MSU RETURNING MATERIALS: PIace in book drop to remove this checkout from LIBRARIES , A!=!!,.,_L_ your record: FINES W111 be charged If book is returned after the date stamped below. Gav-W. CI 13-53? ‘ m. E2331? .1 -- £21333 \ 21995 THE ANALYSIS OF FORAGE HARVEST, STORAGE AND FEEDING SYSTEMS BY Philippe H. Savoie A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1982 ABSTRACT THE ANALYSIS or FORAGE HARVEST, STORAGE AND FEEDING SYSTEMS BY Philippe H. Savoie A computer model was developed in cooperation with other researchers to simulate forage systems on dairy farms. The model simulates alfalfa growth, corn silage and corn grain yields, harvest, storage, feeding and ration formulation for a dairy herd. Alfalfa growth is simulated on a daily basis and harvest is simulated on a half-daily basis. Storage, feeding and ration formulation are simulated once per year. A 26-year series of historical weather data from East Lansing, Michigan was used to estimate the average and the distribution of net returns of forage systems. The analysis focused on alfalfa harvest. Early Imrvest (May 20 for the first cut) resulted in relatively ngh quality, low yield and high net return. Low milk Prmhming cows may however use more efficiently an ‘L...IIII.-___ I o 0.. I Ill t i'." IV'. lg.l ID- at. d ‘ 'o Philippe H. Savoie intermediate maturity harvest (June 1 for the first cut) by substituting yield for quality. Extending the alfalfa harvest period to four weeks reduced the total dry matter and crude protein conserved. The loss in crop value did not however justify the high cost <3f larger machinery, as long as each harvest is done within a.four week period. More dry matter and a higher crude protein concentration can be conserved by reducing the field-curing delay. Additional curing treatments that would increase the drying rate by 20% increased the feeding value of hay by 10 to 15%. Baling hay at a higher moisture content had a similar effect. Shifting from hay to haylage would yield ‘ about 20% more feed per unit area. The feed quality of haylage and hay is practically the same due to the lower i dry matter intake of haylage. The simulation results indicate promising research areas. Applied reseach could be directed towards the development of conditioning treatments that increase the drying rate without increasing dry matter losses, the improvement of conservation of wet hay and the increase of animal intake of alfalfa haylage. More basic research should consider quality changes in silos during filling and fermentation, modeling animal response to hay, haylage and k N .n at: Ir... ECLIFCIF. .1...» SEE... . ,m. 1.3:. y... if... . - .4. .. Ti“. .4 1.. .+ . Philippe H. Savoie large variations in feed quality, and improving estimates of drying rates and dry matter losses. Approved by: Major Professor Department Chairman To my parents ii m.‘ n". a n.7‘ 'I. ACKNOWLEDGEMENTS I would like to express my deepest appreciation to my major professor, Dr. R. C. Brook, for his continual support during my sojourn at Michigan State University. I am very grateful towards Dr. J. R. Black for his financial support and intellectual stimulation through the dairy-forage research group. Dr. C. A. Rotz was also very helpful with suggestions and material support for the field research. The presence of Dr. H. E. Koenig and Dr. M. B. Tesar on my guidance committee added precious insights in the area of multidisciplinary research. The simulation model would only be half done without the faithful cooperation of Luke Parsch. The field mqmriments would not have been done at all without the ufihousiasm of Dr. H. F. Bucholz, director of the Upper Peninsula Experiment Station. The dissertation is dedicated t0“ my parents who Patiently laid the path and bravely let me go on the wonderful adventure of life. Finally I should not forget my affectionate wife and Cheerful children who have shared with me the joys and Pains of the present endeavor. iii I; TABLE OF CONTENTS Page LIST OF T'APLES OCOO...OOO'OOOIOOOOOOOOIO‘.O’COOOOOOOOOOOOOOOviii LIST OF FIGURES COO...I.OOOOOOOOOOOOOOOO-OOOO...0.00.... Xiv Chapter '1. INTRODUCTION 0.0.0.0...‘COOOOOOO'OCOOOOOOOOOOOOOOO l 1.1 The dynamics of forage systems ........... l 1.2 Forage research at Michigan State University .............................. 3 1.3 Objectives 4 2. LITERATUREREVIEW COCO-OOOOC‘OVOOOOOQIOCC00.0.00... 6 3. A GENERAL APPROACH To FORAGE SYSTEMS .......... 12 3.1 The systems' 5 boundaries ................. 12 3 2 The objective function ................... 16 3. 3 A continuous approach .................... 19 3.1 The optimum date to begin harvest . 19 3.2 Harvest rate ...................... 22 3.3 Field curing delay ................ 23 3.4 Problems with the continuous apprOachp......................... 25 3. 3. 3. 3. 3.4 A discrete approach ...................... 27 4. MACHINERY MODEL {000.00.000.00000000000000000000 32 4.1 Forage harvest alternatives .............. 32 4.1.1 Haymaking alternatives ............ 33 4.1. 2 Haylage and direct cutting ........ 38 iv 4 ItIU ‘1 "iv. .o.rm. .aI‘I cilia .I.l!.‘i.l I‘ll .I II n! . . a..-|-{ . . ‘1. .| - flu.“ .|.-. . , . l a a Chapter Page 4.2 Field Capacity ........................... 38 4.2.1 Individual operations ............. 39 4.2.2 Parallel operations ......OOOOOOOOO 41 .3 Power requirements ....................... 47 .4 Energy consumption ....................... 55 .5 Labor requirements ....................... 56 .6 Computer implementation .................. 57 5. FORAGE LOSSES ...00......OOOOOOOOOOOOOOOOOOOOOO 58 5.1 IntrOduction ......IOOOOOOOOOO00.0.0000... 58 5.2 Alfalfa harvest losses due to mechanical treatments 0.0.0.000.........COOOOOOOOOOO 60 5.2.1 Mowing and conditioning ........... 60 5.2.2 Raking ............................ 62 5.2.3 Tedding ........................... 65 5.2.4 Baling ............................ 65 5.2.5 Chopping .......................... 67 5.2.6 The effect of ground speed on material losses ................;. 67 5.3 Alfalfa harvest losses due to environmental factors .................... 69 5.3.1 Dry matter losses from respiration 69 5.3.2 Dry matter losses from rainfall ... 73 5.3.3 Changes in digestibility .......... 74 5.3.4 Changes in crude protein .......... 75 5.4 Alfalfa storage and feeding losses ....... 78 5.5 Corn silage losses ....................... 81 5.6 Summary of losses ........................ 82 6. FIELD DRYINGOF ALFALFA ......OOIOOOOOOOOOOOOOO 85 6.1 Literature review ........................ 85 6.2 Theoretical mOdel ......UOOOOOOOOOCOOOOOOO 89 6.3 Equilibrium moisture content ............. 92 Chapter Page 6.4 Estimating coefficients for the drying mOdel 0.0.0.0000.......OOOOOOOOOOOOOOOOOO101 6.4.1 Experimental results .............. 101 6.4.2 Statistical analysis .............. 102 6.4.3 Rain adsorption ................... 105 6.4.4 Dew adsorption .................... 107 6.5 Additional curing treatments ............. 108 6.5.1 Teddi-n9 0.0.0.... ........ 00.0.00... 108 6.5.2 Maceration .......... ........ ...... 109 6.5.3 Chemical treatment ..... . ..... ..... 110 6.6 Conclusions ... ........ . .................. 111 7. THE DYNAMIC SIMULATION .............. .......... 113 7.1 The commanding subroutine: ALHARV ....... 114 7.2 Direct—cut alfalfa O O O O O O 0.. O O O O O O O O. O. O O O 118 7.3 Field-cured alfalfa .0. 0.. 0. 0.0.0.... 0.... 119 7.3.1 MOWQ: How many plots can be mowed 119 7.3.2 HRVQ: How many plots may be harvested ........... ..... ........ 122 7.3.3 Other field-curing operations ..... 124 7.4 StoragePOlicy......OOOOOOOO0.0.0.0.0....125 7.5 Linking all the subsystems ............... 127 8. COST ESTIMTES ......OCOOCOCOOOOOOOO ........... 130 8.1 Balancing the dairy ration ............... 131 Fixed costs .............................. 135 Variable costs ........................... 136 Economic parameters used in the model .... 138 mam o o o 9“” 8.4.1 Storage structures ................ 138 8.4.1.1 The cost of vertical silos 139 8.4.1.2 The cost of hay barns .... 142 .2 Prices Of feed OOOOOOIOOOOOOOOOOOOO 143 .3 Interest rates .................... 144 vi Chapter ' Page 9 0 SIMULATION RESULTS 0 0 0 . 0 0 0 0 0 0 0 . 0 0 . 0 . 0 . . 0 0 0 0 0 0 0 . 146 9.1 Crop management decisions ................ 147 9.1.1 Maturity at the time of mowing .... 147 9.1.2 Three versus four alfalfa harvests 155 The rate of harvest and forage value ..... 161 9.2 9.3 ield curing delay ....................... 168 Increasing the drying rate ........ 169 Baling at a higher moisture content 172 1 2 3 Hay versus haylage ................ 174 4 Direct-cut alfalfa ................ 185 F 9 9 9 9 9.4 Storage pOIicy 0..........0...0000.00..000 189 10. CONCLUSIONS 00000000000...........00..00.0...0. 194 10.1 General conclusions ..................... 194 10.2 The sensitivity of model assumptions ..... 196 1003 Managing the alfalfa crop 000.00.000.0...0 199 10.4 Comparing hay and haylage systems ........ 202 ll. RECOMMENDATIONS FOR FUTURE RESEARCH ........... 206 APPENDICES A. A SURVEY OF FORAGE HARVEST MACHINERY .......... 210 B. A USER'S GUIDE TO FORHRV ...................... 216 C. AUSER'S GUIDE To ALHARV 000....000000..0..0... 237 D. EXPERIMENTAL DATA OF ALFALFA DRYING ........... 258 E0 LISTING OF THE COMPUTER PROGRAMS 00.0.000.0.... 267 LISTOF REFERENCES 0.000.000.0000.0.000....0.0..0000.. 344 vii Table 4.1 5.1 5.2 5.3 5.4 5.5 5.6 6.1 6.2 7.1 8.1 8.2 LIST OF TABLES Page Rotative power (PTO) requirements ............. 53 Ratio of leaves and stems lost after mowing (data collected in Chatham, Michigan in June 1981) 0000000000.000000000000000000 0000000 0.. 62 Ratio of leaves and stems lost after raking, including mowing losses (data collected in Chatham, Michigan in June 1981) ............. 64 Change in crude protein alfalfa during field drying (from Shepherd et al., 1954) ......... 77 Alfalfa dry matter losses during harvest and curing 00.00000...0..0000.00000...00..0.0.... 83 Storage and feeding dry matter losses of alfalfa (adapted from Kjelgaard, 1979) ...... 83 Changes in the nutritional value of alfalfa during field curing (changes are shown as a fraction of the remaining value per unit mm or h) 00.00.000.00000...000-0000000000.00.00.. 84 Differences in EMC between adsorption and desorption at 15.6 C (from Bakker-Arkema et al.’ 1962) 000.00.000.0000000.00.00.00.000... 97 Differences in EMC between prebloom and mature alfalfa at 15.6 C (from Bakker-Arkema et al., 1962) 00000000.0.000....000000.0000000000000. 98 Labor and energy requirements for feeding (from Kjelgaard' 1979) 0000.00 00000 0000000000000... 127 Daily feed requirements for six types of dairy cows (from NRC, 1978) 00000000000000.0000.... 135 Repair and maintenance cost coefficients (from Hunt, 1973) 0000000....000000000000000000000. 137 viii '91 Table Page 8.3 Prices of vertical concrete silos (quoted from Tristate Silo Inc., Eaton Rapid, MI) ........ 139 8.4 Prices of clear span buildings (quoted from Detroit Steel, Charlevoix, MI and from Lane Clear Span Building, Adrian, MI) ............ 142 8.5 Prices of inputs and outputs used in the ration formUIation mOdel 00000000000000000.000000000 144 8.6 Discount rates and accounting life to estimate yearly cost of durable assets ............... 145 9.1 Date ranges of the first mowing day for harvesting alfalfa at three maturity levels under a three cut system. Dates are shown in Julian days ................................. 148 9.2 Number of years out of 26 when mowing started at the limiting date 00.000.00.00.........0.. 149 9.3 Potential alfalfa yield (tDM/ha) and crude protein at the earliest mowing date ......... 150 9.4 Harvested alfalfa (tDM/ha) available as feed after accounting for harvest, storage and \ feeding losses 00.00.00....0...0.0000.000.... 150 9.5 Feed utilization (tDM/yr) on an 80 ha farm with 128 low yield lactating cows (20 kg milk/cow/day) when alfalfa is harvested at three maturity levels ....................... 151 9.6 Feed utilization (tDM/yr) on an 80 ha farm with 128 high yield lactating cows (35 kg milk/cow/day) when alfalfa is harvested at three maturity levels ....................... 152 9.7 . Comparing non-feed production costs (S/Yr) for harvesting alfalfa at three maturity levels . 152 9.8 Economic comparison (S/Yr) of alfalfa harvest at three maturity levels on an 80 ha farm with 128 lactating cows (20 kg milk/cow/day) 153 ix Table 9.9 9.12 9.13 9.14 9.15 9.16 9.17 9.18 9.19 Page Economic comparison (S/Yr) of alfalfa harvest at three maturity levels on an 80 ha farm with 128 lactating cows (35 kg milk/cow/day) 153 Production costs (S/Yr) of a 3-cut alfalfa system and of a 4-cut alfalfa system over 80 ha 00000000000000.00000000000.000000000000000156 Economic comparison (S/Yr) of a 3-cut and of a 4-cut alfalfa system over 80 ha at four milk production levels 0.0.0....0..........‘0..... 157 Potential yield and actual harvest of the fourth alfalfa cut in specific years when the fourth cut was not profitable ............... 159 Potential alfalfa yield and actual harvest (tDM/ha) from a 4-cut system using the same machinery complement (chopper-round baler) over a wide range of areas .................. 163 Actual harvested feed (tDM/ha) during each of the four alfalfa cuts 0.000000000000000000000 164 Costs and net returns (S/ha) of a haylage machinery system used over a wide range of areas with a low yield dairy herd (20 kg milk/cow/day) ............................... 165 Costs and net returns (S/ha) of a haylage machinery system used over a wide range of areas with a high yield dairy herd (35 kg milk/cow/day) ............................... 165 The average number of calendar days required to harvest each alfalfa cut with a constant size machinery system ............................ 166 Feed costs (s/ha) for low and high milk producing cows with a 4-cut completely hay fixed machinery system over a wide range of areas ....................................... 167 Actual harvested yield (tDM/ha) and average field-curing time using extra treatments to increase the drying rate of baled hay ....... 170 Table Page 9.20 The annual feed cost ($/ha) as influenced by faster drying treatments for an 80 ha alfalfa farm with 128 lactating cows at four milk production levels ........................... 171 9.21 Actual harvested feed (tDM/ha) and average field-curing time when hay may be baled at a higher moisture content ..................... 173 9.22 The annual feed cost (S/ha) when hay may be baled at a higher moisture content for an 80 ha farm with 128 lactating cows at four milk production levels ........................... 173 9.23 Average number of field-curing days of alfalfa before going into storage (80 ha farm) ...... 175 9.24 Alfalfa available as feed (tDM/ha/yr) from fixed machinery systems for hay and haylage harvest over a range of areas ............... 176 9.25 Storage capacity (tDM) and investment cost (5) for a hay system (one hay barn) and for a haylage system (two equal size silos) ....... 177 9.26 The resources required to operate three harvest systems for an 80 ha alfalfa farm ........... 177 9.27 Feed production and utilization (tDM) under four harvest and conservation systems on an 80 ha farm with 128 high milk producing lactating cows (35 kg/cow/day) .............. 183 9.28 Feed production and utilization (tDM) under four harvest and conservation systems on an 80 ha farm with 128 low milk producing lactating cows (20 kg/cow/day) .............. 183 9.29 Net feed costs (S/ha) on an 80 ha alfalfa farm with 128 lactating cows at four milk prOduction levels 0000.00.00.0000000000000.00 188 9.MJ Average haylage quality and standard deviation when one or two silos are used .............. 190 9.I1 Feed utilization under two storage policies with high yield cows (35 kg/day) ............ 191 xi It I i... .1. Fii ¢|‘ [It i). I .55. Table 9.32 9033 9034 A.l A.2 A.3 A.4 A.5 A.6 A.7 A.8 A.9 A.10 A.ll A.12 A.13 A.l4 8.1 Page Feed utilization under two storage policies with low yield cows (20 kg/day) ...... ....... 191 The feed costs (S/Yr) under two storage policies at four milk production levels with a herd of 128 lactating cows ................ 192 The storage investment required under two storage policies ............................ 192 A generic summary of mowers and mower- conditioners on the U.S. market (1981) ..... 211 A generic summary of tedders on the U.S. market (1981) 000000000000000.0000000000.000. 212 A generic summary of side-delivery rakes ...... 212 A generic summary of conventional small rectangular balers .......................... 212 A generic summary of round balers ............. 213 A generic summary of large hay stackers ....... 213 A generic summary of automatic bale wagons that pick and stack small rectangular bales ...... 213 A generic summary of bale ejectors ............ 213 Hay wagons 000000000000000.0000000000.0000 ..... 214 A generic summary of forage harvester cutterheads on the U.S. market (1981) ...... 214 Attachments for cutterheads ............. ..... . 214 Forage wagons with unloading mechanism ........ 215 Forage blowers on the market .................. 215 List of manufacturers quoted for specific examples. Complete addresses are available in Implement and Tractor (1981) ............. 215 Machines used for forage harvest .............. 221 xii Table Page 8.2 Operations modelled in FORHRV ................. 222 3.3 Data required for harvest operations .......... 227 8.4 Example of input data for FORHRV .............. 232 8.5 Example of output from FORHRV ................. 234 CJL General structure of alfalfa harvest management input data file .0000000O00.0.00.00.00.000000 238 C.2 Input data for each alfalfa harvest ......... .. 240 C.3 Example of input data for ALHARV ...... ... ..... 249 C.4 Example of output from ALHARV ................. 251 D.1 Alfalfa drying data collected in Chatham, Michigan in June and July 1980 and in June 1981 00000000000000.000000000.000000000000000 259 0.2 Rain adsorbed by mowed alfalfa. Data collected in Chatham' MiChigan 0.0000000000000000000000 265 D.3 Dew adsorption during the night (between 20:00 and in the evening and 8:00 the next morning) 266 3.1 Listing of CYBER commands to operate the forage simulation model on the MSU computer ........ 269 3-2 Listing of the main program linking FORHRV, ALHARV, ALFMOD and CRNMOD ................... 270 3'3 LiSting Of program FORHRVooooooooooooooooooooo 275 3.4 Listing of program ALHARV ................... .. 294 xiii LIST OF FIGURES Figure Page 3.1 The forage system C O O C O O C C O O I C O O O O O O O O O O C O O O O I 13 3.2 Frequency diagram of total cost of a forage system ......OOOOOOOOOOOOOOOC0.00.00.0000... 18 3.3 The cumulative probability of net profit of two hypothetical forage systems ............ 18 3.4 Yield and protein concentration of alfalfa versus maturity stage during the first harvest (adapted from Gervais, 1974) .. ..... 20 3.5 Flow chart of the discrete approach to analyze forage systems ..................... 28 4.1 Some of the alternatives in forage systems ... 34 4.2 The estimation of cycle time for simultaneous baling, transport and unloading ............ 36 4-3 Independent baling and transport. Transport and unloading occur subsequently to baling . 37 5-1 Leaf dry matter loss from raking, as a fraction of total leaf mass, versus dry basis moisture content (adapted from Hundtoft, 1965) ............................ 63 5-2 Hypothetical relationship between dry matter losses and speed of operation .............. 63 6-1 Adsorption equilibrium moisture content (dry basis) of mature alfalfa versus temperature and humidity. Experimental data are from Bakker-Arkema et a1. (1962) ................ 94 5.2 Adsorption equilibrium moisture content of mature alfalfa in the range of high relative humidities ........................ 95 xiv Figure 6.3 7.1 7.2 7.3 8.1 8.2 9.1 9.2 9.3 9.4 9.5 7% .—.__ w‘_‘ .w. '- Page Predicted equilibrium moisture content (dry basis) versus relative humidity for desorption of prebloom alfalfa at 5 C and 35C......OOOOOOOOOOOOOOOOOI......IOOOOOOOO 100 Interactions between the growth simulator and the alfalfa harvest ........................ 115 The basic algorithm to decide how many plots may be mowed today ......................... 120 The basic algorithm to decide how many plots may be harvested today ..................... 123 The initial cost of vertical concrete silos versus silage capacity ..................... 141 The initial cost of clear span barns for the storage of hay versus storage capacity ..... 141 The cumulative probability of net return per ha for mowing at three maturity levels, identified by the alfalfa crude protein on the first mowing day, with low milk producing cows (20 kg/day/cow) ............. 154 The cumulative probability of net return per ha for mowing at three maturity levels, identified by the alfalfa crude protein on the first mowing day, with high milk producing cows (35 kg/day/cow) ............. 154 The cumulative probability of net return per ha for a 3-cut and for a 4-cut alfalfa harvest systems with low milk producing cows (20 kg/day/cow) ....................... 158 The cumulative probability of net return per ha for a 3-cut and for a 4-cut alfalfa harvest systems with high milk producing cows (35 kg/day/cow) ....................... 158 The cumulative probability of the difference in net returns in favor of a 4-cut system versus a 3-cut system with low yield cows (20 kg/day/cow) ............................ 160 XV Figure 9.6 9.7 9.8 9.9 9.10 9.11 9.12 9.13 Page The cumulative probability of the difference in net returns in favor of a 4-cut system versus a 3-cut system with high yield cows (35 kg/day/cow) ............................ 160 Net cost of a hay system versus area for high milk production (35 kg/day/cow) and real interest rates (i=0.04) ......OOOOOOOOOOOOOO 179 Net cost of a haylage system versus area for high milk production (35 kg/day/cow) and real interest rates (i=0.04) ............... 179 Expected cost of a haylage system and a hay system versus area for high milk production (35 kg/day/cow) and real interest rates (i=0.04) ................................... 180 The cumulative probability of annual net cost of a hay system versus a haylage system under 120 ha of alfalfa with high milk production (35 kg/day/cow) and real interest rates (i=0.04) ............................. 180 Expected costs of a haylage system and a hay system versus area for low milk production (20 kg/day/cow) ............................ 182 Expected costs of a haylage system and a hay system versus area assuming haylage dry matter intake is the same as hay intake (high milk production) ............... ...... 182 Expected costs of a haylage system and a hay system versus area assuming a low real interest rate (i=0.00) and high milk production ................................. 186 xvi CHAPTER 1 INTRODUCTION Jul The dynamics of forage systems An increase in the use of cereal grains and protein concentrates in ruminant feeding has been observed in recent years, partly because of low feed prices (Raymond et al., 1978; Blaser, 1976). The current low feed prices may mfl11 make the practice feasible, but the FAO (1979) EHedicts a long term increase of demand and prices of grain and protein. High quality forages, espacially legumes, are a. good source of protein and can reduce the need of cereal grains and protein meal in the diet of dairy cows (Thomas, 1380). Good harvesting, storage and feeding practices play animportant role in maintaining forage quality. Important technological changes have occurred in the last twenty years in forage systems. Larger machines (round balers, large hay stackers) have been designed especially to reduce labor requirements (Bowers and Rider, 1974). Hoglund (1967) noted that farmers were shifting from dry hay to more haylage. He also reported an increase in corn silage as a forage. Most of the technological changes have meant more capital expenditures (machinery, silos, feeding equipment) and have been justified on the basis of labor and risk reductions. Meanwhile the 1970's have witnessed some important structural changes in the availibility of some resources, especially fossil energy, and capital due to high interest rates. Holtman et a1. (1977) noted that technological adjustments become desirable as the relative scarcity of resources changes with time. In view of these technological and structural changes, a new assesment of forage harvesting, storage and feeding SYStems has become highly desirable. A great deal of agronomic, engineering and nutritional knowledge about fOrages has been published over the last two decades. Modeling tools have become ever more sophisticated. The SYStems approach, including simulation of the forage SYStem, will be useful in assessing the various teChnological and management choices available to the farmer in the 1980's. 1.2 Forage research at Michigan State University Agronomists, animal scientists, economists and engineers have been doing research on various components of the forage system for several years. A multidisciplinary research group was formed in 1979 at Michigan State University to study the dairy-forage system. The group's main objective has been to link the components together and thus gain a better understanding of the whole system. In this context, Sisco (1980) published a detailed model of forage machinery systems. The present dissertation was also initiated within the mulidisciplinary group. A simulation model of forage Growth, harvest, storage, handling and feeding was developed in close cooperation with Parsch (1982). Parsch deals mainly with the impact of various ratios of corn/a1fa1fa production whereas the present dissertation is concerned mainly with machinery and storage alternatives and With management of the alfalfa crop. 1.3 Objectives The broad goal of this thesis is to present a nwthodology and develop a simulation model to analyze and compare forage systems. The model should be versatile enough to allow the analysis of future technological or managerial changes. The specific objectives are: 1. To develop a detailed model of forage harvesting, storage and feeding on the dairy farm. The model will not include field operations other than forage harvesting. The model will include alfalfa harvest as either dry hay, wilted haylage or direct-cut silage as well as harvest of corn silage. The analysis will focus mainly on alfalfa harvest as hay and haylage. 2. To compare forage systems on the basis of a detailed economic analysis that includes income from milk production, income from the sale of excess forages, and fixed and variable costs of harvesting, storage, feeding and ration formulation (purchase of supplemental feeds). Simulation over several years, based on historical weather data, provides samples of annual profits and an insight into the variability of a system. Comparisons will be ta the p: analys To 0: hayla treat be based not only on expected profit but also on. the profit distribution by stochastic dominance analysis (Dillon, 1977). To compare alternative technologies: hay versus haylage, direct-cut alfalfa, additional curing treatments to increase the drying rate (maceration or spraying a chemical solution at mowing), chemical additives to preserve high moisture hay or direct-cut alfalfa. To compare alternative management strategies: alfalfa maturity and starting date for harvest, three versus four alfalfa cuts, the timeliness cost and choice of machinery size with respect to area. CHAPTER 2 LITERATURE REVIEW A brief review of the literature is presented which covers past research efforts to model forage systems and experimental work on various parts of the system. The literature will again be referred to extensively in later chapters to estimate technical parameters required by the model. A number of researchers have analyzed forage systems with respect to the dairy cow performance. McGuckin and Schoney (1980) compared hay and haylage systems as they are affected by weather. They focused on estimating the economic advantage of switching from a highly variable hay SYstem to a less risky haylage system. Under Wisconsin Weather conditions, their model predicted that haylage Systems were both more profitable and less variable than haY 5Ystems on typical dairy farms. Their model did not deal with discrete aspects of harvest and storage. It Chargeci an annual storage cost per unit harvested and "rvesting :5-‘ . "“foe Csop :he basis '4": (D l: (f ‘I - fin nu.=:" b ‘ v’as; ‘0 ‘l H" ~.'.: H assumed a constant dry matter harvest rate independent of yield. 4 Millier and Rehkugler (1972) compared various harvesting rates and harvest starting dates. Using a simple crop model that predicted yield and quality only on the basis of calendar -days, they observed that milk production was negatively affected by slow harvest rates and low forage quality. Some authors have focused on more specific components of forage systems. Bowers and Rider (1974) surveyed forage harvesting equipment in Oklahoma. Kjelgaard and Quade (1975) analyzed forage transport and conveying equipment for Pennsylvania farms. Audsley et a1. (1976) compared various storage and feeding methods in the United Kingdom. These studies, along with otherS' (Hendrix, 1960; Moser, 1980), will provide much of the information needed for a detailed analysis of operations related to forage systems. New technologies abound in the area of forage systems. Btuhn and Koegel (1977) discussed the value of mechanically dewatering alfalfa. Such a process would virtually eliminate all weather risks associated with making haylage. Charlick et a1. (1980) have shown some advantages in using Preservatives for the storage of high moisture hay. Nehrir 9t alt (1978) conducted field studies in which hay Preservatives were shown to reduce dry matter losses on the average by 650 kg/ha, compared with hay on which no I" 6‘ - ::ESEe‘a5‘ '.:‘p.q.'.‘ e. ‘Ov‘uc. ‘ . o 1 . ‘ .crcges a. | I no. p ”H, D .e ""5 Um \ Mrs] M .J'd‘ r1 '5‘ ‘y. i B'na 5 .'.'.e“ ~ “\a I . ‘ w.“ W \::. ".‘; 3“: V. cc; \. ‘1':}. ; e 'L I‘a ‘. L._ I Q, ‘ “'55?“ a“ I n, b . .““:;‘a ‘5 ~‘. 6".“ ‘ \ .fl. §~.a~ H e \., ‘§.E.:‘ \ i P \ '.I c ) preservatives were applied. Harris and Tullberg (1980) and wieghart et a1. (1980) noted that chemical spraying of forages at the time of mowing could accelerate drying and hence reduce exposure and weather risk. Krutz et a1. (1979) proposed a shredding-type conditioner, the macerator, to increase the drying rate. Under Indiana conditions, the macerator has been used to dry alfalfa as hay within one day. The dry matter losses may however be considerable. Some European researchers (Dernedde, 1979; Jones and Harris, 1979) noted increased drying rates by tedding grasses after mowing. Alfalfa is not as well suited for tedding as grasses because of the fragile link between the stem and the leaves, through the petiole, and the higher risk of dry matter losses. A number of harvest models have been presented in the literature. Some authors have used workday probability distributions to establish optimum machinery sets (Hayhoe, 1980; Donaldson, 1968; Sisco et al., 1980; Von Bargen, 1966). As Dumont and Boyce (1974) have observed though, the use of daily weather data is more appropriate in forage harvesting models since the weather of previous days has a dEfinite impact on the work that can be done today and on the forage losses due to weather exposure. In fact, Several researchers have used historical daily weather data in machinery selection models (Edwards and Boehlje, 1980; 'rulll et al., 1974; Wolak, 1980; Van Elderen, 1980). The ¥ use oi his: muse” .‘r.' buag ....' 1 " ‘" 091 b '4'. w t .uE .BVC: ‘9!- «- .IU" "I c ‘ i ‘55 526.4 hare A‘ Si '6 c ‘ . 4“”:av c I“\ i ‘ a, : ; ::5 :.:~ i I ‘w: u a '9 use of historical weather data implies that past trends represent future trends. Van Kampen (1971) showed that weather between 1931 and 1945 in central Netherland was more favorable for grain harvesting than between 1946 and 1965. An optimal machinery complement for the first period was smaller than for the second period. One should be aware of significant weather changes in the same location from one decade to the next. Alfalfa growth simulators have been developed by several researchers. Millier and Rehkugler (1972) presented a simple model where yield and TDN (total digestible nutrients) were a function of the number of calendar days of growth. Pick (1977) and Holt et a1. (1978) have developed more sophisticated models which use daily weather data as input such as solar radiation, Precipitation and degree days. When the harvest of a crop is delayed because of slow harvest rates, there may be yield and quality losses. The decrease in crop value due to slow harvest rates is called timeliness cost. Timeliness costs are sometimes estimated simpuy as a linear function of the number of days required for harvesting (ASAE, 1981). However, two different forage har'Vesting methods, extended over the same time period, might well have a different timeliness cost. Indeed forage ha1-‘Vesting losses should include both quantitative and qUalitative losses. Dale et a1. (1978) simulated alfalfa ¥ ...! 1.": have iecreases .9’5). ' \q-I. .....‘A’. :3'950VHC 533. (a: . . ‘ .u'hn nun“; n um. "UV. e ”‘1‘“. A‘ .Vg“ V. 'flu... “ .hfi§ . .‘ '. 10 dry matter losses during harvest. Alfalfa quality also decreases with harvest delay (National Research Council, 1978). The real measure of quality losses is the additional corn and soybean meal required to re-balance the dairy ration and the possible milk production losses if the minimal nutrient concentration requirement cannot be met. Much literature is available to help build a detailed model of forage harvesting-storage-feeding systems. It is important however that the model be generic in the sense that parameters are specified symbolically throughout the model. Hence adjustments for geographical location, for technological changes or for managerial choices can be made simply by changing these parameters. Basically a forage model should include crop growth, harvest, storage and feed utilization on the farm. Indeed, Corn silage and alfalfa haylage are not easily marketed because of their short conservation period once they are taken out of storage; their value is usually best estimated in the form of milk production and the relative changes in the purchase of concentrates due to forage quality changes. EVen alfalfa hay, which can be sold on the market, is often ”Ore efficiently used on the farm for animal production. The six following chapters describe a general approach t0 forage systems and the details of harvest, storage, handling and ration formulation. Chapter 9 relies on the Simulation model to make inferences about technological and 353838319311 a 11 management alternatives in forage systems. CHAPTER 3 A GENERAL APPROACH TO FORAGE SYSTEMS 3.1 The Systems's Boundaries The primary emphasis of the present dissertation is to refine the simulation of the harvest, storage and feeding components of forage systems. In a sense, it is a continuation of the work done by Sisco (1980) on forage harvesting. While Sisco considered only the harvesting Component, the forage systems's boundaries are now extended to include crop growth, harvest, storage, handling and ration formulation on a dairy farm. Figure 3.1 illustrates the boundaries within. which forage systems will be analyzed. Only two forage crops are considered in the present stUdy: alfalfa and corn silage. An important characteristic of alfalfa is its regrowth in the same year, allow'ing multiple harvests. There can be time conflicts 12 CONTROLLABLE INPUTS ,Land, seed, fertilizers.) labor, machinery, energy Machinery, labor , energy Storage structures, machinery Equipment , labor , energy _:r Supplemental feeds (grain, protein meal) Figure 3.1. 13 SYSTEM COMPONENTS + Forage crop production V Harvest Weather 1 Storage 4\ FE? Handling Ration formulation I 1 Milk production The forage system. I 9’ 4' 7’ betwee. ' Q t2. ...5’. Ma? cu". and 1‘19“" cf 541‘ I’lfiv:. P‘V‘aby a: 6‘. ‘h. :6.a..° “c “)PH 0.. Seare5.“g m (3.}. w; ‘- be 15.: S‘QUSA ‘ \ 14 between the end of corn planting and the beginning of the first alfalfa harvest, between the end of the third alfalfa cut and the beginning of corn silage harvest, and between the end of corn harvest and the fourth alfalfa cut. First priority is given to finishing corn planting, the third alfalfa harvest and the corn harvest before starting the competing operations. The crop growth component is driven by daily weather data: solar radiation, precipitation and growing degree clays. Yields are likely to vary from one harvest to the next and from year to year. Yields and quality of the harvested crop are also affected by the rate of harvest: as tihe calendar time required for harvest increases, more material and quality losses occur. Several other issues ) I:EIated to crop growth will influence the overall system performance: the harvest starting date, the regrowth Pattern, the alfalfa's winterhardiness, the establishment 0f forage fields, fertilization, irrigation. The harvest date and the regrowth pattern are allowed to vary but the Other production parameters (winterhardiness, establishment, fertilizer, irrigation) do not vary in the present growth model. Parsch (1982) has adapted a physiological alfalfa growth model based on research done by Fick (1977). The trtodel predicts growth and regrowth of alfalfa after Subsequent cuttings. Parsch (1982) has also developed a :1 232?. S..BC( C :.' w .‘ I 5.-t.c.l f“ ‘1'" " ‘ 4' 'esben: J 3 P! .ieace q!" C ‘L‘ ‘b Q 5‘ ~ \ w 0‘, 3“: ‘L '45 5:“ ”I 12;; 5 15 corn silage yield model based on Michigan experimental data. Both crop models are included in the present simulation model. What time increment should one use to simulate forage systems? A detailed harvesting model would simulate harvesting activities (machinery operation, field drying, forage quality changes) on a daily or even on an hourly basis. A detailed storage model would simulate fermentation and quality changes on a daily basis. A ration formulation model would allocate various quality forages to dairy animals according to their needs. The quantity of supplements required would be estimated by a Inilk-feed optimization model. It was decided to simulate ‘, growth on a daily basis, harvest on a half daily basis to Provide some management flexibility and storage and feeding on a yearly basis. All the harvested feed is allocated at the end of the year to a dairy herd. The harvest, storage and handling components will be dealt with in more detail in later chapters. Their role is to convert the field crop into a feed ready for animal Consumption. An important aspect of the simulation is to Closely track changes in dry matter and in quality between the time the forages are mowed and the time they are fed. The ration formulation model will estimate amounts of grain and high-protein supplements required to balance the ration of a complete dairy herd. It will also predict milk \ I ”en-Hu‘ F rot-mu» 5 6)! “Mn re p: "' '9. be»; 9 A a $.21 TE: q.._;‘|p‘: P"‘*h4< ‘ O.“ 16 production. The value of the forage crop harvested is converted into milk production and net profit. This is the only realistic way to evaluate forages since in general forages are not sold on the market but are transformed into animal product. Computerized models for ration formulation have been discussed by Black and Hlubik (1980) and also by Waller et a1. (1981). In the present model, rations for a dairy herd composed of lactating cows at four possible milk Production levels are balanced using the harvested crops (alfalfa, corn silage and high-moisture corn) and purchased feeds (corn grain and soybean meal) to satisfy energy and Protein requirements. The ration formulation model is described in section 8.1. 3.2 The Objective Function The inputs of a forage system include labor, energy, Capital, land and supplemental feeds. The outputs are milk production and excess forages that may be sold on the IIlarket. These material flows will be identified in the simulation on a yearly basis. For comparison purposes however, material flows are converted into a monetary value as follows: PR = 1(1) + 1(2) - C(l) - C(2) - C(3) (3.1) L 'e 1r. 0 an 5 same "0 is 1(1) ; 'htc (mach' :ezenéent. Fee: as the “sage PR 82938 J: 33kt» 17 where PR is the total yearly profit; 1(1) is income from milk production; 1(2) is income from the sale of excess forages; C(l) is the annual cost of labor, energy, repair and maintenance for harvest, storage and feeding; C(Z) is the cost of purchased supplemental feeds; arud C(3) is the annualized cost of fixed assets (machinery, silos, land). Tfime objective function above can be used to compare digfferent forage systems. Cost C(3) is practically independent of weather. All other terms are weather dependent. Even milk production might vary from year to Year as the forage quality and the optimum feeding formula Change. The influence of weather can be assesed by simulating tlie same forage system over several years of weather data. Each year will provide a different total annual profit. A ii series of annual profits can be used to draw a histogram or a frequency curve as in figure 3.2. The expected total Yearly profit is simply the average and can be used to compare different systems. The frequency curve provides further information on the relative risk of a system. It can be converted into the cumulative probability of annual profit such as in figure 3.3. The comparison of two systems shows that system 1 generates on the average (probability a 0.5) a greater profit than system 2. I‘iowever system 1 is more variable than system 2: in some Years it may provide unusually large profits; in other Years, it may incur very low profits or even losses. A ”fire 3 18 >. o 2 III .3 o- u 0 '= : "" LOWER aouwo ; EXPECTED '. UPPER souuo : : I l : ' ' ‘ TOTALCOST 3Figure 3.2. Frequency diagram of total cost of a forage system. A : 1.0L--------q-------------.----- :' s: n < O o 2 l 0.5------------ -------_--—- In > h. < 3‘ s1 2 ,. 3 ; 0 PROFIT Figure 3.3 The cumulative probability of net profits of two hypothetical forage systems. :zsk-neutra 335%. I '1' profit but I" I N .0333} t‘ . km: an: 2"“ 0“ ‘c an; l9 risk-neutral manager would choose system 1. A risk-adverse person may prefer system 2: it yields a lower average profit but it is also less risky than system 1. Comparison of forage systems will be based on the expected yearly profit and on the relative riskiness of each system. 3.3 A continuous versus discrete approach Forage systems can be simulated either as continuous sYStems or as discrete systems. The continuous approach implies that small, discontinuous events are aggregated and that average flow rates are used. The discrete approach retains a detailed description of discontinuous events. The discrete approach is usually more complex than the continuous approach but provides a more realistic representation of actual events. The continuous approach 1 5 considered first . 3.3.1 The optimum date to begin harvest The continuous approach is helpful in assessing some important issues in forage systems. A first question that arises is the optimum date to begin harvest. Figure 3.4, adapted from Gervais (1974), illustrates the changes in yield and quality of alfalfa during the first cut. The DRY. MATTE R VIE LD(kg/h.)_ 20 3400 it A 3200 .. ..20 PR TEIN 3000 .. O “13 Jr «.17 zaoo ._ .-16 : ‘ i .L a 1no s‘LOOM PULL SEED U D BLOOM Figure 3.4. Yield and protein concentration of alfalfa versus maturity stage during the first harvest (adapted from Gervais, 1974). pwuoE PROTEIN (76) ::"de 9: :e ‘:e or the “lie-.1 conti :Ee mow‘ fin edh: ‘ ., . 0-1" V‘ w ..: h. eh 'eSteA I a (a T, the ‘A r. I .4 ! ’5 e~"“ 1U: ‘5 § ‘3 is. s ‘ ‘ s. t ‘ ‘ 21 crude protein decreases almost linearly with the mowing date or the maturity stage. Meanwhile the total dry matter gyield continues to increase at least until the full bloom stage. Yield and quality can be expressed as a function of the mowing date: You: fl(t) (3.2) QL = f2(t) (3.3) VAL = f3(QL) = f3(t) (3.4) Where YDMis the total dry matter yield (kg/ha); QL is the forage quality, here expressed as crude protein (dec.); VAL is the value of the crop (5); and t is the calendar date (day). Ir) equation 3.4, crop value is a function of crop quality. This is reasonable since milk production is highly and Positively correlated to feed quality. If alfalfa could be ldarvested instantaneously, then the total value would be: TV a YDM* VAL = f1(t) * f3(t) (3.5) ‘vhere TV is the total value of the crop. The optimal date to harvest would occur at maximum total value. The optimal date is found by differentiating equation 3.5 with respect to time, setting the equation Equal to 0 and solving for t. 6: Solving e: hate to ma: single day . ' I "Mk I a nbu.~e. "- :5 I, IS CalE we“ :te; Vale If A1 *— I 22 I day =- t'm * r. (t) + f (t) * r. (t) = o (3.5) d 1 3 l 3 t Solving equation 3.6 for t will give the optimal harvest date to maximize profit if the harvest could be done in a s i ngle day . 3..3.2 Harvest rate In practice the alfalfa cannot be harvested iristantaneously and the harvest rate becomes an important factor in system performance. The harvest is extended over a. number of calendar days. The average value of the }xarvest period may be estimated as follows: U = A / (EFC * h * r) (3.7) ‘Vhere u is the average number of calendar days required to harvest the crop; A is the total area of harvest (ha): EFC is the effective field capacity calculated from equation 4.2 (ha/h); h is the number of field working hours per day (h/day); (and. r is the average ratio of harvesting days to total calendar days over which the harvest period extends. When the harvest is not instantaneous (u > 0.), the 1:0tal value of the harvested crop is : to + U W =_1_ / f1(t) * f3(t) * dt (3.8) u to m ' optima esp- non "L a “E l (D N 23 The optimal starting date is found by differentiating equation 3.8 with respect to to, equating to 0 and solving for to. to + u m -_1_g_ / fl(t) * f3(t) * dt = o (3.9) dt u dt t0 Solving equation 3.9 for t0 will give the optimal date on wahich harvest should begin to maximize profit. Parameter 11, the average number of calendar days required to complete the harvest is not really a constant and will vary from Year to year depending on weather. 3. 3.3 Field curing delay The quality of alfalfa (f2(t)) is not only affected by tlre date at which it is mowed but also by the amount of time it is left curing in the field. The curing delay is a f“fiction of technology, management, yield and environmental c<>nditions. Quality and value of the alfalfa crop should be expressed as a function of both the date of mowing and the field-curing delay . QL - f2(t,v) (3.10) VAL = f3(t,v) ‘ (3.11) Where v is the field curing delay. A mere U . . ‘ N ‘ 44% Ce: L. “W. ‘u‘. lat? ”‘15 t! Edgy v ~24 A more complete equation for total value is therefore to + u TV =_;_ / fl(t) * f3(t,v) * dt (3.12) u to From the above equation, at least three important roarameters need to be optimized: to, the time when harvest should start; u, the harvest period equal to the average number of calendar days required to complete the harvest (related to harvest rate); arud v, the average field curing delay (days). The total value of the crop (TV) is likely to increase 3if u and v are decreased, i.e. if the harvest period and the field curing delay are decreased. The harvest period Can be decreased by increasing the harvest rate (usually "ivth larger machinery). The annualized fixed costs (C(3)) WC>‘Lild then increase. It is not so clear how C(1), the Yearly variable costs, would be affected. Labor costs “TTUld decrease while energy and machinery maintenance costs “tight remain the same or increase slightly. The field cWiring delay can be decreased by a change in the harvest technology. For example shifting from a hay technology to a. haylage technology will substantially reduce the field Sharing time and will usually result in a higher quality, tmore valuable feed. (The problem of comparing alfalfa hay sizhh yla repcnd d {see secti ielay vij 9"" ... . “V U. o intensive ‘ P An In. R 5; hwn‘.‘ ($0,: I ‘-'n o w "”35 1: C-J 25 with haylage is however compounded by the fact that animals respond differently to hay and haylage of the same quality (see section 5.4.) In general, reducing the field curing delay will increase the value of the crop. However short exposure time technologies are often more capital or energy intensive. So as TV increases, so will C(3). Clearly there will be tradeoffs between the value of the crop that may be obtained and the additional cost of capital and energy required to increase this value. The continuous approach helps to clarify some of the important issues in forage systems, especially with regards to the Size of machinery and the technology used for harvest. 3- 3.4 Problems with the continuous approach Two important parameters, the number of calendar days to complete the harvest (u) and the average field curing delay (V), need to be optimized but vary from year to year because they are weather dependent. Average values of u and v can be used, but information about the magnitude of Year-to-year variations due to weather will be lost. A discrete ‘ approach would allow the estimation of Year-to-year variations and establish distributions of Yearly prof its . k saza " ~ :ear. wad be ‘ 26 Alfalfa can be harvested up to four times per year in the U.S. North-Central region. The starting harvest date (to) and the total harvest period (u) of the first harvest twill affect the yield of all subsequent harvests in the same year. The total value of a multiple harvest crop would be the summation of the value of each harvest: TV = NH) + TV(2) + + TV(n) (3.13) where n is the total number of harvests in a year. Total value of each harvest, TV(i), could be estimated by equation 3.12, but yields (f1(t)) of subsequent harvests would be affected by to and u. Even n, the total number of harvests in a year, might vary from year to year on account of weather and previous harvests. The interaction between previous management decisions and the yield of subsequent harvests can be most efficiently simulated by the inclusin °f an alfalfa growth model in a discrete simulation. Many management decisions are discrete and sequential, especially during forage harvest when there is a field c:llring delay. A discrete approach is more appropriate to a1"lalyze management decisions such as priority between r“Owing and harvest, mowing policy with regards to weather expectations or changing the harvest sequence after Unfavorable weather. The discrete approach is considered next. L_ 27 3.4 A discrete approach The discrete approach to analyze forage systems is summarized by the flowchart in figure 3.5. The discrete rnodel is preferred to the continuous model because it 'follows more closely the discrete decisions and events involved in forage harvesting. It also retains information about year-to-year variations and risk. The discrete model will simulate forage growth and harvest on a daily basis. After accounting for dry matter 1C>sses and quality changes throughout harvest, storage and handling, all forages are used to balance the ration of a cOmplete dairy herd on a yearly basis. The yearly profits alfe estimated according to equation 5.1. After the Simulation has been repeated for a given number of years (1'), a frequency curve of yearly profits can be established as in figure 3.2. More specifically, the discrete model starts by I’eading input data required for the whole simulation. The crop growth information includes first and last growth days each year for alfalfa, the yield distribution for corn silage, the number of years of simulation and the related historical weather data. The machinery information is used to generate harvest rates over a wide range of yields by 28 READ Crop growth information . Machinery information Management information Storage and feeding information #th-d O VL_ Call FORHRV G} (e i Simulate from year 1 to year NYRS DO JYEAR - 1,NYRS \f Simulate alfalfa growth and regrowth from the first growth day the the 4‘ last growth day each year Is the alfalfa ready for harvest’ Historical or simulated daily weather data feeding subroutines 4‘ Call the harvesting, storage and Is the harvest finished? Set the alfalfa for regrowth d5 F1Sure 3.5. Flow chart of the discrete approach to analyze forage systems (continued on the next page). 29 5 another alfalfa harvest scheduled before winter dormancy? Cumulate all feeds harvested in year JYEAR. Use a ration formulation model to estimate income from milk production 1(1) and from the sale of excess forages 1(2), and the cost of supplemental feeds C(Z). f Estimate other variables costs C(l) (labor, energy, repair and maintenance) and the annualized fixed costs C(3) for machinery and storage structures. ‘ Yearly profit No Has the simulation been completed for NYRS? -The distribution of yearly profits can .be used to generate a frequency curve. :Figure 3.5 (continued from the previous page) Flow chart of the discrete approach to analyze forage systems. 0 “Wen - 33...“! c 1.". great: iezision :‘ecis~'- 5V“ . I anal .a‘ :yye..u.x 30 calling a set of subroutines headed by subroutine FORHRV. Chapter 4 and appendix B describe the machinery algorithm ( in greater detail. The management information includes the area under cultivation, the sequence of operations and decision criteria related to harvest and storage. The decision algorithms are documented in chapter 7 and in appendix C. The storage and feeding information concerns fixed assets other than field machinery: silos, hay barns and feeding equipment. The information is later used to estimate the annualized cost of fixed assets. The simulation is repeated for N years. The present weather file being used contains data for 26 years at East Lansing, Michigan (Parsch, 1982). Within each year, the alfalfa growth is simulated on a daily basis. In general, three or four harvest dates per year are defined for 31 falfa. When the calendar date equals the current harvest date, harvest may begin. Growth will continue until the end of the harvest. At this point, the alfalfa is set for regrowth and the cut number (NTHCUT) is increased by one. The second and all subsequent harvests will start at the specified harvest dates. When no more harvests are SCheduled in a given year, all the harvested forages are a<:cumu1ated according to their storage location. The feeds are balanced with supplemental grains and protein-meal to Optimize milk production of a dairy herd. The izportan: harvest re be £03515. «5 . .9 simulate have a‘. I" | ‘ ‘ ‘ betlma be“ 31 The continuous model is helpful in identifying important issues: the date when harvest should start, the harvest rate and the field-curing delay. These issues will .be considered in chapter 9 from the simulation results. The discrete model provides the basic structure to simulate forage growth and harvest on a daily basis and fc>rage allocation to a dairy herd on a yearly basis. Year-to-year, variations in growth and harvest, and L11.timately in available feed and net returns, will be estimated with the use of historical weather data. CHAPTER 4 MACHI NERY MODEL 4 - 1 Forage harvest alternatives The object of the machinery (model is to predict harvest rates and fuel and labor requirements for Practically any combination of machines at any yield. The present chapter establishes the relationships that will be Used to estimate the performance of forage harvesting sYstems. The boundaries of the forage sytem were defined eali'lier, in section 3.1, to include hay, haylage and d 5- rect-cut forages. The more important harvest alternatives will be outlined here. A detailed survey of harvest alternatives can hardly be done without making an inventory of the forage harvest machinery available on the U.S. market. A generic summary of forage harvesting machinery is presented in appendix A. I . . . . t llStS Sizes and capacities of most forage harvest 32 :3“. ‘n “§a‘\ x (I! ‘4 (I) 33 related machines available on the U.S. market in the fall of 1981. 4.141 Hay making alternatives Tseng and Nears (1975) presented a detailed flow chart of most technologies available for forage harvesting. E‘i.gure 4.1 is a simplified version of their flow chart. Pliny making alternatives include all harvest sequences that {Drfloduce dry hay. Dry hay can be packaged in several forms. tPhe more common hay packages are conventional small rectangular bales, large round bales and large hay stacks. Hay making can be broken down into a number of °perations that may occur in the following sequence: 1. Mowing; 2. Conditioning, to enhance drying; 3. Further curing treatment, such as desiccant spraying or tedding; 4. Raking, to bring the material in a narrow windrow for easy pickup; 5. Additional treatment after rain; 6. Pickup and packaging; 7. Hauling to the storage site; 8 . Conveying into storage. 34 A:o_uu:cc~a umou menu E:E«:.: Accuuu:sou s__s u—anuc>v :oqueu ._ucua 2:5 we: uIIIIIIIIIIIIIIIIII _ zc—h<;=zx m=seesuc uvumuso \\ caucum mo—uuuesc mcuusu use can; oz acumen mega—acnsr . canoum moo—uu> uo muo— emmuou dauu>om / _ ouua _ aooum xcsn . . owns _ awesome oz Om z=—h<¢ u2~4=z<= uu<¢OPm .aluunao unsung =q au>quaeusa—c o:u no meow ._.¢ wusudm _— fl .8 U 5 U I) a ass—.1 u9;o=az _ ususmcnuuax acumen - caucus—q: — _.___1_ u.cuoun _ no: you _ 4 ages ..... _ I . zzzus _ a II; _ a:=:_ _ a:ui9m=:5. _ _ _ . _ llllll . _ Panza: _ =ng“ . .23.; Even so: 5".”1 6 “We wane: I I :nemcal Sweat; ”‘ifitib. C 1»; ch .8 0E: 35 Mowing and conditioning are usually simultaneous. Even some additional curing treatments are sometimes simultaneous with mowing-conditioning (e.g. spraying a chemical solution to enhance drying). Tedding however is sequential. Raking is not always necessary. When it is, it is often done just before packaging. Packaging, hauling and conveying may be simultaneous as in the use of a small baler, with an ejector throwing bales into a wagon, operating simultaneously with a transport unit and a bale unloading component at the storage site (figure 4.2). Packaging may also be independent of hauling and conveying when bales are dropped and left in the field (figure 4.3). Round bale and large hay stack systems usually make packaging and transport two independent operations. Bales may be left several days in the field before they are moved into a storage area. These systems are simpler to manage and are less labor intensive than the traditional, simultaneous baling-transport-unloading systems. 36 .ucqvmoncs can uuoamcouu .wcwnon msomcmuusaum now oswu macho mo acuumawumu use .~.¢ muswfim luuoamcmuusumu>umg Essqu: s Fwovmas no Anvm=s .wsqu maemA — ouch wcficmoHss F\‘ mums acacmcacsxuuoamcmuu Esswxmma Amvmeb .mCHcmofics new mafia uuuxm Aevmsh .umvmoacs use has: uuoamcmuu newsman mafia mowuuuucfi Easing: Fx‘ ouch uuoamcwuu Esaaxm=_ _ mums umu>ums Essexmzfl . 4 . men: .uaes A_V¢=a u Auvepe uuoamcmuu a mo mocmmoua .cawuu usu :« vmoH ou wage was as «an» seaweeds: finesse .euoau can on Auv¢=s .eauau <4: .u«:= owououm Eouu Ho>auu On page can :u anode: on page uuoamcmuu a we oucwmam may :a much mcuvcoac= finesse .uuauonm on cause can some Ho>muu cu came .cowm: a fififiu on mafia a~ve=s MEWVQOHED meu~an F l..-..o.w.-..: .— ~ uln...-\~....~ n..- k ‘ t.\u[ru erase...“ zeamnn. 3.3%“.................,.ms.. «...... ......fltrmu. ... , L. 37 .mcqaon ou anacoscuoasn amouo mcqvooacs can uncancaus .uuoaacouu use «sauna unoccoaoccu .n.e ousuum —I much wcuccoassluuoamsauu azsqxam‘ Amuse? .mcaeaosea you can“ spasm Aevmsh .uocooacs new age: uncancmuu manages mafia momuuuucq snags“: H one.“ uuoaocauu a:a«.3=- Fauna acacia: ass—axe:— .c I onzpb .cuuwu may cw mama 0Ht« oz zed: .uwcs Anvzah .vauuu can ou uuoamcouu a uo oucmmoua omuuoum Scum Hu>ouu on mafia , - way :u ouch mcwcuodsz Auvzaa .omQHOum ou macaw .c I Amvzzs <4: .uuc: osu sown Hm>uuu on «Bus . .uamu can“ oz uuoamcmuu a mo oucomam A—vzeb .vHoqu use .c I szmzh may ca ouch acuvmoficz am down (moou on mafia - .oE«u acqtnoucs oz r! 9.3325 2885. N a . I. is 38 4.1.2 Haylage and direct cutting Haylage and direct cutting systems require that harvest and transport to storage or to the feeding bunk be 3 imultaneous operations. Conceptually they are very similar to the baler-transport-unloader system illustrated in figure 4.2. One occasional difference is the parallel use of trucks or wagons pulled by a second tractor in haylage or silage systems. Hitching and unhitching wagons are eliminated. Dump trucks allow rapid unloading into bunk silos. Another difference between the haylage system and the baler-ejector-wagon system is the impossibility of leaving haylage on the ground for later pickup. The option of blowing chopped haylage onto the ground may nonetheless be LlSoeful in dealing with hay which has molded in the windrow. 4 - 2 Field capacity Field capacity is a function of speed, of working width and of field efficiency. It is usually expressed in al‘ea per unit time (e.g. hectares per hour). ThroughPUt capacity is usually expressed in material flow per unit _ ...”... 41.3. 39 time (e.g. tons of dry matter per hour). Throughput may be a function of field capacity and yield for harvesting machines or may be a function of the machine's own ability to process material. Individual and parallel operations are defined aaccording to ASAE (1981), standard 5322. Individual <>perations are continuous and independent from other c>perations. Parallel operations involve two or more nnachinery systems performing their differing functions simultaneously and interdependently. These two types of tnachinery operations will be analyzed in. greater detail below. 4r.2.1 Individual operations Mowing, raking and round baling are examples of individual operations. None can start before a set of management and environmental conditions is met. But once these conditions are met, the individual operation can proceed continuously and independently from other operations. The theoretical field capacity of an individual c’lpeeration is calculated as follows: TFC = (V * WW)/10. (4.1) 40 wwhere TFC is the theoretical field capacity (ha/h); V is the speed (km/h); WW is the working width (m); sand 10. is a conversion factor (km-m/ha). The effective field capacity is lower than the ‘t:heoretical due to turning, idling, minor field adjustments, temporarily slowing down, etc. EFC = (v * ww * FE)/10. (4.2) Where EFC is the effective field capacity (ha/h); ialnd PE is field efficiency (decimal). ASAE (1981) provides some data (D230.3) about the Irange of field efficiencies for various operations. A ifield efficiency of 0.80 will be assumed for all individual iforage harvesting operations (mowing, raking, tedding, laaling, forage chopping independently from transport) (except for round balers (FE - 0.75) and large stack wagons ‘(FE . 0.70). The last two machines need to stop to unload 1:he hay packages. The stack wagon moreover must stop IIDeriodically to compress the stack. These considerations justify the lower field efficiencies. The theoretical throughput is TTP . TFC * YDM (4.3) ‘vlnere TTP is theoretical throughput (t DM/h); Eil1d YDM is dry matter yield (t DM/ha). 41 The effective throughput is ETP = EFC * YDM (4.4) waihere ETP is effective dry matter throughput (t DM/h). 4; .2.2 Parallel operations The moat important parallel operation in forage harvesting is harvest-transport-unloading. Each part of the system can affect the overall efficiency and t:lnroughput. Estimating overall field capacity and material throughput for a given set of parallel operations can be done in three basic steps: 1. Calculate the maximum harvest and transport rates per single unit; 2. Calculate the maximum harvest and transport rates for all units; 3. Balance the) harvest and transport rates by including idle time to one or another of the operations. The concept of cycle time must be introduced to estimate maximum rates. The complete cycle of a forage ‘lmaarvesting machine is the total time required to hitch an 42 empty wagon, to fill it, to unhitch the filled wagon and to :idle while waiting for the transport unit. The hitching sand unhitching times are fairly predictable; they are .ggrouped and called the minimum interface time in the field Iaetween the harvester and the transport unit. TOTHRC = THR(1) + THR(2) + THR(3) (4.5) ‘vwhere TOTHRC is the total harvest cycle time (h); THR(1) is the minimum interface time in the field between the harvester and the transport unit (h); THR(2) is the time required to fill a wagon (h); (aund THR(3) is the idle time the harvester spends waiting for a transport unit (h). The hitching and unhitching times (THR(1)) are fairly Ipredictable and can be provided from experience. Values laetween 0.05 and 0.08 hour are generally used in the model for total interface time. The time to fillawagon, fPHR(2), will depend on throughput of the harvester and ‘Wagon capacity. Throughput is generally expressed as the Inass of dry matter processed per unit time whereas (wagon <=apmcity is in mass of wet matter. The wagon's dry matter Capacity is: DMCAP . WCAP/(l. + m) (4.6) Where DMCAP is the wagon's dry matter capacity (t DM); WCAP is the wagon's actual capacity (t WM); £1116 M is the moisture content (dec, dry basis). 43 The actual time to fill a wagon is THR(2) = DMCAP/ETP (4.7) Assuming no idle time (THR(3)=0), the maximum harvest rate of a single harvester is HR . DMCAP/TOTHRC (4.8) ‘vhere HR is the maximum harvest rate of a single harvester (t DM/h). On very large farms, several harvesters may be working ssimultaneously. The total maximum harvest rate would then Iae XHR . NHU * HR (4.9) ‘Vhere XHR is the overall maximum harvest rate when no idle time is considered (t DM/h); and NHU is the number of harvesting units. ‘Vhen more than one harvester is used, it is implicitly Eissumed that they all are of the same size and capacity. The cycle time of each transport unit is estimated as f ollows: TOTTRC = TTR(1) + TTR(2) + TTR(3) + TTR(4) + TTR(5) + TTR(6) (4.10) where TOTTRC is the total transport cycle time (h); TTR(1) is the minimum interface time in the field between the transport unit and the harvester (h); 44 TTR(2) is the time to travel from the field to storage with a full load (h); TTR(3) is the time to travel from storage to the field with an empty wagon (h); TTR(4) is the minimum interface time at storage, excluding unloading (h); TTR(S) is extra time the transport unit must spend at the storage site to help with unloading (h); 23nd TTR(G) is idle time waiting for the harvester (h). The minimum interface time between the harvester and t:he transport unit TTR(l) is the same as THR(1). Travel t:imes TTR(2) and TTR(3) are calculated by assuming the maximum allowable speed, based on tractor power and jgahysical speed limitations, will be used to travel the ciistance between storage and the field back and forth. The minimum interface time at storage TTR(4) includes IJnhitching and hitching if extra wagons are available and «extra labor is working continuously at the storage site, or t:he time to set up a wagon so it may be ready for IJnloading. If the transport unit can exchange a full wagon for an empty one at storage without any delay besides IJnhitching and hitching, then TTR(S) is zero. In many <=ases however, the transport unit will have to wait for the Ixnloading system to empty the wagon. The waiting time is e st imated as TTR(S) = (DMCAP - QULA)/ULTR (4.11) ‘vlnere QULA is the quantity unloaded during the transport unit's absence (t DM); £1116 ULTR is the unloading rate in the presence of the transport unit (t DM/h). 45 The quantity unloaded during the transport unit's zabsence is estimated as follows: QULA = ULA * (TTR(l) + TTR(2) + TTR(3))/NTU (4.12) *uvhere ULA is the unloading rate in the absence of the transport units (t-DM/h); .aand NTU is the number of transport units. The term ULA will usually have a value of zero in the case of haylage and corn silage but it may be significant i.n the case of baled hay. Hundtoft (1965) reported the ‘Lanloading rate of baled hay as about 6 U.S. tons/man.h. 'IPhis rate was obtained when bales were randomly piled with t:he use of an elevator. In the present model, an unloading Irate of 5 metric tons DM/man.h with a bale elevator and 3.5 mnetric tons DM/man.h without an elevator was assumed. The Ianloading rate in the presence of the transport unit, ULTR, Issually uncreases as the labor available increases. In the case of a mechanical blower, the maximum wet Immaterial flow rate is PTO * XLD * BMECH * 3.6 (4.13) FWM I HEIGHT * G where FWM is the flow of wet matter (t WM/h); PTO is the maximum power available from the power take-off driving tracto (W); XLD is the maximum allowable continuous load (dec); EMBCH is the mechanical efficiency (dec); 3.6 is a conversion factor (t-s/kg-h); G is the earth's acceleration (9.8 m/s ); 46 sand HEIGHT is the silo height (m). The unloading rate expressed in dry matter is ULTR = FWM/(l. + M) (4.14) The maximum allowable continuous load is usually <£iefined as 0.71. The mechanical efficiency is set at 0.08 ifor blowing corn silage and at 0.06 for blowing alfalfa lhmaylage (Kepner et al., 1972; PAMI, 1979). Assuming no idle time (TTR(6)-0), the maximum t:ransport rate per unit is TR . DMCAP/TOTTRC (4.15) Vihere TR is the maximum transport rate per transport unit (t DM/H). The overall transport rate is XTR = NTU * TR (4.16) ‘Vhere XTR is the overall maximum transport rate when no idle time is considered (t DM/h). When more than one transport unit is used, it is implicitly assumed that they all are of the same size and capacity. In general the overall transport capacity XTR will not be equal to the overall harvest rate XI-IR. If the transport treate is greater than the harvest rate, each transport unit \vnill have to idle and wait for the harvester. The average 47 waiting time per transport unit is NTU * TOTHRC - NHU * TOTTRC (4.17) TTR(S) . NHU If the harvest rate is greater than the transport rate, then the harvester will have to idle and wait for a ‘transport unit. The average waiting time per harvest unit 35 NHU * TOTTRC - NTU * TOTHRC (4.18) THR(3) a NTU frhe actual harvest rate is the lowest rate between XTR and :EHR. The above relationships describe the harvest rates for individual and parallel operations. They are used in the <:omputer simulation described in section 4.5. (4.3 Power requirements Designers and analysts of machinery systems must be <:oncerned especially by two types of power requirements: zpeak demand and average demand. The peak power requirement <>ccurs at maximum load or at maximum throughput, under sslippery or sloped conditions. The peak power requirement ciictates what minimum tractor size can be matched with a 48 given implement. The average power requirement occurs at average load, average throughput and under normal soil conditions. It is most useful for estimating average and total fuel consumption. 4 Only average power requirement will be calculated in the present analysis. A safety factor is introduced to Inake sure the actual tractor will also satisfy peak demands. LOAD = PWR/XPWR = 1./SF (4.19) vvhere SF is a safety factor for tractor power design; XPWR is the maximum PTO power available from the tractor (W); ' PWR is the average power requirement (in PTO power equivalent) (W); £3116. LOAD is the ratio of average power required to maximum power available. Typical values of the safety factor range between 1.25 and 1.6, and sometimes beyond. Higher values should be LlSeed when peak demand is considerably higher than average demand, when there are large variations in yield, in slope ‘Eitici in soil conditions. PAMI (1979) has reported that most re<2tangular and round balers require a safety factor of 1.5 ‘:<3 1.6 to make efficient use of high capacity machines in "Ei1:iable conditions. In some types of machines, such as ‘3‘413 grinders, .the tractor may actually stall if the aVailable power is not at least 50% greater than the a"erage power demand due to large variations in peak power 49 demand. It should be noted that PAMI and most other authors neglect power for tractor to move itself which can frequently be more than 20% of total power required for large and heavy tractors. Since the tractor-axle power is already included in the model, a safety factor of 1.4 will be used and should be fairly conservative. The average power required from a tractor (PWR) is distributed into three parts: PWR 8 TRPWR + DBPWR + PTO (4.20) ‘vhere TRPWR is the tractor-axle power to move the tractor itself (W): DBPWR is the tractor-axle power to pull the drawbar w): Eind. PTO is the rotative power from the power take-off shaft to activate some implements (W). The tractor-axle power to move the tractor itself is determined by the tractor weight, the friction force ‘Elszainst the wheels, the tractor speed, the wheel slip and t:he slope of travel. TRPWR = TRM * G * (RRC * c059 + Sine) * v * CFl * SLF/3.6 (4.21) WIIere TRM is the tractor mass (kg); 2 G is the earth's acceleration (9.8 m/s ); RRC is the rolling resistance coefficient; a is the angle of the slope of travel; CFl is a power conversion factor from axle power to PTO equivalent power (CFl=l.10); SLF is the slip factor and is estimated as 1./(1. - SL); SL is slip in decimal form; 50 and V is the tractor speed (km/h). Rolling resistance and slip are estimated from ASAE data D230.3 (ASAE, 1981). The rolling resistance coefficient is RRC = 0.04 + 1.2/cw (4.22) where CN is a soil surface parameter. Typical values are 50 for hard soils, 30 for firm soils, 20 for tilled soils and 15 for soft,sandy soils. Generally a rolling resistance coefficient of 0.08 is used during forage harvesting (firm soil). Predicted slip in decimal form is 1. 0.75 SL :- ——-— ln (4.23) 0.3 * cw 0.75 - (RWTAN + 1.2 + 0.04) RWNOR CN Where RWTAN is the sum of tangential forces against the rear wheels; iirud RWNOR is the normal force of the rear wheels against the soil. The ratio of tangential forces .to normal forces is calculated as follows: RWTAN . DBP + TRM * G * sine (4.24) RWNOR 0.75 * TRM * G * cose where DBP is the drawbar pull (N). “Flue coefficient 0.75 in equation 4.24 assumes that 75% of ‘Ilie tractor weight is distributed on the rear wheels. The cirawbar pull is a function of the weight of the implement and the wagon being pulled. 51 DBP . WIM * G * (sine + RRC * cose) (4.25) where WIM is the mass of the wagon or of the implement pulled by the tractor. For power requirement calculations, the wagon will generally be considered fully loaded except for empty wagons travelling from storage to the field. The second part of power required from a tractor is the tractor-axle power to pull the drawbar. DBPWR 8 DB? * V * SLF * CF2/3.6 (4.26) ‘Where CF2 is the power conversion factor from drawbar power to PTO equivalent power (CF2=l.20). The third power requirement is from the power take-off Shaft to activate rotating implements. Table 4.1 gives Values of PTO power requirements for most harvesting <>Iperations. ASAE (1981) and PAMI (1979) have provided most estimates for power requirements. Power required for u'l<>wing is mainly a function of width while power required 15<>r conditioning is mainly a function of material t1hroughput. Hence a mowing-conditioning operation will be ii function of both width and throughput. Raking and 1=£edding power requirements shown in table 4.1 are I'-‘e=elatively low. All other operations have energy Ii‘equirements proportional to the theoretical flow of dry u~Iatter FDM, expressed in kg of dry matter per second (kg DM/s). FDM is the same as theoretical throughput in 52 equation 4.3 except for units. FDM = TTP/3.6 (4.27) FDM = v * ww * YUM/36. (4.28) where FDM is the theoretical flow of dry matter (kg-DM/s); V is operation speed (km/h); WW is the working width (m); and YDM is the dry matter yield (t-DM/ha). The last equation is obtained by combining equations 4.1, 4.3 and 4.27. The PTO power required from a rotative implement (except mowers) is PTO = PTOW * ww + PTOC * v * ww * YDM/36 ' (4.29) ‘fhere PTO is the power take-off required for a rotating implement (W); PTOW is the power required per unit width in the case of mowers (W/m); ialud PTOC is the power required per unit throughput of dry matter (W/kg-DM/S). E?<:r cutterbar mowers, the power requirement is simply PTO I PTOW * WW (4.30) 53 Table 4.1. Rotative power (PTO) requirements for forage harvesting operations Operation Power requiremment (Watts) Cutterbar mower 1200 * WW Cutterbar mower-cond. 3000 * WW + 2000 * FDM Flail mower-cond. 3000 * WW + 8000 * FDM Drum mower-cond. 6000 * WW + 4000 * FDM Side-delivery rake 1000 * WW Tedder 2000 * WW Baler (rect., alfalfa) 5000 * FDM Baler (rect., wheat) 6000 * FDM Round baler (alfalfa) 7500 * FDM Round baler (wheat) 10000 * FDM .Hay stacker 7500 * FDM Forage harvester Corn silage 15000 * FDM Alfalfa haylage pickup 15000 * FDM Alfalfa green chopping 18000 * FDM Blower: corn silage EMECH = 0.08 Blower: alfalfa EMECH = 0.06 Source: ASAE (1981) and PAMI (1979). The blower power requirement is estimated as follows: PTO = FWM * G * HEIGHT/(EMECH * 3.6) (4.31) where FWM is the flow of wet material (t WM/h); HEIGHT is the silo height (m); Elrid EMECH is the mechanical efficiency (table 4.1). The field operation speed is not constant. Instead it is calculated for each operation to satisfy three criteria: the maximum desirable speed (a user defined limitation), tflne maximum allowable throughput and the maximum allowable 54 tractor load. The maximum speed that satisfies all three criteria will be used for the operation. The maximum desirable speed is a practical speed limitation to prevent excessive wear and tear or malfunction. The maximum throughput is an implement's physical ability to process material. The maximum speed and throughput are both input parameters (e.g. a baling operation may have a 10 km/h maximum speed and the baler may have a 14 t-DM/h maximum throughput). The speed that will satisfy the throughput limitation is estimated from equation 4.28. The speed that will satisfy tractor load .1imitations may be estimated by combining equations 4.19 and 4.20 as follows: (xpwn . TRM*G*(RRC*cose + sin6)*V*CFl*SLF/3.6 S? + DBP*V*SLF*CF2/3.6 + (PTOW*WW) + (PTOC*V*WW*YDM/36) (4.32) Prune above equation can be solved for speed only. v = (xpwn/sr) - (PTOW*WW) TRM*G*(RRC*cose + sine)*CFl*SLF/3.6 4' DBP*SLF*CF2*3 .‘ 6 + PTOC*WW*YDM/36 1 (4.33) 55 The actual operating speed will be the highest speed that will satisfy simultaneously the maximum desirable speed, the maximum allowable throughput and the maximum allowable tractor load. 4.4 Energy consumption Three types of power sources are modeled: gasoline engines, diesel engines and electric motors. Power required from engines is estimated with equation 4.20. Load is estimated with equation 4.19. Fuel consumption equations are taken from ASAE (1981). For gasoline engines, FCONS = 2.74 * LOAD + 3.15 - 0.20 * V597 * LOAD‘ (4.34) where FCONS is fuel consumption (L/kW.h). For diesel engines, FCONS = 2.64 * LOAD + 3.91 - 0.20 * V738 * LOAD + 173 (4.35) Actual fuel consumption rate is approximated by FUEL = FCONS * PWR * (1. + FE)/2. ' (4.36) 56 where FUEL is actual consumption (L/h). The last term in equation 4.36, (l.+FE)/2., is always less than 1. Fuel consumption rate is assumed to be half the normal level when the tractor is idling or turning. The consumption of electricity is expressed in kW.h/h. It is a simple function of the power required. ELECT . PWR/(ELEFF * 1000.) (4.37) where ELECT is electrical power consumption (kW.h/h); PWR is the power required to operate an electric motor (W): and ELEFF is the efficiency of an electric motor (assumed to be generally equal to 0.85). 4.5 Labor requirements One operator is assumed for each harvester and for each transport unit. In the case of a baling-transport-unloading operation, if no bale thrower is Used, then one extra man is assumed to be stacking the IDalesonthe wagon pulled behind the harvester. Extra :Liabor at the unloading site must be specified. The model tI-hen adds up all the labor required for an operation (mamh/h). 57 4.6 Computer implementation The previous equations have been used to write a computer program called FORHRV. It is a static machinery model that estimates the harvest rate, the energy consumption and the labor requirement for 18 different forage harvest operations at any specified yield. The model calculates harvest rates at 6 different yields in a range specified by the user and creates a matrix called RATES(108,8) that retains all the machinery information for use in a dynamic simulation. Program FORHRV is further documented in appendix B. In the dynamic simulation, it is called only once. Information in the RATES matrix is used thereafter to interpolate harvest rates and fuel consumption at various yields generated in a complete simulation. Chapter 7 establishes the link between FORHRV and the dynamic simulation. CHAPTER 5 FORAGE LOSSES 5.1 Introduction Hoglund (1964) presented a useful synthesis of quantitative losses in hay, haylage and silage systems. Since then, a greater recognition has been given to qualitative losses (Waldo and Jorgensen, 1981). In fact, it is the qualitative rather than the quantitative losses that will affect how much corn and soybean meal are required in the ration and whether or not milk production can be maintained. Both qualitative and quantitative losses must be estimated at all stages of forage conservation: harvest, storage and feeding. Losses related to alfalfa harvest are considered in greater detail than losses related to alfalfa storage and feeding or losses related to corn silage. The daily dynamic simulation is used mainly to estimate quality and quantity changes of alfalfa during growth and harvest. 58 59 Changes in storage and feeding are simulated only once per year. Average values are used to estimate storage and ifeeding losses for five different storage methods and seven feeding methods. Quantitative losses of alfalfa in the field are segregated into stem and leaf losses since they are affected differently by various treatments or environmental factors. Losses are expressed as a fraction of the remaining material or nutrient. RF(I) = 1. - LS(I) (5.1) where RF(I) is the remaining fraction of material or nutrient after treatment I is applied; and LS(I) is the fractional loss incurred by applying treatment 1. After several treatments have been applied and after a number of environmental factors have come into play, the final remaining fraction is: FRF . RF(1) * RF(2) * ... * RF(N) (5.2) where FRF is the final remaining fraction of material or nutrients; and N is the number of treatments and environmental factors that account for losses. Let us consider singly the more important treatments and environmental factors that affect losses. 60 5.2 Alfalfa harvest losses due to mechanical treatments Mechanical treatments that produce harvest losses include mowing, conditioning, raking, tedding, baling and chopping. Some treatments are especially harsh on the alfalfa leaves at low moisture content. Mechanical treatments produce material losses but do not generally change the chemical composition of stems and leaves. However the stem to leaf ratio may change and cause a change in the average nutritional composition of the whole plant. This indirect change in quality is estimated at the end of harvest. 5.2.1 Mowing and conditioning Research carried out by this author (Savoie et al., 1981) showed that dry matter losses due to mowers varied between 0.25% and 1% of the yield. Losses were lowest for cutterbar mowers and highest for drum mower-conditioners. Mower-conditioners followed by heavy crimping produced up to 2% of dry matter losses under light yields. Dale et a1. (1978) estimated average mowing losses as 1% of total yield for the cutterbar, 2% for the mower-conditioner and 4.6% 61 for mowing and heavy crimping. These values are about double the ones measured. The measured mowing losses consisted only of detached material shorter than 200 mm that would not likely be raked back in the windrow. The measured losses did not include losses from unmowed alfalfa or small particles within the windrow which might be lost in subsequent handling. In general, total losses of 1% for the cutterbar and 2% for the mower-conditioner were assumed. Dale et a1. (1978) assumed that all mowing losses consisted only of leaves and no stems. This assumption was tested in June 1981, during the first alfalfa cut at the Chatham Experiment Station in Michigan: mowing losses were separated into leaves and stems. The original data are shown in table 5.1. The average dry matter yield at cutting was 4400 kg/ha; it was split as 39% leaves and 61% stems on a total dry matter basis. Relative losses were low, between 0.025% and 0.4%. The measured losses consisted of about 75% leaves and 25% stems. If the total dry matter loss from a mower-conditioner is assumed to be 2%, then the distinct losses are 4% of the leaf mass and 1% of the stem mass. 62 Table 5.1. Ratio of leaves and stems lost after mowing (data collected in Chatham, Michigan in June 1981). Previous Number of Average losses Leaves as a operations(l) samples (kg-DM/ha) fraction of Leaves Stems total loss CB 4 2.88 1.63 0.64 MC 4 4.92 1.80 0.73 MCW 4 13.76 5.70 0.71 CB 4 0.65 0.32 0.67 MC 4 13.25 2.35 0.85 MCW 4 9.33 2.42 0.79 Average losses 7.47 2.37 0.76 (1) Operations are: CB, cutterbar mower; MC, cutterbar mower-conditioner; MCW, cutterbar mower-conditioner- windrower. Data were collected for three operations and for two replications on different days. 5.2.2 Raking Hundtoft (1965) published a curve shatter relating losses to moisture content during raking. It is redrawn here in figure 5.1, adjusted for a change in the abscissa from wet basis to dry basis moisture content. The. statement of shatter losses would lead one to believe that most losses are leaves. Original field data in table 5.2 show that dry matter losses for raking are split almost evenly between leaves and stems. These plots were raked at dry basis moisture levels between 1 and 3. The ratio of leaf to stem losses different under may be dryer FRACTIONAL LEAF 'Loss 63 .10 J- . L I I (15 ‘L0 “L5 21) 2J5 MOISTURE CONTENT I y I I Figure 5.1. Leaf dry matter loss from raking, as a fraction of total leaf mass, versus dry basis moisture content (adapted from Hundtoft, 1965). /\ MM.L———---———-- 100%.. > V Vc SPEED Figure 5.2. Hypothetical relationship between dry matter losses and speed of operation. 64 conditions. Stem losses might be expected to remain constant while leaf shatter is likely to increase considerably as the alfalfa becomes dryer. Stem loss from raking will be set constant at 2% of stem mass and leaf loss will be estimated from figure 5.1, as a fraction of the remaining leaf mass. Table 5.2. Ratio of leaves and stems lost after raking, including mowing losses (data collected in Chatham, Michigan in June 1981). three operation sequences and different days. for three Previous Number of Average losses Leaves as a operations(1) samples ‘(kg-DM/ha) fraction of Leaves Stems total loss CB-R 2 3.83 1.00 0.79 MC-R 2 3.25 0.79 0.80 MCW-R 2 2.80 0.63 0.82 CB-R 2 37.37 34.65 0.52 MC-R 2 22.44 15.53 0.59 MCW-R 2 33.59 27.49 0.55 CB-R 2 19.59 16.78 0.54 MC-R 2 26.79 29.22 0.48 MCW-R 2 15.10 16.43 0.48 Average losses 18.31 15.84 0.54 Operations are: .cutterbar mower; MC, cutterbar mower-conditioner; cutterbar mower-conditioner- windrower; parallel- bar rake. Data were collected for (replications 65 5.2.3 Tedding The tedder spreads the alfalfa across the swath in a rapidly rotating and hitting motion. Dry matter losses measured in the field from tedding were between 1 and 2% of total yield per treatment (Savoie et al., 1981). Tedding was generally applied at high moisture contents (M > 2). No research has apparently estimated tedding losses at very low moisture contents. Leaves would probably shatter in a fashion similar to what can be observed during raking. Dry matter losses from tedding are assumed to be the same as raking losses: only leaves are lost in a proportion given by figure 5.1. 5.2.4 Baling Three types of balers were considered: the conventional baler making small rectangualr bales, the large round baler and the large hay stack wagon. Alfalfa is usually baled only when the hay is dry enough for storage. Leaves are then very dry and brittle. As will be shown, leaves make up the greater part of dry matter losses during baling. 66 Whitney (1966) measured total dry matter losses from a conventional baler between 1.4 and 3.8% of yield, but did *Tnot distinguish stem from leaf losses. He noted that a bale ejector would increase the losses by between 0.3 and 1%. Kjelgaard (1978) used an average of 3% for baling losses from a conventional baler. Friesen (1977) compared the nutritional value of bale chamber losses with the bale itself: losses had a protein concentration of 22% while the baled hay had a protein concentration of 14%. Since alfalfa leaves and stems have a protein concentration of about 28% and 11% respectively and leaves represent initially 40% of the total dry matter at mowing time (Bert et al., 1952), a total dry matter loss of 3% would then be split as 5% of the leaf mass and 2% of the stem mass during baling. An ejector would increase leaf loss to 7.5%. Anderson et a1. (1981) and Kjelgaard (1978) have suggested 10% as an average value for dry matter losses from round balers. PAMI (1979) indicated that round baler losses can vary between 5 and 25%: very high losses are more likely to occur in light and dry alfalfa hay. Whole stems and leaves are lost at the pickup stage while mostly leaves are shattered in the bale chamber. Assuming that 10% of the total dry matter is lost, of which 75% consists of leaves, and that leaves represent initially 40% of the Inass, then 19% of the leaves and 4% of the stems are lost during r Kje matter assmpt leaves . 67 during round baling. Kjelgaard (1979) estimated average stack wagon dry matter losses at 13% of total yield. Using similar assumptions as in the case of the round baler, 24% of the leaves and 5% of the stems are lost during the formation of large hay stacks. 5.2.5 Chopping Losses from chopping and blowing alfalfa into a wagon are estimated at 5% of total yield for wilted alfalfa and 2% for direct-cut alfalfa (Kjelgaard, 1979). Leaves are much more likely to be lost than stems in this operation where air flows are present. Assuming that 75% of the loss consists of leaves and that leaves represent initially 40% of the total dry matter, then 9% of the leaves and 2% of the stems are lost while chopping wilted alfalfa and 4% of the leaves and 1% of the stems are lost while chopping fresh alfalfa. 5.2.6 The effect of ground speed on material losses The operation speed and the alfalfa yield have not (been considered as factors affecting total material losses. IJale et a1. (1978) assumed a linear relationship between 68 raking speed and material losses: relative losses were 0% at 0 km/h and 100% at 10 km/h and above. Meanwhile, . Anderson et al. (1981) measured greater baling losses at 5.6 km/h than at 8.1 km/h. They did not explain this unexpected result. Very little else has apparently been published on the effect of ground speed on material losses. The effect might be important since the physical impact is increased at higher speeds. There is also a relationship between yield and speed: as yields become lower, machines are likely to be operated faster to make more efficient use of the throughput capacity. Low yields are conducive to higher speeds and probably higher relative material losses. Figure 5.2 is a hypothetical relationship between losses and speed. At some average speed V, average losses are expected (100%). Above this speed up to a critical speed Vc , material losses would increase linearly to some maximum level (MAX). Below the average speed, material losses would decrease linearly to a minimum level. This minimum is not likely to be 0, especially in the case of operations using rotative power independently from ground speed. .In the present simulation model no ground speed effect will be assumed in the estimation of material losses (MAX = 100% 8 MIN). More field research would be necessary to test this assumption. 5.3 A1 Me quanti 01959 t Within stem to of the same m: E: (1' 69 5.3 Alfalfa harvest losses due to environmental factors Mechanical treatments were seen to produce important quantitative losses of alfalfa stems and leaves. However these treatments do not alter the nutrient concentration within either stems or leaves. Of course a change in the stem to leaf ratio indirectly changes the composite quality of the whole crop since leaves and stems do not have the same nutrient composition. Environmental factors affect directly both dry matter losses and quality changes. Rainfall, plant respiration and general exposure to the weather are known to alter the digestibility of the alfalfa stems and leaves and, to a lesser extent, to change the crude protein concentration. Dry matter losses and quality changes will alternately be considered. 5.3.1 Dry matter losses from respiration Plant cells of alfalfa remain alive and continue to respire several hours after mowing. Carbohydrates used in respiration are essentially 100% digestible and represent (an important nutritional loss (Moser, 1980). Respiration of ali at t1 70 of alfalfa cell tissues is maximum and relatively constant at temperatures between 30 C and 45 C. It will cease at itemperatures above 55 C or at moisture contents below 35% 'on a wet basis (Wolf and Carson, 1973). Respiration is also practically zero below 0C (Wilkinson and Hall, 1966). The respiration equation is given by Wood and Parker (1971) as: 264 9 CO2 + 108 9 H20 + 677 kcal (5.3) Respiration is often measured in laboratory trials by the concentration of CO2 in the air. Every gram of CO2 measured corresponds to 0.68 g of carbohydrate lost from the alfalfa dry matter. There are some discrepencies in reported values of maximum respiration rates after cutting alfalfa. Wilkinson and Hall (1966) noted a maximum heat generation rate of 36000 BTU per U.S. ton per hour at 27 C and at 80% moisture content on a wet basis. Since one lb of carbohydrate generates about 6770 BTU of heat, the respiration rate is 0.0027 kg-CGleos/kg-DM/h. Wolf and Carson (1973) reported initial rates as high as 0.007 kg-COZ/kg-DM/h or 0.0048 kg-CGHIZOG/kg-DM/h, at 30 C and at 70% moisture content on a wet basis. Wood and Parker (1971) suggested maximum respirat 80% moi: assumed respira‘ and 71 respiration rates of 0.003 kg- COz/kg-DM/h for rye grass at 80% moisture (wet basis) and at 25 C. Dale et a1. (1978) assumed that legumes had respiration rates 50% greater than respiration rates of grasses. The maximum respiration rate of freshly cut alfalfa is likely to be in the range of 0.003 to 0.004 kg- Csleos/kg-DM/h. The respiration rate increases exponentially with an increase in temperature between 0 and 30 C (Wood and Parker,197l). It increases approximately linearly with an increase of the moisture content on a dry basis (Wilkinson and Hall, 1966). A simplified relationship between respiration rate and temperature and moisture content is proposed: R 0: (nae/30)2 * (we) (5.4) where R is the respiration rate (kg-C6H OG/kg-DM/h); TDB is the dry bulb temperature (C7; and M is the moisture content of alfalfa, on a decimal, dry basis (dec, d.b.). This relationship is valid in the ranges 0 < TDB < 30C and 0.5 < M < 4. For temperatures or moisture contents above these ranges, the factors in parenthesis are one. Below the ranges, the respiration rate is zero. For temperatures between 45 and 55 C, the rate actually decreases (Wolf and Carson,1973); such temperatures are not usually encountered during hay making in northern climates. Th« exponen Where ) Integr; 3931ac ma K2 10:31 72 The moisture content decreases approximately as an exponential decay function: M a Mo * exp(-k * t) (5.5) where Mo is the initial moisture gentent (dec, d.b.); k is the drying constant (h )° and T is time (h). I Substituting equation 5.5 in equation 5.4 yields a cx (TDB/30)2 * (Mo/4) * exp(-k * t) (5.6) Integrating over time will give the total respiration loss. Replacing coefficient k by two empirical coefficients kl and k2, the following equation may be used to estimate total respiration loss. (ma 2 (Mo) * kl * (1. - exp(-k2 * t)) - TRL . -- ‘- (5.7) -30 4 where TRL is the total respiration loss (kg- C6H1206/kg-DM). A number of researchers agree that total respiration losses of field cured forages may amount to 10 or 15% of the original dry matter (Watson and Nash, 1960). Assuming a maximum dry matter loss due to respiration of 15% and a maximum total respiration loss of 0.4% in the first hour at 30 C and M = 4.0, values of coefficient kl and k2 are 0.15 and 0.0291 respectively. Re accumu] cannot loss 1! 5.3.2 73 Respiration losses are calculated daily (t = 24 h) and accumulated as long as alfalfa is not harvested. The total cannot however be greater than kl. The same fractional loss is assumed for both leaves and stems. 5.3.2 Dry matter losses from rainfall Rain may increase dry matter losses in several ways: by breaking off leaves through direct impact of rain droplets, by leaching soluble nutrients, by requiring additional machinery treatments to enhance drying and by prolonging respiration of the wet alfalfa. Dry matter losses due to machinery treatments and to respiration have already been dealt with previously. Leaching loss is not likely to represent a large amount of dry matter but it might affect the digestibility. This is discussed in the next section. In laboratory experiments, Collins (1981) estimated that 20% of the leaves were lost after two showers totaling 50 mm of rain. Since no mechanical handling was involved, this loss is presumably due only to the impact of rain. Assuming a linear relationship between leaf loss and rainfall, leaf loss due to rain is 0.4% per mm of rain. 5.3.3 and 74 5.3.3 Changes in digestibility Digestibility of alfalfa leaves and stems is affected primarily by respiration and rainfall. Respiration losses consist practically of 100% digestible nutrients. Digestibility of leaves and stems is corrected as follows at the end of the respiration process: an(r) = (TDN(I) - TRL)/(1. - TRL) (5.8) where TDN(F) is the final digestibility, at the end of field curing (dec); TDN(I) is the initial digestibility, at the time of mowing (dec); and TRL is the total respiration loss. Digestibility is also affected by rainfall. Collins (1981) estimated that cell wall concentration in alfalfa increased from 32.3% to 38.4% after 50 mm of rain. A linear relationship exists between digestibility and cell wall concentration. From data given by the NRC(1977), the relationship for alfalfa is: TDN . 1.06 - cw (5.9) where TDN is total digestible nutrients or digestibility dec ; and CW is the cell wall concentration (dec). *3 equal Assumi level due tc 75 The increase of 6.1% in the cell wall concentration is equal to a drop of the same amount in digestibility. Assuming a linear relationship and an initial digestibility level of 60%, the average relative drop in digestibility due to rain is 0.2% per mm of rain. 5.3.4 Changes in crude protein Many contradicting statements have been published about protein losses during field curing of alfalfa. On the one hand, several authors believe that protein concentration changes very little during field drying of alfalfa (Moser, 1980; Collins, 1981; Watson and Nash, 1960). On the other hand, a number of field experiments have shown substantial drops of crude protein concentration between the time of cut and the time of baling (Bert et al., 1952; Shepherd et al., 1954) or between haylage and hay, the latter being exposed longer in the field and suffering larger protein losses (Hillman et al., 1970). Protein concentration could decrease either through the physical fragmentation of leaves or through a degradation process within the plant tissues. At the time of cutting, the alfalfa leaves have a protein concentration of about 28% and the stems have a protein concentration of about 11% (Bert et al., 1952). If leaves are shattered, the ; cont: prote alfal cons: rapiv I the 76 the protein concentration will certainly decrease. In a controlled laboratory experiment, Collins (1981) found that protein concentration actually increased slightly during alfalfa drying, even after rain. Apparently other cellular constituents, especially carbohydrates, are lost more rapidly through respiration and leaching, thus increasing the concentration of protein. Bert el al. (1952) compared the nutrient content of field cured and barn-cured hays. The barn-cured hay contained 17% crude protein while the field-cured hay had 15.6% protein, an additional relative loss of 8.24%. At the time of cut, leaves represented 48.5% of the alfalfa dry matter. At the end of the harvest and drying processes, the barn-cured hay had 37.9% leaves and the field-cured hay had 33.3% leaves. The difference in leafiness explains about half the difference in protein concentration; the other half would be due to a weathering degradation process. The field-cured hay remained in the field between three and ten days while the barn-cured hay was removed from the field after one or two days. Assuming field-cured hay was exposed three extra days on the average (72 hours), the rate of protein concentration loss would be 0.ll%/h. Shepherd et a1. (1954) also observed a consistent decrease of crude protein concentration between the time of cut and the time of baling. The relevant data are compiled in ta) betweex and 1: table exposu it act '-'as e' Frctei time; on or 77 in table 5.3. The relative loss of crude protein varied between 7 and 11% for non-rained-on alfalfa and between 12 and 18% for rained-on alfalfa. The last column in the (table shows the rate of crude protein loss (%/h of exposure). The rained-on hay had a larger total loss but it actually had a slightly smaller loss rate (%/h) since it was exposed longer to. weather before baling. The crude protein loss appears closely related to total exposure time: about 0.15%/h , no matter whether alfalfa was rained on or not. Table 5.3. Change in crude protein alfalfa during field drying (from Shepherd et al., 1954). Trial No. of Total Hours CP(%) % loss of CP no. showers rain exposed As As Total Rate (mm) in the cut baled (%/h) field 1 0 0. 52. 19.56 18.13 7.31 .1406 2 0 0. 48. 21.57 19.26 10.71 .2231 3 0 0. 61. 18.21 16.88 7.31 .1198 Average rate loss (1,2,3) 1.1612 1a 2 17. 84. 21.58 18.91 12.37 .1473 4 3 27. 131. 20.94 17.19 17.91 .1367 Average rate loss (la,4) .1420 The decrease in protein concentration is probably due to both weathering and a change in the leaf to stem ratio resulting from machinery treatments. The field experiments have not distinguished the contribution of each. Plant respiration and leaching do not decrease the protein concem bleach and N. may al concen rate 0 set a relate 23% cc :0 dai Protei U. C 0“ 5.0 78 concentration. However, other weathering factors such as bleaching, wind and “enzymatic changes" reported by Watson and Nash (1960), which have not previously been mentioned, may all contribute to substantially reduce the protein concentration. On the basis of values estimated above, the rate of protein concentration loss due to exposure only was set at 0.10%/h. This loss does not include machinery related losses. For example, alfalfa initially containing 20% crude protein would lose 24% of its concentration after 10 days (240 h) of field curing and would have a final protein concentration of 15.2%. 5.4 Alfalfa storage and feeding losses For storage and feeding, average dry matter losses presented by Kjelgaard (1979) were used. Storage losses for dry hay are 4%, 12% and 16% for rectangular bales, round bales and hay stacks. The last two values are based on outside storage. Sheltered storage of round bales and hay stacks would probably reduce dry matter losses to the same level as rectangular.bales. Storage losses of wilted alfalfa haylage and of direct cut alfalfa silage are 7% and 13% respectively. Fe rectang 11% £01 silage 79 Feeding dry matter losses are on the average 5% for rectangular bales, 14% for round bales, 16% for hay stacks, .11% for either wilted alfalfa haylage or direct cut alfalfa silage (Kjelgaard, 1979). No published data appears to distinguish between stem and leaf losses during storage and feeding. Consequently the same loss fraction was assumed for stems and leaves. Little quality change occurs during storage of dry hay. Weeks et a1. (1975) observed that digestibility of stacked hay remained around 60% after 10 months of storage. Verma and Nelson (1981) reported that the digestibility of alfalfa in round bales actually increased by 3% after one year of outside storage. Crude protein concentration also increased by 5%. For simulation purposes, quality of alfalfa hay was assumed not to change during storage. While dry hay is chemically stable once it reaches storage, direct-cut or wilted forages undergo substantial changes during the ensiling process. Respiration continues as long as oxygen is present. If the water content is high, considerable seepage may occur and soluble 'nutrients are leached. Low moisture haylage may mold if too much air is present. Few studies have measured specifically the changes of crude protein and digestibility of alfalfa stored as a wet forage. Watson and Nash (1960) reported that ensiling red clover concent feeding haylage changes M: crude éirect- 1961, 80 clover . produced a slight increase in crude protein concentration and a decrease in digestibility. A number of feeding experiments have compared alfalfa hay with alfalfa haylage: these studies may be helpful in understanding the changes that occur during storage of wet alfalfa. Most researchers agree that alfalfa hay contains less crude protein and more crude fiber than haylage or direct-cut alfalfa at the time of feeding (Gordon et al., 1961, 1963; Brown et al., 1963; Thomas et al., 1969). Haylage is generally more digestible than hay. One notable exception is provided by Gordon et al. (1961) who, in an early experiment with sealed silos, estimated hay to be more digestible than haylage. Excess heating during fermentation might have reduced the digestibility of haylage. The lower crude protein and the lower digestibility of hay compared with haylage are probably accounted for mainly by the difference in field curing time and not by storage changes. Little changes in crude protein concentration and in digestibility are likely to occur during fermentation, except in the case where haylage is exposed to excess air and might result in heat-damaged, lower digestibility forage. There are also differences in the intake of hay versus haylage: animals will in general consume more hay than haylage but, as more grain is fed, this intake difference 81 is reduced. These nutritional aspects are left within the ration formulation model by treating hay and haylage as two distinct crops. For simulation purposes, quality of alfalfa haylage or silage was assumed not to change during storage. It should be noted that maintaining high quality throughout the storage period is likely to require more management skills with fermented forages than with dry forages. 5.5 Corn silage losses Kjelgaard (1979) quoted average DM losses of 5% for harvesting, 6% for storage and 4% for feeding of corn silage. Quality changes are likely to occur in the silo. Watson and Nash (1960, p.401) reported that, in one experiment, crude protein concentration increased by 5% and total digestibility decreased by 9%. However no extensive data on these changes seem available. Consequently quality of corn silage was assumed unaltered during storage. 82 5.6 Summary of losses Alfalfa harvest losses are estimated in greater detail than all other losses (storage, feeding, corn silage) because the dynamic simulation is intended primarily to simulate daily growth and harvest of alfalfa. Storage and feeding are simulated only once per year; average loss values are used. Table 5.4 shows values that were used to estimate dry nutter losses of alfalfa leaves and stems during harvest. 'Table 5.5 illustrates dry matter loss values for storage and feeding. The same fractional loss is assumed for both leaves and stems. Quality changes are estimated according to the values given in table 5.6. Important quality changes are estimated during field curing. Quality changes during =3taorage are practically ignored in the present model for lack of extensive data. 83 Table 5.4. Alfalfa dry matter losses during harvest and curing. - Factor Leaf loss Stem loss as a fraction as a fraction of leaf mass of stem mass 1. Mower 0.02 0.005 2. Mower-conditioner 0.04 0.01 3. Rake . (0.02-0.21)a 0.02 4. Tedder ' (0.02-0.21)a 0.00 5. Baler (conventional) 0.05 0.02 6. Bale-ejector 0.075 0.02 7. Round baler 0.19 0.04 8. Stack wagon 0.24 0.05 9. Chopper (wilted) 0.09 0.02 10. Chopper (direct-cut) 0.04 0.01 11. Respiration (0.00-0.15)b (0.00-0.15)b 12. Rainfall 0.004/mm 0.00 (a) Rake and tedder will shatter between 2 and 21% of leaves depending on moisture content, as in figure 5.1. (b) Respiration losses will vary between 0 and 15% f alfalfa stored as a wet forage. Better estimates of liarvest dry matter losses are also needed, especially the distinction between leaves and stems and the effect of Sixpeed and moisture content. CHAPTER 6 FIELD DRYING OF ALFALFA A drying model is developed to be used in the dynamic simulation of forage harvesting. The model is not definitive; much research could still go into improving its ,predictive value. Since the main objective of the present dissertation is to simulate the whole forage system, the stem ratio and whether the alfalfa had been immersed in a Solution of potassium carbonate or not. The model is of 1iJnited use to predict field drying because it is based on 88 laboratory trials dealing with small samples of alfalfa unlike the windrow structure found in the field. Dale et al.(1978), and in a more detailed study Dale (1979), presented a model to predict the drying rate of alfalfa in field conditions under various mechanical treatments. The evaporation model, although presented in a different form, is in fact: gm = -k * (M - EMC) (6.1) dt where M is the moisture content (dec, d.b.); t is time (h); EMC is equilibrium moisture_iontent (dec, d.b.); and k is the drying constant (h ) 'The constant k is a function of solar radiation, wind ‘velocity, plant density, species and type of conditioning. Dale's conceptual model has been useful in understanding the important factors affecting drying. The ritamerical model however has important weaknesses. First, tille model is left implicitly as a difference equation for Computer implementation. This is correct, but the fact that no attention is paid to the size of -the time increment can lead to fairly large errors in the estimation of Inc>Iisture content. Secondly, k is simply calculated by multiplying together solar radiation, a wind velocity faCtor, a crop density factor and a species-conditioning factor. This assumes that doubling the solar radiation W1ll double the drying rate - a rather unlikely outcome. 89 It also neglects convective evaporation due to air temperature. Thirdly, equilibrium moisture content of oats was used in the model for lack of data about alfalfa. A more realistic model of alfalfa equilibrium moisture content is presented further in this chapter. 6.2 Theoretical Model The decreasing rate model is often proposed to simulate the drying of biological products (Brooker et al., 1974). in; - -k * (M - EMC)C (6.2) dt The exponent c is often equated to one. In fact the whalue of c is likely to vary with M. When moisture content is very high, the moisture evaporates almost freely-and at ii constant rate. Then c is equal to 0. As a biological EDIWDduct dries, the drying rate is no longer constant but decreases as the moisture content decreases. The value of <3 :is likely to increase as the material becomes dryer. Since the main objective of this research is to simulate the dynamics of forage harvesting, the drying model is simplified into a single equation to predict drYing in all moisture ranges. For reasons explained in 90 the statistical analysis in section 6.4.1, c is equated to one in equation 6.2. The constant k is tentatively defined as a linear function of environmental and operational factors. k = bo + bl * SR + b2 * TDB + b3 * wv +_b4 * DENS + b5 * xx + b6 * CD + b7 * RNDW + b8 * DAY + b9 * XTR (6.3) where bo, bl, ... , b9 are statistical estimates of parameters affecting drying; SR is the average solar radiation on a horizontal surface (cal/min/cm2); TDB is the dry bulb temperature (C); WV is the wind velocity (m/s); DENS is the dry matter density in the windrow (kg/ha); ‘ RK is a raking factor; CD is a conditioning factor; RNDW is a free water factor, affecting drying rate after rain or dew adsorption; DAY is a factor to distinguish the first day from the subsequent days of field drying; iirld XTR is an extra or additional treatment factor (e.g. chemical application, maceration). Triie last five variables are actually dummy variables with Values being either 0 (no treatment, no rain, first day dtying) or 1 (treatments, adsorption of rain or subsequent days of drying). _ The variable DENS is the alfalfa dry matter density in the windrow in kg/ha. It is estimated as follows: NR = WW/WC (6.4) DENS - YDM/WR (6.5) 91 where YDM is the dry matter yield of alfalfa (kg/ha); ww is the width of the windrow (m); WC is the width of the cut (m); and WR is the width of windrow to width of cut ratio. The raking dummy variable is set on (RKsl) only during the day of raking and set off (RK=0) subsequently. The reason is that raking displaces wet forages from the bottom to the top of the windrow. The beneficial drying effect is present for a limited number of hours and disappears thereafter. With c=l, a simple analytical expression can be derived from equation 6.2. M ' EMC ( \= exp(-k * t) (5-5) Mo - EMC] where Mo is the initial moisture content; and M is the moisture content at time t. A major advantage with the use of the analytical equation 6.6 is that the time increment is not an issue. The actual moisture content of a field curing plot can be estimated at any time in the day by this single equation. (Moreover the time when the plot will be ready for harvest Can also be estimated by solving for t in equation 6.6. On 'the other hand, the use of equation 6.2, expressed as a ‘3ifference equation, for estimating drying poses a serious PFOblem with regards to the choice of a time increment. A 1al'ge time increment would certainly lead to substantial 92 inaccuracies. A very small increment could increase significantly the computation time and cost. The problem of a time increment is avoided by using an analytical equation. 4 The statistical estimation of the coefficients ho to b8 in section 6.4 indicates that some coefficients are not significant. Estimates of b9*XTR, for additional treatments, will be inferred from data published in the literature on various new technologies. 6.3 Equilibrium moisture content Alfalfa left in a specific environment indefinitely will reach an equilibrium moisture content (EMC). Therefore EMC is a very important factor in alfalfa drying: it indicates whether a hay will lose or gain moisture and provides some insight as to the rate of moisture transfer. Zink (1935) measured EMC for various hays, including alfalfa. Dexter et al. (1947) noticed a hysteresis effect in EMC of alfalfa: under the same environment, initially dry alfalfa will reach a lower EMC (through adsorption) ‘than initially wet alfalfa (through desorption). They also °bserved that several samples molded before reaching EMC When they were exposed to a relative humidity above 85%. 93 Bakker-Arkema et al. (1962) did a systematic study of EMC of alfalfa. They measured EMC in the ranges of 4.4 to 48.9 C and 10 to 90% relative humidity. They also measured the difference between adsorption and desorption. They reported that immature alfalfa had a higher EMC than mature alfalfa. A regression model was used to fit the adsorption data provided by Bakker—Arkema et al. (1962). The experimental data and the regression curves are plotted on figures 6.1 and 6.2. The relative humidity was split into four ranges to provide a better fit. In the range 0.10 < RH < 0.60, EMCA = 0.026850 + 0.146462 * RH + 0.045716 * RHZ +0.00036081 * TDB - 0.0013128 * RH * TDB (6.7) where EMCA is the equilibrium moisture content of alfalfa from adsorption (i.e. the alfalfa is initially drier than the environment) (dec, d.b.); RH is the relative humidity (dec); and TDB is the dry bulb temperature (C). In the range 0.60 < RH < 0.90, EMCA = 0.37517 - 1.2816 * RH + 1.4283 * RH2 +0.0065621 * TDB - 0.010839 * RH * TDB (6.8) Data below 10% and above 90% relative humidity are sparse. EMC was assumed to be 0 at 0 relative humidity. In the range 0 < RH < 0.10, simple linear interpolation is used. EQUILIBRIUM MOISTURE CONTENT 94 - — - - -" Regression curve. 60% 50 100 150 200 250 tons DM Figure 8.2. The initial cost of clear span barns for the storage of hay versus storage capacity. 142 8.4.1.2 The cost of hay barns Table 8.4 shows prices and sizes of clear span buildings that may be used for the storage of dry hay. The storage capacity is estimated by substracting 12' from the width for moving in the building, by substracting 1' from the height for clearance and by assuming a hay density of 157 kg/m3. As with silos, a trend exists between cost and capacity. Table 8.4. Prices of clear span buildings (quoted from Detroit Steel, Charlevoix, MI and from Lane Clear Span Building, Adrian, MI). Building size Useful Cost Cost (width x capacity (5) per unit length x (t-DM) storage height) ($/t-DM) 40' x 42' x 12' 58. 4290. 74. 50' x 98' x 12' 182. 6995. 38. 60' x 98' x 14' 272. 8590. 32. 40' x 40' x 14' 65. 3777. 58. 40' x 48' x 14' 78. 4495. 58. 40' x 60' x 14' 97. 4888. 50. 40' x 72' x 14' 117. 5795. 50. 48' x 78' x 14' 150. 6495. 43. The actual barn prices are plotted on figure 8.2 versus storage capacity. For low capacities, the marginal cost is about $75. per ton of dry matter. For capacities above 150 tons DM, the slope becomes practically constant at $20./t-DM. The total cost of a 150 ton barn is $6200. If 143 Assuming that zero capacity will cost nothing, the four boundary conditions are used to fit a cubic equation. For capacities below 150 tons DM, the initial cost of a hay barn is predicted as BC = 75. * CAP - 0.30667 * CAP2 3 + 0.000548 * CAP (8.11) where BC is the predicted initial cost of a clear-span hay barn (S); and CAP is the useful storage capacity of the barn (t-DM). For barn capacities above 150 tons DM, the initial cost is predicted as BC = 6200. + 20. * (CAP - 150.) (8.12) 8.4.2 Prices of feed Table 8.5 shows the prices used for the purchase of supplemental feeds and for the sale of excess forages. Prices are based on recent prices published by Nott et a1. (1981) and by the Michigan Agricultural Reporting Service. To convert U.S. units into metric tons of dry matter, moisture contents of 20% and 15% on a wet basis are assumed for alfalfa hay and corn grain respectively. 144 Table 8.5. Prices of inputs and outputs used in the ration formulation model. Item Price Price (U.S. units) (metric units) ($/t-DM) Milk $13./cwt 286. Soybean meal $225./ton 248. Buying alfalfa hay $60./ton 83. Selling alfalfa hay $50./ton 69. Buying corn grain $3.00/bu 139. Selling high-moisture corn --- 90. Selling corn silage --- 70. Market prices are usually not published for high-moisture corn and corn silage. High-moisture corn has practically the same feeding value as dry corn. However it has a very short life once it is taken out of storage so its marketing is difficult. Corn silage has a lower nutritional value than corn grain and spoils rapidly after it is taken out of storage. Selling prices are set arbitrarily at about 35% below the purchase price of equivalent feeds because of the short preservation period once these fermented feeds are taken out of storage. 8.4.3 Interest rates Interest or discounting rates are required to estimate the annualized cost of durable assets such as machinery or storage structures. Table 8.6 shows the discount rates and 145 accounting lives that are generally assumed in the analysis. Table 8.6. Discount rates and accounting life to estimate yearly cost of durable assets. Item Discount rate Accounting life (years) Machinery 0.15 10. Storage structures 0.13 30. The discount rates in table 8.6 are actually nominal rates because they include the effect of inflation. Real interest rates are closer to 0.04. When comparing alternatives of different capital cost, it may be more appropriate to use real rates. The real and nominal rates will be used alternately to compare hay and haylage systems in chapter 9. In general, nominal rates from table 8.6 will be used. CHAPTER’Q SIMULATION RESULTS The models described in the previous chapters along with those described by Parsch (1982) are linked together to simulate forage harvest, storage and feeding. The simulation model is used to test how various management or technological changes might affect. the forage system's performance. Simulation results in this chapter are based on 26 years (1953-1978) of historical weather data from East Lansing, Michigan. Results are generally shown as an average of 26 .samples. The results may not be wholly applicable to other geographical locations because of different climatic patterns. similar climate. The forage model could actually be used with weather data from other locations. In this sense, the model still has largely unexplored capabilities to analyze forage systems under a wide variety of climates. 146 147 9.1 Crop management decisions Two major crop management decisions are considered in the following discussion: the alfalfa maturity stage at which mowing should start and the value of a fourth alfalfa cut in late fall. 9.1.1 Maturity at the time of mowing The alfalfa growth model does not directly predict maturity, but does predict the crude protein of the whole plant. Crude protein is set at a maximum value of 0.231 as long as the ratio of leaves to stems is greater than one (in the early vegetative stage). As the plant matures, the ratio of leaves to stems decreases and so does the crude protein concentration. The dates on which alfalfa mowing may start are defined in the array BGNCUT(NTHCUT). The number of cuts per year is usually set at 3 or 4; NTHCUT identifies the specific cut (1 to 4). Subroutine ALHARV can interpret BGNCUT (NTHCUT) as the first day to check for alfalfa maturity rather than the first mowing day. Crude protein is used as a measure of plant maturity. When a "mowing 148 crude protein criterion" (appendix C) is specified in the range 0.15 to 0.23, it is compared daily with the standing crop crude protein. If the plant's crude protein is greater than the criterion, the plant is considered immature and mowing is postponed. To prevent overlap with the subsequent mowing dates, postponement is limited to 10 days. Ten days after BGNCUT(NTHCUT), mowing is forced to start even if the crude protein is above the criterion level. Table 9.1 shows the date ranges within which mowing will start for the harvest of alfalfa at three maturity levels. The three maturity levels are identified by the crude protein concentration below which mowing may start: 0.230, 0.200 and 0.170. Table 9.1. Date ranges of the first mowing day for harvesting alfalfa at three maturity levels under a three cut system. Dates are shown in Julian days. CP at Harvest 1 Harvest 2 Harvest 3 mowing Earliest LateSt Earliest Latest Earliest Latest .230 136 145 181 190 226 235 .200 146 155 201 210 256 265 .170 156 165 221 230 286 295 The date ranges were chosen after testing the growth model over 26 years of weather data and observing when each harvest would most likely reach the specified crude protein. Since growth usually starts on day 91 (April 1), the time intervals between cuts are seen to be about 45 149 days, 55 days and 65 days for each maturity level. The objective of such a comparison is to measure whether the additional growth and yield of more mature crops can compensate the quality loss. Table 9.2 illustrates the wide year-to-year variation in the date at which alfalfa reaches the same maturity. For example, the first harvest of early maturity alfalfa (CPa0.23) began at the earliest date (May 16 or day 136) in six years, began at the latest date (May 25 or day 145) in eight years and started between these two dates in 12 years out of 26. . Table 9.2. Number of years out of 26 when mowing started at the limiting date. CP at Harvest 1 Harvest 2 Harvest 3 mowing Earliest Latest Earliest Latest Earliest Latest .230 6 8 4 10 1 16 .200 13 2 6 14 . 8 13 .170 9 5 4 18 4 15 Table 9.3 shows the potential alfalfa yield that was available on the earliest mowing date. Mowing could be postponed up to 10 days after this earliest date if alfalfa was still immature (i.e. the crude protein was still very high). In most cases mowing started later than the earliest date and the actual yield was higher than the potential yield in table 9.3. As expected, the later growth system (CP=0.170) had the greatest potential yield. 150 Table 9.3. Potential alfalfa yield (tDM/ha) and crude protein at the earliest mowing date. CP at Harvest 1 Harvest 2 Harvest 3 Total mowing DM CP DM CP DM CP DM CP .230 3.42 .23 3.36 .23 2.56 .23 9.35 .23 .200 4.56 .21 4.25 .22 2.36 .21 11.17 .21 .170 5.52 .18 4.02 .21 2.27 .20 11.81 .19 Table 9.4 shows actual harvested alfalfa available as feed after accounting for harvest, storage and feeding losses. The total average crude protein decreases steadily as alfalfa is harvested at a more mature stage. Surprisingly the total harvested feed does not increase steadily with maturity. It is maximum for an intermediate maturity (CP=0.20). Although the more mature alfalfa had the greatest potential yield, it incurred greater harveSt losses probably due to the fact that the last harvest was in late October, early November during more adverse weather conditions. Table 9.4 Harvested alfalfa (tDM/ha) available as feed after accounting for harvest, storage and feeding losses. CP at Harvest 1 Harvest 2 Harvest 3 Total mowing DM CP DM .CP DM CP DM CP .230 3.43 .177 3.24 .186 2.00 .182 8.67 .180 .200 4.01 .157 3.39 .172 1.65 .152 9.05 .160 .170 4.68 .144 2.85 .159 1.25 .140 8.77 .146 151' Tables 9.5 and 9.6 show how the harvested alfalfa would be used by a herd of 128 lactating cows producing either 20 or 35 kg of milk per cow per day. The low milk producing herd consumed mostly alfalfa and little corn or soybean meal. Some extra alfalfa had to be bought for the low milking herd. The high milk producing herd required more energy in its ration and consumed a large quantity of corn and also some soybean meal. Consequently some alfalfa was left over and sold as excess forage. Tables 9.5 and 9.6 point out the higher energy need of high production cows compared with low production cows. In the present simulation, only alfalfa is farm grown and all the corn is purchased. With high milk production, it would probably be desirable to reduce the area grown as alfalfa and increase the area grown as corn. Table 9.5. Feed utilization (tDM/yr) on an 80 ha farm with 128 low yield lactating cows (20 kg milk/cow/day) when alfalfa is harvested at three maturity levels. CP at Alfalfa Alfalfa Soy meal Corn grain mowing produced sold purchased purchased .230 693.79 -l73.24 1.63 123.61 .200 723.87 -100.42 1.86 166.12 .170 701.81 '54.31 5.29 . 230.86 152 Table 9.6. Feed utilization (tDM/yr) on an 80 ha farm with 128 high yield lactating cows (35 kg milk/cow/day) when alfalfa is harvested at three maturity levels. CP at Alfalfa Alfalfa Soy meal Corn grain mowing produced sold purchased purchased .230 693.79 67.54 67.93 482.43 .200 723.87 171.28 94.65 527.36 .170 701.81 214.36 112.82 574.34 Table 9.7 shows the non feed costs, i.e. mainly the machinery, storage, labor and energy costs. The fuel, repair and maintenance (RM) and labor costs are proportional to the potential yield and increase with maturity. The storage cost is usually constant except when the hay storage structure is filled and emergency hay storage is required (assumed at $10. per ton DM per year). The greatest amount of feed was harvested under the intermediate maaturity (CP-0.200) and explains. the higher storage cost. 9 Table 9.7. Comparing non-feed production costs (S/Yr) for harvesting alfalfa at three maturity levels on an 80 ha alfalfa farm. CP at Mach. Storage Fuel RM Labor Fert. Total mowing .230 26545. 11155. 2421. 4302. 5917. 15508. 65849. .200 26545. 11399. 2549. 4500. 6154. 15508. 66655. .170 26545. 11321. 2584. 4587. 6159. 15508. 66705. 153 Tables 9.8 and 9.9 illustrate the average return net from harvesting alfalfa at three maturity levels and at two milk production levels. With either low yield milking cows or high yield milking cows, the greatest return is obtained when alfalfa is harvested at the least mature stage (CP=0.230). The benefit of harvesting early is more noticeable with high yield milking cows that use more efficiently high quality feed. Table 9.8. Economic comparison (S/Yr) of alfalfa harvest at three maturity levels on an 80 ha farm with 128 lactating cows (20 kg milk/cow/day). CP at Non-feed Net feed Milk Net mowing costs costs returns returns .230 65849. 31966. 267238. 169423. .200 66655. 31986. 267238. 168597. .170 66705. 38220. 267238. 162313. Table 9.9. Economic comparison ($/yr) of alfalfa harvest at three maturity levels on an 80 ha farm with 128 lactating cows (35 kg milk/cow/day). CP at Non-feed Net feed Milk Net mowing costs costs returns returns .230 65849. 78840. 467667. 322978. .200 66655. '84974. 467667. 316038. .170 66705. 93021. 467667. 307941. The cumulative probability curves of net yearly return are plotted in figures 9.1 and 9.2 from the 26 samples of yearly simulation. For low yield cows the expected net return is largest when alfalfa is harvested early (CP=0.230). However, in a number of years, the greater CUMULATIVE PROBABILITY CUMULATIVE PRo BABILITV 0.6 154 1.0.... 03.- 04.. 0.2 .. ‘ r 1 I 1 1 3 19'00 20'00 21'00 22'00 23'00 (s/Iu)’ NET RETURNS Figure 9.1. The cumulative probability of net return per ha for mowing at three maturity levels, identified by the alfalfa crude protein on the first mowing day, with low milk producing cows (20 kg/day/cow). A 1.0 .. 7 __\I\ 1 1 37'00 38'00 39'00 40'00 41'00 (SIM? NET RETURNS Figure 9.2. The cumulative probability of net return per ha for mowing at three maturity levels, identified by the alfalfa crude protein on the first mowing day, with high milk producing cows (35 kg/day/cow). 155 yield provided .by harvesting later (CP=0.200) would compensate the quality loss. Indeed a profit may sometimes be made by substituting quantity for quality with a low yield milking herd that does not require a very high quality feed. In the case of a high milk producing herd, the advantage of harvesting early is unambiguous (figure 9.2). In general alfalfa harvest should begin early, when the crude protein is between 20 and 23%, to provide the highest quality feed. 9.1.2 Three versus four alfalfa harvests In the preceding section it was observed that alfalfa should be harvested as early as possible to get a high quality feed and a maximum net return to the farm. Under an early harvest system the third cut will start between Julian days 226 and 235 (August 14 and August 23). A fair amount of regrowth is usually expected between the end of the third cut and late October. A comparison was made between the 3-cut early harvest system (CP=0.230) described in the previous section and a 4-cut early harvest system. The fourth cut is scheduled to start between Julian days 286 and 295 (October 13 and October 22). 156 Table 9.10 shows the production (non-feed) costs to harvest 3 or 4 cuts of alfalfa per year. The extra fuel, repair and labor costs to harvest a fourth cut represent $2547. or $31.84 per ha. An additional storage cost of $1061. was also required since the storage structures were already filled after three cuts. The fourth cut was harvested as hay and stored at a temporary storage cost of $10. per tDM per year. In all it costs about $45./ha to harvest and store the fourth cut. Table 9.10. Production costs (S/yr) of a 3-cut alfalfa system and of a 4-cut alfalfa system over 80 ha. System Mach. Storage Fuel RM Labor Fert.‘ Total 3 cuts 26545. 11155. 2421. 4302. 5917. 15508. 65840. 4 cuts 26545. 12216. 2998. 5201. 6988. 15508. 69456. The average feed available from a 3-cut early harvest system is 8.67 tDM/ha with a crude protein of 0.180. The average feed available from a fourth cut harvested as hay after October 13 is 1.32 tDM/ha with a crude protein of 0.141. Hence the yearly total harvested feed under the 4-cut system is 9.99 tDM/ha with an average crude protein of 0.175. Table 9.11 compares the net returns of a 3-cut and a 4-cut system at four milk production levels. In all cases the 4-cut system yields a larger net return. The difference is greatest for low milk producing levels since 157 the fourth alfalfa cut will actually be used in the ration and reduce the purchase of alfalfa hay at $83. per tDM. In the case of a high milk production level the extra alfalfa harvested will not be fed to the herd due to its low quality (CP=0.141) but it will be sold as excess forages at $69. per tDM. In both cases the expected harvested feed (1.32 tDM/ha) and the reduced expense or the increased income cover the additional production cost ($45./ha). Table 9.11. Economic comparison (S/Yr) of a 3-cut and of a 4-cut alfalfa system over 80 ha at four milk production levels. Milk Number Non-feed Net feed Milk Net Diff. level of cuts costs costs returns returns kg/day 20. 3 65849. 31965. 267238. 169424. 6894. 4 69456. 21464. 267238. 176318. 25. 3 65849. 43795. 334048. 224404. 6615. 4 69456. 33573. 334048. 231019. 30. 3 65849. 59046. 400858. 275963. 5623. 4 69456. 49816. 400858. 281586. 35. 3 65849. 78840. 467667. 322978. 4745. 4 69456. 70488. 467667. 327723. Figures 9.3 and 9.4 illustrate how the net return from a 4-cut system is generally superior, or said to be stochasticly dominant, over a 3-cut system with either a low milk producing or high milk producing herd. By comparing the net return on a year by year basis for 26 years, there were actually 2 or 3 years when the 3-cut system would have been more profitable. Table 9.12 CUMULATIVE RROBABILITY CUMULATIVE PROBABILITY 158 1.0.41 08... 0.6... 0.4.1- 0'2" 3 cute 4 cute Le ' e l l ‘ ‘ 19'00 2000 2100 2200 2300 (8/ he) NET RETURNS Figure 9.3. The cumulative probability of net return per ha for a 3-cut and for 'a 4-cut alfalfa harvest systems with low milk producing cows (20 kg/day/cow). 1.0!: 0.8.). 0.5.... 0.4.“. 0.2-.. Hr '. : : l e 3900 4000 41 00 4200 (5/ he ) NET RETURNS Figure 9.4. The cumulative probability of net return per ha for a 3-cut and for a 4-cut alfalfa harvest systems with high milk producing cows (35 kg/day/cow). 159 shows the yields in three years when the fourth cut was not profitable. Figures 9.5 and 9.6 illustrate the cumulative probability of the difference of net returns between a 4-cut and a 3-cut system. In one year out of ten, the 3-cut system would appear more profitable. But the level of increased profits in the other nine years out of ten amply justify the 4-cut system. Table 9.12. Potential yield and actual harvest of the fourth alfalfa cut in specific years when the fourth cut was not profitable. Year Potential Harvested Net return yield (tDM/ha) feed (tDM/ha) ($/ha) 1957 2.92 0.50 _2.21 1962 1.62 0.15 -3.45 1976 2.12 0.00 ~2o.14 A farmer may wish to avoid these losses by defining a minimum yield below which he will not harvest the fourth cut. Since the harvest and storage costs were estimated at $45./ha, the farmer would on the average hope to harvest at least 0.65 tDM/ha valued at $69./tDM. The average potential yield of the fourth cut for a 26-year period was 2.44 tDM/ha. Since the average harvested alfalfa available as feed was 1.32 tDM/ha, the average dry matter loss was 46%. On the basis of average values, a farmer should not harvest a fourth cut unless the potential yield is at least 1.21 tDM/ha. In fact the potential yield was always greater than this minimum value throughout 26 years of 160 CUMULATIVE PROBABILITY 1 7 l 1 L 1 L 1 1 1 ; -2'0 0 2'0 4'0 6'6 8'0 «To 120 140 16'0 (sun) DIFFERENCE IN NET RETURNS Figure 9.5. The cumulative probability of the difference in net returns in favor of a lI-cut system versus a 3-cut system with low yield cows (20 kg/day/ cow). 11ft CUMULATIVE PROBABILITY L 1 l 1 1 1 1 1 I \ -2'0 0 2'0 4b 6'0 ab Ido. 12b 140 160 (sum)7 DIFFERENCE IN NET RETURNS Figure 9.6. The cumulative probability of the difference in net returns in favor of a 4-cut system versus a 3-cut system with high yield cows (35 kg/day/cow). 161 simulation. The two or three years out of 26 when a fourth cut was unprofitable were not due to low yield but rather to exceptionally bad weather conditions during harvest. In the simulation example, the fourth alfalfa cut was harvested as hay and additional temporary storage had to be provided. If unused fixed storage space is available at the time of the fourth cutting, then no additional storage cost would be incurred. 'Moreover, if the fourth cut can be harvested as haylage instead of hay, less losses are likely to occur. If other crops must also be harvested in the fall, the profitability of the fourth alfalfa cut may become questionable because of possible time conflicts. A fourth alfalfa cutting is generally profitable although there is about a 10% chance of a negative return in exceptionally bad years as long as there is no time conflict with the harvest of other crops. 9.2 The rate of harvest and forage value The value of a crop is often affected by the harvest rate. In the case of cash crops such as grains, an extended harvest period usually increases dry matter losses and reduces the overall quality. The decrease in the crop value is called timeliness cost. 162 Alfalfa does not fit well into this simple definition of timeliness cost. Indeed the total alfalfa yield increases almost continuously so that a slower harvest rate will actually produce a greater yield. However quality will decrease. There may sometimes be a tradeoff between quality and quantity as was shown in section 9.1.1. Alfalfa is also different from other crops because of its regrowth mechanisms within the same year. The rate of harvest will affect the yield and quality of subsequent- harvests. A fixed machinery set (medium size chopper and round baler, about 75% haylage and 25% hay) was analyzed over a ,range of areas. If a timeliness cost is associated with alfalfa harvest, it should appear in the form of higher feed costs per cow or per unit area as more time is used to complete the harvest. The size of the storage structures and the number of cows are scaled to the area. Fixed storage capacity is set as 7.5 tDM/ha for silos and as 2.5 tDM/ha for a hay barn. Extra storage is available for hay at a marginal cost of $10. per tDM per year. The ratio between cows and area is set as 1.6 lactating cows per hectare. Table 9.13 shows the potential yield at the. earliest mowing dates and the actual harvested feed. All the beginning harvest dates were the same for all. areas. The 163 potential yield is greatest for low areas because the crop was harvested quickly and more time was available for regrowth. The actual harvest is also greatest for small areas. The differences in actual harvest are smaller than the differences in potential yield. Indeed over large areas the alfalfa continued to grow for a longer time because the harvest was extended over a longer period. Table 9.13. Potential alfalfa yield and actual harvest (tDM/ha) from a 4-cut system using the same machinery complement (chopper-round baler) over a wide range of areas. Area Potential Potential Actual Actual (ha) yield CP harvest CP 20 13.76 .21 10.25 .181 40 13.04 .21 10.15 .178 60 12.42 .22 10.04 .177 80 11.79 .22 10.00 .175 100 11.19 .22 9.95 .174 120 10.59 .22 9.93 .172 Table 9.14 shows in greater detail how the yearly yield was distributed into four harvests. Clearly in the first harvest, a longer harvest period results in higher yields and lower quality. In the second harvest, dry matter and qualtiy are practically the same over all areas. The regrowth has adjusted to the slower harvest rates and adapted itself to a longer harvest period. In the third cut, a longer regrowth period produced slightly higher yields for smaller areas. The fourth cut illustrates two trends opposite to those in the first cut: as the area decreases fourth harvested as alfalfa increases, 164 the and the qualtity increases. Table 9.14. cut shorter regrowth period. was probably at the cost of a lesser yield. of the four alfalfa cuts. not fourth cut yield This is due to the optimal. Actually the date of harvest for The fourth harvest could have started earlier to get a higher quality Actual harvested feed (tDM/ha) during each Area Harvest 1 Harvest 2 Harvest 3 Harvest 4 (ha) DM CP DM CP DM cp DM CP 20 2.91 .199 3.12 .191 2.42 .189 1.79 .127 40 3.13 .190 3.17 .189 2.20 .185 1.65 .134 60 3.29 .183 3.23 .189 2.08 .183 1.43 .141 80 3.43 .177 3.24 .186 2.00 .182 1.32 .141 100 3.56 .172 3.24 .185 1.89 .183 1.25 .140 120 3.69 .167 3.18 .186 1.82 .183 1.24 .148 Tables 9.15 and 9.16 show how the feed costs and net returns vary as a fixed machinery set is used over a larger area. In all cases the decrease in the fixed machinery costs overshadows the increase in the feed costs. For areas above 140 or 150 ha, the system becomes infeasible as the harvest period in some years extends beyond the earliest mowing dates of subsequent harvests. Production costs decrease slightly with area because these costs are proportional to yield. As the machinery set is used over a larger area, more calendar days are required to complete the harvest and less time is available for regrowth. Hence the potential yield is lower and the variable costs related 165 to harvest (labor, energy, repairs) are also lower. Table 9.15. Costs and net returns (S/ha) of a haylage machinery system used over a wide range of areas with a low yield dairy herd (20 kg milk/cow/day). Area Mach. Other Feed Milk Net (ha) costs prod. costs returns returns costs 20 1327. 545. 249. 3340. 1219. 40 664. 544. 252. 3340. 1881. 60 442. 539. 261. 3340. 2098. 80 332. 536. 268. 3340. 2204. 100 265. 534. 277. 3340. 2264. 120 221. 532. 282. 3340. 2305. Table 9.16. Costs and net returns ($/ha) of a haylage machinery system used over a wide range of areas with a high yield dairy herd (35 kg milk/cow/day). Area Mach. Other Feed Milk Net (ha) costs prod. costs returns returns costs 20 1327. 545. 838. 5846. 3136. 40 664. 544. 859. 5846. 3780. 60 442. 539. 870. 5846. 3994. 80 332. 536. 881. 5846. 4097. 100 265. 534. 891. 5846. 4156. 120 221. 532. 898. 5846. 4195. Table 9.17 shows the average number of calendar days required to complete each harvest. The feed costs were seen to increase from $249./ha to $298./ha for low milk yield between a 20 ha farm and a 120 ha farm. The average yearly number of harvest days required for each farm is 17 and 81 respectively. The timeliness loss would be about $0.50/ha/day. Since the average yield is 10 tDM/ha and the value of alfalfa feed can be approximated by $80./tDM, the 166 timeliness coefficient would be about 0.0006/day for low milk production. In the case of high yield cows, the increase of feed cost was about twice as much as for low ’yield cows. The timeliness coefficient would be 0.0012/day for high milk production. Table 9.17. The average number of calendar days required to harvest each alfalfa cut with a constant size machinery system. Area Cut 1 Cut 2 Cut 3 Cut 4 Total 20 3.35 3.50 3.73 6.54 17.12 40 8.00 7.23 6.65 8.92 30.80 60 11.42 11.00 9.08 11.54 43.04 80 15.19 14.35 11.46 14.35 55.35 100 18.96 18.38 13.62 17.31 68.27 120 23.65 21.50 15.65 20.69 81.49 A similar analysis was done with a 100% hay system. The average harvest rate of the hay system was slightly (less than 10%) larger than the haylage system described previously. The medium size conventional baler was simulated over the same area range. From the data in table 9.18, the timeliness coefficients would be about 0.0012/day for low milk yield and 0.0024/day_ for high milk yield. These timeliness coefficients are ASAE relatively low. (1981) suggests 0.0180 for haymaking in Michigan in June in data D230.3. The estimated timeliness coefficients would indicate that a low harvest rate does not really affect the overall value of an alfalfa crop especially when four cuts are made yearly. A slow harvest rate will produce a low 167 quality first cut but the subsequent cuts will be of higher because the regrowth will have adjusted to the harvest rate. Table 9.18. Feed costs (S/ha) for low and high milk producing cows with a 4-cut completely hay fixed machinery system over a wide range of areas. Area Feed costs Feed costs Total calendar (ha) (20 kg/cow) (35 kg/cow) days to harvest 20 285. 806. 24.57 40 295. 844. 33.95 60 296. 861. 45.28‘ 80 330. 885. 55.92 100 330. 904. 68.58 120 339. 920. 81.66 From a practical point of view, the farmer should not worry about taking three or four weeks to harvest the first cut. The subsequent cuts will compensate for the lower quality first cut. Reducing the harvest period to one or two weeks is not worthwhile since this will increase the machinery cost more than it will reduce the feed costs. If the number of cuts per year is reduced from four to three or even two, then the timeliness cost would become more important and so would the machinery size. The effect of rate of fill on haylage quality in not presently considered. If ~slow filling rates cause considerable oxidation, then the timeliness cost for haylage systems would be greater than the one predicted. 168 9.3 Field-curing delay The previous two sections have shown that the time at which harvest of alfalfa begins is more important than the rate at which it procéeds. Another important parameter in forage systems is the field-curing delay. Quality and value of a forage crop will generally decrease with a longer exposure time. The forage harvest technologies presently available provide a number of alternatives to decrease the field curing delay: 1. Increasing the drying rate by additional treatments at mowing or during curing; 2. Baling hay at a higher moisture content and treating the hay against spoilage; 3. Shifting from hay to haylage: 4. Shifting to direct-cut alfalfa harvest and conservation. Hay usually cannot be baled before its moisture content is below 20% (wet basis). The treatment of wet hay could allow harvest at 30% moisture. A haylage system can provide good conservation of alfalfa with moisture as high as 60%. A direct-cut system would require no field curing 169 at all but the technology is not yet feasible because of important seepage losses in storage. This section will Consider the relative advantages and disadvantages of the four technologies outlined above. 9.3.1 Increasing the drying rate New treatments are being proposed to increase the drying rate of forages to decrease the total field curing time. Section 6.5 dealt with some of these treatments (spraying a chemical solution and maceration) and their impact on the drying rate. There are tradeoffs associated with these additional treatments. The reduced field exposure time must be weighed against either higher leaf loss or higher production cost or both. More information is required (especially with regards to leaf loss and production costs) to completely assess some of these new technologies. The impact of an increased drying rate can nonetheless be assessed without all the other technological data. A 100% hay system was simulated under three conditions: with a regular mower-conditioner (control), with an additional treatment that would increase the drying constant by an average of 0.02 similar to the spraying of a chemical solution and with another type of treatment that 170 would increase the constant by 0.05 similar to maceration. (Section 6.5 gives a justification for these numerical values.) No consideration is given to extra dry matter ~losses or to extra production costs. Table 9.19 shows the actual harvest and the average number of days hay was exposed under the three curing conditions: a control (mower-conditioner), spraying a chemical solution and maceration. As the drying rate is increased, the total dry matter harvested and the quality both increase. The results show a reduction of the average exposure time by as much as 1.5 days. Table 9.19. Actual harvested yield (tDM/ha) and average field-curing time using extra treatments to increase the drying rate of baled hay. Extra Assumed Harvest Average exposure treatment value of DM CP days b9*XTR High Low (eq. 6.3) qual. qual. Control 0.00 9.27 .167 4.15 6.63 Chemical 0.02 9.72 .169 3.82 5.87 Maceration* 0.05 10.10 .171 . 3.42 5.19 (*) The extra dry matter losses for maceration are not accounted. The increased quality of the alfalfa is translated into feed cost savings in table 9.20. The feed cost savings are about $40./ha/yr with an increased drying coefficient of 0.02 and $80./ha/yr with an increased drying coefficient of 0.05. The treatment is assumed to be applied over 80 ha for all four cuts. 17.1 Table 9.20. The annual feed cost ($/ha) as influenced by faster drying treatments for an 80 ha alfalfa farm with 128 lactating cows at four milk production levels. Extra treatment 20 kg/day 25 kg/day 30 kg/day 35 kg/day Control 330. 469. 663. 885. Chemical 284. 422. 622. 845. Maceration 246. 381. 577. 797. The cost of spraying a chemical solution on alfalfa would have to be less than $10. per ha per cut or $4. per ton DM to be profitable. This is unlikely given the types of chemical solutions and their concentrations suggested by Wieghart et a1. (1980). Indeed the most promising chemical solution represented an application cost of about $4.50 per ton DM. When the extra labor and equipment costs are added, -the cost of spraying a chemical solution would vary between $5. and $10. per ton DM depending on farm size. A new mechanical hay conditioner such as the macerator suggested by Krutz et al. (1979) appears more promising. It does not have the high variable costs associated with chemical application. If it could actually save $80./ha/yr, a farmer with 80 ha of alfalfa could certainly afford to pay even double the price of an actual mower-conditioner. However the analysis does not include any estimate of extra dry matter losses or of extra fuel requirement of such a machine. A complete analysis should 172 include these technical considerations. ' 9.3.2 Baling at a higher moisture content The total exposure time of alfalfa during field curing can be reduced either by increasing the drying rate or by harvesting at a higher moisture content. Haylage is one way to harvest at a higher moisture content and is considered in section 9.3.3. Baled hay can be harvested at a higher than normal moisture content, provided some treatment is applied to prevent spoilage. In the 1950's and 1960's, barn drying of wet hay was a common practice but energy and labor requirements have outdated such a process. More recently the application of proprionic acid has been suggested to conserve hay baled at a high moisture content (Nahrir et al., 1978). Three simulations were done to compare the effect of being able to harvest hay at a higher moisture content. Table 9.21 shows how a greater amount of yield and quality would be retained if hay could be harvested and stored safely at a higher moisture content. The number of days required for field curing may be reduced by between one half and two full days. 173 Table 9.21. Actual harvested feed (tDM/ha) and average field-curing time when hay may be baled at a higher moisture content. Moisture content Harvested feed Average exposure at baling DM CP days Wet Dry High Low basis basis qual. qual. 20% .25 9.27 .167 4.15 6.63 30% .43 10.16 .173 3.50 5.27 40% .67 10.69 .176 2.97 4.37 The improved quality and quantity represent substantial feed cost savings (table 9.22). About $100./ha/yr may be saved by baling hay at 30% moisture on a wet basis instead of 20%. For such a system to be profitable, the preservative should cost less than $10. per ton of alfalfa DM preserved. Table 9.22. The annual feed cost (S/ha) when hay may be baled at a higher moisture content for an 80 ha farm with 128 lactating cows at four milk production levels. Moisture , at baling 20 kg/day 25 kg/day 30 kg/day 35 kg/day w.b. 20% 330. 469. 663. 885. 30% 238. 368. 560.. 778. 40% 187. 312. 497. 713. 174 9.3.3 Haylage versus hay Alfalfa haylage can be- harvested and stored safely with a moisture content between 50% and 60% (wet basis) whereas hay must be dried down to 20% moisture content. Consequently haylage will be subject to weather risk a shorter time than hay. Haylage technology however is more capital intensive than hay technology for both machinery and storage facilities. A 100% hay system is compared to a 100% haylage system with four alfalfa cuts per year under mid-Michigan climate. The hay machinery system consists of three tractors (60 kW, 40 kW and 20 kW), a large baler (maximum throughput of 14 tDM/h), three bale wagons, a bale elevator, a 2.7 m mower-conditioner, a rake and three men working full-time during hay harvest. Mowing, raking and baling operations are those defined in the example in appendix B (operations 22, 40 and 170). The haylage machinery system uses three tractors (80 ‘kW, 60 kW and 40 kW), a medium size forage chopper (maximum throughput of 11 tDM/h), two forage wagons, a forage blower, a 2.7 m mower-conditioner and two men working full-time during haylage harvest. Mowing and chopping operations are identical to operations 22 and 150 in the 175 example in appendix B. Since there is no raking in the haylage operation, the mower leaves the alfalfa in a narrow windrow 1.35 m wide compared with a wider windrow of 2.16 m for hay making. Table 9.23 shows that the haylage was on the average exposed between 2.4 and 3.2 days for the first and second silos while hay was exposéd on the average 4.2 days for high quality hay (CP > 0.17) and 6.6 days for low quality hay (CP < 0.17). Table 9.23. Average number of field-curing days of alfalfa before going into storage (80 ha farm). High quality Low quality Silo l Silo 2 hay hay System Days CP Days CP Days CP Days CP Hay NA NA NA NA 4.15 .189 6.63 .148 Haylage 2.44 .195 3.24 .169 NA NA NA NA Table 9.24 shows the actual feed available after accounting for harvest, storage and feeding losses and its quality for the hay and haylage systems over a range of areas. 176 Table 9.24. Alfalfa available as feed (tDM/ha/yr) from fixed machinery systems for hay and haylage harvest over a range of areas. Hay Haylage Area Harvested CP Harvested CP (ha) feed feed 20 9.83 .172 11.13 .186 40 9.68 .169 11.17 .183 60 9.61 .167 11.15 .182 80 9.27 .167 11.05 .180 100 9.27 .165 11.08 .179 120 9.21 .163 11.04 .177 For both systems, the storage and the herd size were scaled to area. The storage capacity was set at 12.5 tDM/ha for hay and 15 tDM/ha for haylage. These capacities are larger than the average harvested feed because storage and feeding losses must be accounted and some extra storage space should be provided for exceptional years. The actual size of storage structures is two thirds of the annual storage capacity requirements since harvest extends between late May and late October and the same storage space can be used twice during at least four months per year. Table 9.25 shows the storage capacities required and the storage investment cost for both systems under a range of areas. 177 Table 9.25. Storage capacity (tDM) and investment cost (S) for a hay system (one hay barn) and for a haylage system (two equal size silos). Hay Haylage Area Annual Storage Cost of Annual Storage Cost of (ha) cap. cap. barn cap. cap. silos (tDM) (tDM) (s) (tDM) (tDM) (s) 20 250. 167. 6500. 300. 200. 34700. 40 500. 333. 9900. 600. 400. 58000. 60 750. 500. 13200. 900. 600. 74000. 80 1000. 667. 16500. 1200. 800. 84000. 100 1250. 833. 19900. 1500. 1000. 94000. 120 1500. 1000. 23200. 1800. 1200. 104000. The hay and haylage systems can be compared on the basis of resource requirements. The hay system requires much less capital investment but usually requires more labor (table 9.26). The hay system also requires less fuel than the haylage system. Table 9.26. The resources required to operate three harvest systems for an 80 ha alfalfa farm. System Machinery Storage Fuel Labor investment investment (L/yr) (man.h/yr) Hay $79800. $16500. 5339. 1831. Haylage $110100. $84000. 9387. 1553. Direct-cut* $103900. $102000. 10415. 1223. (*) Equipment and energy necessary for dewatering direct-cut alfalfa are not included. The main advantages of the haylage system over a hay system are a lesser labor requirement, a higher harvested yield and a higher quality (which may however be offset by a lower animal intake). Two disadvantages with the haylage system are the high investment costs and the relatively 178' higher fuel consumption. Figures 9.7 and 9.8 Show the expected net costs of haylage and hay systems versus area. The coSts include the storage and machinery annualized fixed costs, the cost of labor and energy and repair and maintenance for harvest and feeding, the cost of fertilizers for maintaining alfalfa yields and the net cost of feeds for the specified milk production and herd size. Herd size is set at 1.6 lactating cows per hectare of alfalfa.‘ When comparing systems of largely different investment levels, the discount rate used in the analysis becomes very important. The fixed costs of both systems are estimated using a real discount rate of 4% (i=0.04). This is more appropriate than the use of nominal rates because real rates provide an adjustment for inflation. A lO-year accounting life is used for machinery, with a 10% salvage value; a 20-year accounting life is used for storage structures with no salvage value. The upper and lower bounds in figures 9.7 and 9.8 are obtained from the lowest and highest costs in a 26-year simulation. The hay system has wider bounds and more variable costs than the haylage system. In this sense, the hay system is generally riskier than the haylage system. The curves in figures 9.7 and 9.8 are superimposed in figure 9.9 to compare the expected cost of each system. The haylage system is generally more expensive than the hay ($lha) COST NET ($lha) COST NET 179 A 2000 \ \ \ \ ~ \ ~ ~ UPPER BOUND 1500 V \ \ ~ - ---------- x ‘ EXPECTED Io-V'I'E'R' .80-UN; " — ‘- 1000 .L t : : : a : t > 20 40 60 80 100 120 A R E A (ha) Figure 9.7. Net cost of a hay system versus area for high milk production (35 kg/ day/ cow) and real interest rates (i-0.04). 2000 -- \\ \ UPPER BOUND 1500 " ‘ ~~-——__- ‘ ~ - _ __ _ “EXPECTED LOWER BOUND 1000 ~- E I L A I I l I I ‘ I 20 40 60 80 100 120 A R E A (ha) Figure 9.8. Net cost of a haylage system versus area for high milk production (35 kg/day/ cow) and real interest rates (180.04). I COST (S/ha) EXPECTED CUMULATIVE 2000 1 500 1 000 1.0 0.8 PROBABILITY 0.6 0.4 0.2 Haylage a. x i = v 5 I l ) 20 ' 40 60 80 100 120 AREA (ha) Figure 9.9. Expected cost of a haylage system and a hay system versus area for high milk production (35 kg/ day/ cow) and real interestrates (i=0.04). Haylage l n I l a L I 1250 1300 1350 1400 1450 1500 ANNUAL NET COST ($/ha) Figure 9.10. The cumulative probability of 31li net cost of a hay system versus a haylage system under 120 ha of alfalfa with high milk production (35 kg/day/cow) and real interest rates (i=0.04) . 181 system with a high yield milk producing herd (35 kg/day/cow). At 120 ha, both systems cost approximately the same. Figure 9.10 shows that the hay system is more variable than the haylage system at 120 ha. Since they both have the same expected return, a risk adverse farmer would choose the haylage system rather than the hay system at 120 ha. For smaller areas,' the hay system is more profitable but more variable than the haylage system. Some farmers may be willing to forfeit some profit in order to reduce the year to year variation in cost and could then prefer the haylage system to the hay system. Figure 9.11 compares the haylage system and the hay system with a low milk producing herd (20 kg/day/cow). The haylage system becomes less expensive than the hay system for areas above 60 ha. It becomes more profitable more quickly with a low milk producing herd than with a high milk producing .herd because the advantage of haylage over hay is more quantitative than qualitative. Tables 9.27 and 9.28 show the alfalfa feed production and utilization with high yield and low yield dairy herds. COST ($lha) EXPECTED COST ($lha) EXPECTED 182 1500 ‘ Haylage 1000 -- Hay 500 “- i I D l l l I I V I I U I 20 40 60 80 100 120 A R E A (ha) Figure 9.11. ‘ Expected costs of a haylage system and a hay system versus area for low milk production (20 kg/day/cow) . 2000 T 1500.; 1000 . T I ‘ 1 e L I r B O U 20 40 60 80 100 120 A R E A (ha) Figure 9.12. Expected costs of a haylage system and a hay system versus area assuming haylage dry matter intake is the same as hay intake (high milk production). \I 183 Table 9.27. Feed production and utilization (tDM) under four harvest and conservation systems on an 80 ha farm with 128 high milk producing lactating cows (35 kg/cow/day). System Alfalfa Alfalfa Soy meal Corn grain harvested sold purchased purchased DM CP Hay 741.4 .167 56.0 59.8 429.5 Haylage 884.2 .180 263.1 63.1 490.4 Direct-cut 978.3 .195 346.7 55.7 487.3 Direct-cut + 978.3 .195 236.1 25.2 407.2 formic acid Table 9.28. Feed production and utilization (tDM) under four harvest and conservation systems on an 80 ha farm with 128 low milk producing lactating cows (20 kg/cow/day). System Alfalfa Alfalfa Soy meal Corn grain harvested sold purchased purchased DM CP Hay 741.4 .167 -154.7 1.48 94.8 Haylage 884.2 .180 -31.5 0.41 76.2 Direct-cut 978.3 .195 22.6 0.00 36.6 Direct-cut + 978.3 .195 -1.3 0.00 12.7 formic acid The haylage system conserves about 20% more yield and 10% more crude protein than the hay system. The quality advantage of haylage is however offset by a lower intake potential compared with hay. With high milk producing cows, the haylage system indeed requires slightly more soybean meal and corn grain than the hay system to balance the ration. Low milk producing cows require a lower nutrient concentration than high milk producing cows and 184 consume more alfalfa and less soybean meal or corn grain (table 9.28). A large fraction of the haylage cannot be used by the high milk producing herd because the nutrient concentration is not high enough. In the model, excess haylage is sold at $69. per tDM. In practice, a farmer could use about 16% less land with a haylage system than with a hay system to produce the same quantity of feed. A review of literature in section 5.4 showed that dairy cows will generally intake less haylage than hay on a dry basis. This is modelled by decreasing crude protein and digestibility of haylage by 5% in the ration formulation model. The sensitivity of this assumption was tested by assuming that haylage had the same dry matter intake potential as hay. Figure 9.12 shows that the feed value of haylage would increase significantly and the break-even point for the haylage system with high yield lactating cows would be 40 ha instead of 120 ha. A real interest rate of 4% has been used to compare the haylage and hay systems. Some businesses use a real rate of return of 10% for investment comparisons. If such a high rate were used, the hay system would appear even more advantageous than the haylage system because of its lower investment cost for both machinery and storage. Farmers often do not expect such a high rate of return. In- some cases, their loans may be subsidized to a level that. 185 is close to a 0% real discount rate. Between 1975 and 1980, the inflation rate was higher than the interest rates of the Federal Reserve Bank (U.S.D.A., 1981). The average real interest rate was -0.9% during that period. Under those circumstances, the real cost of capital was‘ low because loans were available at a very low real cost. Figure 9.13 shows that the break-even point of a haylage system would shift down to 100 ha with a real interest rate of 0% instead of 120 ha with a real rate of 4%. 9.3.4 Direct-cut alfalfa The ultimate way to reduce the field curing time of alfalfa is by direct cut. The main problem with direct-cut alfalfa is its high moisture content and the large seepage losses that are likely to occur during storage. Bruhn and Koegel (1977) have suggested mechanical dewatering of alfalfa by pressing out up to half the initial water. The dewatered alfalfa may be conserved as haylage without field curing. Table 9.26 compares the resources required to operate a direct-cut system, a hay system and a haylage system. The machinery investment for the direct-cut system is smaller than the one for the haylage system, but the cost of equipment for dewatering and processing the freshly (Slha) COSTS EXPECTED 1700 1 500 1300 A 186 Haylage Hay 1 e 1 1_ 1 l I I ' I I 20 40 60 80 100 120 A R E A (ha) Figure 9.13. Expected costs of a haylage system and a hay system versus area assuming a low real interest rate (i=0.00) and high milk production. > 187 mowed alfalfa is not included. Simulation over 26 years showed that more quantity and quality would be retained with a direct-cut system. Table 9.27 shows that it retains 11% more Yield than the haylage system and 32% more than the hay system. Storage losses for direct-cut are assumed to be the same as for haylage. In practice it is difficult to avoid important seepage losses with direct-cut alfalfa. The quantities of soybean meal and corn grain purchased indicate that hay, despite its lower crude protein concentration, has a very good intake level compared with haylage and direct-cut alfalfa. Waldo and Jorgensen (1981) have suggested the use of formic acid to increase the intake potential of haylage to almost the same level as dry hay. Assuming that the addition of formic acid to wet alfalfa increases its intake to the same level as hay, the more efficient use of direct-cut alfalfa results in substantial savings of soybean meal and corn grain purchases (table 9.27). Table 9.29 compares the net feed costs under the four Iharvest and conservation systems. The benefit of haylage ‘Iersus hay increases with lower milk producing cows. The advantage of haylage would hence be more quantitative than Qualitative since low yield cows make better use of low cinality feed. Similarly the benefit of direct-cut alfalfa ilficreases with lower producing cows. In the case of 188 haylage and direct-cut alfalfa, the decrease in net feed cost is due largely to the increased production of alfalfa (and increased sale of excess forages) and not to the lesser purchase of supplements. Table 9.29. Net feed costs ($/ha) on an 80 ha alfalfa farm with 128 lactating cows at four milk production levels. System 20 kg/day 25 kg/day 30 kg/day 35 kg/day Hay 330. 469. 663. 885. Haylage 268. 420. 623. 881. Direct-cut 48. 219. 436. 720. Direct-cut + 28. 128. 317. ' 582. formic acid The addition of formic acid to direct-cut alfalfa would decrease the purchase of supplemental feeds. The advantage is greatest with high milk producing cows. In fact, the increased dry matter intake assumed for wet alfalfa would allow 110 more tons of alfalfa to be consumed by the herd and would reduce purchases of soybean meal by 30 tons and of corn grain by 80 tons (table 9.27). The benefit of increasing the dry matter intake of wet alfalfa is about $140. per ha per year or about $10. per ton DM with high milk producing cows. The benefit decreases rapidly with lower milk producing cows. In summary, haylage and direct-cut alfalfa do not reduce substantially the amounts of supplements required in ‘the ration compared with good quality hay. Although they ihave a higher crude protein concentration than hay, their 189 lower intake potential makes the overall quality similar to that of hay. Haylage and direct-cut alfalfa do have a quantitative advantage over hay by providing more feed per unit area. Increasing the intake potential of wet alfalfa (with formic acid or any other mean) would be valuable mainly for high milk producing cows. The analysis showed a reduction in feed cost of the order of $10. per ton of alfalfa dry matter harvested. Any haylage treatment to increase animal feed intake would have to cost less than the estimated benefit. 9.4 Storage policy The simulation model includes four possible storage locations for alfalfa: silo one (usually high quality wet alfalfa), silo two, high quality hay and low quality hay. These four locations allow flexibility and greater efficiency in the allocation of forages. Indeed the higher quality alfalfa may be fed to high yield lactating cows and. the lower quality alfalfa can be fed to dry cows and heifers. Two smaller silos usually cost more than one large silo with the same total capacity. The two smaller silos ‘however provide more flexibility in the allocation of forages. They also ensure a faster filling rate which may 190 reduCe oxidation losses in the silo. The present storage model does not simulate varying storage losses. Nonetheless the storage policy may be assesed from the feed allocation point of view. Table 9.30 relates the distribution of harvested alfalfa when one or two silos are used. In addition to the harvested haylage, each system include between 280 and 290 tons of alfalfa baled as hay. Table 9.30. Average haylage quality and standard deviation when one or two silos are used. Policy Silo l Silo 2 DM CP S(CP) DM CP S(CP) 1 silo 507.7 .183 .017 0.0 .000 .000 2 silos 258.4 .194 .012 259.9 .171 .011 Tables 9.31 and 9.32 show how the feed would be utilized with a high milk yield herd and with a low milk yield herd. More soybean meal and more corn had to be purchased with the high milk herd under the one-silo policy. The feed purchases with the low quality herd were curiously lower under the one-silo policy. Apparently under the two-silo policy, alfalfa with CP=0.194 would be tzoo high in quality to be used efficiently with a low milk lfield herd and alfalfa with CP-0.l7l would require the purchase of some supplements. A pooled average CP-0.183 proves to be the most efficient quality level for use by -1-<:w production cows. 191 Table 9.31. Feed utilization under two storage policies with high yield cows (35 kg/day). Policy Alfalfa Soy meal Corn grain Alfalfa produced ‘ purchased purchased sold (tDM) (tDM) (tDM) (tDM) 1 silo 798.42 70.84 482.30 176.95 2 silos 799.62 63.51 474.94 163.46 Table 9.32. Feed utilization under two storage policies with low yield cows (20 kg/day). Policy Alfalfa Soy meal Corn grain Alfalfa produced purchased purchased sold (tDM) (tDM) (tDM) (tDM) 1 silo 798.42 0.92 87.55 -105.38 2 silos 799.62 0.82 94.50 -97.33 This points out a weakness in the ration formulation model. Mixing high quality alfalfa with low quality alfalfa gives numerically an intermediate average quality. But the cows might respond more as if they were fed only low quality instead of an average quality feed. Table 9.30 did in fact show a larger standard deviation in quality ‘with the one-silo policy. The feed model could be improved by taking the variation into account. Table 9.33 shows the difference in feed costs between Storage in one large silo and storage in two smaller silos. ‘Vivth.high milk producing cows a two-silo policy allows betiter allocation of feed and an estimated saving of $1910. Per year (for 128 cows). The feed cost savings become 192 negative under low milk production levels for reasons explained in the above paragraph. In reality we would expect a greater segregation of feed to always reduce feed costs. Table 9.33. The feed costs (S/Yr) under two storage policies at four milk production levels with a herd of 128 lactating cows. Policy 20 kg/day 25kg/day 30 kg/day 35 kg/day 1 silo 21178. 33474. 50196. 72398. 2 silos 21464. 33578. 49816. 70488. Diff. -286. ’98. 380. 1910. Table 9.34 shows the difference in investment costs between the one-silo and the two-silo policies. The difference of $22000 is large and would be minimally compensated only with a high production herd. (The return of $1910. per year represents a negative return over 10 years and a 6% return over 20 years.) At any milk production level lower than 30 kg/cow/day, the two-silo ,policy is not worthwhile. Table 9.34. The storage investment required under two storage policies. ' Policy Storage capacity Total of each silo (tDM) investment (S) 1 silo 600. 52000. 2 silos 300. 74000. 193 As mentioned above however, at least two advantages of the two-silo policy are not accounted for in the model: the lower oxidation of haylage due to a faster filling rate and the lower variation in feed quality within each silo. These two factors should be included in a future more refined storage-feeding model. CHAPTER 10 CONCLUSIONS A systems approach was used to evaluate the production and utilization of forages on dairy farms. The boundaries included crop growth, harvest, storage and feeding to the dairy herd. A computer simulation model was developed to simulate the growth and harvest of alfalfa on a daily basis and the allocation of feed on a yearly basis. Historical weather data from East Lansing, Michigan were used to repeat the simulation over 26 years. 10.1 General conclusions. After having worked over the past two years on a HHJltidisciplinary research project and having completed the Present dissertation, two major conclusions predominate: 194 195 l. The systems approach, by considering simultaneously several interdependent components (namely crop growth, harvest, storage and ration formulation) provides a broader understanding of the relative importance of each component than if one were to consider each component separately; 2. Numerical simulation can be used along with field research to analyze the long term impact of new technologies and their adaptability to a wide range of management conditions. The simulation results showed some interactions between technological choices or management practices and the level of milk production. For example, a hay system was generally less expensive than a haylage system for alfalfa areas below 40 ha. As the area under cultivation increased, the haylage system became profitable more quickly with low milk producing cows than with high milk ;producing cows because the advantage of haylage over hay is Inore quantitative than qualitative. Another example is that early harvest of alfalfa is more profitable with high milk producing cows than with low milk producing cows. Simulation provides the researcher and the extension specialist a broad perspective that a few field or nutritional experiments might not give. 196 Experiments explain physical and biological behavior and are the basis for the simulation model. They can never be replaced by simulation. However simulation may allow the researcher to expand rapidly and at a lesser cost his conclusions to other climatic conditions or to other types of farms. Simulation may also point to promising changes and areas where research priority should be given. 10.2 The sensitivity of model assumptions With the exception of the alfalfa drying model, the simulation model is largely based on research published in the literature. Some technological coeffiecients are more accurate than others. The following section discusses the relative accuracy of those coefficients and the effect of erroneous values. Five aspects of the model are considered: the machinery model, the dry matter loss estimates, the quality loss (estimates, the drying rate model and the feed model. The machinery model should be the most accurate one since it is largely based on physical principles while the other models must incorporate biological or physiological principles that are more difficult to quantify. Some aspects of the machinery model such as time for loading and unloading material and the energy to convey material are 197 only approximate. These approximations should not however have much impact on the overall model. Dry matter losses can vary considerably during harvest, storage and feeding. Losses from mowing and conditioning are generally low; any inaccuracy should be of little consequence. Losses from raking and baling can be considerably high especially with dry and leafy material, and for round balers and hay stack wagons. Some of the loss estimates in the literature may be outdated 'because harvest technology has been changing rapidly. Dry matter losses due to environmental factors, such as respiration and rainfall, are not large and their estimation is relatively adequate. Material losses in the silo and during feeding may be considerable; their estimation would benefit from more detailed modelling compared with the use of a fixed percentage loss in the present model. Quality losses are well modelled during harvest as long as accurate values of leaf and stem losses are available. The model does not deal however with the appearance of mold when alfalfa is left curing for several days under rainy conditions. Quality losses in storage, especially with haylage, is undoubtedly affected by the rate of fill, the silo size and environmental conditions. Modelling quality changes during storage is likely to be the most significant improvement in the analysis of haylage systems. 198 The drying modal predicts the average drying over a whole day. It. does not predict accurately the instantaneous drying rate: this was not an objective of the simulation model. The drying model may suffer from the fact that a single equation was used to estimate drying over the whole range of moisture contents. The parameters in the drying equation may be biased because their estimation is based on data mostly in the higher moisture range. Only a few drying data were obtained for low moisture content alfalfa. The feed model assumes that intake potential is lower for haylage than for hay. The simulation results showed that if the assumption were changed and haylageintake were assumed to be the same as hay intake, the value of haylage would be increased by $150./ha. The haylage system would become more profitable than the hay system at 40 ha instead of 120 ha. The notion of an intake difference between hay and haylage is very crucial and should be further investigated. ‘ The feed model does not deal with quality variability within the storage structure as it would affect animal response. Simulation results show that some storage policies can provide higher and more uniform quality, but no premium value is given to uniformity versus heterogeneity within the storage structure with equal 199 average quality. 10.3 Managing the alfalfa crop The simulations in chapter 9 dealt essentially with the alfalfa crop and how management or technological changes could improve the performance of the forage system. On the basis of historical weather from East Lansing, a number of specific conclusions may be drawn: 1. Alfalfa harvest should start early when quality is still high. The greater yield obtained by postponing the harvest does not generally compensate the quality loss. One exception occurs with low quality demanding animals that can more efficiently use a greater quantity of lesser quality feed provided by late harvest than the smaller quantity of high quality feed provided by early harvest. 2.’ The simulation model indicates that a fourth cut is generally profitable if the three previous cuts start early (around May 20th, July 5th and August 20th). In one year out of ten the fourth cut has a negative return not on account of low yield but because of bad harvesting conditions. If other crops must also be harvested at the same time, the 200 profitability of the fourth alfalfa cut may be more questionable because of the time conflict. A slow harvest rate will result in lower conserved yield and quality than a fast harvest rate, but the differences are small between an instantaneous harvest and a harvest extended over four weeks. An extended first cut will have a relatively high yield and low quality. The subsequent regrowths will adapt themselves to the harvest rate and compensate the low first cut quality with a higher more uniform quality in the subsequent harvests. For haylage systems, a slow harvest rate may cause more damage at storage than in the field because of excessive oxidation during silo filling. For hay harvest, a farmer should not worry about taking three or four weeks for the first cut. The decrease of crop value is relatively small and does not justify the purchase of large machinery to reduce the average harvest period to less than three weeks. For both hay and haylage systems, the rate of harvest and the timeliness costs will become more important as the number of yearly harvests decreases. The field-curing time and weathering of alfalfa can be reduced either by increasing the drying rate or by harvesting at a higher moisture 201 content. A reduction of the field-curing time always results in more yield and crude protein conserved for feed. Conventional hay making with a mower-conditioner for all four alfalfa cuts required an average of 4.2 days for curing to 20% moisture (wet basis) and conserved 9.3 tDM/ha with a crude protein concentration of 16.7%. Increasing the drying rate by about 20%, through additional treatments such as maceration or spraying a chemical solution at mowing, would decrease curing time for hay to 3.4 days and increase harvested yield to 10.1 tDM/ha and 17.1% crude protein. Additional dry matter losses due to the extra mechanical treatment are however not accounted. Baling hay at 30% moisture and treating it against spoilage could conserve 10.2 tDM/ha at 17.3% crude protein after 3.5 days of curing on the average. Conserving alfalfa as haylage allows harvesting at moisture contents as high as 60%. The average curing time for hayalge is decreased to 2.4 days: 11.1 tDM/ha of alfalfa at 18.0% crude protein are available as feed. Direct-cut alfalfa reuires no field-curing at all and could conserve 12.2 tDM/ha at 19.5% crude protein. Seepage losses and other handling losses are, however, not included for the direct-cut 202 system. 5. Technologies that conserve more yield and a higher crude protein concentration will result in lower feed costs.' Increasing the drying rate for hay making or baling at a higher moisture content can represent a saving of $8. to $10. per ton of dry matter harvested, or a premium value for hay of 10 to 15%. The higher crude protein concentration of haylage compared to hay does not however translate itself into a higher per unit feed value because haylage has a lower dry matter intake potential than hay. The higher nominal quality of haylage is offset by a lower dry matter intake compared with hay. Increasing the potential intake of haylage or direct-cut alfalfa with the use of formic acid or other treatments could reduce feed costs by $10./tDM of alfalfa harvested, which is equivalent to a premium value to haylage of about 15%. 10.4 Comparing hay and haylage systems Hay and haylage systems represent different investment levels, different use of energy and labor, different conservation and feeding characteristics. Many factors 203 come into play in the comparison of these two systems. In general, a haylage system requires more investment and more energy but less labor than a hay system. It also retains more yield and more crude protein than the hay system. The nominal quality advantage of haylage is however offset by a lower dry matter intake compared with dry hay. The main advantage of haylage over hay is more quantitative than qualitative. Under mid-Michigan conditions, the haylage system becomes more profitable than the hay system for areas above 120 ha of alfalfa with high yield lactating cows and above 60 ha with low yield lactating cows when a ratio of 1.6 is used for lactating cows to land (cows/ha). Low milk producing cows can consume more haylage than high milk producing cows because the former require relatively low nutrient concentrations that can largely be met by the haylage whereas the latter require high nutrient concentrations that can only be met by the addition of substantial quantities of corn grain and soybean meal. An assumption in the feed model states that intake of haylage is lower than intake of hay. If the assumption is changed and haylage is assumed to have the same intake potential as hay, the haylage system becomes more profitable than the hay system with high yield cows at 40 ha instead of 120 ha. The difference in feed cost is about $150. per ha between the two assumptions. It is important 204 to evaluate more accurately the difference in animal response between alfalfa hay and alfalfa haylage. Interest rates used when comparing hay and haylage systems can be important. A high real interest rate will favor the hay system because of its lower investment cost. Subsidized loans may make the haylage system more attractive than the hay system. Under mid-Michigan conditions, a 100% hay system is generally less expensive than a 100% haylage system for farms growing less than 40 ha of alfalfa. Between 40 ha and 120 ha, haylage may become more profitable than hay depending on a number of assumptions. Low milk producing cows or low interest rates will favor the haylage system. If haylage intake is closer to hay intake than would indicate the few feeding trials published, the feed value of haylage could be significantly higher than the one estimated in the model. The farmer's attitude toward risk will also affect his 3 choice. A risk adverse individual may be willing to forfeit some profit in order to reduce the year-to-year variation. He could thus choose the haylage system which, although more expenxive than the hay system, offers less variability. The hay system requires more total labor than the haylage system and three men instead of two during harvest. 205 Farmers may view hiring and managing temporary labor as representing a higher cost than the $5. per hour assumed in the model. The haylage system does offer this intangible advantage compared with the hay system. Under more humid conditions, haylage might become more profitable than hay under smaller areas. The analysis did not consider corn production at all. Introducing corn silage along with haylage may be a more efficient way to use both machinery and storage structures in the context of the whole farm. A haylage system can produce the same quantity of feed of similar quality as a hay system on about 16% less land. All comparisons were based on equal areas of alfalfa for haylage and hay systems. The excess haylage was given a value of $69. per tDM. In practice, a farmer may have better land use opportunities than producing excess forages. A more realistic comparison between haylage and hay should consider the production of other crops on the land that is freed from forage production when shifting from hay to haylage. Ideally the boundaries of the system should be expanded to cover the whole farm. CHAPTER 11 RECOMMENDATIONS FOR FUTURE RESEARCH 'The simulation model still has a largely unexplored potential for analyzing forage systems under various climates. In addition the simulation results have pointed out some model weaknesses and areas where more experimental research would be helpful. In the short term, the simulation model can be used with minimal changes to expand the analysis of forage systems as follows:' 1. Use weather data from other locations besides mid-Michigan to specify under what general conditions various technologies might become preferable (e.g. under what rainfall pattern and for what alfalfa areas would haylage become more profitable than hay).. Try to include historical values of relative humidity to get better estimates of the drying rate. 206 207 Hay systems appeared to be more profitable than haylage systems in mid-Michigan for farms growing less than 40 ha of alfalfa, and up to 120 ha under certain conditions. For this reason research efforts should continue to improve hay systems. Some short term research priorities could be: 2. The development of improved field curing treatments that would increase the alfalfa drying rate and would not increase dry matter losses. 3. The investigation of treatments to conserve high moisture hay. Early baling can substantially reduce dry matter and nutrient losses. The simulation model dealt with growth and harvest in greater detail than it did with storage and feeding. Consequently more research is needed to model storage and feeding more accurately. Some long term research priorities should include: 4. Experimental measurement of oxidation of. alfalfa haylage as affected by the rate of fill, the silo size and the rate of removal. Little is known about the quality changes within the silo under .various filling rates, environmental conditions and rates of removal. 208 More precise knowledge on the animal intake difference between alfalfa hay and alfalfa haylage and how to model it. Research and development of new physical or chemical means to increase the intake potential of alfalfa haylage. A ration formulation model that deals explicitly with cow response to feeds of variable quality. Validation of the crop model under a wide range of climatic conditions. The prediction of leaf and stem quality is critical for crop valuation and should be further investigated. Validation of the dry matter loss parameters under a wide range of climatic and operational conditions (e.g. rainfall, speed of) operation, crop density). A distinction between leaf loss and stem loss should always be made. 10. Measurement of alfalfa field drying especially at low moisture contents. More data to predict the desorption equilibrium moisture content of alfalfa are also required. The drying model should be broken into several ranges for greater predicting accuracy. 209 In general, when assessing a new technology, field experiments should be done to estimate field losses (distinguishing leaf and stem losses), labor and energy requirements, any change in the drying rate and, ideally, feeding trials. A relatively small number of experiments over a short time can provide values for most parameters needed in a simulation model. The simulation model can then be used to assess the long term value and adaptability of the new technology. APPENDI CES APPENDIX A A SURVEY OF FORAGE HARVEST MACHINERY Appendix A A SURVEY OF FORAGE HARVEST MACHINERY A generic summary of forage harvest machinery is presented. It lists sizes and capacities of most machines available on the U.S. market in the fall of 1981. Also included are average values of machine mass and list price. Such parameters are useful for power requirement calculations and for cost analysis. Costs have been obtained from two sources: NFPEDA (1981) and Michigan dealers through verbal communication. Implement and Tractor (1981) provided an exhaustive listing of specific farm machinery on the U.S. market. Tractors have not been listed although they are required for harvest. Their main characteristics may be simplified as follows: the average tractor weighs about 100 lb per Hp (60 kg/kW) and costs about $300. per Hp ($400./kW) in the fall of 1981. ' One can observe from tables A.1 to A.13 that price is Closely correlated to mass. For most machines the initial cost runs at about $5. to $7. per kg ($2. to $3. per 1b). 210 211 Most costs were based on [those from the large, well established companies. There are some substantial price differences for the same size of equipment when it is manufactured by a small or by a large company. The survey does not show these specific differences. It only provides a generic guide for the potential user. Prices will change quickly and even the sizes and the capacities available are likely to change within the next few years. Table A.1. A generic summary of mowers and mower- conditioners on the U.S. market (1981). Mower Width Mass Cost Specific type (m) (kg) (S) examples (1) Cutterbar 2.1 360. 2000. JD350, IH1300 2.7 385. 2200. JD350, IH1300 4.3 820. 6000. ROWSE D7 5.5 865. 6300. ROWSE D9 Cutterbar 2.2 1140. 6000. JD1207, SNH472 mower-cond. 2.8 1360. 7200. JD1209, SNH472 3.7 1930. 9700. SNH495 Cutterbar 3.6 1860. 10000. JD1308, SNH114 cond.-wind. 4.3 1950. 10800. JD1308, SNH114 Disk 1.6 350. 2300. IH3104, SNH442 2.4 450. 3000. IH3106, SNH462 Drum 1.7 365. 2400. DZKM22, KMN165 2.1 570. 3500. DZKMZS, KMN210 2.7 1000. 5500. D2108, KMN270 3.3 1100. 6000. KMN330 Drum 2.7 1300. 7500. KMN270C mower-cond. 3.3. 1400. 8000. KMN330C (1) See table A.14 for names of manufacturers. 212 Table A.2. A generic summary of tedders on the U.S. market (1981). Width Mass Cost (m) (kg) (S) 2.1 190. 1500. 2.4 195. 1700. 3.0 200. 1850. 4.0 260. 2000. 4.8 400. 2400. 7.2 550. 3300. Table A.3. A generic Specific examples GRIMM 'B' GRIMM '8' KNGFZBN KNGF440 GRIMM '16', KNGF452 KNGF671 summary cf side-delivery rakes. Specific examples JD660, SNH256 JD670, SNH258 JD670-671, SNH258-260 A generic summary of conventional small rectangular balers. Width Mass Cost (m) (kg) (S) 2.6 350. 2500. 2.9 375. 2700. 5.8 790. 5800. Table A.4. Baler Pickup Maximum size width continuous (m) throughput (tDM/h) Small 1.55 6. Medium 1.70 8. Large 1.80' 11. Commercial 1.88 14. Mass (kg) 1230. 1450. 1640. 2000. Cost specific (5) examples 5900. JD336, SNR310 7900. JD346, SNH315 9900. SNH320, JD446 10900. SNH420 213 A generic summary of round balers. Table A.5. Maximum Bale Mass of Cost throughput size baler (S) (tDM/h) (kg) (kg) 7.5 400. 1500. 8000. 12.0 800. 1900. 10500. Table A.6. A generic summary of Maximum Bale Mass of Cost throughput size baler (S) (tDM/h) (kg) (kg) 10. 1350. 2400. 8500. 12. 2700. 4000. 12500. 14. 4500. 4500. 20000. Table A.7. Specific examples JD410, SNH846 JD510, SNH851 large hay stackers. Specific examples OW540, H510 OW560, H530 OW60A A generic summary of automatic bale wagons that pick and stack small rectangular bales. Wagon Maximum capacity loading time (t) rate (min) (tDM/h) 2. 15. 5. 3. 15. 5. 5. 15. 5. Table A.8. Throughput Mass (kg) Same as baler 250. Unloading Wagon mass (kg) 2000. 2500. 4200. Cost ($) 2000. Cost Specific (S) examples 11000. SNH1036 13500. SNH1037 20000. SNH1063 A generic summary of bale ejectors. Specific examples SNH70, JD ejector. 214 Table A.9. Hay wagons. Capacity Mass Cost Specific (t) (kg) (S) examples 4. 320. 1400. JD965 6. 400. 1700. JDlOGSA 8. 550. 2200. JD1075 Table A.10. A generic summary of forage harvester cutterheads on the U.S. market (1981). Typical Type of Maximum Mass Cost Specific PTO power hitch Continuous (kg) (S) examples required throughput (kW) (t-DM/h) 30. Integral 6. 530. 4300. SNH707 45. Pull-type 8. 1130. 6000. SNH718 60. Pull-type 11. 1460. 8000. SNH782 75. Pull-type 14. 1650. 10500. SNH892 90. Pull-type 18. 1700. 12000. GEHL1250 Table A.ll. Attachments for cutterheads. Type of Size Mass Cost attachment (kg) (S) (P): pull-type (I): Integral Row-crop (I) l-row 125. 1500. Row-crop (P) l-row 230. 1800. Row-crop (P) 2-row 360. 2800. Row-crop (P) 3-row 630. 5100. Windrow pickup (I) 1.4 m 175. 1400. Windrow pickup (P) 1.7 m 320. 2200. Windrow pickup (P) 2.2 m 410. 2600. Direct-cut mower (P) 1.8 m 360. 2800. Direct-cut mower (P) 2.3 m 550. 3200. 215 Table A.12. Forage wagons with unloading mechanism. Capacity (m3) 12.2 16.7 19.0 Capacity Mass of Cost Specific (t) wagon (s) examples (kg) 5.4 1350. 7500. KASTEN 21 7.2 1500. 9000. JD714A 9.1 1650. 10000. JD716A Table A.13. Forage blowers on the market. Capacity range PTO power Mass Cost Specific (t-WM/h) range (kg) (S) examples Corn Alfalfa (kw) silage haylage 70-120 35-60 ' 50-90 500. 2500. JD6500 80-140 40-70 60-100 600. 2700. ‘JD66 120-170 60-85 120-170 450. 2500. JD6000 Table A.14. List of manufacturers quoted for specific examples. Complete addresses are available in Implement and Tractor (1981). Company code DZ GEHL GRIMM HS IH JD KASTEN KMN KN OW ROWSE SNH Name and location of company Deutz Corp., Atlanta, GA Gehl Co., West Bend, WI G.H. Grimm Co., Rutland, VT Hesston Corp., Hesston, Kan International Harvester Co., Chicago, IL Deere & Co., Moline, IL Kasten Corp., Allenton, WI KMN Modern Farm Equip. Inc. Kuhn S.A., Vernon, NY‘ Owatonna Mfg. Co., Owatonna, MN Rowse Hydraulic Rake Co., Burwell, NE Sperry New Holland, New Holland, PA APPENDIX B A USER'S GUIDE TO FORHRV APPENDIX B A USER'S GUIDE TO FORHRV Program FORHRV estimates forage harvest rates for a given set of machines. It is a static model, whose results are used later in a dynamic simulation of forage harvest on a day-to-day basis. It calculates actual field capacity (ha/h), actual throughput (tDM/h), fuel consumption (L/h), electricity consumption (kW.h/h) and labor requirements (man.h/h) for up to 18 forage harvest operations, at six yield levels. . A matrix called RATES(108,8) is created by the program. The 108 rows allow a maximum of 18 operations at ' six yield levels. Each column contains the following parameters: RATES(K,1) is dry matter yield (t/ha); RATES(K,2) is effective field capacity (ha/h); RATES(K,3) is effective throughput (tDM/h); RATES(K,4) is actual tractor load (decimal); RATES(K,5) is fuel consumption (L/h); RATES(K,6) is electricity consumption (kW.h/h); 216 217 RATES(K,7) is labor requirement (man.h/h); RATES(K,8) is operating speed (km/h). The reason for calculating rates at six yield levels is to minimize later calculations. For example, alfalfa yield per cut might be expected to vary from a minimum of l tDM/ha to a maximum of 6 tDM/ha. The harvest capacity will also change with yield as three main constraints become alternately limiting: maximum operating speed, maximum machine throughput and maximum continuous tractor load. A 20-year simulation might generate 80 different yields; the harvest capacity and material flow rates need be calculated each time. The RATES matrix provides the data for efficient linear interpolation at various yields. Beyond the minimum and maximum yields, flow rates will be assumed constant except for field capacity which will be calculated from throughput capacity and yield. The input data are read as follows: 1. General information (1 card). 2. Machinery data file (up to 100 cards, one per machine). A last card with 0000 in the first columns will indicate the end of the machinery file. 3. Operations file (up to 18 operations and 60 cards). The last card must show 0000 in the first four columns. 4. Print-out options (1 card). 218 General Information Seven parameters for general use throughout the program are read into the array XINFO(7). They are read under the format 7F10.2. They are: XINFO(1), the power safety factor; XINFO(2), the soil traction number CN as defined in ASAE Data 230.3 (ASAE Yearbook, 1981); XINFO(3), the average soil slope (the tangent): XINFO(4), the absolute minimum alfalfa yield (t DM/ha); XINFO(5), the absolute maximum alfalfa yield (t DM/ha); XINFO(6), the absolute minimum corn silage yield (t DM/ha); XINFO(7), the absolute maximum corn silage yield (t DM/ha). ' The power safety factor is actually the inverse of the allowable continous tractor load. A value of 1.4 will generally be used, based on several observations of measured power requirements and actual tractor size recommendations by PAMI (1979). A firm soil is usually assumed for forage harvesting (CN = 30.). The average soil slope is generally zero. A value greater than zero should however be assigned whenever slopes are important and affect the choice of tractor size. The absolute minimum 219 and maximum yields of alfalfa and corn silage should be based on prior knowledge of extreme values. Machinery Data File Each machinery data card contains 14 parameters to be read under the format 14, 3F8.2, 10F5.l. The first parameter is the machine code and is stored in an array MCODE(100). There can be up to 100 data cards, including the last one (0000). The other 13 parameters are stored in matrix XMDATA(100,13). The parameters are the following machinery characteristics: XMDATA(I,1), mass (kg); XMDATA(I,2), list price (5); XMDATA(I,3), actual value (5); XMDATA(I,4), machine age (h); XMDATA(I,5), annual use other than for forage harvest (h); XMDATA(I,6), width (m); XMDATA(I,7), maximum continuous throughput (tDM/h); XMDATA(I,8), transport capacity (t WM); XMDATA(I,9), self-propelled machine dummy variable: l.' for self-propelled machines, 0. for non self-propelled machines; XMDATA(I,10), engine type dummy variable: 1. for a gasoline engine, 2. for a diesel engine, 3. for an electric motor; 220 XMDATA(I,ll), engine power (kW); XMDATA(I,12), time to load one bale (h); XMDATA(I,13), time to unload a bale wagon (h). Not all data are relevant to all machines. The first five parameters are required for all. When a machine characteristic is irrelevant, zero (0.0) should be inserted on the data card in the appropriate columns. Table 8.1 lists all the machines that are considered for .forage harvesting and the relevant data that are required as input to characterize each machine. Some characteristics, especially maximum continuous throughput and time to load or unload, are difficult to estimate accurately. Some values are given in Appendix A. Others are found in the example at the end of this appendix. Two exceptions to the above parameter defenitions occur 'with machines 0260 and 0270, dump trucks and forage compacting tractors. Ownership is assumed for all machines except for those two cases, for which leasing will be assumed. Input for XMDATA(I,2) should be leasing cost (S/h), excluding labor and fuel costs, instead of the list price. Table 8.1. Code number range 0010-0019 0020-0029 0030-0039 0040-0049 0050-0059 0060-0069 0070-0079 0080-0089 0090-0099 0100-0109 0110-0119 0120-0129 0130-0139 0140-0149 0150-0159 0160-0169 0170-0179 0180-0189 0190-0199 0200-0209 0210-0219 0220-0229 0230-0239 0240-0249 0250-0259 0260-0269 0270-0279 221 Machine Tractor Electric motor Cutterbar mower Cutterbar mower-conditioner Drum mower-conditioner Other types of mowers Side-delivery rake PTO-driven rake PTO-driven tedder Rectangular baler Large round baler Large stack maker Forage harvester cutterhead FH row-crop attachment FH windrow pickup FH direct-cut mower Bale thrower Bale wagon (small rectangular bales) Automatic bale wagon (small rectangular bales) Round bale loader Round bale mover Large stack loader-mover Small bale elevator Forage blower Forage boxes Dump trucks for forages Large tractor for compacting silage in a bunk silo Machines used for forage harvest. Relevant characteristics Power Power Width, Width, Width, Width, Width Width Width Throughput Throughput Throughput Throughput throughput throughput throughput throughput Capacity (tons) Capacity, troughput and time to unload Time to load, time to unload Capacity Time to load, time to unload Throughput Throughput Capacity Power,capacity Power 222 Table 3.2. Operations modelled in FORHRV. Code number Operation name Number of range data cards 0010-0019 Cutterbar mowing 1 0020-0029 Cutterbar mowing-conditioning 1 0030-0039 Drum mowing-conditioning 1 0040-0049 Raking 1 0050-0059 Double-raking 1 0060-0069 Tedding 1 0070-0079 Rectangular baler, with bales l dropped on the ground 0080-0089 Round baler 1 0090-0099 Large stack maker 1 0100-0109 Forage harvester, with windrow pickup, 2 blowing the forage on the ground 0110-0119 Automatic rectangular bale pickup l wagon 0120-0129 Large stack moving 1 0130-0139 Round bale loading-moving 2 0140-0149 Corn silage chopping, transport 5 and unloading 0150-0159 Alfalfa haylage chopping,transport 5 and unloading 0160-0169 Alfalfa direct-cut chopping, 5 transport and unloading 0170-0179 Rectangular baler, with bales 5 simultaneously ejected or stacked in a trailing wagon, transport and unloading 0180-0189 Handpicking rectangular bales dropped 5 on the field, transport and unloading 223 Operation File Some forage harvest operations are simple, involving only a tractor and an implement, while others are more complex, involving a harvester, transport units .and an unloading component. The varying complexity is reflected by varying the number of data cards required for each operation. There are 18 different harvest operations modelled by FORHRV: they are listed in table 3.2. The first nine operations are individual operations, whose working rate depends only on one tractor and one implement (or a multiple of the combination). These operations are fully defined with one data card containing eight data, read under the format 314, 5F10.2. The first three data are read into the matrix ICODE(60,3). They are: ICODE(I,1), the operation code number; ICODE(I,2), the implement code number from the machinery data file; ICODE(I,3), the power source code number from the machinery data file. Implement and power source numbers used here must have been previously defined in the machinery data file, otherwise execution will be stopped and the error will be identified. 224 The other five parameters are. read into matrix XOPER(60,5). They are: XOPER(I,1), the number of units; XOPER(I,2), the maximum allowable speed (km/h); XOPER(I,3), the actual working width (m); XOPER(I,4), the average bale size (kg WM); XOPER(I,5), the average hauling distance (km). The last two data are relevant only for certain operations: when baling or when a transport component is included in the operation. The datum XOPER(I,1) allows the use of multiple, identical machines. Two operations (0100 and 0130) require two data cards. In the case of a forage harvester blowing material on the ground (operation 0100), only one tractor is required but two distinct implements are required: the cutterbar head and the windrow pickup attachment. The first data card is identical to a single-card operation. The second card contains information about the second implement. For operation 0100, all data on both cards are identical except the following: ICODE(I,2) is the cutterbar code number; ICODE(I+1,2) is the windrow pickup code number. 225 In the case of loading and moving large round bales (operation 0130), the first data card identifies the loading implement while the second card specifies the moving wagon if there is one. ICODE(I,2) is the bale loader code number; ICODE(I+1,2) is the bale mover code number. If no distinct multiple bale mover is used (i.e. round bales are moved one by one from the field to storage with the loader), then ICODE(I+1,2) should read 0000. All other data are identical on both cards. Five operations (0140 to 0180) require five data cards. Operation 0180 is a special case and will be dealt with separately. In the case of the other four operations, the first data card describes the harvester: tractor and harvest implement. The second data card specifies any additional attachment to the harvester: a bale thrower, a corn head, a windrow pickup or a direct-cut mower. The third and fourth cards are usually identical and describe the transport system. The fifth data card identifies the unloading system. Table 8.3 shows in detail all the data required for each operation. It should be kept in mind that each operation between 0140 and 0170 includes harvest, transport and unloading. The use of two transport information cards (cards 3 and 4) allows the analysis of a special case: when no distinct 226 transport tractor is available, i.e. the same tractor is used for field harvest and for transport to storage. Such an analysis is done by setting the number of transport units initially at zero on card 3 (TRl - 0.) and by setting the number on card 4 to one (TR2 c 1.). The last operation (code 0180) is hand-picking small rectangular hay bales. Five data cards are also used to define this operation. Table 8.3 shows the information required. Since this is a transport-unloading operation, little information is needed in the first two cards which relate mostly to harvest. Maintaining the same data structure as in the other five-data-card operations simplified the simulation by allowing the use of the same subroutines, especially for transport and unloading calculations. Print-out Options The last data card contains three print-out parameters: IPRl, IPRINT and IPRIN read under the format 312. When IPRl is equal to one (1), values from the RATES matrix are printed out for each operation at six yield levels. When IPRINT is equal to one (1), detailed information is printed out on cycle times for operations that include transport to storage (Operations 0110 to 0180). When IPRIN is equal to one, the input data are printed out. Any value other than one will disactivate the 227 ovoo mugs: mo wouzom opoo nunasz um3om umvwoaca : .m u. = 3 = = : 3 .- 0 9V lav mafiaupaou 1a\aec wsfipafiuxm mufim pound .ommuouw um mcupmoac: mcowws mo uuonmsuuu ovoo oboe mafia monuHMuca um Honda Hones: maaa3oaaw mugs: mo wouumuu comma Snags“: muuxm Hmuoa Huuoa azauxmz nonasz upcomcaua buoamcmua : .m uo>aup one mcavsaoxm .uwcs uncanawuu you paoau may :« uonmg .o .o o . o : .N Aexv moanamae Ax: mxv «ecu waafiaam mafia mama .o .o .c o o aoaaanmao ._ mam—av coaumuomo pumonnumvlo>wm .mwououm ou pagan may Eoum umcmoa msu >3 0:0 as mco pw>oa mum moans Ham can uo>oe mama vacuumfip on ma mumsu ma oooc mu Aw.~+vaoooH .um>oE mama vcsou osu now no nsxuwnusoupcwz ozu you ma ovoo uswsoaasfi «nu .pumo pcoomm. mzu so .umvwoa mama canon may uo anonymouso mzu HOH ma opoo ucosoHnEH may .puwo umuwm msu co .mcofiunuoao vumoamumwnoco Lou m>ono vmcfimov moo ecu ou anon ca Hmofiucopu mum mango numb :uom (Amado «oodcw,mcofiumuomo vumolmuwwnoze fleas 1:: was 1a\axv a..a. mocmumfic ouwm mama AEV Lucas oabmsoaam mugs: mo ovoo mpoo mpoo wcwasm: ouwum>< wcfixuoz Esafixmz umaasz Houomua acoEoHaEH :ofiuwuoao .~ Aomso wa__o .oaoa on o~oov aaoaawamao eaaauaumenmao . mcoaumumao umo>uwn How pouwsvmu mama .m.m dance 228 Any mcwum>aoo mcfipsaoxo .mwmuoum um mafia oumwuoucw Essficaz Aexv mocmumwp wcwasmx wcfivmoass cos wuuxm mo nonesz A23 mew muwm mama mmmum>< mafia: mpou Aav uawwo: usavuofiss mousom opoo oafim mo umnasz Meson umpwonca : .m Aumsv mafia: uuoamcauu : : MO Huh—5:2 : : : . Q An\exv uuoamcuuu mcowwz you woman aumev mafia: opoo mo Hones: manmzoaam unoamcmuu nobomuu ovou comma Hobos adamant mo uoassz uuodmcmua uuonmcmue : .m . Aav Lac: .caoz uuoamcouu w .dummw can umumo>uwn ovoo wumumm>uun ocu ha may.:oosuon Loaouau mama no poaasn comma mafia warehousa ucosnomuum uuoamcmuu a mu Easacqz .. unannouuso : .N 1a\axc umo>ums Ham woman ovou mcou AEV cupwa manmzoaam mugs: mo nouomuu “mama Ho mpoo ucfixuoz E:wamz nonssz wcfiumm>ua= pounumuuso cowumuoao .~ Acmgo ou o<~cv mdowuwummo vumolmuwvlm>fim AeaaaaaaaUV .m.m assay 229 print-out options. An Example Table 3.4 is an example of input data used by program FORHRV. The first page of input data includes general information (first line) and an extensive machinery data file (from the second line to the last line on the page). Sixty-four different machines are specified, between machine 10 (a 20-kW tractor) and machine 270 ( a 150-kW tractor for compacting silage). Of course not all machines will be used. The extensive machinery data file is useful because it provides readily a large number of alternatives. Only the machines atually used are cost accounted. The second page of input data starts with 0000, the separator between the machinery data file and the operations file. Ten operations are identified between operation 40 (raking) and operation 160 (direct-cut alfalfa chopping). Twenty-eight (28) lines are needed to identify the ten operations because some operations require up to five data cards. The first operation is a raking operation (40) and uses machines 70 (a 2.9 m wide rake) and 10 (a 20-kW tractor). Note that the user must define what machines are matched together. 230 Operation 170 is a baling-transport-unloading operation. Tractor 13 (60 kW) pulls a conventional baler 103 (14 tons DM/h as maximum throughput) with a bale ejector 181. One transport unit composed of tractor 12 (40 kW) and hay wagon 181 (5.4 ton capacity) travels an average, distance of one km from the field to storage. A bale elevator (230) and a S-kW electric motor are used to unload bales at storage. As can be seen, each operation can be defined with a fair amount of detail. The present operation file identifies ten operations. Up to 18 operations may be defined in the same file. Not all operations need to be used on a given farm. Only those operations actually done and the machines required are accounted. ‘ The end of the operations file is recognized when 0000 appears in the first four columns. Finally the printout options are read. Here l-O-l means that the rates of each loperation are printed out, without detail, and the input data are also printed. Table 8.5 shows the calculated work rates for the ten (operations defined above. The order in which operations are defined in FORHRV does not matter (the order will matter in the dynamic simulation, in ALHARV). Rates are estimated at six different ‘yields. These rates are conserved in the RATES matrix for subsequent use, by 231 interpolation, in the dynamic simulation. Program FORHRV is independent of the dynamic simulation and can be used alone. It should actually be used to test various minor or major changes in implement matchings (e.g. tractor size, number of transport units) before going on with the dynamic simulation. 232 Example of input data for FORHRV. Table 3.4. .-..-- .. annuaooooufl 640501 oar-000000" 000000000 ooooaoooooonlllllflUQUQ-‘lln oanuaavn.119 O000.00..........OOOOOOOO......OOOOOOOOOOOOOOO0.0.000...00...... aocoooooooooooooooooooooooooooooooonooooooooo no ”000000000 ooaoooogoooooouooooaooooooooogo000080000000001100111000000000 ....0.00.0.0......0......00.000000000000000.........OOOOOOOOOOOO 000000000100000000000300oooooooooooooogooooooooo no 000000000 0 o _ Cocoooooooooooooooooooooooooooosooooooooooooooooooogoooo0000000 o.000.00....00.00..0......OOOOOOOIOOOOOOOOOOOOO......OOOOOOOOOOOO 2000000050000sooooooooooooooooooooooooooooooooooooooooooao00°00qua 23‘6803 5 55 1 11 onaaooaoooooocooooooooooooaooooooooooouoooooooooooogoaooaoooooo O....00.....00.........OOOOOOOOOOOOOOCOO0.0000000000IOOOOOOOOOOO 2229-22130000200000“00000000000oooooooooooooo00000000000000000012 0°00 a00000000ooooooosooooooooooo000000000 6. 20008890375 000092110 co...ooo..ooo..oeo.-ooo..oeo.:ooo-......v.......o..ooo..ooe..ooo..ooo..ooo..ooe OnZEUOEXUOEKUOnXUGnXQOnXYUQZSUOfiXUCninnXQOfiXUOEXUOfiXUU432(2323 «£41344n3200é319aéu 0°0000000000°ooooaoooosooaoooonoooooooooooooooooooooooaooooooooo 0.0.0.0....0.00.00.00.00.....OOOOOOOOOOOOOOOO.......OOOOOOOOOOOO 0°0000300025570000681q7202‘681‘.8000000000000osssooogooaauoosooooo 11111 011 1111 111 111 ‘55 IS FOLLOUS 6000 0:00i2UOfiXU192l75559AXUZflXUOfiXUOfiXEUflKXUOEXG8i:a§11185200fi2006§uUnzuflfiXUOiXUOEXUOnXUO 0..........ooo......ooe.-oo..eoo.....uoo..ooo..oos.ooo......eoo..ooo.uoo..ooi..ooo .UnXUOfiXUOEXéZQ:33iXEDQHLUOfiXQUnXQOnXUOEZQU .197il9:2ZOEXUOEXUDBXUOEXUOn:90n:!005:u0 000000030000ooo0000000000000000000oooooooooaoocoooaooooo 00.0.00...0......0.0.00.0.......OOOOOOOOOOOOOOOOO0.0.... on0000000000000ooooooooooooooooooooooooooooooooooocoo000 .UOfiXQOnIUOfiXU0nfiuOEXUOBXUOEXQOn3005:0052006100520032006290nX20052005290nzuafiruafizu33v o...-00......1.coo..oeo.-ooo.-ooo..co...00................vooo..oo..oee.-o...... 0AXUJEXURhXUOfiZUQnSXUOEIUOBXUOn310629362006103flflu0h:ufli:uflfi!ufld:uflfl!aflfi!uOfififluafifiuln. «a 01 00°00oaooooooaooogoooooo00000000000°00ooooooooooooooooooooonoo00° niaOfiXuOiXJ0n§930§2000130n20OnXu3320002005200nZJOEXUOnzuanXU0h3300620OnXJaéiuaélJQEXJO o...o..ooo_.oo.-on..ooo_-oo..ooo......ooo..ooo..........coo...-...-0......ooo..oon 05200nXuOfiXuOfiXU05200n200529002005390n29092u06200629003200n290n2006X90n3U0fi2006Xu033v .00n10OnKEUOnXU0nXUOnXU0639005200n2006200n5uOfiXXUDEXUOnIQOnKEQOGZHUOnZUOnXUOnXUOnv nXUOn2006280n1¢762014841:0062U0n23fi33506230i28n31QofbaézuQ?ééC:X!0RK!UDfiXUOsilfiixufi 8264 292 267 9 282523 6801818204 6802112512223211213012293 9 53222790 1135541. 2 1?; 1 124 121 «£12 1 002U0n390n32U0nXUOOXEUOnXUOnfiXUOfizgonfifluOnEXUOnKXUOnKEU003200nKEUOnXZUonZZUOnESUOnv FILE FOR FORHRV HAS READ A0200300nXUUEXuOhZu06:00fi2u05200fizuDBEUOEXUOEXUOEXUOn2005§uOfiXUOfiXUOnXuOfifiuOfiXUOEEUHXUOnu .USEZUUnZUOnXUOnZUOnXUOnXUOnXUOfiXU052006290032005200nXUOnXUOnXUOn100nXUOnXUOnHXUOnXUOi:a n2000520083202783118£1200n3290fii32500E1338fi12226nX§U4TAQUSREEUOnXXUOnEZISEEXU .l 8264202 2579282523680181820‘.68021125122232112130122“3‘63222790 U 1135541. 2 1:1 1 124 its 1212 1 D. UnXuOnXuOflfiXu0nXEUOnZUOnXU005:0OnfiguonfizgonXEUOEXUOnXZUUnZXUOOKZUOEXUOnfifluonfiXUOno N n2000320OflfifiguonfiXYUOnfizuOOEXUOnfiguOOEXEUOnZEUOnXEUOOEKEQOOnZEUOQXEUOnXZUOnXEUU Invoooo..ooooo..o0......-..oooo..oood...-oo.f.ooo..ooo..ooooo..oo...ooooo..oooo bJuOnXuOnifluOnXQOnKSDOfiXU0&190nZU0nXUOnXEUfliZEUfliiuflfiIUOEXUOfifluOfiXUOnXQU0200632906100 E 0000000056963007905055000000336502363721655208.0001505600000550500 H128468081313954374529605900551467123613§3523Q50521§QSQ60656435600 .... 11238.61 11141.. 1151211296 1111 22.. 11169 0123956000123001010123010120123401230120100120120101012001201200 111111123444457799000011222333330Q405556678889990011222345455567 1111111111111111111111111111112222222222222222 233 Table 3.4. Example of input data for FORHRV (continued). 00055000550 00000000000 0 a a a o o o o o o o 010 010 0 00000000000 00000000000 . . . . . . . . . . . 00011000000 3 00000000000 77000070000 0 o a a a a o a a a 0 22033020220 00000000000. 00000000000 ............ 00100001000 1 22 1 222 00500005000 00000000000 0 o a o o o o a o o a 11 1111 111 03322044223 11111211111 03011021111 70788335554 1111211222 0000000005500030 0000000500000000 o................ 0000011 0 010 0 0000000000000000 0000000000000000 OOOOOOOOOOOOOOOOO 000000000000000 000 880 00030000000000000 7077777500002000 aaoaoooooaaoaoaoe 2022222102202030 0000000000000000 0000000000000000 OOOOOOOOOOOOOO... 20’-20000100001000 1 111221 2221 2"- 0000000050000500 0000000000000000 o...000.........0 11111111 1111 11 2044444442234423 1111111111111111 1.0211102211121111 49351013 4 55436; 4 11122112221122 000000000000 2.0000000000000000 047777755555 26038334444466666 M 1111111111 11 111111111111 °i°3 1 Exauple of output from FORHRV. Table 3.5. RAKINO KNOUN AS Q0 CALCULATED UORK RATES FOR OPERATION EFCCHA/H’ ETPCTDHIH) LOADCDECI FUELCLIH! ELECIKUHIHD LABOR¢HHIHD SPEED‘KH/H) YDHCT/HA) 999999 999999 0 o o o o 0 999999 999999 999999 0 O I . . . Clio-0'09...“ 999999 999999 0 0-0 0 o 0 999999 999999 999999 . . . O . . nnmnn mono-och sou-«hon o o o o o o «amoeba .4 ”99009” hhhshh o O . O O O CIA-0W 999999 999999 0 o o o o o «NnODO AS BALE EJECT TR UL KNOUN 170 CALCULATED UORK RATES FOR OPERATION FUELCLIH) ELECIKUHIH) LABORIHHIH) SPEEDIKHIH) LOAOIDEC) ETPCTDHIH’ EFCIHA/H) YOHCT/HAI 2234 9990‘0‘0‘ 9999.407 0 o o o o o 99099v~ .40-0 999999 999999 . . . . O . ”1000007010 ”9999“ “99999 9 . . C . . h96~9¢5¢ “NM" 999-00"". 999999 . . . . . . «99050 «99019 0 o o o O o Noland—0.4 999999 999999 . . . . . . ”NMCI'IO AS CHOP (ALF-UPI TR UL KNOUN 150 CALCULATED UORK RATES FOR OPERATION FUELCLIH) ELECCKUH/H) LABORCHHIHI SPEEDCKH/HD LOADCDECO ETPCTDH/H) EFCIHAIH’ voncyluA) ouwumnm OOMMMDN ...... oaumonM) v-l 999999 999999 0 o O o o o NNNNNN 999999 999999 . . . . . . 999999 Odd-odd @FhMfi O O . O O '. encmuoc dunno-no o o o o o o Nam—a 99999:: oooeee o o o o o o nuncmo KNOUN AS CUTTERBAR HOU-COND 22 CALCULATED UORK RATES FOR OPERATION FUELILIHD ELECCKUHIH) LABORCHHIHD SPEEDCKH/H) LOADtDECI ETPCTDHIH) EFCCHA/HI YDHCTIHAD 999 «and 999 «00¢ o o o o o o NNN nah nun-c c-o 999 999 999 999 . O . . O O "'0“ Fork-1 090 «09 COO can . . 0' . . 0 99d '99 c-u-o-o cub-Id ounc'cmua no. no. Nnh W0- IMBO 9019 “I!“ 000 o o o o o o NNN Nut—o 999 999 999 999 o a o o o 0 “N0 (100 Example of output fro- PORHRV (continued). Table 8.5. KNOUN AS TEDDTNB 60 CALCULATED UORK RATES FOR OPERATION FUELCLIH) ELECCKUHIH) LABDRCHHIH’ SPEEDIKH/H) LOADIDEC) ETPCTDHIH) EFCCHAIH) YDHCT/HAD 000000 000000 0 o o o o 0 000000 000000 000000 0 O O O O O 0|!de 000000 000000 0 o o o o 0 000000 000000 000000 0 O O O O O NNNNNN 235 KNOUN AS CHOP ON THE GROUND 100 CALCULATED UORK RATES FOR OPERATION EFCCHA/HD ETP‘TDHIH) LOADCDEC) FUELCLIHD ELECCKUH/H) LABOR(HHIH| SPEED(KHIH! YDM‘T/HAI 0000\00~ 00nn0l~ o o o c o o won-«Nun «...-0 000000 000000 0 O O O O O "ddm 000000 000000 0 O O O O 0 000000 Nahumwu cnhhhh 00.... 00000!- 000». o o o o o o NNNN—o—t 000000 000000 0 O O O O C “0.00 KNOUN A8 ROUND DALTNG 80 CALCULATED UORK RATES FOR OPERATION FUELCLIH) ELECCKUHIH) LABDRCMHIH) SPEED¢KHIHD LOADCDEC! ETPCTDH/H) EFCCHAIHI YDNCTIHA) 00000“ @0000. o O o o o o 00000!~ woo-nun 000000 000000 0 O C O O O I‘d—Odd“ 000000 000000 0 C O O O 0 000000 «ovum ””0000 o O o O o 0 000000 000—!” o o o o o o NO00U‘O‘ nnnnoe 000000 0 o o o o o NNNN-«i 000000 000090 a o o o o o c-INIOOI'MD Example of output from FORHRV (continued). Table 3.5. ROUND DALE HOVER KNOUN AS 130 CALCULATED HORN RATES FOR OPERATION EFC‘HAIH) ETPCTDH/H) LOADIDEC) FUEL(LIHD ELECCKUHIHI LABORCHHIHI SPEEDCKH/H) YDHCT/HAI 000000 000000 I O O O O 0 000000 NNNNN“ 000000 000000 0 O O O O O “dc-00'0"!” unnmumnn eccocc ...... 000000 0000'. o o o o o 0 000000 ocmou Ohan-IO o o o o o o non-cud 000000 000000 0 0 O Q I 0 016000000 236 KNOUN AS CHOP (C8) TR UL IQO CALCULATED UORK RATES FOR OPERATION EFCIHA/HD ETPCTDHIH) LOADIDEC) FUELCLIH) ELECCKUH/H) LABOR(HHIH) SPEED¢KHIHD VDHCT/HA) 5000 an OIDFN I”. e o o o o e 0000 0000 0000 00 0000 00 O O O I O 0 WWW“ NN “on On 0000 can 0 e O o o e 4000m5‘00 new“: no» 0000 O. IMaNUI|~° .40-Ail! “N TR UL KNOUN 169 CALCULATED HORK RATES FOR OPERATION As CHOP (ALFfDCD FUELILIHI ELECCKUHIH) LABOR¢HHIHD SPEEDIKH/H) LOADIDEC! ETPCTDHIHI EFC(HA/H) YDHCT/HA) CNN—0'00 00000“ e o e O o 0 0000mm OI. 000000 000000 e o o e o o NNNNNN @Wdfl Ohmv-h O O O O C 0 000000 000000 e o o o o o «Nth-000 APPENDIX C A USER'S GUIDE TO ALHARV APPENDIX C A USER'S GUIDE TO ALHARV Subroutine ALHARV and all the subroutines called therefrom simulate daily harvest of alfalfa either as direct-silage, field-cured haylage or field-cured hay. A flow chart in chapter 7 describes the algorithm and its location in the overall dynamic simulation. The present appendix explains how to set up the input data and provides an example. The subroutine that reads the input data for alfalfa harvest is called MGTINF. Up to four alfalfa harvests may be simulated per year. For each harvest, the area in hectares, the- sequence of harvest operations and a criterion matrix must be read. Information about. silo capacity and cost and about hay barn capacity and cost is also read. Printout options for alfalfa harvest are then read. Finally the dairy cow herd is specified when subroutine COWFD is used to formulate the rations. Table C.l shows the general structure of the alfalfa harvest management data file. 237 Table C.l. Harvest 1 Harvest 2 Harvest 3 Harvest n 238 General structure of alfalfa harvest management input data file. Line number 6n+l 6n+2 6n+3 6n+4 6n+5 6n+6 6n+7 Input data Format Area F10.2 Sequence of harvest operations 915 Criterion matrix 9F5.2 " " 9F5.2 " ” 9F5.2 " " 9F5.2 Area F10.2 Sequence of harvest operations 915 Criterion matrix 9F5.2 " ' 9F5.2 ” ” 9F5.2 ” " 9F5.2 0.0 F10.2 SILO(1), SILo(2), ALFSIL(1), 7F10.2 ALFSIL(2), HAYST(1), HAYST(2), HAYST(3) IPRZ, IPR3, IPR4 312 XLCOWS, (HERD(I),I=1,6) 7F10.3 1. if another herd is analyzed F10.2 0. if ration analysis is ended XLCOWS, (HERD(I),I=1,6) 7310.2 1. or 0. as above F10.2 etc 0 239 Basicly the input data can be broken down into three parts: the alfalfa harvest parameters, the storage structures and the dairy herd composition. Alfalfa harvest parameters Six input data lines are used to define each harvest. Table C.2 shows all the parameters that define one alfalfa harvest. The first line specifies the area harvested as alfalfa (ha). The second line lists up to nine harvest operations that might be involved in alfalfa harvest. Operations are identified by the same numbers defined previously in the FORHRV program (Appendix B). For example, 00020 would identify a mowing-conditioning operation with specific mower and tractor sizes defined in FORHRV. The nine operations must be identified in the order shown in table C.2. Some operations may be omitted such as extra curing treatment (e.g. tedding), treatment after rain (e.g. tedding or raking) or independent transport of bales (e.g. hauling big bales several days after harvest). When such operations do not exist, 00000 should be inserted for the operation number. The last four lines for each alfalfa harvest contain decision parameters that affect the scheduling of each operation. These decision parameters are stored in the criterion matrix (CRTR,lines 3 to 6). 0240 .ouoz umo>hmz mo .Hnmo> :ofiuouwpu omega on wxmw :«ouonm coo mcwzosw «an: m ca 852. 852. 85% £58 38 we a. 2522 III! III wcmpoom magma mfipoom III 93.5: .555“: 9:30.: 3 ”c 051— .cqu .cqu .cqu .cuoz .Humo> .Humo> .Humo> .Humo> wmoamn mo wmoamn mo smoflmn wo emoama_mo upommcmhu upommcmhu whoamcmuu abommcmpu .ocomopcw .pcoaopcw .wcoaowcm .wcoaowcm pouomw pouomw souomm 0.55 mm 0.85 mm 0.55 mm 0.85 mm ”31.5 IIII muffin mfig um 0:3 6qu .Humo> .uoshumow :wououa .uuzhumow :wouohn fizzy oflums w: saws, sow mxmp ousho pow mxmp ovzho cumzm ou .uabEwm h mu Hmowuflhu Hmowuwpu Hmomufiho Hmowuwuo m2. m2. m2. goppcwz ”v mafia .cnoz . .cuoz .cnoz .cuoz .Humo> flaw.oowv asp oopv .Aaw.oouu .Hnmo> .Humo> .Humo> sogmuzo acoucou acoucou 23:00 N: nu“: “...: 5?» w: 5“: copoum ouzumfloa assumwoe_ chaumwos .cmufigswm. .cmufinawm .cauaoawm moans 0.2 III gag :55wa =3:me III on a :3. on ...x :5 on 2 50 um 0:3 moans mo umm>hma umo>hmc umo>hmz umo>hmn camp acmsumohu woodmcmsu ogu xx: zufipofipd xuwpowpm poumw mcwhso .comuwpcoo acowcoaobcu zohumma coupon vcouom umuwm unusumouh mcwxmm . asuxm -mcwzgz "N ocwq $5 8:. ”H 25 m m n c m e m N H .3952 «:33 sumo how 33 use: .~.u 033. 241 Some explanation may be useful as to the difference between first and second priority harvests. These two operations are usually the same operation. A plot of alfalfa will be shifted to second priority harvest if the actual crude protein is lower than the "critical crude protein” (line 4, column 5) or if silo l is full and silo 2 is not full. In the case of alfalfa silage or haylage, when both silos are full, the alfalfa plots remaining are harvested as dry hay. There are no storage capacity limitations for dry hay except that a marginal yearly storage cost is added if the volume of hay harvested is above the specified barn capacity. The storage policy is further described in chapter 7. It is implied that there can be two silos receiving forages of different quality. A single silo is also allowed. Alfalfa plots may be harvested as soon as their moisture content drops below the "maximum moisture content" specified in the criterion matrix (line 3, column 5, 6 and 7). Another criterion is used to decide if some plots are irremediably wasted because of overexposure. If a plot is exposed for a period longer than the ”critical days for destruction" (line 4, columns 6 and 8), then it is shifted to the harvest operation defined as "destroy the harvest". This operation can be either a baling with transport operation or a chopping operation blowing material on the 242 ground. In either cases, the value of the material is assumed to be zero and the use of machinery for this disposal operation is accounted. Column 6 applies to first and second priority harvests. Column 8 applies to forced hay harvest. The ninth operation, "independent transport of bales", is required when baling dry hay is independent from transport, i.e. bales are dropped on the ground and left for some time before they are hauled to a storage area. If the bales are always transported the same day they are harvested, the criterion "average number of days left in the field" should be 0. Otherwise a constant additional field loss will be accounted for weathering of bales left outside. The windrow to swath ratio (line 4) should be defined for mowing and for all curing treatments. Generally it is 0.8 for mowed alfalfa left in a wide windrow and 0.5 or less for raked material. The drying factors (line 5) refer to coefficients in equation 6.3. The drying factor for the mowing operation is CD in equation 6.3. It is generally 0 for a simple mower and l for a mower-conditioner. In the case of extra curing treatments, the drying factor should be equal to b9*XTR in equation 6.3. For example, a value of 0.05 was suggested for maceration. If there is treatment after rain (tedding or raking), the drying factor is equal to BK in 243 equation 6.3. A value of 1 should be used. Chapter 6 describes more fully the alfalfa drying model and the drying parameters.. The maximum number of days mowing can be ahead of harvest (line 6, column 2) can be used to reduce the risk of having too many plots curing at the same time. The minimum default value is two days (four plots). If a very high value were used, mowing would proceed regardless of the delays with harvesting. The mowing crude protein criterion (line 6, column 3) is the crude protein below which mowing should no longer be postponed. The criterion is used as a mesure of maturity. If the crude protein of the growing alfalfa is higher than the criterion, mowing is postponed for a maximum of ten days on the assumption that the plant is still too immature. The mowing crude protein criterion should be in the range between 0.15 and 0.23 to activate the postponing decision algorithm. If the criterion is outside the range, mowing is not postponed and starts on the first date BGNCUT(NTHCUT). The feeding method for each harvesting operation is a number between 1 and 7. Table 7.1 lists the seven feeding methods considered. It is the model user's responsibility to make sure the feeding method is compatible with the harvest operation. 244 Presently the model is able to read information for up to five alfalfa harvests per year. Any number between 1 and S is allowed (1 < n < 5). A value of 0.0 in line 6n+l, after the last harvest, will indicate the end of alfalfa harvest parameters. Storage structures The next line includes seven parameters for the storage of alfalfa: SILO(1) is the storage capacity of the first silo (t DM); SILO(2) is the storage capacity of the second silo (t DM): ALFSIL(l) is the initial cost of silo 1, including the unloading equipment (s); . ALFSIL(2) is the initial cost of silo 2, including the unloading equipment (s); HAYST(1) is the marginal cost for storing hay once the fixed hay storage capacity is filled ($/t DM/year); HAYST(2) is the initial cost of a hay barn (5); HAYST(3) is the fixed hay storage capacity (t DM). 245 The following line (6n+3) includes three printout parameters. When their value is 1, they activate detailed printouts. Any other value will disactivate the printouts. When IPRZ is l, a daily printout will show how much area is mowed and harvest each day. A seasonal summary will appear at the end of each harvest. When IPR3 is l, a yearly detailed output will show the feeding value of all alfalfa plots harvested in a year. When IPR4 is l, a yearly summary of the use of each machine and the resources required for harvest and feeding is printed out. Dairy herd composition The last lines, starting at 6n+4, are required only when subroutine COWFD, written by this author, is used for the ration formulation of the dairy herd. While all the previous lines are read from subroutine MGTINF, the last line is read from COWFD. The seven variables read in are: XLCOWS, the number of lactating cows (representing the total of fractions HERD(1), HERD(2), HERD(3) and HERD(4)); HERD(l), the fraction of the total herd as high yield lactating cows (35 kg milk/day); HERD(2), the fraction of the total herd as medium yield lactating cows (30 kg milk/day); 246 HERD(3), the fraction of the total herd as medium low yield lactating cows (25 kg milk/day); HERD(4), the fraction of the total herd as low yield lactating cows (20 kg milk/day); HERD(5), the fraction of the total herd as dry cows; HERD(6), the fraction of the total herd as heifers. The sum of HERD(1) to HERD(6) must be equal to 1. Each group of cows is fed farm grown feeds (alfalfa, corn silage, high moisture corn). Additional corn grain or soybean meal may be purchased to satisfy the net energy and the crude protein requirements. Any excess farm grown feeds are sold on the market. Subroutine COWFD is further explained in chapter 8. The input on the following line is either 0 or 1.. A value of 0 means the end of the feed analysis. A value of 1. means another herd with other values for XLCOWS and HERD will be read. The same harvested feed over 26 years will be allocated to this different dairy herd. Again the next line must specify either 0 (end) or 1 (continue with another herd). There must always be an even number of data lines in the dairy herd composition section, and the last card must always read 0. 247. An example Table C.3 lists the input data read for the dynamic simulation using the ALHARV set of subroutines for daily harvest simulation and the COWFD subroutine for ration formulation. The second page of table C.3 lists input data read from the alfalfa growth model (Parsch,1982). Four alfalfa harvests per year are simulated in this example. The four earliest mowing dates are defined as Julian days 135, 180, 225 and 285. No area is grown as corn. On the first page, all four harvests are seen to cover 100 ha. The sequence of operations is the same in all four harvests: operation 22 (mowing-conditioning) is followed by raking (40) and by chopping alfalfa haylage (Operation 150). The 26th line indicates that there are two silos with a 375-ton capacity each. There is also a hay barn with a 250-ton DM capacity. When the first silo is filled, haylage goes into the second silo. When both silos are filled, operation 80 (round baling) takes over the haylage operation. Note that operation 130 (transport of large bales) is also required. If the crop is left field curing more than 14 days, it will be destroyed by operation 100 (chop and blow on the ground). 248 All the machines used for these Operations (22, 40, 150, 80, 100, 130) are those defined in the FORHRV program explained in appendix B. Table C.4 is a partial output from _the dynamic simulation based on input from table C.3. The first page shows the potential yield and quality of alfalfa on the earliest mowing date for each harvest over a 26-year simulation. The second page shows the actual harvested alfalfa available as feed from each harvest. The third page provides information on the starting and ending dates of alfalfa harvest. The fourth page shows how the total alfalfa was distributed in the four storage locations: first silo, second silo, high quality hay and low quality hay. The fifth page shows the feed utilization with 160 low milk producing cows. The sixth page lists costs, milk income and net return. The seventh page is a summary of the resource utilization. Example of input data for ALHARV. Table C930 GOOC!C¢ “GOOD 0" I O O O HOOD 00699 @0009 F0 0 C C C OG’CO ochoo and—coo 0490. HI") Hoot-'5 'fiC‘JOfiO --4 e o e e und'CHD '0 06500 moi—coo —o o e o o 0‘ CMD (360°C 00°C) 0 O O 0 06°C (50°C,) 'OWON C O 0 C '4 Ca 249 OCOOQ 700096 H O O O O HGOC.‘ enacnpca cuacuacz F10... tween: F0 ¢3cfl~cfl= awn—«3:» O... .00 eacfiao [90.1600 '1 e o o e v-Q‘HQ o-n echo: lan-iocz 0'4 0 o e e F! awn 00°09 690° 0 O O 0 660° OGOOO') comow o e o o H O 900°C WOQOG '4 O O O 0 “COO 00°00 00000 d o O o o OQ‘OD pa CONDO com-«co e e g e For") anoao tnNcoo «a o o o e u-IC‘Oxo pi cohoo IDNHOO c-a e o e o o-c CHD ODOOD GOOD 0 0 e a 006° Deccan QDWON e o e o u-i C 06039 ”GOOD F. O O O 0 “COO DOOOO OOOOO H I O O O D¢O° c-c DONOO com—coo e o e o '0') COCK-AC.“ IDNOOO c-I e e e e dcoso ..4 OONC!O IDNv-OOD r4 0 o o 0 CI! cub 996°C 09°C I O O 0 000° oooccn tame“! o e o o d C 06250.00 10.00 9000.00 250000 06253.03 375.00 HANAGEMENY INPUTS FOR AREA AND OPERATION SEQUENCE HERE READ AS FOLLOWS c:cocc~ocooooc.oococococc~Docs: o cocoa 90699 ©0909 occaoo U. 00000 00.00 000000 Io can-mo cccmo occmc- sooner) I»: c a a he: «mecca—ommooc‘nmoooc—omocoo v0 NOQOO Nome: mecca women: a O O O O O O O O O I O O O O O O c: cuc> co udc- ca v4=~ => —u: 'c~ The following data were read in COWFD .300 .100 .600 .000 .000 .000 160. Example of input data for ALHARV (continued). Table C.3. ALFIN INPUI VILUES FOR ALFALF‘ SIMULATION RUN READ INTO SUBROUTINE 250 SUBROUTINES CRNIN AND CORN INPUT VALUES FOR CORN SIMULATION: NBLOJQ (HCOOEI 6: NTBLOU (NCOOE) Q=NIRAC (NCOOE) 0000 0000 00 (NANHRS/HR’ =XHEN 301.09 118.58 227.73 207.11 0.00 0.00 1C6: IHAD: U ‘50 013 D I E 1 l 3 I .90 II NN'O'OCID I0 I L pa. U aruue h-«(>¢o edxoooouuxmo O 62099603 >( 10° 6 2 CO :00 v-t N “(60 I \Q 2 o A- 0 ..IO- .IHZZCII <MIIOCIO¢IO ”(D-IMO- OUQ‘C‘J matamzmo N‘:h¢v\u U! 3 GOSUC‘ 0 "02¢ 0.0‘4 Z 0" 232*“ UOUZF-O- KG JUdO-“DQZUZ ocznh- m o-O\nu—nhl ¢ ”0- at, UVUUUUB «panama-o on zzczHoot-‘Q\¢¢¢J zz>hINUU C PZU:C(¢(J>¢ZUH UN (NOPOOHIO. 1m 0. ¢¢¢ CK camera-mu HCbZOOMOOWIH‘UKSP‘ ”(”ifl33UUUUIO-JBOUW2 Example of output from ALHARV. Table 0.6. QUALITY. PRE'HARVEST ALFALFA YIELD. MATRIX YALF. SUMMARY OUTPUT FOR 26 SIMULATION YEARS. 1953-1978: A=CF 5260085. =CP 3=OIO_ m 11 12 13 14 15 16 17 10 19 20 21 22 23 2A 10 251 OOOOOOOOOOOOOOOOOOOOOOO0.. 089.4”?\DmOJQQOIOIDIOOWDOQOOOnOOO‘ @nO‘QOO‘NOU-MONOIDJCWCFU‘OIDU'MDNIINO IDIDO‘flmONIDGDFU‘QOOIDOhO‘\DIDFCOU'MDC’ «HHHHdHdddnndfidddddddnndud h~moho05900090000909000990 NNNNNNNNNNNNNNNNNnNNNNNNNN .................p........ 0‘0 0‘0 00‘ W U‘ OOOU‘QODOQU‘OCOOOOOG asboomomoh~o~~hooo~~hhhhhb ......OOOCOOOOOOOOOOOC.... annual—Nuwwnnwnnnduwnwnwnnnn NNNNNNNNNNNNNNNNNNNNNNNNNN OOOOCDOOOIIOOIOIIOOOOGIOOOIIOOO ONQQ$CNDNWNFflDhHflNONOflOdON 090090—cmmtohnmowoonoomhun .....b.........¢.....Ib...1p... 99mum—ummuaawummomunnnammamammua Md‘ddu‘d IOU-I «dd dd Cite-I ..1....Ileooqloootl....|...¢I... 0995900900009.mooonocnunhm m~¢wnmr~n~r~9omhhnancr~0~9nooma~ chant:mmnmmococmooncocnccc chunocwuwuawunawmumwccmumoohmwma nnnumnnnnnnuununnnnnnumwmn .....r.........1....w|....1n... nflmunu¢4wm099m4uaou‘r91wwmhoan. oonoooooonhohs900090000999 .......................... cansoohaann9nn9nohnuw9unnn uwunuuuunNNNNNNunaNNNNNNNN 0.1!...(IOOOIIOIOIDOOOCDOOOIDOOO 32 fi .3 00 32 a? 19 07 23 56 $3 i 90 78 30 AT AA 55 $6 2% .......................... NNNNNNNNflanNuNflflNuflNNNNuN 0h00009000cn00099Nh0900090 «unuwunuwnnnwwwnnnuunwwwww ............o............. T T 7 T T 7 7 7 7 T T 7 7 T T 7 T T i T 7 i nnnnnnnnnnnnnnnnnnnnnnnnnn NNNNNN~NNNNNNNNNNNNNNNNNNN ............ooooeeooooeooo OOGODENGFDnOO$ODOOOdOflflfiNt nmhnaooo0~¢ouwnn9nmunuosuw ...»...1....1....¢....II...1.... NNNNNdNNdflfld «unnn dflddwfld 0.1!...IIOOOIDOOOGIOOOIDIOOCI.... 9~mnnnhmmcunmwmw0009a¢0~muI n—hn9hhnoo9uewowhonmro9nnmI ¢¢¢¢cn¢nncnmcc¢cncmnnnteen: 00905900900909000099900000I NNnNNNnNNNnnNNNNNNnNNNNNNNI 0.1!...IIOOOOIMOOOOIDOOOOIDOOOOI 99999099900999999990090909 hhhhhhhhfihOhhhhhhhhhhhfihhh ......OOOOOCOCOOOOO0...... nnnnnnnnnnuwnnnnnnnnnnnnnn NNNNNNNNNNNNNNNNNNNNNNNNNN ......OOOOOOOOOOOOOOOO.... cmohhmoownwuwnonnonumc0n—N atuahuosonuwnawnwmnmwhchnn ...I...1|...1DOO(OOOIDOOO|DOO(DO. Nunwunnwunotnuunnnnuuwuwnw 0.1!...(IOOOIIOOOGDOOOGDIOOCDI... MNBOdU‘nNDIfidQOth O‘DQQdQ Ch“ I o—WMDoGWMGOFMMOGCMMmDBcumrmcnuamI thdNNNNdNNnNd«NNNNNNNNNnd: «chcflnnd¢nm¢°u¢n¢NNnNNNmQI NNNNNNNNNNNNNNNNNNNNNNNNNNI ...1...1....v...t...¢p...¢...t...l °°°°O°°a°°°°°°°OQOOOCOQOCQ fifiohfifihfififihfififihofifihhhhfififih ...................C...... nnmnnnnnnnnwnnnwnmnnnnnnn NNdNNNNNNNNNNNNNNNNNNNNNN ......»....1p...¢|.....HD.... mCWNOQdDEandDNNnnQdeOOhD ~00n090~009u0onnwom—nnooho ......o...oo...o..o.0.ooo «wouwonnwnc¢nwncnnnnnn~nnw . .23 .«mncrwm~womuuuncmfim~ocwm~~runnc dududddnduNNNNNNN OF VARIATION 1=HEAN 2=STANOARO OEVIATION 3=COEF. STATISTICS FOR SIMULATION OUTPUT. ROUS: SAMPLE .03 .19 .22 .56 .01 .14 03. I: 30 5 2 69. .07 .10 :83 .05 .20 .03 .13 2.00 .08 .20 .39 070. o 2 71 .01 .13 :88 :38 .00 2:3? .36 .28 031. § .01 50. .05 .12 5% iii .23 236. 3.;0 .01 51. . 0 .01 .07 .22 .17 _.0A .10. 5 3:23, :3: :32 3 Table C.4. Example of output from ALHARV (continued). (TINAI. AVERAGE CRUDE PROTEIN IDECI AND AVERAGE DIGESTIBILITY (DEC) FEED TAL AS I HARVEST I CP DIG TOTAL YEARLY DIG CP DIG HARVEST 2 HARVEST 3 CP DIG DM CP DIG HARVEST I OH CP YR 252 up“:mnowomm~o9nnonmmn~or~o~ o¢mn¢nnmmcomnmwmuootmccmh 00000900000000000000000000 .......................... "WOWhctdédNNnNGODOOOnfictfl‘n I FNNQFOQDOFFOONnOOQthhhhw I Mdfiflduflddmdfldflflfldwd I ....ecoeooooooooooooooeoool I I uc~m¢nNNOONDOO¢IO~NDQOOOmOO¢ I Odwhndmna‘mNOQv-OCONFDOIDNNNO I ..........ooeoooooooooooool BOOOOONOOFDGOO‘O—IO'IOOQO‘O‘O‘O‘O I .490.“ 0.0400 dd III-"II u-I utnsDMOMOcOOnOOONmtwa I 9nmu909a~0~cnmmnov~¢n~~9on I owe-00000000000110.3000000900 I ..........................I 9 I I Otui'cflhc-IOIBNOCDO‘ONNWOMFODN I ncmunwwwounmocumencoeom I “mudmmd—omm ...-.900..— ..........ooeaeeooooooooeol 9 I uhMMtfiOfld$han0¢omnm I omnmouhuucnmco90ncv~ov~0uh I ......................... I duh-I." «...-1 «Inn—I m I nhuOTONtconmdhhdmoQoh-fiedcw I Inna—...owuoocoshhnnuoochntho I 60mOOOQOQGIOOOOOOOOOOQoOOO I ..........oeeoeooooeooeoool I I :OwflnODOCU‘NHI’nOflOHdeOOO I H09"! 090009090090909999v~099 I «MdNMNdNfi-INMGNNdNMN I O.........OOOOOOOOOOOOOOOOI I I onmnuocumnhaehcooaoowoee I «wooehcnmonmnahntmoowunnhh I ..........................I «umNnNN—o No-I woo-INN“ "Nanak-... I 9¢~nnn~oc~ncmhcoowcmmhnnm~¢s I 00mm\DsDnO‘IDOIOO‘flOONNhOthhmnh I 00wouoooooooooooooooocooo I ..oo......0...............I I I \DU‘MHUIOOIDFUIU‘OODNIDMNNOOOBO I cocoa-h OOhdhhoth-FQOOdOOONQG I umuuunwumwmnu—Nuufinnuw I ..........................I I I I I I I mom~~m¢ueomwm0¢ocuhr~9co~0 Nuchnuomnnnhocncm-ao0990mm .......o.................. nnmnnnnunmwnnnnnnuunnnnm P‘IOIOOOhdrIU‘OQCOOOGOQOOFVDNQFfi I nowann¢ncmc¢¢nnnmn~cn¢c~o I 00004!$600000000006000~000~00 I ..........o...............I 9¢mn9t~m9n¢nmc~cwv~mmscnmhtno msmohohsscoohhmmowowhossw dunnddnnd—Iununduduuunudnn—I ........................... momnuchmhoomchncucmccammo NCDQ'OFOIDIDIDOIDCC'FQFOC’OIDVDIDCN .......................... If) I I I I I I I I I I nnmnnnnnnnnnnnnnnnntnnnn I “N" I'I OhOU‘Oo-INI'I CID Oh OOBdNnQIflW ddddfidddHc-INNNNNNN OF VARIATION ROU 3=COEF. ROU 2=STANDARD DEVIATION. ROH 1=MEANI SAMPLE STATISTICS FOR SIMULATION OUTPUT. Example of output from ALHARV (continued). Table C.4. FOR THE UHOLE SIMULATION AND ENDING HARVEST DATES OF ALFALFA STARTING SPAN >¢UI SPAN HARVEST 2 SIARTING ENDING 0 TE DATE- SPAN ENDING DATE HARVEST 1 STARTING DATE YR E D 253 4......«I.....1I.....I....«I... 15¢Owuw~cawwohdmnvOrhu¢AwmunWWan unhduMHumnwouwunouwmwwwwNu—wuwuu .......................... compo»ommmw9usom~9~~~~n9om 0990990999n99—9999u9009909 wnnmnnwnnnnnnnnnnnnnwwnnwn .......................... 00.00000000000009000000500 OGOQOOQOGO$OQOQQOOOQOQOOOQ NNNNNNNNNNNNNNNNNNNNNNNNNN o.......o..o.............. annwou¢¢hncwu9cw0non9c0~nw Hunnfldndfiudwnuu unfinuflndd ............o............. OOQ¢°OOhNOOfiOhDfldOflOfiOflmOh ocean:otno¢onoccccn¢ccno¢c NNNNNNNNNNNNNNNNNNNNNNNNNN ............e...o......... annmuncnmm9nnmnowanmnnnnnm nnnnnnnnnnnmnnnanwnnnnnnnn NNNNNNNNNNNNNNNWNNNNNNNNNN 0.00000000000..00000...... FFOOOOOFFOODOFOFDN‘DOOOOOF uddddflddflddflddddflNfldddddfld ............o............. hhomDQWOththONNodnncm00h cu=9cuus9cumamcmua9cum9—mna9cumao NNNNNNNNNNdNNNNNNNNNNNNNNN .......................... 99n999¢090du99$fih¢9°°999¢9 00000000009000099000000000 «wannaunuuuunundunuudunuuu ............o............. 500006d09h000hmenaoahfimbhh deudNNuNuudenNNNddeNddu .......................... «00000annncwmuuowmo90nh~mw commonmoommnmoomomnoooocmo dududfinduduundduuddnnmadam ............o............. nnwmncocnumownuwwomwwcmmom ocnoonncccnnnt¢nnnn¢ccccn¢ «wannadunnuwduuuuunududdnn «Nachohco9uwncmuhom0n~ncmo dqududuu—uNNNNNNN OF VARIATION 33C0EF. zésrnuoano DEVIATION. now RON ROH 1=MEANI SAMPLE STATISTICS FOR SIMULATION OUTPUT. “OK: ”GNU .00 NC NNOI ~09 ... mo fifluI 0&9 .00 F” Nat OWN ... mm a 09M dNn Example of output from ALHARV (continued). Table C.4. 0 STANDARD DEVIATION OF CP SIDIGI DH T DIG SIDIGI :LI T HAY DH G SIDIGI T SILO I ma TR 254 wnxcnonuuoohmucmomcnn«ONhhI «HM~NNHNNONdosonnmoonunudoI OOQOOOOOOOOOOOOO9906666990 I .0...........O..00......0OI I OFOFNQDQOCNOdQnONOhNnFQOnmI umwmmuocowumnwuobomenmuwmnI moommomomoooooommmomoomowoI 000.000.0000..ooooooooooooI I NOOdQDNONefldfldflOndnHQOONFOI docududndcodeoQdNNOOOOddOflI DOODOOOOOOOOOOQOOOOO099600I ooooocoo-oooooooooooooooool I tuhtunnhanwmuecw~eoaoo¢~ouI ccnn~¢nnwom¢cocmwnn¢n¢omnmI dunnunduduuduunn«uduuduqudI ooooooooooooooooooooooooool owNFFODONOFdQfiODNndOONhnOD oooooooooooooooooooooooooo on¢oncnnooo$n~¢oocm@uummww fimwonmmohnaohcmnwhccoomnnn I I I I : «Nunwmnnd NW ¢nc NHde I nnpcm¢wuononnunhhwfi¢~06c0hI daudeoeOdoouudweodccuuuedoI 9990999aeaooooooeooocoooooI ooooooooooooooooooooooooool I hoawcoocnnconnhomoconom0NOI .OFOOmOODQwNQFQFOFOQQOFONFOI wommuwowemoooooooooocoomooI oono.oooooooooooooooooooool I o—oommnnnohnwooooowce~000hI 9999096ouuounoooooneud«°doI 66°OOOOOGGGGBOOOOGOOOOBOOGI ooooooooooo.ooooooooooooool I notowmnonuownhcoohhnwnncwcI uoecoaoheqoeooencherGQOOQI ddwuuuuduunfldnNununwufluunwI ooooooooooooooooooooooooool a I ‘ I cantoohmooennnooaeenonnecuI ooooooooooooooooooooooooooI «nonwomnmehnocamoecohmooanI nowc9hfl¢~u°0fl000fih¢0¢fl~0~hI d u an ddede “and“ Ndl cooonaswncomunnunmununooouI umuuwfluoflddOnNudNONnndNOdNI 9066060099OOGGGOOOBOOOOBQOI oooooooooooooooooooooooooou muonooanoounmhonhoe¢0honooI nndmnnnNCnHNONNNHNOOdONanI oowomoooooomwowoowoowoooomI ooooooooooooooooooooooooooI I 099N0¢dOOOOF¢00¢NOOwwdanhI OdHMOOnONOOONOOdOOdNOfidODdI 90900000006000OOOOOOQDODOQI 0000000000.000000000000000. I ~¢nm¢mneohwwcowwaoanccnmowI aomsomowhoomhmcmmoohmsooooI dudddddflflddddddfldflddflddfinflg ooooooooooooooooooooooooooI ”hueOdONndOFOODNOanOOOdOn 0......0....000..0......0. ”NnhdfldfihahmOfi@OnNOQNOODFFI NNfldhflNndnufldfiddNnNdNNNNNu| nnnnnnnnnnnnnnnnnnnnnnnnnnI notwoonum¢ooconmowop¢~n~~cI fidNfinddNfldedddddCOfldedddI ccoooeeeeeoeooeeoaooeoeoonI ooooooooooooooooooooooooooI I OOOdbOmnéoanhmofibNQOOOONQdI chmawhoommmmoohmcmhhsohhomI weomeoowoo¢wowoowmommmomooI oooooooooooooooooooooooCOOI n¢~conchnwwnmnumcoomcmwmwmI ouaohnnsonouonwomoanwuuuanI ocheOGmoevQQOOOOQOOOGOOOOOI Nfldundnuwduuwu«uanNudwnuNI 00000000000000.0000...-cool wscnhoommccnmhhoontdhnccn 00.00.coco-oooooooooooooo aQhuth—nOOhthDOoneNOuwoh fiddwunundnmnuwnddNnNNNNduu nnnnnnnnnnnnnnnnnnnnnnnnnnI 9.9 uwncmohomeuuncnoseoo~~n¢mm «unduuunndNNNNNNN OF VARIATION HEANI RON 2=STANDARD DEVIATIONI ROU 3=COEFo ROU I SAHPLE STATISTICS FOR SIMULATION OUTPUT. Example of output Iron ALHARV (continued). Table C040 0.000 0.000 .600 .100 .300 fioups 1N IHE FOLLOVING Pnopontxous: 0.000 n: couro Y 60 G FEEDS PRODUCED ON THE FARM NET FED FEEDS SOLD FEEDS PURCHASED YR CG CS HMC CG $8M CG ALF CS HMC ALF 255 tcd'Oc-Oc'tCC-O'Octtd'tftcct'tcc I nmnnnnnnnnnnnnnnnnnnnnnnn I ...‘0...ID..0II....1D..1I00.1|.0I 99999999999999999999999999 I C¢¢¢ot¢¢¢ct0¢¢ct¢cO¢¢C¢¢c¢ I NNNNNNNNNNNNNNNNNNNNNNNNNN I unwnH—wM4dwwuuu—umnwwund—wmwu—wunI I I I I I I c-cc¢¢¢¢¢¢c¢¢o¢co¢cc¢¢ccccc comnnnnnnnnnnnnnnnnnnnnnnn ..............0.0..O....O. 99999999999999999999999999 I Ottttoctétcc¢¢c¢¢¢¢¢¢¢¢¢¢¢ I NNNNNNNNNNNNNNNNNNNNNNNNNN I .mnuwwunu«wwwu—nuwnwwuwnuumn—wmnn cocoooeooooooooeuoeeaeoeoe I 90999906699990960066999996 I 000..000000...0..00..0.0.0I ONOOOQOOOOOOOOMGOOOOOOQ I aeoeeoooooeoceemoooome B“DO°OG°°°9°°°°G°°°°OO°9° 000...0000000.0000000.0.00 09996999699969909999999990 0w «woooaunonomumuomamon owe~o~o~ohr~uauoa~uom¢~oo~o~¢nwcnn ooo1.00000Inooocnnoooocunooooc nmnwwnuouuwooumhhmnonu 9*999Nnhm99009Cd9anFnF-mfl I'll Idl I IdNI INc-Iv-I I NNI—Idc-I—INI I I I I I I I I I I I I I I «mammamomnhoounuomnnua I nmnhmoumowooomcuomomnnuu I 0001!...IIOOIIOOOIIOOIIIOOOIIIOOI I090 twnomnoonooonnounuhosnu I GM9999AM9NC~¢9~DO¢OO9FDMN I chumscumuuumn—wmwuuumn-un‘umkumnt omomonomooonononomnoocooc ODOOOOIDOIIIDDGQDOONU‘F39(50ch oooooooooooooooooooooooooo oeoona 9N099N9 9”".‘0‘999990 99999999999999999999999999 I 99999 99 999999999999999 9999 I ooooooooooooooooooooooooool 99999999909999999999999999 I BEDDDOODOOOOGGOOOOOGOOOOOQ ooocooeooeoeaoaeecaoeceoao 00.0.000000000....0....00. O”°°O°°°B°O°Q°°°900°°O°°° 99999999999999999999990990 GOOGQCGQOOOGODOOE’.990909900 000000.oooooooooooooooooo. 99999999999999999999999999 OOflOOFOI’INIflIfiU‘hflNtNI’Iv-IC‘VDO‘U‘IOID v-INU‘IODC'FMOO‘FnOFOnNU‘GQN‘DNIflNW ..0..0.........0000..0.000 “ONOVOWHGMN'IIDU‘U‘G‘U‘FIFQUIQFCWWC OWIDFID'IU‘IOU‘IDNC’Fv-IC’IDFFIDU‘IDNNNQ 999GOOdOU‘NQODU‘CW-IOOIOOOO‘U‘U‘U‘Q I Hod—IF. FCC-Iv! Hv-I VIC-“II H “M5 'In W 90‘9HNIOOIIOFQD‘9'INIOOIDW dflddddv-I—u-IdNNNNNNN OF VARIATION 3=COEF. ROU 2=STANDARD DEVIATION. ROU =MEAN. RDH I SAMPLE STATISTICS FOR SIMULATION OUTPUT. 0.00 0.00 00.0 “117063 890I6 ‘076 .98 1.39 I.A2 9.00 0.00 0.00 00:3 0.0 0.0 0.0 990.65 98066 .ID I 2 3 Example of output from ALHARV (continued). Table ColIo NRET SUMIIO-IZI II=MILK 15 :C6 13 FSC 8:CUSTCG 9: AND NET RETURNS. gINED DAIRY-FORAGE SYSTEMS MODEL (DAFOSVMI. . LABFEED 7 I GROSS :12 U R u ? "$2 F R CTIO U A O EAR. RMM 5=LAGFLD 6 R S T H 2 C EL 4 DRYCG IO=SUM(I-9I II=FNET 12 256 .00..OOOOOOOOOOOOOOOOOOOOO mowed—thoummc‘mcmnrmnm—Iaahanw nhncm—ummcuauswhwnnunmmmmm cwccnhohwnumcnwommmmncodu emhundcn—Ioummoommmhommm—com WNNPONI'HO"NHnNfiNNnNnHHflNNNNv-fi NNNNNNNNNNNNNNNNNNNNNNNNNN 00000.0.00.0....0.00.0.000 OQQQQOCGOQODQDQQQQOODODQQO O'OCCCOQtttC'C'C'CCCC’CQOOOC 9999909000OOOGOOOOODOQGOGO OCCCCO¢¢¢¢¢CC¢CV'¢¢OCOO... nonnnnnnnnnnnnnnnnnn-onnnnn nnnnnnnnnnnnnnnnnnnnnnnun-mo ......ooooooooooooooooooo. mwwchsuchonomcmmmnnhoc‘dunno ohdewnnomennNhNh—ou—In—uwuow ohooonnwoaamooomon—ocmsmo-cco ccmwowoownwmosnmmooh-aocomwhn 9999G969d~99d9d090dfl999fi°01 rod—Inco— “'4de wt no.4 cum-0.4." oooooooooooooooooooooooooo 99d¢000hh90l~f~¢lfllflt9~bccn909$ IDOID99¢I~09d909900N—N9m—dcwt Nunnhnh9m9ouDdOnPnO9D9—I9C9 #3109ch CIDQIDCNOQOQNOU‘OHCQU‘IDO «Maw—dwflunflnfldwmmudM—I—I .......OOOOOOOOOOOOOOOO... :00“:me«woonoonohomommhmuc coauehnmnhaosownmnmmumno tone-«ohahhnaoowmmocmnonwcho nOONIBOMNONQr-IOo-Icq—onnomcutwu Nils-I I I “N dude-IN ......000.0000.000..0.0000 CMOOCMO'NCONNOONQQODfidOCF fidfiOOflv-IMNVDOONOCNUIMOCDIOF” CIDFU‘NNCC’QCIFIOOKIGDNOMONNC\DI‘I d999U‘deQONOhU‘Onv-IIOMFIU‘D‘OU‘F QQOOFOQOFFQOFNFOQGD»DNFNFN oooooooooooooooooooooooooo mnmnnnnnnnnnmnnnmnmnnmmumn 00099099990999900099990999 nnnnnnnnnnnnnnnnnnmnton-mom OOO‘U‘O‘O‘O‘U‘U‘U‘O‘O90909900090990 dududnuddfldndudnduflnnnmud .0.....00.000000.000000... Odntfimhfn5090$°0nfiCQFNONI-IQ U‘ONNIOQOHNI‘IQQ‘DOQCCOMOU'IODIDN nNdeNOCQCMfl‘DOOIflnFMCOv-‘U‘OF COCCOCCOnnCCWOIfl'CC'OIOCCQIOCI'I cooooooooooooooooooooooooo Ohfimnntnhflhutflnfl‘hc900n90‘n9 nNNou—INNI‘DNGnNOGOOQdN9NNboN emceeohmcuonncnhmhmomtnmn OCCCO'COOQOOOO¢¢COOC'O¢00¢. cooooooooooooooooooooooooo wanna—snohuowhncnoow«mama—unm— eweunhoonoo—ohc-Nensouocnnemc nnnvwcmonaoounwhcomcowwc—a ooowomuomowwwooooomww«mam ......ooooooooooooooooooo. \onhhho-hnwetom—ocmc-Ohcmoc-Nun N90h¢¢n¢09¢9mdc\DONC’IOON-Iwohc moshohmomnmonmmmoo¢cv~.mmmoc nnnnmnnnnnnnnnnencnnnnnnnn ......oooooooooooooooooooo hamooonr~¢nwr~mm~o~mn-m~taunt mwnhmwhnncunehm—m—ooum—oonce _unhmanhuonwswe—owonmmwhmcoh unmoun-mowcnmnnmchohnnocctmn uwuuunnununnuuunu—«unnnnuu 00000.0.000000000000000... nmmnmunmInmmmmmmmmmmmmmmmmmmm CC...CCOCOC¢CCOQCCCCCOCCOC mnmmmmmnnnnnnnnmnnnuwmmnnmn OOOOQ\OOfiOOQ‘DW‘DOQOIflW‘OQOOOOfi NNNNNNNNNNNNNNNNNNNNNNNNNN «NncmOhwwouwncmohomouwntmo ududuuunadflmw NNNN 2=STANDARD DEVIATION 3=COEF OF VARIATION ROHS I=MEAN SAMPLE STATISTICS FOR SIMULATION RUN: I 0'0 NOO no \D'~ (U 2 0 on 099 33AOQ 00F- .09 ..m Example of output from ALHARV (continued). Table Co‘ I CGIDMTI 9: CGIHAI 6=LABFEEDIHRSI T=CROPSIHAI LABFLDCHRSI INVESTMENT. 5: AND YEAR. Q=HIRH$ I FUELILI OU MU 3: 257 0...... 0.............. O O 99°06” ”GOOQOOOBOOODOOO ..1I...II...O|...II...II...II... mtumnnrunnntuwnnmwnmnnmumnnmumnn OHWNOMWNQMUV«NNWNGNWNGMNNNMKWNQNWN .wu—«mu—wuwuumwfiwuukunncumnu—nfimu ..1I...IIO..I|...II...1|...II.O. cacwmhohcwuh—munwtoomww~o—mnwuaua O oncoumnch ("Mon—um OOO90dNO—IO «noounmnmcrumnnhdnuhowmwunocuunh ...II.....II....II...I|...II... I~@fhmflflfflbufl~9ffl”m9FFJWDNCMMmO0 N9NNQNONU‘ OndthmHm90N9Q‘hu-MD Inwowmnaowwumamaumnoowwmnowcum»a OdhbfidhflfilNMDOINMDO¢NMDOUMDO¢NMDG o001.000.0130ooocnooocpoooqu000 Nhwnnnrunassaww~o~mnuwouhmuumna 9 O oNomnm QNGMDCNDIDC'NOOh-I—IU‘OO 0') 0NNQNFMQOODFO-‘99quhonD-I (WAowwnNGMW‘cMhm9—umflwflhmuawuwnrun dthNduwmfiwuuwumnnwwuwuww«ndwumu o ...1DOOII...1|O.II.O.1I...II.OO 99999999999999999999999999 999999999 99999999999999999 In :mn mmmmm mmmmmmmmmmmmmmmmm chum—wumnducumnd—umud—wnud—wumnu 999999999 99999999999999999 awumuuuumnnuumnuwwmuuwwunnwwumnn O1...!IOOOOII.OOCIOOOOIIOOIDOOO. CH=9CMMDOCKM59cmMm99cumDocumaocua 9c":ocunocxmacnaocuMDccu:ocuacmnac o«homunmooounmooqwmmoqumoounmoo \oouwmomuumomuhmoounmowuhmoeuhmom nwnmnnwwmmonwwmonrwmonwwmonwuwon n—uwu-wuwuuwwmuu—wmuduwmunuwum—d .uMnomhohanumuanmtndnuo¢=w«mo¢nwa uuwucwuwuwwmnaNWNwomuv MEAN 2=STANDARD DEVIATION 3=COEF OF VARIATION RDUS I FOR SIMULATION RUN: STATISTICS SAMPLE ..m ‘009 N1). ..n «N39 “GM? APPENDIX D EXPERIMENTAL DATA OF ALFALFA DRYING APPENDIX D EXPERIMENTAL DATA OF ALFALFA DRYING Field experiments were conducted in Chatham, Michigan during the first and second alfalfa cuts in 1980 and during the first cut in 1981. Appendix D lists the original data that were collected during those three experiments. The measurement technique is described in Savoie et al. (1981). Table D.1 represents drying rate measurements as a function of several machinery and environmental factors. Table D.2 shows how rain was adsorbed by field curing alfalfa. Table D.3 illustrates how dew was adsorbed under a variety of environmental conditions. 258 DMDT .329 .101 .301. .279 .130 .0119 .h72 .3111. Table 0.1. in June and July 1980 and in June l98l. contains fourteen variables. average values during the drying period. #0 \Dmua‘mbWN-i o .0000. dd —I N 13. 1h. M0 3-655 2.617 3.720 2.807 2.361 2.112 3.398 2.1.00 259 Alfalfa drying data collected in Chatham, Michigan Each observation Environmental variables are The variables are: DMDT, drying rate (dec. d.b. moisture content per hour); M0, the initial moisture content (dec., d.b. - dry basis); HF, the final moisture content; SR, solar radiation intensity (cal/min/cmZ): TDB, dry bulb temperature (C); THE, wet bulb temperature (C); WV, wind velocity (m/s); YDH, yield of dry matter (kg/ha); AM, alfalfa maturity factor equal to the ratio in equation 6. l8; HR, windrow to swath ratio (equation 6.h); RK, raking dummy variable RK - l, on the day of raking RK - 0 otherwise CD. conditioning dummy variable CD - O for cutterbar mowing CD - l for mower-conditioner CD - 2 after a second conditioning treatment; RNDW. rain and dew dummy variable RNDW - 0 if no rain or dew has occurred RNDW - i if all the moisture that evaporated during the trial was from rain or dew. RNDW can be a fraction between 0 and 1 if part of the evaporated water was dew or rain and the other part was moisture initially in the plant; DAY, a day factor DAY . 0 on the first curing day DAY - l on all subsequent curing days. MF SR TDB TNB WV YDH AM WR RK C0 RNDW DAY .617 1.17 19.6 lh.0 2.8 h129. 1.0 .782 0. 0. O. 0. .222 0.h3 15.0 11.0 0.8 #129. 1.0 .782 0. O. 0. 0. .807 0.81 13.0 10.8 A.1 2260. .90 .782 0. O. O. O. .361 0.86 13.3 10.5 3.h 2260. .90 .782 0. 0. 0. 0. .112 0.72 13.h 10. 7 3.8 2260. .90 .782 O. O. 0. O. .926 0.22 11. 0 9.0 2.8 2260. .90 .782 0. 0. 0. 0. .AOO 1.02 23. 6 18.6 A.5 2579. 0. 6 .782 O. 0. 0. 0. .23h 0.51 23.3 19.6 3.1 2579. 0.6 .782 '0. 0. 0. 0. DMDT .050 .219 .271 .617 .313 .179 .082 .080 .237 .123 .635 .237 .113 .015 .196 ~09? .095 .067 .087 .115 .109 .601 .276 .261 .070 .237 .277 .100 .298 .207 .130 .035 .239 .132 .286 .110 .225 .231 .153 .000 .760 .202 -337 WNWNNWWNw—ONWNWWNUPNW‘C‘r-‘HNdN—‘NUNWUNWrNNU-PU-‘U M0 .302 .650 .607 .006 .202 .009 .122 .108 .688 .715 .932 .212 ~057 .525 .502 ~759 .007 .881 .350 -753 .968 .006 .015 .256 .879 .108 .610 .509 .932 .396 .229 .525 .767 ~655 .593 .720 .316 .868 .628 .398 -993 .302 HNNNNNUNNd-‘N-‘NW-‘NWNNUrd-i-‘d-I-i-‘NHNWNNWdNNWNO-i HF SR .650 0.73 .357 0.36 .081 0.27 .202 0.99 .009 0.97 .122 0.60 .833 0.20 .688 1.10 .715 1.18 .001 0.02 .212 1.06 .057 1.20 .509 0.00 .502 0.85 .759 1.20 .221 0.51 .881 0.60 .606 0.20 .897 0.02 .220 0.00 .386 0.51 .015 1.11 .256 .93 .879 0.08 .601 0.16 .610 0.73 .509 1.16 .976 0.55 .396 1.00 .229 1.20 .558 0.08 .763 0.98 .767 1.20 .082 0.05 .593 1.17 .122 0.03 .316 0.70 .868 0.86 .628 0.72 .055 0.22 .993 0.80 .206 0.51 .863 0.70 TDB 26.6 25.8 20.8 20.0 21.0 21.0 19.0 10.1 20.8 16.3 17.9 20.8 20.2 20.8 23.9 22.2 21.0 19.0 16.3 20.2 22.2 20.5 21.0 21.0 19.0 12.2 17.9 16.3 17.9 20.8 20.2 20.8 23.9 22.2 19.6 15.0 13.0 13. 13.0 11.0 25.0 23.3 25.3 TWB 22. 21. 18. 15. 15. 16. 15. 12. 15. 12. 10. 15. 10. 16. 18. 16. 16. 15. 12. 10. 16. 16. 15. 16. 15. 10. 10. 12. 10. 15. 10. 16. 18. 16. 10. 11. 10. O‘OON#N-flNWkD-HNDNOUIONdmNONt’N-‘U‘IWWWONOU'INL‘NU‘ d 0 U1 d O \J 9.0 20.2 19.6 21.8 260 Z < “Us!«PNWWPONUUUN—‘ONWWN-‘OOWNNNdWWWN-‘ONHWN—fioowd—I o oo o 0 ea 0. e. o. oo 00 e a one. o 00.0. e. o 00 a e . o . 0.0 . d—mmoor—moo-omN-amoomoowmrmm-mmr~m-mmmmwmrmmow~ YDM 1515. . 1515. . 2398. . 0181. . 0181. . 0181. . 0181. . 0817. . 0702. . 0817. . 0702. . 0702. . 0702. . 0019. . 0019. . 0019. . 0820. . 0820. . 0868. . 3350- - 0121. . 0005. . 0005. . 0005. . 0005. . 0060. . 0060. . 0060. 0162. . 0162. . 0162. . 0026. . 0026. . 0026. . 0685. 0685. 6005. . 6005. . 6005. . 6005. . 3138. . 3138. . 2809. . HR RK CD RNDW DAY .782 .782 .782 .852 .852 .852 .852 .852 .852 .852 .852 .852 .852 .852 .852 .852 .020 .020 .020 .020 .020 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 .003 OOOOOOOOOOOCOOOOOOOOOO-I-I—d-‘OOOOOOOOOOOOOOOO 0. 000000000 0 OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO DNOT .157 .261 .281 .101 ~153 .128 .118 .570 .306 .263 ~075 -377 .309 .126 .000 .280 .122 .677 .267 -097 .263 .102 .119 .100 -097 -079 .001 .056 .003 .000 .130 .061 .110 .000 ~099 .109 .022 .067 .030 .025 .050 .031 ~035 c>c>c>c>c>-c>--a—--a—-—o----—--hanina-Raua-auauanauas-Ranauac-—-—-nanaumua—a MO .863 .607 .156 .800 .536 .988 .675 .006 .715 .883 .098 .108 .517 .271 .932 .007 .772 .525 .701 .552 .609 .293 .028 .905 -337 -937 .006 .537 .186 .350 .912 .368 .900 .002 ~397 .250 .601 .182 .768 .508 0 .816 0 .762 0 .811 0 oooooddfl—‘do-‘d—‘o—‘d-‘N-fldN-d-dW-‘NWNNNWdd-‘NNNO NF .919 .306 .800 -059 .685 ~372 .087 .715 .883 .098 .229 .517 .271 .571 .007 .772 .115 .701 .552 -057 ~293 .938 .389 ~173 .868 .006 .207 .186 -935 .099 .368 .029 .002 .198 .868 .582 .509 .768 .590 .363 .509 0. 0. 0. 0. ooooocuo—ooooooooooo—oo—oo—-ooo--ooooo . 0 0 0 . 0 00 0 .573 0.80 .605 0.80 TDB 25.7 20.0 21.0 19.0 16.3 20.2 22.2 20.3 21.0 21.0 19.0 13.6 17.9 16.3 16.2 20.8 20.2 21.9 23.9 22.2 21.0 19.0 16.3 20.2 22.2 10.1 17.2 10.1 17.2 17.2 20.9 20.2 20.9 20.2 20.2 19.8 22.2 19.8 22.2 22.2 20.3 20.3 20.3 Twa 21. 18. 16. 15. 12. 10. 16. 15. 15. 16. 15. 11. 10. 12. 13. 15. 10. 17 16. 16. 15. 12. 10. 16. 12. 13. 12. 13. 15. 10. 15. 10. 10. 15. 16. 15. 16. l6. 10. 10. ‘10. fiWO‘W-‘NNOWLON-‘WNOWN .1 NMNNNNNNNNWNWU‘IU‘IOU‘ON-‘mNON3‘ 261 Z < 0000000... 00.. eeoooeoeoooooaooeoo0.00.... oooduau—oomomoooouoou-odmmr—mni~mmmooums-totodammrow NNNWWWUWNN-‘NHNNUNWWNNN-‘WWUN-‘ONWWN—‘OOWNNN-‘W-fl 0 0 YDM 2980. . 2093. . 0006. . 0006. . 0358. . 3099. - 0196. . 0967. . 0967. . 0967. . 0967. . 0160. . 0160. . 0160. . 0150. 0150. . 0150. . 0597- - 0597- - 0597- . 0908. . 0908. . 3906. . 3968. . 0305. . 3910. . 3910. . 0820. . 0820. . 0080. . 0072. . 0072. . 0936. . 0936. . 5037. . 5072. . 5072. . 5162. . 5162. . 3872. . 3290. . 0052. . 0526. . --oo--oooo-oooo~oooo-—----ooooooooooooo--—--oo n D eoeeeeoeoooooooe.ooeooooeoeoeooeo...eeooooo RNDW DAY OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO .ooooeoeoeooaeeeee0....eoooooooeooooeoeoaoe —--—-—-—-—-—-—a—o--—-—---n—-—‘—-—°OOOOOOOOOOOOOOOOOOOOOOOOO 262 nnor 'Mo HF SR 103 Twa Z < , YDM AN NR RK ("I U RNDW DAY .055 1.635 1.279 0.87 22.0 17.0 3.1 5206. .80 .852 0. 0. 0. 1 .082 1.302 0.838 0.57 22.3 16.7 3.1 5206. .80 .852 0. 0. 0. 1 .071 1.315 0.859 0.87 22.0 17.0 3.1 3350. .80 .020 0. 0. 0. 1 .083 0.859 0.388 0.57 22.3 16.7 3.1 3350. .80 .020 0. 0. 0. 1 .079 1.226 0.791 0.57 22.3 16.7 3.1 5002. .80 .020 1. 0. 0. 1 .087 1.070 0.722 1.05 20.5 16.5 2.2 5136. .80 .852 0. 0. 0. 1 .086 0.908 0.600 1.05 20.5 16.5 2.2 0198. .80 .020 0. 0. 0. 1 .070 1.106 0.831 1.05 20.5 16.5 2.2 5276. .80 .020 1. 0. 0. 1 .103 1.170 0.596 1.05 20.5 16.5 2.2 3962. .75 .852 0. 0. 0. 1 .098 1.085 1.090 1.05 20.5 16.5 2.2 0101. .75 .020 0. 0. 0. 1 .125 1.130 0.638 1.05 20.5 16.5 2.2 0013. .75 .020 1. 0. 0. 1 .131 2.015 1.595 0.88 10.1 12.0 3.3 0913. .95 .705 0. 1. 0. 1 .112 1.900 1.208 0.88 10.1 12.0 3.3 0908. .95 .390 0. 1. 0. 1 .070 1.590 1.109 0.70 17.2 13.5 2.8 0913. .95 .705 0. 1. 0. 1 .018 1.208 1.102 0.70 17.2 13.5 2.8 0908. .95 .390 0. 1. 0. 1 .082 1.588 1.113 0.70 17.2 13.5 2.8 5097. .95 .390 1. 1. 0. 1 .060 0.803 0.008 0.80 20.3 10.7 2.0 5295. .95 .705 0. 1. 0. 1 .010 0.570 0.087 0.80 20.3 10.7 2.0 5022. .95 .390 0. 1. 0. 1 .000 0.780 0.510 0.80 20.3 10.7 2.0 0532. .95 .391 1. 1. 0. 1 .111 1.019 0.968 1 20 20.9 15.3 1.5 3606. .85 .705 0. 1. 0. 1 .090 1.186 0.822 1 20 20.9 15.3 1.5 3906. .85 .390 0. l. 0. 1 .073 0.952 0.535 0.00 20.2 10.2 2.0 3603. .85 .705 0. 1. 0. 1 .051 0.822 0.535 0.00 20.2 10.2 2.0 3906. .85 .390 0. 1. 0. l .067 0.970 0.603 0.00 20.2 10.2 2.0 5002. .85 .390 1. 1. O. 1 .063 0.708 0.323 0.90 19.8 15.7 3.1 3721. .85 .705 0. 1. 0. 1 .036 0.595 0.375 0.90 19.8 15.7 3.1 0670. .85 .390 0. 1. 0. 1 .010 0.320 0.261 0.76 22.2 16.7 3.1 3721. .85 .705 0. 1. 0. 1 .026 0.375 0.219 0.76 22.2 16.7 3.1 0670. .85 .390 0. 1. 0. 1 .008 0.322 0.275 0.76 22.2 16.7 3.1 3572. .85 .390 1. 1. 0. 1 .081 1.277 0.768 0.87 22.0 17.0 3.1 0198. .80 .705 0. 1. 0. 1 .092 1.322 0.751 0.87 22.0 17.0 3.1 3967. .80 .390 0. 1. 0. 1 .063 .782 0.019 0.57 22.3 16.7 3.1 0198. .80 .705 0. 1. 0. 1 .035 0.751 0.508 0.57 22.3 16.7 3.1 3698. .80 .390 0. 1. 0. 1 .016 0.730 0.636 0.57 22.3 16.7 3.1 0237. .80 .390 1. 1. 0. 1 .100 0.871 0.296 1.05 20.5 16.5 2.2 3336. .80 .705 0. 1. 0. 1 .087 '0.966 0.610 1.05 20.5 16.5 2.2 0102. .80 .390 0. 1. 0. 1 .116 0.851 0.391 1.05 20.5 16.5 2.2 5061. .80 .390 1. 1. 0. 1 .090 1.020 0.655 1.05 20.5 16.5 2.2 0733. .75 .705 0. 1. 0. 1 .098 0.971 0.578 1.05 20.5 16.5 2.2 0305. .75 .390 0. 1. 0. 1 .098 1.151 0.768 1.05 20.5 16.5 2.2 0618. .75 .390 1. 1. 0. 1 .122 2.678 1.905 0.88 10.1 12.0 3.3 0081. .95 .079 0. 1. 0. 1 .099 2.071 1.803 0.88 10.1 12.0 3.3 0006. .95 .390 0. 1. 0. 1 .080 1.905 1.038 0.70 17.2 13.5 2.8 0081. .95 .079 0. 1. 0. 1 263 DMDT M0 MF SR TDB THE 1: < YDN AM WR RK n U RNDW DAY .098 1.803 1.367 0.70 17.2 13.5 2.8 0006. .95 .390 0. 1. 0. 1. .098 1.718 1.162 0.70 17.2 13.5 2.8 0099. .95 .390 1. 1. 0. 1. .029 0.861 0.687 0.80 20.3 10.7 2.0 0060. .95 .079 O. 1. 0. 1. .038 0.810 0.587 0.80 20.3 10.7 2.0 0252. .95 .390 0. 1. 0. 1. .063 1.010 0.636 0.80 20.3 10.7 2.0 0099. .95 .390 1. 1. 0. 1. .127 1.731 1.236 1.20 20.9 15.3 1.5 0701. .85 .079 0. 1. 0. 1. .095 1.323 0.950 1.20 20.9 15.3 1.5 0358. .85 .390 0. 1. 0. 1. .037 0.950 0.702 0.00 20.2 10.2 2.0 0358. .85 .390 0. 1. 0. 1. .070 1.207 0.803 0.00 20.2 10.2 2.0 0701. .85 .079 0. 1. 0. 1. .085 1.190 0.716 0.00 20.2 10.2 2.0 0019. .85 .390 1. 1. 0. 1. .060 0.925 0.567 0.90 19.8 15.7 3.1 5013. .85 .079 0. 1. 0. 1. .072 0.870 0.001 0.90 19.8 15.7 3.1 0188. .85 .390 0. 1. 0. 1. .035 0.525 0.313 0.76 22.2 16.7 3.1 5013. .85 .079 0. 1. 0. 1. .023 0.001 0.305 0.76 22.2 16.7 3.1 0188. .85 .390 0. 1. 0. 1. .005 0.601 0.339 0.76 22.2 16.7 3.1 0069. .85 .390 1. 1. 0. 1. .113 1.699 0.982 0.87 22.0 17.0 3.1 0022. .80 .079 0. 1. 0. 1. .107 1.525 0.805 0.87 22.0 17.0 3.1 3099. .80 .390 0. 1.‘ 0. 1. .061 0.931 0.581 0.57 22.3 16.7 3.1 0022. .80 .079 0. 1. 0. 1. .067 0.805 0.063 0.57 22.3 16.7 3.1 3099. .80 .390 0. 1. 0. 1. .031 1.061 0.888 0.57 22.3 16.7 3.1 0305. .80 .390 1. 1. 0. 1. .110 0.916 0.058 1.05 20.5 16.5 2.2 0331. .80 .079 0. 1. 0. 1. .118 1.210 0.730 1.05 20.5 16.5 2.2 3902. .80 .390 0. 1. 0. 1. .125 1.221 0.726 1.05 20.5 16.5 2.2 0510. .80 .390 1. 1. 0. 1. .152 1.185 0.571 1.05 20.5 16.5 2.2 0919. .75 .079 0. 1. 0. 1. .106 1.002 0.852 1.05 20.5 16.5 2.2 0196. .75 .390 0. 1. 0. 1. .190 1.157 0.010 1.05 20.5 16.5 2.2 3671. .75 .390 1. 1. 0. 1. .225 3.815 2.505 .38 15.9 15.2 3.2 3967. .95 .852 0. 0. 1. 1. .113 2.505 1.825 .57 20.6 17.8 2.5 3967. .95 .852 0. 0. .70 1. .090 2.638 2.093 .38 15.9 15.2 3.2 0820. .95 .020 0. 0. 1. 1. .091 2.093 1.505 .57 20.6 17.8 2.5 0820. .95 .020 0. 0. .82 1. .179 0.022 3.376 .38 15.9 15.2 3.2 0973. .95 .705 0. 1. 1. 1. .161 3.376 2.011 .57 20.6 17.8 2.5 0980. .95 .705 0. 1. 1. 1. .192 3.972 2.851 .38 15.9 15.2 3.2 0908. .95 .390 0. 1. 1. 1. .107 2.851 1.968 .57 20.6 17.8 2.5 0908. .95 .390 0. 1. 1. 1. .179 0.526 3.078 .38 15.9 15.2 3.2 0088. .95 .079 0. 1. 1. 1. .150 3.078 2.552 .57 20.6 17.8 2.5 0088. .95 .079 0. 1. .89 1. .092 0.179 3.603 .38 15.9 15.2 3.2 0006. .95 .390 0. 1. 1. 1. .153 3.603 2.762 .57 20.6 17.8 2.5 0006. .95 .390 0. 1. 1.- 1. .550 5.280 2.230 .83 22.3 19.0 3.1 2097. .50 .852 0. 0. .90 1. .130 2.230 1.311 .20 21.8 19.0 2.0 2603. .50 .852 0. 0. 0. 1. .133 2.060 1.170 .20 21.8 19.0 2.0 2291. .50 .020 0. 0. 0. 1. .637 5.003 1.953 .83 22.3 19.0 3.1 2110. .50 .852 0. 1. .88 1. .138 1.893 0.965 .20 21.8 19.0 2.0 2110. .50 .020 0. 1. 0. 1. DMDT .560 .131 .607 .101 .106 .601 -137 .508 .052 ~r~r-m~r M0 .991 .878 .310 .176 .010 .690 .388 .128 .202 o—lo—O—O-IIN—l—l HF .921 .002 .176 .161 .006 .606 .002 .308 .803 SR .83 .20 .83 .20 .20 .83 .20 .83 .20 T08 22. 21. 22. 21. 21. 22. 21. 22. 21. com com 00000» out» THE 19. 19. 19. 19. 19. 19. 19. 19. ' 090900909 260 Z < NUNWNNWNU 0 0 0 0 0 0 0 0 0 tariff—0rd YDM 2858. 2858. . 2033. 2863. 1355- 2067. 2067. 1690. 1690. AM .50 .50 .50 .50 .50 .50 .50 .50 NR RK 1.000 .020 .079 .079 .390 .079 .390 1.000 .390 OOOOOOOOO ..oooeooo n C 000.00... RNDW 0- OOO O. o. \I on 0 \DO ddddddddd 0 0 0 0 0 0 0 0 0 o 0‘. mo 0‘ 0 DAY 265 Table 0.2. Rain adsorbed by mowed alfalfa. Data collected in Chatham, Michigan. Previous No of Moisture cont. (d.b.) YDM NR RAIN Percent treatments samples Before After Change (kg/ha) (mm) of rain (1) rain rain absorbed C8 6 1.999 3.815 1.816 3967. .852 5.3 16.5 CB-R 2 1.606 2.638 0.992 0820. .020 5.3 20.7 NC 6 2.297 0.022 2.125 0973. .705 5.3 28.3 MC-R 2 1.938 3.972 2.030 0908. .390 5.3 50.0 MCW 6 2.657 0.526 1.869 3872. .079 5.3 28.5 MCW-R 2 2.059 0.179 1.720 0006. .390 5.3 01.0 C8 0 2.508 5.280 2.738 2067. .782 30.7 2.8 CB-CR 2 2.378 5.003 3.065 2110. .782 30.7 2.7 CB-TD 2 2.582 0.991 2.009 2858. 1.000 30.7 2.2 MCW 0 2.031 5.310 2.879 2109. .007 30.7 0.9 MCW-CR 2 2.218 0.690 2.072 2067. .007 30.7 0.9 MCW-TD 2 2.272 0.128 1.856 1690. 1.000 30.7 1.0 CD 0 0.838 2.008 1.210 5207. .852 28.2 2.6 CB-R 0 0.589 1.706 1.157 0198. .020 28.2 0.1 MC 0 0.019 2.373 1.950 0198. .705 28.2 0.1 MC-R 0 0.592 2.303 1.751 0102. .390 28.2 6.5 MCW 0 0.581 2.650 2.069 0022. .079 28.2 6.8 MCW‘R 0 0.675 2.605 1.970 3902. .390 28.2 6.9 CD 6 1.138 2.079 0.901 3978. .852 28.2 1.6 CB-R 2 1.386 2.371 0.985 0101. .020 28.2 3.0 MC 6 1.110 2.661 1.551 0695. .705 28.2 3.7 MC-R 2 0.868 2.028 1.560 0305. .390 28.2 6.0 MCW 6 1.097 2.365 1.268 0503. .079 28.9 0.2 MCW-R 2 1.087 2.775 1.688 0196. .390 28.2 6.0 (1) Previous treatments are: CB, cutterbar mower; MC, mower- conditioner; MCW, mower-conditioner-windrower; R, rake and T0, tedder. 266 Table 0.3. Dew adsorption during the night (between 20:00 in the evening and 8:00 the next morning). Previous Moisture contents (d.b.) Temperatures (C) treatments Previous Previous Morning Dew TDB TWB Minimum (1) morning evening after (C) (C) night TDB C8 + rain 3.815 1.825 1.937 .112 19.5 16.0 7.8 CB-R + rain 2.637 1.505 1.537 -.008 19.5 16.0 7.8 CD 1.852 0.999 1.250 .255 18.8 13.2 7.2 CB'R 1.968 1.033 1.283 .150 18.8 13.2 7.2 CB 3.932 1.597 1.635 .038 18.8 13.2 7.2 CB-R 3.932 1.220 1.315 .091 18.8 13.2 7.2 CD 2.008 0.876 1.090 .210 10.2 10.9 7.0 CB-R 1.708 0.827 0.908 .121 10.2 10.9 7.0 CB 2.653 1.366 1.752 .386 20.2 19.6 13.3 CB-R 2.370 1.086 1.270 .180 20.2 19.6 13.3 C8 1.752 0.606 1.260 .618 23.3 21.1 11.1 CB-R 1.270 0.072 0.822 .350 23.3 21.1 11.1 CB + rain 5.007 1.000 1.888 .308 18.2 17.6 13.9 CB-R + rain 5.513 1.170 1.360 .190 18.2 17.6 13.9 C8 1.579 0.322 1.157 .825 18.5 16.3 16.7 CB-R 1.731 0.532 0.856 .320 18.5 16.3 16.7 CD 3.398 1.357 2.077 1.120 20.0 17.0 7.8 CD 3.302 0.322 1.655 1.333 23.3 21.1 11.1 APPENDIX E LISTING OF THE COMPUTER PROGRAMS APPENDIX E LISTING OF THE COMPUTER PROGRAMS The computer models developed by this author are listed on the following pages. Before they are presented however, the list of control statements and the organizing main program prepared by Parsch (1982) and this author are briefly explained. Table 8.1 shows the commands that were used on the MSU Cyber 750 to link five input data files and five binary coded files to run the complete simulation of forage systems. The input data files were MACHINPUT for the FORHRV program (appendix B), MGTALFINPUT .for the ALHARV algorithm (appendix C), ALFCRNINPUT for the alfalfa and corn models (Parsch, 1982), ELANSWTHR5378 for historical weather data and BMATRIXLP for the stochastic corn yield distributions. The five binary files were FORHRVBIN from program FORHRV listed in table 8.3, ALHARVBIN from program ALHARV listed in table 8.4, ALFMDDBIN from the alfalfa growth Inodel (Parsch, 1982), CRNMODBIN from the corn yield, 267 268 planting and harvest model (Parsch, 1982) and BIGMODBINPS from the organizing main program listed in table 8.2. The organizing main program (table 8.2) sets up reading of the input data and links the binary files together. Subroutine REPORT follows immediately after BIGMOD and generates the end of simulation printout tables. Table 8.3 lists program FORHRV, the static machinery model. It can be run independently as described in appendix B. Table 8.4 lists program ALHARV, the dynamic harvest, storage and feeding model for alfalfa. It cannot be run independently: it requires FORHRV and the models developed by Parsch (1982), namely the alfalfa growth model (ALFMOD) and the corn model (CRNMOD). The listing includes a main program, TEST, that was used to run ALHARV for testing purposes only with fixed growth and weather parameters. When using TEST as the main program, only ALHARVBIN and FORHRVBIN are required as binary files along with their corresponding input data files, MACHINPUT and MGTALF I NPUT . 269 Table E.l. Listing of the CYBER commands to operate the forage simulation model on the MSU computer. *JOBCARD*,R01,JC2000.CM170000,L100. 100 ATTACH,B,WOLBBIN1T. 110 HAL.CCEXEC.B. 120 ATT.DATA1,MACHINPUT. ‘ 130 ATT,DATA2,MGTALFINPUT1. 100 ATT,DATA3.ALFCRNINPUTBO. 150 EDITOR.E-DATA1. 160 EDITOR,E-DATA2. 170 EDITOR,E-DATA3. 180 ATT,WEATHR,ELANSWTHR5378. 190 ATT,8MATRX,BMATRIXLP. 200 RETURN,DATA1,DATA2,DATA3. 210 ATT,FORHRV.FORHRVBIN. 220 ATT,ALHARV,ALHARVBIN. 230 ATT.ALFMOD.ALFMDDBIN. 200 ATT,CRNMOD.CRNMODBIN. 250 ATT,B|GMDD,BIGMODBINPS. 260 LOAD.FORHRV. 270 LOAD,ALHARV. 280 LOAD,ALFM00. 290 LOAD.CRNM00. 300 LOAD.BIGMOD. 310 EXECUTE. 320 EXIT,C,S. 330 R EH1 ND . ZZZZZMP , OUTPUT . 300 3*E05 SAVE.MACH,NS. 360 *E05 SAVE,MGTALF,NS. 380 ’*E05 SAVE.ALFCRN.NS. l100 r1r1r1 flflf? (ICICIFI 270 Table E.2. Listing of the main program linking FORHRV, ALHARV, ALFMOD and CRNMDD. C C ************t******************************************************* PROGRAM BIGMOD C ******************************************************************** c c COMMON/ALF123/SLA,DTL,SDCLAI.XLDLAI.CSF.DTS.XMLOSC.RCTNC,RGR, + XMLBUD,XMLTNC,XFRDST,ALCROP,ALSOIL,U,ALPHA,XL,PTF,XLAT, + x1RRIc,AwFC,AwFs,Aw1N1T,wTHR(365.5).DAY1(39).DEc(39), + DAY2(10),SRAD(10) COMMON/CTRL20/BGNCUT(5),NTHYR,NTHCUT,NDAYSC,NDAYSH,YLD(0), + QUAL(3,0),GDDCUM,METRIC,JYEARF,JYEARL,IPRTl,IPRTZ. + JDAYF,JDAYL,JPRT,NYRS,lPRTO,NCUTS,JYEAR,JLALHR,CPLANT COMMON/Y3/NMDATA.NOPER.IN,IO an 0PEN(1,FILE-'MACH') 0PEN(2,FILE-'MGTALF') 0PEN(3,FILE-'8MATRX') 0PEN(0.FILE-'HEATHR') 0PEN(5.FILE-'ALFCRN') OPEN(6,FILE-'0UTPUT') READ IN ALL USER-INPUTTEO DATA FROM FILES. lN-I CALL FORHRV IN'Z CALL MGTINF CALL ALFIN(IFEED,ICDF) CALL CRNIN(NYRS,IPRTO) .- BEGIN SIMULATION CYCLE. LOOP 10-YEARS. LOOP 20-DAYS. DO 10 JYEAR-JYEARF,JYEARL NTHYR'JYEAR-JYEARF+1 READ IN CLIMATOLOGICAL DATA FOR JYEAR. INITIALIZE RELEVANT VARIABLES. PLANT CORN CROP FOR JYEAR. READ(0,200)((WTHR(JDAY,ITYPE),lTYPE-1,5),JDAY-1,365) CALL YRINIT ‘ CALL CRNPLT(NTHYR.CPLANT) 130 100 110 120 130 100 150 160 170 180 190' 200 210 220 230 200 250 260 270 280 290 300 310 320 330 300 350 360 370 380 390 000 010 020 030 000 050 060 070 080 090 500 510 520 0 non nnnnnn O nnnnnnnnn nnnnn-on C 200 271 OUTPUT CONTROL OPTION (DAILY) FOR PHENOLOGICAL ALFALFA CROP GROWTH MODEL. IF(IPRT1.NE.999)CALL ALFOUT(1) DO 20 JDAY=JDAYF,JDAYL CROw ALFALFA CROP FOR JDAY. DETERMINE YIELD. QUALITY 0N DAILY BASIS. IF APPROPRIATE, HARVEST AND STDRE ALFALFA CROP. SAVE FIRST-DAY STANDING YIELD. QUALITY VALUES (ALFOUT). CALL ALMAIN(JDAY) IF(JDAY.EQ.BGNCUT(NTHCUT))CALL ALFOUT(2) CONTINUE SUMMARIZE AND STORE END-OF-YEAR STANDING ALFALFA YIELD AND QUALITY MEASURED ON FIRST DAY OF EACH CUTTING. HARVEST CORN CROP FOR JYEAR ONCE 3RD CUT ALFALFA HARVEST HAS FINISHED. WRITE OUT END-OF-YEAR CORN RESULTS IF APPROPRIATE. CALL ALFOUT(3) CALL CRNHRV(NTHYR,JLALHR) CALL wRITAL(2) IF(|PRTO.EQ.1)CALL CRNOUT(NTHYR,NYRS,1) CONTINUE SUMMARIZE AND PRINT STANDING YIELD/QUALITY ESTIMATES OF ALFALFA AT END OF SIMULATION. SUMMARIZE AND PRINT OUT RESULTS OF CORN SIMULATION. CALL REPORT(NTHYR.NYRS) FORMAT(F7.0,1X,F0.0,1X.F0.0,1X,F5.0,1X,F0.0,1X) C ****************************************************fi** SUBROUTINE REPORT(NTHYR.NYRS) C ******************************************************* C + CDANDN/z1/AREA(6),N30(6).NDPSQ(5.9).CRTR(5.0,9),SIL0(2) COMMON/Z7/ALHRFD(26.15).AFEEDIZ6.23) COMMON/ZlO/TCDSTS(26,20),TRESS(26,20) COMMON/SUMRYl/YCORN(26.19).SCORN(0,19).CCDST(26,16).SCOST(0,16) CDNNDN/cowDTA/........ COMMON/PR1CE/PLABOR,PFUELD,PFUELC,RATEIM,PDRYCG,PHRVCG,COEFSV(3), PFSCAl,PFSCA2.PFSCCS,PFSCHM,ALFYRS,RATEIS,RATEIL,XLIFE(3) 530 500 550 560 S70 580 590 600 610 620 630 600 650 660 670 680 690 700 710 720 730 700 750 760 770 780 790 800 810 820 830 800 850 860 870 880 890 900 910 920 930 900 950 960 970 980 990 1000 1010 1020 nnnnnnn nnnnnnnnnnnnn nnnnnnnn 272 COMMON /SUMRY2/ TRESP(26,20),TCOSTP(26,20),TCDST(26,20), + STCOST(0,20),TRES(26.20).SRES(0.20) DIMENSION HERD(5) DATA TRESP,TCOSTP,TCOST,STCOST,TRES,SRES/520*O.,520*0..520*O., + 80*0.,520*0.,80*0./ 0PEN(7.F1LE-'FEED') WRITE OUT SIMULATION-END RESULTS GENERATED IN THE INDIVIDUAL SUB-MODELS. CALL ALFDUT(0) CALL wRITAL(3) CALL CRNOUT(NTHYR,NYRS.2) CALL COWMOD(NYRS) GENERATE THE SUB-RESOURCE AND SUB-COST MATRICES, (TRESP,TCOSTP). COLUMNS REPRESENT: (TRES) I-MACHINE INVESTMENT, S 3-FUEL USE. LITERS 5-FIELD LABOR. MAN/HRS 7-AREA IN CROPS, HA 9-CG PRODUCTION, DMT DO 10 N-1.NYRS TRESP(N,1)-CCO$T(N.11) TRESP(N.2)-CCOST(N.12) TRESP(N,3)-CCDST(N.5) TRESP(N,0)-CCDST(N,1) TRESP(N,5)-CCOST(N,6) TRESP(N.61-CCOST(N.7) Z-FEED STORACE INVESTMENT. S 0-REPAIR, MAINTENANCE, s 6-FEEDING LABOR. MAN/HRS 8-AREA HARVESTED AS CG. HA TRESP(N,7)-YCORN(N,5)+AREA(1)+(AREA(1)/ALFYRS) TRESP(N.8)-YCORN(N.8) . TRESP(N.9)-YCORN(N.19) COLUMNS REPRESENT: (TCOST) 1-MACHINE FIXED COST, ANNUAL $ 2-STORAGE FIXED COST. S/YR 3-FUEL COST, 5 5-FIELD LABOR, $ . 7-FERT/SEED/CHEMS, $ 9-DRY00wN (CC), 5 TCOSTP(N.l)-CCOST(N,13) TCOSTP(N.2)-CCOST(N.10) TCDSTP(N,3)-CCOST(N.2) TCDSTP(N.0)-CCOST(N,1) 0-REPAIR/MAINT, MACHINES. s 6-FEED LABOR. $ 8-CUSTOM HARVEST (CG). 5 1030 1000 1050 1060 1070 1080 1090 1100 1110 1120 1130 1100 1150 1160 1170 1180 1190 1200 1210 1220 1230 1200 1250 1260 1270 1280 1290 1300 1310 1320 1330 1300 1350 1360 1370 1380 1390 1000 1010 1020 1030 1000 1050 1060 1070 1080 1090 1500 1510 1520 O A nnnnn—on N N nnnnngnn man 273 TCOSTP(N.5)-CCOST(N.3) TCOSTP(N.6)-CCOST(N.0) TCDSTP(N.7)-CCDST(N,10)+(AREA(l)*(PFSCA2+PFSCA1/ALFYRS)) TCOSTP(N,8)-CCOST(N,8) TCO$TP(N.9)-CCDST(N,9) CONTINUE DO THE SUB-RESOURCE AND SUB-COST MATRICES TO GENERATE THE TOTAL RESOURCE USE (TRES) AND TOTAL COST/RETURNS (TCOST) MATRICES. DO 20 N-1,NYRS DO 20 I-I.20 TRES(N,I)-TRESP(N,I)+TRESS(N,I) TCOST(N,I)-TCOSTP(N,I)+TCOSTS(N.I) CONTINUE DO 22 JCOL-21.23 AFEED(N.JCOL)-YCORN(N.JCOL-0) CONTINUE WRITE(7.300)(AFEED(N.JCOL).JCOL-l.23) CONTINUE DAIRY HERD INFORMATION IS READ IN. THE HARVESTED FEED IS ALLOCATED TO COWS AND SUPPLEMENTS ARE PURCHASED TO BALANCE THE RATION IN COWFD. CALL COWFD(NYRS,XLCOWS.HERD) WRITE OUT THE COST/PROFIT AND RESOURCE MATRICES. WRITE(6,100)NYRS DO 30 N-1,NYRS WRITE(6,110)N,(TCOSTIN,I),l-1,15) CALL SSTAT(15.TCOST,NYRS,STCOST) wRITE(6.118) WRITE(6,120) DO 35 I-1.2 WRITE(6,110)I,(STCOST(|.J),J-l,15) WRITE(6,125)I,((STCOST(|,J).J-1.15).l-3,3) WRITE(6,200) DO 00 N-1,NYRS WRITE(6,210)N,(TRES(N.I),I-1,15) ' CALL SSTAT(15.TRES.NYRS.SRES) wRITE(6.118) 1530 1500 1550 1560 1570 1580 1590 1600 1610 1620 1630 1600 1650 1660 1670 1680 1690 1700 1710 1720 1730 1700 1750 1760 1770 1780 1790 1800 1810 1820 1830 1800 1850 1860 1870 1880 1890 1900 1910 1920 1930 1900 1950 1960 1970 1980 1990 2000 2010 2020 270 WRITE(6,120) DO 05 I-1.2 05 WRITE(6.210)l,(SRES(l,J),J-l,15) WRITE(6,225)I.((SRES(I.J).J-l.15).1-3,3) 100 FORMAT('1'.'END OF SIMULATION RUN RESULTS FOR COMBINED'. ++++++++++ DAIRY-FORAGE SYSTEMS MODEL (DAFOSYM).',/. SUMMARY OUTPUT FOR ',12.' SIMULATION YEARS.'./. MATRIX TCOST-TOTAL COST OF PRODUCTION. GROSS AND NET'. RETURNS.',/. EACH Row EQUALS ONE SIMULATION YEAR.',/, COLUMNS REPRESENT:',/. l-FCM 2-FCSTG 3-EUEL 0-RMM 5-LABELD', 6-LABFEED 7-FSC 8-CUSTCG 9-DRYCG', ' 10-SUM(1-9) ll-FNET lZ-CG 13-SUM(IO-12) 10-MILK ', 'ls-NRET'.///) 110 FORMAT(I3,2(1X,F7.0),1X,F6.0.13(1X,F7.0)) 125 FORMAT(I3,2(1X,F7.2),1X,F6.2,13(1X,F7.2)) 118 FORMAT(//) 120 FORMAT(' SAMPLE STATISTICS FOR SIMULATION RUN:', + - Rows I-MEAN 2-STANDARO DEVIATION 3-COEF 0F VARIATIDN',/) 200 FORMAT('1',IMATRIX TRESS-TOTAL RESOURCE USE AND INVESTMENT.'./. +++++ C EACH Row EQUALS ONE SIMULATION YEAR.'./. COLUMNS REPRESENT:'./. I-M/INVS 2-STC/INVS 3-FUEL(L) 0-M/RMS '. 5-LA8FLD(HRS) 6-LABFEED(HRS) 7-CROPS(HA) ', 8-CG(HA) 9-CC(DMT)'.///) 210 FORMAT(I3,2(1X,F8.0).2(1X,F7.0).11(1X,F6.0)) 225 FORMAT(I3,2(1X,F8.2).2(1x.F7.2),11(1X,F6.2)) C RETURN END 2030 2000 2050 2060 2070 2080 2090 2100 2110 2120 2130 2100 2150 2160 2170 2180 2190 2200 2210 2220 2230 2200 2250 2260 2270 2280 2290 2300 2310 2320 2330 2300 2350 2360 2370 2380 275 Table E.3. Listing of program FORHRV. C ********************************it********************************** PROGRAM DUMMY C *********************A********************************************** OPEN (5.FILE-'MACH') OPEN (6,F1LE-'OUTPUT') CALL FORHRV STOP END C ****************************************A**********t**************** SUBROUTINE FORHRV *kttttt************************AAAAAA***A**********A**************** C C C PROGRAM FORHRV ESTIMATES FORAGE HARVEST RATES FOR A GIVEN SET OF C MACHINES. C IT WAS WRITTEN BY PHILIPPE SAVOIE. AGRICULTURAL ENGINEERING DEPT., C MICHIGAN STATE UNIVERSITY, EAST LANSING, MICHIGAN, USA 08820 C A USER"S GUIDE IS AVAILABLE IN APPENDIX B OF THE AUTHOR"S DOCTORAL C DISSERTATION (1982). C IT CAN BE RUN INDEPENDENTLY WITH THE USE OF PROGRAM DUMMY. C C C SUBROUTINE FORHRV AND ITS APPENDED SUBROUTINES WERE HOWEVER WRITTEN TO BE USED WITH THE DYNAMIC HARVEST MODEL ALHARV. COMMON /Y1/ XINFO(7).MCDDE(100),XMDATA(100,13) COMMON /Y2/ ICODE(60.3).XOPER(6O,5) COMMON /Y3/ NMDATA,NOPER.IN.IO COMMON /Y0/ x0PMD(6O.26) COMMON /Y5/ A(15).OPNAME(5.6O).XTTP,JOP COMMON /Y6/ RATES(108,8).YAR(6) COMMON /Y7/ NBOP(18).NBMACH(18.7).XNBM(18,7) COMMON /Y9/ IPRINT,IPR1 DIMENSION IEXTRA(18) DATA IEXTRA /O.O.O,O.O,O,O.O.O.1.0.0.1.0.0.0.0.0/ DATA IN/5/.IO/6/ CALL READ IOP-I 110 120 130 100 150 160 170 180 190 200 210 220 230 200 250 260 270 280 290 300 310 320 330 300 350 360 370 380 390 000 010 020 030 000 070 080 C NBMACH INCLUDES THE MACHINERY NUMBERS OF ALL MACHINES USED IN OPERAT050 C NBO(IOP). THERE MAY BE UP TO 7 DIFFERENT MACHINES IN AN OPERATION. 060 C XNBM IS THE NUMBER OF UNITS OF EACH MACHINE USED IN AN OPERATION. DO 29 I-I,18 DO 29 J-1,7 XNBM(I,J)-0. 29 NBMACH(|,J)-O I-I C THERE ARE NOPER OPERATION CARDS 10 CALL DCODEI (I) 090 500 510 520 530 500 276 CALL DCODET (I) 550 JOP-ICODE(I.I) 560 DO 50 J-1.18 570 JLow-J*10-l 580 JHIGH-JLOW+10 590 IF (JOP.LE.JLOV.OR.JOP.OT.JHICH) GO TO 50 600 JEXTRA-IEXTRAIJ) 610 50 CONTINUE 620 IF (JEXTRA.EQ.O) 60 T0 30 630 Ix1-I+I 600 IX2-I+JEXTRA 650 DO 00 IJ-le,IX2 660 CALL DCOOEI (IJ) 670 00 CALL DCODET (IJ) 680 30 CALL BUILDA (I) 690 CALL RATE (I.IOP) 700 NBOP(IOP)-JOP 710 NBMACH(IOP.1)-ICODE(I.2) 720 NBMACH(IOP.2)-ICODE(I.3) 730 XNBMIIDP,1)-XOPER(I.1) 700 XNBM(IDP,2)-XOPER(I,1) 750 |F(JEXTRA.EQ.0) GO TO 35 760 NBMACH(IOP,3)-ICODE(I+1.2) 770 XNBM(IOP,3)-XOPER(I,I) 780 IF (JEXTRA.LE.1) GO TO 35 790 NBMACH(IOP.0)-ICODE(I+2.2) 800 NBMACH(IOP,5)-ICODE(I+2,3) 810 NBMACH(IOP,6)-ICDDE(1+0,2) 820 NBMACH(IOP.7)-ICODE(1+0.3) 830 XNBM(|0P,0)-XOPER(I+2,3) 800 XNBM(IOP.5)-XOPER(I+2.1) 850 XNBM(IDP,6)-XDPER(I+0.1) 860 XNBM(|0P,7)-XOPER(I+0.I) 870 35 IF (IPR1.NE.1) DO TO 21 880 WRITE (IO.200) JOP.(0PNAME(J.I).J-1.5) 890 200 FORMAT I ///.5X.'CALCULATED WORK RATES FOR OPERATION'.I6.' KNO9OO +WN AS ',5A0.///.11X,'YDM(T/HA) EFCIHA/H) ETP(TDM/H) LOAD(DEC910 +) FUEL(L/H) ELEC(KwH/H) LABOR(MH/H) SPEED(KM/H)',///) 920 DO 20 K-l,6 930 IR-(IOP-I)*6+M 900 WRITE (IO.2IO) (RATES(IR.J).J-I.8) 950 210 FORMAT (10x,8r12.2) 960 20 CONTINUE 970 21 IOP-IOP+1 980 l-I+JEXTRA+1 990 IF (I.LE.NOPER) GO TO 10 1000 RETURN - 1010 END 1020 c *******************************AAAAAAAAAAAAAAAA*AAAAAAAAAAAAAAAAAAAA 1030 SUBROUTINE READ 1000 277 C *ickk*****a'ct************1:**********1!it3010********************I’c********* 1050 an nnnnnnnnnnnn nnnnn nnn nnnn nn COMMON /Y1/ XINFO(7).MCODE(100).XMDATA(100,13) 1060 COMMON /Y2/ ICODE(60.3).XOPER(60,5) 1070 COMMON /Y3/ NMDATA,NOPER,IN.IO 1080 COMMON /Y9/ IPRINT,IPRI 1090 1100 THIS SUBROUTINE READS THE MACHINERY DATA FILE AND THE OPERATION FILEIIIO IT INITIALLY READS A GENERAL INFORMATION ARRAY 1120 x1NFO(1) IS THE POWER SAFETY FACTOR (USUALLY 1.0) 1130 XINFO (2) IS THE SOIL CONDITION PARAMETER ,ASAE CN NUMBER -- 30 FOR F1100 XINFO (3) IS THE AVERAGE SOIL SLOPE (ITS TANGENT) THE SOIL SLOPE IS 1150 CONVERTED INTO AN ANCLE(RADIANS) IN THE PRESENT SUBROUTINE 1160 x1NFO(0) Is THE ABSOLUTE MINIMUM ALFALFA YIELD (TDM/HA) 1170 x1NFO(5) IS THE ABSOLUTE MAXIMUM ALFALFA YIELD (TDM/HA) 1180 x1NFO(6) IS THE ABSOLUTE MINIMUM CORN SILACE YIELD (TDM/HA) 1190 x1NFO(7) IS THE ABSOLUTE MAXIMUM YIELD OF CORN SILAGE (TDM/HA) 1200 1210 READ (IN,110) (x1NFO(I),I-1.7) 1220 x1NFO(3)-ATAN(x1NFO(3)) 1230 1200 READ THE MACHINERY DATA FILE 1250 1260 I-O 1270 1 I-I+1 1280 READ (IN,12O) MCODE(I).(XMDATA(I,J),J-I,13) 1290 IF (MCODE(I).CT.0) GO TO 1 1300 1310 THE LAST CARD AT THE END OF THE MACHINERY DATA FILE MUST HAVE 0000 1320 IN THE LAST FOUR COLUMNS. 1330 THE NUMBER OF MACHINES IN THE FILE Is NMDATA. 1300 1350 NMDATA-I-I 1360 ‘ 1370 READ THE OPERATION FILE 1380 1390 1-0 1000 2 I-I+1 1010 READ (IN,I3O) (ICODE(I.J).J-1,3).(XOPER(I.J).J-1.5) 1020 IF (ICODE(I,1).CT.O) CO T0 2 1030 1000 THE LAST CARD AT THE END OF THE OPERATION FILE MUST BE ZERO 1050 NOPER IS THE NUMBER OF OPERATION CARDS 1060 1070 NOPER-I-I 1080 HHEN IPRINT IS 1 (ONE). A DETAILED PRINTOUT OF CYCLE TIMESOF HARVEST1090 TRANSPORT MACHINES WILL APPEAR 1500 READ (1N.100)IPR1,IPRINT,IPRINP 1510 IF (IPRINP.EO.O) RETURN 1520 wRITE (10,105) 1530 wRITE (10.110) (x1NFO(I).I-I.7) 1500 DO 10 1-1,NMDATA 1550 10 wRITE (10,120) MCODE(I),(XMDATA(I,J),J-1,I3) 1560 wRITE (10.125) 1570 DO 20 1-1,NOPER 1580 20 wRITE (10,130) (lCODE(I,J),J-1,3),(XOPER(|,J),J-l.5) 1590 WRITE (10,125) 1600 wRITE (10.100) IPR1,lPRINT,IPRlNP 1610 105 FORMAT (/.5x.'THE INPUT DATA FILE FOR FORHRV WAS READ As FOLLOWS')1620 110 FORMAT (7F10.2) 1630 120 FORMAT (I0,3F8.2,10F5.1) 1600 125 FORMAT ('0000') 1650 130 FORMAT (310,5FIO.2) 1660 100 FORMAT (312) 1670 RETURN 1680 END . 1690 C *kkicmttitxitktimthatASIAitAttics!**********************Aidcxims'dcicidcmwc*iddmic 1700 SUBROUTINE DCODEI (I) 1710 C ************************tttttttttkttfiimmundane*AAAAAMIAAAAAAAAAMHMA 1720 COMMON /Y1/ x1NFO(7).MCODE(IOO),XMDATA(IOO,13) 1730 COMMON /Y2/ ICODE(60,3).x0PER(6O.5) 1700 COMMON /Y3/ NMDATA.NOPER.IN.IO 1750 COMMON /Y0/ XOPMD(60,26) 1760 C q 1770 C THIS SUBROUTINE DECODES THE IMPLEMENT NUMBER FOR A GIVEN OPERATI01780 C AND INSERTS THE MACHINERY DATA IN A WORKING MATRIX XOPMD(I.J).J-I,13179O c 1800 K-0 1810 C wHEN THE IMPLEMENT NUMBER IS ZERO. THE wORKING MATRIx IS INITIALIZED1820 C 05 ZERO. 1830 C THIS CAN HAPPEN IN AT LEAST TWO CASES 1800. C 1. WHEN ROUND BALEs ARE HAULED ONE BY ONE FROM THE FIELD T0 STORAGE.1850 C THERE IS ONLY A LOADER AND NO MULTIPLE BALE WAGON (MOVER). 1860 C ZEROES APPEAR ON THE SECOND DATA CARD. 1870 C 2. WHEN NO EJECTDR Is USED IN THE BALER-WAGON SYSTEM. BUT INSTEAD 1880 C ONE MAN STACKS THE BALES IN THE HAGON BEHIND THE BALER. 1890 IF (ICODE(I,2).NE.O) GO TO 0 1900 DO 1 J- 1,13 1910 1 XOPMD(I,J)-0 1920 GO TO 7 1930 0 K-K+l 1900 IF (ICODE(I,2).NE.MCDDE(K).AND.K.LT.NMDATA) GO TO 0 1950 IF (ICODE(I,2).EQ.MCODE(K)) GO TO 5 1960 c 1970 C AT THIS POINT THE DATA FILE DOES NOT CONTAIN THE SPECIFIC MACHINE 1980 C GIVEN IN THE OPERATION DATA CARD 1990 c 2000 wRITE (10,100) lCDDE(|,1) 2010 100 FORMAT (///.'THE IMPLEMENT NUMBER FOR OPERATION'.IIO.' +xIST IN THE DATA FILE'./.'MAKE THE CORRECTION') STOP 278 DOES NOT E2020 2030 2000 C0 C31 [CECE 279 5 DO 6 J-l.l3 2050 x0PMD(I,J)-XMDATA(K,J) 2060 6 CONTINUE 2070 7 RETURN 2080 END 2090 C AAAAAAAAAAAAAittaunt*1:MIAMIAtAtticin":Amulet*ttkttttitttkidmttkttAkttktttt 2 100 SUBROUTINE DCODET (I) 2110 C A*1:AAAAAAAMIAAAA*7:**************************tt********************** 2 120 COMMON /Y1/ x1NFO(7).MCDDE(100),XMDATA(100,13) 2130 COMMON /Y2/ lCDDE(60.3).XOPER(60.5) 2100 COMMON /Y3/ NMDATA,NOPER.IN.IO 2150 COMMON /Y0/ x0PMO(60,26) 2160 C 2170 C THIS SUBROUTINE DECODES THE TRACTOR (OR POWER SOURCE) NUMBER FOR 2180 C A GIVEN 2190 C OPERATION AND INSERTS THE TRACTOR MACHINERY DATA IN A NORKING MATR1x2200 C XOPMD(I,J).J-l0,26 2210 K-0 2220 C IN THE CASE OF A SELF-PROPELLED MACHINE. TRACTOR CODE Is 0000. 2230 C IN SUCH A CASE. ALL THE POWER AND ENGINE SPECIFICATIONS ARE GIVEN 2200 c WITH THE IMPLEMENT 2250 IF (ICODE(I,3).NE.O) GO TO 7 2260 DO 9 J-l0.26 2270 x0PMD(|,J)-0. 2280 9 CONTINUE 2290 GO TO 10 2300 7 K-K+1 2310 IF (ICODE(I,3).NE.MCDDE(K).AND.K.LT.NMDATA) GO TO 7 2320 IF (ICODE(I,3).EQ.MCDDE(K)) GO TO 8 2330 C A *' 2300 C AT THIS POINT THE DATA FILE DOES NOT CONTAIN THE SPECIFIC MACHINE 2320 c 23 O WRITE (10,150) ICODE(I.1) 2370 150 FORMAT(///.'THE TRACTOR NUMBER FOR OPERATION'.110,' DOES NOT EX152380 +T IN THE DATA FILE'./.'MAKE THE CORRECTION') 2390 STOP 2000 8 DO 11 J-l0,26 2010 JJ-J-l3 2020 XOPMD(|.J)-XMDATA(K.JJ) 2030 11 CONTINUE 2000 10 RETURN 2050 END 2060 C ******************************************************************** 2070 SUBROUTINE BUILDA (I) 2080 C Atktidct***********AAAAAAA*******************tth’dctAAAAAAAAAAAAAAAAAA 2090 COMMON /Y1/ XINFO(71.MCODE(100),XMDATA(100,13) 2500 COMMON /Y2/ ICODE(60.3).XOPER(60,5) 2510 COMMON /Y3/ NMDATA.NOPER.IN.IO 2520 COMMON /Y0/ x0PMO(60,26) 2530 COMMON /Y5/ A(15).OPNAME(5.60).XTTP,JOP 2500 nnnnnnnnnnnnnnnnnnnnnnnn C C C '280 2550 THIS SUBROUTINE CREATES THE A ARRAY WHICH INCLUDES PARAMETERS FOR 2560 ESTIMATING SPEED. LOAD. FIELD CAPACITY, THROUGHPUT RATE 2570 2580 A(l) IS TRACTOR POWER (KW) 2590 A(2) IS TRACTOR MASS (KG) 2600 A(3) Is IMPLEMENT MASS, INCLUDING WAGON IF PULLED (KG) 2610 A(0) IS PTO POWER (CONSTANT.INDEPENDENT OF THROUGHPUT, KW) 2620 A(5) IS PTOC (COEFFICIENT, DEPENDENT OF THROUGHPUT. KW/KGDM/S) 2630 A(6) IS THE POWER SAFETY FACTOR 2600 A(7) IS THE SOIL CONDITION CN 2650 A(8) IS THE SOIL SLOPE ANGLE (RADIANS) 2660 A(9) IS THE MAXIMUM ALLOWABLE SPEED (KM/H) 2670 A(IO) Is THE WORKING WIDTH (M) 2680 A(11) Is THE DRY MATTER YIELD (T/HA) 2690 A(12) IS THE ACTUAL OPERATING SPEED (KM/H) 27OO A(13) IS THE TRACTOR LOAD (DEC) 2710 A(I0) IS THE FIELD EFFICIENCY (DECIMAL) 2720 A(15) IS THE ENGINE TYPE 1. GAS 2. DIESEL 3. ELECTRIC 2730 XTTP IS THE MAXIMUM MACHINE THROUGHPUT (TDM/H) 2700 JDP IS THE OPERATION CODE (SAME As ICODE(I.1)) 2750 DNAME(I.J) CONTAINS THE NAMES OF EACH OPERATION 2760 THE OPNAME MATRIX CONTAINS THE NAME OF EACH OPERATION 2770 2780 DIMENSION EFF(18).PT0w(18),PTOC(18),DNAME(5.18) 2790 DATA EFF/.8..8..8,.8..8..8,.8,.75,.7O,.8..8..8..8,.8..8..8,.8..8/ 2800 DATA PTOW/1.2,3.0,6.00,1..1..2.,0.,O.,0.,0.,0.,0.,0.,0.,0.,0., 2810 +0..0./ 2820 DATA PTOC/O..2.,0..0..0..O..5..7.5,7.5.15..6.,O.,O.,15..15..18., 2830 +5..0./ _ 2800 DATA DNAME /0HCUTT,0HERBA,0HR MO,0HwING,0H .0HCUTT.0HERBA.0HR M2850 +0.0HW-C0.0HND ,0HDRUM,0H MOW,0H CON,0HD ,0H .0HRAKI.0HNG . 2860 +0H ,0H ,0H ,0HOOUB,0HLE R,0HAKIN,0HG ,0H ,0HTEDD.0H1287O +NG ,0H ,0H ,0H ,0HRECT,0H BAL.0HING .0H(DRO.0HP) ,0HROUN2880 +,0HD BA,0HLING,0H ,0H .0HLARG,0HE ST.0HACK ,0H BAL.0HING , 2890 +0HCHOP.0H ON , 2900 + 0HTHE .0HGROU.0HND .0HAUT0.0H BAL.0HE WA.0HGON ,0H .2910 +0HLARG,0HE ST,0HACK ,0HMOVE.0HR + ,0HCHOP,0H (CS.0H) TR,0H UL ,0H ,0HROUN,0HD BA.0HLE M,0HOVER.0H 2920 ,0HCHOP,0H (AL,0HF-WP.0H) TR2930 +,0H UL ,0HCHOP,0H (AL,0HF-DC,0H) TR,0H UL .0HBALE.0H EJE.0HCT T,0H2900 +R UL .0H ,0HHAND,0HPICK,0H BAL,0HES T,0HR UL/ JOP-ICODE(|.1) A(1)-XOPMD(I.20) A(2)-XOPMD(I.10) A(3)-XOPMD(I.I)+XOPER(I,0) A(15)-XOPMD(I.23) CHECK IF THE IMPLEMENT IS A SELF-PROPELLED MACHINE IF (XOPMD(I,9).NE.1.) GO TO 1 2950 2960 2970 2980 2990 3000 3010 3020 3030 3000 no 0 n nonnnnnnn 281 A(1)-XOPMD(I,II) A(2)-A(3) A(3)-O. A(15)-XOPMD(I.IO) 1 A(6)-XINFO(1) A(7)-XINFO(2) A(8)-XINFO(3) A(9)-XOPER(I.2) A(IO)-XOPER(I,3) IF (JOP.GE.100.AND.JOP.LT.110) A(3)-A(3)+XOPMD(I+1,I) IF(JOP.GE.110.AND.JOP.LT.120) A(3)-A(3)+XOPMD(I,8)*1000. IF (JOP.LT.100.0R.JOP.GE.180) GO TO 6 A(3)-A(3)+XOPMD(I+1,1)+XOPMD(1+2.1)+XOPMD(I+2,8)*1000. IF (XOPER(I+1,2).EQ.O.) A(3)-A(3)-XOPMD(I+2.8)*1000. 6 DO 22 J-1,18 JLOW~10*J-l JHIGH-JLOH+IO IF (JOP.LE.JLOW.0R.JOP.GT.JHIGH),GO TO 22 A(0)-PTOW(J)*A(10) A(5)-PTOC(J) A(10)-EFF(J) XTTP-XOPMD(I,7) IF (XTTP.LE.O.) XTTP-IOOO. THIS MEANS THAT MAXIMUM THROUGHPUT WILL NOT BE A CONSTRAINT DO 21 K-1,5 21 OPNAME(K,I)-DNAME(K,J) 22 CONTINUE NEXT CONSIDER THE CASE OF A BALE THROWER. THE PTO REQUIREMENT IS INCREASED BY 0.5 KW/KG/S IF A BALE THROWER IS PRESENT. IF (JOP.LT.170. OR.JOP.GE.180) GO TO 5 IF (ICODE(I+I,2).NE.0) A(5)-A(5)+0.5 5 RETURN END AAAAA*******************ttt***************************************** SUBROUTINE RATE (I.IOP) Atttttttta*ttttttt*tttxttidcttmwctimtttkfl:*tktttttttawmtx*ttttiztttttt COMMON /Y1/ x1NFO(7).MCODE(IOO),XMDATA(IOO.13) COMMON /Y2/ ICODE(6O.3).XOPER(6O,5) COMMON /Y5/ A(15).OPNAME(5.60),XTTP.JOP COMMON /Y6/ RATES(108.8).YAR(6) COMMON /Y10/ XLD.XLABOR THIS SUBROUTINE CALCULATES RATES OF HARVEST FOR ALL OPERATIONS AND INSERTS THE VALUES IN A WORKING MATRIX RATES(108.8) FOR LATER USE 3050 3060 3070 3080 3090 3100 3110 3120 3130 3100 3150 3160 3170 3180 3190 3200 3210 3220 3230 3200 3250 '3260 3270 3280 3290 3300 3310 3320 3330 3300 3350 3360 3370 3380 3390 3000 3010 3020 3030 3000 3050 3060 3070 3080 THE RATES(108.8) MATRIX WILL CONTAIN INFORMATION ABOUT HARVEST RATES3090 AT 6 DIFFERENT YIELD VALUES FOR EACH OPERATION HARVEST RATES ARE ESTIMATED FOR A MAXIMUM OF 18 OPERATIONS AT SIX YIELDS. THERE ARE THUS 108 ROWS. THE SIX YIELD LEVELS ARE EQUALLY SPACED BETWEEN MINIMUM AND MAXIMUM YIELDS SPECIFIED IN THE GENERAL INFORMATION 3500 3510 3520 3530 3500 nonnnnnnnnn nnn ARRAY. 282 THE EIGHT PARAMETERS IN EACH ROW ARE RATES(I,1) Is RATES(I,2) IS RATES(I.3) Is RATES(I,0) IS RATES(I.5) IS RATES(I.6) Is RATES(I.7) IS RATES(I,8) IS THE DRY MATTER YIELD (T/HA) EFFECTIVE FIELD CAPACITY (HA/H) THE THE THE THE THE THE YMAx-XINFO(5) YMIN-XINFO(0) IF (JOP.LT.100.0R.JOP.GT.109) GO TO 2 YMAx-XINFO(7) YMIN-XINFO(6) 2 DIFF-(YMAx-YMIN)/5. YAR(1)-YMIN DO 1 J-2,6 EFFECTIVE THROUGHPUT (TDM/H) TRACTOR LOAD (DECIMAL) FUEL CONSUMPTION (L/H) ELECTRICITY CONSUMPTION (KW-H/H) 3550 3560 3570 3580 3590 3600 3610 3620 LABOR REQUIREMENT PER UNIT OPERATION TIME (MAN-H/H363O OPERATING SPEED (KM/H) 1 YAR(J)-YAR(J-l)+DIFF IF (JOP.GE.120.AND.JOP.LT.100) GO TO 00 K-(IOP-1)*6 XLABOR-XOPER(I.1) IF (JOP.LT.100) GO TO 7 IF (JOP.LT.180) GO TO 8 HAND PICKING BALES IN THE FIELD XLABOR-(I.+XOPER(I+1.3))*XOPER(I+3,1)+XOPER(I+2,0) GO TO 05 8 IF (JOP.LT.I70) GO TO 9 THE BALER WITH A wAGON PULLED BEHIND IF (ICODE(I+1.2).EQ.O) XLABOR-2.*XLABOR INCLUDING LABOR AT UNLOADING SITE (STORAGE) AND TRANSPORT OPERATORS 9 XLABOR-XLABOR+XOPER(I+2.0)+XOPER(1+2,1) 7 DO 30 J-1,6 A(ll)-YAR(J) CALL SPEED K-K+1 RATES(K.I)-A(11) XOPER(I.2) Is THE NUMBER OF UNITS DOING THE SAME OPERATION SIMULTANEOUSLY. THE SINGLE UNIT HARVEST RATES TIMES XOPER(I.1). RATES(K.2)'A(12)*A(10)*A(l0)/10. RATES(K,3)=RATES(K,2)*A(11) RATES(K,0)-A(13) TOTAL HARVEST RATES ARE RATES(K,7)-XLABOR RATES(K.8)-A(12) XLD-A(13) PWR-A(I) ENG-A(15) EFF-A(I0) 3600 3650 3660 3670 3680 3690 3700 3710 3720 3730 3700 3750 3760 3770 3780 3790 3800 3810 3820 3830 3800 3850 3860 3870 3880 3390 3900 3910 3920 3930 3900 3950 3960 3970 3980 3990 0000 0010 0020 0030 0000 C ******************************************************************** SUBROUTINE SPEED C AAA*AAAA********A*ttktAAA*tttttttA********************************** COMMON /Y3/ NMDATA.NOPER.IN,IO COMMON /Y5/ A(15).0PNAME(5,60),XTTP,JOP nnnnn 3O 00 05 25 50 THIS SUBROUTINE CALCULATES OPERATING SPEED AND TRACTOR LOAD FUI-1.10 FUEL-O. ELECT-0. 283 CALL ENERGY (XLD.PWR.ENG.EFF,FUI,FUEL.ELECT) RATES(K.5)-FUEL RATES(K.6)-ELECT CONTINUE IF (JOP.GE.110.AND.JOP.LT.100) CALL TRCYCI (I.IOP) IF (JOP.GE.100.AND.JOP.LT.180) CALL HRTR (I.IOP) IF (JOP.GE.180) CALL HAYPCK (I.IOP) IF (JOP.GE.100.0R.XOPER(I.1).EQ.1.) GO TO 50 DO 25 J-l,6 K-(IOP-l)*6+J RATES(K.2)-RATES(K.2)*XOPER(I,1) RATES(K,3)-RATES(K,3)*XOPER(I,1) RATES(K.5)-RATES(K,5)*XDPER(I,1) RATES(K,6)-RATES(K,6)*XOPER(I,1) CONTINUE RETURN END THREE CONSTRAINTS MUST BE RESPECTED: DATA CF1/1.10/.CF2/1.20/ TTP-A(9)*A(IO)*A(11)/10. IF (TTP.LE.XTTP) GO TO 1 MAXIMUM ALLOWABLE THROUGHPUT. MAXIMUM ALLOWABLE SPEED AND MAXIMUM ALLOWABLE TRACTOR LOAD 0050 0060 0070 0080 0090 0100 0110 0120 0130 0100 0150 0160 0170 0180 0190 0200 0210 0220 0230 0200 0250 0260 0270 0280 0290 0300 0310 0320 0330 0300 0350 0360 0370 REDUCE MAXIMUM ALLOWABLE SPEED SO THROUGHPUT WILL NOT EXCEED MAXIMUM0380 AISI-XTTP*10./(A(10)*A(11)) 1 V-A(9) TETA-A(8) RRc-O.O0+1.2/A(7) DBP-A(3)*9.8*(RRC*COS(TETA)+SIN(TETA)) FR-(DBP+A(2)*9.8*SIN(TETA))/(0.75*A(2)*9.8*C0$(TETA)) CHI-0.75-(FR+RRC) IF (CHI.GT.O.) GO TO 5 wRITE (10.10) JOP 0390 0000 0010 0020 0030 0000 0050 0060 0070 10 FORMAT (///.'SLIP IS EXCESSIVE AND CANNOT BE CALCULATED FOR OPERAT0080 STOP 5 SL-(l./(0.3*A(7)))*ALOG(0.75/CH1) SLF-1./(1.-SL) TRPWR-A(2)*9.8*(RRC*COS(TETA)+SIN(TETA))*V*CF1*SLF/3600. +ION',IIO./.'REDUCE SLOPE, 0R INCREASE TRACTOR MASS 0R REDUCE TRAIL0090 +ING IMPLEMENT MASS ') 0500 0510 0520 0530 0500 280 DBPWR-DBP*V*CF2*SLF/3600. PTo-A(0) PTDV-A(5)*A(10)*A(11)*V/36. PWR-TRPWR+DBPWR+PTO+PTOV ALOAD-PwR/A(I) XLOAD-1./A(6) IF (ALOAD.LE.XLOAD) GO TO 15 C AT THIS POINT. MAXIMUM SPEED ASSUMEO RESULTS IN EXCESSIVE LOAD. C REDUCE LOAD TO XLOAD AND RECALCULATE SPEED v-(A(l)*XLOAD-PTO)*V/(TRPWR+DBPWR+PTOV) ALOAD-XLOAD 15 A(12)-V A(13)-ALOAD RETURN END C thiamineAtatttatkictticttttttttAida:inkAtticAAAAAAAAAAAICAAAAAAAAAA********** SUBROUTINE ENERGY (XLD.PWR,ENG.EFF,FUI,FUEL,ELECT) ARRAAAAARRAA****************A«AA******AA*************R***********00* FOR ELECTRIC MOTORS. XLD IS THE POWER SOURCE LOAD (DECIMAL) PWR IS THE MAXIMUM POWER (KW) EFF IS THE MACHINE FIELD EFFICIENCY EQUAL TO 1.10). nnnnnnnnnnnnn IF (ENG.LE.2.) GO TO 1 C WE HAVE AN ELECTRIC POWER SOURCE Fc-O. ELECT-XLD0PHR*EFF*FUI GO TO 3 1 IF (ENG.LT.2.) GO TO 2 C WE HAVE A DIESEL POWER SOURCE ELECT-0. Fc-2.60*XLD+3.91-0.2*(738.*XLD+173.)**0.5 GO TO 3 2 ELECT-0. C WE HAVE A GASOLINE ENGINE , Fc-2.70*XLD+3.15-0.2*(697.*XLD)**0.5 3 FUEL-FC*PHR*EFF*XLD*FUI RETURN END C ARA***A***A**A*AAAAAAAAAAAA***ARAAAAAAAAAAARARRAAAAARAAAAAAARAAARRAA SUBROUTINE TRCYCI (I.IOP) C AAAARRAAARAAAA**A***AAAAARAAAAAAARAAA*AAAR*AAAAAAAARAAAAARAAAAAAAAAA COMMON /Y1/ XINFO(7).MCODE(100),XMDATA(100,I3) 0550 0560 0570 0580 0590 0600 0610 0620 0630 0600 0650 0660 0670 0680 0690 0700 0710 0720 0730 THIS SUBROUTINE CALCULATES ENERGY FOR FARM OPERATIONS. EITHER LIQUID0700 FUEL FOR TRACTORS (GASOLINE OR DIESEL ENGINES) 0R ELECTRICAL ENERGY 0750 0760 0770 0780 0790 ENG IS THE ENGINE TYPE 1. FOR GAS 2. FOR DIESEL AND 3. FOR ELECTRIC08OO 0810 FUI IS THE FUEL USE FACTOR TO ACCOUNT FOR IDLING OR TURNING (USUALLY0820 0830 0800 0850 0860 0870 0880 0890 0900 0910 0920 0930 0900 0950 0960 0970 0980 0990 5000 5010 5020 5030 5000 nnnnnnnnnnnnnn nnn COMMON COMMON COMMON COMMON COMMON COMMON THIS SUBROUTINE CALCULATES TRANSPORT CYCLE TIMES FOR INDIVIDUAL TRANSPORT OPERATIONS (110,120,130) WHICH ARE NOT AFFECTED BY /Y2/ /Y3/ /Y0/ /Y5/ /Y6/ /Y9/ 285 ICODE(60,3).XOPER(6O.5) NMDATA,NOPER,1N,IO x0PMO(60,26) A(15).OPNAME(5.60),XTTP.JOP RATES(108.8).YAR(6) 1PRINT,1PR1 EXTERNAL HARVEST OR OPERATIONS UNLOAOING OPERATION OPERATION OPERATION T1 15 THE T0 IS THE T2 IS THE T3 IS THE XMC IS THE MOISTURE CONTENT ON A DRY BASIS DMCAP IS THE DRY MATTER CAPACITY OF A TRANSPORT WAGON (T) CODE BETWEEN 110 AND 119 IS FOR AUTOMATIC BALE WAGON 5050 5060 5070 5080 5090 5100 5110 5120 5130 5100 5150 5160 CODE DETHEEN 120 AND 129 IS FOR A LARGE STACK LOADER-MOVER517O CODE BETHEEN 130 AND 139 IS FOR A ROUND BALE LOAOER-MOVER 5180 LOADING TIME IN THE FIELD (H) UNLOAOING TIME AT STORAGE (H) TIME TO TRAVEL FROM FIELD TO STORAGE WITH A FULL LOAD (H) 5210 TIME TO TRAVEL FROM STORAGE TO FIELD WITH AN EMPTY NAGON (5220 XMc-O.25 DMCAP-x0PMD(I.8)/(1.+XMC) IF (OMCAP.GT.O.) GO TO 6 WRITE (10.101) JOP,I 101 FORMAT (///.IX,'THE DRY MATTER CAPACITY OF THE TRANSPORT UNIT IN 05290 +PERATION',I6,'IS CALCULATED TO BE LESS OR EQUAL TO 0'./.IX.'CHECK 5300 +0PERATION DATA CARD NUMBER.'.I6.' STOP 6 T0-XOPMD(I,13) IF (JOP.GE.120.AND.JOP.LT.130) T1-XOPMD(I,12) IF (JOP.LT.130) GO TO 1 HERE WE CALCULATE THE NUMBER OF ROUND BALES THAT WILL BE MOVED AT EACH TRIP FROM THE FIELD (XNBL) AND THE LOADING AND UNLOAOING TIMES IF (ICODE(I+1,2).EQ.0) XNBL-1. IF (ICODE(I+1.2).NE.0) T1-XOPMD(I.12)*XNBL IF (XNBL.GT.1.) T0-T0+T1/3. IF (XNBL.GT.1.) TRAVELLING WITH A FULL LOAD 1 A(3)-XOPMD(I.I)+OMCAP*(1.+XMC)*IOOO. 1F (JOP.GE.130) A(3)-A(3)+XOPMO(I+1,I) A(0)-o. A(5)-O. A(9)-XOPER(I.2) XTTP-IOOO. CALL SPEED VFULL-A(12) T2-XOPER(I.5)/A(12) XL02-A(13) AND DATA FILE FOR ERROR') XNBL-XOPMO(I+1,8)*1000./XOPER(I.0) DMCAP-XOPMD(1+1,8)/(1.+XMc) 5190 5200 5230 5200 5250 5260 5270 5280 5310 5320 5330 5300 5350 5360 5370 5330 5390 5000 5010 5020 5030 5000 5050 5060 5070 5080 5090 5500 5510 5520 5530 5500 286 C TRAVELLING WITH AN EMPTY WAGON A(3)-A(3)-DMCAP*(1.+XMC)*1000. CALL SPEED T3-XOPER(1.5)/A(12) VEMPT-A(12) XLD3-A(13) PWR-A (1) ENG-A(15) FUEL-O. ELECT-O. FUI-I. K-(IOP-I)*6 IF (JOP.GT.119) GO TO 2 C HERE WE CONSIDER THE AUTOMATIC BALE WAGON AT 6 DIFFERENT YIELDS 3 DO 3 J-1,6 K-K+1 TI-DMCAP/RATES(K.3) XLDI-RATES(K,0) AVLD-(XLD1*T1+XL02*T2+XLD3*T3)/(T1+T2+T3) RATES(K,3)-DMCAP/(T1+T2+T3+T0) RATES(K.2)-RATES(K.3)/RATES(K,1) RATES(K,0)-AVLD EFF-(A(10)*T1*1.1+T2+T3)/(T1+T2+T3) CALL ENERGY (AVLO,PWR,ENG,EFF.FUI,FUEL.ELECT) RATES(K,5)-FUEL RATES(K,6)-ELECT IF (IPRINT.NE.I) GO TO 3 WRITE (6.100) JOP,T1,T2,T3,T0.VFULL,VEMPT CONTINUE GO TO 0 C HERE WE CONSIDER THE LARGE STACK MOVER AND THE ROUND BALE MOVER 2 100 ETP-DMCAP/(T1+T2+T3+T0) AVLD-(XL02*T2+XLD3*T3)/(T2+T3) EFF-(T2+T3+(T1+T0)/2.)/(T1+T2+T3+T0) CALL ENERGY (AVLD,PNR,ENG.EFF.FUI,FUEL,ELECT) DO 5 J-1,6 K-K+1 RATES(K.1).YAR(J) RATES(K.2)-ETP/YAR(J) RATES(K,3)-ETP RATES(K,0)-AVLO RATES(K,5)-FUEL RATES(K.6)-ELECT RATES(K.7)-XOPER(I.I) RATES(K.8)-VFULL CONTINUE IF (IPRINT.NE.I) GO TO 0 wRITE (6,100) JOP,T1,T2,T3,T0,VFULL.VEMPT FORMAT (///.5x,'FOR OPERATION',I6,' +0F10.3,//,5X,'SPEEDS FULL AND EMPTY ARE (KM/H)‘.2FIO.3) PARTIAL CYCLE TIMES ARE'. 5550 5550 5570 5580 5590 5600 5610 5620 5630 5600 5650 5660 5670 5680 5690 5700 5710 5720 5730 5700 5750 5760 5770 5780 5790 5800 5810 5820 5830 5800 5350 5860 5870 5880 5890 5900 5910 5920 5930 5900 5950 5960 5970 5980 5990 6000 6010 6020 6030 6000 [CCCCCCCCCCC C 287 0 RETURN 6050 END 6060 C ***********mumtit*iddckicktidkic*tttimkiddmttkttkttim*tktidmtidmictkicttkt 6070 SUBROUTINE TRANSP (I, IOP) 6080 C *kmuttin":******M********t****izttimktttimttttktimktickimktttktttttttt 6090 COMMON /YI/ XINF0(7).MCODE(IOO),XMDATA(IOO,I3) 6100 COMMON /Y2/ ICODE(60.3).XOPER(6O,5) 6110 COMMON /Y3/ NMDATA,NOPER,IN,IO 6120 COMMON /Y0/ XOPMD(6O,26) 6130 COMMON /Y5/ A(15).OPNAME(5,60),XTTP,JOP 6100 COMMON /Y6/ RATES(108, 8) .YAR(6) 6150 COMMON /Y8/ TTR(6) ,THR(3), XMC, FUELTR, FUELUL, ELECTT, ELECTU, DMCAP, UT616O COMMON /Y10/ XLD, XLABOR 6170 THIS SUBROUTINE CALCULATES THE MINIMUM CYCLE TIME OF ONE TRANSPORT 6180 UNIT. THE TRANSPORT CYCLE TIME INCLUDES 6190 TTR(I). MINIMUM INTERFACE TIME IN THE FIELD WITH THE HARVESTER 6200 on TTR(2), TIME TO TRAVEL FROM THE FIELD TO STORAGE WITH A FULL WAGON 6210 TTR(3). TIME TO TRAVEL FROM STORAGE TO THE FIELD WITH AN EMPTY WAGON6220 TTR(0), MINIMUM INTERFACE TIME AT STORAGE 6230 TTR(S). EXTRA TIME AT STORAGE TO HELP UNLOAD 6200 TTR(6), IDLE TIME HAITING FOR THE HARVESTER 6250 THE HARVEST CYCLE TIME INCLUDES 6260 THR(1), MINIMUM INTERFACE TIME IN THE FIELD HITH THE TRANSPORT UNIT 6270 THR(2). TIME TO FILL A WAGON 6280 THR(3). IDLE TIME WAITING FOR A TRANSPORT UNIT 6290 TTR(I)-XOPER(I+1,1) 6300 WHEN THE WAGON IS PULLED BY A VEHICLE OTHER THAN THE HARVESTER. INTE6310 TIME IN THE FIELD ALSO INCLUDES TIMEFOR THE HARVESTER TO FILL A WAG06320 IF (XOPER(I+I,2).EQ.O.) TTR(I)-TTR(I)+THR(2) 6330 CREATE VECTOR A TO CALCULATE TRAVEL SPEED To AND FROM STORAGE 6300 A(1)-XOPMO(I+2.20) 6350 A(2)-XOPMO(I+2.10) 6360 A(3)-XOPMD(I+2,1)+XOPMO(I+2,8)*1000. 6370 A(15)-XOPMD(I+2,23) ~ 6380 IF THE HARVESTER MUST ALSO TRANSPORT, THEN ADD THE MASS OF BOTH 6390 THE HARVESTER AND THE ATTACHHENT. 6000 IF (XOPER(I+2,I).EQ.O.) A(3)-A(3)+XOPMD(I.1)+XOPMO(I+1,1) 6010 CHECK IF A DUMP TRUCK IS BEING USED FOR TRANSPORT 6020_ IF (ICODE(1+2,2).LT.260) GO TO 5 6030 A(1)-XOPMD(I+2.11) 6000 A(2)-A(3) 6050 A(3)-O. 6060 A(15)-XOPMD(I+2,10) 6070 5 A(0)-0. 6080 A(5)-0. 6090 A(6)-x1NFO(1) 6500 A(7)-XINFO(2) 6510 A(8)-XINF0(3) 6520 A(9)-XOPER(I+2,2) 6530 A(10)-0. 6500 nnnn fin nn nnn nn nnn 288 A(H)-o. A(10)-1. XTTP-IOOO. CALL SPEED TTR(2)-XOPER(I.5)/A(12) VFULL-A(12) XL02-A(13) - FROM STORAGE TO THE FIELD. THE HAGON Is EMPTY A(3)-A(3)-XOPMD(I+2,8)*1000. CALL SPEED TTR(3)-XOPER(1.5)/A(12) VEMPT-A(12) XLD3-A(13) TTR(0)-XOPER(I+2.5) CALCULATE UNLOAOING RATES IN THE ABSENCE (ULA) AND IN THE PRESENCE OF THE TRANSPORT UNIT (ULTR) TTR(5)-o. QULA-O. ULTR-O. FUELUL-O. ELECTu-O. 6550 6560 6570 6580 6590 6600 6610 6620 6630 6600 6650 6660 6670 6680 6690 6700 6710 6720 6730 6700 6750 IF ICODE(I+0,2) IS NOT ZERO, THERE IS AN UNLOAOING DEVICE AND ENERGY676O REQUIRED FOR UNLOAOING WILL BE CALCULATED IF (ICODE(I+0.2).NE.O) GO TO 21 IN THE CASE OF HAND UNLOAOING RECTANGULAR BALES. NO MECHANICAL ENERGY ISREQUIRED. BUT THE IMPACT ON UNLOAOING TIME MUST BE CALCULATED CALL NUMBER 22. IF (JOP.GE.170) GO TO 22 GO TO 20 6770 6780 6790 6800 6810 6820 6830 6800 POWER AND ENERGY REQUIREMENTS ARE CALCULATED HER FOR THE BLOWER. THE6850 ELEVATOR AND THE COMPACTING TRACTOR (BUNK SILOS). THE ENERGY FOR SE6860 UNLOAOING HAGONS Is INCLUDED IN TRANSPORT 21 PWR-x0PMO(I+0,20) ENG-XOPMD(I+0.23) IN THE CASE OF A COMPACTING TRACTOR. POWER AND ENGINE INFORMATION IS GIVEN WITH THE IMPLEMENT. 1F (ICODE(I+0,2).LT.270) GO TO 25 PWR-XOPMD(I+0.11) ENG-XOPMD(I+0,10) 25 EFF-1. FUI-I. XLD-1./XINF0(1) AVERAGE LOAD OF A COMPACTING TRACTOR IS ASSUMED AS 0.5 AVERAGE POHER REQUIRED TO OPERATE A BALE ELEVATOR IS ASSUMED TO BE 0 KW. , IF (ICODE(I+0,2).GE.270) XLD-O.5 IF (ICODE(I+0,2).LT.200.AND.PWR.GT.5.) XLD-0./PWR CALL ENERGY (XLD,PHR.ENG.EFF.FUI.FUEL.ELECT) FUELUL-FUEL 6870 6880 6890 6900 6910 6920 6930 6900 6950 6960 6970 6980 6990 7000 7010 7020 7030 7000 non nn no on nnnnn 289 ELECTu-ELECT IF (JOP.LT.170) GO TO 10 CONSIDER THE CASE OF UNLOAOING RECTANGULAR BALES. UNLOAOING RATES ARE ASSUMED TO BE 5 TONNES (METRIC) OF NET MATTER PER MAN-HOUR WITH AN ELEVATOR AND 3.5 TWM/MAN.HOUR FOR HAND STACKING. 22 RUL-5.O IF (ICODE(I+0,2).EQ.0) RUL-3.5 ULA-RUL*XOPER(I+2.0)/(1.+XMC) QULA IS THE QUANTITY UNLOADED BETWEEN EACH HAGON"S ARRIVAL QULA-ULA*(TTR(I)+TTR(2)+TTR(3))/XOPER(1+3,1) IF (JOP.GE.180) GO TO 23 TLABOR-XOPER(I+2,0)+1. 1F (ICODE(I+I.2).EQ.O.AND.XOPER(I+2.1).EQ.O.) TLABOR-TLABOR+1. GO TO 20 . 23 TLABOR-XOPER(I+2,0)+XOPER(I+1,3)+1. 20 ULTR-RUL*TLABOR/(I.+XMC) GO TO 15 10 IF (ICODE(I+0.2).LT.200.0R.ICODE(I+0.2).GE.250) GO TO 20 CONSIDER HERE THE CASE OF A BLOWER. UNLOAOING RATE DOES NOT TAKE INTO ACCOUNT TIME FOR SETTING UP THE WAGON AT STORAGE TTR(0) HEIGHT-XOPER(I+0,2) MECHANICAL EFFICIENCY FOR BLOWING IS ASSUMED AS .08 FOR CORN SILAGE 0.06 FOR ALFALFA HAYLAGE EMECH-O.O6 IF (JOP.GE.I00.AND.JOP.LT.150) EHECH-O.08 FWM-PWR*XLD*EMECH*3600./(HEIGHT*9.8) ULTR-FwM/(1.+XMC) 15 TTR(5)-(DMCAP-QULA)/ULTR IF (TTR(5).LT.O.) TTR(5)-O. CALCULATE AVERAGE FUEL CONSUMPTION (L/H) FOR TRANSPORT, CONSIDERING IDLE TIME AS ZERO (IDLE TIME WILL BE IDENTIFIED 1N SUBROUTINE HRTR) 20 PWR-XOPHD(I+2,20) ENG-XOPMO(I+2,23) FUI-I. XLD-(XL02*TTR(2)+XLO3*TTR(3))/(TTR(2)+TTR(3)) TTc-TTR (1)-1-TTR (2) +TTR (3) +TTR (0) +TTR (5) EFF-(TTR(2)+TTR(3)+O.5*TTR(5)1/TTC IF (JOP.GE.170) EFF-(TTR(2)+TTR(3))/TTC IF (XOPER(I+I,2).EQ.O) EFF-EFF+TTR(1)/TTC CALL ENERGY (XLD.PHR.ENG.EFF.FUI.FUEL.ELECT) FUELTR-FUEL ELECTT-ELECT UT IS THE UNLOAOING TIME TO TRANSPORT TIME RATIO. SINCE ENERGY REQUIREMENTS FOR UNLOAOING ARE CALCULATED FOR CONTINUOUS UNLOAOING. UT WILL BE USED TO ESTIMATE ACTUAL ENERGY USED FOR UNLOAOING ATR AND AUR ARE ACTUAL TRANSPORT AND UNLOAOING RATES ATR-DMCAP*XOPER(I+3,1)/TTC AUR-ULTR*XOPER(I+0,1) 1F (AUR.NE.O) UT-ATR/AUR 7050 7060 7070 7080 7090 7100 7110 7120 7130 7100 7150 7160 7170 7180 7190 7200 7210 7220 7230 7200 7250 7260 7270 7280 7290 7300 7310 7320 7330 7300 7350 7360 7370 7380 7390 7000 7010 7020 7030 7000 7050 7060 7070 7080 7090 7500 7510 7520 7530 7500 Cir??? nnnnn nnn nonnnn on 290 IF THE UNLOAOING RATE IS 0. HE MIGHT HAVE EITHER A COMPACTING 7550 TRACTOR. IN _ 7560 HHICH CASE UT-O.5 0R HE MAY HAVE NO UNLOAOING DEVICE AT ALL (UT=0.) 7570 IF (AUR.EQ.O.) UT-O.5 7580 IF (ICODE(I+0.2).EQ.O) UT-O. 7590 RETURN 7600 END 7610 *******************************************************************k 7620 SUBROUTINE HRTR (I.IOP) 7630 **************k***tk************************k*********************** 7600 COMMON /YI/ x1NFO(7).MCODE(IOO),XMOATA(IOO.13) 7650 COMMON /Y2/ ICODE(60,3).XOPER(6O,5) 7660 COMMON /Y3/ NMDATA,NOPER.IN.IO 7670 COMMON /Y0/ x0PMD(6O,26) 7680 COMMON /Y5/ A(15).0PNAME(5,60),XTTP,JOP 7690 COMMON /Y6/ RATES(108.8).YAR(6) 7700 COMMON /Y8/ TTR(6),THR(3).XMC,FUELTR.FUELUL.ELECTT,ELECTU,DMCAP.UT77IO COMMON /Y9/ IPRINT,IPR1 7720 THIS SUBROUTINE LINKS THE HARVEST SYSTEM TO THE TRANSPORT SYSTEM 7730 IT CALCULATES MINIMUM HARVEST AND TRANSPORT RATES AND ALLOCATES IDLE7700 TIME TO HHICHEVER SYSTEM IS FASTER .SO BOTH RATES BECOME EQUAL. 7750 IT ALSO CALCULATES ENERGY REQUIREMENTS FOR THE ENTIRE OPERATION 7760 (HARVEST. TRANSPORT AND UNLOAOING). . 7770 XMc-2.33 ‘ 7780 IF (JOP.GE.170) XMc-o.25 7790 IF (JOP.GE.150.AND.JOP.LT.I60) XMC=1.O 7800 DMCAP-XOPMD(I+2,8)/(1.+XMC) 7810 THR(I)-XOPER(I+1,1) ‘ 7820 THE FOLLOWING FIVE VARIABLES ARE INITIALIZED WITH DUMMY VALUES. THE7830 ACTUAL VALUE IS CALCULATED SUBSEQUENTLY IN EITHER HRTR OR TRANSPORT 7800 SUBROUTINE. 7850 THR(2)-O.5 7860 THR(3)-O. 7870 TTR(6)-O. 7880 HTOT AND TTOT ARE RATIOS OF HARVEST TIME AND TRANSPORT TIME 7890 TOTAL OPERATION TIME. IN THE CASE OF A HARVESTER ALSO TRANSPORTING 7900 MATERIAL TO STORAGE, TIME MUST BE ALLOCATED IN PART TO HARVEST AND 7910 IN PART TO TRANSPORT. , 7920 IN THIS CASE HTOT AND TTOT HILL BOTH BE LESS THAN 1 A 7930 THEIR SUM HILL BE EQUAL TO I. 7900 HTOT-I. 7950 TTOT-I. 7960 CALL TRANSP(I,IOP) 7970 K-(IOP-1)*6 7980 DO 10 J-1,6 7990 K-K+I 8000 THR(2)-DHCAP/RATES(K.3) 8010 TRANSPORT RATES HILL BE INDEPENDENT OF YIELD EXCEPT HHEN THE HAGON 8020 IS PULLED BY THE HARVESTER. 8030 IF (XOPER(I+1,2).EQ.0.) CALL TRANSP (I.IOP) 8000 n an an 291 THc-THR(1)+THR(2) 8050 HR-OMCAP*XOPER(I.1)/THC 8060 TTc-TTR(1)+TTR(2)+TTR(3)+TTR(0)+TTR(5) 8070 TR-DMCAP*XOPER(I+3.1)/TTC 8080 IF (XOPER(I+2.1).NE.O.) GO TO 15 8090 HTOT-TR/(TR+HR) 8100 TTOT-HR/(TR+HR) 8110 AHR-HR*HTOT 8120 GO TO 30 8130 15 IF (HR.GT.TR) GO TO 20 8100 HERE TRANSPORT UNIT HILL IDLE TTR(6) HOUR PER UNIT HARVESTER 8150 TTR(6)-(XOPER(I+3,I)*THC-XOPER(I.1)*TTC)/XOPER(I.I) 8160 AHR—HR 8170 THR(3)-O. . 8180 GO TO 30 8190 HERE HARVEST RATE IS GREATER THAN TRANSPORT RATE. 8200 HARVESTER HILL IDLE THR(3) HOUR PER TRANSPORT UNIT 8210 20 THR(3)-(XOPER(I,1)*TTC-XOPER(I+3.1)*THC)/XOPER(I+3,1) 8220 AHR-TR 8230 TTR(6)-O. 8200 NOH LET US MAKE CHANGES TO HARVEST RATES AND ENERGY CONSUMPTION SO 8250 THEY MAY INCLUDE IDLE TIME. 8260 30 RATES(K.3)-AHR . 8270 RATES(K.2)-AHR/YAR(J) 8280 THc-THC+THR(3) 8290 FUELHR-RATES(K.5)*THR(2)/THC 8300 ELECTH-RATES(K.6)*THR(2)/THC 8310 ACTUAL ENERGY CONSUMPTION RATES ARE CALCULATED ON A TOTAL OPERATION 8320 BASIS. 8330 EH-FUELHR*HTOT*XOPER(I.1) 8300 FT-FUELTR*TTOT*XOPER(1+3.I)*TTC/(TTC+TTR(6)) 8350 FU-FUELUL*TTOT*UT*XOPER(1+0,1)*TTC/(TTC+TTR(6)) 8360 EH-ELECTH*HTOT*XOPER(I.1) 8370 ET-ELECTT*TTOT*XOPER(1+3.1)*TTC/(TTC+TTR(6)) 8380 EUIELECTU*TTOT*UT*XOPER(1+0,1)*TTC/(TTC+TTR(6)) 8390 RATES(K.5)-FH+FT+FU 8000 RATES(K.6)-EH+ET+EU 8010 1F(1PRINT.NE.I) GO TO 10 8020 HRITE (10.100) JOP.YAR(J).(THR(KK).KK-1.3).(TTR(KK).KK-1.6) 8030 100 FORMAT (//.5X.'0PERATION '.I8./5X.'YIELD ',F10.2.' TDM/HA',/.5X.8000 +'HARVEST CYCLE TIMES (HOURS) ',/,IOX,'T1, INTERFACE TIME HITH TRAN8050 +SPORT ',F10.0./.10X,'T2, TIME TO FILL A HAGON IN THE FIELD ',F108060 +.0,/10X,'T3. HARVESTER IDLE TIME ',F10.0./.5X,'TRANSPORT CYCLE TI8070 +MES (HOURS) './ ,10X,'TI, INTERFACE TIME HITH HARVESTER '.F10.808O +0./.10X,'T2, TIME TO TRAVEL HITH A FULL LOAD '.F10.0./.10X.'T3, T8090' +IME TO TRAVEL HITH AN EMPTY HAGON ',F10.0./10X,'T0, MINIMUM 1NTER85OO +FACE TIME AT STORAGE '.F10.0,/,10X,’T5, TIME HELPING HITH UNLOA0|85IO +NG 1. F10.0,/10X.'T6. TRANSPORT UNIT IDLE TIME '.F10.0./) 8520 HRITE (10.110) FUELHR.FH.FUELTR.FT.FUELUL.FU.ELECTH,EH.ELECTT.ET. 853D +ELECTU.EU.HTOT.TTOT.UT 8500 292 110 FORMAT (//.60X,'PER S1NGLE UNIT',10X,'FOR ALL UNITS',/6OX,'ON A CO8550 +NTINUOUS',10X,'WITH RESPECT TD',/60X,'BASIS'.20X,'TOTAL OPERATION 8560 +TIME'./.5X,'FUEL CONSUMPTION RATES (L/H) HARVEST',19X,F10.2,15X,8570 +F10.2./.36X,‘TRANSPORT',17X.FIO.2,15X,F10.2./.36X,'UNLOADING',17X,858O +F10.2,15X,F10.2.//.5X.'ELECTRICITY CONSUMPTION RATES (KW-H/H) HA8590 +RVEST',9X,F10.2.15X,F10.2,/06X,'TRANSPORT',7X,F10.2,15X,F10.2./. 8600 +06X,'UNLOADING',7X,FIO.2,15X,F10.2.///.5X,'THE HARVEST TIME TO TOT8610 +AL OPERATION TIME RATIO IS '.F10.0./95X.'THE TRANSPORT TIME TO T08620 +TAL OPERATION TIME RATIO IS ', FI0.0./.5X,'THE UNLOAOING TIME TO 8630 +TRANSPORT TIME RATIO IS '.F10.0) 8600 10 CONTINUE 8650 RETURN 8660 END 8670 C ************************t******************************************* 8680 SUBROUTINE HAYPCK (I.IOP) 8690 C Mk*ttttttictt***********ttth’ctttietittttitmkttktt*i¢********************* 8700 COMMON /YI/ XINFO(7).MCOOE(IOO).XMDATA(IOO,13) 8710 COMMON /Y2/ ICODE(60,3).XOPER(6O.5) 8720 COMMON /Y3/ NMDATA.NOPER.IN.IO 8730 COMMON /Y0/ XOPMD(6O.26) 8700 COMMON /Y5/ A(15).OPNAME(5.6O).XTTP.JOP 8750 COMMON /Y6/ RATES(108.8).YAR(6) 8760 COMMON /Y8/ TTR(6).THR(3).XMC.FUELTR.FUELUL,ELECTT,ELECTU.DMCAP.UT877O COMMON /Y9/ IPRINT.IPRI - 8780 COMMON /YIO/ XLO.XLABOR 8790 c' THIS SUBROUTINE LINKS FIELD HAND-PICKING OF RECTANGULAR HAY BALES 8800 C AND UNLOAOING AT A STORAGE SITE. 8810 C THIS OPERATION IS CONSIDERED INDEPENDENT OF AND SUBSEQUENT TO HAY 8820 C BALING. 8830 XMc-O.25 8800 DMCAP-XOPMD(I+2.8)/(1.+XMC) 8850 THR(1)-XOPER(I+1,1) 8860 THR(3)-O. 8870 TTR(6)-O. 8880 K-(IOP-1)*6 8890 DO 10 J-1.6 8900 K-K+1 ‘ 8910 C PICKING RATE OF BALES IS A FUNCTION OF YIELD AND LABOR AVAILABLE IN 8920 C THE FIELD. 8930 C VARIABLES BALES AND RMASS ARE BALES PICKED PER HOUR AND TONNES OF 8900 C DRY HATTER PICKED PER HOUR. 8950 FLABOR-XOPER(I+I,3)+1. 8960 BALEs-(08.+0.*YAR(J))*FLABOR 8970 RMASS-BALES*XOPER(I,01/(1000.*(1.+XMC)) 8980 THR(2)-DMCAP/RMASS 8990 CALL TRANSP (I.IOP) ~ 9000 TTCa-TTR (1) +TTR (2) +TTR (3) +TTR (0) +TTR (5) 9010 AHR-DMCAP*XOPER(I+3,1)/TTC 9020 RATES(K.1)-YAR(J) 9030 RATES(K,2)-AHR/YAR(J) 9000 293 RATES(K.3)-AHR - 9050 RATES(K.0)-XLD 9060 FT-FUELTR*XOPER(I+3.1) 9070 Fu-FUELUL*UT*XOPER(I+0.I) 9080 ET-ELECTT*XOPER(I+3,1) 9090 Eu-ELECTU*UT*XOPER(I+0.I) 9100 RATES(K.5)-FT+FU 9110 RATES(K.6)-ET+EU 9120 RATES(K.7)-XLABOR 9130 RATES(K.8)-8. 9100 IF (IPRINT.NE.I) GO TO 10 9150 HRITE (10.100) JOP,YAR(J).(THR(KK),KK-I.3).(TTR(KK),KK-1,6) 9160 100 FORMAT (//.5X,'OPERATION '.I8,/.5X.'YIELD ',F10.2,' KG/HA'./.5X.9I70 +'HARVEST CYCLE TIMES (HOURS) ',/,10X.'T1, INTERFACE TIME HITH TRAN9180 +SPORT ',E10.0./.10X,'T2, TIME TO FILL A HAGON IN THE FIELD ',F109190 +.0,/10X,'T3, HARVESTER IDLE TIME ',F10.0./.5X,'TRANSPORT CYCLE T19200 +MES (HOURS) './ .10X,'T1, INTERFACE TIME HITH HARVESTER ',F10.9210 +0,/,10X,'T2. TIME TO TRAVEL HITH A FULL LOAD ',F10.0./.10X,'T3, T9220 +IME TO TRAVEL HITH AN EMPTY HAGON ',F10.0,/10X,'T0, MINIHUM INTER923O +FACE TIME AT STORAGE ',F10.0./.IOX,'T5, TIME HELPING HITH UNLOADI920O +NG 1. F10.0,/10X.'T6. TRANSPORT UNIT IDLE TIME '.F10.0./) 9250 HRITE (10.110) FUELTR.FT.FUELUL.FU.ELECTT.ET.ELECTU.EU.UT 9260 110 FORNAT (//.6OX.'PER SINGLE UNIT'.10X.'FOR ALL UNITS'./60X.'0N A C0927O +NTINUOUS'.10X,‘WITH RESPECT TO'./6OX.IBASIS'.2OX.'TOTAL OPERATION 9280 +TIME'./.5X,'FUEL CONSUMPTION RATES (L/H)’. 9290 + 'TRANSPORT',17X,F10.2.15X,F10.2./.36X.'UNLOADING',17X.9300 +F10.2.15X,F10.2.//.5X.'ELECTRICITY CONSUMPTION RATES (KW-H/H)‘. 9310 + 'TRANSPORT',7X,F10.2.15X.F10.2./a 9320 +06X,'UNLOADING'.7X,F10.2.15X,F10.2.///o5X. 'THE UNLOAOING TIME TO 9330 +TRANSPORT TIME RATIO IS ',F10.0) 9300 10 CONTINUE 9350 RETURN - 9360 END 9370 C 290 Table E.0. Listing of program ALHARV. C ********************************************A*********************** SUBROUTINE ALHARV(REMCUT,REMHRV,ICUTON.JDAY) C ************************************A******************************* nnnnnnnnnnnnnnnnnnnnnnnnnnnnnn COMMON /HI/ NPLOTS.NMOW.NHRV,NSTO.AREAPL.HARMAT(00.29).ZRT(9,5) COHMON /H2/ TPL(9).RAIN,JJDAY.NDAYHR COMMON /w0/ NPDCA.NDCTD.IDAH ' COMMON /21/ AREAI6).NBO(6).NOPSQ(5.9).CRTR(5.0.9).SILO(2) COMMON /CTRL20/ BGNCUT(S).NTHYR.NTHCUT,NDAYSC.NDAYSH,YLD(0), 100 110 120 130 100 150 I60 170 180 +QUAL(3.0),GDDCUM,METRIC,JYEARF,JYEARL.IPRTI,IPRT2,JDAYF,JDAYL,JPRT190 +,NYRS,IPRTO,NCUTS,JYEAR,JLALHR,CPLANT COMMON /ALFARG/ GDDB5.AVTA,DAYLIN,DAYLEN,YDAYL,DECR,XLAI,AW, +SUMSI,SUMSZ,T.WSF,SRADF,DWS.PPT,ESO.ESR,XLEAF,BUDS.STEM,TOPS,TNC, +XMATS,TNCS,TMAXC,TMINC THIS SUBROUTINE IS CALLED FROM THE ALFALFA GROHTH SIMULATOR HRITTEN BY LUKE PARSCH. AGICULTURAL ECONOMICS DEPARTMENT. MSU THE PRESENT SUBROUTINE ALHARV AND ALL THE ATTACHED SUBROUTINES CALLED HEREFROM HERE HRITTEN BY PHILIPPE SAVOIE. AGRICULTURAL ENGINEERING DEPARTMENT. MICHIGAN STATE UNIVERSITY. ALHARV IS CALLED ONCE EACH ALFALFA HARVEST DAY. HARVEST HILL NOT BEGIN IF CORN PLANTING (CPLANT)IS NOT FINISHED. IF THE MOHING CRUDE PROTEIN IS SPECIFIED IN THE REASONABLE RANGE. MOHING CAN BE POSTPONED UP TO 10 DAYS BEYOND BGNCUT(NTHCUT) IF ALFALFA IS CONSIDERED IMMATURE. ON THE FIRST DAY OF MOHING. AN INITIALIZATION SUBROUTINE IS CALLED. THE HHOLE AREA To BE HARVESTED IS DIVIDED INTO NPLOTS. THE NUMBER OF PLOTS. FOR DIRECT-CUT ALFALFA, IDENTIFIED BY IDAH-I IN THE INITIALIzATION SUBROUTINE. SUBROUTINE DCALF IS CALLED. FOR FIELD CURED ALFALFA. EITHER FOR HAY OR HAYLAGE. SUBROUTINES HRVQ, MOHQ. HRVQ AND UPDATE ARE CALLED IN THAT ORDER. FIRST PRIORITY IS GIVEN TO HARVEST (REMOVING ALFALFA FROM THE FIELD). SECOND PRIORITY IS GIVEN TO MOHING. HRVQ IS CALLED A SECOND TIME IN CASE SOME PLOTS MOHED IN THE MORNING COULD BE READY FOR HARVEST LATER IN THE AFTERNOON. ALL PLOTS MOHED AND NOT YET HARVESTED (STILL CURING IN THE FIELD) ARE THEN UPDATED FOR HEATHERING LOSSES AND FOR DRYING. FINALLY HHEN ALL PLOTS ARE HARVESTED THEY ARE AGGREGAGATED INTO THE HFEED MATRIX BY CALLING ENDHRV. NHTOAY IS THE NUMBER OF PLOTS HARVESTED TODAY NMTDAY IS THE NUMBER OF PLOTS MOWED TODAY ICUTON-O 200 210 220 230 200 250 260 270 280 290 300 310 320 330 300 350 360 370 380 390 000 010 020 030 000 050 060 070 080 090 500 510 520 530 500 an an 295 IF (NDAYHR.GT.0) GO TO 5 IF (CPLANT.GE.FLOAT(JDAY)) RETURN KFIRST-MAXI(CPLANT+1..BGNCUT(NTHCUT)) IF THE CP CRITERION IS UNREASONABLE. BYPASS IT AND HARVEST. 550 560 570 580 IF (CRTR(NTHCUT,0,3).LT.0.15.0R.CRTR(NTHCUT,0,3).GT.0.23) GO TO 5 590 ON THE FIRST CHECKING DAY. SET THE PREVIOUS DAY'S CP AND RETURN. 1F (JDAY.EQ.KFIRST) THEN PDCPI-CRTR(NTHCUT.0,3)+0.0001 PDCP2-QUAL(3.2)+O.OOOI POCP-AHAXI(POCP1,PDCP2) RETURN ENDIF ON SUBSEQUENT DAYS. THE NUMBER OF DAYS MOHING HAS BEEN POSTPONED IS CALCULATED. IF IT IS GREATER OR EQUAL TO 10. POSTPONEMENT IS STOPPED AND MOHING MUST START. A CHDAYS-FLOAT(JDAY)-BGNCUT(NTHCUT) CHMAx-IO. IF (CHDAYS.GE.CHMAX) THEN KFIRST-JDAY GO TO 5 ENDIF IF (QUAL(3.2).GT.PDCP) GO TO 3 IF (QUAL(3.2).GT.CRTR(NTHCUT.0.3)) RETURN HERE THE QUALITY IS LOH ENOUGH TO HARVEST. CHECK IF TODAY IS THE FIRST DAY OF HARVEST. IF (PDCP.GT.CRTR(NTHCUT.A.3)) THEN KFIRST-JDAY ENDIF PDCP-QUAL(3.2) GO TO 0 IF (NHRV.EQ.NPLOTS) RETURN CONTINUE I-NTHCUT JJDAY-JDAY RAIN-PPT NHTOAY-O NMTDAY-O IF(JDAY.EQ.KFIRST) NDAYHR-I IF (NDAYHR.EQ.I) CALL INHRV IF (NDAYHR.GE.39) CALL ENDHRV IF (NDAYHR.GE.39) GO TO 30 IF (IDAH.EQ.1) GO TO 10 IF (NHRV.LT.NMOH) CALL HRVQ (NHTOAY) 1F (NMOH.LT.NPLOTS) CALL MOHQ (NHTOAY.NMTDAY) NMOH-NMOH+NMTDAY IF (NHRV.LT.NMOH) CALL HRVQ (NHTOAY) NHRv-NHRV+NHTDAY CALL UPDATE 600 610 620 630 600 650 660 670 680 690 700 710 720 730 700 750 760 770 780 790 800 810 820 830 800 850 860 870 880 890 900 910 920 930 900 950 960 970 980 990 1000 1010 1020 1030 1000 296 GO TO 20 1050 10 CALL DCALF 1060 20 CONTINUE 1070 IF (NHRV.EQ.NPLOTS) CALL ENDHRV 1080 NDAYHR-NDAYHR+I 1090 30 CONTINUE IIOO REHCUT-I.—FLOAT(NMOH)/FLOAT(NPLOTS) 1110 REMHRv-I.-FLOAT(NHRV)/FLOAT(NPLOTS) 1120 IF (NMOH.GT.O) ICUTON-I 1130 IF (NMOH.GE.NPLOTS) ICUTON-O 1100 IF (NMTDAY.GE.NPLOTS) ICUTON-I 1150 CALL HRITAL(I) 1160 RETURN 1170 END 1180 C tint*******************kicktmu::1:*s‘c{duettt*ktktfi*itkticiutttimttttimtimtttt 1 190 SUBROUTINE MGTINF 1200 C ***************at*Akidttiddck'ktktt*ttttttttttttttttktimtic*icictidmtimtttt 12 10 COMMON /zI/ AREA(6),NBO(6),NOPSQ(5.9).CRTR(5,0.9).SILO(2) 1220 COMMON /25/ IPR2.IPR3.IPRO 1230 COMMON /28/ ALFSIL(2).HAYST(3) ’1200 COMMON /Y3/ NMDATA.NOPER.IN.IO 1250 C THIS SUBROUTINE READS MANAGEMENT INFORMATION RELATED TO ALFALFA 1260 c HARVEST. THIS INCLUDES THE AREA. THE SEQUENCE OF OPERATIONS. THE 1270 C CRITERION MATRIX FOR EACH ALFALFA HARVEST AND ALFALFA STORAGE 1280 C CAPACITIES AND INITIAL COSTS. THERE CAN BE UP TO 0 DISTINCT 1290 C ALFALFA HARVESTS IN A GIVEN YEAR. THE NUMBER MAY VARY. HHEN AREA 1300 c READ IN IS ZERO. NO MORE HARVESTS ARE CONSIDERED. 1310 C 1320 HRITE (IO. 95) 1330 95 FORMAT (/. 5X. 'THE MANAGEMENT INPUTS FOR AREA AND OPERATION SEQUENCI30O +E HERE READ AS FOLLOHS ) 1350 1-0 1360 15 I-I+1 1370 READ (IN. 100) AREA(I). NBO(I) 1380 HRITE (10.100) AREA(I). NBO(I) I390 100 FORMAT (F10. 2. 12) 1000 IF (AREA(I). EQ. O. ) GO TO 10 1010 READ (IN.IIO) (NOPSQ(I.K).K-I.9) 1020 HRITE (10.110) (NOPSQ(I.K).K-I.9) 1030 110 FORMAT (915) 1000 READ (IN,120) ((CRTR(I.J.K).K-1.9).J-1.0) 1050 HRITE (10.120) ((CRTR(I,J,K),K-I,9).J-1.0) 1060 120 FORMAT (3(9F5.2./).9F5.2) 1070 GO TO 15 1080 10 READ (IN,130) (SILO(I).I-1.2).(ALFSIL(I).I-1.2).(HAYST(I).I-I.3) 1090 HRITE (10.130) (SILO(I).I-1.2).(ALFSIL(I),I-1.2).(HAYST(I).I-1.3) 1500 130 FORMAT (7F10.2) 1510 READ (1N.100) IPR2.|PR3,IPRO 1520 HRITE (10.100) IPR2.IPR3,IPRO 1530 100 FORMAT (312) 1500 297 RETURN END C c *k****************************************tt************************ SUBROUTINE YRINIT C *immk12**tkkiricicmkkkaktk*****2‘c*****Akin“:Okinawan!*t********************* COMMON /21/ AREA(6).NBO(6).NOPSQ(S.9).CRTR(5.0.9).SILO(2) COMMON /23/ HARDEX.TMSTO(0),NPST(5,5),NCUM(5).0PUSE(5,9) COMMON /Z0/FDLABR.FDENER.HRLABR.HRFUEL.HRELEC COMMON /Z6/ CSLABR.CSFUEL.CSELEC.CSFDLB.CSFDEN.DMCS COMMON /z7/ ALHRFD(26.15).AFEED(26.23) COMMON /YYI/ USEMCH(IOO).UNITS(IOo) C THIS SUBROUTINE PROVIDES AN INITIALIzATION OF PARAMETERS THAT MUST C BE SET TO O AT THE BEGINNING OF EACH YEAR. DATA ALHRFD.AFEED /390*0.0.598*0.0/ HARDEx-I. 1F (SILO(I).EQ.O.) HARDEx-z. IF (SILO(I).EQ.O.O.AND.SILO(2).EQ.O.) HARDEx-3. FDLABR-o. FDENER-O. HRLABR-O. HRFUEL-o. HRELEc-O. CSLABR-O.~ CSFUEL-O. CSELEc-O. CSFDLB-O. CSFDEN-O. OMcs-O. DO 3 I-1.0 3 TMSTO(I)-O. DO 5 I-I.5 NCUM(I)-O DO 0 J-1,9 0 OPUSE(I.J)-0. DO 5 J-1,5 5 NPST(I.J)-O DO 6 I-I.IOO USEMCH(I)-O. 6 UNITS(I)-O. RETURN END C . C fittttttkttttttt*********ticidt{admit*tttttkm’cttic********************* SUBROUTINE INHRV C ***************************t**********************fc***************Mc COMMON /HI/ NPLOTS.NMOW.NHRV.NSTO,AREAPL,HARMAT(00.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN.JJDAY.NDAYHR COMMON /W0/ NPDCA.NDCTD.IDAH COMMON /21/ AREA(6),NBO(6).NOPSQ(5.9).CRTR(5.0.9).SILO(2) 1550 1560 1570 1580 1590 1600 1610 1620 1630 1600 1650 1660 1670 1680 1690 1700 1710 1720 1730 1700 1750 1760 I770 1780 1790 1800 1810 1820 1830 1800 1850 1860 I870 1880 1890 1900 1910 1920 I930 1900 1950 1960 1970 1980 1990 2000 2010 2020 2030 2000 nnnnnnnnnnnnnnonnnnnnnnnnnnnnnnnnnnnnn 298 COMMON /Z3/ HARDEX,TMSTO(0),NPST(5,5).NCUM(5).0PUSE(5,9) COMMON /CTRL20/ BGNCUT(S).NTHYR,NTHCUT,NDAYSC,NDAYSH,YLD(0), 2050 2060 +QUAL(3.0),GDOCUM,METRIC.JYEARF.JYEARL.IPRTI,IPRT2.JDAYF.JDAYL.JPRT207O +,NYRS,IPRTO,NCUTS,JYEAR.JLALHR,CPLANT COMMON /ALFARG/ GDD85.AVTA.DAYLIN.DAYLEN.YDAYL.DECR.XLAI.AH. +SUMSI.SUMsz.T.HSF.SRADF.DHS.PPT.ESO.ESR.XLEAF.BUDS.STEM.TOPS.TNC. +XMATS.TNCS.TMAXC.TMINC COMMON /Y3/ NMDATA.NOPER.IN.ID COMMON /Y6/ RATES(108.8).YAR(6) COMMON /Y7/ NBOP(18).NBMACH(18.7).XNBM(18.7) COMMON /221/ ADATES(26,12).SDATES(0.12) DATA ADATES /312*O./ THIS IS AN INITIALIzATION SUBROUTINE. IT IS CALLED ON THE FIRST HARVEST DAY. IT IS CALLED ONLY ONCE FOR EACH HARVEST (OR CUT). UP TO NINE OPERATIONS MAY BE INCLUDED IN A HARVEST SEQUENCE. THE NUMBER OF OPERATIONS IN A SEQUENCE IS EITHER 1 FOR DIRECT CUT ALFALFA OR CORN SILAGE HARVEST (IDENTIFIED BY IDAH-I) OR UP TO 9 SEQUENTIAL OPERATIONS (IDAH-9) FOR FIELD-CURED ALFALFA. HHEN ALFALFA IS FIELD CURED. THERE MAY BE UP TO 6 SEQUENTIAL OPERATIONS AND 3 OPTIONAL HARVEST OPERATIONS FOR EACH CUT I IN ANY YEAR. THE POSSIBLE OPERATIONS ARE NOPSQ(I.I). MOHING FOR FIELD CURING NOPSQ(I.2). ADDITIONAL CURING TREATMENT OR 0000 NOPsq(I.3). RAKING JUST BEFORE HARVESTING OR 0000 NOPSQ(I,0), ADDITIONAL TREATMENT AFTER RAINFALL OR 0000 NOPSQ(I.5), FIRST PRIORITY HARVEST OF FIELD CURED FORAGES 0R DIRECT-CUT ALFALFA HARVEST. NOPSQ(I.6). SECOND PRIORITY HARVEST NOPSQ(I,7), FORCED HAY HARVEST HHEN SILOS ARE FULL 2080 2090 2100 2110 2120 2130 2100 2150 2160 2170 2180 2190 2200 2210 2220 2230 2200 2250 2260 2270 2280 2290 2300 2310 2320 2330 2300 2350 NOPSQ(I,8), LAST RESORT HARVEST OPERATION TO DESTROY FORAGES AFTER2360 EXCESSIVE EXPOSURE . NOPSQ(I.9). TRANSPORT OF BALED HAY DROPPED IN THE FIELD DURING HARVEST. FOR EACH OPERATION (J) DURING HARVEST (I). FIVE PARAMETERS ARE ESTIMATED. THEY ARE ZRT(J,1). THE HARVEST RATE AT A SPECIFIC YIELD (HA/H) 2RT(J.2). THE FUEL CONSUMPTION RATE (L/H) 2RT(J.3). THE ELECTRICITY CONSUMPTION RATE (KH.H/H) ZRT(J,0). THE LABOR REQUIREMENT (MAN.H/H) ZRT(J.5), THE AVERAGE SPEED OF THE HARVESTING IMPLEMENT (KM/H) 2370 2380 2390 2000 2010 2020 2030 2000 2050 2060 2070 THE HARMAT MATRIX CONTAINS ALL THE USEFUL CHARACTERISTICS OF ALFALFA2080 BETWEEN MOWING AND STORAGE TIME. IT KEEPS TRACK 0F DRYING, DRY MATTER AND QUALITY CHANGES OF BOTH STEMS AND LEAVES. FOR EACH PLOT (I). THE CHARACTERISTICS ARE 2090 2500 2510 HARMAT(I,1), A MOWING DUMMY VARIABLE (1. WHEN MOWED. O. OTHERWISE)2520 HARMAT(1.2). LEAF YIELD AT TIME OF MOHING (KG-DM/HA) HARMAT(I.3), STEM YIELD AT TIME OF MOHING (KG-DM/HA) 2530 2500 nonnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn C C 299 HARMAT(I.0), CRUDE PROTEIN IN THE LEAVES AT MOHING (DEC.) 2550 HARMAT(I.5). CRUDE PROTEIN IN THE STEMS AT MOHING (DEC.) 2560 HARMAT (1.6). DIGESTIBILITY OF LEAVES AT MOHING (DEC) 2570 HARHAT(I.7). DIGESTIBILITY OF STEHS AT MOHING (DEC) 2580 HARMAT(I.8). CRUDE FIBER 0F LEAVES AT MOHING (DEC) 2590 HARMAT(I.9). CRUDE FIBER OF STEMS (DEC) 2600 HARMAT(I.IO), INITIAL MOISTURE CONTENT EACH DAY AT 8AM (DEC. DB) 2610 HARMAT (1,11), FINAL MOISTURE CONTENT AT TIME OF HARVEST (DEC. DB)2620 HARMAT(I.IZ). HARVEST DUMMY VARIABLE (1. WHEN HARVESTED. O. OTHERW263O HARMAT(I.I3). STORAGE DUMMY VARIABLE (1. WHEN STORED. O. OTHERWISE2600 HARMAT(I.I0), NUMBER OF EXPOSURE DAYS SINCE MOHING 2650 HARMAT(I.Is). NUMBER OF EXPOSURE DAYS SINCE HARVESTING (IN THE 2660 CASE OF BALES LEFT OUTSIDE FOR STORAGE) 2670 HARMAT(I.I6). CUMULATIVE RAINFALL ON BALED HAY LEFT IN THE FIELD 2680 (MM) 2690 HARMAT(I.I7), HINDROH TO SHATH HIDTH RATIO 2700 HARMAT(I.IB). RAKING FACTOR FOR DRYING (1. ON THE DAY MATERIAL IS 2710 RAKED. 0. OTHERHISE) 2720 HARMAT(I.IS). MOHING-CONDITIONING FACTOR FOR DRYING 2730 HARMAT(1.2O). EXTRA TREATMENT FACTOR FOR DRYING 2700 HARMAT(I,21), HARVEST INDEX (1. WHEN FIRST PRIORITY. 2. WHEN SECON2750 PRIORITY. 3. WHEN FORCED BALED HAY AFTER FILLING SILOS, 0. WH276O DESTROYING EXCESSIVELY EXPOSED FORAGES) 2770 HARMAT(1.22). STORAGE TYPE INDEX (1. FOR DRY HAY. 2. FOR HET STORA2780 HARMAT(1.23). THIS PARAMETER HAS NO USE AT PRESENT 2790 HARMAT(I.20), REMAINING LEAF FRACTION (DEC) 2800 HARMAT(1.25). REMAINING STEM FRACTION(DEC) 2810 HARNAT(I.26). REMAINING DRY MATTER FRACTION AFTER RESPIRATION LOSSZ820 HARMAT(1.27). CUMULATIVE RAINFALL DURING FIELD CURING (MM) 2830 HARMAT(1.28), AVERAGE TIME AFTER 8AM AT HHICH PLOT(|) IS MOWED 2800 HARMAT(1.29). MOISTURE CONTENT RIGHT AFTER RAIN 0R AT 8AM IN THE 2850 CASE OF A NON-RAINY DAY (DEC. 08) 2860 2870 CONVERT YIELD INTO TDM/HA AND INCREASE BY 10 PERCENT TO ESTIMATE 2880 AVERAGE HARVEST RATE THROUGHOUT THE HARVEST SEASON 2890 K-(NTHCUT-1)*3+1 2900 ADATES(NTHYR.K)-FLOAT(JJDAY) 2910 YDM-TOPS*0.0I*I.1 2920 DO 10 J-1.9 2930 DO 10 K-I.5 2900 10 ZRT(J.K)-0. 2950 I-NTHCUT 2960 Naox-9 2970 IF (NOPSQ(I,I).GE.100.AND.NOPSQ(I.1).LT.150) NBOX-I 2980 THE FOLLOWING DO LOOP IDENTIFIES EACH OPERATION AND USES INFORMATION299O IN THE RATES MATRIX T0 INTERPOLATE ACTUAL PARAMETERS IN THE ZRT MATR3000 1 D0 20 J-1,NBOX 3010 II-O 3020 II-I|+1 3030 IF (NOPSQ(I,J).EQ.0) GO TO 20 3000 non on an 300 IF (NDPSQ(I.J).NE.NBOP(II).AND.II.LT.18) GO TO 1 IF (NOPSQ(I,J).EQ.NBOP(II)) GO TO 2 HRITE (10.100) NOPSQ(I,J) 3050 3060 3070 100 FORMAT (/.5X,'OPERATION NUMBER ',I6,' HAS NOT BEEN DEFINED INITIAL308O +LY IN SUBROUTINE FORHRV'./.5X.'MAKE THE CORRECTION') STOP 2 K-(II-I)*6 ' YDMLOH-RATES(K+1.1) YDMHGH-RATES(K+6.I) X1NCR-(YDMHGH-YDMLOH)/5. IF (YDM.LE.YDMLOH) GO TO 3 IF (YDM.GE.YDMHGH) GO TO A DIFF-(YDM-YDMLOH)/XINCR IDIFF-IFIX(DIFF) KI-I+IDIFF FH-DIFF-FLOAT(IDIFF) FL-I.-FH GO TO 5 3 FL-I. FH-O. KI-I GO TO 5 0 FL-O. FH-I. KI-5 5 KL-K+KI ZRT(J.1)-RATES(KL.2)*FL+RATES(KL+1.2)*FH ZRT(J,2)-RATES(KL.5)*FL+RATES(KL+1,5)*FH ZRT(J.3)-RATES(KL,6)*FL+RATES(KL+1,6)*FH 2RT(J.A)-RATES(KL.7) ZRT(J,5)-RATES(KL,8)*FL+RATES(KL+I,8)*FH IN THE CASE OF A YIELD ABOVE THE HAXIMUM USED IN FORHRV. HE SHOULD ASSUME A CONSTANT THROUGHPUT INSTEAD OF A CONSTANT FIELD CAPACITY ALSO REDUCE FIELD OPERATING SPEED PROPORTIONATELY IF (YDM.GE.YDMHGH) ZRT(J,1)-RATES(KL+1.3)/YDM IF(YDM.GE.YOMHGH) 2RT(J.5)-2RT(J.5)*YDMHGH/YDN 20 CONTINUE 3090 3100 3110 3120 3130 3100 3150 3160 3170 3180 3190 3200 3210 3220 3230 3200 3250 3260 3270 3280 3290 3300 3310 3320 3330 3300 3350 3360 3370 3330 3390 3000 3010 NMO.NHRV AND NSTO ARE THE TOTAL NUMBER OF PLOTS MOWED, HARVESTED AND3020 STORED DURING THE CURRENT HARVEST SEASON NPDCA IS THE NUMBER OF PLOTS THAT WILL BE HARVESTED AS DIRECT CUT ALFALFA DURING THE PRESENT HARVEST NMOW-O NHRv-O NSTO-0 NPDCA-O NDCTD IS THE NUMBER OF PLOTS THAT ARE HARVESTED AS DIRECT CUT ALFALFA TODAY. NDCTD=0 3030 3000 3050 3060 3070 3080 shao 3500 3510 3520 IOAH IS THE IDENTIFICATION NUMBER FOR ALFALFA HARVEST. ITS VALUE IS 3530 1 FOR DIRECT CUT ALFALFA. ANY OTHER VALUE MEANS THE ALFALFA WILL BE 3500 nnnnnnn C C ******************************************************************** C ******************************************************************** 3OI FIELD CURING. IN THIS CASE, IDAH IS USUALLY 9. IDAH-9 IF (NOPSQ(I,5).GE.160.AND.NOPSQ(I.5).LE.169) IDAH-1 IF (HARDEX.EQ.3.0.AND.NOPSQ(I,1).NE.O) IDAH-9 WE MUST CALCULATE HOW MANY PLOTS WILL BE HARVESTED THE BASIC ASSUMPTION FOR ALFALFA HARVEST IS THAT ONE PLOT IS EQUIVALENT TO 5 HOURS OF FIRST PRIORITY HARVEST TIME. AS CAN BE SEEN LATER IN SUBROUTINES HRVQ (FOR FIELD CURED ALFALFA) AND DCALF (FOR DIRECT CUT ALFALFA). A MAXIMUM OF 2 PLOTS MAY BE HARVESTED THE SAME DAY. ARE NOT NECESSARY. IF (NOPSQ(NTHCUT.I).GE.IAO.AND.NOPSQ(NTHCUT.1).LE.IA9) RETURN HRR-ZRT(5.I) IF (HARDEX.EQ.3.O.ANO.NOPSQ(NTHCUT.1).NE.O) HRR-ZRT(7.1) XAREA-HRR*5. NPLOTS-IFIX(AREA(1)/XAREA)+1 AREAPL-AREA(I)/FLOAT(NPLOTS) IF (NPLOTS.LE.A0) GO TO 8 HRITE (10.110) NPLOTS.AREA(I).HRR.YDM 110 FORMAT (/.5X.'THE NUMBER OF PLOTS TO BE HARVESTED IS GREATER THAN 3700 +00, THE MAXIMUM ALLOWED',/.5X.'IT IS ACTUALLY '.I6./.5X,'EITHER TH3750 +E AREA TO BE HARVESTED IS EXCESSIVE OR THE HARVEST RATE IS UNREALI376O +STICALLY LOW',/.5X,'AREA IS '.F12.2.' HA AND HARVEST RATE IS '.F123770 +.2.' HA/H FOR A DRY MATTER YIELD OF ',F12.2,' KG/HA',/.5X,'A CHANG3780 +E MUST STOP BE MADE') 8 DO 30 I-I.NPLOTS DO 30 J-I.29 30 HARMAT(I.J)-O. CALCULATE THE TIME TO DO EACH OPERATION OVER ONE PLOT DO 00 J-I,9 TPL(J)-AREAPL/ZRT(J.1) IF (ZRT(J,1).LE.O.) TPL(J)-0. 0O CONTINUE RETURN END SUBROUTINE HRVQ(NHTDAY) COMMON COMMON COMMON COMMON COMMON COMMON /HI/ NPLOTS.NMOH.NHRV.NSTO.AREAPL.HARMAT(AO.29).ZRT(9,5) /H2/ TPL(9),RAIN,JJDAY,NDAYHR /H3/ HFEED(A.16O.5) /HA/ NPDCA.NDCTD.IDAH /z1/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) /CTRL20/ BGNCUT(S).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 3550 3560 3570 3530 3590 3600 3610 3620 3630 FOR CORN SILAGE HARVEST. THESE CALCULATIONS360O 3650 3660 3670 3680 3690 3700 3710 3720 3730 3790 3800 3810 3820 3830 3800 3850 3860 3870 3880 3890 3900 3910 3920 3930 3900 3950 3960 3970 3930 3990 0000 +QUAL(3,0).GDDCUM,METRIC,JYEARF,JYEARL,IPRTI,IPRT2,JDAYF,JDAYL.JPRT0010 +.NYRS.IPRTO.NCUTS.JYEAR.JLALHR.CPLANT COMMON /ALFARG/ GDDBS,AVTA,DAYLIN,DAYLEN,YDAYL,DECR,XLAI,AW, 0020 0030 +SUMSI,SUMSZ,T.WSF,SRADF,DWS,PPT,ESO.ESR.XLEAF,BUDS.STEM,TOPS,TNC, 0000 nnnnnnnnnnnnnnnnnnnnnnnnnnnnn 302 +XMATS,TNCS.TMAXC.TMINC A050 COMMON /z3/ HARDEX.TMSTO(A).NPST(5.5).NCUM(5).OPUSE(5.9) A060 COMMON /Y3/ NMDATA.NOPER,IN.IO AO7O SUBROUTINE HRVQ DETERMINES IF ANY FIELD-CURING (ALREADY MOHED) PLOT A080 MAY BE 0090 HARVESTED TODAY. PLOTS ARE CONSIDERED IN REVERSE CHRONOLOGICAL ORDEAIOO STARTING HITH THE LAST MOHED PLOT. A MAXIMUM OF THO PLOTS MAY BE AIIO HARVESTED THE SAME DAY. A120 (EACH REQUIRES 5 HOURS OF EFFECTIVE FIELD TIME) A130 FOR ONE PLOT TO BE HARVESTED. THE FOLLOHING CRITERIA MUST BE SATISFIAIAO 1. THE PLOT MUST NOT HAVE BEEN HARVESTED ALREADY A150 2. LESS THAN THO PLOTS MUST HAVE BEEN ALREADY HARVESTED ON THAT A160 DAY. A170 3. THE MOISTURE CONTENT OF ALFALFA IN THE PLOT MUST BE BELOH THE A180 CRITICAL MOISTURE CONTENT FOR HARVEST BY APM. A190 A. IN THE CASE OF A HARVEST INDEX OF A. , THE PLOT IS HARVESTED A200 TODAY HITHOUT REGARDS TO MOISTURE CONTENT A210 FOR A SECOND PLOT TO BE HARVESTED ON THE SAME DAY. HE NEED A220 5. ONE OF THE PLOTS READY FOR HARVEST BY IOAM A230 GENERALLY 2 PLOTS MAY BE HARVESTED ON THE SAME DAY IF THE FIVE 0200 CONDITIONS A250 ABOVE ARE SATISFIED. HOHEVER THERE ARE AT FOUR SPECIAL CASES HHERE A260 ONLY ONE PLOT MAY BE HARVESTED IN A GIVEN DAY A270 1. HHEN RAKING IS REQUIRED AND CANNOT BE SIMULTANEOUS HITH A280 HARVEST A290 2. HHEN INDEPENDENT TRANSPORT OF BALES IS REQUIRED. IS NOT A300 SIMULTANEOUS AND MUST BE DONE THE SAME DAY AS HARVEST A310 3. HHEN HE ARE DESTROYING PLOTS (HARVEST INDEX A). HIGHER A320 PRIORITY IS THUS GIVEN TO MOHING. 0330 A. HHEN HE HAVE A HARVEST INDEX OF 2. OR 3. AND THE RATES OF 0300 HARVEST FOR THESE TYPES ARE SLOHER THAN FOR HARVEST INDEX I A350 A360 I-NTHCUT A370 C TIMEFP IS A DUMMY VARIABLE HHOSE VALUE BECOMES 1. IF A PLOT IS A380 c FOR HARVEST BY 10AM. A390 TIMEFP-O. 0000 C NFIRST IS THE NUMBER OF THE FIRST ALFALFA PLOT IN A HARVEST SEASON 0010 C THAT IS LEFT CURING IN THE FIELD (EITHER FOR HAY OR HAYLAGE). 0020 C USUALLY NFIRST HILL BE 1 EXCEPT IN THE CASE OF A SHITCH FROM DIRECT 0030 C CUT ALFALFA HARVEST T0 DRY HAY HARVEST ON ACCOUNT OF FILLED SILOS. AAAO C NPDCA IS THE NUMBER OF PLOTS THAT HERE PREVIOUSLY HARVESTED AS AA50 C DIRECT CUT ALFALFA DURING THE PRESENT HARVEST. 0060 NFIRST-NPDCA+I 0070 J-NMOW+1 AA80 DD 10 II-NFIRST.NMOH 0090 J-J-I . ' A500 C HRITE(ID.18A) JJDAY,J,NHTDAY,HARMAT(J,12) , A510 C 180 FORMAT(5X,'JJDAY,J,NHTDAY,HARMAT(J,12) - ',318,F8.1) A520 IF (NHTOAY.GE.2) RETURN A530 IF (HARMAT(J,12).EQ.1.) GO TO 10 0500 303 NBHR-IFIX(HARMAT(J.2I))+A IF (NHTDAY.EQ.D) GO TO 2 C HERE CONSIDER THE SPECIAL CASES HHEN NHTOAY-I IF (NOPSQ(I.3).NE.O.AND.CRTR(I,I.3).NE.1.) RETURN 1F (NOPSQ(I,9).EQ.O.OR.CRTR(I,3.NBHR).EQ.O.) GO TO 1 C HERE HE CONSIDER INDEPENDENT TRANSPORT OF BALES IF (CRTR(I.1.9).EQ.O.O.AND.CRTR(1.2.9).EQ.O.) RETURN 1 IF (HARMAT(J,2I).EQ.A.) RETURN IF (HARMAT(J.21).EQ.1.) GO TO 2 RI-TPL(NBHR)/TPL(5) IF (RI.GT.I.) RETURN C HERE HE ARE ALLOHED TO CONSIDER HARVESTING A PLOT 2 IF (HARMAT(J.21).EQ.A.) GO TO 20 CRMc-CRTR(I.I.NBHR) CALL DRY (J.TIME.FMCAM.CRMC) HRITE(IO.IOI)JJDAY.J.TIME.CRMC IOI FORMAT(SX.'WITHIN HRVQ. JJDAY- J- TIME- CRMC-‘./, + 15x.216.2F8.2) IF (TIME.GT.8.) GO TO 10 IF (NHTOAY.LT.1) GO TO 3 IF (TIME.GT.2.O.AND.TIMEFP.EQ.O.) GO TO 10 3 IF (TIME.LE.2.O) TIMEFP-I. IF (NOPSQ(I.3).NE.O) CALL QUANTC(J,3) CALL QUANTC (J.NBHR) CALL PLOTCD (J.NS.NBHR) HARMAT(J.12)-1. THE FOLLOHING IS TO CHECK HHETHER SILOS ARE FULL OR NOT. HHEN THE FIRST SILO 15 FULL. ALL PLOTS HITH AN INDEX OF I. MUST BE CHANGED TO AN INDEX OF 2. (SECOND SILO). HHEN BOTH SILOS ARE FULL. HARVEST INDEX 15 SHIFTED TO 3. (FORCED HAY HARVEST) IF (NS.GT.2) GO TO 15 ‘ IF (HARDEX.EQ.3.) GO TO 15 IF (NS.EQ.2) GO TO 35 IF (TMSTO(1).LT.SILO(I)) GO TO 15 IF (SILO(2).EQ.O.) GO TO 35 DO 30 JJ-NFIRST.NMOH IF (HARMAT(JJ.I2).EQ.I.) GO TO 30 IF (HARMAT(JJ.22).EQ.1.) GO TO 30 IF (HARMAT(JJ.21).NE.I.) GO TO 30 HARMAT(JJ,21)-2. IF (NOPSQ(I.6).LT.150.OR.NOP50(I.6).GT.159) HARMAT(JJ,22)-I. C HRITE(ID.132) J C 132 FORMAT (5x.'SILO I IS FILLED. REASSIGNED PLOT J-'.I0) 30 CONTINUE HARDEx-z. 35 IF (TMSTO(2).LT.SILo(2)) GO TO 15 D0 A0 JJ-NFIRST.NMOW IF (HARMAT(JJ.12).EQ.I.) GO TO AO IF (HARMAT(JJ.22).EQ.1.) GO TO 00 IF (TMSTO(1).LT.SIL0(1)) THEN nnn nnnn 0550 0560 0570 0580 0590 0600 0610 0620 0630 0600 0650 0660 0670 0680 0690 0700 0710 0720 0730 0700 0750 0760 0770 0780 0790 0800 0810 0820 0830 0800 0850 0860 0870 0880 0890 0900 0910 0920 0930 0900 0950 0960 0970 0980 0990 5000 5010 5020 5030 5000 C nnn n O nnnnnnnnnn 300 HARMAT(JJ.2I)-I. 5050 ELSE 5060 HARMAT(JJ,21)-3. 507D HARMAT(JJ.22)-I. 5080 ENDIF 5090 HRITE (10,133) J 5100 133 FORMAT (5X,'SILO 2 IS FILLED. REASSIGNED PLOT J-',I0) 5110 A0 CONTINUE 5120 IF (TMSTO(1).GE.SIL0(1)) HARDEx-3. 5130 GO TO 15 51AO 20 NPST(I.5)-NPST(I.5)+I 5150 NCUM(5)-NCUM(5)+I 5160 15 OPUSE(I,3)-OPUSE(I,3)+TPL(3) 5170 OPUSE(I.NBHR)-OPUSE(I.NBHR)+TPL(NBHR) 5180 IF (CRTR(I.3.NBHR).EQ.I.) OPUSE(1.9)-OPUSE(I.9)+TPL(9) 519D NHTOAY-NHTOAY+1 5200 HARMAT(J,12)-1. 5210 HRITE (10.131) J,HARDEX,TMSTO(1),TMSTO(2) 5220 I31 FORMAT(5X,'HARVESTED PLOT J-‘.I0,' HARDEX-',F0.0,' THSTO(1)-'.523O +F8.I.' TMSTO(2)-'.F8.I) 5200 10 CONTINUE 5250 RETURN 526D END . 5270 *Atkin’dcttidcttti:***************M:thunkkid:kid:*******tttkin’cictttimicimAME 5280 SUBROUTINE PLOTCD (J.NS.N8HR) 5290 *ktttttttifldfltA*ttttttkttthtt************************************** 5300 COMMON /H1/ NPLOTS. NMOH. NHRV, NSTD.AREAPL. HARMAT(AO. 29). zRT(9. 5) 5310 COMMON /H2/ TPL(9). RAIN. JJDAY, NDAYHR 5320 COMMON /H3/ HFEED(A. I60. 5) 5330 COMMON /HA/ NPDCA.NDCTD.IDAH 53AO COMMON /21/ AREA(6).NBD(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) 5350 COMMON /CTRL20/ BGNCUT(S).NTHYR,NTHCUT,NDAYSC.NDAYSH,YLD(0), 5360 +QUAL(3.0).GDDCUM,METRIC.JYEARF.JYEARL.IPRTI.IPRT2,JDAYF,JDAYL,JPRT537O +,NYRS,IPRTO,NCUTS.JYEAR,JLALHR,CPLANT 5380 COMMON /ALFARG/ GDDBs.AVTA.DAYLIN.DAYLEN.YDAYL.DECR.XLAI.AH. 5390 +SUMSI.SUM52.T.HSF.SRADF.DHS.PPT.ESO.ESR.XLEAF.BUDS.STEM.TOPS.TNC. 5AOO +XMATS.TNCS.TMAXC.TMINC 5010 COMMON /z3/ HARDEX.TMSTO(A).NPST(5.5).NCUM(5).OPUSE(5.9) 5020 COMMON /zA/FDLABR.FDENER.HRLABR.HRFUEL.HRELEC 5A3O COMMON /27/ ALHRFD(26.15).AFEED(26.23) 5000 SUBROUTINE PLOTCD CONDENSES THE INFORMATION CONCERNING ONE PLOT AT 5A50 THE TIME OF HARVEST. IT SPECIFIES IN HHICH OF A STORAGE STRUCTURES 5A6O THE PLOT GOES. THE STORAGE STRUCTURES ARE 5A7O 1. NET STORAGE. HIGH QUALITY 5A80 2. NET STORAGE. LOH QUALITY 5A9O 3. DRY STORAGE. HIGH QUALITY 5500 A. DRY STORAGE. LOH QUALITY 5510 MATRIX HFEED(NS.NBPL.NCHAR) CONTAINS ALL THE FEED INFORMATION FOR 5520 EACH PLOT. 5530 NS 15 THE STORAGE STRUCTURE NUMBER (1 TO 0) 5500 305 NBPL IS THE PLOT NUMBER DURING A GIVEN YEAR THAT GOES INTO NS. A MAXIMUM OF 160 PLOTS IS ALLOHED PER STORAGE STRUCTURE. IN THE CASE OF SILOS (HET ALFALFA). A CHECK EXISTS IN SUBROUTINE HRVQ TO PREVENT THE SILO FROM OVERFLOHING. HAY STORAGE VOLUME OR CAPACITY IS ASSUMED UNCONSTRAINED NCHAR REPRESENTS ONE OF 5 CHARACTERISTICS OF FORAGES STORED 1. TOTAL DRY MATTER (METRIC TONS) 2. CRUDE PROTEIN (DECIMAL) 3. DIGESTIBILITY (DECIMAL) A. MOISTURE CONTENT (DECIMAL. DRY BASIS) 5. NUMBER OF DAYS OF EXPOSURE HHILE CURING. nnnnnnnnnnnn ~ DIMENSION XLBR(7).XENE(7) DATA XLBR /I..O.25.0.5.0.2O.O.AO.O.5.O.15/ DATA XENE /O..O.5.I.5.O.5.I.5.O.15.O.1/ I-NTHCUT RFRESP-HARMAT(J,26) RFRESP-AMAXI(0.85.RFRESP) IF (IDAH.NE.I) RFRESP-AMINI(O.97.RFRESP) TRL-1.5RFRESP DML-HARMAT(J,2)*HARMAT(J,20)*RFRESP OMS-HARMAT(J.3)*HARMAT(J,25)*RFRESP DM-DML+DMS PCL-DML/DM IF (PCL.LT.O.290) PCL-O.29O IF (PCL.GT.O.5OO) PCL-O.5OO Pcs-1.-PCL C LOSS OF CRUDE PROTEIN DUE TO EXPOSURE TIME ET-HARMAT(J,10)*20.+8. PLE-ET*0.001 CPL-HARMAT(J.0)*(I.-PLE) CPS-HARMAT(J.5)*(I.-PLE) CP-CPL*PCL+CPS*PCS IF (CP.LT.O.IO) CP-O.IO C LOSS OF DIGESTIBILITY DUE TO RESPIRATION AND RAINFALL TDNBL-HARMATIJ.6)*PCL+HARMAT(J,7)*PCS DLR-HARMAT(J,27)*0.002 TDN-((TDNBL-TRL)/(1.-TRL))*(1.-DLR) IF (TDN.LT.O.AO) TDN-0.00 C DECIDE IN HHICH STORAGE LOCATION THE PLOT HILL GO IF (HARMAT(J.22).EQ.I.) GO TO 10 Ns-I IF (HARMAT(J.21).EQ.2.) Ns-z GO TO 20 10 Ns-3 IF (HARMAT(J,21).GT.I.) GO TO 12 IF (CP.LT.CRTR(NTHCUT.2.5)) Ns-A GO TO 20 12 IF (HARMAT(J,21).GT.2.) GO TO 1A Ns-A A MA5550 5560 5570 5530 5590 5600 5610 5620 5630 5600 5650 5660 5670 5680 5690 5700 5710 5720 5730 5700 5750 5760 5770 5780 5790 5800 5810 5820 5830 58AO 5850 5860 5870 5880 5890 5900 5910 5920 5930 5900 5950 5960 5970 5980 5990 6000 6010 6020 6030 6000 (fir-ant“, I0 20 mnnnnmmnn nnnn on nnnnnnnnn 306 GO TO 20 10 IF (CP.LT.CRTR(NTHCUT.2.7)) NS'0 20 NPST(I,NS)-NPST(|,NS)+1 ' NCUMINS)'NCUM(NS)+1 NBPL-NCUM(NS) CALL STORE (J.NBHR,DMCH.CPCH,TDNCH,NFEED) HFEED(NS.NBPL.1)-DM*AREAPL*0.001*(I.-DMCH) HFEED(NS.NBPL,2)-CP*(1.-CPCH) HFEED(NS.NBPL,3)-TDN*(1.-TDNCH) IF (HARMAT(J,11).EQ.O.) HARMAT(J,11)-HARMAT(J,IO) HFEED(NS,NBPL.A)-HARMAT(J.II) HFEED(NS.NBPL.5)-HARMAT(J.1A) TMSTO(NS)-TMSTO(NS)+DM*AREAPL*0.001 CUMULATIVE LABOR AND ENERGY REQUIRED FOR FEEDING FDLABR, CUMULATIVE LABOR REQUIRED FOR FEEDING THE FORAGES (MAN.H) FOENER, CUMULATIVE ENERGY REQUIRED FOR FEEDING THE FORAGES (LITERS OF DIESEL FUEL). WM-HFEED(NS,NBPL,1)*(1.+HFEED(NS.NBPL,0)) FDLABR-FDLABR+XLBR(NFEED)*WM FDENER-FDENER+XENE(NFEED)*WM KK-(NTHCUT-1)*3+I ALHRFO(NTHYR,KK)-ALHRFD(NTHYR.KK)+HFEED(NS,NBPL,1) ALHRFD(NTHYR.KK+1)-ALHRFD(NTHYR.KK+I) + +HFEED(NS,NBPL.1)*HFEED(NS.NBPL,2) ALHRFD(NTHYR.KK+2)-ALHRFD(NTHYR,KK+2) + +HFEED(NS.NBPL.1)*HFEED(NS.NBPL,3) RETURN END *z’ct****************************************3!************************ SUBROUTINE STORE (J,NBHR,DMCH,CPCH,TDNCH,NFEED) ****t***************************ttttttkk**************************** COMMON /WI/ NPLOTS.NMOW,NHRV,NSTO.AREAPL,HARMAT(00.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN.JJDAY.NDAYHR COMMON /21/ AREA(6),NBO(6).NOPSQ(5.9).CRTR(5.0.9).SILO(2) COMMON /CTRL2A/ BGNCUT(5),NTHYR,NTHCUT.NDAYSC.NDAYSH,YLD(A). 6050 6060 6070 6080 6090 6100 6110 6120 6130 6100 6150 6160 6170 6180 6190 6200 6210 6220 6230 6200 6250 6260 6270 6280 6290 6300 6310 6320 6330 6300 6350 6360 6370 6380 6390 6000 +QUAL(3,0).GDDCUM.METRIC.JYEARF.JYEARL.IPRTI.IPRT2,JDAYF,JDAYL,JPRT6010 +,NYRS,IPRTO,NCUTS,JYEAR,JLALHR,CPLANT COMMON /ALFARG/ GDDBS,AVTA,DAYLIN,DAYLEN,YDAYL.DECR.XLAI,AW, 6020 6030 +SUMSI,SUMSZ,T,WSF,SRAOF,DWS.PPT,ESO,ESR,XLEAF,BUDS,STEM,TOPS,TNC, 6000 +XMATS,TNCS.TMAXC.TMINC THIS SUBROUTINE ESTIMATES QUALITY AND QUANTITY LOSSES IN STORAGE AND6060 FEEDING. THERE ARE 5 STORAGE METHODS 10 2. 3. “I 5. THERE 1. ANY DRY HAY STORED INSIDE (O.OA DM Loss) ROUND BALES STORED OUTSIDE (0.12 DM Loss) HAY STACKS STORED OUTSIDE (0.16 DM LOSS) ALFALFA IN A VERTICAL SILO (0.07 DM LOSS) ALFALFA IN A BUNK SILO (0.13 DM LOSS) ARE 7 FEEDING HETHODS RECTANGULAR BALES. HAND FED (0.05 DM LOSS) 6050 6070 6080 6090 6500 6510 6520 6530 6500 307 . ROUND BALES. SELF FED (0.10 DM LOSS) ROUND BALES. GROUND (0.05 DM LOSS) HAY STACKS. SELF FED (0.16 DM LOSS) . HAY STACKS. SHREDDED (0.05 DM LOSS) . VERTICAL SILO AND UNLDADER (0.11 DM LOSS. 0.10 DIGESTIBILITY . BUNK SILO AND SCOOP (0.11 DM LOSS. 0.15 DIGESTIBILITY LOSS) AT PRESENT. NO CHANGES IN CP OR TDN IS ASSUMED FOR ALL METHODS nnnnnnnn NO‘m-F'WN O. DIMENSION STOLS(5).FEEDLS(7) DIHENSIDN CPCHST(7).TDNCHS(7) DATA STOLS /O.OA.O.12.O.16.O.O7.O.13/ DATA FEEDLS /O.05.O.1A.O.05.O.I6.O.05.O.II.O.I1/ DATA CPCHST /O..O..O..O..O..O..O./ DATA TDNCHS /O..O..O..O..O..O..O./ I-NTHCUT NFEED-IFIX(CRTR(I,A.NBHR)) IF (NFEED.LT.1.OR.NFEED.GT.7) NFEED-I C FIND NST. THE STORAGE METHOD. FROM PREVIOUS INFORMATION IF (HARMAT(J,22).EQ.2.) GO TO 10 C CONSIDER DRY HAY NST-I IF (CRTR(I.I.9).EQ.O.) GO TO 20 NST-2 IF (NOPSQ(I.NBHR).GE.0090.AND.NOPSQ(I.NBHR).LE.0099) NST-3 GO TO 20 10 NST-A IF (CRTR(I.A.NBHR).EQ.7.) NST-5 . 20 RFDM-1.*(1.-STOLS(NST))*(1.-FEEDLS(NFEED)) DMCH-1.-RFDM CPCH-o. TDNCH-O. CPCH-CPCHST(NFEED) TDNCH-TDNCHS(NFEED) RETURN END C C timid“!*********************************t**************************** SUBROUTINE UPDATE C *tttttth’cttttk*ttttttttimid:*tfitthAtttttkttttkttttfi****************** COMMON /H1/ NPLOTS.NMOH.NHRV.NSTO.AREAPL.HARMAT(AO.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN.JJDAY.NDAYHR COMMON /HA/ NPDCA.NDCTD.IDAH COMMON /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A,9).SILO(2) COMMON /CTRL2A/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 6550 6560 6570 6580 6590 6600 6610 6620 6630 6600 6650 6660 6670 6680 6690 6700 6710 6720 6730 6700 6750 6760 6770 6780 6790 6800 6810 6820 6830 6800 6850 6860 6870 6880 6890 6900 6910 6920 6930 6900 6950 6960 6970 6980 +QUAL(3,0),GDDCUM,METRIC,JYEARF.JYEARL,IPRTI,IPRT2,JDAYF,JDAYL.JPRT699O +,NYRS,IPRTA.NCUTS.JYEAR.JLALHR.CPLANT COMMON /ALFARG/ GDDBs.AVTA.DAYLIN.DAYLEN.YDAYL.DECR.XLAI,AH. +SUMSI.SUM52.T.HSF.SRADF.DHS.PPT.ESO.ESR.XLEAF.BUDS.STEM.TDPS.TNC. +XMATS.TNCS.TMAXC.TMINC COMMON /23/ HARDEX.TMSTO(A).NPST(5.5).NCUM(5).OPUSE(5.9) 7000 7010 7020 7030 7000 nnnn no nnnn 308 COMMON /Y3/ NMDATA,NOPER.|N,IO 7050 THIS SUBROUTINE PROVIDES A DAILY UPDATE OF ALL INFORMATION IN HARMAT7060 FOR EXPOSURE LOSSES AND FOR CHANGES IN THE MOISTURE CONTENT. UPDATES ARE MADE ONCE PER DAY. ONLY FOR PLOTS THAT ARE CURING IN THE FIELD AND ARE NOT YET HARVESTED AND STORED IF (NMOH.LT.1) RETURN IF (NHRV.EQ.NMOH) RETURN NFIRST-NPDCA+I DO 20 J-NFIRST.NMOH IF (HARHAT(J.12).EQ.1.) GO TO 20 CRMc-I. IF (RAIN.LT.2.) GO TO 5 HERE HE CHECK IF THERE IS TEDDING OR RAKING AFTER RAIN IF (NOPSQ(NTHCUT.A).EQ.O) GO TO 5 HARMAT(J,I7)-CRTR(NTHCUT,2.0) HARMAT(J,18)-I. HARMAT(J.2O)-CRTR(NTHCUT.3.A) OPUSE(NTHCUT.A)-OPUSE(NTHCUT,A)+TPL(A) 5 CALL DRY (J,TIHE.FMCAM.CRMC) HRITE(ID.102)J.FMCAM 102 FORMAT (5X,'WITHIN UPDATE. J- '.I0,' FMCAM- ',F10.0) IF (NOPSQ(NTHCUT.A).NE.O.AND.RAIN.GE.2.) CALL QUANTC(J,0) HARMAT(J.18)-O. HARMAT(J.28)-O. HARMAT(J,27)-HARMAT(J.27)+RAIN AMC IS THE AVERAGE MOISTURE CONTENT TO ESTIMATE RESPIRATION LOSSES AMc-HARMAT(J.IO) CALL RESP (AMC.RF) HARMAT(J.26)-HARMAT(J.26)*RF HARMAT(J,IO)-FMCAM HARMAT(J,10)-HARMAT(J.10)+1. IF (HARMAT(J.21).GT.1.) GO TO 1A 7070 7080 7090 7100 7110 7120 7130 7100 7150 7160 7170 7180 7190 7200 7210 7220 7230 7200 7250 7260 7270 7280 7290 7300 7310 7320 7330 7300 7350 7350 MAKE A PROJECTION OF CRUDE PROTEIN CONCENTRATION OF EACH FIELD CURIN737O CURING ALFALFA PLOT. IF CRUDE PROTEIN GOES BELOH A CRITICAL LEVEL. SHIFT THE PLOT TO LDHER PRIORITY HARVEST. XL-HARMAT(J.2)*HARMAT(J,20)*HARMAT(J,26) XS-HARMAT(J.3)*HARMAT(J,25)*HARMAT(J,26) ACCOUNT FOR FUTURE RAKING AND HARVESTING LOSSES XL-XL*0.95 IF (NOPSQ(NTHCUT.3).NE.O) XL-XL*O.95 XCP-(XL*HARMAT(J.0)+XS*HARMAT(J.5))/(XS+XL) XCP-XCP*(1.-0.00I*(8.+HARMAT(J,10)*20.)) IF (XCP.GT.CRTR(NTHCUT.2.5)) GO TO 20 IF (NOPSQ(NTHCUT.6).EQ.O) GO TO 12 IF (NOPSQ(NTHCUT.6).LT.150.OR.NOPSQ(NTHCUT.6).GT.159) GO TO 10 IF (SIL0(2).EQ.O.O.OR.TMSTo(2).GE.SILD(2)) GO TO 12 HARMAT(J,21)-2. HARMAT(J,22)-2. GO TO IA 7330 7390 7000 7010 7020 7030 7000 7050 7060 7070 7080 7090 7500 7510 7520 7530 7500 12 C 309 10 HARMAT(J.21)-2. HARMAT(J.22)-I. GO TO 1A IF (TMST0(1).LT.SIL0(1)) GO TO 1A HARMAT(J.21)-3. HARMAT(J.22)-1. GO TO 16 IA IF (HARMAT(J.21).GT.2.) GO TO 16 IF (CRTR(NTHCUT.2.6).LE.O.) GO TO 20 IF (HARMAT(J,10).GT.CRTR(NTHCUT.2.6)) HARMAT(J.21)-0. GO TO 20 16 IF (HARMAT(J.21).GT.3.) GO TO 20 IF (CRTR(NTHCUT.2.8).LE.O.) GO TO 20 IF (HARMAT(J,10).GT.CRTR(NTHCUT.2.8)) HARMAT(J.21)-A. 20 CONTINUE RETURN END C *****************************************t************************** SUBROUTINE DRY (J.TIME,FMCAM.CRMC) C ******************************************************************** nnnnnnnnnnn COMMON /HI/ NPLOTS.NMOH.NHRV.NSTO.AREAPL.HARMAT(AO.29).ZRT(9.5) COMMON /H2/ TPL(9).RAIN,JJDAY,NDAYHR COMMON /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) COMMON /CTRL2A/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 7550 7560 7570 7530 7590 7600 7610 7620 7630 7600 7650 7660 7670 7680 7690 7700 7710 7720 7730 7700 7750 7760 7770 7730 7790 +QUAL(3,0),GDDCUM,METRIC.JYEARF,JYEARL,IPRTI.IPRT2.JDAYF,JDAYL,JPRT7800 +,NYRS,IPRTO.NCUTS.JYEAR,JLALHR,CPLANT COMMON /ALFARG/ GDD85.AVTA, DAYLIN. DAYLEN, YDAYL, DECR. XLAI, AW, +SUMSI, SUMSZ. T, WSF, SRADF, DWS, PPT, ESO, ESR, XLEAF, BUDS, STEM, TOPS ,TNC, +XMATS, TNCS, TMAXC, TMINC COMMON /Y3/ NMDATA. NOPER.IN,IO THE SUBROUTINE DRY HAS TWO MAIN PURPOSES 1. IT ESTIMATES THE TIME AT WHICH A PLOT WILL REACH CRITICAL MOISTURE CONTENT (CRMC) FOR HARVEST UNDER TODAY"S DRYING CONDITIONS. TIME IS ESTIMATED IN HOURS AFTER 8AM. 7810 7820 7830 7800 7850 7860 7370 7880 7890 2. IT ALSO ESTIMATES MOISTURE CONTENT OF THE PLOT ON THE NEXT DAY79OO THIS ESTIMATE INCLUDES DESORPTION FROM 8AM TO 8PM ON A NORMAL 7910 DAY AND ADSORPTION THROUGH THE NIGHT FROM DEW. REWETTING IS A7920 ALSO CONSIDERED ON A RAINY DAY (ON SUCH A DAY. DRYING TIME IS 7930 REDUCED FROM 12 TO 6 HOURS). SOLAR RADIATION IS CONVERTED FROM A DAILY ACCUMULATION TO A RADIATION INTENSITY AVERAGED OVER 12 HOURS (CAL/MIN.CM2) SR-SRADF/720. TDB-(TMINC+2.*TMAXC)/3. 1F (HARMAT(J.17).LE.O.) HARMAT(J.I7)-O.75 DENs-(HARMAT(J.2)+HARMAT(J.3))/HARMAT(J.17) RK-HARMAT(J,I8) CD-HARMAT(J.I9) XTR-HARHAT(J.201 RAIN-PPT 7900 7950 7950 7970 7930 7990 8000 8010 8020 8030 8000 310 XKK-(-0.016009)+(.073060*SR)+0.0055086*TDB+(-0.00000730*DENS) + +0.019722*RK+0.029609*CO+XTR IF (XKK.LT.0.0I) XKK'0.0I XMO-HARMAT(J,10) TORY-12. DTRAIN-O. IF (RAIN.LE.0.) GO TO 10 8050 8060 8070 8080 8090 8100 8110 8120 8130 8100 8150 8160 8170 8180 8190 8200 8210 8220 8230 8200 8250 8260 8270 8280 8290 8300 8310 8320 8330 8300 8350 8360 8370 8380 8390 8000 8010 8020 8030 8000 8050 8060 8070 8080 8090 8500 8510 8520 8530 C IF THERE IS RAIN. THE MOISTURE CONTENT IS INCREASED C RAIN IS ASSUMED TO OCCUR IN THE MORNING. DRYING RESUMES IN THE C AFTERNOON. THE DAILY DRYING PERIOD IS REDUCED BY 6 HOURS. DTRAIN-6. FCR-I. IF (HARMAT(J,19).NE.O.) FCR-1.A IF (RAIN.LE.5.) DMR-0.25*RAIN*FCR IF (RAIN.GT.5.) DMR-(I.25+0.03*(RAIN-5.))*FCR IF (DMR.GT.3.) DMR-3. , IF (HARMAT(J.I0).GT.O.) DMR-DMR*(2./3.) XMo-XMO+DMR IF (XMO.GT.5.5) XMo-5.5 C CALCULATE TIME AT HHICH CRMC HILL BE REACHED 10 EMc-O.15 _ IF (NTHCUT.EQ.2.OR.NTHCUT.EQ.3) EMc-O.IO XMR-(CRMC-EMC)/(XMO-EMC) IF (XMR.LT.O.OI) XMR-O.OI TIME-(-ALOG(XMR))/XKK TIME-TIME+HARMAT(J.28)+DTRAIN C CALCULATE FINAL MOISTURE AT THE END OF THE DAY ADT-TDRY-(DTRAIN+HARMAT(J,28)) IF(ADT.LT.O.)ADT-O. XMR-EXP(-XKK*ADT) XMc-XMR*(XMO-EMC)+EMC c CALCULATE DEH PICKUP THROUGH THE NIGHT DMPV-HARMAT(J,IO)-XMC IF (DMPv.LT.O.) DMPv-O. FCD-1. ,. IF (HARMAT(J.19).NE.O.) FCD-1.2 RH-RANDRH(JJDAY) RH-O.5 DMDEW-DMPV*HARHAT(J,17)*(RH-O.5)*FCD IF (RH.LT.O.5) DMDEH-O. FMCAM-XMC+DMDEH C MOISTURE CONTENT AFTER RAINFALL IS NEXT RECORDED HARMAT(J.29)-XMO C HE NEED MOISTURE CONTENT DURING HARVEST IN CASE THE PLOT IS C HARVESTED TODAY. TIMEHR-TIME+2. XMR-EXPI-XKK*TIMEHR) HARMAT(J,11)-XMR*(XMO-EMC)+EMC C HRITE(IO.102)J.XKK.XMO.XMC.FMCAM C 102 FORMAT(5X.'HITHIN DRY. J- '.I0.' XKK. XMO. XMC. FMCAM - ',0F10.8500 n no C C C C C 311 +3) 8550 RETURN 8560 END 857D 858D ktat**********Attktt************************************************ 8590 FUNCTION RANDRH (JDAY) 8600 At****************************************************************** 8610 THIS FUNCTION GENERATES PSEUDO RANDOM VALUES OF RELATIVE HUMIDITY 8620 FOR ESTIMATING DEH ADSORPTION. A TRIANGULAR DISTRIBUTION IS ASSUMED863O FOR RELATIVE HUMIDITY, HITH RH-o.5 THE MOST LIKELY OCCURRENCE 86AO THIS FUNCTION IS CALLED FROM SUBROUTINE DRY (ABOUT LINE 68) 8650 BUT Is NOT PRESENTLY USED. 8660 IT SHOULD BE DISCARDED IT HISTORICAL HEATHER DATA INCLUDE 8670 RELATIVE HUMIDITY OR HET BULB TEMPERATUR OR DEH POINT. 8680 X1-FLOAT(JDAY)/I.387 '8690 II-IFIX(x1) 8700 x2-(1.+X1-FLOAT(I1))**2.02 8710 Iz-IFIx(x2) 8720 RN-xz-FLOAT(12) 8730 RANDRH-SQRT(RN/2.) 87AO IF (RN.GT.O.5) RANDRH-I.-SQRT((1.-RN)/2.) 8750 RETURN 8760 END 8770 8780 At****************************************************************** 8790 SUBROUTINE RESP (AMC.RF) — 8800 ******************************************t************************* 8810 COMMON /H1/ NPLDTS.NMOH.NHRV.NSTO.AREAPL.HARMAT(AO.29).ZRT(9.5) 8820 COMMON /H2/ TPL(9).RAIN,JJDAY.NDAYHR 883D COMMON /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) 8800 COMMON /CTRL2A/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC,NDAYSH.YLD(A), 8850 +QUAL(3.0).GDDCUM,METRIC.JYEARF.JYEARL,IPRTI,lPRT2.JDAYF,JDAYL,JPRT886O +,NYRS,IPRTO,NCUTS,JYEAR,JLALHR,CPLANT 8870 COMMON /ALFARG/ GDDB5.AVTA.0AYLIN.DAYLEN.YDAYL.DECR.XLAI.AH. 8880 +SUMSI.SUMsz.T.HSF.SRADF.DHS,PPT.ESO.ESR.XLEAF.BUDS.STEM.TOPS.TNC. 8890 +XMATS.TNCS.TMAXC.TMINC 8900 SUBROUTINE RESP CALCULATES THE REMAINING FRACTION (RF) OF DRY MATTER89IO LEFT AFTER 2A HOURS OF RESPIRATION 8920 K1-O.15 ' 8930 K2-O.0291 8900 TIME-2A. 8950 ATc-(TMINC+TMAXC)/2. 896D TF-(ATC/30.)*(ATC/30.) 8970 IF (TF.GT.I) TF-I. 8980 TRL-TF*(AMC/0.)*K1*(1.-EXP(-K2*TIME)) 8990 IF (TRL.LT.O.) TRL-0. . 9000 RF-1.-TRL ‘ 9010 RETURN 9020 END 9030 C **********************************************k********************* 9000 C kittid:**************************kid:*a'dtkittab’nb’n'ddc********i¢************ nnnnn nnnnnnn on 312 SUBROUTINE MOWQ (NHTOAY.NMTDAY) COMMON /HI/ NPLOTS.NMOH.NHRV,NSTO.AREAPL.HARMAT(AO.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN.JJDAY.NDAYHR COMMON /zI/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SIL0(2) COMMON /CTRL20/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 9050 9060 9070 9080 9090 9100 +QUAL(3,0),GDDCUM.METRIC,JYEARF.JYEARL.IPRTI,IPRT2,JDAYF,JDAYL.JPRT9110 +,NYRS,IPRTO,NCUTS,JYEAR.JLALHR.CPLANT COMMON /ALFARG/ GDDBS.AVTA,DAYLIN,DAYLEN,YDAYL,DECR,XLAI,AW, 9120 9130 +SUMSI,SUMSZ,T.WSF,SRADF,DWS,PPT,ESO,ESR.XLEAF.BUDS.STEM,TOPS.TNC, 9100 +XMATS,TNCS,TMAXC.TMINC COMMON /23/ HAROEX,TMSTO(0),NPST(5,5),NCUM(5).OPUSE(5.9) COMMON /Y3/ NMDATA.NOPER,IN,IO 9150 9160 9170 PLOTS ARE MOWED IN A GROUP SUCH THAT MOWING MAY BE CONTIMUOUS FOR 5 9180 OR 10 HOURS. A HALF DAY OR A FULL DAY. THE NUMBER OF PLOTS MOWED IN A FULL DAY 9190 9200 IS MAXMOW(2) AND IN HALF A DAY 15 MAXMOW(1). THE NUMBER OF PLOTS 159210 AN INTEGER IN BOTH CASES AND IS AT LEAST EQUAL TO ONE. DIMENSION MAXMOH(2) NMIO-IFIX(IO./TPL(1)) NM5-IFIX(5./TPL(I)) MAXMOH(2)-MAXO(I.NMIO) MAXMOH(I)-MAXO(I,NM5) THE MAMIMUM NUMBER OF PLOTS THAT MAY BE MOWED IN A DAY IS REDUCED IF MAXMOW VALUES PRESENTLY ESTIMATED PRODUCE TOO MANY CURING PLOTS. CRTR(NTHCUT.0.2) IS USED TO SPECIFY THE MAXIMUM NUMBER OF DAYS MOWING CAN PROCEED AHEAD OF HARVESTING. A MINIMUM OF 2 DAYS OR 0 PLOTS AHEAD IS ALWAYS ALLOWED. CURING-FLOAT(NMOH-(NHRV+NHTDAY)) ALLWD-2.*CRTR(NTHCUT.0.2) ALLHD-AMAX1(ALLHD.A.) DO 5 Iv-1.2 TOT-CURING+FLOAT(MAXMOH(IV)) IF (TOT.GT.ALLHD) THEN IMAx-IFIx(ALLHD-CURING) MAXMOH(IV)-MAXO(O.IMAX) ENDIF CONTINUE I-NTHCUT NO PLOTS ARE MOHED TODAY IF 1. THERE IS MORE THAN 2 MM OF RAIN 2. MORE THAN 1/2 THE TOTAL AREA IS FIELD CURING 3. THO PLOTS ARE BEING HARVESTED AND MOHING CANNOT BE SIMULTANEOUS HITH HARVEST IF (RAIN.GT.2.) RETURN IF (NHTDAY.GE.2.AND.CRTR(I.1.1).NE.I.) RETURN NBMH IS THE RELATIVE MOHING TIME IN A DAY (0 IS NO TIME. 1 IS A HALF-DAY. 2 IS A FULL DAY) 9220 9230 9200 9250 9260 9270 9280 9290 9300 9310 9320 9330 9300 9350 9360 9370 9380 9390 9000 9010 9020 9030 9AAO 9050 9060 9070 9080 9090 9500 9510 9520 9530 9500 313 C HOH MUCH MOHING MAY BE DONE TODAY IS DETERMINED AS FOLLOHS 9550 C NORMALLY IF 2 PLOTS ARE HARVESTED TODAY, No MOHING IS DONE 9560 C IF I PLOT IS HARVESTED TODAY. HALF A DAY IS SPENT MOHING 9570 C IF 0 PLOT IS HARVESTED TODAY. ALL DAY IS SPENT MOHING 9580 C THE FOLLOHING EXCEPTIONS ARE CONSIDERED 9590 C 1. IF MOHING MAY BE SIMULTANEOUS HITH HARVEST. THEN MOHING MAY 9600 C DE CARRIED OUT ALL DAY 9610 C 2. IF TEDDING IS REQUIRED AND CANNOT BE SIMULTANEOUS HITH MOHING9620 C THE MOHING PERIOD IS REDUCED BY HALF A DAY 9630 C 3. IF RAKING IS REQUIRED AND CANNOT BE SIMULTANEOUS HITH HARVEST96AO C THE MOHING PERIOD IS REDUCED BY HALF A DAY 9650 C A. IF CRTR(I.A,I) SPECIFIES THAT THE MAXIMUM PERIOD IS HALF A 9660 C DAY, THEN ANY TIME A FULL MOHING DAY IS SPECIFIED IT MUST BE 9670 C REDUCED. 9680 NBMH-O 9690 NRK-O 9700 IF (NHTDAY.EQ.O) NBMH-2 9710 IF (NHTDAY.EQ.1) NBMH-1 9720 IF (CRTR(I.I.I).EQ.1.) NBMH-2 9730 IF (NOPSQ(I,2).EQ.O) GO TO 10 9700 IF (CRTR(I.I.Z).EQ.O.) NBMH-NBMH-I 9750 10 IF (NOPSQ(I.3).EQ.O) GO TO 20 9760 IF (CRTR(I.I.3).EQ.I.) GO TO 20 9770 IF (NHTDAY.NE.O) NRK-I 9780 20 NBMH-NBMH-NRK 9790 IF (NBMH.LE.O) RETURN 9800 IF (NBMH.EQ.2.AND.CRTR(I.A.I).EQ.I.) NBMH-I 981D NMTDAY-MAXMOH(NBMH) 9820 C HRITE(IO.IOI)NMTDAY 9830 C 101 FORMAT(5X.'WITHIN MOHQ. NMTDAY- ',I0) 9800 C INITIALIZE EACH NEH MOHED PLOT 9850 IA-NMOH+1 9860 IB-NMOH+NMTDAY 9870 TIMEMW-TPL(1)*O.5 9880 IF (IB.LE.NPLOTS) GO TO 25 9890 IB-NPLOTS 9900 NMTDAY-NPLOTS-NMOH 9910 25 DO 30 J-IA.IB 9920 HARMAT(J,I)-1. 993D HARMAT(J.2)-XLEAF*10. 9900 HARMAT(J.3)-STEM*10. 9950 HARMAT(J.A)-QUAL(1.2) 996D HARMAT(J.5)-QUAL(2.2) 9970 HARMAT(J,6)-QUAL(1,3) 9980 HARMAT(J.7)'QUAL(2.3) 9990 HARMAT(J.8)-QUAL(1.A) 10000 HARMAT(J.9)-QUAL(2.A) IOOIO HARMAT(J.IO)-XINMC(NDAYHR.T1MEMH.NTHCUT) 10020 HARMAT(J.IA)-1. 10030 HARMAT(J.I7)-CRTR(NTHCUT.2.1) IOOAO an n on nnnnnnnnnn 31A HARMAT(J.I9)-CRTR(NTHCUT,3,1) HARMAT(J,20)-CRTR(NTHCUT.3.2) HARMAT(J,21)-I. HARMAT(J.22)-1. IF (NOPSQ(I.5).LT.1AO.OR.NOPSQ(I.5).GT.169) GO TO 35 HERE HE HAVE HAYLAGE OR DIRECT CUT AS FIRST PRIORITY HARVEST HARMAT(J.2I)-HARDEX IF HARDEX IS 3.. HE HAVE THE FORCED HAY HARVEST OPTION SINCE SILOS ARE FULL. IF (HARDEX.GE.3.) GO TO 35 HARMAT(J,22)-2. 35 HARMAT(J.20)-1. HARMAT(J,25)-I. HARMAT(J.26)-I. HARMAT(J.28)-TIMEMW HARMAT(J,29)-HARMAT(J,10) TIMEMH-TIMEMH+TPL(I) CHECK IF THE CRUDE PROTEIN CRITERION IS SATISFIED AT MOHING TIME IF (HARMAT(J,21).GT.1.) GO TO 3A IF (QUAL(3.2).LT.CRTR(NTHCUT.2.5).AND.TMSTO(2).LT.SILO(2)) THEN HARMAT(J.21)-2. ENDIF 3A CONTINUE CALL QUANTC (J.I) OPUSE(NTHCUT.I)-OPUSE(NTHCUT.I)+TPL(I) IF (NOPSQ(NTHCUT.2).EQ.O) GO TO 30 NOH HE CONSIDER AN EXTRA TREATMENT (TEDDING) HARMAT(J,17)-CRTR(NTHCUT,2,2) HARMAT(J.18)-I. CALL QUANTC(J.2) OPUSE(NTHCUT.2)-OPUSE(NTHCUT.2)+TPL(2) 3O CONTINUE RETURN END AtA*AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA*AAAR*AAAAAAAAAAAAAAAAAAAAAAAAAAAA FUNCTION XINMC (NDAYHR.TIMEMW.NTHCUT) ******************************************************************** THIS IS A SIMPLIFIED APPROXIMATION OF INITIAL MOISTURE CONTENT OF ALFALFA. THE MAXIMUM MOISTURE CONTENT IS 0.5 ON THE FIRST DAY OF HARVEST OF FIRST AND FOURTH CUTS AT 8AM. IT IS 0.0 FOR THE SECOND AND THIRD CUTS. THE MOISTURE DECREASES BY 0.05 PER HOUR FOR MOWING OCCURRING AFTER 8AM ON A GIVEN DAY. IT IS FURTHER DECREASED BY 0.05 PER DAY FOR EACH CALENDAR DAY AFTER THE BEGINNING OF HARVEST. XINMC-0.5 , IF (NTHCUT.EQ.2.0R.NTHCUT.EQ.3) XINMC-0.0 IF (TIMEMH.GT.10.) TIMEMH-IO. A...— _. ‘ .A—M-W“ 10050 10060 10070 10080 10090 10100 10110 10120 10130 10100 10150 10160 10170 10180 10190 10200 10210 10220 10230 10200 10250 10260 10270 10280 10290 10300 10310 10320 10330 10300 10350 10360 10370 10380 10390 10000 10010 10020 10030 10000 10050 10060 10070 10080 10090 10500 10510 10520 10530 10500 «no-o C 315 XINMC-XINMC-0.05*TIMEMW XINMC-XINMC-0.05*FLOAT(NDAYHR) RETURN END C ******************************************************************** SUBROUTINE QUANTC(J,N) C *********************************t********************************** nnnnnnnnnnnnnnnnn COMMON /H1/ NPLOTS.NMOH.NHRV.NSTO.AREAPL.HARMAT(AO.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN,JJDAY,NDAYHR COMMON /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) COMMON /CTRL2A/ BGNCUT(S).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 10550 10560 10570 10580 10590 10600 10610 10620 10630 10600 10650 10660 +QUAL(3.0),GDOCUM,METRIC,JYEARF,JYEARL,IPRTI,IPRT2,JOAYF,JDAYL.JPRT1067O +,NYRS.IPRTO.NCUTS.JYEAR,JLALHR.CPLANT 0 COMMON /ALFARG/ GDDB5.AVTA.DAYLIN.DAYLEN.YDAYL.DECR.XLAI.AH. _ +SUMSI.SUMsz.T,HSF.SRADF.DHS.PPT.ESO.ESR.XLEAF.BUDS.STEM.TOPS.TNC. +XMATS.TNCS,TMAXC,TMINC COMMON /Y7/ NBOP(18).NBMACH(18.7).XNBM(18.7) THIS SUBROUTINE ESTIMATES LEAF AND STEM LOSSES DUE TO MECHANICAL TREATMENT. THERE ARE 11 TYPES OF LOSS. NB STANDS FOR THE MACHINE TREATMENT. 1. MOHER 2. MOHER-CONDITIONER . RAKE TEDDER BALER (CONVENTIONAL. RECTANGULAR BALEs) BALER-EJECTOR ROUND BALER STACK HAGON . CHOPPER (HILTED ALFALFA) 10. CHOPPER (DIRECT-CUT ALFALFA) 11. CHOPPER (DIRECT-CUT CORN SILAGE) XLL REPRESENTS LEAF LOSS XSL REPRESENTS STEM LOSS FIRST HE HAVE TO IDENTIFY HHICH OPERATION HE ARE DEALING HITH. DIMENSION XLL(11).XSL(11) DIMENSION VAL(7).ARG(7) DATA XLL /.02..OA.O..O...05,.O75..19..2A..09..OA..05/ DATA XSL /.005..OI..02.O...02..02..OA..05..02..DI..05/ DATA VAL /.2I..IA..08..OA5..028..023..020/ DATA ARG /.25..AO..67.I.O.1.5.2.O.2.5/ I-NTHCUT KK-7 NB-II IF (NOPSQ(I.N).LE.0019) NB-I IF (NOPSQ(I.N).GE.20.AND.NOPSQ(I.N).LE.39) NB-z IF (NOPSQ(I.N).GE.AO.AND.NOPSQ(I.N).LE.69) GO TO 10 IF (NDPSQ(I.N).GE.7O.AND.NOPSQ(I.N).LE.79) NB-5 IF (NOPSQ(I.N).GE.8O.AND.NOPSQ(I.N).LE.89) NB-7 1F (NOPSQ(I.N).GE.9O.AND.NOPSQ(I.N).LE.99) NB-8 \OCDN O‘UI rm 0 10680 10690 10700 10710 10720 10730 10700 10750 10760 10770 10780 10790 10800 10810 10820 10830 10800 10850 10860 10870 10880 10890 10900 10910 10920 10930 10900 10950 10960 10970 10980 10990 11000 11010 11020 11030 11000 316 IF (NOPSQ(I.N).GE.OIAO.AND.NOPSQ(I.N).LE.1A9) NB-II IF (NOPSQ(I.N).GE.15O.AND.NOPSQ(I.N).LE.159) NB-9 IF (NOPSQ(I.N).GE.I6O.AND.NOPSQ(I.N).LE.169) NB-IO IF (NOPSQ(I,N).GE.17O.AND.NOPSQ(I.N).LE.179) GO TO 30 GO TO 00 C HERE HE CONSIDER RAKING AND TEDDING 10 XMc-HARMAT(J.IO) 1F (RAIN.GT.2.) XMc-HARMAT(J.29) C IN THE CASE OF RAKING AND TEDDING. LEAF LOSS IS A FUNCTION OF C MOISTURE CONTENT. NB-3 IF (NOPSQ(I.N).GE.6O.AND.NOPSQ(I.N).LE.69) NB-A XLL(NB)-TABLI(VAL.ARG.XMC.KK) GO TO AO C CHECK IF THERE Is AN EJECTOR 3O NB-5 II-O 1 II-II+1 IF (NOPSQ(I.N).NE.NBOP(II).AND.II.LT.18) GO TO 1 IF (NBMACH(II.3).NE.O) NB-6 AO HARMAT(J.20)-HARMAT(J,20)*(1.-XLL(NB)) HARMAT(J.25)-HARMAT(J,25)*(1.-XSL(N8)) RETURN END C C ******************t************************************************* SUBROUTINE ENDHRV C **********************t********************************************* .COMMON /HI/ NPLOTS.NMOH.NHRV.NSTO.AREAPL.HARMAT(AO.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN.JJDAY.NDAYHR COMMON /H3/ HFEED(A.16O.S) COMMON /ZI/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5,A.9).SIL0(2) COMMON /CTRL2A/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 11050 11060 11070 11080 11090 11100 11110 11120 11130 11100 11150 11160 11170 11180 11190 11200 11210 11220 11230 11200 11250 11260 11270 11280 11290 11300 11310 11320 11330 11300 11350 11360 11370 +QUAL(3,0),GDDCUM,METRIC,JYEARF,JYEARL.IPRTI,IPRT2,JDAYF,JDAYL,JPRT11380 +,NYRS,IPRTO,NCUTS,JYEAR.JLALHR,CPLANT COMMON /ALFARG/ GDDBs,AVTA.DAYLIN.DAYLEN.YDAYL.DECR.XLAI.AH. +SUMSI.SUM52.T.HSF.SRADF.DHS.PPT.ESO.ESR.XLEAF.BUDS.STEM.TOPS.TNC. +XMATS.TNCS.TMAXC.TMINC COMMON /z3/ HARDEX.TMSTO(A),NPST(5.5).NCUM(5).OPUSE(5.9) COMMON /zA/FDLABR.FDENER.HRLABR.HRFUEL.HRELEC COMMON /25/ IPR2.IPR3.IPRA COMMON /z7/ ALHRFD(26.15).AFEED(26.23) COMMON /YY1/ USEMCH(100).UNITS(IOO) COMMON /Y1/ XINF0(7).MCODE(IOO).XMDATA(IOO.13) COMMON /Y3/ NMDATA.NOPER.IN.IO COMMON /Y7/ NBOP(18).NBMACH(18.7).XNBM(18.7) COMMON /z2I/ ADATES(26.12).SDATES(A.12) COMMON /222/ DELAY(26.12).SDELAY(A.12) DATA DELAY /312*0.0/ DATA NDAYHR /0/ 11390 11000 11010 11020 11030 11000 11050 11060 11070 11080 11090 11500 11510 11520 11530 11500 C C C 10 C C nnn on C SUBROUTINE ENDHRV PROVIDES A SUMMARY OF HOW PLOTS WERE HARVESTED 317 11550 THE END OF EACH CUT AND A DETAILED OUTPUT AT THE END OF EACH YEAR ON11560 QUANTITY AND QUALITY. AT THE END OF EACH CUT, SUM UP LABOR,FUEL AND ELECTRICITY REQUIRED K-(NTHCUT-1)*3+2 ADATES(NTHYR.K)-FLOAT(JJDAY) ADATES(NTHYR,K+1)-ADATES(NTHYR,K)-ADATES(NTHYR.K-1) I-NTHCUT IF (NDAYHR.LT.39) GO TO IO NLM-NPLOTs-NMOH NLH-NPLOTS-NHRV OPUSE(I,I)-OPUSE(I,1)+NLM*TPL(I) OPUSE(I.8)-0PUSE(|.8)+NLH*TPL(8) IF (CRTR(I.3.8).EQ.1.) OPUSE(I,9)-OPUSE(I.9)+NLH*TPL(9) NCUM(5)-NCUM(5)+NLH NPST(I,5)-NPST(I,5)+NLH CONTINUE NMOW-NPLOTS NHRV-NPLOTS FOR HARVEST. 50 JLALHR IS THE LAST ALFALFA HARVEST DAY DURING THE THIRD HARVEST. IT WILL BE USED TO ESTABLISH ANY TIME CONFLICT BETWEEN ALFALFA DO 50 J-I.9 HRLABR-HRLA8R+OPUSE(I.J)*ZRT(J.0) HRFUEL-HRFUEL+OPUSE(|.J)*ZRT(J.2) HRELEc-HRELEC+OPUSE(I.J)*ZRT(J.3) CONTINUE HARVEST AND CORN SILAGE HARVEST. IF (NTHCUT.EQ.3) JLALHR-JJDAY CHECK IF THIS IS THE LAST CUT OF TH E YEAR AT THE END OF EACH YEAR. SUM UP MACHINE USE FOR EACH OPERATION NDAYHR-'00 , IF (NTHCUT.LT.NCUTS) RETURN AND FOR EACH INDIVIDUAL MACHINE. 65 60 AT THE END OF EACH YEAR, SUMMARIZE THE TOTAL FEED HARVESTED DO 60 I-I.5 DO 60 J-1,9 IF (OPUSE(I,J).LE.0.) GO TO 60 11-0 II-II+1 IF (NOPSQ(I,J).NE.NBOP(II)) GO TO 1 DO 65 K-I.7 IF (NBMACH(II.K).EQ.O) GO TO 65 lJ-O IJ-IJ+1 IF (NBMACH(II.K).NE.MCODE(IJ)) GO TO 2 UNITS(IJ)-AMAX1(UNITS(IJ),XNBM(II.K)) USEMCH(IJ)-USEMCH(IJ)+OPUSE(I,J)*XNBM(II,K) CONTINUE CONTINUE 11570 11580 11590 11600 11610 11620 11630 11600 11650 11660 11670 11680 11690 11700 11710 11720 11730 11700 11750 11760 11770 11780 11790 ‘ 11800 11810 11820 11830 11800 11850 11860 11870 11880 11890 11900 11910 11920 11930 11900 11950 11960 11970 11980 11990 12000 12010 12020 12030 12000 C C C C nnnnnn nn 318 MATRIX ALHRFD(26,15) CONTAINS DM ,T/HA), CP (DEC), AND TDN (DEC) FOR EACH ALFALFA HARVEST FOR EACH YEAR. DM. CP AND TDN FOR UP TO 0 ALFALFA HARVESTS. COLUMNS 1 T0 12 CONTAIN COLUMNS U0 TO 15 CONTAIN ANNUAL AGGREGATE INFORMATION. 55 MATRIX AFEED(26.23) CONTAINS OM (TOTAL T). CP (DEC), STANDARD DEV. OF CRUDE PROTEIN. TDN (DEC) AND STANDARD DEVIATION OF TDN FOR ALL LOCATION 1 h TCP-O. TDIG-O. TOMA-O. TDM-O . DO 55 K-1,A KK-(K-I)*3+1 DM-ALHRFD(NTHYR.KK) IF (DM.LE.O.) GO TO 55 CP-ALHRFD(NTHYR.KK+1)/DM DIG-ALHRFD(NTHYR.KK+2)/DM ALHRFD(NTHYR.KK+1)-CP ALHRFD(NTHYR,KK+2)-DIG ALHRFD(NTHYR.KK)-DM/AREA(K) TDM-TDM+DM TOMA-TDMA+DM/AREA(K) TDIG-TDIG+DM*DIG TCP-TCP+CP*DM CONTINUE ALHRFD(NTHYR.I3)-TDMA ALHRFD(NTHYR.IA)-TCP/TDM ALHRFD(NTHYR.15)-TDIG/TDM IF (TDM.LE.O.) THEN ALHRFD(NTHYR.1A)-O. ALHRFD(NTHYR.15)-O. ENDIF STORAGE LOCATIONS. IS FIRST SILO. 2 IS SECOND SILO. 3 IS HIGH QUALITY HAY, 0 IS LOW QUALITY HAY. THE LAST THREE COLUMNS ARE RESERVED FOR DRY MATTER OF HARVESTED CORN: CALCULATE TOTAL DM, AVERAGE CP, BIASED STANDARD ERROR OF CP, AVERAGE DO 35 NS-I.0 NPSS-NCUM(NS) IF (NPSS.LE.0) GO TO 35 CORN SILAGE. HIGH MOISTURE CORN AND DRY CORN GRAIN. DIG AND BIASED STANDARD ERROR OF DIG. SDM-O. SCP-O. SDIG-O. SSCP-O. SSDIG-O. DO 36 J-1.NPSS SDM-SDM+HFEED(NS.J.I) SCP-SCP+HFEED(NS,J.2) SSCP-SSCP+HFEEO(NS,J.2)*HFEED(NS,J,2) SDlG-SDIG+HFEED(NS.J.3) 12050 12060 12070 12080 12090 12100 12110 12120 12130 12100 12150 12160 12170 12180 12190 12200 12210 12220 12230 12200 12250 12260 12270 12280 12290 12300 12310 12320 12330 12300 12350 12360 12370 12380 12390 12000 12010 12020 12030 12000 12050 12060 12070 12080 12090 12500 12510 12520 12530 12500 82 81 C A******************************************************************* c t******************************************************************* COMMON COMMON COMMON COMMON COMMON COMMON +QUAL(3,0),GDDCUM,METRIC,JYEARF.JYEARL.IPRTI.IPRT2,JDAYF.JDAYL.JPRT12970 +,NYRS.IPRTO,NCUTS.JYEAR.JLALHR.CPLANT 36 35 319 SSDIG-SSDIG+HFEED(NS,J.3)*HFEED(NS,J,3) CONTINUE KK-5*NS-0 AFEED(NTHYR.KK)-SDM AFEED(NTHYR.KK+1)-SCP/NPSS AFEED(NTHYR.KK+3)-SDIG/NPSS VARCP-(SSCP-SCP*SCP/NPSS)/NPSS VARDIG-(SSDIG-SDIG*SDIG/NPSS)/NPSS IF(VARCP.LT.O.) VARCP-O. IF(VARDIG.LT.O.) VARDIG-O. AFEED(NTHYR.KK+2)-SQRT(VARCP) AFEED(NTHYR,KK+A)-SQRT(VARDIG) CONTINUE DO 81 Ns-I.A TD-O. K-(NS-1)*3+1 NPss-NCUM(NS) IF (NPSS.LE.O) GO TO 81 DO 82 NBPL-I.NPSS TD-TD+HFEED(NS,NBPL.5) TDS-TDS+HFEED(NS.NBPL.5)*HFEED(NS.NBPL.5) XN-FLOAT(NPSS) DELAY(NTHYR.K)-XN AD-TD/XN SDD-(TDS-TD*TD/XN)/(XN~1.) IF (XN.LE.1.) SOD-O. IF (SDD.LT.O.) SOD-O. DELAY(NTHYR.K+I)-AD DELAY(NTHYR.K+2)-SQRT(SDD) CONTINUE RETURN END SUBROUTINE DCALF /H1/ NPLOTS.NMOH.NHRV,NSTO.AREAPL.HARMAT(AO.29),zRT(9.5) /H2/ TPL(9),RAIN.JJDAY,NDAYHR /H3/ HFEED(A.I6O,5) /HA/ NPDCA.NDCTD.IDAH /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) /CTRL2A/ BGNCUT(S).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). COMMON /ALFARG/ GDDBS,AVTA.DAYLIN.DAYLEN.YDAYL.DECR,XLAI,AW, +SUMSI,SUMSZ,T,WSF,SRADF,DWS,PPT.ESO,ESR,XLEAF,BUDS,STEM,TOPS,TNC, +XMATS,TNCS,TMAXC,TMINC COMMON /z3/ HARDEX.TMSTO(A).NPST(5.5).NCUM(5).OPUSE(5,9) COMMON /2A/FDLABR.FDENER.HRLABR.HRFUEL.HRELEC COMMON /25/ IPR2.IPR3.IPRA 12550 12560 12570 12580 12590 12600 12610 12620 12630 12600 12650 12660 12670 12680 12690 12700 12710 12720 12730 12700 12750 12760 12770 12780 12790 12800 12810 12820 12830 12800 12850 12860 12870 12880 12890 12900 12910 12920 12930 12900 12950 12960 12980 12990 13000 13010 13020 13030 13000 rs n n r1 ‘ I C TH! E HARI t HA1 —- 7 ——- 7 '7 7 , H H n -— ._ __ n m— mww -—-'-11 ,_. z (n o z zzzzzzzra C 320 COMMON /z7/ ALHRFD(26.15).AFEED(26.23) COMMON /YY1/ USEMCH(IOO),UN1TS(IOO) COMMON /YI/ XINFO(7).MCODE(100).XMDATA(IOO.I3) COMMON /Y3/ NMDATA.NOPER.IN.IO COMMON /Y7/ NBOP(I8).NBMACH(18.7).xNBM(18,7) THIS SUBROUTINE IS USED FOR ALFALFA GREEN CHOPPING (DIRECT CUT) C HARVEST WILL OCCUR IF RAIN IS LESS THAN 2 MM. C nnn C DATA STOLS/0.07/.FDLS/O.II/ DATA DCLL/0.0A/.DCSL/0.0I/ IF (RAIN.GT.2.) RETURN HARVEST LOSSES HLEAF-XLEAF*(I.-DCLL) HSTEM-STEM*(1.-DCSL)' HDM-HLEAF+HSTEM PCL-HLEAF/HDM IF (PCL.LT.O.290) PCL-O.29 1F (PCL.GT.O.5O) PCL-O.5O Pcs-1.-PCL CP-PCLtQUAL(I,2)+PCS*QUAL(2.2) DIG-PCLAQUAL(I.3)+PCS*QUAL(2.3) DM-HDM/IOO. NDCTD-I NHPD-I FIND THE STORAGE STRUCTURE IN HHICH THE ALFALFA HILL BE STORED 5 Ns-I IF (CP.GE.CRTR(NTHCUT.2.5).AND.TMSTO(1).LT.SILO(1)) GO TO 10 Ns-2 IF (TMST0(2).LT.SILO(2)) GO TO 10 IF QUALITY DICTATES TO STORE IN SILO 2 AND SILO 2 15 FULL HHILE 13050 13060 13070 13080 13090 13100 13110 13120 13130 13100 13150 13160 13170 13180 13190 13200 13210 13220 13230 13200 13250 13260 13270 13280 13290 13300 13310 13320 13330 SILO 1 IS NOT, THEN STORE THE LOW QUALITY SILAGE IN SILO 1 (HIGH QL)13300 INSTEAD OF FORCING HAY HARVEST N531 10 CONTINUE NMTDAY-NMTDAY+1 NHTOAY-NHTOAY+1 NHRV-NHRV+1 NMOW-NMOW+1 . NPST(NTHCUT,NS)-NPST(NTHCUT.NS)+1 NCUM(NS)'NCUM(NS)+I NPDCA'NPDCA+1 NBPL-NCUM(NS) 'TOTAL DRY MATTER AFTER STORAGE AND FEEDING LOSSES HFEED(NS.NBPL,1)'DM*AREAPL*NHPD*(1.-FDLS)*(1.-STOLS) HFEED(NS.NBPL,2)-CP HFEED(NS.NBPL,3)-DIG .XMCI-XINMC(NDAYHR,5,NTHCUT) HFEED(NS.NBPL.0)-XMCI HFEED(NS.NBPL.5)-O. 'TMSTO(NS)-TMSTO(NS)+DM*AREAPL*NHPD NBHR-0+NS 13350 13360 13370 13380 13390 13000 13010 13020 13030 13000 13050 13060 13070 I 13080 13090 13500 13510 13520 13530 13500 no an 321 OPUSE(NTHCUT,NBHR)-OPUSE(NTHCUT,NBHR)+TPL(NBHR)*NHPD KK-(NTHCUT-I)*3+I ALHRFD(NTHYR.KK)-ALHRFD(NTHYR.KK)+HFEED(NS.NBPL.1) ALHRFD(NTHYR.KK+I)-ALHRFD(NTHYR.KK+I) + +HFEED(NS.NBPL.1)*HFEED(NS.NBPL.2) ALHRFD(NTHYR.KK+2)-ALHRFD(NTHYR,KK+2) + +HFEED(NS,NBPL.1)*HFEED(NS.NBPL.3) IF (TMSTO(1).LT.SILO(I)) GO TO 15 HARDEx-z. IF (TMSTO(2).LT.SILo(2)) GO TO 15 IF (NOPSQ(NTHCUT,1).EQ.O) GO TO 15 IF BOTH SILOS ARE FULL. SHIFT FROM DIRECT CUT HARVEST TO DRY HAY HARVEST AS LONG AS THE EQUIPMENT IS AVAILABLE. THE FIRST HAY HARVEST DAY HILL NOT START UNTIL TOMORROH HARDEx-3. IDAH-9 RETURN 15 NLEFT-NPLDTS-NHRV IF (NLEFT.GE.I) GO TO 25 NHRv-NPLOTS NMOH-NPLOTS RETURN CHECK IF A SECOND PLOT MAY BE HARVESTED TODAY AS DIRECT CUT ALFALFA 25 IF (NDCTD.GE.2) RETURN IF (CRTR(NTHCUT.0,1).EQ.1.) RETURN NDCTD'NDCTD+1 GO TO 5 END ***********a'tta’c****************************************************** SUBROUTINE HRITAL(ILINE) *********************kt********************************Ak*********** COMMON /w1/ NPLOTS.NMOW.NHRV,NSTO,AREAPL.HARMAT(00.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN.JJDAY.NDAYHR COMMON /H3/ HFEED(A.I6O.5) COMMON /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) COMMON /CTRL20/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A), 13550 13560 13570 13580 13590 13600 13610 13620 13630 13600 13650 13660 13670 13680 13690 13700 13710 13720 13730 13700 13750 13760 13770 13780 13790 13800 13810 13820 13830 13800 13850 13860 13870 13880 13890 13900 13910 +QUAL(3,0),GODCUM,METRIC,JYEARF,JYEARL,IPRTI,IPRT2,JDAYF,JDAYL.JPRT13920 +,NYRS,IPRTO,NCUTS.JYEAR,JLALHR,CPLANT COMMON /ALFARG/ GDDBS.AVTA.DAYLIN.DAYLEN,YDAYL.DECR,XLAI,AW, +SUMSI.SUMSZ.T,WSF,SRADF,DWS,PPT,ESO,ESR,XLEAF,BUDS.STEM,TOPS,TNC, +XMATS.TNCS.TMAXC.TMINC . COMMON /23/ HARDEX.TMSTO(A),NPST(5,5).NCUM(5).DPUSE(5.9) COMMON /Z0/FDLABR,FDENER.HRLABR,HRFUEL.HRELEC COMMON /25/ IPR2.IPR3.IPRA COMMON /z6/ CSLABR.CSFUEL.CSELEC.CSFDLB.CSFDEN.DMCS COMMON /Z7/ ALHRFD(26,15).AFEEO(26.23) COMMON /221/ ADATES(26.12).SDATES(A,12) COMMON /YY1/ USEMCH(IOO).UNITS(IOO) COMMON /Y1/ XINFD(7).MCDDE(IOO).XMDATA(IOO.13) 13930 13900 13950 13960 13970 13980 13990 10000 10010 10020 10030 10000 nnnnnnn n 102 101 C C 105 FORMAT (5X,'WARNING--- 110 FORMAT (5X.'OURING CUT', 322 COMMON /Y3/ NMDATA,NOPER.IN,IO COMMON /Y7/ NBOP(18).NBMACH(18,7).XNBM(18.7) COMMON /222/ DELAY(26,12).SDELAY(A.12) DIMENSION STALHR(A. 15). STFEED(0. 23) DATA STALHR. STFEED /60*0. .92*0. / THIS SUBROUTINE CONTAINS ALL THE HRITE STATEMENTS FOR THE ALFALFA HARVEST. THE ARGUMENT ILINE REFERS TO 3 PRINTOUT LEVELS. ILINE-1 IS FOR DAILY AND SEASONAL OUTPUT. ILINE-2 IS FOR YEARLY OUTPUT. ILINE-3 IS FOR END-OF-SIMULATION OUTPUT. DAILY AND YEARLY PRINTOUTS HILL APPEAR ONLY IF INPUT DATA IPR2. IPR3 OR IPRA ARE EQUAL TO 1. GO TO (1.2.3) ILINE DAILY PRINTOUT IF (IPR2.NE.I) RETURN IF (NTHCUT.EQ.1.AND.NDAYHR.EQ.1) HRITE (10.102) NTHYR.JYEAR FORMAT (//.5X,'DETAILED OUTPUT FOR YEAR '.12.' ('.I0.')',//) NHD-NDAYHR-I IF (NHD.EQ.1) HRITE (10.101) FORMAT (/.6X,'DAILY ALFALFA HARVEST INFORMATION'./.6X.'JDAY'.6X, + 'PLOTS NMOH NHRV PPT TOPS'. + 8x.'CP DIG'./) 10050 10060 10070 10080 10090 10100 10110 10120 10130 10100 10150 10160 10170 10180 10190 10200 10210 10220 10230 10200 10250 10260 WRITE (10,100) JJDAY,NPLOTS,NMOW,NHRV,PPT,TOPS,QUAL(3.2).QUAL(3,3)10270 100 FORMAT (5X,0(15,5X),F10.2,F10.0.2F10.3) IF (NHRV.NE.NPLOTS) RETURN I-NTHCUT A HARNING IS GIVEN IF THE END OF THE ALFALFA HARVEST HAS CAUSED 'BY NDAYHR BEING GREATER THAN 39. IF (NDAYHR.LT.39) GO TO 10 NLM-NPLOTs-NMOH NLH-NPLOTS-NHRV HRITE (10.105) I,NLM.NLH +W FOR THE GIVEN AREA'./.5X.'DURING CUT' 10280 10290 10300 10310 10320 . 10330 10300 10350 10360 THE HARVEST RATE MAY BE UNREALISTICALLY LOI037O ,I0,',',I0,' PLOTS WERE UNM1038O +OWED AND'.I0.' PLOTS WERE UNHARVESTED FOR LACK OF TIME'./.5X.'MORElh39° + THAN 39 DAYS WOULD BE REQUIRED TO HARVEST THE WHOLE AREA') 10 IF (IPR2.NE.1) GO TO 15 WRITE (10,110) I,AREA(I),NPLOTS,(NPST(I,J),J-1,5) I0,', AN AREA OF',F8.2.' +TO',I0,' PLOTS. +DEAO. +ITY HAYLAGE'./.10X,I0,' PLOTS AS HIGH QUALITY HAY'./.IOX,I0, 10000 10010 10020 HA WAS DIVIDED IN1003O PLOTS WERE HARVESTED AND STORED AS FOLLOWS'./,10X10000 PLOTS AS HIGH QUALITY HAYLAGE', /,IOX,I0,' PLOTS AS LOW QUAL1005O ' PL010060 +TS AS LOW QUALITY HAY'./10X,I0,' PLOTS DESTROYED BECAUSE OF OVEREX10070 +PDSURE') HRITE (10.115) (OPUSE(NTHCUT.J).J-1.9) 10080 10090 115 FORMAT (/.5X.'THE NINE OPERATIONS WERE EACH CONDUCTED FOR THE FOLL10500 +OWING AMOUNT OF TIME (H) DURING THE PRESENT HARVEST',/,5X,9F10.2) 10510 C 15 CONTINUE RETURN YEARLY PRINTOUT. IF IPR3 IS 1, 10520 10530 A DETAILED DESCRIPTION OF THE VALUE 10500 Mnnn 323 VALUE OF ALL ALFALFA PLOTS HARVESTED IN THE YEAR IS PROVIDED. 10550 IF IPRO IS 1, A DETAILED DESCRIPTION OF ALL MACHINERY USE AND 10560 RESOURCE REQUIREMENT IS PROVIDED. 10570 CALL ANCOSTINTHYR) 10580 IF (IPR3.NE.1) GO TO 25 10590 WRITE (10,102) NTHYR,JYEAR 10600 WRITE (10.150) 10610 150 FORMAT (/.5X,ITHE PRESENT CUT IS APPARENTLY THE LAST OF THE YEAR'.10620 +/,5X,'THE FEEDING VALUE OF ALL THE FORAGES HARVESTED IN THE YEAR 110630 +8 GIVEN BELOH') , 10600 D0 A0 Ns-1.A IA650 NPSS-NCUM(NS) 10660 IF (NPSS.LE.O) GO TO 20 10670 HRITE (10.120) NS 1A680 120 FORMAT (5X,' IN STORAGE STRUCTURE NS - ',I0,', THE FOLLOHING PLOTSIA69O + HERE ACCUMULATED'./.9X.'DM (T) C? DIG MC DAY10700 +5 EXP.') 1A710 DO 30 NBPL-I.NPSS 1A720 HRITE (10.130) (HFEED(NS.NBPL.J).J-1.5) 1A73D 130 FORMAT (7X.AFIO.3.F8.O) 10700 30 CONTINUE 1A750 GO TO AO 1A76O 20 HRITE (ID.1AO) NS 10770 IAO FORMAT (5X.'NOT A SINGLE PLOT HAS STORED IN STORAGE STRUCTURE Ns- 1A78O +',I0. ' DURING THE CURRENT YEAR') ' 1A79O 0O CONTINUE 1A800 HRITE (l0.105) NCUM(5) 1A810 1A5 FORMAT (5X,'THE NUMBER OF ALFALFA PLOTS DESTROYED BECAUSE OF OVERE1A82O +XPOSURE IN THE YEAR EQUALS '.15) 1A83O 25 CONTINUE 1A8AO IF (IPRA.NE.1) RETURN 1A850 HRITE (10.102) NTHYR,JYEAR 1A860 DO 70 K-1.NMDATA 10870 IF (USEMCH(K).LE.O.) GO TO 70 1A880 IF (UNITS(K).NE.1.) GO TO 71 10890 HRITE(ID.17O) MCODE(K).USEMCH(K) 10900 170 FORMAT (5X,'A SINGLE UNIT OF MACHINE',I6,' HAS USED',F10.2.' HOURSIA91O + DURING THE YEAR FOR FORAGE HARVEST') 1A920 GO TO 70 10930 71 HRITE (10.171) UNITS(K).MCODE(K).USEMCH(K) 10900 171 FORMAT (5X,F0.0,' UNITS OF MACHINE'.I6,' HERE USED ALLTOGETHER A T1A950 +OTAL OF',F10.2.' HOURS DURING THE YEAR FOR FORAGE HARVEST') 10960 70 CONTINUE 10970 HRITE (10.180) HRLABR.HRFUEL.HRELEC.FDLABR.FDENER 1A980 180 FORMAT (/.5X,'THE TOTAL YEARLY RESOURCE REQUIREMENTS', 10990 +1 FOR ALFALFA HARVEST AND FEEDING WERE'. 15000 +/,10X,'FOR HARVESTING, ',F10.2.' MAN.HOURS'./.26X.F10.2,' LITERS',15010 + ' OF FUEL'./.26X.F10.2,' KW.H OF ELECTRICITY'./.10X,'FOR FEEDING,15020 + '.F13.2.' MAN.HOURS'./26X,F10.2,' LITERS OF FUEL OR ELECTRICAL EQ15030 +UIVALENT') 15000 32A WRITE (10.190) CSLABR.CSFUEL.CSELEC.CSFDLB.CSFDEN 190 FORMAT (/,5X,'THE TOTAL YEARLY RESOURCE REQUIREMENTS', C 3 +1 FOR CORN SILAGE HARVEST AND FEEDING HERE'. ‘ +/,10X,'FOR HARVESTING, ',F10.2,' MAN.HOURS',/,26X,F10.2,' LITERS', 15050 15060 15070 15080 + ' OF FUEL'./.26X.F10.2,' KW.H OF ELECTRICITY'./.10X.'FOR FEEDING,15090 + ',F13.2.' MAN.HOURS'./26X,F10.2.' LITERS OF FUEL OR ELECTRICAL EQ15100 +UIVALENT') RETURN END-OF-SIMULATION PRINTOUT. CONTINUE HRITE (10.125) 125 FORMAT ('1'.////. 131 + /. 5X,'AVERAGE ALFALFA DM YIELD AVAILABLE AS FEED (T/HA), 15110 15120 15130 15100 15150 15160 15170 +AVERAGE CRUDE PROTEIN (DEC) AND AVERAGE DIGESTIBILITY (DEC)',/, 5X1518O +,'FOR UP TO 0 HARVESTS AND THE ANNUAL TOTAL'.//.' YR'.7X.'HARVEST 15190 +1'.12X. +'HARVEST 2',12X.'HARVEST 3'.12X,'HARVEST 0',12X,'TOTAL YEARLY'./, +IOX,'DM 'CP DIG DM CP DIG DM CP DIG + OM CP DIG .DM CP DIG'.//) DO 32 1-1.NYRs HRITE (10.131) 1. (ALHRFD(I.J).J-1.15) FORMAT (2X.12.5(F9.2.2F6.3)) 32 CONTINUE HRITE (IO.13A) 13A FORMAT (9X,' .......................................... . '7'. ...................................... .D//) CALL SSTAT (15.ALHRFD.NYRS.STALHR) HRITE (10.133) 133 FORMAT (///.5X.'SAMPLE STATISTICS FOR SIMULATION OUTPUT. '. 73 + 'ROW 1-MEAN, ROW 2-STANDARD DEVIATION, ROW 3-COEF. 0F ', + 'VARIATION',/) 00 73 I'I.3 WRITE (10.131) I,(STALHR(I,J),J-1,15) WRITE (10.130) WRITE (10.132) 132 FORMAT ('1',////, + /,15X,'TOTAL ALFALFA FEED AVAILABLE FROM FOUR STORAGE LOCA15010 +TIONS',/,15X,'THE INFORMATION INCLUDES TOTAL DM (T). AVERAGE CP. 315020 135 +IASED STANDARD DEVIATION OF CP'./.15X. +‘AVERAGE DIG AND BIASED STANDARD DEVIATION', + ' OF DIG‘.//.' YR'.7X,'ALFALFA IN FIRST SILO'.IOX.'ALFALFA'. + . IN SECOND SILO'.11X,'HIGH QUALITY HAY'.13X,'LOW QUALITY HAY'. +/,8X.'DM CP S(CP) DIG S(D1G) DM CP S(CP) DIG S(DIG)‘, + . DM CP S(CP) DIG S(D1G) DM CP S(CP) DIG S(DIG)‘, +//) DO 30 1-1.NYRS HRITE (10.135) 1. (AFEED(I.J).J-I.20) FORMAT (1X.12.2X.A(F7.1.2x.A(FA.3.1x).1x)) 3A CONTINUE WRITE(IO.137) 15200 15210 15220 15230 152AO 15250 15260 15270 15280 15290 15300 15310 15320 15330 153AO 15350 15360 15370 15380 15390 15AOO 15030 15000 15050 15060 15070 15080 15090 15500 15510 15520 15530 15500 70 I36 I37 201 202 36 200 207 209 208 210 211 + + + + + + + + + + + + + + + + + 325 CALL SSTAT (23.AFEED.NYRS.STFEED) HRITE (10.133) DO 7A I-1.2 HRITE (10.135) I.(STFEED(I.J).J-I.ZO) HRITE (10.136) (I,(STFEED(I.J).J-1.20).I-3.3) FORMAT(1X.12.2x.A(F7.2.2X.A(FA.3,1X).1x)) HRITE(ID.137) FORMAT (7x.' -------------------------- +' ------ . + 1 ------ './/) HRITE (10.201) FORMAT ('1',//,5X,'STARTING AND ENDING HARVEST DATES OF'. '----'.//) DD 36 I-I.NYRS HRITE (10.202) l,(ADATES(I,J),J-I,12) FORMAT (2X.12.1x.F7.O.11(3X,F7.O)) CONTINUE CALL SSTAT (12.ADATES.NYRS.SDATES) HRITE (10.133) DO 38 I-I.3 HRITE (IO.2OA) l.(SDATES(I,J),J-1,12) FORMAT (2X.12.3X.F7.2.11(3X.F7.2)) HRITE (10.207) FORMAT ('1',//.5X,'THE AVERAGE NUMBER OF DAYS ALFALFA HAS'. . FIELD CURING BEFORE GOING INTO STORAGE'.//.IX.'YR'. 10X,‘LOW QUALITY HAY',/. 6X,'PLOTS',0X,'DAYS',5X,'S(DAY)', 6x,'PLOTS'.0X,‘DAYS',5X,'S(DAY)'. 6X,'PLOTS'.0X,'DAYS'.5X,'S(DAY)'. 6X.'PLOTS'.0X,'DAYS'.5X,'S(DAY)'. //) DO 209 I-1.NYRS HRITE (10.208) I.(DELAY(I.J).J-1.12) FORMAT (1x.12.3X.A(FA.O.5x.F5.2.AX.F6.3.6X)) CALL SSTAT (12.DELAY.NYRS.SDELAY) HRITE (10.133) DO 210 1-1.3 HRITE (10.211) I,(SDELAY(I.J),J-1.12) FORMAT (Ix.12.3x.A(F6.2.3X.F6.3.3x.F6.3.6X)) RETURN END ' ALFALFA FOR THE HHOLE SIMULATION'.//.2X.'YR'.0X. 'HARVEST 1'.21X,'HARVEST 2',21X.'HARVEST 3'.21x,'HARVEST A'. /.8X.'STARTING ENDING SPAN STARTING ENDING ' 'SPAN STARTING ENDING SPAN STARTING '. 'ENDING SPAN'./.8X,'DATE',6X.'DATE'.16X.'DATE'.6X, 'DATE',I6X,'DATE',6X.'DATE'.I6X,'DATE',6X.'DATE'./,8X. I ........................ I I ........................ I ...I----------------------:f’-‘:...,. .................... 10X.'FIRST SILO',19X,'SECOND SILO',17X,'HIGH QUALITY HAY', 15550 15560 15570 15580 15590 15600 15610 15620 15630 15600 15650 15660 15670 15680 15690 15700 15710 15720 15730 15700 15750 15760 15770 15780 15790 15800 15810 15820 15830 15800 15850 15860 15870 15880 15890 15900 15910 15920 15930 15900 15950 15960 15970 15980 15990 16000 16010 16020 16030 16000 C C ******************************************************************** c ***************************************A**************************** COMMON /w1/ NPLOTS.NMOW,NHRV,NSTO.AREAPL.HARMAT(00.29).ZRT(9,5) nnnnnn nnnn 326 FUNCTION CSRATE (YDM.NOPCS) COMMON /W2/ TPL(9),RAIN,JJDAY,NDAYHR COMMON /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.0.9).SILO(2) COMMON /Z6/ CSLABR.CSFUEL.CSELEC,CSFDLB.CSFDEN.DMCS COMMON /29/ NBOPCS.ZRTCS(5) COMMON /CTRL2A/ BGNCUT(S).NTHYR,NTHCUT,NDAYSC.NDAYSH,YLD(0), 16050 16060 16070 16080 16090 16100 16110 16120 16130 16100 +QUAL(3.0),GDDCUM.METRIC.JYEARF,JYEARL,IPRTI,IPRT2,JDAYF,JDAYL.JPRT16150 +,NYRS.IPRTO,NCUTS.JYEAR.JLALHR.CPLANT COMMON /ALFARG/ GDDBS,AVTA.DAYLlN.DAYLEN,YDAYL.DECR.XLAI,AW, +SUMSI,SUM52,T,WSF,SRADF,DWS,PPT,ESO.ESR,XLEAF.BUDS,STEM,TOPS,TNC. +XMATS,TNCS,TMAXC.TMINC COMMON /Y3/ NMDATA.NOPER.IN,IO COMMON /Y6/ RATES(108.8).YAR(6) COMMON /Y7/ NBOP(18).NBMACH(18.7).XNBM(18.7) COMMON /W0/ NPDCA.NDCTD.IDAH THIS FUNCTION ESTIMATES THE HARVEST RATE (HA/H) FOR THE CORN SILAGE OPERATION. RETAIN CURRENT VALUES OF TOPS.NTHCUT,IDAH AND NOPSQ(1.1). THESE VALUES MUST BE CHANGED BEFORE CALLING INHRV FOR CORN SILAGE. AFTER THE CORN SILAGE HARVEST RATE IS ESTIMATED, THE ORIGINAL VALUES HILL BE REASSIGNED TO THE A VARIABLES. DMCS-YDM NBOPcs-NOPCS ATOPs-TDPS JCUT-NTHCUT JAM-IDAH NALFM-NOPSQ(1.I) CHANGE THE VARIABLES FOR CORN SILAGE HARVEST. TOPs-YDM*100./1.1 NTHCUT-I IDAH-1 NOPSQ(1.1)-NOPCS CALL INHRV CSRATE-2RT(1.I) ZRTCS(1)-ZRT(I.1) zRTCS(2)-2RT(1.2) ZRTCS(3)-ZRT(I.3) ZRTCS(A)-2RT(1.A) 2RTC5(5)-ZRT(1.5) NOPCS".Iho/o HRITE (10.152) NOPCS.((ZRT(I.J).J-1,5).I-1.9) 152 FORMAT (5X.'2RT MATRIX FOR CORN SILAGE. + 9(10x.5FIO.2./)) REASSIGN THE ORIGINAL VALUES. TOPs-ATOPS NTHCUT-JCUT IDAH-JAM 16160 16170 16180 16190 16200 16210 16220 16230 16200 16250 16260 16270 16280 16290 16300 16310 16320 16330 16300 16350 16360 16370 16380 16390 16000 16010 16020 16030 16000 16050 16060 16070 16080 16090 16500 16510 16520 16530 16500 C C *************************************************t****************** C ******************************************************************** nnnnn nnnn 327 NOPSQ(I.1)-NALFM RETURN END SUBROUTINE ENDCS (CSAREA. CSFED) COMMON /H1/ NPLOTS. NMOH. NHRV. NSTO. AREAPL. HARMAT(AO. 29). ZRT(9, 5) COMMON /H2/ TPL(9).RAIN.JJDAY.NDAYHR COMMON /H3/ HFEED(0,160,5) COMMON /21/ AREA(6).NBO(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) COMMON /CTRL20/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 16550 16560 16570 16580 16590 16600 16610 16620 16630 16600 16650 16660 +QUAL(3,0).GDDCUM,METRIC.JYEARF.JYEARL,IPRTI,IPRT2,JDAYF,JDAYL,JPRT16670 +,NYRS.1PRT0.NCUTS.JYEAR.JLALHR.CPLANT COMMON /ALFARG/ GDDBS,AVTA.DAYLIN,DAYLEN,YDAYL.DECR.XLAI,AW, +SUMSI,SUMSZ.T,WSF.SRADF,DWS,PPT,ESO.ESR.XLEAF,BUDS,STEM,TOPS,TNC. +XMATS.TNCS,TMAXC,TMINC , COMMON /z3/ HARDEX.TMSTO(A).NPST(5.5).NCUM(5).OPUSE(5.9) COMMON /zA/FDLABR.FDENER.HRLABR.HRFUEL.HRELEC COMMON /25/ IPR2. IPR3. IPRA COMMON /26/ CSLABR. CSFUEL. CSELEC. CSFDLB. CSFDEN, DMCS COMMON /29/ NBOPCS. ZRTCS(5) COMMON /YY1/ USEMCH(IOO). UNITS(IOO) COMMON /Y1/ XINFO(7).MCODE(100).XMDATA(1OO.I3) COMMON /Y3/ NMDATA.NOPER.IN,IO COMMON /Y7/ NBOP(18).NBMACH(18.7).XNBM(18.7) THIS SUBROUTINE ACCOUNTS FOR THE USE OF ALL MACHINES INVOLVED IN 16680 16690 16700 16710 16720 16730 16700 16750 16760 16770 16780 16790 16800 16810 THE CORN SILAGE OPERATION AND ESTIMATES LABOR AND ENERGY REQUIREMENT16820 LABOR AND ENERGY REQUIRED FOR FEEDING CORN SILAGE ARE APPROXIMATED AS 0.8 MAN.H/TDM AND 0.05 L FUEL EQUIVALENT PER TON OF DRY MATTER. DATA FDLB,FDEN /0.8.0.05/ CSUSE-CSAREA/ZRTCS(1) WRITE (10,101) (ZRT(1,JJ),JJ-1,5),CSAREA.DMCS.CSUSE 101 FORMAT (5X.'PRINTOUT TO CHECK THE SOURCE OF CORN SILAGE '. + ' ERROR',/,5X.'ZRT - '.5F10.2./.5X.'CSAREA I ',F10.2,' DMC5-'. + F10.2.' CSUSE-‘.F10.2) 11-0 1 II-II+1 IF (NBOPCS.NE.NBOP(II)) GO TO 1 DO 65 K-1,7 IF (NBMACH(II,K).EQ.O) GO TO 65 IJ-O 2 IJ-IJ+1 IF (NBMACH(II,K).NE.MCODE(IJ)) GO TO 2 UNITS(IJ)-AMAX1(UNITS(IJ),XNBM(II.K)) USEMCH(IJ)-USEMCH(IJ)+CSUSE*XNBM(II,K) 65 CONTINUE CSLABR-CSUSE*ZRTCS(0) CSFUEL-CSUSE*ZRTCS(2) 16830 16800 16850 16860 16870 16880 16890 16900 16910 16920 16930 16900 16950 16960 16970 16980 16990 17000 17010 17020 17030 17000 C ***********t******************************************************** C ***********************t******************************************** nnnnnnnnnn nnn nnn— nnn 328 CSELEc-CSUSE*ZRTCS(3) CSFDLB-FDLB*CSFED CSFDEN-FDEN*CSFED RETURN END SUBROUTINE ANCOST(NTHYR) COMMON /z3/ HARDEx.TMSTo(A).NPST(S.5).NCUM(5).OPUSE(5.9) COMMON /2A/FDLABR.FDENER.HRLABR.HRFUEL.HRELEC COMMON /26/ CSLABR.CSFUEL.CSELEC.CSFDLB.CSFDEN.DMCS COMMON /28/ ALFSIL(2).HAYST(3) COMMON /210/ TCOSTS(26.20).TRESS(26.20) COMMON /YYI/ USEMCH(IOO).UNITS(IOO) COMMON /Y1/ x1NFO(7).MCODE(IOO).XMDATA(IOO.13) COMMON /Y3/ NMDATA.NOPER.IN.IO COMMON /PR1CE/PLABOR.PFUELD.PFUELG.RATEIM.PDRYCG.PHRVCG.COEFSV(3), + PFSCAI,PFSCA2,PFSCCS,PFSCHM,ALFYRS,RATEIS.RATEIL,XLIFE(3) DIMENSION RMCOEF(27) DATA RMCOEF /2*1.2,0*12.,3*7.,3*3.1.0*2.9,3.1,1.8,5*3.0,2.5, + 3.0,0..0./ DATA TCOSTS,TRESS/520*O.,520*0./ THIS SUBROUTINE ESTIMATES THE ANNUAL USE OF RESOURCES AND THE ANNUALIZED COSTS FOR ALFALFA AND CORN SILAGE OPERATIONS. L. PARSCH HAS WRITTEN ANOTHER SUBROUTINE THAT ESTIMATES COSTS FOR HIGH MOISTURE CORN AND GRAIN CORN OPERATIONS. ALL THESE COSTS ARE MERGED IN SUBROUTINE REPORT AT THE END OF THE SIMULATION. IN THE FIRST YEAR ONLY, THE FIXED COSTS OF MACHINERY AND OF ALFALFA STORAGE STRUCTURES ARE ESTIMATED. IF(NTHYR.NE.I) GO TO 20 TOTAL CAPITALIzATION OF MACHINERY Is CALACULATED. TMCAP-O. DO 10 K-1.NMDATA IF (USEMCH(K).LE.O.) GO TO 10 IF (MCODE(K)-GE.26O.AND.MCODE(K).LE.279) GO TO 10 TMCAP-TMCAP+XMDATA(K.3)*UNITS(K) CONTINUE TOTAL CAPITALIZATION OF ALFALFA SILOS AND HAY BARN. TSCAP-ALFSIL(1)+ALFSIL(2)+HAYST(2) ESTIMATE THE ANNUALIZED FIXED COSTS FOR MACHINERY AND SILOS. ANMACH-ANPV(TMCAP.COEFSV(2).XLIFE(2).RATEIM) 17050 17060 17070 17080 17090 17100 17110 17120 17130 17100 17150 17160 17170 17180 17190 17200 17210 17220 17230 17200 17250 17260 17270 17280 17290 17300 17310 17320 17330 17300 17350 17360 17370 17380 17390 17000 17010 17020 17030 17000 17050 17060 17070 17080 17090 17500 17510 17520 17530 17500 15 nnnn nnn nnn W O nnnn n nnn 329 ANSILo-ANPV(TSCAP.CDEFSV(I),XLIFE(1).RATEIL) DO 15 K-1.26 TRESS(K.1)-TMCAP TCOSTS(K.1)-ANMACH TRESS(K.2)-TSCAP TCOSTS(K.2)-ANSILD CONTINUE CONTINUE ANNUAL VARIABLE COSTS: FUEL. LABOR AND REPAIR AND MAINTENANCE. ESTIMATE FUEL REQUIREMENTS AND COSTS FIRST. TFUEL-HRFUEL+FDENER+CSFDEN+CSFUEL+HRELEC/6. TRESS(NTHYR,3)-TFUEL TCOSTS(NTHYR,3)-TFUEL*PFUELD LABOR REQUIREMENTS AND COSTS. TLABHR-HRLABR+CSLABR TLABFD-FDLABR+CSLABR TRESS(NTHYR.5)-TLABHR TRESS(NTHYR.6)-TLABFD TCOSTS(NTHYR.5)-TLABHR*PLABOR TCOSTS(NTHYR.6)-TLABFD*PLABOR REPAIR AND MAINTENANCE COSTS. TRMC-O. DO 30 K-1,NMDATA IF (USEMCH(K).LE.O.) GO TO 30 KRM-MCODE(K)/10 TRMC-TRMC+XMDATA(K.2)*USEMCH(K)*RMCOEF(KRM)*0.0001 CONTINUE TRESS(NTHYR.0)'TRMC TCOSTS(NTHYR.0)-TRMC THERE MAY ALSO BE A VARIABLE STORAGE COST FOR DRY HAY IF THE VOLUME HARVESTED EXCEEDS THE NOMINAL STORAGE CAPACITY. TOTHAY-TMSTO(3)+TMST0(A) IF (TDTHAY.LE.HAYST(3)) RETURN VARSTO=(TOTHAY-HAYST(3))*HAYST(1) TCOSTS(NTHYR.2)-TCOSTS(NTHYR.2)+VARSTO RETURN END ******************************************************************** SUBROUTINE COWFD (NYRS.XLCDWS.HERD) **AAAAAAARAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA*AAAAAAAAAAA*AAAAAAAAAA THIS SUBROUTINE ESTIMATES MILK PRODUCTION. THE SALE OF 17550 17560 17570 17580 17590 17600 17610 17620 17630 17600 17650 17660 17670 17680 17690 17700 17710 17720 17730 17700 17750 17760 17770 17780 17790 17800 17810 17820 17830 17800 17850 17860 17870 17880 17890 17900 17910 17920 17930 17900 17950 17960 17970 17980 17990 18000 18010 18020 18030 18000 nnnnnnnnnnnnnnnnnnnnnnnn nnnnn nnnnn on 330 EXCESS FORAGES AND THE PURCHASE OF SUPPLEMENTAL FEEDS. IT WAS WRITTEN BY PHILIPPE SAVOIE. APRIL 1982 THE ARRAY HERD CONTAINS THE DISTRIBUTION OF ANIMALS WITHIN THE DAIRY HERD INTO THE SIX GROUPS SPECIFIED BELOW. TYPICAL VALUES COULD BE: DATA HERD /O.30,0.30,0.00,0.00,0.10,0.30/ XLCOWS IS THE TOTAL NUMBER OF LACTATING COWS REPRESENTING HERD(I) + HERD(2) + HERD(3) + HERD(0). LACTATING AND DRY COHS ARE ASSUMED TO HEIGH 650 KG. THE HERD IS DIVIDED INTO SIX GROUPS OF ANIMALS: 1. LACTATING COHS PRODUCING 35 KG MILK PER DAY . LACTATING COHS PRODUCING 30 KG MILK PER DAY . LACTATING COHS PRODUCING 25 KG MILK PER DAY . LACTATING COHS PRODUCING 20 KG MILK PER DAY . DRY COHS . HEIFERS (AVERAGE 300 KG LIVE HEIGHT) mmrwu A FEW PRINTOUTS ARE AVAILABLE TO SHOW DETAILS OF THE RATION 18050 18060 18070 18080 18090 18100 18110 18120 18130 18100 18150 18160 18170 18180 18190 18200 18210 18220 18230 FORMULATIONS AND HOW COWS ARE FED. THESE ARE PRESENTLY DISACTIVATED1820O BY COMMENT SIGNS IN THE FIRST COLUMN. THEY ARE LOCATED JUST ABOVE THE DO 60 STATEMENT (0 LINES). ABOVE THE 50 CONTINUE STATEMENT (2 LINES) AND BELOW THE DO 80 STATEMENT (3 LINES). COMMON /27/ ALHRFD(26,15).AFEED(26.23) COMMON /21/ AREA(6).NBD(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) COMMON /SUMRY2/ TRESP(26.2O).TCOSTP(26.2O).TCOST(26.2O). + STCOST(A.20).TRES(26.2O).SRES(A.2O) COMMON /Y3/ NMDATA.NOPER.IN,IO DIMENSION HERD(6),CNEL(6).CCP(6),TNEL(6).TCP(6).CS(3) DIMENSION HMC(3).SBM(3).YFR(6).PURALF(5).ADUMMY(A.6) DIMENSION ALFM(5.6).FR(5).ALFNEL(5).RATION(5.6.5) DIMENSION XMILK(A).FEEDUT(26.12).SFDUT(A.12),STTCST(A.2O) DIMENSION TCIO(26).TC13(26).TCI5(26).TCUA(26).TNRUA(26) CNEL AND CCP ARE THE MINIMUM CONCENTRATIONS OF NET ENERGY (LACTATION) AND CRUDE PROTEIN REQUIRED IN THE RATION FOR EACH OF THE FIVE GROUPS OF COWS (MCAL/KG AND DEC. CP) DATA CNEL/1.656.1.59O.1.51A,1.A26.1.35.1.35/ DATA CCP/O.I63.O.153.O.IA1.O.128.O.I1.0.12/ TNEL AND TCP ARE THE TOTAL NET ENERGY OF LACTATION (MCAL) AND TOTAL CRUDE PROTEIN (KG) REQUIRED PER ANIMAL PER DAY FOR EACH OF THE FIVE GROUPS OF COWS. DATA TNEL/30.05.31.00.27.55.20.10,13.39.7.25/ DATA TCP/3.385,2.975.2.565,2.155.0.980,0.706/ THE STANDARD QUALITY OF FEEDSTUFFS USED IN THE RATION IS 18250 18260 18270. 18280 18290 18300 18310 18320 18330 18300 18350 18360 18370 18380 18390 18000 18010 18020 18030 18AAO 18050 18060 18070 18080 18A90 18500 18510 18520 18530 18500 nnnnnnnnnnnn nnnnnnn 111 331 CHARACTERIZED BY 1-NET ENERGY OF LACTATION (MCAL/KG). 2-CRUDE PROTEIN (DEC). 3'TDN (DEC). FIVE TYPES OF FEED ARE CONSIDERD IN THE RATION: ALFALFA, CORN SILAGE, HIHGGH MOISTURE GRAINCORN. DRY CORN GRAIN AND SOYBEAN MEAL. DHE FIRST THREE ARE FARM GROWN AND ARE ALWAYS INCLUDED IN THE RATION. THE LAST TWO ARE ADDED ONLY WHEN WE MUST INCREASE EITHER THE NET ENERGY CONCENTRATION (ADD PURCHASED CORN GRAIN) OR THE CRUDE PROTEIN CEONCENTRATION (ADD SOYBEAN MEAL). NOTE THAT NO STANDARD VALUE IS USED FOR ALFALFA. BUT RATHER VALUES OF QUALITY FROM THE AFEED MATRIX WILL BE USED. DATA CS/1.589,0.08,0.70/ DATA HMC/1.80,0.10,0.80/ DATA SBM/1.86.0.096,0.81/ DATA PURALF/10000...13,0.,.52,0./ 18550 18560 18570 18580 18590 18600 18610 18620 18630 18600 18650 18660 18670 18680 18690 18700 DATA XMILK.PMILK,PSOYM.PCORN.PALF/35..30.,25..2O..286..2A8..139..8187IO +3./ DATA SCG.SHMC.SALF.SCS/129..90..69..7O./ DATA RATION/150*O./ . READ (2,99) XLCOHS.(HERD(I).I-1.6) FORMAT (7FIO.3) DO 5 NTHYR-1.NYRS TCORN-O. TSOYM-O. TALF-AFEED(NTHYR.1)+AFEED(NTHYR.6)+AFEED(NTHYR.11) + +AFEED(NTHYR.16) TALFI-TALF TCS-AFEED(NTHYR.21) TCSI-TCS THMc-AFEED(NTHYR.22) THMCI-THMC TCG-AFEED(NTHYR.23) TCG1-TCG THE YFR ARRAY CONTAINS THE TOTAL YEARLY FEED REQUIREMENT (TONS OF DRY MATTER) FOR EACH GROUP OF COHS. XLCOHS IS THE TOTAL NUMBER OF LACTATING COHS. TCOHS IS THE TOTAL NUMBER OF CDHS IN THE HERD. INCLUDING DRY COHS AND HEIFERS. FRLACT-HERD(1)+HERD(2)+HERD(3)+HERD(A) TFRAc-FRLACT+HERD(5)+HERD(6) IF (FRLACT.LE.O.O.OR.TFRAC.NE.1.) THEN HRITE (10.111) FORMAT ('1',///,5X,'***WARNING***',/.5X,'THE TOTAL FRACTION', ' WAS LESS 0R EQUAL TO 0. ACCORDING TO INPUT'.5X./. + + + + ' OF LACTATING COWS WITH RESPECT TO ALL COWS IN THE HERD'. 5X,'OR THE TOTAL OF ALL FRACTIONS WAS NOT EQUAL TO 1',5X./. 5X.'THE FOLLOWING DEFAULT VALUES WERE GIVEN TO THE SIX'. 18720 18730 18700 18750 18760 18770 18780 18790 18800 18810 18820 18830 18800 18850 18860 18870 18880 18890 18900 18910 18920 18930 18900 18950 18960 18970 18980 18990 19000 19010 19020 19030 19000 nnnnn— N nnnnnnnnnn— 20 332 + ' COH GROUPS: HERD(1)-O.3O HERD(2)-0.30 HERD(3)-0.00 HERD(A)-O.OO HERD(51-O.IO HERD(6)-O.3O FRLACT-HERD(1)+HERD(2)+HERD(3)+HERD(A) ENDIF TCOH5-XLCOHS/FRLACT DO 7 JCOH-1.6 YFR(JCOW)-TCOWS*HERD(JCOW)*TNEL(JCOW)*365./(CNEL(JCDW)*1000.) FEEDUT(NTHYR.12)-YFR(1)+YFR(2)+YFR(3)+YFR(A)+YFR(5)+YFR(6) DO 10 NS-1.A K-(NS-1)*5+I ADUMMY(NS.1)-AFEED(NTHYR.K) ADUMMY(Ns.2)-AFEED(NTHYR.K+1) ADUMMY(NS.3)-AFEED(NTHYR.K+2) ADUMMY(NS.A)-AFEED(NTHYR.K+3) ADUMMY(Ns.5)-AFEED(NTHYR.K+A) CONTINUE 0.30, 0.30, 0.00, 0.00, 0.10 AND O.30.',//) FOR WET ALFALFA, A 5 PERCENT REDUCTION OF CRUDE PROTEIN AND OF DIGESTIBILITY IS ASSUMED TO REFLECT THE REDUCED INTAKE WHEN COMPARED WITH DRY ALFALFA. DO 12 NS-1.2 ADUMMY(NS.2)-ADUMMY(NS,2)*O.95 ADUMMY(NS,0)-ADUMMY(NS,0)*0.95 RANK THE FOUR ALFALFA STORAGE LOCATIONS BY QUALITY. THE HIGHEST CRUDE PROTEIN BEING THE FIRST ROW IN ALFM MATRIX. A FIFTH ROW IS INCLUDED FOR PURCHASED ALFALFA IN CASE NOT ENOUGH ROUGHAGE IS PRODUCED ON THE FARM. THE QUALITY OF PURCHASED ALFALFA IS DEFINED IN A DATA STATEMENT FOR PURALF(5). THE FIVE COLUMNS IN MATRIX ALFM REPRESENT: TOTAL DM (METRIC TONS), CRUDE PROTEIN (DEC). BIASED STANDARD DEV. OF CP, DIGESTIBILITY (DEC) AND BIASED STANDARD DEVIATION OF DIG. DO 30 I-1.A KMAx-I XCP-ADUMMY(1.2) DO 20 Ns-2.A IF(XCP.GT.ADUMMY(NS.2)) GO TO 20 XCP-ADUMMY(NS.2) KMAx-NS CONTINUE ALFM(I.1)-ADUMMY(KMAX.1) ALFM(I,2)-ADUMMY(KMAX.2) ALFM(I.3)-ADUMMY(KMAX.3) 19050 19060 19070 19080 19090 19100 19110 19120 19130 19100 19150 19160 19170 19180 19190 19200 19210 19220 19230 19200 19250 19260 19270 19280 19290 19300 19310 19320 19330 19300 19350 19360 19370 19380 19390 19000 19010 19020 19030 19000 19050 19060 19070 19080 19090 19500 19510 19520 19530 19500 30 n O nnnnnnnnnr nnnnn 333 ALFM(I.A)-ADUMMY(KMAX.A) ALFM(I.5)-ADUMMY(KMAX.5) ADUMMY(KMAX.2)--1. CONTINUE A ALFM(5.1)-IOOOO. ALFM(5,2)-PURALF(2) ALFM(5.3)-PURALF(3) ALFM(5.A)-PURALF(A) ALFM(5.5)-PURALF(5) TDM-TALF+TCS+THMC THE NET ENERGY FOR LACTATION IS CALCULATED FOR ALL FIVE ALFALFA SOURCES. NE 15 A FUNCTION OF DIGESTIBILITY. DO 00 Ns-1.5 ' TDN-ALFM(NS.A) ALFNEL(NS)-1.15+(TDN-0.52)*2.5 IF (TDN.LT.O.52) ALFNEL(NS)-1.15 IF (TDN.GT.O.68) ALFNEL(NS)-1.55 CONTINUE THE FOLLOWING DO LOOPS (60 AND 50) ESTABLISH BALANCED RATIONS FOR ALL COMBINATIONS OF FARM GROWN ROUGHAGES (S DISTINCT ALFALFA GROUPS) AND OF FIVE ANIMAL GROUPS. HRITE (10.136) 136 FORMAT (//.SX.'THE RATION FORMULATIONS FOR ALL COMBINATIONS'. + /.SX.'NS + . NEL DO 60 Ns-1.5 IF (ALFM(NS.1).LE.O.) GO TO 60 DO 50 JCOW-1.6 FR(1)-TALF/TDM FR(2)-TCSITDM FR(3)-THMC/TDM FR(A)-O. FR(5)-O. FRI-1. FRA-O. FRs-O. AVENEL-ALFNEL(NS)*FR(1)+CS(1)*FR(2)+HMC(1)*FR(3) AVECP-ALFM(NS.2)*FR(1)+CS(2)*FR(2)+HMC(2)*FR(3) IF (NS.EQ.5) THEN AVENEL-ALFNEL(Ns) AVECP-ALFM(NS.2) ENDIF IF (AVENEL.GE.CNEL(JCOW)1 GO TO 55 JCOW ALF CS CP'./) HMC CG SB". , HERE WE MUST INCREASE THE CONCENTRATION OF NET ENERGY BY ADDING MORE CORN GRAIN. THE NEW CONCENTRATIONS IN THE RATION ARE CALCULATED 19550 19560 19570 19580 19590 19600 19610 19620 19630 19600 19650 19660 19670 19680 19690 19700 19710 19720 19730 19700 19750 19760 19770 19780 19790 19800 19810 19820 19830 19800 19850 19860 19870 19880 19890 19900 19910 19920 19930 19900 19950 19960 19970 19980 19990 20000 20010 20020 20030 20000 330 R-(CNEL(JCOW)-AVENEL)/(HMC(1)-CNEL(JCOW)) FR(A)-R/(1.+R) FR(3)-FR(31/(1.+R) FR(2)-FR(2)/(1.+R) FR(1)-FR(I)/(I .+R) AVECP-ALFM(NS. 2)*FR(I)+CS(2)*FR(2)+HMC(2)*(FR(3)+FR(0)) IF (NS. E0 5) THEN FRI-I./(1.+R) FRA-R/(1.+R) AVECP-ALFM(NS.2)*FR1+HMC(2)*FRO ENDIF IF (AVECP.GE.CCP(JCOW)) GO TO 51 HERE HE NEED TO ADD BOTH CG AND SBM. RECALCULATE PROPORTIONS OF FEEDS BY SOLVING THO EQUATIONS SIMULTANEOUSLY FOR CCP AND CNEL. BALANCE THE FOLLOHING EQUATIONS: AVENEL+HMC(1)*RC+SBM(1)*RS-CNEL(JCOW) AVECP +HMC(2)*RC +SBM(2)*RS-CCP(JCOW) XI-CCP(JCOH)-(AVECP+HMC(2)*(CNEL(JCOH)-AVENEL)/HMC(1)) X2-SBM(2) -HMC(2)*SBM(1)/HMC(1) Rs-XI/Xz Rc-(CNEL(JCOH)-(AVENEL+SBM(1)*RS))/HMc(1) X3-1./(1.+RS+RC) FR(1)-FR(1)*X3 FR(ZI-FR(2)*X3 FR(3)-FR(3)*X3 FR(01-RC*X3 FR(S)-RS*X3 IF (NS.EQ.5) THEN FRI-FR1*X3 FRO-RC*X3 FR5-RS*X3 ENDIF GO TO 51 IF (AVECP.GE.CCP(JCOW)) GO TO 51 nnnnnnnnnnnnnnnnnnnn HERE WE MUST INCREASE THE CONCENTRATION OF CRUDE PROTEIN BY ADDING SOME SOYBEAN MEAL. THE NEW CONCENTRATIONS IN THE RATION ARE CALCULATED. nnnnnmnnnn R-(CCP(JCOW)-AVECP)/(SBM(2)-CCP(JCOW)) FR (5) -R/ (1 .+R) FR (01-FRI01/(1 .+R) FR (3)-FR(3)/(1.+R) FR (2)-FR(2)/(1.+R) FR(1)-FR(1)/(1.+R) IF (NS.EQ.5) THEN FRI-FR1/(I.+R) 20050 20060 20070 20080 20090 20100 20110 20120 20130 20100 20150 20160 20170 20180 20190 20200 20210 20220 20230 20200 20250 20260 20270 20280 20290 20300 20310 20320 20330 20300 20350 20360 20370 20380 20390 20000 20010 20020 20030 20000 20050 20060 20070 20080 20090 20500 20510 20520 20530 20500 51 58 C C 50 60 can nnnnn nn nnnn FRO-FRO/(1.+R) FR5-R/(1.+R) ENDIF DO 58 II-I.5 RATION(NS.JCOW.II)-FR(II) IF (NS.EQ.5) THEN RATION(NS.JCOW.1)-FRI RATION(NS.JCOH.2)-O. RATION(NS,JCOW,3)-O. RATION(NS.JCOH.A)-FRA RATION(NS.JCOH.5)-FR5 ENDIF 335 AVENEL-ALFNEL(NS)*RATION(NS.JCOW.1)+CS(1)*RATION(NS.JCOW.2) + +HMC(1)*(RATION(NS.JCOW.3)+RATION(NS.JCDW.0))+SBM(1)* + RATION(NS.JCOW.5) AVECP-ALFM(NS,2)*RATION(NS.JCOW,I)+CS(2)*RATION(NS.JCOW.2) + +HMC(2)*(RATION(NS.JCOW.3)+RATION(NS,JCOW.0)) + +SBM(2)*RATION(NS.JCOW.5) HRITE(ID.137) NS,JCOW,(RATIDN(NS.JCOW.I),I-I.5).AVENEL.AVECP FORMAT (5X,12,I7.7F8.3) CONTINUE CONTINUE 137 FEED EACH GROUP OF COWS ONE AFTER THE OTHER STARTING WITH LACTATING THE BALANCED FEEDS WILL BE ALLOCATED STARTING WITH THE HIGHEST QUALITY ALFALFA UNTIL THE YEARLY FEED REQUIREMENT IS MET. COWS. DO 70 JCOW-1.6 IF (HERD(JCOW).LE.O.) GO TO 70 DO 80 N5-1,5 HRITE (10.126) JCOW.NS.ALFM(NS.1).ALFM(NS.2).YFR(JCOW) 126 FORMAT (5X.'FEEOING THE COWS: JCOW NS ALFDM ALFCP YFR'. + /,5X.20X,I3,I0,F7.1,F7.3,F7.1) IF (ALFM(NS.1).LE.O.) GO T0 80 ALFRQ-YFR(JCOW)*RATION(NS.JCOH.1) IF (ALFM(NS.1).GT.ALFRQ) THEN THE FEED REQUIREMENT FOR JCOW IS COMPLETELY MET. REDUCE THE FEED LEFT IN STORAGE LOCATION NS. ALFM(NS,1)-ALFM(NS,1)-ALFRQ TALF-TALF-ALFRQ THMc-THMC-YFRIJCOW)*RATION(NS.JCOW.3) TC5-TCS-YFR(JCOW)*RATIONINS.JCOW.2) TCORN-TCORN+YFR(JCOW)*RATION(NS.JCOW.0) TSOYM-TSOYM+YFR(JCOW)*RATION(NS.JCOW.5) GO TO 70 ENDIF HERE ALL THE FEED IN NS IS NOT ENOUGH TO SATISFY THE FEED REQUIRED BY COW GROUP JCOW. USE ALL NS. REDUCE YFR(JCOW) BY EMPTYING ALL THE FEED 20550 20560 20570 20580 20590 20600 20610 20620 20630 20600 20650 20660 20670 20680 20690 20700 20710 20720 20730 20700 20750 20760 20770 20780 20790 20800 20810 20820 20830 20800 20850 20860 20870 20880 20890 20900 20910 20920 20930 20900 20950 20960 20970 20980 20990 21000 21010 210207 21030 21000 C 80 nnn n 35 ... 336 IN STORAGE LOCATION NS. TDMNS-ALFM(NS,I)/RATION(NS.JCOH.1) YFR(JCOH)-YFR(JCOH)-TDMNS TALF-TALF-ALFM(NS.1) ALFM(NS.1)-O. THMc-THMC-TDMNS*RATION(NS.JCOW,3) TCS-TCS-TDMNS*RATION(NS,JCOW.2) TCORN-TCORN+TDMNS*RATION(NS.JCOW.0) TSOYM-TSOYM+TDMNS*RATION(NS,JCOW,5) CONTINUE CONTINUE MILK PRODUCTION, INCOME FROM MILK. INCOME FROM THE SALE OF EXCESS CROPS AND COST OF PURCHASED FEEDS ARE ESTIMATED BELOW. TMILK-(TCOWS*(HERD(I)*XMILK(1)+HERD(2)*XMILK(2) +HERDI3)*XMILK(3)+HERD(0)*XMILK(0)))*365./1000. VMILK-TMILK*PMILK CSOYM-TSOYM*PSOYM IN THE CASE OF CORN PURCHASES (TCORN). CHECK IF ANY FARM HARVESTED CORN IS LEFT AS HMC OR AS DRY GRAIN BEFORE MAKING OUTSIDE PURCHASES IF (TCORN.GT.THMC) THEN TCORN-TCORN-THMC THMCIO. ELSE THMC-THMC-TCORN TCORN-O. ENDIF IF (TCORN.GT.TCG) THEN TCORN-TCORN-TCG TCG-O. ELSE TCG-TCG-TCORN TCORN-O. ENDIF CCORN-TCORN*PCORN VCG-TCG*SCG VHMCITHMC*SHMC IF (TALF.LT.O.) THEN VALF'O. CALF-(-TALF)*PALF ELSE VALF-TALF*SALF CALF-O. ENDIF VCS-TCS*SCS TT-O. DO 85 I-I,9 TT-TT+TCOST(NTHYR,I) 21050 21060 21070 21080 21090 21100 21110 21120 21130 21100 21150 21160 21170 21180 21190 21200 21210 21220 21230 21200 21250 21260 21270 21280 21290 21300 21310 21320 21330 21300 21350 21360 21370 21380 21390 21000 21010 21020 21030 21000 21050 21060 21070 21080 21090 21500 21510 21520 21530 21500 ('1an 88 89 101 103 95 102 337 TCOST(NTHYR.10)-TT NET COST OF FEEDS: SBM MINUS INCOME FROM EXCESS ALF. CS. HMC TCOST(NTHYR.11)-CSOYM+CALF-(VHMC+VALF+VCS) NET COST OF CORN PURCHASES TCOST(NTHYR.12)-CCORN-VCG TCOST(NTHYR.13)-TCOST(NTHYR.IO)+TCOST(NTHYR.11)+TCOST(NTHYR.12) TCOST(NTHYR.1A)-VMILK TCOST(NTHYR.15)-TCOST(NTHYR.IA)-TCOST(NTHYR.13) . MATRIX FEEDUT IS A FEED UTILIZATION MATRIX. FEEDUT(NTHYR.1)-TALF1 FEEDUT(NTHYR.2)-TCSI FEEDUT(NTHYR.3)-THMCI FEEDUT(NTHYR.A)-TCG1 FEEDUT(NTHYR.5)-TSOYM FEEDUT(NTHYR.6)-TCORN FEEDUT(NTHYR.7)-TALF FEEDUT(NTHYR.8)-TCS FEEDUT(NTHYR.9)-THMC FEEDUT(NTHYR.10)-TCG TT-O. DO 88 1-1.6 TT-TT+FEEDUT(NTHYR.I) DO 89 1-7.IO TT-TT-FEEDUT(NTHYR.I) FEEDUT(NTHYR.11)-TT CONTINUE HRITE (10.101) XLCOHS.(HERD(I).I-1.6) FORMAT ('1',//.5X.'SUMMARY OF HOH FEEDS HERE USED EACH YEAR'. + /.5X.'THE NUMBER OF LACTATING COWS IS '.E6.0,/. + 5X,'THE DAIRY HERD IS DIVIDED INTO SIX GROUPS IN THE'. + ' FOLLOWING PROPORTIONS: ',6(F5.3,2X), + /.5X.'UNITS ARE METRIC TONS OF DRY MATTER',/,5X, + 'RATIONS WERE FORMULATED BY SUBROUTINE COWFD'.//.3X.'YR'. + 10X,'FEEDS PRODUCED ON THE FARM'.9X,'FEEOS PURCHASEO',18X, + 'FEEDS SOLD',16X,'NET FED',3X,'MAXIMUM‘,/. + 11X. + 'ALF CS HMC CG SBM 'CG', + ' ALF CS HMC CG' , 15X, ' INTAKE') WRITE(|0,103) FORMAT(9x. ------------------------------------ .5x. ' ---------- '. + ----- ' 0X, ' ------------------------------------ ',0X, 7" . TTTTTTTTTTTTTTTT '9//) DO 95 I-1.NYRS HRITE (10.102) I,(FEEDUT(I.J).J-1.12) FORMAT (3x.12.12FIO.2) HRITE(ID.103) CALL SSTAT(12.FEEDUT.NYRS.SFDUT) WRITE (10,133) 21550 21560 21570 21580 21590 21600 21610 21620 21630 21600 21650 21660 21670 21680 21690 21700 21710 21720 21730 21700 21750 21760 21770 21780 21790 21800 21810 21820 21830 21800 21850 21860 21870 21880 21890 21900 21910 21920 21930 21900 21950 21960 21970 21980 21990 22000 22010 22020 22030 22000 112 78 113 21' 211 21L 20 (“Dr—7n” 338 133 FORMAT (///.5X.'SAMPLE STATISTICS FOR SIMULATION OUTPUT. '. + 'ROW I-MEAN, ROH 2-STANDARD DEVIATION. ROH 3-COEF. OF '. + 'VARIATION',/) DO 96 1-1.3 96 HRITE (10.102) I.(SFDUT(I.J).J-1,12) HRITE (10.103) DO 77 I-I.NYRS TCIO(I)-TCOST(I.IO) TC13(I)-TCOST(I.I3) TC15(I)-TCOST(I.15) TCUA(I)-TCOST(I,13)/AREA(1) TNRUA(I)-TCOST(I.15)/AREA(1) 77 IF(AREA(1).LE.O.) TCUA(I)-O. CALL RANK(TCIO.NYRS) CALL RANK(TC13.NYRS) CALL RANK(TC15,NYRS) CALL RANK(TCUA.NYRS) CALL RANK(TNRUA,NYRS) HRITE (10.112) 112 FORMAT ('1',//.5X.'TOTAL COSTS RANKED IN INCREASING ORDER'./. + 5X.'TOTAL COST (1-9)'.5x,'TOTAL COST (10-12)‘.5X. + 'NET RETURN'. + 5X.'TC(10-12)/HA',5X.'TNR/HA',//) DO 78 I-1.NYRS 78 HRITE (10.113) TCIO(I).TCI3(I).TC15(I).TCUA(I).TNRUA(I) 113 FORMAT (5X.FIO.O.2(11X.FIO.O).2(6X.FIO.O)) HRITE (10.210) 210 FORMAT ('1',///,5X,'TOTAL COSTS IN THE ORIGINAL YEARLY', + ' ORDER FOR THE HERD SPECIFIED ABOVE',//.3X,'YR'.3X, + '10-SUM(I-9) 11-FNET 12-CG 13-SUM(IO-12) 10-'. + 'MILK 15-NET RETURN'.//) * DO 211 I-I,NYRS 211 HRITE (10.212) I.(TCOST(I,J).J-10,15) 212 FORMAT (3x.12.6FI0.0) CALL SSTAT (20.TCOST.NYRS.STTCST) HRITE (10.133) DO 213 1-1.3 213 HRITE (IO.21A) I.(STTCST(I,J),J-IO,15) 21A FORMAT (3x.12.6F10.2) READ (2.201) Izz 201 FORMAT (110) IF (Izz.EQ.1) GO TO 1 RETURN END C it”:*tttttttttttkttizttt**********mudm*MAAAA************************* SUBROUTINE RANK(AR.K8) *ttti'kfitttttic**********************ttkttktttttttkttttt*ktttfitkttitktak C C C 'THIS SUBROUTINE REORDERS NUMBERS IN A ONE-DIMENSIONAL ARRAY C AND RANKS THEM IN INCREASING ORDER. 22050 22060 22070 22080 22090 22100 22110 22120 22130 22100 22150 22160 22170 22180 22190 22200 22210 22220 22230 22200 22250 22260 22270 22280 22290 22300 22310 22320 22330 22300 22350 22360 22370 22380 22390 22000 22010 22020 22030 22000 22050 22060 22070 22080 22090 22500 22510 22520 22530 22500 Inn arc-16.158.158.158 C C OH 339 THERE ARE KB NUMBERS TO BE RANKED IN ARRAY AR. DIMENSION AR(26).DUM(26) IF (KB.LE.1) RETURN DO 1 I-I,KB DUM(I)-AR(I) FIND THE MINIMUM VALUE AND RANK IT. DO 3 J-1,KB IMIN-I VALMIN-DUM(I) DD 2 I-2.KB IF (VALMIN.GT.DUM(I)) THEN VALMIN-DUM(I) IMIN-I ENDIF CONTINUE AR(J)-VALMIN DUM(IMIN)-9.E+20 CONTINUE RETURN END *****************************************t************************** PROGRAM TEST *************************ttt**************************************** PROGRAM TEST Is A DUMMY PROGRAM USED TO TEST ALHARV. IT ALLOHS TO RUN ALHARV AND FORHRV TOGETHER HITHOUT THE CORN AND ALFALFA GROHTH MODELS BY ASSUMING FIXED YIELDS AND HEATHER CONDITIONS. IT SHOULD BE REPLACED BY THE BIGMOD PROGRAM. ALFMOD AND CRNMOD HRITTEN BY PARSCH (1982) TO SIMULATE THE HHOLE DYNAMIC FORAGE MODEL. COMMON /H1/ NPLOTS.NMOH.NHRV,NSTO.AREAPL.HARMAT(AO.29).ZRT(9,5) COMMON /H2/ TPL(9).RAIN.JJDAY,NDAYHR COMMON /H3/ HFEED(A.I6O,5) COMMON /21/ AREA(6).N30(6).NOPSQ(5.9).CRTR(5.A.9).SILO(2) COMMON /CTRL2A/ BGNCUT(5).NTHYR.NTHCUT.NDAYSC.NDAYSH.YLD(A). 22550 22560 22570 22580 22590 22600 22610 22620 22630 22600 22650 22660 22670 22680 22690 22700 22710 22720 22730 22700 22750 22760 22770 22780 22790 22800 22810 22820 22830 22800 22850 22860 22870 22880 22890 22900 22910 +QUAL(3.0).GDDCUM,METRIC.JYEARF,JYEARL,IPRTI,IPRT2.JDAYF,JDAYL.JPRT22920 +,NYRS,IPRTO,NCUTS,JYEAR,JLALHR,CPLANT COMMON /ALFARG/ GDDB5,AVTA,DAYLIN.DAYLEN.YOAYL.DECR.XLAI,AW, +SUMSI.SUMSZ.T,WSF.SRADF,DWS,PPT.ESO.ESR.XLEAF.BUOS,STEM,TOPS,TNC. +XMATS,TNCS,TMAXC.TMINC COMMON /z3/ HARDEX.TMSTO(A).NPST(5.5).NCUM(5).OPUSE(5.9) COMMON /2A/FDLABR.FDENER.HRLABR.HRFUEL.HRELEC COMMON /25/ IPR2.IPR3.IPRO COMMON /Z6/ CSLABR.CSFUEL.CSELEC,CSFDLB.CSFDEN,DMCS COMMON /27/ ALHRFD(26,15).AFEED(26.23) COMMON /z10/ TCOSTS (26. 20) .TRESS (26.20) COMMON /YY1/ USEMCH (100) .UNITS (100) COMMON /Y1/ XINFO(7).MCODE(IOO).XMDATA(IOO.13) 22930 22900 22950 22960 22970 22980 22990 23000 23010 23020 23030 23000 IIEIII. 300 26 28 IF (JDAY.LT.BGNCUT(NTHCUT)) GO TO 20 COMMON /Y3/ NMDATA,NOPER.IN.IO 23050 COMMON /Y6/ RATES(108.8).YAR(6) 23060 COMMON /Y7/ NBOP(18).NBMACH(18,7).XNBM(18,7) 23070 COMMON /PRICE/PLABOR.PFUELD.PFUELG.RATEIM,PDRYCG,PHRVCG.COEFSV(3).23080 + PFSCAI.PFSCA2.PFSCCS,PFSCHM.ALFYRS.RATEIS.RATEIL.XLIFE(3) 23090 COMMON /SUMRY2/ TRESP(26.20).TCOSTP(26.2O).TCOST(26.2O). 23100 + STCOST(A.2O).TRES(26.20).SRES(A.2O) 23110 DIMENSION HERD(6) 23120 OPEN(1.FILE-'MACH') 23130 0PEN(2,FILE-'MGTALF') 23100 OPEN (6,FILE-'OUTPUT') 23150 DATA IN/5/.IO/6/ 23160 DATA QUAL /.AA..56,1...28..13..196..75..60..666..1A..29..22A/ 23170 DATA PLABOR.PFUELD /5.00.0.309/ 23180 DATA RATEIM,RATEIL /O.15.0.13/ 23190 DATA COEFSV,XLIFE /O.O.O.1.O.2.3O..IO..7./ 23200 DO 28 I-1.26 23210 DO 26 J-1.2O 23220 AFEED(I.J)-O. 23230 DO 28 J-1.15 23200 ALHRFD(I.J)-O. 23250 NTHCUT-I 23260 BGNCUT(1)-1. 23270 BGNCUT(2)-5O. 23280 BGNCUT(3)-IOO. 23290 BGNCUT(A)-365 23300 NTHYR-I 23310 JDAYF-I 23320 JDAYL-ISO 23330 JYEARF-I 23300 JYEARL-Z 23350 CPLANT-O. 23360 NYRS-JYEARL+1-JYEARF 23370 TMINc-15. 23380 TMAxc-25. 23390 SRADF-5OO. 23AOO PPT-20. 23A10 XLEAF-220. 23A20 STEM-280. 23030 TOPS-500. 23000 IN-I 23A50 CALL FORHRV 23060 IN-2 23A7O CALL MGTINF 23A80 YCS-IO. 23090 CSAREA-IOO. 23500 DO 30 JYEAR-JYEARF.JYEARL 23510 CALL YRINIT 23520 DO 20 JDAY-JDAYF,JDAYL 23530 23500 96 107 108 109 97 nnnnnnnnnnnn 20 120 30 53 130 +'COLUMN REPRESENTS:',/.5X,'1-MACH INV. +'0-R6M (S) 100 00 101 50 301 X1-FLOAT(JDAY)/0. II-IFIX(XI) x2-X1-FLOAT(II) IF (X2.EQ.O.) PPT-IO. CALL ALHARV (REMCUT.REMHRV.ICUTON.JDAY) IF (JDAY.EQ.1.0R.JDAY.EQ.50) GO TO 96 IF(JDAY.EQ.100) GO TO 96 IF(JDAY.GE.109.AND.JDAY.LE.111) GO TO 96 GO TO 97 HRITE(IO.IO7) JYEAR.JDAY,NTHCUT.HARDEX.(TMSTO(J).J-1.0) HRITE(ID.108)(NPST(NTHCUT.J).J-1.5).(OPUSE(NTHCUT.J).J-1.9) HRITE(ID.109)(2RT(J.1),J-I,9) FORMAT(SX.'JYEAR-',I0,/.5X.'JDAY-',I0./,5X,'NTHCUT-'. + I0,/,5X,'HARDEx-',F10.0,/.5X,'TMSTO-'.0F10.2) FORMAT(SX,'NPST-'.5I10./.5X.'0PUSE-'.9F10.2) FORMAT(5X.'ZRT-',9FIO.2) CONTINUE PPT-O. IF (REMHRV.EQ.O.) NTHCUT-NTHCUT+I CONTINUE CSHR-CSRATE(YCS.1AO) CSFED-YCS*CSAREA*O.8 CALL ENDCS(CSAREA.CSFED) CALL HRITAL(2) HRITE (IO.12O) YCS.CSAREA.CSHR.CSLABR.CSFUEL.CSELEC FORMAT (//.10x,'CORN SILAGE HARVEST INFORMATION'./,10X, 6F12.2) XLEAF-132. STEM-168. TOPS-300. NTHCUT-I NTHYR-NTHYR+I CONTINUE CALL HRITAL(3) DO 53 I-1.NYRS DO 53 J'1.20 TCOST(I.J)-TCOSTS(I.J) CALL COHFD(NYRS.XLCOHS.HERD) HRITE (10.130) , FORMAT (//.5X.'PRINTOUT OF RESOURCES AND COSTS. EACH'. 2-SILO INV. 3-FUEL (L) 5-FIEL LB (MAN.H) 6-FEDD LB',//) DO 00 K-1.NYRS HRITE (IO.1AO) (TRESS(K,J),J-1.10) FORMAT (5X.6(F10.1.1X).AF6.1) CONTINUE HRITE (10.130) DO 50 K-1.NYRS HRITE (IO.1A1) (TCOST(K.J).J-1.15) FORMAT(1X.15(1X.F7.O)) CONTINUE 23550 23560 23570 23580 23590 23600 23610 23620 23630 23600 23650 23660 23670 23680 23690 23700 23710 23720 23730 237AO 23750 23760 23770 23780 23790 23800 23810 23820 23830 23800 23850 23860 23870 23880 23890 23900 23910 23920 23930 '23900 23950 23960 23970 23980 23990 20000 20010 20020 20030 20000 302 STOP END C . C ************************************k******************************* FUNCTION TABLI (VAL.ARG.DUMMY.K) C ***t**************************************************************** DIMENSION VAL(K).ARG(K) DUM-AMAX1(AMINI(DUMMY.ARG(K)).ARG(1)) DO 1 I-2.K IF (DUM.GT.ARG(I)) GO TO I TABLI-(DUM-ARG(l-I))*(VAL(I)-VAL(I-1))/ +(ARG(I)-ARG(I-1))+VAL(I-I) RETURN 1 CONTINUE RETURN END . C **A***************************************************************** FUNCTION ANPV(PP.COEFSV.XLIFE.RATEI) C *At***************************************************************** IF ((PP.LE.O.).OR.(XLIFE.LE.O.)) THEN ANPv-O. RETURN ELSE CRF-(RATEI*((1.0+RATEI)**XLIFE))/(((I.0+RATEI)**XLIFE)-1.0) ANPv-((PP*(1.0-COEFSV))*CRF)+((PP*COEFSV)*RATEI) ENDIF RETURN END C . C *****************************************t************************** SUBROUTINE SSTAT(NVAR,SMPL.NOBS.XMOMNT) C **************************************************A***************** C C SSTAT CALCULATES MEAN, STANDARD DEVIATION. COEFFICIENT OF C VARIATION, AND SKEHNESS OF A SAMPLE DISTRIBUTION. c (L. PARSCH. DEPT OF AG ECON. MSU. 12/81) C DIMENSION SMPL(26,25).XMOMNT(A.25) DIMENSION SUM(26).SX(25).SV(25).SS(25) DO 10 I-I.NVAR SUM(I)-O.O DO 20 J-1.NOBS 20 SUM(1)-SUM(I)+SMPL(J.I) IO sx(I)-SUM(I)/NOBS DO 30 II-1.NVAR SUM(II)-0.0 DO 00 JJ-1.NOBS Do SUM(Il)-SUM(II)+(SMPL(JJ,|I)-SX(II))**2. 20050 20060 20070 20080 20090 20100 20110 20120 20130 20100 20150 20160 20170 20180 20190 20200 20210 20220 20230 20200 20250 20260 20270 20280 20290 20300 20310 20320 20330 20300 20350 20360 20370 20380 20390 20000 20010 20020 20030 20000 20050 20060 20070 20080 20090 20500 20510 20520 20530 20500 ’1 6O 50 303 SV(II)-SUM(II)/(NOBS-1) IF(NOBS.LE.1)SV(II)-0.0 DO 50 III-I,NVAR SUM(III)'0.0 DO 60 JJJ-1,NOBS IF(SV(III).EQ.0.0)G0 T0 50 SUM(III)-SUM(III)+((SMPL(JJJ,III)-SX(III))**3./(SV(III)**.5)) SS(III)-SUM(III)/NOBS DO 70 1-1.NVAR XMOMNT(1,I)-SX(I) XMOMNT(2.I)-SQRT(SV(I)) XMOMNT(3,I)-XMOMNT(2.I)/XMOMNT(1.I) IF(SX(I).EQ.O.O)XMOMNT(3.I)-O.O XMOMNT(A,I)-SS(I) RETURN END 20550 20560 20570 20580 20590 20600 20610 '20620 20630 20600 20650 20660 20670 20680 20690 20700 20710 20720 20730 LIST 05' REFERENCES LIST OF REFERENCES Amir, 1., J. 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