. .. . . .VV., ..V.. V V V . V. . . . ._4 .. .,.V‘. V , V ,4 . , , , V ,V .V . ,.. . . . V A . . V _ . i v. .. . . V v . V ....V.,. 3 V .. . V. .V . . . . ,, V , . , V , ., , .. . . .. V .V . V. , . .V. V . . . , ,. ,0. . , . , .V VV . . . , . .. ., . . V , V V V V A . V . .. V. . ... , V V ; .V, .V. V V V Q r. V . V . . _ V . V . . V . . , , . . , , . . . . . . . . . . V V . V V . . .V V A V . . . A .A . \ . V . , V ’l l \ VI. MI ICHIGANS \allllljfilllw ”Willi F T LIBRARY Michigan State University k , l This is to certify that the dissertation entitled VALUE OF ALFALFA LOSSES IN THE DAIRY FORAGE SYSTEM presented by Dennis R. Buckmaster has been accepted towards fulfillment of the requirements for Ph.D. degree in Agr. Engr. cfléflr Major professo Date {gal // W MS U is an Affirmative Action/ Equal Opportunity Institution 0-12771 PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. DATE DUE DATE DUE DATE DUE MSU It An Affirmdivo Action/Equal Opportunity Initiation mans-m VALUE OF ALFALFA LOSSES IN THE DAIR! FORAGE SYSTEM By Dennis R. Buckmaster A DISSERTATION Submitted to‘ Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1989 ABSTRACT VALUE OF ALFALFA LOSSES IN THE DAIRY FORAGE SYSTEM 33! Dennis R. Buckmaster Value lost because of dry matter loss and quality deterioration during production and utilisation of forages was determined using a whole-farm simulation model (DAFOSYM). Improved submodels of dry matter loss and changes in neutral detergent fiber and crude protein during harvest, storage and feeding were developed. The silo storage model is comprehensive and describes the preseal, fermentation, infiltration and feedout phases. The animal model combines nutrient requirements with an intake function to predict animal response to forage quality. Elimination of all losses on a representative, Midwestern, 100-cow dairy farm increases the annual net return by $12,ufl8 when milk production is 8,000 kg/cow-y. Reduction of alfalfa value during harvest (:13.u2/r DH) is larger than during storage ($7.87/T DM). Rate of respiration loss during field curing was modeled as a function of moisture content and temperature, with the loss being- non- protein, non-fiber material. The loss in alfalfa value due to respiration is relatively small ($1.32/T DH). Rain loss, modeled as a function of rainfall and alfalfa quality at the time of rain occurrence, decreases alfalfa value by $2.9u/r DM. _ Machinery-induced losses were modeled as functions of moisture Dennis R. Buckmaster content and yield. Of these losses, raking loss reduces alfalfa value most ($7.01/T DH). For a moderate milk production level, hay storage causes the greatest value reduction ($8.91/T DH); silo loss reduces value by 36.n2/T DH. The value of alfalfa losses depends greatly on the milk production potential of the dairy animals. At high milk production levels (production is limited only by forage quality), the value of all alfalfa losses combined is 2.2 times greater than the value at moderate (8,000 kg/cow-y) milk production. The ranking of the most costly losses changes as milk production increases. Hay storage ($19.58/T DH), silo storage ($28.93/T DH), raking ($10.00/T DH) and rain ($7.08/T DH) cause the largest value reductions at the high milk production level. Improvement in feed allocation over a method typical of the better dairy farmers can ,increase net return by $3,271 to $11,059/y. The improvement in feed allocation has as much impact on net return as the elimination of all storage losses. APPROVED BY (f?-4422:: Lyéé:g;*”’.’”" HajoF Profesgdr APPROVED BY 1 12' €',//¢QQ / Department Chairman ACKNOWLEDGMENTS The list of thank yous is nearly endless, yet in this finite space, I'd like to express my gratitude to my major professor and friend, Dr. 0. Alan Rotz, for his helpful, motivating and knowledgeable guidance over the past four years. His approach toward life beyond career has been particularly meaningful. A I thank Dr. J. Roy Black for the unselfish discussions and his help during construction of the animal and silo models. I am appreciative to Dr. Fred W. Bakker-Arkema, Dr. Roy S. Emery and Dr. James V. Beck for their instruction during coursework and for their constructive criticism of this dissertation. Without input from Dr. David R. Hertens and Dr. Richard E. Huck, the animal and silo models could not have been possible. For their giving of time and knowledge, I am grateful. I thank my wife, Corinne, for encouragement, support and lack of complaints as time slipped away. Still others have given insight, motivation and simply friendship, which makes graduate work all the more worthwhile. Hy hope is that those who have helped might have received something in return for their unselfishness. Host importantly, I wish to thank my heavenly Father for giving me a real purpose in life, mental abilities and friends, which together have made my life enjoyable and productive thus far. iv TABLE OF CONTENTS Chapter Page LIST OF TABLES O O O O O O O O O 0 O O O O O O O O O O O O O O 0 x1 LIST OF FIGURES ..... . . . . . . . . . . . . . . . . . . . .xiii NOMENCLATURE . . . . . . . ..... . . . . . . . . . . . . . . xvi 1. INTRODUCTION . . . . . . . . ..... . . . . . . . . . . . . . 1 1.1 Background and motivation . . . . ......... . . . . 1 1.2 Objectives . . . . . . . . ............ . . . . . 3 2. LITERATURE REVIEW . . . . . ...... . . . . . . . ..... H 2.1 Forage Losses . . . . . . . . . . . . . . . . . . . . . . . u 2.1.1 Field losses . . . . . . . . . . . . . . . . . . . . . H 2.1.1.1 Losses during field curing . . . . . . . . . . 5 2.1.1.2 Machinery induced losses . . . . . . . . . . 7 2.1.2 Storage losses . . . . . . . . . . . . . . . . . . . . 12 2.1.2.1 Losses during hay storage . . . . . . . . . . 12 2.1.2.2 Losses during silage storage . . . . . . . . 15 2.1.3 Feeding and feed allocation . . . . . . . . . . . . . 17 2.2 Animal utilization/assessment of feed nutritional value . . . 18 2.3 Forage system models . . . . . . . . . . . . . . . . . . . . 21 2.l|DAFOSYM..........................30 3. HARVEST LOSS MODELS . . . . . . . . . . . . . . . . . . . . . . 36 3.1 Respiration . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 Rain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 TABLE OF CONTENTS - continued Chapter Page 3.3 Machinery . ......... . ........... . . . . ”0 n. STORAGE AND FEEDING MODELS . . . . . .............. 52 ”.1 Inside hay storage . . . . . . ......... . ..... 52 ”.2 Outside hay storage ..................... 5A A 4.3 Silo storage . . . . . . . . . . . . . . . . . . . . . . . . 55 A.3.1 Model development .................. 56 A.3.1.1 Overall model structure . . . . . . . . . . . 56 u.3.1.2 Phase 1: Preseal ............... 61 fl.3.1.3 Phase 2: Fermentation ...... . ..... 66 u.3.1.u Phase 3: Infiltration ........... _. 69 “ fl.3.1.5 Phase A: Feedout ............... 7A u.3.2 Model validation ................... 75 u.3.3 Sensitivity analysis ................. 78 A.” Feeding loss . . . . .................... 8A 5. ANIMAL UTILIZATION MODEL ............. . ...... 88 5.1 Lactating cow model . . . . . . . . . . . . . . . . . . . . 89 5.1.1 Dry matter intake . . . . ............... 89 SCLZanr . ... ... ... ... ... ... ... .. 91 5.1.3 Energy . . . . I ......... . . . . . . ..... 92 5.1.” Protein . . . . . . . . . . . . . . . . . . . . . . . . 93 5.1.5 Linear programming implementation . . . ...... . 97 vi TABLE OF CONTENTS - continued Chapter Page 5.2 Growing heifer model ................ . . . . 98 5.2.1 Energy .................... . . . . 98 5.2.2 Protein . . . ...... . ........ . . . . . . 100 5.3 Feed characteristics . .................. . 101 5." Verification . . . . .................... 103 5.“.1 Lactating cow model . . . . . . . ..... . . . . . 103 5.“.2 Growing heifer model . . . . . . . . . . . . . . . . . 108 5.5 whole-herd model . . . . .............. . . . . 111 5.5.1 Herd composition ..... ‘ .............. 112 5.5.2 Feed allocation . . . . . ............. . 117 5.5.2.1 Decision rule . . . . . . . .......... 118 5.5.2.2 Linear programming .............. 121 FORAGE VALUE . . . . . . . . . . . . . . . . . . . . . . . . . . 12“ 6.1 Simulation approach . . . . . . . . . . . . . . . . . . . . . 12“ 6.1.1 Determination of alfalfa value . . ...... . . . . 125 6.1.2 Animal characteristics ..... . . . . ....... 126 6.1.3 Feed characteristics . . . . . . . . . . . . . . . . . 127 6.2 Effect of alfalfa quality on value ........ . . . . . 128 6.2.1 Moderate milk production . . . . . . . . . . . . . . . 128 6.2.2 High milk production -- determined by forage quality . 129 6.3 Relative feed value . . ............. . ..... 132 vii TABLE OF CONTENTS - continued Chapter Page 6.“ Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . 136 PROCEDURE FOR DETERMINING THE ECONOMIC VALUE OF FORAGE LOSSES . . 1uo 7.1 Simulation approach . . . . . . ........... . . . 1A0 7.2 Farm description ..... . ................ 1A1 7.3 Evaluation . . . . . ........ . . . . . . . . . . 1A3 VALUE OF FORAGE LOSSES ..................... 148 8.1 In-field and harvest ............ . ..... . . 149 8.1.1 Respiration .............. . ....... 1A9 8.1.2 Rain . . . . . . . . . . . . . . . . . . . . . . . . 151 8.1.3 Machinery . . . . . . . . . . . . . . . . . . . . . . 152 8.1.A Total harvest loss . . . . . . . . . . . . . . . . . . 15” 8.2 Storage . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.2.1 Hay storage . . . . . . . . . . . . . . . ..... 158 8.2.2 Silo storage . . . . . . . . . . ......... . . 158 8.2.3 Total storage loss . . ...... . . . . . ..... 159 8.3 Feeding . ........ . . ............... '. 159 8.” Total loss ............. . ..... . ..... 160 8.5 Feed allocation . . . . . . . . . . . . . . . . . . . . 160 8.6 Sensitivity of feed loss value . . . . . . . ........ 162 8.6.1 Farm parameters . ............ . . . . . . . 163 8.6.1.1 Farm size . . . . . . . . ........... 163 viii TABLE OF CONTENTS - continued Chapter Page 8.6.1.2 Type of alfalfa harvest ............ 16“ 8.6.1.3 Type of baler ................. 169 8.6.1." Harvest moisture .- .............. 170 8.6.1.5 Silo type ................... 173 8.6.2 Animal parameters ................... 179 8.6.2.1 Body weight .................. 179 8.6.2.2 Ingestive capacity .............. 180 8.6.3 Economic parameters .................. 180 8.6.3.1 Milk price . . ............... . 180 8.6.3.2 Price of protein supplements ......... 181 8.7 Summary ..... . ..................... 182 9. CONCLUSIONS AND RECOMMENDATIONS . . ............... 183 9.1 Conclusions . . . . . . ................... 183 9.1.1 Loss models . . . ................... 183 9.1.2 Animal utilization model . . . . . . . . . . . . . . . 18“ 9.1.3 Value of alfalfa losses . . . . . . . . . . ..... . 18“ 9.2 Recommendations for future work . . . ...... . . . . . . 186 10. REFERENCES ........... . ............... 189 ix TABLE OF CONTENTS - continued Appendix FORTRAN code for the hay and silo storage models in DAFOSYH . . FORTRAN code for the animal models in DAFOSYM . . . . . . . . . Formulated rations and feed use data used to determine alfalfa value as presented in Chapter 6 ....... . . . . . . . . . . DAFOSYM farm and machinery input files used in the simulation experiments . ........... . . . . . . . . DAFOSYM output used to determine the value of alfalfa losses Page . 197 - 224 ° 249 ° 261 ° ° 286 LIST OF TABLES Table Page 2.1 3.1 3.2 ”.1 H2 n.63 5.1 5.2 5.3 5.“ 5.5 5.6 5.7 5.8 Level of detail in the component models of several economic forage system models . . . . . . . . . . . . . . . . . 29 Models of machinery-induced losses . . . . . . . . . . . . . . H2 Baler pick-up and chamber loss (small rectangular bales) . . . A7 Initial crop characteristics used to develop fermentation relationships 0 O O O O O O O O O O O O O O O O O O O O O O O O 68 Dry matter losses predicted by the silo model compared to losses reported for actual silos . . . . . . . . . . . . . . . . . . 77 Predicted and published concentrations of non-protein nitrogen in alfalfa silages . . . . . . . . . . . . . . . . . . . . . . 79 Animal model subroutines and their function in the prediction of animal performance . . . . . . . . . . . . . . . 90 Characteristics of various ruminant feeds . . . . . . . . . . 102 Formulated rations for 600 kg cows producing varying amounts of ”1 fat milk while gaining 0.33 kg/day . . . . . . . . . . . . . 105 Net energy of lactation (Meal/kg) of formulated rations for 600 kg cows producing varying amounts of “1 fat milk while gaining 0e 33 kg/day e e e e e e e e e e e e e e e e e e e e e 106 Neutral detergent fiber content (fraction) of formulated rations for 600 kg cows producing varying amounts of MS fat milk while gaining 0.33 kg/day .. . . . . . . . . . . . . . 107 Crude protein content (fraction) of formulated rations for 600 kg cows producing varying amounts of ".01 fat milk while gaining 0.33 kB/day O O O O O O O O O O O O O O O O O O O O O 107 Impact of body weight change of a 600 kg cow producing 30 kg of “1 fat milk on net energy and absorbed protein requ1rements O O O O O O O I C O O I O O O O O O O O O O O O O 109 Formulated rations for heifers gaining 0.70 kg/day . . . . . . 110 xi LIST OF TABLES - continued Table 5.9 5.10 5.11 5.12 6.1 7.1 702 7-3 70“ 8.1 8.2 8.3 8.“ 8.5 Crude protein (fraction) and metabolizable energy (Meal/kg) concentrations of formulated rations for heifers gaining o O 70 kslday O O O O O O O O O O O O O O O O O O O O I O O O 0 Impact of body weight change of a "00 kg heifer on metabolizable energy and absorbed protein requirements . . . . Parameter values used to describe animals in the dairy herd . Description of a herd with 26% primiparous cows . . ..... Combinations of crude protein and neutral detergent fiber for which rations were balanced for three stages Of lactation O O O O O O I O O O O O O O O O O O 0 O O O O 0 Sources of loss analyzed in this study . . . . . . . . . . . Storage structures on the representative 100 ha farm . . . . Machinery available on the representative 100 ha farm . . . . Prices of various inputs and feeds in the simulation studies Value of individual harvest losses and their effect on total feed costs for a medium-sized farm . . . . . . . . . . Value of storage and combined losses and their effect on total feed costs for a medium-sized farm . . . . . . . . . . Increase in net return and value of optimal feed allocation on different sized farms . . . . . . . . . . . . . Value of losses during ensiling of alfalfa in three types of silos on the medium-sized farm . . . . . ..... . Ranking of alfalfa value losses on a medium-sized farm due to individual processes . . . . . . . . . . . . . . . . . xii Page 111 112 115 116 . 128 1H2 um um ms . 151 157 161 176 182 LIST OF FIGURES Figure Page 2.1FlowchartofDAFOSYM..................... 32 3.1 Model for moisture variation throughout the day for determination of respiration loss . . . . . . . . . . . . . . . 38 3.2 Raking loss as affected by yield and moisture content . . . . . 44 3.3 Baler loss as affected by moisture content . . . . . . . . . . 48 3.4 Chopper loss as affected by moisture content . . . . . . . . . 51 4.1 Order of filling and unloading of plots in silos . . . . . . . 59 4.2 Geometry of 1-dimensicnal steady state oxygen diffusion into forage material (Pitt, 1986) . . . . . . . . . . . . . . . 63 4.3 Moving-front concept for modeling loss due to oxygen infiltration into silos . . . . . . . . . . . . . . . . . . . . 70 4.4 Algorithm used for modeling the moving-front concept of loss due to oxygen infiltration . . . . . . . . . . . . . . . . 72 4.5 Effect of silo permeability on predicted dry matter loss in silos filled with alfalfa silage (150 T DM capacity) emptied over 360 days . . . . . . . . . . . . . . . . . . . . . 80 4.6 Effect of silo size on predicted dry matter loss . . . . . . . 82 4.7 Effect of feedout rate and temperature during feedout on dry matter loss in a 6.1 x 21.3 m top-unloaded tower silo . . . 83 4.8 Effect of dry matter content and temperature on predicted non-protein nitrogen content in alfalfa silage . . . . . . . . 85 4.9 Effect of dry matter content and temperature on predicted non-protein nitrogen content in corn silage . . . . . . . . . . 86 5.1 Division of the lactation curve of a typical cow producing 6,700 kg of milk in a 390-day lactation cycle . . . . 113 5.2 Linear programming format for simultaneous feed allocation and ration formulation . . . . . . . . . . . . . . . 122 xiii LIST OF FIGURES - continued Figure Page 6.1 Effect of alfalfa quality on relative value of alfalfa in a mixed forage diet fed to a 600 kg cow producing 7,9"0 k8 Of milk/y O O O O O O O O O O O O O O I O O O O O O O 130 6.2 Effect of alfalfa quality on potential milk production from a 600 kg cow fed a mixed forage diet . . . . . . . . . . . 131 6.3 Effect of alfalfa quality on relative value of alfalfa in a mixed forage diet fed to a 600 kg cow producing as much milk as the forage will allow . . . . . . . . . . . . . . 133 6.4 Range in alfalfa's relative feed value from three methods or determination O O O O O O O O O O O O O O C O O O O O O O O 135 6.5 Impact of increased cow size on the effect of alfalfa quality on potential milk production from a cow fed a mixed forage diet 0 O O O O O O O O O O O O O O O O O O O O O O 137 6.6 Impact of forage source on the effect of alfalfa quality on potential milk production from a 600 kg cow . . . . . . . . 139 8.1 Value of alfalfa losses attributable to single sources for two milk production levels . . ....... . ..... . 150 8.2 Value of combined harvest, storage and all alfalfa losses and optimal feed allocation on three sizes of farms when forage quality determines milk production . . . . . . . . . . . 155 8.3 Value of combined harvest, storage and all alfalfa losses and optimal feed allocation on three sizes of farms when milk production is 8,000 kg/y per cow . . . . . . . . . . . ._. 156 8.4 Effect of farm size on alfalfa value lost during individual processes through harvest, storage and feeding when forage quality determines milk production . . . . 165 8.5 Effect of farm size on alfalfa value lost during individual processes through harvest, storage and feeding when milk production is 8,000 kg/y per cow . . . . . . 166 8.6 Effect of type of alfalfa harvest on alfalfa value lost during individual processes through harvest, storage and feeding when forage quality determines milk production . . . . 167 xiv LIST OF FIGURES - continued Figure . Page 8.7 8.8 8.11 Effect of type of alfalfa harvest on alfalfa value lost during individual processes through harvest, storage and feeding when milk production is 8,000 kg/y per cow. . . . . . . 168 Effect of baler type and storage location on alfalfa value lost during baling, storage and feeding when forage quality determines milk production . . . . . ...... 171 Effect of baler type and storage location on alfalfa value lost during baling, storage and feeding when milk production is 8,000 kg/y per cow . . . . ......... 172 Impact of harvest moisture content on the value of combined harvest, storage and all alfalfa losses when forage quality determines milk production . . . . . . . . 174 Impact of harvest moisture content on the value of combined harvest, storage and all alfalfa losses when milk production is 8,000 kg/y per cow . . . . . . . . . . 175 XV NOMENCLATURE Chapter 3: Harvest loss models ATD ATN asses lag-FFP‘H as: . RLR average temperature during daytime hours, °C average temperature during nighttime hours, °C baler chamber loss, fraction of dry matter crude protein content of leaves or stems, fraction chopper spout loss, fraction of dry matter drying constant, 1/h hours of daylight each day fraction of less material that is leaves fraction of whole plant that is leaves leaf loss, fraction of leaf dry matter leaf loss from raking, fraction of dry matter moisture content, decimal wet basis moisture content at 8 a.m. of next day moisture content at 8 p.m. of current day moisture content at 8 a.m. of next day neutral detergent fiber content of leaves or stems, fraction of dry matter rainfall amount, mm respiration loss, fraction of dry matter raking loss, fraction of dry matter respiration loss rate, fraction of dry matter/h rain loss due to leaching, fraction of dry matter rain loss due to leaf shatter, fraction of leaf dry matter fraction of loss material that is stems fraction of whole plant that is stems stem loss, fraction of stem dry matter stem loss from raking, fraction of dry matter time, h temperature, 0C - total loss, fraction of dry matter yield, T DM/ha Subscripts i f initial (before a given treatment) final (after a given treatment) xvi NOMENCLATURE - continued Chapter 4: Storage loss models A ADIP ADIPCP AL ASH area of moving front, m2 acid detergent insoluble protein content, fraction of dry matter acid detergent insoluble protein content, fraction of crude protein accumulated dry matter loss in a section (summed from time 0 to date), kg ash content, fraction of dry matter depth to moving front, cm crude protein content, fraction of dEy matter diffusion coefficient for oxygen, on /h depth of forage fed per day, cm/d dry matter content, decimal relative respiration rate as dependent on carbon dioxide concentration, dimensionless feed rate cut of the silo, kg DM/d depth of plot or depth of bunker, cm height of bunker, m soluble carbohydrate formed from acid and enzyme hydrolysis of hemicellulose, fraction of DM heating, degree-days > 35°C Michaelis-Henten constant, dimensionless dry matter loss rate, g/h dry matter loss, fraction of dry matter depth at which oxygen concentration gradient is zero, cm infiltration loss, kg/10 d " moisture content, decimal wet basis initial mass of dry matter in the plot (tower silo) or the vertical section (bunker silo), kg neutral detergent fiber content, fraction of dry matter ammonia content, fraction of dry matter non-protein nitrogen, fraction of total nitrogen silage pH after fermentation rate of oxygen reaching the front, cm3/h total sensible heat production, kJ/kg DM-6 months radius, m respirable substrate, fraction of dry matter time, d temperature, °C permeability, cm/h depth, on xvii NOMENCLATURE - continued approximation variable, cm 2'. (Km+¢)/Y oxygen concentration, decimal forage porosity, dec mal respiration rate, cm 02/g silage-h forage density, g/cm3 constant incorporatigg density, respiration and diffusion parameters, 1/cm 1 tortuosity of diffusional paths in silage (2/3), dimensionless O «01:96 Subscripts in air effective front initial infiltration time increment, multiples of 10d silo to date phase 1 phase 2 phase 3 phase 4 phase 4, in-silo phase 4, in-bunk ”‘0” kg *9 3 g.” A WN—guuHV-a 1:3: 0'” Chapter 5: Animal utilization model AESCPdiet available escape protein of the diet, kg/d AESCPi available escape protein content of feed 1, kg/d ALFSIL amount of alfalfa silage in the diet, kg DM/d ANDF1 adjusted HDF content of feed 1, fraction of DH ANDFdiet adjusted MDF content of the diet, fraction of DH APr absorbed protein requirement, kg/d ARV annual roughage value requirement for group j, kg/y AR annual roughage value requirement for the herd, kg/y ASRV amount of alfalfa silage roughage value in storage, kg/y ATDH adjusted total digestible nutrient content of diet, fraction of DM BASEMP average milk production of a mature cow in the herd, kg/y BASEVT average weight of a mature cow during the second stage of lactation indicating size of animals in the herd, kg xviii NOMENCLATURE - continued BCPdiet bacterial crude protein yield of the diet, kg/d BTDNdiet baseline total digestible nutrient content of the diet, fraction of DM BTDN1 baseline total digestible nutrient content of feed 1, fraction of DM BV body weight, kg ABV change in body weight, kg/d C daily intake capacity of the animal Cic ingestive capacity coefficient, kg ANDF/d per kg BW CP1 crude protein content of feed 1, fraction of dry matter CPdiet crude protein content of the diet, fraction of dry matter CS amount of corn silage in the diet, kg DM/d CSRV amount of corn silage roughage value in storage, kg ”Edict digestible energy content of the diet, Meal/kg DH DEGR1 protein degradation rate of feed 1, fraction of crude protein DMI dry matter intake, kg DM/d DMIm dry matter intake reserved for maintenance, kg DH/d DMIB dry matter intake reserved for gain, kg DM/d DPA difference protein absorbed, kg/d F fill effect of diet FAIF fraction of alfalfa in the forage FFC fraction of cows experiencing first lactation FFID fraction of the diet that is forage FHAYIA fraction of the alfalfa in the diet that is hay FPN absorbed protein requirement for fecal output, kg/d GR grain to total dry matter ratio in corn silage HAY amount of alfalfa hay in the diet, kg DM/d I daily intake IC ingestive capacity, kg ANDF/d IDM indigestible dry matter intake, kg DM/d LPN absorbed protein requirement for lactation, kg/d “Edict ' metabolizable energy content of the diet, Mcal/kg DH ME} metabolizable energy content of feed 1, Meal/kg DH ME metabolizable energy requirement, Meal/d HHNT multiples of maintenance MNTP absorbed protein requirement for maintenance, kg/d HPD milk production, kg/d MPDI1n minimum basis of milk production for determining net energy concentration of approximate diet, kg/d NDF neutral detergent fiber content, fraction of dry matter "Eg,diet net energy for gain of the diet, Meal/kg DH HEB net energy for gain requirement, Meal/d NE1,1 net energy for lactation of feed 1, Meal/kg DH “El,i,adjusted net energy for lactation of feed 1, adjusted for xix NEl,diet NEl,m "El-99 "31.8 NOMENCLATURE - continued multiples of maintenance, Meal/kg DM net energy for lactation of the diet, Hcal/kg DM net energy for lactation requirement for maintenance, Meal/d net energy for lactation requirement for pregnancy, Meal/d net energy for lactation requirement for weight change, Meal/d net energy for lactation requirement for lactation, Mcal/d net energy for lactation requirement, Meal/d we ’1 ,adjusted net energy for lactation requirement adjusted for NEp,diet “Em NH3diet NPN PDMI PFAT PRICEi RAPr RAPdiet RELLN RELMBS RPN RV 1 xi Y YEN multiples of maintenance, Meal/d net energy for maintenance of the diet, Meal/kg DM net energy for maintenance requirement, Meal/d ammonia pool of the diet, kg/d non-protein nitrogen content, fraction of crude protein approximate dry matter intake, kg DM/d milk fat, 1 relative price of feed 1 for LP solution of linear inequalities rumen available protein requirement, kg/d rumen available protein in the diet, kg/d relative live weight relative metabolic body size absorbed protein requirement for retained protein, kg/d roughage value of feed i, fraction of DM amount of feed 1 in the diet, kg DM/d animal characteristic in Table 5.11 for group j absorbed protein requirement for pregnancy, kg/d Chapter 6: Alfalfa value AA ACS ADF AMP ASFC CP L MPD NDF P PMP RFV UAI V annual alfalfa used, kg/cow-y annual corn silage used, kg/cow-y acid detergent fiber content, fraction of dry matter annual milk production, kg/cow-y annual supplemental feed cost, $/cow-y crude protein content, fraction of dry matter fraction of milk income required to cover non-feed, variable costs milk per day, kg/cow-d neutral detergent fiber content, fraction of dry matter price, $lkg potential milk production, kg/cow-y relative feed value unallocated income, $/cow-y alfalfa value, $/T dry matter XX NOMENCLATURE - continued Subscript ref with respect to reference quality alfalfa Chapter 7: Determining loss value MP milk production, kg/y RTFC reduction in total feed costs per unit of milk produced, 1 TFC total feed costs, $/y xxi 1. INTRODUCTION 1.1 Background and motivation Of the 141 million tonnes (T) of hay that 0.8. farmers produced on 25 million hectares (ha) during 1986, 59 percent was alfalfa (USDA, 1987). In Michigan alone, the value of hay production exceeded $300 million (USDA, 1987). During the mid-19803, annual hay production in the United States was valued at approximately $9.5 billion. This was nearly 53 percent of the value of U.S.-produced corn grain and 92% of the value of U.S.-produced soybeans. Dry matter loss and quality deterioration during forage production can vary among farms, but the typical loss of 22 percent during hay production implies an annual economic loss of $2.7 billion to American farmers. Forage losses during silage production result in additional economic losses. Forages are an interdisciplinary topic. From the time of planting through growth, harvest and storage and eventually animal utilization, the processes in forage production are affected by agronomists, engineers, animal scientists and economists. Each of these disciplines has been involved in research to improve forage efficiency. The work of the agronomists has resulted in yields of high quality alfalfa that exceed 22.2 tonnes of dry matter per hectare (Tesar, 1982). The primary interest of the engineer has been to design machinery and processes that harvest, store and feed the animal. Animal scientists are increasing the efficiency with which forages and other feeds are converted into 65 million tonnes of milk per year (USDA, 1987). To the economists, who focus on overall efficiency, it must be satisfying to see the progress made in forage utilization over the past years, yet at the same time discouraging to recognize that national averages of forage production and utilization are consistently far below levels shown possible by research. Some of the controllable factors in forage production that have significant impacts on farm net return are planting time and date, seeding rate, fertilization practices, soil conditions, length of growth period, date of harvest, machinery types and technologies used, labor availability, allocation of land and financial resources, rations as fed to the animals and allocation of produced feeds. To maximize production, all of these factors -- as well as some beyond the farmer's control, such as weather -- must be considered. Interdisciplinary topics rely heavily on a common interest in improvement. It is obvious that, because numerous disciplines affect the production and utilization of forages, gains in efficiency should not be limited to any one or two fields. Improvement of forage quality during growth has no impact if the gain is lost during harvest and storage, or if the animal does not utilize the improved quality. Many times, researchers have fine-tuned one specific area while leaving another far less efficient area untouched or even inadvertently making it more inferior. Properly evaluating the various steps in the chain of forage production and utilization events can identify the weakest links, the processes in which large losses occur. A thrust of this present work was to connect some of the links between disciplines. Determining the value of losses at each stage in the production and utilization chain enables researchers to direct studies to those areas with the greatest economic potential. Farmers could also use such information to make decisions about various technologies or management strategies. Perhaps the cheapest gains in efficiency can be achieved by informing farm operators that good management can minimize losses and increase profits. 1.2 Objectives The primary goal of this work was to evaluate, in economic terms, the losses of dry matter and reduction of quality that occur during alfalfa harvest and storage. The specific objectives were: 1. To develop models of dry matter loss and quality change during harvest and storage of alfalfa and incorporate these models into DAFOSYM, a simulation model of the dairy forage system. ' 2. To develop and incorporate into DAFOSYM a model that determines feed value by predicting potential milk production from a given quantity of feeds of given quality. 3. To use DAFOSYM to determine the value of losses that occur at each stage of alfalfa production and utilization. 2. LITERATURE REVIEN Minimizing losses has long been recognized as a way to improve forage systems. Total losses between crop growth and animal utilization vary widely as management practices and crop and weather conditions change. Total losses typically range from 5 to 261 of the initially available crop (Moser, 1980; Hundtoft, 1965), so the value of losses is of obvious importance. The intent of this chapter is to review data and models of losses, methods of determining feed value and several forage system models. 2.1 Forage losses Losses of forage dry matter and quality begin during crop growth and continue to occur during harvest, storage and feeding. Losses in legumes differ from those in grasses, even though the processes may be identical. Under similar conditions and given identical treatments, legume loss between cutting and baling was 38.91 (Klinner, 1976); less in grass hay was approximately half as much. 2.1.1 Field losses For hay made under good conditions, total dry matter loss is typically 15 to 221 of the initial yield (Rotz and Abrams, 1988). For silage systems, field losses range from 5 to 18$ (Hundtoft, 1965). When rain severely hampers field-curing conditions, the loss can reach 100%. Field losses are caused by improper timing of harvest, machinery treatment, rain or respiration during curing. 2.1.1.1 Losses during field curing Respiration of the cut plant, the activity of microorganisms and rain can each contribute to dry matter and quality loss during field curing of forages. The dry matter lost during field curing is primarily non-structural carbohydrate; when rain occurs, some protein is lost (Rotz and Abrams, 1988). Zimmer (1977) plotted dry matter loss vs. curing time for grass harvested under poor, moderate and good weather conditions. Loss per day ranged from 1 to 4%. Dry matter loss ranged from 2.5% (good weather and silage harvest) to 30$ (poor weather and hay harvest). Honig (1980) plotted grass respiration loss per hour as a function of dry matter content and temperature. The respiration rate ranged from 0 to 0.351/h, increased with increasing temperature and decreased with increasing dry matter content. Hood and Parker (1971) presented a model of grass respiration that includes the effects of moisture and temperature. The Honig and Wood models differ in that the Honig model allows for slight respiration at moistures as low as 20%, while the Hood and Parker model does not allow for respiration below 27.3% moisture. (Hood and Parker acknowledge that a small but insignificant amount of respiration occurs between 20 and 271 moisture.) Rees (1982) reviewed models and data of respiration loss and concluded that the models of Honig (1980) and Hood and Parker (1971) are in close agreement; however, Rees used an oversimplified drying model in his comparison. McGechan (1986) combined the functional forms of these two models and incorporated maturity effects (water soluble carbohydrate and digestibility) as well. The data of Greenhill (1959) show that the respiration of legumes is approximately 50% greater than that of grass (Dale et al., 1978). Rotz and Abrams (1988) reported alfalfa curing loss to range from -8 to 191, with an average of 3.21. Because of large errors associated with the measurement of field curing loss, Rotz et a1. (1987) did not measure a significant decrease in respiration loss as curing time decreased. Rain is another environmental factor causing alfalfa loss. Rain— induced losses can reach 1001 if cr0p quality deteriorates below a useful level. Experimental results on the effects of rewetting forage crops have been reported by Collins (1982, 1983, 1985) and Fonnesbeck et a1. (1982). Rain-induced losses are affected by the amount of rainfall, the number of showers, the types of mechanical treatment and the moisture content when rain occurs (Collins, 1982). Although experimental conditions can be described accurately, it is difficult to identify the independent effects of these factors on rain-induced losses. Alfalfa hay exposed to 25 mm of rain during a 3-day period had a combined leaching and respiration loss of 11.91 (Collins, 1985); the comparable loss without rain was 3.91. Leaf loss (shatter during rain impact and simulated raking) was 9.8 and 2.41 for wetted and non-wetted alfalfa, respectively. The rain-induced loss of an additional 15.41 caused acid detergent fiber (ADF) and neutral detergent fiber (NDF) 'concentrations to increase, while in vitro dry matter digestiblity (IVDMD) and crude protein (CP) concentrations decreased. Total NDF, ADF and CP losses (as a fraction of initial amount) were -1, 1 and 91, respectively, for non-wetted hay; for wetted hay these losses were 4, 6 and 311, respectively. In these experiments, the curing time to achieve 251 moisture were 3 and 7 days for unwetted and wetted alfalfa, respectively. Separating leaching and respiration loss into leaching loss directly caused by rain and respiration loss due to extended exposure for curing is difficult (Collins, 1983). Collins (1983) reported 3.1- to 4.8-fold increases in losses during drying when wetting occurred. Netting that occurred after drying (rain on 151 moisture hay) caused losses as high as 55.21. Fonnesbeck et al. (1982) reported dry matter losses of 4.6 and 9.71 caused by 5 and 20 mm of rain, respectively. Crude protein losses associated with the 5 and 20 mm of rain were 4.3 and 10.21; these were approximately equal to the dry matter losses. Fonnesbeck et a1. (1982) suggested that because fiber components (NDF and ADF) are insoluble, they are immune to leaching by rain; however, the Collins data (1985) indicate some fiber loss because of rain.. Pizzaro and James (1972) reported that the respiration rate of hay wetted to a given moisture content is similar to the respiration rate of the hay at that moisture content before wetting. This suggests that respiration after rain occurrence can be treated like respiration before rain occurrence. 2.1.1.2 Machinery-induced losses Machinery-induced (mechanical) losses occur each time the crop is manipulated. It is difficult to attribute losses to a given machine because of interactions among treatments. For example, mower loss is difficult to determine because subsequent raking may pick up material the mower has left behind (Straub et al., 1986). Similarly, it is difficult to determine if some material was left behind by the mower or if the material could have been considered a preharvest loss (i.e., leaves that fell before mowing). It is clear that not all machine losses are additive (Rotz and Abrams, 1988). Despite the difficulty in quantifying mower loss, it is useful to determine its magnitude. Koegel et al. (1985) evaluated 3 mower- conditioners. 0f the dry matter loss occurring during hay harvest, approximately 501 occurred during mowing and raking. Mower-conditioner losses averaged 3.9, 5.9 and 7.21 for a reciprocating mower with a fluted roll conditioner, a disk mower with a fluted roll conditioner, and a disk mower with a flail conditioner, respectively. Straub et al. (1986) reported mower conditioner losses of approximately 2.51 for a cutterbar mower with an intermeshing roll conditioner. Rotz et al. (1987) found mowing plus raking loss to be from 0.9 to 6.61. Losses for reciprocating cutterbar mowers averaged 2.91 without mechanical conditioning and 3.41 with fluted roll conditioning over three cuttings. Mower loss was not affected by chemical conditioning. Loss expressed as a fraction of yield was higher for cuttings with low yields (Rotz et al., 1987). This was also shown by Savoie et a1. (1982) -— absolute losses were nearly the same regardless of yield. For cutterbar mowers, losses were approximately 9 and 20 kg DH/ha without and with a fluted-roll conditioner, respectively. This resulted in relative losses of 0.2 and 0.41 for first cutting and 0.4 and 0.81 for second cutting, during which the yield was 501 lower. Losses due to mowing and conditioning were approximately the same regardless of the time of conditioning (Savoie et al., 1982); however, a second mechanical conditioning treatment increased loss significantly. The combined mowing, conditioning and raking loss was about 31 for disk and cutterbar mowers with either flail or roll conditioners; losses with a flail mower were double those of cutterbar mowers (Rotz and Sprott, 1984). Dale et al. (1978), using data from Hundtoft (1965), modeled mowing losses to be 1.0, 2.1 and 4.61 for cutterbar mowers, cutterbar mowers with conditioning and mowing with heavy crimping, respectively. All mower- conditioner loss was considered to be leaf material. Pitt (1982) modeled mowing losses as 1 and 1.51 without and with conditioning. Rotz et al. (1989a) modeled mower—conditioner loss of leaves (41) and stems (11) separately to quantify quality changes attributable to the mower. Field treatment options following mowing or mowing with conditioning include conditioning, tedding, inverting and raking. The added mechanical conditioning of crimping previously conditioned alfalfa increased losses by 25.9 kg/ha (Savoie et al., 1982). Crimping alfalfa previously conditioned with a flail conditioner increased losses by 8.9 kg/ha. Tedding alfalfa hay at low moisture contents can result in large leaf losses. Vhen following a cutterbar mower with a roll conditioner, tedding resulted in an average of 54.4 kg/ha loss during second cutting (Savoie et al., 1982). With a yield of 2,350 kg/ha, this constitutes a loss of 2.31. Tedding of ryegrass hay resulted in about 41 loss per tedding (Bockstaele et al., 1980). Parke et al. (1978) modeled tedding loss with a maximum value of 2.51 for grass; tedding loss was inversely related to moisture content. Raking can result in a net gain if, during the raking operation, material previously scattered or left behind is gathered (Koegel, et al., 1985). Rotz and Abrams (1988) reported that the greatest loss of forage quality during harvest occurred during raking, with 3.71 of the digestible dry matter and 3.81 of the crude protein lost. Loss for 10 alfalfa hay raked at 35 to 451 moisture at a speed of 5.1 km/h with a parallel bar rake averaged 3.51; raking loss was inversely proportional to yield raised to the 2.42 power. When a second raking was required, 0.21 additional loss was observed (Rotz and Abrams, 1988). Savoie et al. (1982) reported raking losses of 50 to 80 kg/ha (1 to 41) for alfalfa hay raked at 60 to 661 moisture. Nhen separated from mowing and conditioning losses, the raking loss was approximately 11 (Koegel et al., 1985). Dale et al. (1978) modeled raking loss as a function of moisture content, plant species, rake type, raking speed and conditioning using multiplicative factors. All raking loss is considered to consist of leaves that fall from the stems. With a maximum moisture content factor of 1.0, the effect of moisture on raking loss decreases nearly exponentially as moisture content increases. Grasses are assumed to have 501 less loss than legumes. The rake type factor is a function of how gently the crop is treated. Rake speed increases loss linearly up to 10 km/h, where further increases in rake speed do not increase raking loss. Raking loss without prior mechanical conditioning is 951 of that with prior conditioning. Pitt (1982) modeled raking loss as 81 with previous conditioning and 7.61 without conditioning. Rotz et al. (1989a) modeled raking loss of stems as 21 of initial stem mass. Raking loss of leaves ranged from 2 to 211 of leaf dry matter and was inversely related to moisture content (Savoie, 1982). An inverse function of this type was previously used for total (leaf and stem) loss by Hundtoft (1965). Following mowing and other swath manipulation treatments, the final source of mechanical harvest loss is the harvester. Losses incurred 11 during baling include material missed by the pick-up mechanism and material lost from the baler chamber. The'combination of these two losses exceeded the loss incurred during chopping (Straub et al., 1986). Baler pick-up loss averaged 2.11; chamber losses for a variable chamber round baler averaged 3.11, with higher losses associated with drier hay (Straub et al., 1986). Baler chamber losses for three types of balers were measured by Koegel et al. (1985). Chamber losses for hay baled at 181 moisture were 2.8, 3.8 and 10.9 for a rectangular, round variable chamber and round fixed chamber baler, respectively. Baler pick-up losses were 2.0 to 2.41 and were the same regardless' of baler type because all balers had similar pick-up mechanisms. The pick-up loss during first cutting was less than half that of later cuttings. This is likely ,due to a nearly constant absolute loss with changing yield as reported by Savoie et al. (1982). Because of differences among cuttings, Koegel et al. (1985) found that neither baler chamber nor total loss can be adequately predicted as a function of baling moisture; however, within cuttings baling moisture did explain a considerable amount of the differences in baler chamber and total losses. Rotz and Abrams (1988) reported pick-up and chamber losses for a small rectangular baler to be 1.8 and 1.11, respectively. Even though the quality of the chamber less material was higher than that of pick-up loss, the total loss of nutrients due to pick-up loss was higher because more material was lost. Pitt (1982) modeled baler loss, including both pick-up and chamber losses, for a rectangular baler with ejector as 41 unless the hay was baled at 301 moisture; then baler loss was 21. Rotz et al. (1989a) modeled baler loss of leaves (7.51) and stems (21) separately. i.e., 7.51 of leaf mass and 21 of stem mass were lost 12 during baling. Dale et al. (1978) modeled baler loss as a function of moisture, plant species and conditioning treatment and assumed all baler loss was leaf material. Loss during chopping of forages is highly operator dependent. Differences in turning frequency, turning speed and spout alignment could very easily result in losses an order of magnitude larger or smaller than published values. Chopper loss is a function of feed rate, wind speed, moisture content, length of cut and wagon type (roof, air vents, etc.). Straub et al. (1986) reported chopper losses of 2.51 for alfalfa placed in a windrow by the mower-conditioner and 1.41 for alfalfa raked into a windrow from a swath. Based on Whitney (1966), Dale et al. (1978) modeled chopper losses to be 431 higher than baler losses and 251 higher than losses with a baler with a bale ejector. Pitt (1982) modeled chopper losses to be 21.1 Rotz et al. (1989a) modeled chopper loss during corn silage harvest as 61. It is clear from a study of published loss values that losses from various machines vary widely within experiments and among experiments. The loss data collected during the 19805 has contributed significantly to the knowledge in this area. Although previous models were presented in this discussion, loss models should be based on the latest experimental data because changes in machine design affect machinery- induced losses. 2.1.2 Storage losses 2.1.2.1 Losses during hay storage Much research .has been devoted to the measurement of dry matter losses and quality changes during hay storage (Buckmaster, 1986; 13 Buckmaster et al., 1988; Rotz et al., 1988; Rotz and Abrams, 1988; Davies and Warboys, 1978; Nehrir et al., .1978; Johnson and McCormick, 1976, Knapp et al., 1975; Weeks et al., 1975; Nelson, 1972, 1968, 1966; Miller et al., 1967; Shepherd et al., 1954). This research has been conducted primarily as comparative experiments to evaluate chemical preservatives. Dry matter loss during indoor storage of hay with less than 201 moisture may range from 5 to 101 (Martin, 1980). Waldo and Jorgensen (1981) suggested a rule of thumb: 11 dry matter loss for each 11 decrease in moisture content during storage. Martin (1980) suggested that hay which contains more than 151 moisture will heat during storage. The amount of heating that occurs depends on moisture concentration (Buckmaster, 1986; Buckmaster et al., 1988; Nelson, 1966, 1972; Miller et al., 1967; Rotz et al., 1988). With sufficient heating, nitrogen becomes bound to fiber and becomes unavailable for ruminant use. In extreme cases, heat development can result in combustion. If hay is baled at a low moisture level and stored inside, few nutrient changes occur during storage (Moser, 1980). Weeks et al. (1975) reported little chemical change in loosely stacked hay harvested at 401 moisture. Other research indicates that when hay is baled at moisture levels exceeding 201, heating and mold development occur and affect nutrient retention (Miller et al., 1967). Quality changes have been significant as baling moisture was increased (Miller et al., 1967; Nehrir et al., 1978; Nelson, 1966, 1968, Buckmaster et al., 1988). Buckmaster et al. (1988) combined empirical analysis of experimental data and thermodynamic theory to develop a model for predicting dry matter loss and major quality changes during indoor 14 storage of alfalfa hay. In their model, dry matter loss is related to total heat generated from consumption of dry matter. They found that fiber and ash concentrations increase because of the loss of other components. Crude protein loss during storage was less than soluble carbohydrate loss. The increase of acid detergent insoluble (bound) nitrogen was related to the heating of the hay in degree-days above 35°C. Losses during outside storage of hay vary with weather conditions, bale covering, bale condition (solid or loosely formed) and soil conditions under the bale (Moser, 1980; Martin, 1980). Currence et al. (1976) reported 10.6 and 17.21 dry matter loss for 23.91 moisture alfalfa in round bales stored inside and outside, respectively. The bales were placed on gravel and were stored for approximately 6 months. Belyea et al. (1985) reported dry matter losses in large round bales of alfalfa to be 2.5 and 151 for inside and outside uncovered storage respectively. The rain penetrated 10 to 25 cm into the uncovered bales and as a result, nearly 401 of the original bale dry matter deteriorated. Covered bales stored outside lost only 5.8 to 6.61 of initial dry matter (Belyea et al., 1985). Anderson et al. (1981) reported alfalfa dry matter losses of 31 for inside storage and 141 for outside storage. The quality of the interior of the bales stored outside was near the quality of the bales stored inside, but because of weathering of the outer 20 cm which made up 42 to 491 of the total bale, bales stored outside had lower digestibility and higher fiber than those stored inside. Burch and Balk (1978) reported losses in bermudagrass hay to be 3.11 and 101 after 5 1/2 months of storage inside and in the field, respectively. 15 Lechtenberg (1978) reported dry matter loss in grass hay to be 12.8, 9.3 and 81 for hay stored on the ground outside, on crushed rocks outside and inside, respectively, each for 5 months. The unweathered portions of the same bales were 76.8, 85.5 and 921 of the original weight, respectively. In vitro dry matter digestibility (IVDMD) of weathered grass was 161 lower than that of unweathered grass. For an alfalfa/grass mixture, weathering reduced IVDMD by 221 (Lechtenberg et al., 1979). These literature data suggest that round bale losses for inside or covered storage are on the order of 51, near that of square bales stored inside. Outside uncovered storage results in larger dry matter loss (10 to 201) with the loss very dependent on the length of storage and the soil conditions. Typical dry matter loss during outdoor hay storage is about 141. Data are insufficient to describe accurately any crude protein changes, but it is clear that digestibility decreases and fiber content increases during outside storage. 2.1.2.2 Losses during silage storage Loss and quality change is more dramatic during ensiling than during hay storage. Dry matter loss during ensiling typically varies from 3 to 251 (McDonald, 1981). The loss can be attributed to respiration, fermentation and effluent production. Loss due to initial aerobic respiration should be less than 11 (Pitt, 1986) but depends on fill rate and compaction within the silo. Losses due to fermentation and effluent are on the order of 1 to 21 and 0 to 71 respectively, and effluent losses are negligible in wilted forages. The remainder of ensiling loss is due to aerobic respiration made possible by oxygen 16 penetration into the forage material throughout the storage period (Pitt, 1986). Dry matter lost during aerobic respiration is generally accepted to be non-structural carbohydrates; therefore, fiber and protein contents increase and energy content decreases as a result of aerobic respiration. If aerobic respiration results in a sufficient temperature rise, protein may bind to fiber and become unavailable to ruminant animals. Quality changes during fermentation include transformation of protein nitrogen into non-protein nitrogen, production of ammonia, breakdown of hemicellulose and change of soluble carbohydrates into organic acids (Pitt et al., 1985). Researchers have taken numerous approaches to describe the changes that occur to ensiled forage. Some modelers have used simple expressions to relate dry matter less to moisture content (McIsaac and Levering, 1980; Levering and HcIsaac; 1981c). Empirical relationships have also been used to evaluate quality changes during ensiling (Helter, 1983; Levering and McIsaac, 1981c). Pitt et al. (1985), Leibensperger and Pitt (1987) and Neal and Thornley (1983) have modeled the anaerobic phase of ensiling with detailed models based on a theoretical understanding of the processes. Pitt (1986) developed a model to predict dry matter losses caused by oxygen infiltration into the forage material. Effluent (seepage er runoff) less has also been modeled (Daynard et al., 1978; Pitt and Parlange, 1987; Weisbach and Peters, 1983; Bastiman and Altman, 1985). Although these models have been useful in certain applications, a comprehensive model of the ensiling process has not been developed. 17 2.1.3 Feeding and feed allocation A final process contributing to feed loss is that of feeding. During handling, hauling, unloading and conveying, some feed is lost before it reaches the animal. This loss should be negligible under good management; however, depending on the feeding system and other conditions, the loss of feed during the feeding process may be considerable. Kjelgaard (1979) modeled feeding loss to be 51. Partenheimer and Knievel (1983) estimated feeding loss to be 41 for silages and 51 for hay. These values should be increased for round bales. Belyea et al. (1985) reported round bale feeding losses of approximately 131 for bales stored inside or covered and near 251 for bales stored outside uncovered. Because feeding does not alter the feed , chemically and the loss consists of both leaf and stem components, the quality of' fed material should not be affected by feeding less. An exception might occur if animals selectively eat the ration. (e.g., if they selectively reject stems or cebs.) An obvious yet often overlooked loss of feed is that of inefficient allocation and/or improper ration balancing. Although the feed is not lost in the sense that it is no longer useful, a loss due to improper feed use is an economic loss. The goal of optimal feed use has prompted application of linear programming (LP) to the area of animal nutrition (Black and Hlubik, 1980; Waller et al., 1980; Klein et al., 1986). Optimum feed use can be determined using LP by optimizing an objective function (e.g., maximize milk production, maximize return over feed cost, or minimize purchased feed cost) subject to various constraints imposed pen the diets of the animals. Although the specific rules of ration formulation may vary among scientists, for any one set of ration 18 constraints, there is only one optimal way to feed a given set of feeds. Feed use in any other manner could be considered a loss of feed value. Applying LP makes determining least cost rations rather easy. A more complicated problem is that of feed allocation. That is, given certain feeds' availability and prices, what is the most economical method of dividing these feeds among the entire dairy herd? Milligan et 'al. (R.A. Milligan, 1988 personal communication) have worked on a model titled "Max Profit", which maximizes income over feed costs. Though this .model goes beyond the boundaries of feed allocation and animal conversion of feedstuffs into milk, an important component of this model describes the animal. 2.2 Animal utilization/assessment of feed nutritional value The purpose of this section is to review methods previously used to determine feed value. Feed value purely depends on animal utilization of feeds; thus, some discussion is directed toward this issue. This discussion is not a review of animal nutrition; rather it is a summary of current models of animal conversion of feedstuffs into milk, with some information on animal and feed interactions that affect this conversion. It is common knowledge that forage quality is important. Relative importance, however, is unclear. For example, what difference does an increase of 11 in fiber content make in the value of alfalfa hay? Obviously the answer to such a question has a long list of qualifying statements related to the animal considered and the ration that it is fed. To answer such a question requires experimentation and an accurate model of animal conversion of feedstuffs into milk. The primary 19 considerations for determining forage value are intake, fiber, energy and protein. Numerous dairy models have been used in the past (Levering and McIsaac, 1981a; Partenheimer and Knievel, 1983; Rotz et al., 1989a; Parke Let al., 1978; Doyle et al., 1983; Savoie, 1982; Parsch, 1982; Waller et al., 1980; Street, 1974). The basis of most models has been a balance of energy and crude protein requirements for described animals within dry matter intake limitations. Energy, protein and intake represent the minimum criteria for ration formulation and have limitations because the intake and protein availability of forages of varying quality are different. The "Max Profit" model (R.A Milligan, 1988 personal communication) balances rations for several animal groups at one time. Constraints include criteria for each ration as well as limits on feed use based on availability. Unlike most ration balancers, this model determines optimal milk production. Milk production during later stages of lactation depend on milk production during the first few weeks of lactation. Likewise, body weight change is not predetermined, although body weight lost during early lactation must be gained later in lactation. A The work of Conrad (1966) and Hertens (1987) illustrate the effect of diet characteristics on intake. The intake theory is based on intake limitation .by either physical fill or physiological energy demand (see waldo, 1986, for a thorough review of intake literature). The physical fill limitation implies that an animal has a limited capacity to ingest feed. The physiological energy demand limitation implies that an animal will not ingest more energy than it requires. Hertens (1987) 20 presented a simple algebraic model for predicting intake based on the NDF and net energy (NE) content of the diet. Recognizing that not all NDF is equal, the Hertens model stands out from the other countless equations for predicting intake (as functions of milk production, body weight, body weight change and other animal factors) because it clearly separates the effects of animal characteristics on intake from those of feed characteristics. Although the French fill unit system (Jarrige et al., 1986) uses different terminology than the Hertens model, the concept is nearly the same. An advantage of the French system is that it addresses substitution rates of concentrates for forages; however, a close look at their substitution rate model shows that the functional form can have no biological basis. The lack of data for estimating parameters in the French system renders it less useful for new. Requirements of energy and protein for dairy animals are Ipresented by NRC (1988). Equations predicting requirements as functions of body weight, body weight change, milk production, days pregnant and animal type are presented. The absorbed protein system (NRC 1985, 1988) has the advantage over a crude protein system (NRC, 1978) in that it addresses protein degradability. An absorbed protein system allows for issues concerning forage conservation methods that affect protein quality to be addressed in the context of animal conversion of feedstuffs into milk. The fiber content of dairy cattle diets is inversely related to the energy content (Waldo and Jorgensen, 1981). However, a minimum amount of fiber of the proper quality and physical form is necessary in the diet of dairy cattle to obtain maximum intake, to maintain normal 21 ruminal fermentation and milk fat percentage, and possibly to aid in the prevention of postcalving disorders (NRC, 1988). It is suggested that at least one-third of a dairy cow's diet should be long hay, chopped silage or other forage (NRC, 1988). NRC (1988) also suggests minimum concentrations of ADF and NDF. Perhaps the simplest and most comprehensive fiber "rule" is that 751 of the NDF in the diet should come from a forage source (Mertens,-1985b; NRC, 1988). 2.3 Forage system models Several forage system models have been developed over the past several years as researchers have recognized the strengths of systems analysis. Van Keuren (1974) stated it well when he wrote: " ...system analysis provides the best procedure for integrating all of many variables involved in growing and getting forage to the animal..." Forage system models have varied widely in their extent of detail, range of applicability and, as a result, usefulness. Two types of system models are available: simulation and linear programming. Simulation must be used to consider the stochastic effects of weather on harvest and growth of forages. Modeling of harvest interactions is also best suited to simulation models. Linear programming models are better suited to those studies concerning optimal allocation of resources. This section discusses several forage system models. The emphasis is on model strengths, weaknesses and applications. It is interesting to follow the progression of forage system models to see the increases in detail, the number of processes considered and the range of applicability. Von Bargen (1966) used probability theory to study hay harvest 22 systems. By modeling the probability of rainfall and hay harvest rate, he was able to recommend sizes and combinations of implements necessary to achieve harvest of a given area. The Von Bargen study did not evaluate quality changes occurring during harvest nor the effects of late harvest on whole-farm economics. It related harvest to weather conditions, with a required drying period of two consecutive "open haying days". The probability of this occurrence was modeled only for Nebraska; therefore, the results were not widely applicable. Hillier and Rehkugler (1972) simulated the effect of harvest dates, harvesting rate and weather on the value of forage for dairy cows. With empirical functions for harvested yield as functions of time, they could model the effect of harvest rate and harvest date on harvested yield. Quality was modeled using digestible dry matter converted into total digestible nutrients (TDN). From harvested TDN they determined the number of cow-days of feeding. The model did not include effects of forage protein but was useful nevertheless. The model also did not include losses during harvest and storage. Oddly enough, the work -- done by agricultural engineers -- seemed to jump from the agronomist's viewpoint (harvested yield) to the animal scientist's viewpoint (value to the animal), skipping those processes better related to the engineering field (with the exception of harvest rate, which was considered). Hillier and Rehkugler (1972) recognized that their simulation dealt with only a part of the overall milk production system. Their discussion gave results applicable for northeastern U.S. dairy farmers. Street (1974) briefly discussed a dairy system model and an application of it. The details of the model were not described but the 23 conceptual diagram included many of the animal and feed production factors that affect conversion of feedstuffs into milk. His system simulated harvest and feeding on a weekly basis. With the small time step, the seasonality of milk production, feed demand and feed supply were studied. The model contained considerable detail on the dairy herd and effects on demand for nutrients, but it contained little detail on the effects of weather, harvest procedures and storage on the whole farm system. The goal of the work by Tseng and Hears (1975) was to outline a framework for connecting component models into a comprehensive model for forage production. Two approaches were discussed: a flow chart descriptive of the entire forage production system, and an LP model for determining the optimal land use plan and forage production scheme. The usefulness of the flow chart was to define the components needed within the model. The LP model maximized the total value of forage produced. Forage quality was maintained by implementing a constraint on the ratio of digestible protein to TDN. Constraints dealing with land and machine use and labor requirements were also implemented. Although it was presented as an optimizing model, its usefulness was limited because it did not consider losses and quality changes during harvest and storage and animal utilization of the produced feeds. A model dealing with energy and time requirements for forage transport and handling was developed by Kjelgaard and Ouade (1975). This model primarily dealt with harvest capacity and efficiency of various harvesting methods. This work was important because it modeled interactions among harvest operations. The model was far from a whole- farm systems model, but it addressed some issues of concern on dairy- 24 forage farms. Russell et al. (1977) used a simulation model to evaluate effects of harvest system capacity, number of cuts, crop area, harvest starting date and nutrient value on forage yield and quality for 30 harvest seasons. . The harvested crop was direct-cut timothy silage. Crop yield and quality were expressed as empirical functions of days of growth (first cutting) or regrowth (subsequent cuttings). Because the crop was harvested as direct-cut silage, constraints on timing of harvest were easily met. Higher harvest system capacities resulted in more cuttings per year, but lower capacities resulted in higher yield per cutting. The crop value was determined by assigning values of $100/tonne protein and $6.93/tonne metabolizable energy. Adding the protein and energy values of the timothy silage gave the total crop value. This approach did not give any value to the fiber in the forage. Bebernes and Danas (1978) presented a model of hay harvest that combined a standard machinery cost analysis with a hay quality analysis. This simulation model simulated dry down of alfalfa, rewetting from dew, dry matter content and quality (digestible dry matter, TDN, CP) during alfalfa growth, and dry matter losses due to machinery operations and rainfall. The computer model output included drying time requirements, an itemization of dry matter losses, labor requirements and hay nutrition and economic information. Bebernes and Danas (1978) assigned an economic value to the hay based on protein content; thus they were able to determine the economic value of machine- and rain-induced dry matter losses. The combination of machinery cost and hay quality analyses made it possible to compare various harvesting methods on a cost basis and show the effect of farm size on optimal harvest method. 25 The model of Parke et al. (1978) was the first simulation model to cover all bases between crop growth and animal utilization of the feedstuffs produced. Major components of the model included crop growth, crop harvest, crop storage and animal utilization. The simulation model was limited to one cutting of grass hay with subsequent growth used as pasture (value was assigned to pasture). Effects of weather on dry-down of the crop were modeled, and dry matter losses during harvest due to respiration, machine treatment and rainfall were estimated. Loss during storage was a function of moisture content. The animal model consisted of an LP ration balancer that formulated rations for a herd of lactating cows. The difference in feed cost between 1001 purchased feeds vs. properly supplemented farm-produced feeds was used to determine the value of farm-produced feeds. Limitations of the model were: only grass was considered, only one cutting was allowed and data concerning losses were not available. As a first attempt at whole- system modeling, this work was quite useful. For the model to be improved, individual components needed more supporting data; ideally deterministic or mechanistic models of phenomena would be used. Kjelgaard (1979) evaluated machine activities within the forage system. This study provided the type of information necessary to develop a whole-farm simulation. The emphasis was on energy and labor requirements for various harvest and feeding operations. Using assumptions about dry matter less at various stages, specific efficiencies (e.g., energy units or hours of labor per unit of forage fed) were calculated for several operations. Although comparisons of harvest systems were made, the quality of the forage produced with various harvest systems was not considered. 26 Boyce et al. (1980) used the model of Parke et al. (1978) to evaluate the uses of an acid hay preservative, mechanical conditioning and barn drying. To compare systems, they used both the means and variations in hay value. The results showed variations from year to year were so high that studies of this nature carried out solely through experimentation would not likely give significant results because of added experimental error. Levering and McIsaac (1981b) presented a forage-dairy model containing five submodels: forage growth, harvest, storage, conversion of feeds to milk and milking of cows and disposal of manure. The forage crop considered was timothy, and all growth functions were .developed for eastern Canadian conditions. The amount of dry matter and the CP content were simple functions of harvest date. The harvest model included the three harvest options of direct-cut silage, wilted silage and hay. A drying and rewetting model was a critical part when considering hay systems. Dry matter loss during hay storage was modeled as 101, with nutrient concentrations unchanged. The model considered ten silo types; dry matter, energy and protein losses were functions of silo type. Feed to milk conversion was modeled by splitting a 365-day lactation cycle into three periods and balancing digestible protein and metabolizable energy requirements for each period. The barn model computed labor and energy requirements associated with milking and manure removal. The Levering and McIsaac (1981b) model is non-optimizing, like most large simulation models. The applications of this type of model are to rank management and technology options available to the farmer and to determine sensitivity of economic or biological changes. Limitations of 27 the model were listed by the authors in two areas: decisions not addressable, and poorly represented physical or biological processes. Management issues unaddressable by their model include: choosing the forage crop, allocating land to various crops, incorporating pasture, raising heifers and replacing cows. Poorly represented proCesses include: feed to milk conversion, dry matter loss and quality changes and effect of barn type on milk production. Levering and McIsaac (1981a) used the same model to compare silo types and evaluate trade-offs between barn costs and labor requirements. The analyses were based on a 30-cow dairy in eastern Canada. They made specific recommendations on silo and barn type to maximize net return. McIsaac and Levering (1982) also used the model to compare timothy harvested as hay, wilted silage or direct-cut silage. Pitt (1982) developed a forage harvest model based on probability theory. By considering the probabilistic influence of weather on delay in. cutting, drying time, leaching losses and respiration losses, the model determined the long-term average and variance of forage yield from various harvesting systems. By computing energy requirements as functions of harvester type, transport distance and storage type, the model was used to develop a criterion for evaluating forage harvest systems in terms of yield vs. energy use. Though it was a very powerful application of probability theory, the model had serious drawbacks in that it did not consider forage quality and feed conversion into milk. Improved models require simulation to represent more accurately losses and quality changes that occur during harvest and storage. Doyle et al. (1983) developed a mathematical model that combined forage conservation techniques with grazing. The model included four 28 sections: calculation of the herd feed requirements, assessment of the grass growth on grazed lands, determination of cut yields and integration of grazing and conservation. The model did not consider in any detail the harvest of conserved forages, quality change over time or loss due to conservation. This model is unique in considering pasture in combination with harvested forages. Partenheimer and Knievel (1983) presented a model with the stated purpose "to illuminate some of the interrelationships between forage production practices and other parts of the farm business." The model was set up as a large LP matrix that included such processes as alfalfa production, corn production, changes in feed quality, loss of feeds and conversion of feeds into milk. As is common in most forage system models, the Partenheimer and Knievel model was not uniform in the degree of simplification of various parts of the system. Their model had considerable detail on forage quality and animal nutrient needs, but much less detail on farm growth, machinery complement and product markets. Use of the model was demonstrated by illustrating the effects of urea treatment of corn silage and the price of soybean meal on farm net return. The authors mentioned that limitations of the model were due to data deficiencies and the lack of strong component models. McGechan (1986) did a major revision of the Parke et al. (1978) model. The structure was not changed, but the component models were improved using data collected during recent years. Improved component models included loss, swath drying and rewetting prediction and the animal conversion of feed to milk. Table 2.1 summarizes the level of detail for several processes in the economic forage system models discussed. The level of detail is 29 Table 2.1 Level of detail in the component models of several economic forage system models. Detail of Model‘ Date Developer(s) Yield Harvest Storage Animal Losses and Forage util. quality crop changes 1966 Von Bargen 0 2 0 0 0 alfalfa 1972 Hillier and Rehkugler 2 0 0 0 1 alfalfa 1974 Street 0 1 1 2 1 grass 1975 Tseng and Hears 0 3 0 0 0 --- 1975 Kjelgaard and Quade 0 3 0 o o --- 1977 Russell et al. 2 2 1 2 1 timothy 1978 Bebernes and Danas 1 3 0 O 2 alfalfa 1978 Parke et al. 2 2 1 2 1 grass '1979 Kjelgaard o 3 o o o --— 1981 Levering and HcIsaac 2 1 1 2 1 timothy 1982 Pitt 2 3 0 O O alfalfa - 1982 Parsch an Savoie 3 3 1 2 1 alfalfa 1983 Doyle et al. 2 0 0 2 0 grass 1983 Partenheimer and Knievel 1 1 1 2 2 alfalfa 1986 McGechan 2 3 2 2 2 grass ' 0: not included in the model; 1: very little detail; 2: moderate + detail; 3: very detailed component model DAFOSYM. 30 indicated on a scale from 0 to 3. The models of Levering and HcIsaac (1981b) and the Parke et al. (1978) are the only system models discussed that simulate crop growth, harvest, storage and animal utilization and predict losses and quality changes throughout. The McGechan (1986) revisions to the Park et al. (1978) model increased the detail with which harvest, storage, losses and quality changes are modeled. His improvements have made the model more useful for the analyzing of grass forage systems. The weak links in most forage system models are the loss relationships and the conversion of feed into milk. Lack of sufficient data has hindered development of accurate models of loss and quality change during forage harvest and storage. The biology of the ruminant is so complex and varying that accurate assessment of feed value is extremely dependent on feed quality and the individual animal. Simulation models can be based on average animals, but this basis must be considered and put into perspective when analyzing results for a complete dairy herd. 2.4 DAFOSYM DAFOSYM is a dairy forage system model begun in the late 1970s at Michigan State University (Parsch, 1982; Savoie, 1982). DAFOSYM was used for this study because it was the only comprehensive forage system model available that was tailored to the harvest, storage and feeding of alfalfa. As noted in Table 2.1, it modeled the growth and harvest of alfalfa in considerable detail, though the level of detail with which losses, storage and animal utilization were modeled was relatively low. Several changes had been made to DAFOSYM before this work began. 31 Rotz (1985) modified the field drying model. DAFOSYM was also modified to accept corn yield data generated by the CERES-MAIZE model of Jones and Kiniry (1986). Other changes were made to the harvest components of the model. e.g., harvest of alfalfa, originally performed in sets of two plots per day, was changed to allow harvest of three plots per day. For DAFOSYM to be used to evaluate value of alfalfa losses, the loss and quality components as well as the animal model needed to be strengthened because these components were relatively weak (objectives 1 and 2, Section 1.2). DAFOSYM was written as a mainframe computer model that simulated alfalfa growth, corn silage and corn grain yields, harvest, storage, feeding and ration formulation for a dairy herd (Parsch, 1982; Savoie, 1982; Savoie et al., 1985). DAFOSYM was developed as a tool for evaluating alternative technologies and management strategies on a dairy farm. The model has been restructured for use on microcomputers to make it more versatile and usable. Figure 2.1 illustrates the structure and flow of DAFOSYM. As noted in Table 2.1, it models the growth and harvest of alfalfa in considerable detail. Although several changes had been made to the original model, the components that consider crop storage, losses and quality changes and animal utilization had remained unchanged. Crop growth and harvest depend on the weather, which is site specific and is entered via a data file. The model is structured to simulate as many years (replications, not sequential years) as desired, if weather data are available. Inputs describing the farm and available machinery are also entered via a data file and can be easily changed. DAFOSYM best describes dairy operations in the Great Lakes states. 32 READ FARM INFORMATION FARM DATA FILE / MACHINERY DATA FILE J CALCULATE MACHINE CAPACITY. FUEL S LABOR REOM'T wk READ CORN PRODUCTION CORN DATA FILE INFORMATION A ' WEATHER DATA :l TODAY WEATHER DATA FILE J ‘ 4r LAST DAY OF YEAR? CORN REQUIRED? DETERMINE CHANGES IN STORED FEED .L DETERMINE FEED UTILIZATION ALFALFA READY FOR HARVEST? HARVEST ALFALFA TODAY DETERMINE come a. ECONOMIC RETURN iNCREMENT To 7 i am YEAR. IwcwEiaewT To NEXT on I RESET ALFALFA l SIMULATE ANOTHER YEAR? CORN READY YES FOR HARVEST? FOR REGROWTH RESULTS l , ll HARVEST CORN J REPORT OF SIMULATION Figure 2.1 Flow chart of DAFOSYM. 33 Past uses of DAFOSYM include an analysis of the effects of maturity at the time of mowing (Savoie et al., 1985; Rotz et al., 1989a), a comparison of three vs. four alfalfa cuttings (Savoie et al., 1985), a comparison of conservation systems (Savoie et al., 1985; Savoie and Marcoux, 1985), a study of the effect of field curing delays and crop value (Savoie, et al., 1985), a study of the economic returns from chemical conditioning (Rotz, 1985; Rotz et al., 1989a) and an analysis of the effects of harvester size and silo type on farm net return (Rotz et al., 1989b). The most recent analyses (Rotz et al. 1989a, 1989b) incorporated improved component models. Changes to DAFOSYM are currently being documented (Rotz, 1989). Because the present work includes modifications to the storage and feed conversion components, a review of the original storage and feed conversion submodels is in order. The storage of alfalfa hay or silage was assumed to cause dry matter loss only; i.e., quality characteristics remained unchanged. Research completed since 1982, however, indicates that quality changes do occur during storage. Dry matter loss during storage (in the original model) depended on storage type. These simple quantity adjustment factors did not adequately reflect the real world phenomena and so were replaced with the more detailed models developed in this dissertation. Two feed to milk conversion models (sometimes referred to as the animal or cow models) were developed with the original DAFOSYM. The Savoie (1982) version was a simple ration balancer based on nutrient requirements (NRC, 1978) and feed quality (TDN and CP). Because nutrient requirements were based on net energy, TDN was converted to net energy for lactation (NE1). 34 Farm-produced alfalfa, high moisture ear corn and corn silage were allocated so as to be depleted at the same time. The rations were balanced for six groups of dairy animals (four lactation levels, dry cows and heifers) using the mixed feed of alfalfa, high moisture corn and corn silage. If the mixture could not provide the minimum energy concentration, corn grain was added to the diet until the energy concentration was suitable. Similarly, if the crude protein concentration was low, soybean meal was added to the diet. When the supply of farm-produced feeds was depleted, purchased medium quality hay and corn grain became the alternative feedstuffs. Although the Savoie (1982) animal model followed NRC (1978) guidelines correctly, it had serious flaws. Maximum dry matter intake was a fixed percentage of body weight, even though intake varies throughout the lactation cycle and is affected by forage quality (NRC, 1988). The original model did not allow milk production to vary with feed quality. Because corn grain and soybean meal could be continually added to the diet, it was possible for the lactating cow rations to contain only corn grain and soybean meal. Clearly this is unrealistic because forage fiber is necessary for proper rumen function (NRC, 1988). The user was required to input the fraction of the lactating cows that corresponded to the four production levels. However, all cows were assumed to be in late lactation, of the same weight and producing milk containing 3.51 fat. No allowance was made for the cyclical nature of requirements through the lactation cycle or for the growth of primiparous cows. The use of soybean meal as the only protein supplement in diets containing large amounts of silages can also be an unfair assessment of reality. 35 It should be recognized that these flaws may not affect certain forage system analyses, particularly those that evaluate efficiency of harvest systems. However, when results are sensitive to the animal model, as were the results of Savoie and Marceux (1985), these flaws can have a major impact on the analysis. For example, the Savoie and Marceux (1985) study was so sensitive to intake prediction for high producing herds that a complete shift of preference from hay to silage resulted when slight changes were made in intake parameters. To obtain a more generic model suitable for addressing a wide range of issues, the factors mentioned above must be considered. The animal model used by Parsch (1982) was that of Waller et al. (1980). Though the structure of the Parsch animal model was the current "state 'ef the art," it was not incorporated into DAFOSYM because of difficulties in linking ’ferage quality to the ration balancer and subsequently following feed disappearance. The Parsch (1982) animal model assigned prebalanced rations from which feed disappearance was predicted. This approach negated one of the abilities of DAFOSYM -- the prediction of feed quality as it reaches the animal. 3. HARVEST LOSS MODELS The .purpose of this chapter is to present improved models of alfalfa losses that occur during field curing and harvest. The losses can be attributed to plant respiration, rain and mechanical treatment. 3.1 Respiration Plant cells of freshly cut forages remain alive and continue to respire. During respiration, carbohydrate material in the plant is oxidized into water and carbon dioxide. Dry matter loss from respiration is estimated using the model of Wood and Parker (1971) in combination with assumptions concerning the change in moisture ever time. ’The fellowing equation relates rate of respiration loss (RLR) to moisture content (m) and temperature (T): RLR_= c.00067u(m-o.273)e°-°59T [3.1] (Wood and Parker, 1971). Equation [3.1] is applicable for temperatures between 5 and 25°C; the respiration loss can occur only when the crop moisture exceeds 27.311. To predict total respiration loss, equation [3.1] is integrated over time with appropriate time courses of moisture and temperature. For each day simulated by DAFOSYM, a minimum and maximum temperature are required inputs from a data file. Rather than model the temperature change throughout the day, it uses average temperatures for 1 All moistures are expressed as wet basis unless otherwise noted. 36 37 daytime and nighttime. Average daytime (ATD) and average nighttime (ATN) temperatures are computed as: ATD (Tminimum + 2Tmaximum)/3 [3-2] AT" (ZTminimum + Tlaximum)/3 [303] The moisture of the alfalfa is assumed to follow the pattern shown in Figure 3.1. The moisture contents at daybreak (m1 and m3) and dusk (m2) are computed by the drying and rewetting models of DAFOSYM. Since the alfalfa is drying exponentially, a drying constant (DC) for the daytime hours can be determined: DC = ln(m2/m1)/HDL [3.4] Using [this drying constant, the moisture over time (Figure 3.1) during the daytime hours is: m(t) = m1-e'DC't [3.5] To get respiration loss during the daytime hours, equation [3.1] is integrated with [3.5] as the relationship of moisture over time: t‘ -DC t o 069ATD RELday = [ 0.000674[m1 e - O.273]e ° dt [3.6] t=0 The upper limit (t1) can range from 0 to the number of daylight hours (HDL) until the moisture content of the crop drops to 27.31 within that day (Figure 3.1). Assuming that overnight rewetting occurs at a constant rate, the moisture rises linearly during the nighttime hours (Figure 3.1): I(t) = mg + (33-mZ)(t-HDL)/(24-HDL) [3.7] To calculate respiration loss during the nighttime hours, equation [3.1] is integrated with [3.7] as the relationship of moisture over time: 38 .mweH cewumuwemou we cowuoCHEueuoo Hem zoo ecu usoswoousu cowumwpm> ousumaoe new Hope: fi.m omowwm Ase x3 wouoemmm we mmofi meeoezo q.m epomwm 38a 8; EV EEzoo mmaemaz ON 00 on 0.? . _ L _ r h _ O mmo_ 95.er mmo_L:oam (z) SSO‘I ElEddOl-IC) 4. STORAGE AND FEEDING MODELS Forages are stored either as hay or as silage. The processes that occur during these two storage methods differ greatly and so are modeled diffErently. This chapter discusses incorporation of a hay storage model into DAFOSYM and develops and evaluates a comprehensive model of silo storage. Quantity loss during feeding is also modeled. (FORTRAN code for the storage model is included as Appendix A.) 4.1 Inside hay storage The model of indoor hay storage incorporated in subroutine HAY was developed by the author (Buckmaster, 1986; Buckmaster et al., 1988). Dry matter loss and quality changes are predicted for pure, untreated alfalfa hay. Several assumptions about bale density and stack size were made to make the model more computer efficient. Based on thermodynamic theory, the model for dry matter loss in alfalfa hay is: 86.40 + 2u33[M1-mf(1-l1)/(I-flf)] L: [ALI] [1-m1] [14206-2433flf/(1-Mf)] To use equation [4.1], initial and final moisture contents and the amount of heat generated during storage must be known. Within DAFOSYM, the moisture content of the hay entering storage is determined by the harvest model. Ray is stored for enough time to allow complete drying; therefore, a final moisture content of 0.12 is used. The total respiration heat generated during storage is: 0 = 3289(m1>2-‘8(o,)°-5° + 3179(m1)‘°23(o,)°-9” [1.2] 52 53 Estimates of the initial density of rectangularly baled hay as a function of initial moisture content are based on the data of Buckmaster (1986): p1 = 0.100 + 0.44mi [4.3] Dry matter loss is estimated using equation [4.1] with the simulated initial moisture content (from harvest), a final moisture content of 0.12 and the total respiration heat generation estimated using equations [4.2] and [4.3]. Quality characteristics of concern within DAFOSYM are fiber and protein. Buckmaster et al. (1988) presented an assessment of acid detergent fiber changes during storage. The data of Rotz and Abrams (1988) suggest a similar relationship for neutral detergent fiber. The change in NDF during storage is predicted by: NDFf = NDFi/(1-L) [4.4] Three protein characteristics are important in the animal model (of DAFOSYM: crude protein content, acid detergent insoluble protein content, and protein degradability. Changes in crude and acid detergent insoluble protein (ADIP) contents during storage are modeled as in Buckmaster et al. (1988): CPf = CP1(1-0.4L)/(1-L) [4.5] 1019f = (ADIP; + 0.0000373HDD)/(1-L) -[4.6] where ADIPi is equal to 0.81 of dry matter (i.e., ADIP1 = 0.008). Simulation of heating in degree-days (HDD) for numerous stacks through prediction of temperature over time is extremely time consuming. Rather than performing these calculations repetitiously, time/temperature relationships were simulated for stacks of 1,000 bales for initial moisture contents ranging from 12 to 251. Corresponding densities were 54 computed using equation [4.3]. Simulated data for heating in degree- days were empirically modeled as a function of the initial moisture content of the hay: 48460(m1-0.12)1’836 1r m1 > 0.12 2 1r m1 < 0.12 [4.7] (r = 1.000) A - HDD Acid detergent insoluble protein content is predicted using equations [4.6] and [4.7]. Caution should be used when applying these relationships. Equation [4.6] is based on data ranging from 0 to 354 HDD. Extrapolation beyond 400 HDD (m1 of approximately 201 according to equation [4.71) can be unrealistic and should be avoided. This caution does not pose a problem in DAFOSYM, however, because moisture content at baling should not exceed 201 without the use of a chemical preservative. For use in the animal model, the acid detergent insoluble protein 1 has to be expressed as a fraction of crude protein. That is: ADIPCPf = ADIPf/Cpf [4.8] Degradability of alfalfa hay is assumed to be 701 and is discussed further in Chapter 5. 4.2 Outside hay storage Based on the published data reviewed in Chapter 2, dry matter loss during outside storage of round hay bales is approximately 51 (L:0.05) if the bales are covered and 141 (L=O.14) if uncovered. Published data indicate that fiber concentration increases during outside storage. Assuming that (as in indoor storage) fiber is not lost, the fiber content as a fraction of dry matter increases with the loss of non-fiber material: werf = NDF1/(1-L) [4.4] 55 In published data, protein changes during outside hay storage did not show any significant trends (Anderson et al., 1981; Currence et al., 1976). It is reasonable to expect some protein loss due to leaching by rain. This, in addition to the slight protein loss that occurs during indoor storage, implies that the loss of crude protein as a fraction of crude protein would be approximately the same as total dry matter loss as a fraction of dry matter. Thus, the crude protein content during outside storage remains unchanged: err = or, [4.9] Because ADIP is related to heat development (Buckmaster et al., 1988), heat development in round bales stored outside must be modeled. An attempt to model heating in round bales stored outside is futile with available data because factors such as wind conditions, rainfall occurrence, ambient temperature and bale condition are variable. Considering the size of round bales, it is reasonable to assume that heat generated within the bale is dissipated before the bale temperatures reach 35°C for a significant length of time as long as bale moisture is relatively low (251 moisture or less). For this reason, the ADIP content of hay stored outside is censidered to remain constant: ADIPf = ADIP1 [4.10] 4.3 Silo stogege DAFOSYM offers several silage structure options and allows up to four alfalfa and two corn silage structures. The silos can be bunker, top-unloaded tower or bottom-unloaded tower silos. Two of the four alfalfa silos can be designated for low quality forage; the other two, for high quality forage. Associated with each silo are its dimensions, 56 capacity and initial cost and the permeability of the wall or cover. It is assumed that structures containing similar quality forage are emptied over a 12-month period and that only one structure of each quality is open at a time. Because it is common for alfalfa silage silos to be refilled within one year, the silo capacity is increased for later cuttings if some alfalfa silage was harvested during an earlier cutting. It is assumed that there are 30 days between cuttings; therefore, a silo filled with alfalfa silage during first cutting can hold an additional 1/12 of its capacity for each of the following cuttings. Bacause of the nature of filling and unloading a bunker silo, refilling of this type of structure is not permitted._ The literature contains several models dealing with silage storage (Pitt, 1986; Pitt et al., 1985; Neal and Thornley, 1983; Holter, 1983; Levering and HcIsaac, 1981c; Leibensperger and Pitt, 1987), but an integrated model that simulates the entire ensiling process --including filling, fermentation and feedout -- is not available. The following sections contain the development and evaluation of such a model. 4.3.1 Model development 4.3.1.1 Overalleodel structure The ensiling process can be divided into four phases. The first phase' is before sealing (preseal). A silo is filled by plots (in DAFOSYM one plot is the material harvested in three hours). The first phase considers changes caused by aerobic respiration that occurs from the time a plot is placed into the silo until it is covered with another plot or, in the case of the last plot in the silo, until the silo is covered with plastic. The second phase is fermentation which includes 57 all dry matter and quality changes that occur under anaerobic conditions. During the third phase, infiltration, oxygen penetrates the silo wall (tower silos) or the cover (bunker silos) into the stored material to allow aerobic respiration. During the last phase, feedout, dry matter loss is due to aerobic respiration both on the silage surface inside the silo and in the silage in the feed bunk. The intended use of the model is for wilted silages with dry matter contents above 301. Therefore, effluent production is not considered. The concept on which the silo model is based is that of oxygen diffusion into the forage material. For a tower silo, assuming angular symmetry, the diffusion is two dimensional: axially downward and radially inward. The two-dimensional problem is approximated by three one-dimensional phases, each describing different time periods. During the first phase, preseal, the top surface of the forage material is uncovered; thus, radial diffusion of oxygen is negligible over a short period of time. The fourth phase, feedout, considers oxygen diffusion similarly because the opened surface is again exposed to air. The third phase, infiltration, considers the long-term effects of radial oxygen diffusion. Because of relative dimensions and the fact that the feedout phase deals with the opened surface, the silo is considered to be a tube with insulated ends for the modeling of the infiltration phase. Even though oxygen is diffusing into the forage material, the rate of diffusion is slow enough that forage undergoes anaerobic farmentation. Fermentation is modeled as phase two even though phase three, infiltration, occurs simultaneously. Similar arguments hold for the breakdown of the phases in a bunker silo. The four phases are linked together to simulate the entire ensiling 58 process. The linkage is different for tower and bunker silos. Because plots are removed in different order, there are also slight differences between top- and bottom-unloaded tower silos. In a top-unloaded tower silo, the plots are numbered in the order in which they were harvested and in the reverse order in which they are removed from the silo (Figure 4.1a). Each of the four phases is simulated sequentially for each plot in a tower silo. The density during phase 1 is the uncompacted density. It is assumed that the silo is filled prior to fermentation; therefore, the density during farmentation is higher and depends upon the position of the plot within the silo. Forage density is estimated as by Pitt (1983). The depth to the top of a given plot is computed using the density of each plot above the given plot and the cross-sectional area of the silo. The temperature of the ensiled crop as it enters the fermentation phase includes the temperature rise from phase 1. Once fermentation has been simulated for a plot, the third phase, infiltration, is simulated. The length of time each plot is in the silo is the time required to remove all of the material above (or below in a bottom-unloaded silo) the current plot plus half of the current plot. This time is determined from the feed rate, which is computed in such a way that the silo is emptied over a given length of time. Feedout, phase 4, is simulated following infiltration. The density of the exposed silage surface is the compacted density; uncompacted density is always used for in-bunk respiration. Total dry matter loss from all phases cannot exceed the total respirable substrate available in the feed. Refilling of a tower silo complicates the model. In the top- 59 Filling, Unloading Fill in}: Unioudlny, 7 l 7 7 6 V 2 6 ' 6 5 3 5 5 4 4 .. 4 11 3 V) 3 3 2 6 2 2 l 7 1 l (A) (13) TOP- UNLOADED TOWER SILO BOTTOM- UNLOADED TOWER SILO (c) A (D) UNLOADING OF A BUNKER 311.0 Figure 4.] Order of filling and unloading of plots in silos. 6O unloaded tower silo, refilling is modeled by increasing the length of time the original plots in the bottom of the silo are in the silo. Plots placed into the silo during refilling or plots replaced by the refill are treated identically in the model. It is assumed that refilling. does not change the density of the original plots in the bottom of the silo. ‘ The bottom-unloaded tower silo is modeled like the top-unloaded silo. A difference is the length of time each plot remains in the silo. Because plots are removed from the silo in the same order that they were harvested (Figure 4.1b), the length of time a plot is in the silo is the time required to remove the plots below the given plot plus half of that plot. The effect of refilling a bottom-unloaded silo is an increase in the density of the original plots that remain in the silo. For plots removed prior to refilling, density remains the same as in a silo without refilling. For the original plots that remain in the silo during refilling, the time in the silo is split into two segments: one before refilling and one after refilling. Infiltration losses are simulated for the first time period using the original density of the plots. The density after refilling depends on plot position within the silo. Figure 4.1c illustrates plot placement in a bunker silo. A bunker silo is not emptied one plot at a time; rather, vertical sections that contain material from several plots are removed (Figure 4.1d). Phase 1 in the bunker silo is simulated for each plot as it is placed into the silo. Estimation of surface area is based on a slope of one-half during filling. After preseal has been simulated, fermentation 61 is simulated for each plot using the compacted density. Because infiltration is vertically downward into a bunker silo and the silo is emptied in vertical sections, the plots become indistinguishable once the silo is filled. Following preseal' and fermentation, which are simulated for each plot, the moisture content and quality at any point in the bunker is considered to be the average moisture content and quality of all material in the silo. Thus, before infiltration, the vertical sections each contain material of identical quantity and quality. The time that each vertical section remains in the silo is the amount of time required to remove the vertical sections in front of the current section. With this overview of how the four phases are linked together, the mathematical models for each phase will be developed. 4.3.1.2 Phase 1: Preseal Changes that occur before the sealing of a plot are the result of plant respiration, and they include dry matter loss, a change in dry matter content and a temperature rise. Proteolysis is assumed to be negligible until fermentation begins. During the preseal phase, oxygen infiltration is assumed to be vertically downward into the forage material. Oxygen infiltration through the silo wall is assumed to be negligible compared with the infiltration into the open surface. The preseal portion of the silo model is a modification of the work of Pitt (1986). Pitt (1986) modeled respiration rate (u) in forage material as: not +v)r1: u: a m a c [21.11] Va (Km ‘1' V) The relationship for estimating the respiration rate in air (pa) as a 62 function of crop type, pH, temperature and dry matter content is given by Pitt (1986). For the geometry shown in Figure 4.2, the differential equation and boundary conditions describing the steady state diffusion of oxygen is: 92¢ 0 Ha (Km + Va) 1’’C W ._. - = O [H.128] :11:2 0111113 (xmwp) subject to: . m(x=0) = Va [4.12b] dv(h) ——, = 0 [4.12c] dx (Pitt, 1986). At a constant temperature, silage pH, silage dry matter content and density, equation [4.12a] can be simplified to: d‘v 1 1 ___ - ________ = 0 [4.13] dx2 (xIll + t) where y : pua(Km+1pa)fC/(D¢Twa) is a constant. Equation [4.13] is a non-linear equation that can be solved numerically to estimate the oxygen concentration profile. Using superscripts to denote iterations, equation [4.13] can be rewritten as: dzw(k+1) Y 9(k+1) _ - : O [HUN-l] dx2 (x. + o)‘“*" Using a Taylor series expansion on the second term gives: (k+1) (k) (k) 7 V = 7 V + Y C(V/(Km+w)) (¢(k+1)-w(k) (Kn+V)(k+1) (Kn+W):k dw [4.15] which can be simplified to: (k+1) (R) Y = x + Y (1 -v ) (x.+v)(k*‘7 (x,+1)(k5 (x.+v(k))2 [4.16] 63 .Acwofi .uufimv Hmwuoume emmpew eucw cewmowwwo cowxxe eumum zomeum HmoowmceEHolfi we hpueEeeo N.q onowwm e 1 a... m \\\\\\\\\R\\\R\\\\\\\\\\\\\\\\\\\\\\\\\R\ .e . w u .\ x .8 .6 \ o .11: . 1:28- ... “ \ 3e 8 x x a \ X<+M NU \ \ ...1 33.. u once \ a Md‘O: I “$0.35“ I II. II I ”luv iiiii H iiiiiiiiii WL slime. llllllllllllllllll x. .. a a 1w \ N ”W P0401 u :mU fi“ x x 2. \ _ ._.. ._. 3...: - 1F 64 Substitution of equation [4.16] into [4.14] with rearrangement gives: d2¢(k+1) Km (k+1) Y (V‘k))2 3;; - Y ZE;:ETE7;§ m : (R;:TTESI§ [4.17] Equation [4.17] can be used with finite differences to obtain a set of simultaneous equations to be solved at each iteration. It is noteworthy that few iterations are necessary for adequate convergence and that the set of simultaneous equations is in tridiagonal form for quick solving. Once the oxygen concentration profile is known, a respiration rate profile is computed using equation [4.11]. From this, the average respiration rate over the depth of the plot (i) can be calculated. Within DAFOSYM, an approximate oxygen concentration profile is used rather than the finite difference approach. Starting with the non- linear diffusion equation [4.13], a linear approximation is incorporated (Pitt, 1986): d211 2 ' ——— - w/o = o [4.18] dx2 where o2 approximates (Km + m)/y. Because w varies only from O to 0.21 (oxygen concentration in air), this approximation can be made with little change in predicted dry matter loss. The zero intercept on the approximation is necessary for the oxygen concentration profile to approach zero at infinite depth. The solution of equation [4.18] subject to the boundary conditions of [4.12b] and [4.120] is: wa e-I‘I/Q ‘va eh/Q 0(x) = eX/a + e'X/a V [4.19] eh/o + e-h/o eh/o + e-h/o For ensiled forages, o ranges from 5 to 25 cm; therefore, h/o is large and equation [4.19] can be further approximated by: V(x) = 1. e'“/° [4.20] 65 To estimate the dry matter less, the average respiration rate over the depth of the plot is needed. Substituting the oxygen concentration relationship [4.20] into equation [4.11] and integrating over the depth of the plot gives: h ua rc (Kr-v.) v. e"’“ ' 1/h I dx [4.21] O Iva (Km ‘I’ Iva e-X/a) 'E II -uf(K+1[I)G - = a C m a [ln(Km + wae-h/o) _ ln(Km + Va)] [4.22] 93 d A value of o = 1/(9y)°'5 was chosen so the error in dry matter loss prediction was small. Because the oxygen concentration in silage is low, it is important that the approximation of o2 : (Km + u)/y fits more closely, at w near 0.0 rather than at 0 near 0.21. With the approximation of a : 1/(9y)°'5, agreement between preseal dry matter less (expressed as a fraction) as predicted by the finite difference approach and the approximation as presented was to the fourth decimal place for several typical plots of silage. For modeling the oxygen concentration profile, h refers to the depth of a plot in a tower silo. Because the plots in a bunker silo are much shallower, h refers to the total depth in the bunker to date; i.e., preseal loss in a bunker silo is not confined to the top. plot alone. Dry matter loss during the preseal phase can be related to the average respiration rate and the duration of the preseal phase (t1). Using the respiration reaction: 05H1205 + 602 --> 6H20 + 6002 + HEAT (2870 kJ/mole) [4.23] and assuming the density of oxygen is 0.00133g/cm3 (ideal gas at 20°C): 66 L1 = 0.0299 n t1 / OH [4.24] where: 0.0299 = (0.001333 02/cm3 02) (180g silage lost/192g 02 used)(24h/day) Within DAFOSYM, dry matter loss during the preseal phase is estimated using equation [4.24] with the average respiration rate approximated by equation [4.22]. The change in dry matter content during the preseal phase is estimated by equation [4.23]: for each 180 g of dry matter lost, 108 g of water is produced. Temperature rise before sealing is computed with the assumption that the heat generated raises the temperature of the ensiled material. A lumped analysis is used; thus the plot is assumed to have uniform temperature. Using the specific heat estimate of Pitt (1983), the temperature rise (AT) is: 2870 L1 AT = O.18(1.89+4.19(1/DM-1)) 6932L1 / (1.82/DM - 1) [4.25] The preseal model uses a time step of one day, thus the temperature rise due to respiration on the first day affects the respiration rate on the second day, etc. All dry matter lost is respirable substrate (i.e., sugars and starch). Therefore, as dry matter is lost during the preseal phase, neutral detergent fiber and crude protein concentrations increase: NDF1 = NDF1 / (1-L1) [4.26] CP1 = CPI / (1~L1) [4.27] 4.3.1L3 Phase 2: Fermentation Models of changes in alfalfa silage and corn silage due to fermentation and respiration of air trapped during ensiling were 67 developed using the model of Pitt et al. (1985) as modified by Leibensperger and Pitt (1987). Numerous runs of their simulation 'model were used to develop a data base of important quality changes for different values of initial temperature, air to herbage ratio and dry matter content. Two methods of modeling these data were considered. The first was to treat the generated data as a table for interpolation. The second, reported here, was to fit empirical functions to the generated data. The model of Pitt et al. gives time courses of quality, yet only final quality characteristics were of interest for the ‘comprehensive model. Simplifying the detailed model to empirical equations greatly decreased the execution time of the comprehensive model, with the difference in each predicted quality change being 31 or less. Some quality changes are very non-linear and at times not monotonic with respect to the input variables; therefore, the generated data were divided into subsets such that, within the subsets, the effect of each variable could be modeled with a mathematically simple function. Transformations were performed to assure continuity among the functions. Least squares regression was used to fit functions to the data. For both alfalfa and corn silage, the temperature rise in degrees Celsius during fermentation is nearly equal to the air to herbage ratio. The alfalfa fermentation functions are applicable for initial temperatures (T) from 5 to 45°C, dry matter contents (DM) from 20 to 601 and air ‘16 herbage ratios from 0.5 to 3.4. Values for other initial characteristics are listed in Table 4.1. Fermentation functions for alfalfa silage were: L2 = 0.0156 - 0.0364(DM-0.20) [4.28] 68 Table 4.1. Initial crop characteristics used to develop fermentation relationships. Characteristic Alfalfa Corn pH 5.9 5.7 Buffering capacity . (mEq/g DH) 0.597 0.244 Water soluble carbohydrate (1 DH) 8.0 30.5 Crude protein (1 DH) 17.0 8.5 Insoluble protein (1 CP) 67.0 84. Hemicellulese 9.5 34.0 Lactic acid bacteria population * (#lg DH) f(T) 1(10)6 *Lactic acid bacteria population : 895(1o)0-0699T T = Temperature (°C) urn = 0.4187 + 0.0114(7-5) - 0.169(0w-0.20) + 0.0128(T-5)(DM-0.20) - 0.0515(T-5)(DM-0.20)2 ‘ 0.765 [4.29] PH = 4.43 - .2o<0w<.33, 5 1 7‘ ------------- K $11.0 WALL \ \\ MOVING FRONT REGION LNAFFECTED BYLDSS TONER SILD 311.0 WALL RESPIRABLE SUBSTRATE DEHJHED MOVHKSFRONT REGION UNAFFECTED EYLDSS Figure 4.3 Moving—front concept for modeling loss due to oxygen infiltration into silos. 71 of dry matter loss (and thus the amount lost) is determined by the rate of oxygen infiltration. The oxygen must penetrate the silo and forage material to reach the front; therefore, the effective permeability has contributions from both the silo wall (tower) or cover (bunker) and any forage to the outside of (tower) or above (bunker) the moving front. Aerobic respiration resulting from oxygen infiltration occurs slowly; any heat generated is assumed to be readily dissipated. The rate of oxygen infiltration to the front is: 10000 Ueff-A-(wa - vr) ZIOO-Ueff-A [4.38] q since Va 2 0.21 and tr = 0.0. The rate of dry matter loss (linf) is related Vto the oxygen infiltration rate using the density of oxygen and stoichiometry of the respiration reaction [4.23]: linf 9 (0.00133 g Og/cm3 02)(180 g silage lost/192 g 02 used) 0.001247 q [4.39] The location of the front affects the infiltration rate and thus affects the rate of dry matter loss. The movement of the front is modeled using a 10-day time step (Figure 4.4). For each 10 days, [the amount of dry matter loss is estimated by: LIJ = 0.24 11hr [4.40] where: 0.24 = (10 d)(24 h/d)(1 kg/1000 g). Combining equations [4.38], [4.39] and [4.40] and simplifying gives: L13 = 0.628 Ueff°A [4.41] Because a bunker silo is unloaded in vertical sections, the bunker is divided into vertical sections with each section containing the amount of material removed during 10 days. The duration of phase 3 (t3) 72 ( START ) INITIALIZE FRONT POSITION DAYS : 0 SET DAYSsim Ueff = Usilo [COMPUTE LOSS FOR 10 DAYSF: Lpnrs = DAYS + 10 J l V UPDATE ACCUMULATED LOSS AND—7 ,_ POSITION or MOVING FRONT I - -.- ._.-“*— ALL RESPIRABLE SUBSTRATE COHPUTE "eff \l/ EVALUATE CHANGE IN QUALITY STOP Figure 4.4 Algorithm used for modeling the moving— front concept of loss due to oxygen infiltration. 73 is different for each vertical section and is determined by the feedout rate (FR). For a bunker silo, the effective permeability is a function of the cover permeability (Hoover): silage porosity (e), the position of the front (B) and diffusion parameters (D and T). Ueff = (i/Uferage + I/Ucover)-1 [4.42] where: ”forage = DOT/B The depth of the moving front at any given time is computed by: B = 100 Ltd-H / RS [4.43] where the respirable substrate (RS) and fraction of dry matter lost to date (Ltd) are given by: 9 RS = 1 — NDF - cp - ASH [4.44] Ltd = AL/M [4.45] The area of the moving front (A) in a bunker silo is the horizontal area of the vertical section: A = 10 FR / (p-DM-H) [4.46] Similar relationships were used to model infiltration into a tower silo. The function for "forage changed because of differences in geometry: Ueff = (I/Ufopage + 1/uw311)" [4.47] where: “forage = DOT/(100 Rf 1R(Rs/Rf)) The position of the moving front at any given time is given by: R, = (R§ (1-Ltd/RS))O'5 [4.48] where the dry matter lost to date (Ltd) is computed in a similar manner as above but using the initial amount of dry matter in the plot. In the case of a tower silo, the area of the moving front is: A = 2 n Rf‘d [4.49] 74 Because all dry matter lost is respirable substrate, the neutral detergent fiber and crude protein contents of the forage increase: NDF3 = NDF2 / (1-L3) [4.50] 093 = CP2 / (1-L3) [4.51] where: L3 = Ltd when accumulated loss (AL) is summed from time 0 to t3. 4.3.1.5 Phase 4: Feedout Dry matter loss during feedout is divided into two portions: that occurring inside the silo, and that occurring in the feed bunk. Feeding loss from. handling or animal rejection is considered separately from feedout loss, which models only dry matter loss due to respiration. The feedout phase in the silo is similar to preseal in that the surface is exposed to air and oxygen can diffuse into the forage. For feedout in a top-unloaded tower silo, diffusion is one-dimensional downward into the forage; in a bottom-unloaded tower silo, diffusion is one-dimensional upward into the forage; in a bunker silo, diffusion is one-dimensional from the opened end inward. Feedout loss is modeled using the same procedure used for modeling preseal loss, but the density and other factors affecting the respiration rate are different during feedout. Once the respiration rate profile during feedout is ' determined, the in-silo feedout loss is computed by incorporating the duration of phase 4: L1,, .0299 '11 tha / 011 [4.52] where: t4a L'lDF. During bunk exposure, the density of the crop is assumed to be its uncompacted density. The in-bunk loss is estimated by converting respiration rate in air to dry matter loss using a form of equation 75 [4.24]: Lab = 0.0299 ua tub / DM [4.53] Total dry matter loss during feedout is the combination of in-silo and in-bunk losses: Ln = an + L4b [4.54] Again, because all dry matter lost is respirable substrate, the neutral detergent fiber and crude protein contents increase: worn = NDF3 / (1-Lu) [4.55] 094 = CP3>/ (1-Lu) [4.56] 4.3.2 Model validation True validation (proof of model accuracy) of a silo storage model is difficult primarily because of errors in measurement of real data. Data on losses during ensiling contain considerable error due to errors in dry matter content and weight measurements. Ideally, losses during each of the four phases should be measured and compared to simulated losses during the corresponding phases; however, it is difficult if not impossible to obtain such data in a real silo. To check the validity of the model, predicted dry matter losses and final non-protein nitrogen concentrations were compared to published results from Bolsen et al. (1980, 1981), Bolsen and Ilg (1981, 1982), Jackson and Lessard (1977), Kung et al. (1987) and Woodford (1987). Data collected from alfalfa and corn silages in bunker as well as tower silos were included. In the literature, not all of the necessary input data were published. In many cases, initial quality was not given. Typical NDF, CP and ash contents were assumed when necessary to determine the amount of respirable substrate in the forage. Frequently, 76 the temperatures at filling and feedout times were not reported. Feedout temperature was assumed to be 15°C; the temperature at the time of filling, if unavailable, was assumed to be 20°C for alfalfa and 8°C for corn silage. Published information on fill rate, storage time and feedout rate was used for model input. In several cases, the silo size was given, but the amount placed into storage was not. In these cases, the model computed capacity, but capacity estimates could not be validated with experimental data. Table 4.2 contains comparisons of predicted and published dry matter loss values. The model validity was tested by linear regression of actual vs. predicted dry matter loss. Comparisons of the resulting slope and intercept to 1.0 and 0.0, respectively, indicated a valid model (p = 0.07). When the intercept of the actual vs. predicted data was forced to be 0.0, the actual dry matter loss averaged 1.11 higher than the predicted. Although the model predicts reasonable values for dry matter loss and can be identified as an unbiased estimator, the variance between the predicted and actual values was high (r2 = 0.06). Much of the variance can be attributed to the difficulty in gathering accurate data from silo experiments. Errors in the estimate of silo wall (tower silos) or cover (bunker silos) permeability are likely to be a cause of discrepancy between predicted and actual losses. To investigate sensitivity to silo permeability, the permeability necessary for agreement between predicted and actual dry matter loss was determined (Table 4.2). In most cases, the permeability value is reasonably close to the estimated value. In the four cases where such an analysis yields unreasonable silo 77 Table 4.2. Dry matter losses predicted by the silo model compared to losses reported for actual silos, Descriptiop Source of ”estimated Dry Matter Loss (1) BT cropa silo published data cm/h actual predicted cm/h A SB Woodford, 1987 3.0 9.8 12.6 1.8 A SB Woodford, 1987 2.0 7.5 3.7 --- A SB Woodford, 1987 2.0 6.0 6.3 1.9 A SB Woodford, 1987 4.0 8.5 8.2 4.5 A ST Kung et al., 1987 1.8 4.6 11.5 0.5 A ST Kung et al., 1987 2.0 12.7 12.4 2.1 A ST Bolsen and Ilg, 1982 3.0 10.5 9.0 4.0 A ST Bolsen et al., 1980 3.0 6.9 7.9 2.4 A ST Bolsen et al., 1981 3.0 12.8 8.8 7.1 C ST Bolsen and Ilg, 1981 3.0 6.7 8.4 2.2 C LT Jackson and Lessard, 1977 4.0 9.2 4.0 --- 0 LT Jackson and Lessard, 1977 4.0 8.8 5.5 --- 0 LT Jackson and Lessard, 1977 4.0 13.6 8.9 --- mean 9.0 8.2 A = alfalfa, C = corn 3 SB = small bunker, ST = small tower, LT = large tower U = permeability of silo wall or cover at which predicted loss equals published loss (missing data implies errors in addition to permeability estimate causes discrepancies) cot-In. permeabilities, the differences between predicted and actual losses could have easily been caused by errors in determining other simulation parameters such as initial quality, feedout temperature or feedout rate or measurement errors in the actual data. The sensitivity analysis section that follows illustrates the effects of these factors on dry matter loss. Non-protein nitrogen (NPN) concentration in alfalfa silages is predicted using equation [4.29]. Published results of NPN concentration 78 were compared to simulated values (Table 4.3). Comparisons of predicted and published NPN concentrations again indicate that the model predicts reasonable values. Over the small range of values available, the use of the slope/intercept hypothesis for comparison was not warranted. 4.3.3 Sensitivity analysis The silo model was used to determine the sensitivity of predicted dry matter loss to several factors: silo permeability, silo size, silo type, feedout rate, initial quality, crop type and moisture content of the forage. Because the dry matter lost is respirable substrate, a study of dry matter loss gives a good indication of changes in NDF and CP as well. In the sensitivity analysis, unless otherwise noted, all silos are of the same capacity and emptied over a 360-day time span, feedout temperature is 15°C, the crop is alfalfa silage, and permeabilities are 2, 4 and 4 cm/h for the wall of a bottom-unloaded tower silo, the wall of a [top-unloaded tower silo and the cover of a bunker silo, respectively. Crop moisture contents are 50, 60 and 651 (wet basis) for bottom-unloaded, top-unloaded and bunker silos, respectively, unless specified otherwise. Figure 4.5 illustrates the effect of tower silo wall and bunker cover permeability on dry matter losses for three types of silos, each with a capacity of 150 tonnes dry matter. The tower silos are 6.10 m in diameter by 21.3 m high; the comparably sized bunker is 9.14 x 3.05 x 28.9 m. Permeability of stave silo walls ranges from 3 to 10 cm/h (Pitt, 1986). Because of probable pinholes in bunker silo covers, the permeability of silo covers is most likely in the same range (Pitt, 79 Table 4.3. Predicted and published concentrations of non-protein nitrogen in alfalfa silages. Dry matter NPN cencentration content actual predicted 34.0 53.0 62.0 31.6 63.5 65.0 35.2 59.5 55.0 27.3 62.1 56.0 34.8 51.7 66.0 mean 58.0 60.8 7 Source: Woodford (1987) 1986). Bottom-unloaded "sealed" silos would have lower oxygen permeabilities. Decreased silo condition (increased permeability) can have a large impact on silo 'losses. An increase in silo wall permeability from 2 to 4 cm/h caused 2.0 and 3.21 more less in the top and bottom-unloaded silos, respectively. Figure 4.5 indicates that at a common silo wall permeability, a bottom-unloaded silo has higher loss. The susceptibility of forage to dry matter loss is very dependant on density -- less dense silage experiences more less. In a bottom-unloaded tower silo, the most susceptible (least dense) forage material remains in the silo the longest time. The opposite is true for a top-unloaded tower silo. Comparisons of silo type must accurately reflect silo permeability because of this sensitivity. For typical permeabilities (2, 4 and 4 cm/h for bottom-unleaded, top-unloaded and bunker silos, respectively), dry matter losses are nearly the same regardless of silo type for this size silo. 80 .mkmv com no>e amended Asoaonaoo so a cmov common nofineen roe: common women Ce mmeH Leanne zoo neuowoona co zuwawcmesoee eaww we OOOLLm m.q onomwm €\Eov $50 mo j<>> cam no Egmfizmma S m m e N o r n 1— L l— l— F b L b O . 1N 1 nu 1e mm 4 W [Q m 1 fid— Tm a a WI l: w r n\u \ Ll \l/ . \\\1. NF % \x. 1 /l\ \1 \\ o.__m mmxzam .4 11.11.. o.__m E39 8232: zotom .... V: o.__m «use 8232: a9 1.. . 81 Figure 4.6 illustrates the effect of silo capacity on dry matter less. All silos were emptied over 360 days; therefore, the feedout rate was different for each capacity. Loss as a fraction of initial dry matter is decreased with larger tower silos because of the higher density in the lower portion of the silo which allows less oxygen penetration. In bunker silos, which have a more constant dry matter density, the dry matter loss levels off at about 71. This occurs because of a maximum feasible bunker depth. Increasing the capacity of large bunkers adds surface area in proportion to the increase in capacity as depth is held constant. A Because most dry matter loss occurs during the infiltration phase, the length of time that forage remains in the silo has a large impact on dry matter loss; therefore, feedout rate affects dry matter loss. Figure 4.7 illustrates the relationship for a 6.1 x 21.3 m top-unloaded tower silo. As storage time increased, total dry matter loss during enkiling increased almost linearly. Approximately 0.61 dry matter was lost per month. For the medium-sized tower silo, reducing the days to empty the silo from 360 to 120 days resulted in 4.51 less dry matter loss. The effect was similar for a bottom-unloaded tower silo and a bunker silo of comparable capacity. Figure 4.7 also illustrates the effect of feedout temperature on total dry matter loss. Feedout temperature affects only losses that occur during the fourth phase, but the effects are important. It was assumed that feedout temperature was constant over the entire feedout period. An increase in feedout temperature of 25°C can cause 1.0 to 3.51 more dry matter loss. Temperature at feedout had more effect on losses in less dense material (near the top of a tower silo or in a 82 .mmoH Rename zap oeuOflvoua :0 Oman oHHw Lo uooLLm c.¢ ouzmflm 6E6 moccoyv C5uu co LooOOOL mayhem emoumLereu cam sumo uzcooow Lo uooLLm n.e_euowflm Onzm tnzzm OF m>mo _ 3% co on om . . 1L L L L mum” coo, 19. 1mm 1mm l U) [\ ' (Nlaiead 30080 %) .LNELLNOO NdN 86 V .oamfiwm :Lou Ca occuceu concouwc CHOLOLEICOC oeuOfioeuo co enoumneeEOu one uncuceo oeuumE Sup we nooLem m.q on:uwm HZmfiZOQ wake/12 ED om cs om . _ . _ _ L p O N \\ 0 mm O (\l l O ["7 1 CD v. (Nlaioad 301183 %) lNElNOO NdN 87 unchanged. Feeding loss for all feeds except round hay bales was assumed to be 51. Loss during feeding of round bales was assumed to be 141 if they had been stored inside and 251 if they had been stored outside. 5. ANIMAL UTILIZATION MODEL When considering forage value on the dairy farm, the importance of the animal cannot be overemphasized. Production of milk is the capstone of all processes that converts labor, resources and capital into a salable product. The conversion of feed into milk reveals the effects of alternative technologies and management strategies on feed value. The objectives of the animal model are to predict feed intake and milk output for a given supply of feed. In effect, the model provides an estimate of forage value based on dairy cow performance and so provides feedback on how crop growth, harvest and storage affect animal perfOrmance. The model developed here is not intended to be a ration balancer for on-farm use. Rather, it estimates the potential milk production from given feed and the required supplemental feed for 'producing a given amount of milk. The three major sections of this chapter describe models for lactating cows, growing heifers and the combined dairy herd. The single-animal models are used to determine rations that yield given milk production levels or daily gains. The whole-herd model uses the single- animal models and includes the division of the herd as well as feed allocation to the various herd groups. It is the purpose of this chapter to define clearly the new animal models in DAFOSYM. Care is taken to give considerable detail on structure with some insight into why the structure was chosen; however, an understanding of the biology behind the relationships will require a 88 89 review of the referenced work. Several subroutines make up the animal'model. Table 5.1 lists these subroutines and their functions and tells from which subroutine they are called. 5.1 Lactatigg cow model The objective of the lactating cow model is to formulate rations that meet the nutrient requirements of a given lactating or dry cow. It was assumed that forage quality will have negligible impact on mineral supplementation, so the model deals with only intake, fiber, energy and protein. Rations are formulated using linear programming. Constraints imposed on the formulated diet are developed in the following sections. 5.1.1 Dry matter intake It is commonly accepted that intake is controlled by two mechanisms: physiological energy demand and physical .fill (Conrad, 1966). The control of intake by physiological energy demand implies that a cow will ingest no more energy than she requires. When balancing a ration, energy intake is forced to equal the energy requirement so the ration meets the requirement withOut providing excess energy. The intake constraint of concern, then, is physical fill; i.e., the ingestive capacity of the animal cannot be exceeded. Using the work of Hertens (1987): I-F 5 C [5.1] Of the chemical analyses available, NDF should be most highly correlated to the space-occupying or fill effect of the diet. Soluble constituents in feeds dissolve and contribute very little to the fill effect of diets. Fiber displaces volume in the rumen and NDF is the 90 Table 5.1 Animal model subroutines and their function in the prediction of animal performance. Subroutine Called by Function COWFD REPORT Main animal model program. Transfers feed - information, sets up feeding order, transfers feed utilization information back to REPORT. FEEDUM COWFD Calls ration formulators, assures feed stocks are fed correctly. FEED FEEDUM Allocates feeds according to decision rules. SINGLE FEEDUM Ration formulator for lactating cows and heifers. Sets up constraint equations and calls for LP solution. ROWSET SINGLE Sets up the auxiliary matrix for LP solution. LPSOLV SINGLE Solves the ration LP problem. only fiber routinely used that isolates all fibrous components: cellulose, hemicellulose and lignin. Neutral detergent fiber has been shown to be highly correlated with the volume or bulk density of feeds (Hertens, 1980; 1985a). Because not all NDF is equal in its fill effect, an adjusted NDF (ANDF) is used for fill units and [5.1] is rewritten as: ONT-Anna“, : IC [5.2] Ingestive capacity (10) is directly related to the frame size of the animal: IC = 1310-3115ch [5.3] (Hertens, 1987). The ingestive capacity coefficient (Cic) is a function 91 of stage of lactation and maturity of the animal. Hertens (1987) reported that daily NDF intake was 1.2 3 0.11 of body weight per day (i.e., IC = 0.012 3 0.01). This typical value was adjusted over the lactation cycle. Daily ingestive capacity for cows in early lactation was calculated from an experiment in which an alfalfa/corn/soybean meal ration containing 301 NDF was fed to cows for the first 14 weeks ‘of lactation (Dado and Hertens, unpublished). Other IC's were calculated from the data of Hertens (1985b). The frame size indicator (BASEWT) is the body weight during the second stage of lactation and is discussed further in Section 5.5. The physical fill constraint gives a maximum amount of fiber that the cow can ingest per day. To incorporate this intake constraint in a linear programming approach, the following notation is used: 2 xi-ANDFI < Cic-BASEWT [5.4] 5.1.2 Fiber Both the amount and the physical characteristics of fiber have important effects on rumen function and milk synthesis. Long fiber is needed to promote the rumination activity and rumen function necessary to maintain the rumen environments that result in the production of the necessary amounts and types of fermentation products for milk synthesis. Rations are constrained so that, of the fiber in the total ration, 751 comes from long or coarsely chopped forage (NRC, 1988). This is implemented' with the concept of roughage value (RV). The RV is equal to the ANDF value of forages; it is 0.0 for concentrates and protein supplements. The constraint is, then, that the roughage value in the diet must exceed 751 of the ANDF in the diet; i.e., 92 Z xi'Rvi : 0.75 X xi-ANDFi [5.5] Rearranged for easy implementation into an LP format, [5.5] becomes: 2 x1(RV1 - 0.75ANDF1) 3 0 [5.6] If the diet cannot meet energy and protein requirements and satisfy the RV and intake constraints, the LP problem statement will have no feasible solution. When this occurs, milk production is reduced 21 and a new ration is attempted. 5.1.3 Energy The net energy for lactation requirement is estimated using the National Research Council (1988) relationships. The total net energy requirement (NEE) includes needs for maintenance, pregnancy, weight change and lactation: NEi = NEl’m + NEl,p + NEl:8 + NEI,1 [5.7] where: NEl’m = 0.083v0-75 [5.8] NE1,p = 0.02qu0’75 if days pregnant 3 210 = 0 if days pregnant < 210 [5.9] = n.92ABv ifsABV < 0.0 [5.10] N31,1 = (0.3512 + 0.0962PFAT)MPD [5.11] The net energy content of feeds depends on the level at which the cow is producing milk (i.e., multiple of maintenance). Using a a percent reduction in feed N31 content for each multiple of maintenance (HUNT) (NRC, 1988), the energy content of feed 1 is adjusted from the energy content for three times maintenance if intake is other than three times maintenance: 93 where: HMNT = NEE / NEl’n [5°13] Alternatively, since the “1 per multiple of maintenance reduction is used for all feeds, the requirement can be increased by the inverse of the factor in equation [5.12] and the energy content of the feeds held constant at the three times maintenance level; i.e., adjust NEE by: NEE’adJusted = 0.92NE{/(1-0.0u(MMNT-1)) [5.1a] Although this approach is somewhat misleading, it simplifies implementation because the energy content of the feedstuffs does not change as rations are balanced for different animals. I The energy constraint to the LP ration balancer is then: X xi'NEl,i = NE[,adjusted [5°15] and the energy contents of each feed, NEl,1 is three times maintenance. The equality assures that the ration will not provide excess energy. 531.” Protein Protein requirements are calculated as in NRC (1988), using the absorbed protein system (NRC, 1985). The absorbed protein requirement (AP’) includes needs for maintenance, weight change, pregnancy, lactation and fecal output: AP” = NNTP/O.67 + DPA + YPN/O.5 + LPN/0.7 + FPN [5.16] where: NNTP = 0.000213w°-6 + 0.002753w°°5 [5.17] DPA : 0.256ABN (lower limit of -0.1875) [5.18] rpm = 0.0011368V0'7 1r days pregnant 3 210 = 0 if days pregnant < 210 [5.19] LPN = (0.019 + 0.00nPFAT)MPD [5.20] FPN = 0.09OIDM [5.21] 9” The lower limit on difference protein absorbed (DPA) incorporates an upper boundary on mobilized protein. The indigestible dry matter (IDM) is estimated from the intake and digestibility of an approximate diet: IDM = PDNI(1-ATDN) (5.22) In the approximate diet, intake is predicted from the energy requirement and recommended energy concentrations for the diet (NRC, 1988): PDMI z NEE/ual,diet [5.23] where: “El,diet 1.25 g for dry cows 1.02 + 0.01(NPD-NP 1n)(R£LMBS)(0.7u)/ (O.3512+0.09 2(PFAT)) (lower limit of 1.u2, upper limit of 1.72) for lactating cows [5.2”] 10(RELMBS)(0.7u)/(0.3512+0.0962(PFAT)) [5.25] ((Bv)/600)°°75 [5.26] “PDmin RELHBS To estimate the digestiblity from the energy concentration, a production level of three times maintenance is assumed: ATDN = 0.92(N21,d18t + 0.12) / 2.15 [5.27] The requirement for absorbed protein is used in ration formulation. The procedure discussed here differs slightly from that of NRC (1988) to provide a more clear description of what occurs in the digestive tract of a ruminant. Protein absorbed through the intestine walls is available from two sources: microbial protein (NCP) formed in the rumen, and available rumen escape protein (AESCP : CP(1-DEGR-ADIP)). The efficiency of converting HCP into absorbed protein is 6”} (NRC, 1988). This 6"} comes 95 from 801 efficiency in converting ammonia to bacterial true protein in combination with a bacterial true protein digestibility of 801. NRC (1988) suggests that escape protein is 80% digestible. Previous dairy (NRC, 1978) and current beef (NRC, 198V) requirements are based on total absorbability of 75 to 801 and 901, respectively. Nith an escape protein digestibility of 80%, this cannot be true (i.e., a weighted average of 6H1 and 80% cannot be in the range of 80 to 901); thus, escape protein must have a digestiblity of approximately 951. The use of 80% digestibility is warranted when unavailable (or fiber-bound) undegraded protein is not explicitly subtracted from total undegraded protein as in NRC (1988). The following is based on the 6H and 951 conversion rates for NCP and AESCP, respectively, because unavailable protein is subtracted from total undegraded protein. Following NRC (1988), the absorbed protein requirement is split into two fractions: degradable and undegradable, or escape. Degraded protein is converted into microbial protein in the rumen and is later absorbed through the walls of the small intestine. Undegraded protein (sometimes referred to as bypass protein) passes through the rumen unchanged before being absorbed from the small intestine. The bacterial crude protein yield (BCPdiet) is determined by the amount of substrate in the diet for microbial growth. For a lactating cow, the substrate is expressed in terms of the net energy in the diet: acpdie, = 6.25(0.01)NE{ [5.28] (D.R. Hertens, 1988 personal communication). Though this relationship is not applicable with diets containing fatty sources of energy, within DAFOSYM it poses no problem. This substrate must be matched with an appropriate amount of ammonia in the rumen for proper microbial 96 activity to occur. If the conversion efficiency of rumen ammonia into bacterial protein is 901 (NRC, 1988), the rumen available protein (RAP?) (ammonia) requirement is: RAPr : acpdiet/0.9 [5.29] The ammonia comes from degraded intake protein as well as recycled ammonia. If 151 of intake protein is recycled (NRC, 1988), the rumen available protein is: RAPdiet = un3diet + 0.1scrdiet [5.30] where: NH3diet = z xi’DEGR1°CP1 [5.31] CPdiet = z x1°CP1 [5.32] If excess degraded protein is allowed in the diet, the rumen available protein requirement can be expressed as the following ration constraint: 2 xi'CP1(DEGR1+O.15) Z BCPdiet/o'g [5.33] Given a total absorbed protein requirement, that that is not provided by microbial protein must be provided as available escape protein. Using the 6H1 and 95% efficiencies discussed above: 0.6uBCPdiet + 0.95AESCPdiet : AP” [5.3u] Equation [5.3“] can be rewritten so the available escape protein requirement is an explicit ration constraint: 2 0.95x1-AESCP1 : APr - 0.648CPdiet [5.35] The protein constraints on the diet are then [5.33] and [5.35]. These constraints will allow excess protein in the diet but will assure protein requirements are satisfied. 97 5.1.5 Linear program implementation The ration constraints outlined above' are in the form of inequalities. These constraints are solved using linear programming (LP) as algorithm which solves simultaneous inequalities. The feed options in the LP solution are forage, high moisture ear corn, dry corn grain, soybean meal and distiller's grains. The objective function for the LP is rigged to maximize forage use. To accomplish the goal of maximum forage use, a "ration cost" is minimized: Minimize: 2 xi-PRICEi where the relative prices (PRICE) of forages, corn grain, soybean meal and distiller's grain are 0, 1.0, 2.2 and 1.6, respectively. The forage mix is determined from feed allocation rules discussed in Section 5.5.2.1. High moisture ear corn (HNEC) cannot be readily sold, so it is desirable to deplete HMEC supplies before feeding corn grain, if possible. For the purpose of balancing rations, the relative price of HNEC is set to zero if HMEC is available. 'If it is unavailable, the relative price is set higher than that of corn grain so it will not become part of the diet. The ration constraints for lactating and dry cows are repeated here fer clarity: z xi-ANDFi : Cic-BASENT [5.u] z x1(RV1 - 0.75ANDF1) 3 0 [5.6] F xi'NEl,i = NEf,ad]usted [5-15] x x1°CP1(DEGR1+O.15) 3 BCPdiet/O.9 [5.33] 2 0.95x1-AESCP1 : AP” - 0.6uscpdiet [5.35] It is possible that for given feeds and a given animal, these five constraints present an unsolvable problem. Reducing the milk production 98 level until the feeds can satisfy all animal requirements (Section 5.5.2.1) resolves this problem. 5.2 Growing heifer model The objective of the growing heifer model is to formulate rations that meet the nutrient requirements of a described heifer. Again the assumption is that forage quality will have negligible impact on mineral supplementation, so the model is concerned with only intake, fiber, energy and protein. The structure is exactly the same as that of the lactating cow model. Intake and fiber constraints are the same except that the ingestive capacity of a growing heifer is a function of her current body weight (BW): IC = Clo-8N [5.36] The differences in energy and protein requirements are outlined in the following sections. 5.2.1 Energy The common energy system for growing animals includes requirements for net energy for both gain and maintenance. Because intake is being predicted rather than predetermined, it was necessary to simplify these relationships to one equivalent energy requirement. Because net energy for maintenance (NEm) and gain (NEE) energy concentrations in feeds are computed from metabolizable energy (NE) (NRC 198“), the equivalent system was based on NE. The metabolizable energy concentration in the diet is specified as a function of relative weight (RELLV) (NRC, 1988): “Ediet = 2.67 - 1.072(RELLH-.125) 2.00 5 “Ediet : 2.67 [5.37] 99 where: RELLN = Bv/800 [5.38] for large-breed females. The maintenance and gain energy concentrations in the diet are functions of the metabolizable energy concentration of the diet: NEg,diet = 0 3?§§?§§°t’ -30 1711(MEdiet)2 + diet) 65 [5.39] NEm,diet = 1 37(MEdiet) -3° 133(MEdiet)2+ 0. 0105(MEdiet) 12 [5.u0] Based on these concentrations, the amount of intake necessary to meet requirements is computed: PDMI z 0M1In + 01118 [5.n1] where: DMIm = wag / NEm,diet [5.u2] 01118 = nag / wag,diet [5.n3] (NRC, 1988). The maintenance and gain energy requirements are: up; = 0.0863w0-75 [5.Au] NEE = 0.0353w0-75A3w‘°“9 + ABN [5.u5] for large-breed female dairy animals. A metabolizable energy requirement (MEr) is computed from the NE concentration and the approximate intake: ME” = NEdiet-PDHI [5.u6] In an LP format, the energy constraint for the diet of a growing heifer is: Z *i‘MEi = MEr [5.97] Equation [5.15] for lactating cows is replaced by equation [5.”7] when formulating rations for growing heifers. 100 5.2.2 Protein The absorbed protein system (NRC, 1985; 1988) is also used for growing heifers. The requirements are the same as those of lactating cows except for protein requirements due to weight change and fecal output. For the growing heifers, difference protein absorbed (DPA) is zero. In its place is retained protein (RPN). The absorbed protein requirement for growing heifers is: AP” NNTP/0.67 + RPN/0.65 + YPN/0.5 + FPN [5.u8] where: RPN ABv(0.211 - 0.0262ng/A3v) [5.u9] and NNTP, FPN and YPN are computed as outlined for lactating cows. For growing heifers, the adjusted total digestible nutrient content in the diet (ATDN) (necessary for prediction of FPN) is estimated from digestible energy (DE): ATDN = 0°92°Ediet/“-u09 [5.50] where: media, = ("Ediet+0-“5)/1-01 [5.51] The structure of the dietary protein constraints is exactly the same as that for the lactating cow: i.e., two protein inequalities -- one for degraded protein (5.33), one for available escape protein (5.35). A difference is that the bacterial protein yield is expressed as a function of baseline TDN rather than net energy of lactation. For growing heifers (D.R. Martens, 1988 personal communication): BCPdiet = 6.25(0.0230)BTDNdiet [5.52] where: BTDNdiet = z xi-BTDNi [5.53] 101 Baseline TDN is related to metabolizable energy (NRC, 1988): Bron, = (MEi + 0.u5) / [(1.01)(u.u09)] [5.5n] 5.3 Feed characteristics Ration constraints are composed of a right-hand side, which specifies a limit or requirement of the animal, and a left-hand side summation, which specifies nutrients provided by the feeds. Feed characteristics traced through growth, harvest and storage of all forages include neutral detergent fiber (NDF) and crude protein (CP). DAFOSYM also models non-protein nitrogen (NPN) content of silages and the acid detergent insoluble protein (ADIP) content of hay. The ration constraints involve these as well as other characteristics that can be assumed constant for a given forage or can be determined from NDF and CP. I Table 5.2 includes quality characteristics for typical ruminant feedstuffs. The first five lines of the table summarize characteristics of purchased feeds and farm-produced non-forage feeds. These characteristics are assumed to be constant (i.e., do not vary from year to year). Adjusted NDF content of forages is equal to the NDF content. The roughage value for alfalfa hay and alfalfa silage is also equal to the NDP content. The roughage value of corn silage is credited to the stover and husks in the silage; thus, the roughage value is the total NDF content less the NDF contribution from the grain in the corn silage: RVcorn silage = NDFcorn silage ’ GR NDFgrain [5°55] The grain to total dry matter ratio (GR) is computed in the corn growth model. The NDF concentration of corn grain is 0.12 (Table 5.2). 102 Table 5.2 Characteristics” of various ruminant feeds. Feed NDF ANDF av N31 ME CP 0203* ADIP Corn grain High-moisture ear corn Distilleris grain Soybean meal Medium quality alfalfa hay Low quality alfalfa hay High quality alfalfa hay Medium quality alfalfa silage Low quality alfalfa silage Corn silage O O O 0 88888 \00 w 5 ‘1 000 0000000 ‘ m o o m Nd-meo—a dmm-IWO o 0 mm 000 0000000 0‘1 0 o dad-add“) o o mmw zmwmomo 0 0 ca: cmzazma Ch-d-d -a-4-acn:=;=:= ~D~I~I OOOUIO‘unm O O O O zow zeamzmz as: zmzoooo omw meaoogw mm» Ndewmw 0 mm: mmzoamq 000 0000000 O 000 ooooooo mca owa dumb—D 0 6L- mom .3 00¢ 00 3mm ca 000 OOOOOQO O GMOO‘ oquztom 000 0000000 oosoas ONflzt’asN ’ NDF neutral detergent fiber, fraction of dry matter ANDF adjusted neutral detergent fiber, fraction of dry matter RV roughage value, fraction of dry matter NEl net energy for lactation, Mcal/kg ME metabolizable energy, Meal/kg CP crude protein, fraction of dry matter DECR protein degradability, fraction ADIP acid detergent insoluble protein, fraction of crude protein + D.R. Mertens (1988 personal communication); NRC (1988) 5 RMEC contains approximately 15 percent cobs Protein degradability of alfalfa hay is assumed to be constant at 0.70. Degradability of silages is computed from the non-protein nitrogen (NPN) content as predicted by the silo model. It is assumed that all NPN is degraded and that 501 of the true protein is soluble and degraded. Thus, the insoluble true protein is the undegraded portion. DEGRsilages = NPN + 0.5(1-NPN) : 0.5 + 0.5NPN [5.56] Forage net energy for lactation values are predicted from NDF content (D.R. Mertens, 1988 personal communication): 103 NEl,alfalfa = 2°323 ' 2'15NDFalfa1fa [5-57] NEl,corn silage = 2°592 ‘ 2°‘191NDFcorn silage [5°58] Based on published data (NRC, 1988), NE of forages (alfalfa and corn silage) is 65% greater than the NE1; thus, for forages: "£1 = 1-55NEl,i [5.59] 5.4 Verification Verification (does the model perform as intended) of the animal model was carried out by formulating rations for describable animals, and comparing the nutrient contents of those rations to those suggested by NRC (1988). The intent was to replicate (or agree with) Table 6.5, ”Recommended nutrient content of diets for dairy cattle," in NRC (1988). It should be recognized that the NRC table contains only typical values. As NRC warns and this validation study points out, nutrient concentrations in the diet should vary with the feeds in the diet and should not be considered as fixed standards. Rather, with intake limitations recognized, emphasis should be placed on meeting requirements per day. 5.”.1 Lactating cow model For verification of the lactating cow model, rations were formulated with several forage options. Each diet was balanced using the described forage, corn grain, soybean meal and distiller's grain. Descriptions of the animals matched those of Table 6.5 (NRC, 1988); i.e., weight of 600 kg, weight change of +0.33 kg/day, milk production from 0 to 50 kg/day and milk fat of H.01. In addition to these characteristics, the animal model requires the number of days pregnant and the daily ingestive capacity of ANDF as a fraction of body weight. 10“ These were assumed to be 0 and 0.011, respectively. Table 5.2 lists the characteristics of each feed, and Table 5.3 lists each diet as formulated by the model. The values for dry matter intake predicted by the new model agreed very well with the values in Table 6.1 of NRC (1988). Nutrient concentrations in the formulated diets are listed in Tables 5.“, 5.5 and 5.6 along with the NRC recommendations. Nith medium quality alfalfa hay (MQH) or corn silage (CS) as the forage source, the maximum sustainable milk production was approximately 30 kg/day (Table 5.3). This would correspond roughly to 7,100 kg per lactation. At higher milk production levels, it was not possible to meet the nutrient requirements while satisfying the fiber intake limitation. Note that use of high quality alfalfa resulted in higher potential milk production. The intake increased as milk production increased because the bulky forages were replaced by concentrates to meet nutrient requirements. By method of solution, each diet as formulated maximizes intake, given the nutrient requirements to be satisfied. For all milk production levels (0 to ”0 kg/day), the formulated diets had energy concentrations that agreed very well with those suggested by NRC (1988) (Table 5.“). Differences in energy concentration can be explained solely by intake differences because the total daily energy requirement was satisfied. For the dry cow (0 kg milk/day), the energy concentrations were higher than the NRC recommendations for all diets except the low quality hay (LQH) diet. For the dry cow, the most suitable forage is LQM with little or no corn silage. 105 Table 5.3. Formulated rations for 600 kg cows producing varying amounts of ”1 fat milk while gaining 0.33 kg/day. Forage. Amounts in diet (kg/day) Daily source HAY AS CS CG SBM DST U Z 1—1 0 kg milk/day HQH MQH+CS HQMF HQH LQH MQAS CS 10 kg milk/day MQH MQH+CS MQMF HQH LOH MQAS CS 20 kg milk/day HQH MQH+CS HQMF HQH LQH MQAS CS 30- kg milk/day HON MQH+CS MQMF HQH LOH HQAS “0 kg milk/day HQH 12.9 d d oozomob ooobboo m «cowom wawomow 00aoooo‘ooobbob d d O O d000000 d O O O O O O 0—b‘OO‘UIO NWWNQNW OBOWQO‘Q GOdNUIWN —.-b O O O O O O O O O O O O O .5 d00w00 0&00300 owoozoo 00000000 0 AWWNNNU “VD—50000 O aoqoooo d‘ddddd d 00—b0‘hO‘N 00MNBO‘W 0000(380 040000 CONNUJ-f—‘O OOWW—bO-fi OOQNORN dd 0 O O O O O O O C d . C C . . —h—bd0-N0 0000000 0000000 doooooo dd‘d“d a444mm4 O O _m 003:WUI—b c o o o o O . o omombo aoooooo bwooooo wzwomoo . dd 0 O O O O O O 0 ‘ 000L900 N000J=O~0 0000300 d000mk0 O d00-b00 o m NNNNBN 4400 —b o d o o m 3QWNW~I «BOO—bNQ—b o 0 0—b00-30 0 N .B \O ... . N #00440 0N3—bN-fi3 00‘0000 0000000 0 mooooo omoowob 0‘00aoo obboooo o ooocmo mooowzb oooodoo wooooco 0 0 rFeed characteristics are in Table 5.2. MON 100% medium quality alfalfa hay HQH+CS 501 medium quality alfalfa hay, 50$ corn silage MQMF 331 medium quality alfalfa hay, 331 medium quality alfalfa silage, 33$ corn silage ROM 1001 high quality alfalfa hay LOH 100$ low quality alfalfa hay MQAS 100% medium quality alfalfa silage 106 Table 5.“. Net Energy of lactation (Meal/kg) of formulated rations for 600 kg cows producing varying amounts of “1 fat milk while gaining 0.33 kg/day . Forage Milk production (kg/day) source 0 10 20 30 “0 111101 1.25 1.u2 1.52 1.62 1.72 now 1.31 1.31 1.99 1.63 * non+cs 1.uu 1.93 1.5“ 1.66 * MQMF 1.u1 1.90 1.53 1.66 9 non 1.“6 1.“6 1.99 1.63 1.73 LQH 1.21 1.29 1.99 1.62 P MQAS 1.35 1.3“ 1.99 1.63 9 cs 1.55 1.55 1.59 * * ‘ Missing data implies that forage quality was too low to + satisfy all ration criteria. Nutrient concentration recommended in NRC (1988). Comparing the NDF content in all diets to those suggested by NRC show that, in general, the model allows more NDF in the diet than necessary (Table 5.5). The higher NDF concentration is in agreement with recommendations made by animal scientists at Michigan State University (J.U. Thomas, 1987 personal communication). Perhaps diets with poorer quality forages would better agree with the NRC guidelines. Nith the forages used in these verification diets, the amounts of concentrate in the diet were relatively low. The crude protein contents of the diets, although generally higher, were in satisfactory agreement with NRC suggestions (Table 5.6). Diets containing excess degraded protein are noted in Table 5.6. In these diets, either the forages used were simply of higher quality than necessary or the use of soybean meal was more economical than the use of 107 Table 5.5. Neutral detergent fiber content (fraction) of formulated rations for 600 kg cows producing varying amounts of “1 fat milk while gaining 0.33 kg/day . Forage Milk production (kg/day) source 0 10 20 30 “0 1130* .35 .28 .28 .28 .25 MON .“7 .“7 .38 .32 * MQH+CS .“5 .“6 .“O .32 9 MQMF .“5 .“6 .39 .32 * HQH .“O .“0 .38 .32 .2? LOB .51 .“8 .38 .31 * MQAS .“5 .“5 .38 .32 * CS .“3 .“3 .“1 * * iMissing data implies that forage quality was too low to + satisfy all ration criteria. Nutrient concentration recommended in NRC (1988). Table 5.6. Crude protein content (fraction) of formulated rations for 600 kg cows producing varying amounts of “1 fat milk while gaining 0.33 kg/day . Forage Milk production (kg/day) source 0 10 20 30 “0 NRC'F .12 .12 .15 .16 .17 11011 .1 _._fi ,_1_6_ .15 1* 11011+cs :11 .15 .15 .15 1* "9115‘ all 15. .-1_5 ~15 ' 11011 .21 Q ._29 _._1_8 .16 L011 :15 45; .15 .15 1* 110115 g .20 ;1_9 .17 1* cs g 711 .15 T 1* iMissing data implies that forage quality was too low to satisfy all ration criteria. Underlined entries indicate diets containing excess + degraded protein. Nutrient concentration recommended in NRC (1988). 108 distiller's grain. This resulted in diets providing more crude protein than necessary. To assure effects of body weight change on requirements were computed correctly, sample calculations were performed for a 600 kg cow producing 30 kg milk containing “1 fat. The cow was assumed to be less than 210 days pregnant. Table 5.? contains the results. These calculations showed that the impact of body weight change on requirements was correct. 5.“.2 Growing heifer model To verify the growing heifer model, rations were formulated with several forage options, as previously described. Descriptions of the animals matched those of Table 6.5 in NRC (1988); i.e., weights of 150, 250 and “00 kg and weight change of +0.70 kg/day. In addition to these characteristics, the animal model requires the number of days pregnant and the daily ingestive capacity of ANDF as a fraction of body weight. These were assumed to be 0 and 0.011 respectively for each heifer group. Each ration as formulated by the model is listed in Table 5.8. Average dry matter intakes of “.0, 6.0 and 8.8 kg/day for the three groups agreed closely with the corresponding values of 3.8, 5.6 and 8.9 kg/d computed using the NRC relationships. Table 5.9 gives the crude protein and metabolizable energy concentrations in the diet for each ration. The differences in energy concentration are relatively small and can be explained by the differences in intake. The crude protein concentrations are higher than NRC (1988) recommendations. In all but two diets (CS-based diets for older heifers), excess degraded protein was provided in the diet because the forage source was of higher quality 109 Table 5.7 Impact of body weight change of a 600 kg cow producing 30 kg of “1 fat milk on net energy and absorbed protein requirements. - Body weight Net energy Absorbed protein change requirement requirement (kg/day) (Meal/day) (kg/day) -1.5 23.8 1.85 -1.0 26.3 1.90 -0.5 28.7 2.00 0.0 31.2 2.18 0.5 33.8 2.36 1.0 36.3 2.53 1.5 38.9 2.71 110 Table 5.8. Formulated rations for heifers gaining 0.70 kg/day. Forage. Amounts in diet (kg/day) Daily source HAY AS CS CG SBM DST E 3—6 months (150 k ) LQH 2.9 0.0 0.0 0.6 0.0 0.6 “.1 LQH+CS 1.5 0.0 1.5 0.0 0.1 0.0 “.0 LQMF 1.0 1.0 1.0 0.0 0.0 0.9 “.0 MQH 3.3 0.0 0.0 0.“ 0.0 0.“ “.1 MQH+CS 1.5 0.0 1.7 0.0 0.3 0.“ “.0 HQHF 1.1 1.1 1.1 0.0 0.2 0.5 “.1 CS 0.0 0.0 2.9 0.0 0.8 0.0 3.8 6-12 months (250 kg) LQH 5.0 0.0 0.0 1.2 0.0 0.0 6.2 LQH+CS 2.7 0.0 2.7 0.6 0.0 0.0 5.0 LQMF 1.8 1.8 1.8 0.7 0.0 0.0 6.0 "CH 5.? 0.0 0.0 0.5 0.0 0.0 6.2 HQH+CS 2.9 0.0 2.9 0.2 0.0 0.0 5.9 MQMF 1.9 1.9 1.9 0.2 0.0 0.0 6.0 CS 0.0 0.0 5.3 0.0 0.3 0.0 5.6 > 12 months (“00 kg) LQH 8.2 0.0 0.0 1.2 0.0 0.0 9.“ LQH+CS “.“ 0.0 “.“ 0.2 0.0 0.0 9.0 LQMF 2.9 2.9 2.9 0.3 0.0 0.0 9.1 “OH 9.“ 0.0 0.0 0.0 0.0 0.0 9.“ NQH+CS “.“ 0.0 “.“ 0.0 0.0 0.0 8.7 MQMF 3.0 3.0 3.0 0.0 0.0 0.0 8.9 CS 0.0 0.0 7.5 0.0 0.6 0.0 8.0 ; Feed characteristics are in Table 5.2 MOM 1001 medium quality alfalfa hay MQH+CS 501 medium quality alfalfa hay, 501 corn silage MQMF 331 medium quality alfalfa hay, 331 medium quality alfalfa silage, 331 corn silage : HQH 1001 high quality alfalfa hay L08 1001 low quality alfalfa hay MQAS 1001 medium quality alfalfa silage 111 Table 5.9 Crude protein (fraction) and metabolizable energy (Meal/kg) concentrations of formulated rations for heifers gaining 0.70 kg/day. CRUDE PROTEIN: METABOLIZABLE ENERGY Forage Age (months) Age (months) source 3-6 6-12 >12 3-6 6-12 >12 111105 .16 .111 .12 2.60 2.117 2.27 LOH .16 .1“ .1“ 2.38 2.2“ 2.16 LQH+CS .16 .12 .12 2.“? 2.3“ 2.26 LQMF .17 .13 .13 2.““ 2.31 2.22 MON .18 .18 .17 2.39 2.25 2.16 MQH+CS .18 .13 .13 2.“? 2.35 2.32 MQMF .19 .15 .15 2.““ 2.32 2.28 CS .18 .11 .12 2.61 2.51 2.52 iAll diets except CS diets for older heifers contained excess degraded protein. + Average weights of 150, 250 and “00 kg for 3-6, 6-12 and >12 month old heifers, respectively. Nutrient concentration recommended in NRC (1988). than necessary or the use of soybean meal was more economical than the use of distiller's grain. To assure that the effects of body weight change on requirements were computed correctly, sample calculations were performed for a “00 kg heifer assumed to be less than 210 days pregnant. Table 5.10 contains the results which show that the impact of body weight change on requirements was correct. 5.5 Whole-herd model The whole-herd model uses a time step of one year. The objective was to describe a complete dairy herd, balance rations for all animals in the herd and, in doing so, predict feed disappearance and milk 112 Table 5.10 Impact of body weight change of a “00 kg heifer on metabolizable energy and absorbed protein requirements. Body weight Metabolizable Absorbed protein change energy requirement requirement (kg/day) (Meal/day) (kg/day) 0.0 12.5 0.278 0.5 17.8 0.“56 1.0 23.9 0.635 1.5 30.3 0.815 , production. The whole-herd model uses the single-animal models described above, incorporates a distribution of animals in the herd and allocates available feeds to them. The model does not simulate day-to-day feeding, although it can be used to give insight into optimal feed allocation. 5.5.1 Herd composition A standard distribution of animals was assumed for the herd. From these standard data, milk production of the herd, the number of first- lactation cows, the sizes of the animals and the number of heifers can be adjusted. The cows are separated into groups according to the time spent in each of three stages of lactation and the dry period. A typical 390-day lactation cycle (13-month calving interval) is divided into four sections (Figure 5.1). The groups correspond to the first 60, the next 90 and the next 180 days of lactation with 60 days allotted for the dry period. A typical cow spends 15.“1 of the time in the first stage of lactation, etc. The average number of days pregnant for cows in the feur stages are 0, 0, 130 and 250, respectively. The young and 113 .osoao :oeooooos aocuomm a ca 2205 so am cea.o mafiusvoua Boo Hmowazu m we m>uao coaumuoma mLu mo cowmw>wa H.m madman $33 ozzézmmE 20% m2: Own. 00m. O¢N om_ ON_ 00 O p _ _ PI. r _ L p LIIIIIL: 11-1L11;-11|I._11-111|L..-1.i11+l. O QOEMQ >mo 1. m I O F /.x/ n mofim // // (“DP/51) 1101130009.] >1‘HW 11“ old heifers average 0 and 180 days pregnant, respectively. The standard herd is described in Table 5.11. The data in the table are adjusted to change herd characteristics as follows. The table is based on a mature cow that produces 6,270 kg of milk per year (6,700 kg per 390-day lactation). To adjust the herd average milk production, the milk production per day for each group is found by: MPD = (BASEMP/6270)(table value for milk production) [5.60] If forage quality limits milk production during the first stage of lactation, milk production in the later stages is decreased proportionately. The body weights and changes in body weights are based on a mature cow's having an average weight of 622 kg during the second stage of lactation. If the animals are of a different size, body weight and body weight change for each animal are adjusted from the table values by: ‘ av = (BASENT/622)(table value for an) [5.61] ABw = (BASENT/622)(table value for Ann) [5.62] The fraction of cows experiencing their first lactation is accounted for by computing a weighted average milk production, milk fat, body weight, _body weight change and intake factor (C10) for all cows in each of the four stages of lactation: Y3 = FFC-Yj,rirst lactation +_(1-FFC)YJ,nature [5-531 where Y refers to the animal characteristics in Table 5.11 and Y3 is the value used for ration formulation when considering group j. For a typical herd with 261 primiparous cows, the resulting characteristics are in Table 5.12. Rations should not be balanced for the average animal in ‘a given group because this would limit the production level of the higher 115 Table 5.11 Parameter values used to describe animals in the dairy herd. Animal Number Milk Milk Body Body 010 group in * production fat weight weight group change (kg ANDF/ (ks/d) (1) (k8) (kg/d) k8 3") Young heifers XYHEIF 0.0 --- 200 0.82 0.0105 01d heifers XOHEIF 0.0 --- “00 0.“8 0.0110 Stage 1 mature cows .15“(XLCT)(1-FFC) 29.0 3.8 639 -0.72 0.0107 Stage 2 mature cows .231(XLCT)(1-FFC) 26.2 3.“ 622 0.10 0.0120 Stage 3 mature cows .“61(XLCT)(1-FFC) 16.1 3.6 66“ 0.“1 0.0130 Mature dry Stage 1 first lactation cows .15“(XLCT)(FFC) 23.6 3.8 52“ -0.““ 0.009“ Stage 2 first lactation cows .231(XLCT)(FFC) 21.3 3.“ 52“ 0.30 0.0106 Stage 3 first lactation cows .“61(XLCT)(FFC) 13.0 3.6 588 0.55 0.011“ First lactation dry . cows .15“(XLCT)(FFC) 0.0 --- 658 0.66 0.0110 n XYREIF (number of young heifers), XOHEIF (number of old heifers), XLCT (number of lactating cows) and FFC (fraction of cows experiencing first lactation) are user-defined inputs. 116 Table 5.12 Description of a herd with 261 primiparous cows. Animal Number 11111.1 Milk Body Body cm group in * production fat weight weight group change (kg ANDF/ (ks/d) (1) (k8) (ks/d) k8 3“) Young heifers XYHEIF 0.0 --- 200 0.82 0.0105 01d heifers XOHEIF 0.0 --- “00 0.“8 0.0110 Stage 1 .15“(XLCT) 27.6 3.8 609 -0.65 0.010“ Stage 2 .231(XLCT) 2“.9 3.“ 596 0.15 0.0116 Stage 3 .“61(XLCT) 15.3 3.6 6““ 0.“5 0.0126 Dry cows .15“(XLCT) 0.0 --- 705 0.65 0.0117 0 XYREIF (number of young heifers), XOHEIF (number of old heifers) and XLCT (number of lactating cows) are user-defined inputs. + Based on a 390-day lactation cycle with a herd average milk production level of 6,225 kg per lactation. producing animals. Likewise, it is not advisable to formulate rations for the highest producer because lower producers would receive too rich a diet and this would waste feed. A balance between these options is to incorporate lead factors. A lead factor is a multiplicative constant by which the average production level in a group is adjusted upward to account for variability within the group. Based on the data of Stallings and McGilliard (198“), the lead factors used for the whole herd model were 1.12, 1.07 and 1.07 for the first, second and third stages of lactation, respectively. Ration requirements are computed 117 using MPD multiplied by the respective lead factor. This results in a diet in which nutrient concentrations are higher than needed for the average cow, though feed disappearance is computed for the average animal. 5.5.2 Feed allocation Feed allocation is of utmost importance in correctly evaluating forage systems. Least cost rations usually do not provide least cost or maximum profit production because of less than optimal decisions concerning feed allocation. The feeds potentially produced on the DAFOSYM farm may include any combination of the following: high quality alfalfa silage, low quality alfalfa silage, high quality alfalfa hay, low quality alfalfa hay, corn silage, high moisture ear corn and corn grain. Possible purchased feeds include corn grain, medium quality alfalfa hay, soybean meal and distiller's grain. It is the objective of the feed allocation scheme to distribute these feeds pr0perly so that purchased feed costs are low and, at the same time, all animal requirements are met. Two feed allocation methods are discussed in this chapter. The first, decision rule feed allocation, is typical of the better dairy farmers. In this apprdach, the best forages are allocated to those animals with the highest nutrient requirements according to decision rules. The second, linear programming, combines feed allocation and ration formulation into a relatively large linear programming problem. DAFOSYM uses the decision rule allocation. The LP approach outlined here is intended as an ancillary model. In either approach, feeds are 118 alloCated at the end of the year. The model assumes that feed stocks and the quality of the feeds in storage are known. 5.5.2.1 Decision rule Feed allocation was designed to make the best use of available feeds. The best way to use low quality alfalfa is to feed it to non- lactating cows, the animals with the lowest requirements. The highest producing lactating cows should be fed the highest quality alfalfa. If there is no feasible solution to the LP ration constraints, the forages are not of sufficient quality to support the target milk production. when there is no feasible solution, the model reduces milk production (MPD) by 21 and attempts a new ration. This is repeated until a feasible ration is formulated. Silages (alfalfa and corn) are not readily sold. The allocation scheme is set up to uSe ensiled forages at a faster rate than those that are not ensiled; however, there is no guarantee that stocks of ensiled forages will be completely depleted during the year. The term "feeding order" is used here to imply a priority order for determining which feeds should be fed to which animals. The simulation of dairy herd feeding assumes that, once rations are determined for each group, the groups will be fed the corresponding rations for the entire year. The feeding order was set for best feed use. Feeding animals with low nutrient requirements (dry cows and heifers) first allows for low quality alfalfa to be utilized where needed. Feeding the highest producing lactating cows next allows for the best alfalfa to be utilized where needed. Feeding of the lower producing cows last is beneficial 119 because low quality alfalfa is used for animals with lower requirements when stocks of high quality alfalfa are depleted. Similarly, feeding younger heifers after feeding dry cows and older heifers will assure .that, if a shortage of low quality alfalfa exists, the animals with higher requirements will receive the better feeds. One might suggest feeding lactating cows with high quality hay, then feeding dry cows and heifers with low quality hay. With this feeding order, a shortage of sufficient high quality hay poses the following question: should the lower producing lactating cows be fed low quality hay or purchased medium quality hay? Purchasing hay with stocks still on the farm may not seem sensible; yet, if low quality hay stocks are also low, some lactating cows may get low quality hay while heifers or dry, cows get medium quality hay. The priority order set in the previous paragraph eliminates this decision. Feeding priority starts at the high and low ends of the requirement spectrum; if purchased hay of medium quality is needed, it is provided to those animals with moderate needs. The relative pricing of HMEC and CG within the LP ration formulator assures that HMEC will be used before CG, if possible, but the forage must be preassigned for the single-animal model. The preferred forage for stage 1 lactating cows is high quality alfalfa. For other lactating cows, the preferred forage is a mix of corn silage and high quality alfalfa. For dry cows and growing heifers, the preferred forage is corn silage plus low quality alfalfa. Alternative forages are used only when preferred forage stocks are depleted. If corn silage is not available, alfalfa is the only forage. If high quality alfalfa hay is in the preferred forage and is unavailable, low quality alfalfa hay is used 120 and vice versa (similarly for alfalfa silages). If all farm-produced alfalfa has been fed, purchased medium quality alfalfa hay is used. The forage mix is determined using an approximate forage requirement for the entire herd. The objective of the forage assignment is to completely use ensiled feeds before using alfalfa hay, if possible. Because forage use is maximized, intake for all animals is maximized. Using the roughage value constraint, we can obtain an estimate of the annual forage requirement for each group: ARVJ = 0.7501c-BN-365(number in group j) [5.6“] In practice, the ARV of dry cows should be decreased by approximately 301 because their intake is not typically at maximum. Summing the ARV over all groups (j:1 to 6) gives an estimate of the annual forage requirement: ARVH = z ARVJ [5.65] Because it is preferred to use all the corn silage on the farm, the fraction of alfalfa in the forage is computed so that all corn silage will be used: FAIF = 1 - CSRV/ARVH [5.66] The corn silage roughage value (CSRV) available is the amount of corn silage stored on the farm times its roughage value. Based on that part of the annual roughage value requirement estimate that is not supplied by corn silage, the fraction of hay in the alfalfa portion of the forage is computed so that alfalfa silage is used up, if possible. FHAYIA = 1 - ASRV/(ARVH-CSRV) [5.67] Computing the amount of a given feed in the ration involves using these fractions along with the ration as determined by the single-animal model. For example, the amounts of corn silage, alfalfa silage and 121 alfalfa hay in the ration would be: cs = FFID(1-FAIF)DMI . [5.68] ALFSIL = FFID'FAIF(1-FHAYIA)DMI [5.69] HAY = FFID'FAIF'FHAYIA'DMI [5.70] Once a ration has been formulated, the next step is to determine how many animals in the current group can be fed the given ration for 365 days with current feed stocks. If all animals in the group can be fed the given ration, no problem exists; if not, as many as can be fed are fed, and the balance are fed with alternate feeds. If milk production within the group is different because different rations were used, a weighted average milk production level is computed for the group. Remaining feed quantities are updated each time a group of animals is fed. 5.5.2.2 Linear programming The linear programming approach to feed allocation and simultaneous ration formulation is illustrated in Figure 5.2. This tabular format of LP setup can be used to minimize feed costs, meet ration criteria for all animal groups and, at the same time, meet constraints on the use of farm-produced feeds. The LP format has 37 rows and 66 columns. (Implementation using Fortran code involved “3 rows -- 6 being inactive -- to allow for easier experimentation with the model.) All non-labeled entries in Figure 5.2. are zeros. The LP format has seven major regions of importance, each with a corresponding right-hand side. 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Nu .u “O~.E.C.E c u . c n. . c N . oooooooooooooooo nuuununuuunfiflnuun IIuOPIIOIIvIIuCII n mango .wE.cm n cacao pQE.cm _ anouo _IE.cm by no. momma by no; mucus 0a to; momma n 0 xII b I d QDOKU BDOCU Q3010 cacao 13010 123 of the matrix assures that constraints on the supply of farm-produced feeds are not violated. The feed prices used in the objective function are the selling price for farm-produced feeds and the purchase price of other feeds, as these represent the true opportunity prices of the feeds. Formulation of rations using this LP structure will result in true, least-cost rations from the whole farm perspective. The purpose of the LP structure is to evaluate the "loss" due to less than optimal use of feeds. All output from the LP allocator should be recognized as an upper bound on efficiency of allocation because logistics of feeding may impose constraints that is does not take into consideration. It is important to note that the only difference between the decision rule and LP approaches is in feed allocation. Ration constraints for the approaches are identical, as are descriptions of the herd and available feeds. 6. ALFALFA VALUE The cow model developed in the previous chapter is useful for determining the milk production potential from a given supply of forages and the required supplemental1 feeds to produce that amount of milk. This chapter outlines a procedure and includes some results of an attempt to use the animal model of Chapter 5 to evaluate, in economic terms, incremental changes in alfalfa quality. The value of alfalfa is determined by considering the effects of forage quality on milk production and supplemental feed costs throughout the lactation cycle. Relative feed value is determined by dividing the value of the alfalfa by the value of a reference alfalfa. 6.1 Simulation approach The process of determining alfalfa value included several steps and assumptions about the animal and feeds. The first step was, for various levels of alfalfa quality, to balance rations for an animal in each of the three lactation stages. Based on these rations, annual milk production (AMP), annual supplemental feed cost (ASFC), annual corn silage use (ACS) and annual alfalfa use (AA) were computed. The third step was to arbitrarily assign a value to a reference alfalfa quality. Based on this reference, the value of alfalfa at other quality levels 1 Supplemental feeds in the context of this chapter include corn grain, soybean meal and distiller's grain. 12“ 125 was ascribed by assuming a constant amount of unallocated income per 00" 0 6.1.1 Determination of alfalfa value unallocated income (UAI) was computed as milk income less feed costs and the extra cost of labor, veterinarian bills or other factors that increase as milk production increases. “AI = Pmilk AMP - ASFC - Poor" silage ACS - V AA - L Pm11f6A?§ Expenses due to increased milk production were assumed to be 121 of the milk price (L=0.12, J.R. Black, 1988 personal communication); therefore, when an increase in milk production was associated with a change in the ration, only 881 of the increased revenue could be credited to the ration change because 121 was required to cover other costs. Corn silage price (value) was assumed to be $0.05/kg DM and milk price was assumed to be $0.25/kg. Therefore, equation [6.1] can be rewritten as: UAI 0.25AMP - ASFC - 0.05ACS - V AA - 0.12(0.25)AMP 0.22AMP - ASFC - 0.05ACS - 7 AA [6.2] Alfalfa value was determined by maintaining a constant value of unallocated income: UAI = UAIref '[6.3] The reference alfalfa containing “51 NDF and 181 GP was assigned a value of $0.05/kg DM. Substitution of equation [6.2] into [6.3] with rearrangement gives: V = 0.22(AMP-AMPref)-(“SEC-ASFCref)-0.05(ACS-ACSpef)+Vref AAref AA [6.9] 126 Values for AAref, AMPref and ASFCref are determined from the diets formulated with the reference alfalfa (Appendix C). 6.1.2 Animal Characteristics , Forage value was determined only for lactating cows. Therefore, only the three stages of milk production were considered. Body weight, weight gain, milk fat and intake characteristics of the animal are presented in Table 5.12. (Although the base cow weighs 596 kg, the cow is hereafter referred to as a 600 kg cow). Potential milk production per day for the animal was varied to reflect two milk production levels (7,9“0 kg/cow-y and milk production limited by forage quality only). Using the production levels in Table 5.12 as a base, milk per day (MPD) corresponding to the three stages associated with the annual production level of 7,9“0 kg was: MPDstage 3 = 15.3 (79“0/6225) [6.7] For evaluating rations in the case where milk production was limited by forage quality, target milk production was set to an unreasonably high value. All rations for the animal in the first stage of lactation were then unsolvable. Milk per day was therefore decreased by 0.1 kg/day until all requirements could be satisfied. The milk production in later stages was set in proportion to milk production during the first stage. For this reason, this analysis is perhaps more sensitive to forage quality than is reality. 127 6.1.3 Feed characteristics Rations were formulated in which the forage was 1/3 alfalfa hay, 1/3 alfalfa silage and 1/3 corn silage. Potential supplemental feeds were corn grain, soybean meal and distiller's grain, with prices of $100, $220 and $15“/T DM, respectively. All characteristics of the supplemental feeds and corn silage are included in Table 5.2. Degradability and acid detergent insoluble protein (ADIP) concentrations of alfalfa were as listed in Table 5.2. The Neutral detergent fiber (NDF) content of alfalfa was varied from 0.36 to 0.56 in increments of 0.03; the crude protein (CP) content was varied from 0.12 to 0.2“ in increments of 0.03. Because alfalfa CP and NDF contents are inversely related, not all combinations of CP and NDF are realistic. Based on typical composition (Rohweder et al., 1978), the following equation gives realistic combinations of CP and NDF: CP = 0.““ - 0.6NDF [6.8] Obviously other combinations exist, this relationship simply gives approximate values that are reasonable. Table 6.1 gives the CP/NDF combinations used to formulate rations; these combinations encompass combinations suggested by equation [6.8]. For this analysis of forage value, it was assumed that the same forage source was fed throughout the year, regardless of stage of lactation. That is, to evaluate the value of alfalfa containing “51 NDF and 181 CP, it was assumed that the forage available to the animal through all three stages of lactation contained alfalfa containing “51 NDF and 181 CP. This differs from the allocation scheme of DAFOSYM and 128 Table 6.1 Combinations of crude protein and neutral detergent fiber for which rations were balanced for three stages of lactation. Crude protein Neutral detergent fiber (fraction of DM) (fraction of DM) 0.36 0.39 0.“2 0.“5 0.“8 0.51 0.5“ 0.57 0.12 x x x 0.15 x x x x x 0.18 x x x x x x 0.21 x x x x x 0.211 x x x most farms, where forage allocation is based on animal requirements and forage quality. 6.2 Effect of alfalfa quality on value 6.2.1 Moderate milk production 1 For a fixed milk production level of 7,9“0 kg/cow-y, the fiber content of alfalfa affects only supplemental feed costs. Because fiber content is inversely related to energy content, increases in alfalfa fiber content result in increased supplemental feed costs over the lactation cycle. Increased protein content, on the other hand, reduces supplemental feed costs because increased alfalfa protein content reduces the need for supplemental feeds. Alfalfa value was computed as outlined in Section 6.1.1, then 129 converted to a relative value by dividing the computed value by the value of the reference alfalfa ($0.05/kg DM for alfalfa with “51 NDF, 181 CP). Figure 6.1 illustrates the effects of fiber and protein contents on the value of the alfalfa fed over the lactation cycle of a 600 kg cow. Because milk production was fixed, the differences in value among alfalfa qualities is solely attributed to the change in supplemental feed costs. From Figure 6.1, it is evident that the reduction in supplemental feed costs associated with a 11 increase in GP content are approximately equal to those associated with a 21 decrease in NDF content; i.e., the slope with respect to CP is double that with respect to NDF and of opposite sign. 6.2.2 High milk production -- determined by forage quality At high production levels, the effect of forage fiber on value is twofold. As fiber content increases, energy concentration decreases; this results in higher supplemental feed costs. More importantly, fiber content can limit milk production and so decreases income. Though the former is true regardless of milk production, the latter can have a much more dramatic consequence with high producing animals. To determine the effect of fiber content on potential milk production, the simulated data were analyzed using linear regression. For alfalfa fed in a mixed forage diet to a 600 kg cow, potential milk production (PMP) was related to NDF content by (Figure 6.2): PMP = 12360 - 79“0NDF [6.9] (r2 = 0.991) If the genetic potential or ability of the cow is lower than a given point of interest on Figure 6.2, the effect of fiber on alfalfa value is 130 ->\xHHE mo wx obo.n w:wu:voaa Boo wx ooc m Cu vow uowv mmmHOM vwaE m :« mwammam mo msfim> m>wumamp co kuwamsv maammfim mo uumwmm ~.c mumwwm 920 to § E886 ..52 mm on me O... on F. 1L 1% h .V.nv / Imd / film-O a 10; T n6 ”KN— ml». mo NW. To // 1N.— mo NE old m0 NPN Elm fl n6 New Olo r: enlo/x pee] eAglolea 131 .uwwv mambo» vowa m to» Boo ax occ m E0pw :ofiuostpa xfiwe Hmwucwuoo cc xuwamsc mmamwfim we uumumm N.c mumwwm 30 Lo 8:85 ezmezoo .52 00.0 med 20.0 30.0 Ndd eta-o _ L _ . L e _ .. L 2 Doom Gaza-0318mm 7% 100mm ..ooom 100mm (V58) Nomencoad >nlw 132 due to a reduction in feed costs only. In those cases where forage quality limits milk production, a change of 11 in NDF content of the alfalfa results in a change of approximately 80 kg/cow-y in milk production. For a typical herd of 100 cows, this corresponds to an increase in gross income of approximately $2000 for each 1$ decrease in alfalfa NDF content. In these cases where the lactating animal can txtilize increased quality alfalfa (lowered NDF) to increase milk production, the fiber content dramatically affects economic value of the alfalfa. Figure 6.3 illustrates the effect of alfalfa quality on relative \Ialue‘ when forage quality determines milk production. Because fiber (zontent affects milk income as well as supplemental feed costs, the (effect .of fiber content is much more dramatic in this case than for a :fflxed milk production level (Section 6.2.1). The effect of increased Eilfalfa protein content is primarily a decrease in supplemental feed (cost. The slight effect on milk production is negligible. For this ‘cmse of an animal with high production potential, the increase in value associated with a 11 increase in CF content is approximately equal to ‘the increase in value associated with a 0.51 decrease in NDF content. 6.3 Relative feed value Rohweder et al. (1978) proposed a relative feed value system for malfalfa hay. The intent was to relate economic value to laboratory quality measures. The system of Rohweder et al. (1978) was based on dry matter intake (a function of NDF) and dry matter digestibility (a .function of acid detergent fiber -- ADF). Protein effects on alfalfa value were not considered or were eliminated from the model because of 133 .BCHHm HHHB wwmucw mSu mm xHHE £035 mm wcfioavoum 300 wx 000 m 00 www umfiv wumu00 vwaE m CH mmflmwam we mDHm> m>fiumamp so zuHstc memMHm 00 000000 m.0 wusmwm Ea .5 NV €880 52 mm 00 0v 0¢ mm. r!\\\ _ _ _ mvxu 13 10.0 IN.— 10; n6 RE I - . no N? «In [ON 10 NE mlm - do new. ole amp/x pee; eAgiolea 134 correlation among quality characteristics. Another shortcoming of the Rohweder et al. system was that the equations developed are applicable only when forage is the only source of dietary energy and protein. The relative feed value for legumes was modeled as: RFV = o.025(65.5+97.5ADF-2.77ADF2)(39+268NDF-u.1NDF2) [6.10] A better assessment of relative value would include effects of protein content and energy and protein supplementation of a balanced ration. The relative feed values determined in the previous sections incorporated both' these factors and are based on the economics of feeding a lactating cow throughout the lactation cycle. For comparison purposes, the Rohweder system was converted to the following equation by assuming ADF concentration is 10% lower than NDF concentration (i.e., ADF=NDF-0.10) and rescaling the relative value so that the relative value of alfalfa with 451 NDF was 1.00: arv = 0.025[65.5+97.5(NDF-0.1)-277(NDF-.1)2] 2 [39+268NDF-u1ounr ]/126 [6.11] where 126 is the RFV of alfalfa with n51 NDF, 35$ ADF according to the Rohweder system. Figure 6.4 illustrates the range in RFV using the procedure outlined above compared with the Rohweder system as approximated by equation [6.11]. The current cow model indicates that alfalfa value is more sensitive to quality than the relative feed value system of Rohweder et al. suggests. For high milk production, the effects of fiber on milk production dominate the determination of alfalfa value. Clearly, much work could be done to extend the range of applicability of such a relative feed value model, but this is beyond the scope and intent of this dissertation. The procedure is outlined 135 .cowumcprmuow 00 mvocuoe mwusu Scum wsfim> comm m>wumfiwp m.mwamwam Cw wmcmm «.0 mu:&wm 0,5 .6 N0 E880 ..52 mm om 9v 0* mm. F _ _ h 0.0 / . / / I // / {v.0 / / .I I, / z/ / I, / 10.0 IN.— 10.— :0303003 sz LEI ..l // cozoavoca 5:: 39.2002 ..1 / .. £398 ._0 yo Lmnmzcom II / 10.N enlo/x pee; eAnolea 136 here to illustrate that the cow model developed in the previous chapter can be used to develop such a model of relative feed value and that alfalfa value depends on both feed and animal characteristics. 6.” Sensitivity The approach outlined in Section 6.1.1 for determining alfalfa value is sensitive to many factors. The partial budgeting concept works in the context of comparing two alternatives, but more work is needed to improve the concept for its use in the context of determining the value of alfalfa. For example, the relative feed value curves (Figures 6.1 and 6.3) can be changed dramatically with changes in the reference forage, the value of the reference forage or the coefficient concerning V costs of producing more milk (L). (With the sensitivity of the analysis to these economic issues it is difficult to adequately address the topic of alfalfa value. 0 The value of alfalfa is also sensitive to assumptions made about the animal and the type of forage in the diet. One critical assumption is the size of the lactating cow. Repetition of the analysis for a larger cow illustrated that a larger cow can produce more milk from a given forage than can a smaller cow. Linear regression of simulated data indicated that milk production from a 101 larger cow was approximately 1000 kg more per year, given the same alfalfa quality fed in balanced, mixed forage diets (Figure 6.5): PMP560 ks co" = 13510 - 8u30NDF [6.12] (r2 = 0.991) The production of extra milk requires more feed, so the increase in milk income would be partially offset by an increase in supplemental feed 137 .uoflw mampow woxfia m to» Boo a soy» :cwuuscoho xHHE Amwucmuoa cc >uwamsw muamuam mo uomwwm map so omwm 3oo vommmuocH we uowasb m.0 ousawm 05 B 8:85 ezEzoo 1.52 as . .....o . ....o _ ....o , as _ is. 1111111111111 100mm 3W” my. 101010 111111111111 H.0000 1oomm g8 9. .08 1802 -oomo_ (V51) Nouonooad mm 138 costs. It was suspected that the type of forage in the diet also affects potential milk production. For this reason, diets were formulated in which the forage source was either all alfalfa hay or all alfalfa silage. ' For alfalfa hay- and silage-based diets, maximum milk production fit the following models: PMPall hay = 1uu80 - 12380NDF [6.13] (r2 = 0.983) PMPall silage = 1u1uo - 11880NDF [6.1u] (r2 = 0.985) Figure 6.6 illustrates the effect of forage type on potential milk production. Note that for poor quality alfalfa (>171 NDF), adding corn silage to the diet increased milk production. At high quality levels (<451 NDF), alfalfa as a forage source resulted in the highest milk production. Alfalfa hay-based diets had slightly higher milk production levels than alfalfa silage-based diets, regardless of alfalfa quality. 139 .300 0x 000 m EC»; :cwuo:vc»0 x_HE Hmwucouc: :c zuwfimsc «gammfim 00 000000 0:0 :0 mobzom wumu00 00 uumaap 0.0 whswfim 020 .6 8:080 EEzoo 52 00.0 0x0 0N0 .320 mumww wwwmxlaum—«m m0—00H< 00000 manqu wwxwz muowv cmmmnl>05 00H00—< Nxd 0¢.0 0000 10000 (WEN) Nonlonooad >fllw 7. PROCEDURE FOR DETERMINING THE ECONOMIC VALUE OF EORAGE LOSSES The models of harvesting and storage losses (Chapters 3 and H) and the model of animal conversion of feedstuffs into milk (Chapter 5) were the foundation for determining the value of forage losses. These models were incorporated into DAFOSYM to perform a simulation study of alfalfa losses. (After these modifications, the level of detail in the Submodels of DAFOSYM was increased to the equivalent of a 3 in Table 2.1). This chapter includes an outline of the approach toward determining 'the value of alfalfa losses and a description of the simulated farm. The following chapter contains results from the analysis as well as some results showing the sensitivity of forage loss value to various parameters. 7.1 Simulation approach The dairy forage system model (DAFOSYM1) was used for the simulation study to properly model the interaction among losses during harvest, storage and animal conversion. With alfalfa harvest driven by weather and machinery available, the impact of machinery set (harvest- rate) on respiration and rain losses is considered. Because crop growth is also a part of the model, the changing effects of harvest losses as the crop becomes more mature are inherently considered. The greatest 1 DAFOSYM Version 3.5, the latest version as of December 1988, which included all models developed in Chapters 3, fl and 5, was used for this study. 1H0 1N1 benefit of the simulation approach is that animal performance (feed conversion) depends on the value of the crop available to the animal. With all losses and quality changes between growth and feeding simulated, animal performance becomes dependent on every factor influencing alfalfa production. The value of losses is determined by the subsequent milk production potential and the total feed cost necessary for producing the given amount of milk. The previous chapter briefly outlined an approach for using the animal model of Chapter 5 to determine feed value. The value of losses was determined in a different manner, which is best illustrated with an example. To determine the value of raking loss under a describable set of farm, animal and economic conditions, the net return above feed costs from a simulation with raking loss eliminated (raking loss set equal to 0.0) was compared with net return above feed costs where raking loss was modeled (Chapter 3). The difference in net return above feed costs was attributed to raking loss. This approach was applied to each source of loss (Table 7.1) independently. Some combinations of losses were also eliminated to determine their combined impact on net return. 7.2 Farm description A medium-sized farm located in East Lansing, Michigan was modeled as the representative farm. The farm consisted of 50 ha of alfalfa and 30 ha of corn. The animal herd included 100 lactating cows (26% primiparous), 36 heifers less than one year old and 30 heifers over one year old. Two levels of milk production potential were used: 8,000 kg/y herd average and milk production limited by forage quality only. The lower milk production level was used to illustrate the impact of forage 1M2 Table 7.1 Sources of loss analyzed in this study. Loss Abbreviation Respiration RESP Rain RAIN Mowing/conditioning MOW Baling BALE Chopping CHOP Hay storage HAY Silo storage SILO Feeding FEED losses on a typical dairy farm. The higher milk production level was used to show the effect of milk production level on the value of losses and to illustrate the impact of forage losses on the animals of the future. For the higher milk production level, an unrealistic target (12,000 kg milk/y) was set. (The author recognizes that 12,000 kg/y is not unrealistic on some dairy farms; however, with a herd of 600 kg cows split into four groups, 12,000 kg/y simply cannot be achieved using only total mixed rations.) When the target milk production level was not met, it was reduced to a level that could be supported with the available feeds. Because animals with higher production potential could utilize higher quality forages more effectively (Section 6.2), response to increases in forage quality was always greater for the 12,000 kg/y herd. Crops were grown on a clay loam soil with a water-holding capacity of 130 mm. A four-cut alfalfa harvesting system was used, with first and fourth cuttings harvested as silage; second and third cuttings were harvested as dry hay. All alfalfa was mowed with a mower-conditioner 1113 and field cured. Alfalfa intended for silage was laid into narrow swaths until harvest; alfalfa intended for hay harvest was laid into full-width swaths and raked before baling. First, second and third cutting alfalfa harvest was begun on May 25, July 5 and August 20, respectively, or when the crude protein of the growing plant dropped to 21$, whichever was earlier. Fourth cutting was started on October 11 regardless of quality level. All feeds produced were stored on the farm. Storage structures (Table 7.2) include an open-front hay shed, two stave silos for alfalfa silage, one stave silo for corn silage and a stave silo for high moisture ear corn. Prices of the storage structures reflect 1988 values (Table 7.2). Useful life for the structures was 20 years. The machinery complement for the medium-sized farm is listed in Table 7.3.' Included are four tractors (35, 35, 65 and 100 kW), a 3.7 m mower-conditioner, a 2.7 m rake, a medium-sized baler and a medium-sized forage chopper. The useful life of all machinery was 10 years. Prices of other farm inputs and feeds are listed in Table 7.4. Farm and machinery input files containing this and all other simulation information are included in Appendix D. All simulations were performed over 26 years of historical weather data for East Lansing, Michigan, so loss values reported are averages over 26 years. 7.3 Evaluation Each pair of simulations (with and without simulating a particular loss or set of losses) gives information including milk income, total feed costs and net return above feed costs as well as information on production and use of feeds. Though net return above feed costs is a 1H9 Table 7.2 Storage structures on the representative 100 ha farm. Structure Capacity No. of Initial cost (T DM) units (:) Hay shed 300 1 17000 Corn silage silo (stave) 142 1 23000 Alfalfagsilage silo (stave) 112 1 19000 Alfalfa silage silo (stave) 175 1 26000 High moisture ear corn silo (stave) 112 1 19000 Table 7.3 Machinery available on the representative 100 ha farm. Machine Size No. of Initial cost units ($) Tractor 35 kw 2 15800 Tractor 65 kw 1 29250 Tractor 100 kw 1 “5000 Mower-conditioner 3.7 m 1 13200 Rake 2.7 m 1 3150 Baler Medium 1 9500 Bale elevator -- 1 3600 Bale wagons ”.5 T DM 3 1900 Forage chopper Medium 1 11300 Forage blower 30 T/h 1 3150 Forage wagons 9 T DM 3 7200 1 Corn planter 6 row 12600 145 Table 7.” Prices of various inputs and feeds in the simulation studies. Item Price Units Labor 8.00 $/h Diesel fuel 0.31 $/l Electricity 0.08 t/kWh Corn drying 1.00 $/pt./T Milk 0.25 $/kg Annual cost of tillage and ground preparation - for corn grain production 8fl.00 $/ha — for alfalfa production 100.00 $/ha Annual cost of fertilizer seeds and chemicals - for new alfalfa production 260.00 $/ha - for established alfalfa 130.00 $/ha - for silage corn production 250.00 $/ha - for corn grain production 190.00 $/ha Annual cost for corn grain harvest 55.00 $/ha Selling price of feeds ' - corn grain 100.00 $/T DM - high moisture ear corn 85.00 $/T DM - alfalfa hay 65.00 $/T DM - corn silage “8.00 $/T DM Buying price of feeds - soybean meal 220.00 t/T DM - distillers grain 159.00 $/T DM - corn grain 106.00 $/T DM alfalfa hay 69.00 $/T DM useful number, it should be used with the definition in mind. It is not the profit that can be realized on the described farm; rather, it is the income left after covering feed costs. Expense items not included are barns, milking labor, manure hauling, milking parlor, etc. Recognizing that these expenses are likely to be constant for a farm, the 1H6 differences in net return above feed costs for different scenarios represent the differences in farm profit. The actual value of the profit is unknown, but the difference is simulated. The value of each loss was the change in net return above feed costs realized when the loss was eliminated. To make loss values more general, two other units of comparison were also used: loss value per unit of alfalfa available ($/T DM) and reduction of total feed costs ($). While loss value in dollars represents the profit potential of eliminating a given loss, loss value per unit of alfalfa available indicates the value of loss in proportion to production. The loss value per unit of production was computed with the source of loss defining the amount of alfalfa available. For example, rake, baler and hay storage loss per unit of alfalfa available was the difference in net return divided by the amount of alfalfa hay available (i.e., silage is not raked nor baled). Similarly, chopper and silo loss value per unit available were determined as the difference in net return divided by the amount of alfalfa silage available. The value of respiration, rain, mower and feeding loss was determined using the total amount of alfalfa available to the animal. Reduction of total feed costs (RTFC) was computed as: (TFC/"P)without loss RTFC = 1oo[1- ] [7'1] (TFC/“P)with loss This indicator of loss value illustrates the changes in feed efficiency and the impact losses can have on profit. For example, if total feed costs per unit of milk produced are reduced by 31 when a given loss is eliminated and if feed costs are approximately ”01 of total production costs, then elimination of that loss decreases total costs (increases 19? profit) by 1.2%. Admittedly, the three methods of evaluating the value of losses are related; however, each gives its own insight into the interpretation of loss value. 8. VALUE OF ALFALFA LOSSES The value of alfalfa losses was determined by comparing net return above feed costs with and without a given loss or set of losses simulated. With all losses simulated on a representative medium-sized farm (Section 7.2), the net return above feed costs averaged $116,338 when milk production was 8,000 kg/cow-y and $1u3,115/y when production was determined by forage quality1 (Appendix E). Milk production limited by forage quality ranged from 8,609 to 9,569 kg/cow-y and averaged 9,300 kg/cow-y. Alfalfa available to the animals averaged #79 T DM. Alfalfa was nearly equally divided between hay and silage, with more hay being of low quality (> 41% NDF) than of high quality (< 41% NDF). The quality split of alfalfa silage is determined by the silo capacities, but the quality within each silo varied from year to year. Corn production -- in the form of corn silage, high moisture ear corn (HMEC) and corn grain -- averaged 123, 98 and 137 T DM, respectively. The total feed costs (including the sale of excess alfalfa and corn grain and purchase of protein supplements) averaged $83,663 and $89,390/y for low and high production levels, respectively. The lower producing herd utilized more alfalfa, and in doing so, required less corn grain and protein supplement. For this base case in which all losses were simulated, feed ,cost 1 For discussion purposes, 8,000 kg/cow-y will be called low production. Milk production as determined by forage quality will be called high production. 1&8 1H9 per unit of milk production was $0.0961/kg of milk produced when forage quality limited milk production. If animal potential limited milk production to 8,000 kg/cow-y, feed cost was $0.1096/kg of milk produced; thus, with the milk price at $0.25/kg, 38 to “21 of milk income went toward feed costs. 8.1 In-field and harvest 8.1.1 Respiration Simulating a scenario in which respiration loss was eliminated increased net return for the high producing herd by $2,987/y (Table 8.1, Figure 8.1). Because material lost during respiration is totally digestible, respiration loss decreases quality as well as quantity. The change in quality due to respiration loss resulted in 110 kg less milk per cow per year (Appendix E); this in combination with increased feed costs resulted in an increase in total feed cost per unit of milk produced of 1.u:. For each tonne of alfalfa available to the animal, respiration loss costs $6.29 (Table 8.1). With a high producing herd, respiration caused the third largest loss of value during harvest. For the 8,000 kg/cow-y herd, response to respiration loss was not as great. Because the lower producing herd is less sensitive to quality, and because the amount of material lost during respiration is relatively small, net return increased only $630/y with the elimination of respiration loss (Table 8.1, Figure 8.1). This change in net return above feed costs corresponded to a decrease in feed costs per unit of milk produced of 0.81. Respiration loss reduced the value of alfalfa available to the animal by $1.32/T DM. 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O- Eupm>m m0<.__m m ..m Ewbm>m mo<.__m um >m >000000 000000 0003 0000000 000 0000000 .000000 000020 0000 0:00> 0000000 00 00000000 0000000 000 0000 00000 00 00000m w.w 000000 mmO._ mmon. mmo._ 02.0mm... wo 0000000 00 00000000 0000000 000 0000 00000 00 00000m m.w 000000 mm0... mm0._ mm00 02.0mm... mo000 00000000 00 0000> 0:0 00 0000000 0000000E 000>000 00 000000 c0.w 000000 mmmmO... mm00 mmO.. .....4. 090m. 95 Hmm>m\0x ooo.w 00 0000000000 x00E 0003 000000 0000000 000 000 0000000 .000>00; 00000E00 00 0:00> 0:0 00 0000000 00000005 000>000 00 000050 00.0 000000 mumm00 mm00 mmo._ ..3< mom 35 C DIGESTIBLE PART OF THE CRUDE PROTEIN LOST FEEDING METHOD FOR HAY THE FEEDING METHODS ARE 1 RECTANGULAR BALES, HAND FED (0.05 DH LOSS) 2 ROUND BALES, SELF FED (0.14 DH LOSS) 3 ROUND BALES, GROUND (0.05 DH LOSS) 4 HAY STACKS, SELF FED (0.16 DH LOSS) 5 HAY STACKS, SHREDDED (0.05 DH LOSS) INITIAL MOISTURE, FRACTION FINAL MOISTURE, FRACTION FEEDING METHOD (FMHY OR FMHL+5) NUMBER OF PLOT CURRENTLY CONSIDERED NUMBER OF PLOTS IN CURRENT STORAGE STRUCTURE STRUCTURE INDICATOR . ALF SILAGE ALF SILAGE ALF SILAGE ALF SILAGE ALF HAY INSIDE ALF HAY INSIDE . ALF HAY OUTSIDE ALF HAY OUTSIDE 50 SILAGE 60 CORN SILAGE TOTAL HEAT GENERATED OVER STORAGE PERIOD, KJ/KG STORAGE DRY MATTER LOSS, FRACTION 2132!: bbbbbbbo m-QONU‘J‘me-e grmrmI—‘r‘ E! --- SUBROUTINE SILO --- DIMENSIONLESS PARAMETER USED TO PREDICT DENSITY (SEE PITT, 1983) AHOUNT OF CRUDE PROTEIN, TONNE AMOUNT OF DIGESTIBLE FORAGE, TONNE AMOUNT OF DIGESTIBLE FORAGE INITIALLY, TONNE AIR TO HERBAGE RATIO AMOUNT OF DRY MATTER, TONNE AMOUNT OF DRY MATTER IN THE SILO, TONNE AMOUNT OF NEUTRAL DETERGENT FIBER, TONNE AMOUNT OF NON-PROTEIN NITROGEN, TONNE ASH CONTENT, FRACTION OF D.H. AVERAGE CRUDE PROTEIN CONTENT, FRACTION OF D.H. AVERAGE DIGESTIBILITY, FRACTION OF D.H. AVERAGE DIGESTIBILITY INITIALLY, FRACTION OF D.H. OOOOOOOOOOOOOOOOnOOOOO000000OOOOOOOOOOOOOOOOOOOOOOOOO AVNDF AVNPN CAP CROP CSA CSAF CSFDRT CSTBE DENS DENSC DENSE DEPTH DIM1 DIM2 DM DMAX DMLEFT DRHO EXPAR FDRTE FDTMP FMHL GAMO NPSTAR NSILO NVS NVSCS 199 AVERAGE NEUTRAL DETERGENT FIBER CONTENT, FRACTION OF D.M. AVERAGE NON-PROTEIN CONTENT, FRACTION OF D.M. SILO CAPACITY, KG D.M. CROP TYPE INDICATOR (1 FOR ALFALFA SILAGE, 2 FOR CORN SILAGE) CROSS SECTIONAL AREA, SQ METERS CROSS SECTIONAL AREA OF FEEDOUT SURFACE, SQ METERS CORN SILAGE FEEDRATE, KG/DAY TIME BEFORE (STARTING TO) EMPTY THE CORN SILAGE SILO, DAYS SILAGE DENSITY, KG/CUBIC METER SILAGE DENSITY AT CENTER OF SILO, KG/CUBIC METER SILAGE DENSITY AT EDGE OF SILO, KG/CUBIC METER DEPTH OF CURRENT PLOT IN SILO, M DIAMETER (TOWER) OR WIDTH (BUNKER), M HEIGHT OF A BUNKER, M DRY MATTER CONTENT, FRACTION MAXIMUM POSSIBLE DENSITY (AT ZERO VOID SPACE), KG/CUBIC M AMOUNT OF DH HARVESTED "NOT IN SILO YET", KG DRY MATTER DENSITY, KG/CUBIC M EXPOSED AREA DURING PRESEAL, SQ H FEED RATE, KG D.M./DAY TEMPERATURE AT FEEDING TIME, DEGREES C FEEDING METHOD FOR HAYLAGE THE FEEDING METHODS ARE 1 AUTOMATIC FEEDING 2 CART OR TRUCK FEEDING UNCOMPACTED FORAGE DENSITY, KG/CUBIC M NUMBER OF PLOT WHICH MAKES THE SILO FULL INDICATOR FOR WHICH SILO IS CONSIDERED 1,2 H.Q. ALF SILAGE 3,4 L.Q. ALF SILAGE 5,6 CORN SILAGE NUMBER OF VERTICAL SECTIONS (IN BUNKER SILO) NUMBER OF VERTICAL SECTIONS IN CORN SILAGE BUNKER SILO PLOT(I,J) QUALITY AND QUANTITY OF PLOT ARRAY RAD REFILL I: PLOT NUMBER; J:INFORMATION TYPE AS FOLLOWS CROP TYPE (1.:ALFALFA SILAGE, 2.: CORN SILAGE) AMT OF DH IN PLOT INITIALLY, KG DRY MATTER CONTENT, DECIMAL NDF CONTENT, FRACTION OF D.M. CRUDE PROTEIN CONTENT, FRACTION OF D.M. NPN CONTENT, FRACTION OF CRUDE PROTEIN ADF CONTENT, FRACTION OF D.M. TEMPERATURE, DEGREES C EXPOSURE TIME OF THIS PLOT BEFORE BEING COVERED, DAYS 10 PH OF THE SILAGE 11 AMT OF DH AFTER PRESEAL, KC 12 AMT OF DH AFTER FERMENTATION, KG 13 AMT OF DH AFTER INFILTRATION, KC 14 AMT OF DH AFTER FEEDOUT, KG RADIUS OF TOWER SILO, M AMOUNT OF FORAGE PUT IN SILO DURING REFILL, KG D.M. QmNOWtWN—b GOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO RI RX RMUK 200 RADIUS TO INFILTRATION FRONT, M PARAMETER FOR COMPUTING DENSITY PARAMETER FOR COMPUTING DENSITY (GREEK MU TIMES K) SILDAT(I,J) SILO INFORMATION ARRAY SILTYP SWETWT SWT T3B TBE TIME3 TOTDM USILO I ' SILO NUMBER; J = INFORMATION TYPE AS FOLLOWS DIM1 ABOVE, M DIM2 ABOVE, M CAPACITY, TONNE COST, $ PERMEABILITY TO OXYGEN, CM/ATM-H SILO TYPE (1 UPRIGHT TOP UNLOADED, 2 UPRIGHT BOTTOM UNLOADED, 3 BUNKER) SUM OF WET WEIGHT, KG SUM OF DRY WEIGHT, KG D.M. TIME TOP PLOT OF TOWER SILO IS IN THE SILO AND EXPERIENCES TOP DOWN INFILTRATION, DAYS TIME BEFORE EMPTYING CURRENT SILO, DAYS DURATION OF STAGE 3 (TIME PLOT IS IN THE SILO), DAYS TOTAL AMOUNT OF DRY MATTER, KG SILO PERMEABILITY TO OXYGEN, CM/ATM-H “kWN—nu --- SUBROUTINE ENDSTR --- FEEDLS IBUNK RESET VARCP VARDIG VARNDF VARNPN WSSCP WSSDIG HSSNDF WSSNPN FEEDING LOSS FACTORS INDICATOR FOR A BUNKER SILO INDICATOR AS TO WHETHER OR NOT THIS IS THE FIRST'SILO OF THE APPROPRIATE QUALITY OF FORAGE VARIANCE OF CRUDE PROTEIN CONTENT VARIANCE OF DIGESTIBILITY VARIANCE OF NEUTRAL DETERGENT FIBER CONTENT VARIANCE OF NON-PROTEIN NITROGEN CONTENT WEIGHTED SUM OF SQUARES FOR CRUDE PROTEIN CONTENT WEIGHTED SUM OF SQUARES FOR DIGESTIBILITY WEIGHTED SUM OF SQUARES FOR NDF CONTENT WEIGHTED SUM OF SQUARES FOR NON-PROTEIN NITROGEN CONTENT --- SUBROUTINE PRESEAL --- C D DELT DML1 DMLPD DMLTD EXPTHE . FC FD FPH GAMMA MUBAR MUMAX TERM USED TO APPROXIMATE OXYGEN CONCENTRATION PROFILE DIFFUSION COEFFICIENT OF OXYGEN THROUGH AIR, SQ CM/H TEMPERATURE RISE, DEGREES C. DRY MATTER LOSS DURING STAGE 1, FRACTION DRY MATTER LOSS PER DAY, FRACTION DRY MATTER LOSS DURING CURRENT DAY, FRACTION DURATION OF STAGE 1, DAYS RESPIRATION RATE AS DEPENDENT UPON C02 CONCENTRATION RESPIRATION RATE AS DEPENDENT UPON DRY MATTER CONTENT RESPIRATION RATE AS DEPENDENT UPON pH RESPIRATION RATE AS DEPENDENT UPON TEMPERATURE TERM USED TO APPROXIMATE OXYGEN CONCENTRATION PROFILE AVERAGE RESPIRATION RATE THROUGH DEPTH OF FORAGE MAXIMUM RESPIRATION RATE OF FORAGE IN AIR, CUBIC CM OXYGEN/G SILAGE-H OCOCOCOO00000OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO MUTAU PHI RHO RHOMAX TAU THICK 201 RESPIRATION RATE OF FORAGE, CUBIC CM OXYGEN/G SILAGE-H POROSITY, FRACTION DENSITY, G/CUBIC CM MAXIMUM DENSITY, G/CUBIC CH TORTUOSITY OF DIFFUSIONAL PATHS THICKNESS (OF CURRENT PLOT IN TOWER SILO, OF ALL MATERIAL IN BUNKER), CH --- SUBROUTINE FERMENT --- DML2 HCCHG NPN PH T DRY MATTER LOSS DURING STAGE 2, FRACTION CHANGE IN NDF DUE TO HEHICELLULOSE BREAKDOWN, FRACTION NON-PROTEIN NITROGEN CONTENT, FRACTION OF CRUDE PROTEIN pH OF FORAGE TEMPERATURE OF FORAGE, DEGREES c --- SUBROUTINE TOWER --- A ADJ AI B BPAST DTAU LDEC LDECB LKGACB LKGACC PI PSIT RI RIPAST RS T3 THE UCOV UEFF UMAT USILO AREA FOR INFILTRATION LOSS IN TOP PLOT (DowNwARD INFILTRATION), 50 M ADJUSTMENT TO AIR OXYGEN CONCENTRATION TO OXYGEN CONCENTRATION ON THE TOP SURFACE OF FORAGE IN THE SILO AREA OF INFILTRATION FRONT, SQ M DISTANCE BETWEEN SILO WALL AND MOVING FRONT, M DISTANCE BETWEEN SILO WALL AND MOVING FRONT LAST TIME PERIOD, M D*TAU (SEE ABOVE) DRY MATTER LOSS, FRACTION DRY MATTER LOSS DUE To TOP PLOT DOWNWARD INFILTRATION, FRACTION ACCUHULATED'LOSS OVER TIME IN TOP PLOT DOWNWARD INFILTRATION, KG ACCUMULATED LOSS OVER TIME, KC 3.14159 OXYGEN CONCENTRATION AT TOP OF FORAGE SURFACE RADIUS TO MOVING FRONT, M RADIUS TO MOVING FRONT DURING LAST TIME PERIOD, H RESPIRABLE SUBSTRATE, FRACTION OF D.M. DURATION OF STAGE 3, DAYS TIME, DAYS OXYGEN PERMEABILITY OF SILO COVER, CM/ATM-H EFFECTIVE PERMEABILITY OF SILO WALL AND FORAGE MATERIAL OUTSIDE THE MOVING FRONT, CH/ATH-H PERMEABILITY OF FORAGE‘MATERIAL OUTSIDE THE MOVING FRONT, CM/ATM-H PERMEABILITY OF SILO WALL, CH/ATH-H --- SUBROUTINE BUNKER --- A AVGPH AVGT SURFACE AREA ON TOP OF 10 DAYS' WORTH OF MATERIAL, 50 M AVERAGE PH OF FORAGE AVERAGE TEMPERATURE OF FORAGE, DEGREES C CDCDCTCICTCI CICDCIC)CDC!(If)C)CDCDCDCDCTCIC)(ICICDCICDC!C)C)C)CIC)C)CICI CCP CNDF CNPN HEIGHT IVS SPHXWT STXWT TPAST U 202 CRUDE PROTEIN CONTENT, FRACTION OF D.M. NEUTRAL DETERGENT FIBER CONTENT, FRACTION OF D.M. NON-PROTEIN NITROGEN CONTENT, FRACTION OF CRUDE PROTEIN HEIGHT OF BUNKER, M COUNTER FOR VERTICAL SECTIONS SUM OF PH TIMES WEIGHT SUM OF TEMPERATURE TIMES WEIGHT DISTANCE BETWEEN BUNKER TOP AND MOVING FRONT, M DISTANCE BETWEEN BUNKER TOP AND MOVING FRONT LAST TIME PERIOD, M PERMEABILITY OF BUNKER SILO COVER, CM/ATM-H --- SUBROUTINE FEEDOUT --- CSAF DML4 DML4A DML4B DF THICK CROSS SECTIONAL AREA OF FEEDOUT SURFACE, SQ M DRY MATTER LOSS DURING STAGE 4, FRACTION DRY MATTER LOSS IN-SILO DURING STAGE 4, FRACTION DRY MATTER LOSS IN-BUNK DURING STAGE 4, FRACTION THICKNESS OF FORAGE FED PER DAY, CM DEPTH AT WHICH OXYGEN CONCENTRATION GRADIENT IS ZERO, CM --- SUBROUTINE CSSILO --- ADM(J) AMOUNT OF DRY MATTER IN SILO J (J=1,2), TONNE CLOSSS(3) STORAGE DRY MATTER LOSS IN CORN SILAGE SILO, FRACTION CSMC CSTBE JDAYCS CORN SILAGE MOISTURE CONTENT, DECIMAL WET BASIS TIME BEFORE EMPTYING CORN SILAGE SILO, DAYS DAYS TO FILL CORN SILAGE SILO IQHNNNHNWiifiiiiiiiiiiiiiNiiiifi§****iiii‘iflfii§§****N**§***NHNNHHMHMHHH SUBROUTINE HAY (NSTR,NPSS) onxiiixResetAisxxxwxaxexxxxexxxxexaxxxxxeexixa{xxxxxxxixxnxxxxxxxexxxx DETERMINES THE LOSSES AND QUALITY CHANGES WHICH OCCUR DURING THE STORAGE OF DRY HAY (D.R. BUCKMASTER, AGRIC. ENGINEERING DEPT., MSU, JUNE 1988) LOGICAL FULL REAL H0,HF INTEGER*4 NYRS,JYEARF,IPRT1,HGHT,IRAIN,NCUTS,ISIL(7) INTEGER*4 IHOWER,IRAKE,IBALER,IFHRV,ICRNPL,IHHCHV,NHOWER,NRAKE, + + + NBALER,NLOADE,NELEVA,NFHRV,NBLOWE,NCRNPL,NHMCHV,NTRANS, KMOWER,KRAKE,KBALER,KLOADE,KTRANS,KFHRV,KBLOWE,KCRNPL, KHMCHV,LHAY,LHAYLG,LSILG,LHMC COMMON /w3/ HFEED(8,160,8) COMMON /z1/ AREA(6),NBO(6),NOPSQ(S.9).CRTR(5.4,9),IRAIN COMMON /27/ ALHRFD(26,20),AFEED(2,26,33),FEEDUT(26,14) COMMON /CTRL24/ BGNCUT(5),NTHYR,NTHCUT,NDAYSC,NDAYSH,YLD(4), + + QUAL(3.4),GDDCUH,HETRIC,JYEARF,JYEARL,IPRT1,IPRT2,JDAYF, JDAYL,JPRT,NYRS,IPRT4,NCUTS,JYEAR,JLALHR,CPLANT,HGHT COMMON /STRG/ SILDAT(32,5),FCAP,ISIL,HAYST(3),FMHY,FMHL COMMON /OPER/ HOWER(5),JRAKE(2),JBALER(6),JFHRV(4),JCRNPL(4), + JHMCHV(3).JTRAC(7),IMOVER,IRAXE,IBALER,IFHRV,ICRNPL,IHMCHV, 203 + NHOWER,NRAKE,NBALER,NFHRV,NCRNPL,NHHCHV,NLOADE,NTRANS, + NBLOWE,NELEVA,KHOWER,KRAKE,KBALER,KFHRV,KCRNPL,KHHCHV, + KLOADE,KTRANS,KBLOWE,KELEVA,LHAY,LHAYLG,LSILG,LHHC COMMON /z3/ HARDEX,TMSTO(8),RF(5),NPST(5.9).NCUM(9),OPUSE(5,9), + FULL(4) COMMON /SDATA/ PLOT(52,14) COMMON /CSSIL/ AMTDM,CSFDRT,CSTBE,NVSCS COMMON/LOSS/ILOSS INTEGER*4 ILOSS(12) DATA HF/O.12/ DRY HAY STORED INSIDE OR SQUARE BALES STORED OUTSIDE (COVERING ASSUMED IN THE LATTER CASE) 0000 IF( (NSTR.EQ.5).0R.(NSTR.EQ.6) .OR. + ((NSTR.GT.4).AND.(NSTR.LE.8).AND.(IBALER.GE.1).AND. + (IBALER.LE.6)) ) THEN c CONVERT MOISTURE TO WET BASIS FOR USE IN HAY STORAGE MODEL NFEED = FMHY DO 5 NPL=1,NPSS M0 = HFEED(NSTR,NPL,5)/(1.+HFEED(NSTR,NPL,5)) IF(MO.LT.O.12) HO:.12 D0 = 100. + 440.*MO QSTOR = 104.*HO**2.18 * DO**O.5 + 5.72*HO**1.23 * DO**0.94 C DETERMINE DRY MATTER LOSS SDML = (QSTOR + 2433. * (Mo-MF*(1.-MO)/(1.-MF)))/ + ((1.-H0) * (14206. - 2433.*MF/(1.-MF))) C c TOGGLE FOR SHUTTING OFF HAY STORAGE LOSS IF(ILOSS(7).EQ.1) SDML = 0. C DETERMINE QUALITY CHANGES DPCPL = O.4*HFEED(NSTR,NPL,2)“SDHL HFEED(NSTR,NPL,1)=HFEED(NSTR,NPL,1)*(1.-SDML) HFEED(NSTR,NPL,2)=HFEED(NSTR,NPL,2)*(1.-o.4*SDML)/(1.-SDML) HFEED(NSTR,NPL,3):(HFEED(NSTR,NPL,3) - DPCPL*0.929 - + (SDML-DPCPL)*1.)/(1.-SDML) IF(MO.GT.O.12) THEN DD = 48640. * (Mo-.12)**1.836 ELSE DD = O. ENDIF IF(ILOSS(7).EQ.1) DD = 0. BP = .O1*(o.8 + 0.00373*DD)/(1.-SDHL) BPFCP = BP/HFEED(NSTR,NPL,2) HFEED(NSTR,NPL,4)=HFEED(NSTR,NPL,4)/(1.-SDML) HFEED(NSTR,NPL,5)=HF/(1.-HI-‘) HFEED(NSTR,NPL,8) = BPFCP 5 CONTINUE c C ROUND BALES STORED OUTSIDE ELSEIF((NSTR.EQ.7).OR.(NSTR.EQ.8).AND.(IBALER.GE.7)) THEN NFEED : FMHY ' D0 7 NPL=1,NPSS 204 SDML : 0.14 IF(ILOSS(7).EQ.1) SDML = O. HFEED(NSTR,NPL,1):HFEED(NSTR,NPL,17*(1.-SDHL) HFEED(NSTR,NPL,2) = HFEED(NSTR,NPL,2) HFEED(NSTR,NPL,3)=(HFEED(NSTR,NPL.3) - DPCPL'O.929 - + (SDHL-DPCPL)*1.)/(1.-SDHL) ' HFEED(NSTR,NPL,3) = HFEED(NSTR,NPL,3) - SDML HFEED(NSTR,NPL,4):HFEED(NSTR,NPL,4)/(1.-SDHL) BP : 0.01.0.8 BPFCP = BP/HFEED(NSTR,NPL,2) HFEED(NSTR,NPL,8) = BPFCP 7 CONTINUE 000 HAY STACKS STORED OUTSIDE ELSEIF((NSTR.EQ.7).OR.(NSTR.EQ.8).AND.(A STACKER)) THEN NFEED : FMHY DO 9 NPL:1,NPSS SDML = 0.16 HFEED(NSTR,NPL,1)=HFEED(NSTR,NPL,1)*(1.-SDML) ASSUMED NO QUALITY CHANGE IN STACKS 9 CONTINUE ENDIF CALL ENDSTR(NSTR,NPSS,O,O,NFEED) RETURN END 000000000 C C §§iiififiifiii§ii§****§*§**§i*iiifiiifiiiiflfliiiiifiiiifiiNiii*iifififlfiflNKiiifiMM SUBROUTINE SILO (NSTR,NPSS) C {NifiiiiiiiliififiiifiiiWiiiiiii}!*WHNHNNNWWWWWN{iiiiifiiiiNHWNNWHWHNWNNNWW C DETERMINES THE LOSSES AND QUALITY CHANGES WHICH OCCUR DURING THE C STORAGE OF SILAGE C (D.R. BUCKHASTER, AGRIC. ENGINEERING DEPT., MSU, JUNE 1988) C LOGICAL FULL REAL R1(52) INTEGER*4 NYRS,JYEARF,IPRT1,HGHT,IRAIN,NCUTS,ISIL(7),SILTYP INTEGER*4 IHOWER,IRAKE,IBALER,IFHRV,ICRNPL,IHHCHV,NHOWER,NRAKE, + NBALER,NLOADE,NELEVA,NFHRV,NBLOWE,NCRNPL,NHHCHV,NTRANS, + KHOWER,KRAKE,KBALER,KLOADE,KTRANS,KFHRV,KBLOWE,KCRNPL, + KHHCHV,LHAY,LHAYLG,LSILG,LHHC COMMON /w3/ HFEED(8,160,8) COMMON /z1/ AREA(6),NBO(6),NOPSQ(5.9).CRTR(5.4,9),IRAIN COMMON /27/ ALHRFD(26,20),AFEED(2,26,33),FEEDUT(26,14) COMMON /CTRL24/ BGNCUT(5),NTHYR,NTHCUT,NDAYSC,NDAYSH,YLD(4), + OUAL(3,4),GDDCUH,HETRIC,JYEARF,JYEARL,IPRT1,IPRT2,JDAYF, + JDAYL,JPRT,NYRS,IPRT4,NCUTS,JYEAR,JLALHR,CPLANT,HGHT COMMON ISTRG/ SILDAT(32,5),FCAP,ISIL,HAYST(3).FMHY,FMHL COMMON /OPER/ MOVER(5),JRAKE(2),JBALER(6),JFHRV(4),JCRNPL(4). + JHMCHv(3).JTRAC(7),IMOVER,IRAXE,IBALER,IFHRV,ICRNPL,IHMCHV, + NHOWER,NRAKE,NBALER,NFHRV,NCRNPL,NHMCHV,NLOADE,NTRANS, + NBLOWE,NELEVA,KHOWER,KRAKE,KBALER,KFRRV,KCRNPL,KHHCHV, + KLOADE,KTRANS,KBLOWE,KELEVA,LHAY,LHAYLG,LSILG,LHHC COMMON /z3/ HARDEX,TMSTO(8),RF(5),NPST(5.9),NCUM(9),0PUSE(5,9), 00 (3 0 205 FULL(4) COMMON /SDATA/ PLOT(52,14) COMMON /CSSIL/ AMTDM,CSFDRT,CSTBE,NVSCS INTEGER IBUNK IF(NSTR.LE.4) THEN ALFALFA IN A SILO NSILO = NSTR CROP : 1. TOTDM = 1000.*THSTO(NSILO) EMPTY OUT SILOS OF "SAME" QUALITY OVER APPROX. 365 DAYS IF((NSILO.EQ.1).OR.(NSILO.EQ.2)) THEN H.Q. HAYLAGE FDRTE = 1000.*(TMST0(1)+TMSTO(2))/365. IF 2ND SILO, ADD TIME OF EMPTYING 1ST SILO T0 STORAGE TIME IF(NSILO.EQ.2) THEN TBE - TMSTO(1)*1000./FDRTE ELSE TBE = 0.0 ENDIF ELSE L.Q. HAYLAGE FDRTE = 1000.*(THSTO(3)+THSTO(4))/365. IF(NSILO.EQ.4) THEN TBE = TMSTO(3)F1000./FDRTE ELSE TBE : 0.0 ENDIF ENDIF ASH = 0.07 TRANSFER ALFALFA QUALITY DATA REGARDLESS OF SILO TYPE 100 DO 100 NPL : NPSS,1,-1 PLOT(NPL,1) = CROP PLOT(NPL,2) = HFEED(NSTR,NPL,1) * 1000. PLOT(NPL.3) = 1. / (1. + HFEED(NSTR,NPL,5)) PLOT(NPL,4) = HFEED(NSTR,NPL,4) PLOT(NPL,5) : HFEED(NSTR,NPL,2) PLOT(NPL,7) = 0.19 PLOT(NPL,8) = 18. IF(NPL.EQ.NPSS) THEN PLOT(NPL,9) = 0.125 ELSE PLOT(NPL,9) = HFEED(NSTR,NPL+1,7) - HFEED(NSTR,NPL,7) ENDIF IF PLOT WAS EXPOSED FOR MORE THAN 3 DAYS, ASSUME IT WAS CAPPED AFTER 3 DAYS: IF(PL0T(NPL,9).GT.3.) PLOT(NPL,9) 3. IF(PL0T(NPL,9).LT.0.) PLOT(NPL,9) o. PLOT(NPL,10) = 5.8 CONTINUE ELSE HHOLE PLANT CORN SILAGE IN A SILO NSILO = NSTR/10 0 0 0 000 0 CROP: AMOUNT, SUBROUT TOTDM: FDRTE : TBE ASH ENDIF 206 2. FEED RATE AND CROP QUALITY ASSIGNED INTO PLOT ARRAY IN INE CSSILO AMTDM*1000. CSFDRT CSTBE 0.05 INITIALIZATION OF IMPORTANT VARIABLES USILO : SILDAT(ISIL(NSILO).5) CAP : 100 FDTMP : 1 DATA RMUK DIM1 : SI DIM2 = SI IF((ISIL( BUNKER IF(CRO ALFA DRH ELSE CORN DRH ENDIF CSA CSAF = EXPAR : ELSE TOWER IF(CRO ALFAL DRHO = 0.*SILDAT(ISIL(NSILO),3) 8. /O.25/ LDAT(ISIL(NSILO),1) LDAT(ISIL(NSILO),2) NSILO).GE.26).AND.(ISIL(NSILO).LE.32)) THEN P.EQ.1.) THEN LEA O = 190. 0 = 225. : TOTDM/(DRHO*DIM2) DIM1*DIM2 SQRT(5.) * DIM1 * DIM2 P.EQ.1) THEN FA 130. : 0.00003 ELSE CORN DRHO = 180. RX : ENDIF RAD = CSA = CSAF = EXPAR : ENDIF IF((ISIL( 0.000012 DIM1/2. 3.14159'RAD**2 CSA CSA NSILO).GE.2).AND.(ISIL(NSILO).LE.13)) THEN TOWER WITH TOP UNLOADER SILTYP: 1 NFEED IBUNK TIME3 DEPTH é TBE O.5*(PLOT(NPSS,2)/(DRHO*CSA)) u u u u 0+ 0 000 000 207 REFILL = 0. DHLEFT = TOTDH DO 200 NPL = NPSS,1,-1 DMAX = 1000./(1.-PLOT(NPL,3) + (PLOT(NPL.3)/1.5)) REFILL CHECKING ROUTINE IF((DHLEFT.GE.CAP).AND.(DMLEFT-PLOT(NPL,2).LE.CAP)) THEN SHITCH FROM REFILL PLOTS TO ORIGINAL PLOTS TIME3 = 0. REFILL = TOTDM - DMLEFT DEPTH = O.5*(PLOT(NPL,2)/(DRHO*CSA)) ELSEIF((DHLEFT.GE.CAP-REFILL).AND. + (DMLEFT-PLOT(NPL,3).LE.CAP-REFILL)) THEN CONSIDER PLOTS HHICH HERE IN THE SILO BEFORE AND AFTER REFILL TIME3 = TIME3 + (TIME3 - TBE) ENDIF DMLEFT = DMLEFT - PLOT(NPL,2) END OF REFILL CHECKING TIME3 = TIME3 + O.5*PLOT(NPL,2)/FDRTE GAMO = DRHO/PLOT(NPL,3) A = 2. * RMUK / (RAD * RK * GAMo * 9.807) DENSE = GAMO * (1. + 1./(A-1.) + 1*(1.-EXP((1.-A)VRRVGAMOM.8074DEPTH))) IF(DENSE.GT.DMAX) DENSE = DMAX DENSC = GAM0*EXP(RX*GAM0*9.807*DEPTH) IF(DENSC.GT.DMAX) DENSC = DMAX DENS = (DENSE+DENSC)/2. AHR = DMAX/DENS - 1. PRESEAL LOSS CALL PRESEAL(NPL,SILTYP,EXPAR,DRHO) FERMENTATION CHANGES ‘ CALL FERHENT(NPL,AHR) INFILTRATION LOSS RI(NPL) = 0. CALL TOWER(USILO,RAD,NPL,NPSS,DENS,TIHE3,TBE,RI(NPL),ASH) TIME3 = TIME3 + O.5*PLOT(NPL,2)/FDRTE FEEDOUT Loss CALL FEEDOUT(SILTYP,NPL,CSAF,FDTMP,FDRTE,DENS,ASH) IF(CROP.EQ.1.) THEN SDHL = 1. - PLOT(NPL,14)/PLOT(NPL,2) HFEED(NSTR,NPL,1) = PLOT(NPL,14)/1000. HFEED(NSTR,NPL,2) = PLOT(NPL,5) HFEED(NSTR,NPL,3) = (HFEED(NSTR,NPL,3)-SDML)/(1.-SDML) HFEED(NSTR,NPL,4) = PLOT(NPL,4) HFEED(NSTR,NPL,5) = 1./PL0T(NPL,3) - 1. HFEED(NSTR,NPL,8) = PLOT(NPL,6) ENDIF DEPTH = DEPTH + (PLOT(NPL,2)/(PLOT(NPL,3)*DENS*CSA)) READY FOR NEXT PLOT TO BE CONSIDERED 208 200 CONTINUE IF(CROP.EQ.2.) RETURN ALL PLOTS CONSIDERED ELSEIF( (ISIL(NSILO).GE.14).AND.(ISIL(NSILO).LE.25) ) THEN TOWER WITH BOTTOM UNLOADER 000 0 SILTYP = 2 NFEED 7+FMHL IBUNK 0 REFILL = O. AMTIN = 0. DO 220 NFL = 1,NPSS IF(AMTIN + PLOT(NPL,2).GT.CAP) THEN c THERE HAS SOME REFILLING —- DETERMINE HHAT PLOTS HERE C IN THE SILO ORIGINALLY REFILL = TOTDM - CAP T3B = CAP/FDRTE GO TO 235 ELSE C HE HAVE NOT GOTTEN THE SILO FILLED YET AMTIN = AMTIN + PLOT(NPL,2) NPSTAR = NPL T3B = AMTIN/FDRTE ENDIF 220 CONTINUE c NPSTAR IS THE TOP PLOT BEFORE REFILLING 235 TIHE3 = TBE + CAP/FDRTE - IF(NPSTAR.NE.NPSS) THEN C THERE HAS REFILLING, SIMULATE INFILTRATION FOR THE TIME 0 PERIOD PRIOR To REFILL DEPTH = O.5*(PLOT(NPSTAR,2)/(DRHO*CSA)) DO 250 NPL = NPSTAR,1,-1 DMAX = 1000./(1.-PLOT(NPL,3) + (PLOT(NPL,3)/1.5)) TIME3 : TIME3 - 0.5*PL0T(NPL,2)/FDRTE IF(TIHE3.GT.TBE+REFILLIFDRTE) THEN C SHORTEN TIME3 TO TIME BEFORE REFILL TIME3 = TBE+REFILL/FDRTE ENDIF GAM0 = DRHO/PLOT(NPL,3) A = 2. * RHUK / (RAD * RK * GAMO * 9.807) DENSE = GAMo * (1. + 1./(A-1.) + *(1.-EXP((1.-A)‘RK*GAHO*9.807‘DEPTH))) IF(DENSE.GT.DMAX) DENSE = DMAX DENSC = GAMO!EXP(RX*GAM0*9.807*DEPTH) IF(DENSC.GT.DMAX) DENSC = DMAX DENS = (DENSE+DENSC)/2. AHR = DMAX/DENS - 1. C PRESEAL LOSS FOR ORIGINAL PLOTS CALL PRESEAL(NPL,SILTYP,EXPAR,DRHO) c FERMENTATION CHANGES FOR ORIGINAL PLOTS CALL FERMENT(NPL,AHR) C C 0000 00 00 209 INFILTRATION LOSS FOR ORIGINAL PLOTS BEFORE REFILL RI(NPL) = 0. CALL TOHER(USILO,RAD,NPL,NPSS,DENS,TIME3,T3B,RI(NPL),ASH) TIME3 = TIME3 - O.5FPLOT(NPL,2)/FDRTE IF(NPL.LE.NPSS-NPSTAR) THEN IT WAS A "REPLACED" PLOT, SO SIMULATE FEEDOUT ETC. CALL FEEDOUT(SILTYP,NPL,CSAF,FDTMP,FDRTE,DENS,ASH) IF(CROP.EQ.1.) THEN SDML = 1. - PLOT(NPL,14)/PLOT(NPL,2) HFEED(NSTR,NPL,1) = PLOT(NPL,14)/1000. HFEED(NSTR,NPL,2) = PLOT(NPL,5) HFEED(NSTR,NPL,3) = (HFEED(NSTR,NPL,3)-SDML)/ + (1.-SDML) HFEED(NSTR,NPL,4) PLOT(NPL,4) HFEED(NSTR,NPL,5) = 1./PLOT(NPL,3) - 1. HFEED(NSTR,NPL,8) = PLOT(NPL,6) ENDIF ENDIF DEPTH : DEPTH + (PLOT(NPL,2)/(PLOT(NPL,3)*DENS*CSA)) READY FOR NEXT PLOT TO BE CONSIDERED 250 CONTINUE ENDIF SIMULATE INFILTRATION FOR EITHER PLOTS IN THE SILO AFTER REFILL OR FOR A SILO THAT HAS NOT REFILLED. IF REFILLED: DO NOT CONSIDER TIME BEFORE EMPTYING FOR REFILLED PLOTS ELSE: TIME3 = TBE + CAP/FDRTE AS ABOVE IF(REFILL.GT.0.) TIME3 = CAP/FDRTE DEPTH = 0.5F(PLOT(NPSTAR,2)/(DRHOFCSA)) DO 280 NPL:NPSS,NPSS-NPSTAR+1,-1 DMAX = 1OOO./(1.-PLOT(NPL,3) + (PLOT(NPL,3)/1.5)) TIME3 = TIME3 - 0.5FPLOT(NPL,2)/FDRTE GAMO = DRHO/PLOT(NPL,3) A = 2. F RMUK / (RAD F RK F GAMO F 9.80?) DENSE = GAMO F (1. + 1./(A-1.) + F(1.-EXP((1.-A)FRXFGAMOF9.807FDEPTH))) IF(DENSE.GT.DMAX) DENSE = DMAX DENSC = GAMOFEXP(RRFGAMOF9.807FDEPTH) IF(DENSC.GT.DHAX) DENSC = DMAX DENS = (DENSE+DENSC)/2. AHR = DMAX/DENS - 1. IF((NPL.GT.NPSTAR).OR.(NPSTAR.EQ.NPSS)) THEN THESE PLOTS HAVE NOT BEEN CONSIDERED AT ALL YET. PRESEAL LOSS FOR REFILL PLOTS ONLY, OR IN SILO HHICH HAS NOT REFILLED CALL PRESEAL(NPL,SILTYP,EXPAR,DRHO) FERMENTATION CHANGES FOR REFILL PLOTS ONLY, OR IN SILO HHICH WAS NOT REFILLED CALL FERMENT(NPL,AHR) RI(NPL) : O. ENDIF INFILTRATION LOSS FOR ALL PLOTS REMAINING AFTER REFILL -- EFFECT OF PREVIOUS INFILTRATION IS TAKEN INTO CONSIDERATION BY THE RI(NPL) VALUE. CALL TOWER(USILO,RAD,NPL,NPSS,DENS,TIHE3,T3B,RI(NPL),ASH) 210 TIME3 = TIME3 - 0.5FPLOT(NPL,2)/FDRTE c STORAGE COMPLETED, SO SIMULATE FEEDOUT LOSS CALL FEEDOUT(SILTYP,NPL,CSAF,FDTMP,FDRTE,DENS,ASH) IF(CROP.EQ.1.) THEN SDML = 1. - PLOT(NPL,14)/PLOT(NPL,2) HFEED(NSTR,NPL,1) = PLOT(NPL,14)/1OOO. HFEED(NSTR,NPL,2) PLOT(NPL,5) HFEED(NSTR,NPL,3) = (HFEED(NSTR,NFL,3)-SDML)/(1.-SDML) HFEED(NSTR,NPL,4) = PLOT(NPL,4) HFEED(NSTR,NPL,5) = 1./PLOT(NPL,3) - 1. HFEED(NSTR,NPL,8) = PLOT(NPL,6) ENDIF DEPTH = DEPTH + 1.0*(PLOT(NPL,2)/(PLOT(NPL,3)*DENS*CSA)) 280 CONTINUE IF(CROP.EQ.2.) RETURN ELSE C C BUNKER SILO C SILTYP = 3 NFEED : 1O IBUNK : 1 DO 300 NFL = NPSS,1,-1 DENS = DRHO/PLOT(NPL,3) DMAX = 1000./(1.-PLOT(NPL,3) + (PLOT(NPL,3)/1.5)) AHR = DMAX/DENS - 1. C PRESEAL LOSS CALL PRESEAL(NPL,SILTYP,EXPAR,DRHO) FERMENTATION CHANGES CALL FERMENT(NPL,AHR) 300 CONTINUE C INFILTRATION LOSSES CALL BUNKER(CSA,USILO,NPSS,DRHO,FDRTE,TBE,NVS,ASH) C RESET SUMMING VARIABLES ANDF - O. ANPN : O. ACP : O. C AADF = O. C STXWT : O. C C 0 SWT = O. SPHXWT : O. SWETWT = O. FEEDOUT LOSSES AND SUM FOR WEIGHTED AVERAGE QUALITY DETERMINATION DO 330 IVS : 1,NVS CALL FEEDOUT(SILTYP,IVS,CSAF,FDTMP,FDRTE,DENS,ASH) IF(CROP.EQ.1.) THEN ANDF : ANDF + PLOT(IVS,14)*PLOT(IVS,4) ACP = ACP + PLOT(IVS,14)*PLOT(IVS,5) ANPN : ANPN + PLOT(IVS,14)*PLOT(IVS,5)*PLOT(IVS,6) AADF : AADF + PLOT(IVS,14)*PLOT(IVS,7) STXWT : STXWT + PLOT(IVS,14)*PLOT(IVS,8) SPHXWT : SPHXWT + PLOT(IVS,14)'PLOT(IVS,10) SWT : SWT + PLOT(IVS,14) 000 330 211 SHETHT = SHETHT + PLOT(IVS,14)/PLOT(IVS,3) ENDIF CONTINUE IF(CROP.EQ. NVSCS = RETURN ENDIF THEN 2 ) NVS C GET AVERAGE QUALITY IN THE BUNKER -- EACH PLOT TO HAVE THE AVERAGE C QUALITY SINCE YOU CANNOT EMPTY A BUNKER 1 PLOT AT A TIME 365 370 C (ICICICDCTCDCDCDCD CDC? DH = SHT/SHETHT AVCP = ACP/SWT AVNDF ANDF/SHT AVNPN ANPN/ACP A0101 0. DO 365 NFL ADIOI : CONTINUE AVDIGI = ADIGI/TMSTO(NSTR) SDML = 1. - SHT/(TMSTO(NSTR)F1OOO.) DO 370 NPL = 1,NPSS = 1,NPSS ADIGI + HFEED(NSTR,NPL,3)FHFEED(NSTR,NPL,1) HFEED(NSTR,NPL,1) = SHT/(FLOAT(NPSS)F1000.) HFEED(NSTR,NPL,2) = AVCP HFEED(NSTR,NPL,3) = (AVDIGI-SDML)/(1.-SDML) HFEED(NSTR,NPL,4) = AVNDF HFEED(NSTR,NPL,5) = 1./DH - 1. HFEED(NSTR,NPL,8) = AVNPN CONTINUE END OF BUNKER ENDIF CALL ENDSTR(NSTR,NPSS,IBUNK,NVS,NFEED) RETURN §§**§**§§**§HHNHNNHNHHHHNHHNMHNHHHHHHNGHINNHKKHHHHMHHKNKMMifiifiiifififiififi SUBROUTINE ENDSTR (NSTR,NPSS,IBUNK,NVS,NFEED) CNNHHHHMNNNNHNifiifiiiiflfififiiiNHNNNNMHINNRHHNNHNHHHKHHHHHHNHNHHNNNHKNNHNK GET AVERAGE QUALITY IN THE STRUCTURE IF WE ARE DEALING WITH ALFALFA MATRIX AFEED(3,26,33) CONTAINS DM (TOTAL T), CP (DEC), IVDMD (DEC) AND NDF (DEC) FOR ALL 4 STORAGE LOCATIONS. LOCATION 1 IS FIRST SILO, 2 IS SECOND SILO, 3 IS HIGH QUALITY HAY, 4 IS LOW QUALITY HAY. THE LAST 12 COLUMNS ARE RESERVED FOR DRY MATTER AND QUALITY OF CORN SILAGE, HIGH MOISTURE CORN AND DRY CORN GRAIN. DIMENSION FEEDLS(10) COMMON /SDATA/ PLOT(52,14) COMMON /W3/ HFEED(8,160,8) COMMON /Z7/ ALHRFD(26,20),AFEED(2,26,33),FEEDUT(26,14) COMMON /CTRL24/ BGNCUT(5),NTHYR,NTHCUT,NDAYSC,NDAYSH,YLD(4), QUAL(314),GDDCUM,METRIC,JYEARF,JYEARL,IPRT1,IPRT2,JDAYF, JDAYL,JPRT,NYRS,IPRT4,NCUTS,JYEAR,JLALHR,CPLANT,MGMT INTEGER*4 ILOSS(12),NYRS,JYEARF,IPRT1,MGMT,NCUTS COMMON/LOSS/ILOSS 212 INTEGER IBUNK DATA FEEDLS /0.0S,O.14,0.05,0.16,0.0S,0.05,0.0S,0.05,0.0§,0.0S/ C . C TOGGLE FOR SHUTTING OFF FEEDING LOSS IF(ILOSS(12).EQ.1) THEN DO 100 K:1,1O FEEDLS(K) = O. 100 CONTINUE ENDIF C C CALCULATE TOTAL DM, AVERAGE CP, BIASED STANDARD ERROR OF CP, AVERAGE C DIG AND BIASED STANDARD ERROR OF DIG. C SET ACCUMULATING TERMS TO ZERO FOR FIRST NSTR OF NS, TO FORMER VALUE IF C SECOND NSTR OF NS RESET = O. IF((NSTR.EQ.1).OR.(NSTR.EQ.2)) THEN KK = 1 IF(NSTR.EQ.2) RESET : 1. ELSEIF((NSTR.EQ.3).OR.(NSTR.EQ.4)) THEN KK = 6 IF(NSTR.EQ.4) RESET : 1. ELSEIF((NSTR.EQ.5).OR.(NSTR.EQ.7)) THEN KK : 11 IF(NSTR.EQ.7) RESET' ELSE KK = 16 IF(NSTR.EQ.8) RESET ENDIF IF(RESET.EQ.O.) THEN ANDF : O. ACP = O. ADIG O. ANPN O. TOTDM : O. WSSNDF : O. WSSCP = O. WSSDIG O. WSSNPN O. ELSE TOTDM : AFEED(1,NTHYR,KK) ANDF = AFEED(1,NTHYR,KK+3)*TOTDM. ACP = AFEED(1,NTHYR,KK+1)*TOTDM ADIG AFEED(1,NTHYR,KK+2)*TOTDM ANPN AFEED(1,NTHYR,KK+4).ACP IF(AFEED(2,NTHYR,KK+3).EQ.O.) THEN WSSNDF = O. ELSE WSSNDF : TOTDM*(AFEED(2,NTHYR,KK+3)**2) ENDIF IF(AFEED(2,NTHYR,KK+1).EQ.O.) THEN WSSCP : O. ELSE WSSCP : TOTDM*(AFEED(2,NTHYR,KK+1)**2) 1. H «._. o 213 ENDIF IF(AFEED(2,NTHYR,KK+2).EQ.O.) THEN WSSDIG : O. ELSE WSSDIG : TOTDH*(AFEED(2,NTHYR,KK+2)**2) ENDIF IF(AFEED(2,NTHYR,KK+4).EQ.O.) THEN WSSNPN : O. ELSE WSSNPN : ACP*(AFEED(2,NTHYR,KK+4)**2) ENDIF ENDIF IF(IBUNK.EQ.1) THEN C A BUNKER SILO: IN A BUNKER, PLOTS ARE NOT REMOVABLE ONE AT A TIME, C QUALITY IS AVERAGED AND EACH PLOT IS ASSIGNED THE AVERAGE QUALITY C ACCOUNT FOR FEEDING LOSS (NO CHANGE IN QUALITY) AT THIS TIME DO 590 J=1,NPSS HFEED(NSTR,J,1) : HFEED(NSTR,J,1) * (1.-FEEDLS(NFEED)) ADIG : ADIG + HFEED(NSTR,J,3)*HFEED(NSTR,J,1) 590 CONTINUE DO 600 IVS:1,NVS PLOT(IVS,14) = PLOT(IVS,14) ’ (1.-FEEDLS(NFEED)) TOTDM : TOTDM + PLOT(IVS,14)/1000. ACP : ACP + PLOT(IVS,5)‘PLOT(IVS,14)/1000. ANDF = ANDF + PLOT(IVS,4)*PLOT(IVS,14)/1000. ANPN : ANPN + PLOT(IVS,6)*PLOT(IVS,5)*PLOT(IVS,14)/1000. 600 CONTINUE AVCP : ACP/TOTDM AVDIG = ADIG/TOTDM AVNDF : ANDF/TOTDM AVNPN : ANPN/ACP DO 615 IVS=1,NVS WSSCP = WSSCP + PLOT(IVS,14)F(PL0T(IVS,5)-AVCP)FF2/1000. WSSNDF HSSNDF + PLOT(IVS,14)F(PL0T(IVS,4)-AVNDF)FF2/1OOO. WSSNPN HSSNPN + PLOT(IVS,14)FPLOT(IVS,5)* + (PL0T(IVS,6)-AVNPN)FF2/1000. 615 CONTINUE DO 620 J=1,NPSS WSSDIG = HSSDIG + HFEED(NSTR,J,1)F(HFEED(NSTR,J,3)-AVDIG)FF2 620 CONTINUE ELSE C NOT A BUNKER SILO DO 560 J:1,NPSS 0 ACCOUNT FOR FEEDING LOSS (NO CHANGE IN QUALITY) AT THIS TIME HFEED(NSTR,J,1) = HFEED(NSTR,J,1)F(1.-FEEDLS(NFEED)) TOTDH = TOTDM + HFEED(NSTR,J,1) ACP = ACP + HFEED(NSTR,J,2)FHFEED(NSTR,J,1) ADIG : ADIG + HFEED(NSTR,J,3)’HFEED(NSTR,J,1) ANDF = ANDF + HFEED(NSTR,J,4)*HFEED(NSTR,J,1) ANPN : ANPN + HFEED(NSTR,J,2)*HFEED(NSTR,J,1)* + HFEED(NSTR,J,8) 560 CONTINUE AVCP : ACP/TOTDM AVDIG : ADIG/TOTDM AVNDF : ANDF/TOTDM AVNPN : ANPN/ACP DO 580 J=1,NPSS WSSCP : WSSCP + HFEED(NSTR,J,1)‘(HFEED(NSTR,J,2)-AVCP)'*2 HSSDIG : WSSDIG + HFEED(NSTR,J,1)F(HFEED(NSTR,J,3)-AVDIG)FF2 HSSNDF = HSSNDF + HFEED(NSTR,J,1)F(HFEED(NSTR,J,4)-AVNDF)FF2 HSSNPN = WSSNPN + HFEED(NSTR,J,2)FHFEED(NSTR,J,1)F + (HFEED(NSTR,J,8)-AVNPN)FF2 580 CONTINUE ENDIF VARCP : AMAX1(O.,(HSSCP/TOTDM)) VARDIG = AMAX1(O.,(HSSDIG/TOTDM)) VARNDF = AMAX1(O.,(HSSNDF/TOTDM)) VARNPN = AMAX1(O.,(HSSNPN/ACP)) AFEED(1,NTHYR,KK):TOTDM AFEED(1,NTHYR,KK+1):AVCP AFEED(1,NTHYR,KK+2):AVDIG AFEED(1,NTHYR,KK+3):AVNDF AFEED(1,NTHYR,KK+4):AVNPN AFEED(2,NTHYR,KK+1):SQRT(VARCP) AFEED(2,NTHYR,KK+2):SQRT(VARDIG) AFEED(2,NTHYR,KK+3)=SQRT(VARNDF) AFEED(2,NTHYR,KK+4)=SQRT(VARNPN) C QUANTITY AND AVERAGE QUALITY IN STORAGE COMPUTED RETURN END C c NHCHHHHHHHMNHN*HNHi*fiiiH}H*HHiiiiiii*§*§§§*§§*HNHNNMHCHHQHMMHHHHHHHNHN - SUBROUTINE PRESEAL(NPL,SILTYP,EXPAR,DRHO) C §§§§************HifiiiiflifiiiiiifififiHNHNNHHH§**§*§§*K!****N§****§N*N§**i* C PRE-SEALING SURFACE LOSS (DML1) COMMON /SDATA/ PLOT(52,14) COMMON/LOSS/ILOSS INTEGERF4 ILOSS(12),SILTYP REAL MUTAU,MUMAX,KM,MUBAR,K C INITIALIZE EXPOSURE TIME AND STAGE 1 DRY MATTER LOSS EXPTME = PLOT(NPL,9) DML1 : O. IF(PLOT(NPL,1).EQ.1.) THEN C HAYLAGE MUMAX=4.8*PLOT(NPL,3) ELSE C CORN SILAGE MUMAX = 2.9’PLOT(NPL,3) ENDIF C FOR A BUNKER, THE DEPTH (THICK) IS THE TOTAL DEPTH SO FAR IF(SILTYP.EQ.3) THEN C BUNKER TOTDM : 0. DO 10 I : 1,NPL TOTDM : TOTDM + PLOT(I,2) 1O CONTINUE 215 ELSE C TOWER TOTDM : PLOT(NPL,2) ENDIF THICK = 1OO.FTOTDM/(EXPARFDRH0) D = .0086F(273.+PLOT(NPL,8))FF2 TAU .6667 RHO - 0.001FDRHO/PLOT(NPL,3) RHOMAX = 3./(3.-PLOT(NPL,3)) PHI = 1. - RHO/RHOHAX IF(PLOT(NPL,3).GT.0.693) THEN FD : 0.0384 ELSEIF(PLOT(NPL,3).GT.0.20) THEN FD = 1.93 - 5.46FPLOT(NPL,3) + 3.94FPLOT(NPL,3)FF2. ELSE FD = 1.0 ENDIF C C REPEAT WHAT FOLLOHS HHEN EXPTME > 1 DAY 0 40 IF(PLOT(NPL,8).GE.25.0) THEN FT = 1.0 ELSE FT = O.178FEXP(0.069FPLOT(NPL,8)) ENDIF FPH = (PLOT(NPL,10)-3.)/3.5 MUTAU = MUMAXFFDFFTFFPH DATA K,KM,FC,PSIA/9.0,O.055,O.756,0.21/ GAMMA = RHOFMUTAUF 0.0, THE STOCK OF FEED TYPE "LIMF" IS NOW 0.0 AND SOME ANIMALS ARE LEFT TO BE FED C C C C C C CDCJC) CDCJC) CDCICIC) CICICDCICICDCT (if) 236 ANIMAL(3,TYPE,1) = ANIMAL(3,TYPE,1) - FED IF (ANIMAL(3,TYPE,1).GT.O.0) STOR(LIMF,1) = 0.0 ACCMP = FEDFANIMAL(3.TYPE,7) + ACCMP NOTICE ANIMAL(3,TYPE,1) NOW REFERS T0 NUMBER LEFT TO BE FED, NOT THE TOTAL NUMBER IN SET "TYPE". ANIMAL(31A,B) SHOULD BE RESET FOR EACH YEAR SIMULATED ----- DETAILED OUTPUT 0F RATION INFORMATION ----- IF(IPRS.EQ.1) THEN WRITE(6,900) TYPE,FED,(RATION(I),I=1,11),(OTPT(I),I:1,4) 900 FORMAT(1X,I3,F8.1,11F8.1,4F8.3) ENDIF ----- END OF RATION DETAILED OUTPUT ----- IF (ANIMAL(3,TYPE,1).GT.0.0) GOTO 10 THIS "GOTO" IS FOR FEEDING THE LEFTOVER ANIMALS WHICH WERE NOT FED IF ANIMAL(3,TYPE,1) : 0.0, ALL ANIMALS WERE FED WITH NO SHORTAGE OF FEED ANIMAL(3,TYPE,7) : ACCMP/ANIM1(TYPE) RETURN END NHNNNNNNNNHNNNNHNNNNNK*********KNNNflifiifiifiiflifiifiiiiiiifiiiN********** SUBROUTINE FEED(ANIMAL,ANIH1,TYPE,STOR,STOR1,FAIF,FHAYIA;HAYUSE, .+ HLGUSE,CRNUSE) NCNNNNNKNNNNNNNNNNNNNNNNMNMNNNN*NKNNNNN**N****N§*§**NNNNNNNNNNNNNNNNN SUBROUTINE FEED ASSURES THAT THE ANIMALS ARE FED CORRECT FEEDS INTEGER HAYUSE,HLGUSE,CRNUSE,TYPE REAL STOR(11,S),STOR1(11),FAIF,FHAYIA,ANIMAL(3:6,7),ANIM1(6) COMPUTE CURRENT STOCKS HLGNOW : STOR(1,1)+STOR(2,1) HAYNOW = STOR(3,1)+STOR(4,1) GET ROUGH ESTIMATE OF ANNUAL RV RQMT BASED ON MAX FORAGE DIETS FOR THE HHOLE HERD I.E., FORAGE RQMT = SUM OVER GROUPS OF:. O.75(IC)(NUHBER IN GROUP)(365DAYS) FIC FACTOR HEIGHT 5 INTAKE FACTOR O.75*O.O1FANIMAL(3,2,5)FANIMAL(3,2,4)FANIM1(2)F365.F1. O.75FO.01FANIMAL(3,3.5)FANIMAL(3,2,4)FANIM1(3)F365.F1. 0.75FO.01FANIMAL(3,4,5)FANIMAL(3,2,4)FANIM1(4)F365.F.7 0.75FO.01FANIMAL(3.5.5)FANIMAL(3.5,4)FANIM1(5)F365.F1. 0.75FO.01FANIMAL(3,6,5)FANIMAL(3,6,4)FANIM1(6)F365.F1. ARV1 + 0.75FO.01FANIMAL(3,1,5)FANIMAL(3.1,4)FANIM1(1)F365.F1. ARV1 + + + + u + + + + n ARV2 + ROUGH ESTIMATE OF ALFALFA RQMT = FORAGE ARV - CORN SILAGE ARV 237 GR : (STOR(5,4) - 0.67)/(-O.33) CSRV = STOR1(5)*(STOR(5.4)-0.12*GR) ALFRV = ARV2 - CSRV C TRY TO GET RID OF ALL CORN SILAGE IF((HLGNOW+HAYNOW.EQ.0.).AND.(STOR(5,1).GT.O.)) THEN C ONLY CORN SILAGE ON THE FARM FAIF : 0. ELSEIF(STOR(5,1).GT.0) THEN C BOTH CORN SILAGE AND SOME ALFALFA ON THE FARM FAIF : AMAX1( 0., (1. - CSRV/ARV1) ) ELSE C NO CORN SILAGE ON THE FARM FAIF : 1. ENDIF BUT FOR FIRST STAGE COWS, DO NOT USE CORN SILAGE UNLESS IT IS THE ONLY ON-FARM FORAGE ( I.E., THERE IS NO ALFALFA ON THE FARM) IF((TYPE.EQ.1).AND.(FAIF.NE.0.)) FAIF : 1. CHOOSE PREFERRED ALFALFA QUALITY FOR THE ANIMAL TYPE GIVEN IF (TYPE.LE.3) THEN FEED HIGH QUAL ALFALFA TO MILKING COWS IQUAL : 1 ELSE C FEED LOW QUAL ALFALFA TO YOUNG HEIFERS, OLDER HEIFERS AND DRY COWS IQUAL = 2 ENDIF 0 000 00 SET ALFALFA USE FACTORS IF(HLGNOW.NE.0.0) THEN THERE IS SOME ALFALFA SILAGE IN STORAGE SET PREFERRED HAYLAGE TO BE USED HLGUSE : IQUAL IF HAYLAGE OF PREFERRED QUALITY IS DEPLETED, USE THE ALTERNATIVE IF((HLGUSE.EQ.1).AND.(STOR(HLGUSE,1).EQ.O.).AND. + (STOR(2,1).GT.0.)) HLGUSE : 2 IF((HLGUSE.EQ.2).AND.(STOR(HLGUSE,1).EQ.0.).AND. + (STOR(1,1).GT.0.)) HLGUSE : 1 C TRY TO GET RID OF ALL ALFALFA SILAGE HLGRV : (STOR1(1)*STOR(1,4) + STOR1(2)*STOR(2,4)) FHAYIA = AMAX1( 0. , (1.-HLGRV/ALFRV) ) 0 00 000 ELSE C NO HAYLAGE IN STORAGE, USE ONLY HAY C SET HLGUSE TO IQUAL TO AVOID ACCESSING STOR(O,?) HLGUSE : IQUAL FHAYIA : 1. ENDIF IF(HAYNOW.NE.0.0) THEN C THERE IS SOME ALFALFA HAY IN STORAGE C SET PREFERRED HAY TO BE USED 238 HAYUSE = IQUAL + 2 IF((HAYUSE.EQ.3).AND.(STOR(HAYUSE,1).EQ.0.).AND. + (STOR(4,1).GT.0.)) HAYUSE = 4 IF((HAYUSE.EQ.4).AND.(STOR(HAYUSE,1).EQ.O.).AND. + (STOR(3,1).GT.O.)) HAYUSE = 3 ELSE C NO HAY IN STORAGE, USE PURCHASED HAY HAYUSE = 10. ENDIF C C CORN USE PRIORITY: 1) ON FARM PRODUCED CORN GRAIN 2) PURCHASED CORN GRAIN C IF (STOR(7,1).NE.0.0) THEN CRNUSE : 7 ELSE CRNUSE : 9 ENDIF RETURN END C C *N!**N****§****N§§i*§§*§i*1!-‘Iii*N‘IN‘HNHN‘Ii1H*NNNI'NNHNHNN‘INl-iififii*****KM SUBROUTINE SINGLE(TYPE,ANIMAL,FAIF,FHAYIA,HAYUSE,HLGUSE, + CRNUSE,STOR,RATION,OTPT) c CHMHNHNHHHHHNHNHHNXHHHNH*NxxxxxxxxxxxxxxxxxxxxHHHHHHHHHNHHHHHHHHHHHH C THIS SUBROUTINE DETERMINES A FEED RATION FOR AN ANIMAL GROUP 0 (D.R. BUCKMASTER, SEPT. 1988) C IMPLICIT REAL (I-N) LOGICAL INFEAS - INTEGERF4 IPR5 INTEGER I,J,K,N,TYPE,NCOLS,HAYUSE,HLGUSE,CRNUSE,IFEED DOUBLE PRECISION DPM,RHS,OBJ,ZC INTEGERF2 INACT,ISACOL,IRWTY REAL NEL(7).CP(7),ESCF(7).NH3(7).FPD(5).DEGR(7),STOR(11.5) REAL NDF(7),OTPT(4),GOST(7).Rv(7).ADIP(7),ANIMAL(3.6,7),RATION(11) COHMON/LPDT/DPH(6,20),RHS(6),OBJ(20),ZC(20),INACT(6),ISACOL(6), + IRHTY(4O),INFEAS GOMMON/FDGOH/XLCOHS,AHRDAV,PFGH,BASEHT,XOHEIF,XYHEIF,PMILK, + PSOYM,PDST,PCORN,PALF,SCG,SHMC,SAFA,SGS,IFEED,IPR5,PRCE(11) CHARACTERISTICS OF AVAILABLE FEEDS WHERE FEEDS ARE: 1. ALFALFA SILAGE . ALFALFA HAY . CORN SILAGE . HIGH MOISTURE EAR CORN . DRY CORN . SOYBEAN MEAL . DISTILLERS GRAIN 0000000000 «1001an 10 DO 20 I = 1,7 IF(I.EQ.1) THEN J : HLGUSE ELSEIF(I.EQ.2) THEN 20 SET SET 00000000000 239 J : HAYUSE ELSEIF(I.GT.2.AND.I.LT.7) THEN J = I + 2 ELSEIF(I.EQ.7) THEN J = 11 ENDIF NDF(I) = STOR(J,4) IF(I.LE.2) THEN RV(I) = NDF(I) ELSEIF(I.EQ.3) THEN RV(I) = 1.249*NDF(I) - 0.154 ELSE RV(I) = 0. ENDIF NEL(I) = STOR(J,3) CP(I) : STOR(J,2) IF((I.EQ.1).0R.(I.EQ.3)) DEGR(I) : 0.5 + 0.5*STOR(J,5) IF(I.EQ.2) ADIP(I) : STOR(J,5) CONTINUE "COSTS" TO MAXIMIZE FORAGE USE DATA (COST(I),I=1,7)/0.0,0.0,0.0,0.0,1.0,2.2,1.6/ IF(STOR(6,1).GT.0.0) THEN COST(4):0. ELSE COST(4)=900. ENDIF RELATIVE UNIT COSTS USING INPUTS OF PRCE ARRAY READ IN FARM FILE GOST(1) = PRCE(HAYUSE) GOST(2) = PRGE(HLGUSE) COST(3) = PRCE(5) IF(STOR(6,1).GT.O.) THEN GOST(4) = PRCE(6) ELSE GOST(4) = 900. ENDIF IF(STOR(7,1).GT.0.) THEN GOST(S) = PRCE(7) ELSE COST(5) = PRCE(9) ENDIF COST(6) = PRCE(3) COST(7) = PRCE(11) DEGRADABILITY OF NON SILAGES AND ADIP OF NON-HAY AS CONSTANTS DATA DEGR(2),DEGR(4),DEGR(5),DEGR(6),DEGR(7)/O.7O,O.44,O.48, + 0.65.0.47/ DATA ADIP(1),ADIP(3).ADIP(4),ADIP(5),ADIP(6),ADIP(7) + /0.05,0.059,0.09,0.02,0.0062,0.087/ DO 40 I = 1,7 NH3(I) = CP(I)*DEGR(I) C 240 ESCP(I) = CP(I)-NH3(I)-CP(I)*ADIP(I) 40 CONTINUE C CALCULATE FORAGE NUTRIENT CONCENTRATION 0000 45 NDFF = FAIF*FHAYIA*NDF(2) + FAIF*(1.-FHAYIA)*NDF(1) + + (1.0-FAIF)FNDF(3) RVF = FAIFFFHAYIAFRV(2) + FAIFF(1.-FHAYIA)FRV(1) + + (1.0-FAIF)FRv(3) NELF = FAIFFFHAYIAFNEL(2) + FAIFF(1.-FHAYIA)FNEL(1) + + (1.0-FAIF)FNEL(3) ESCPF = FAIFFFHAYIAFESCP(2) + FAIFF(1.-FHAYIA)FESCP(1) + + (1.-FAIF)FESCP(3) NH3F = FAIFFFHAYIAFNH3(2) + FAIFF(1.-FHAYIA)FNH3(1) + + (1.0-FAIF)FNH3(3) COSTF : FAIFFFHAYIAFCOST(2) + FAIFF(1.-FHAYIA)FCOST(1) + + (1.0-FAIF)FGOST(3) CPF = FAIFFFHAYIAFCP(2) + FAIFF(1.-FHAYIA)FCP(1) + + (1.0-FAIF)FCP(3) ETERMINE REQUIREMENTS FOR RHS OF LP. FLEAD IS LEAD FACTOR FOR SET- ING NUTRIENT CONCENTRATION RICHER THAN NECESSARY FOR THE AVERAGE COW MILK = ANIMAL(3,TYPE,2) IF(TYPE.EQ.1) THEN FLEAD : 1.12 ELSE FLEAD = 1.07 ENDIF C INGESTIVE CAPACITY IF(TYPE.LE.4) THEN IO = O.O1FANIMAL(3,TYPE,5)FANIMAL(3.2,4) ELSE IC = O.O1FANIMAL(3,TYPE,5)FANIMAL(3,TYPE,4) ENDIF IF(TYPE.EQ.3) THEN DPREG = 130. ELSEIF(TYPE.EQ.4) THEN DPREG = 250. ELSEIF(TYPE.EQ.5) THEN DPREG = 180. ELSE DPREG = O. ENDIF C ENERGY (NEL FOR LACT COWS, ME FOR GROWING HEIFERS) 50 IF(TYPE.LE.4) THEN NELM = 0.08*ANIMAL(3,TYPE,4)**.75 IF(DPREG.LT.210.) THEN NELP : 0. ELSE NELP : 0.024'ANIMAL(3,TYPE,4)**.75 ENDIF IF(ANIMAL(3,TYPE,6).GT.O.) THEN NELG = 5.12FANIMAL(3,TYPE,6) ELSE NELG : 4.92*ANIMAL(3,TYPE,6) ENDIF NELL :(O.3512 + 0.0962'ANIMAL(3,TYPE,3))*MILK*FLEAD NELR : NELM + NELL + NELP + NELG MMNT : NELR/NELM C RATHER THAN ADJUST FEED ENERGY CONTENT, ADJUST REQUIREMENT FOR MMNT C NELREQ : NELR * 0.92/(1.-0.04*(MMNT-1.)) C ABSORBED PROTEIN REQUIREMENT C FECAL NITROGEN CONTRIBUTION C BCP = 6.25 F (-.03093 + .O1145FNELR) BCP = 6.25FO.O1FNELR RAPREQ = BCP/0.9 RELMBS = (ANIMAL(3,TYPE,4)FF0.75)/(6OOFFO.75) XMIN = 1O.FRELMBSFO.74/(O.3152+O.O962FANIMAL(3.TYPE,3)) NELDD = 1.42 + 0.01F(MILK-XMIN)FRELMBSFO.74/ (O.3512+O.0962FANIMAL(3.TYPE,3)) IF(MILK.EQ.O) THEN ELSE NELD : 1.25 ELSE NELD = AMIN1(1.72,AMAX1(1.42,NELDD)) ENDIF ATDN = O.92F(NELD+O.12)/2.45 PDMI = NELR/NELD IDM = PDMIF(1.-ATDN) RELLW = ANIMAL(3,TYPE,4)/8OO. HED = AMAX1( (AMIN1(2.67,(2.804-1.072FRELLH))) . 2.0 ) NEMD = 1.37FMED - 0.138FMEDFF2 + 0.0105FMEDFF3 - 1.12 NEGD = 1.42FMED - 0.174FMEDFF2 + 0.0122FMEDFF3 - 1.65 NEMR = 0.086FANIMAL(3,TYPE,4)FF.75 NEGR = O.O35FANIMAL(3,TYPE,4)FF.75FANIMAL(3,TYPE,6)FF1.119 + ANIMAL(3,TYPE,6) PDMI NEMR/NEMD + NEGR/NEGD MEREQ : MED*PDMI DED = (MED+0.45)/1.01 ATDN = O.92F(DED/4.4O9) BTDN = DED/4.409 BCP = 6.25 F (-.03186 + .02612FBTDNFPDMI) BCP : 6.25*0.0230*BTDN*PDMI RAPREQ : BCP/0.9 IDM = PDMI*(1.-ATDN) ENDIF FPN = 0.090’IDM C MAINTENANCE NITROGEN CONTRIBUTION MNTP : 0.0002*ANIMAL(31TYPE,4)**0.6 + 0.00275'ANIMAL(3,TYPE,4)**0.5 C CONTRIBUTION FROM CHANGE IN BODY WEIGHT IF(TYPE.LE.4) THEN 0 RPN . I AMAX1(-.1875,.256*ANIMAL(3,TYPE,6)) DPA ELSE RPN = ANIMAL(3,TYPE,6)F(O.211 - O.0262FNEGR/ANIMAL(3,TYPE,6)) DPA = O. ENDIF C CONCEPTUS PROTEIN CONTRIBUTION IF(DPREG.GE.21O.) THEN YPN = 0.001136FANIMAL(3.TYPE,4)FF.7 ELSE YPN : 0. ENDIF C MILK PROTEIN CONTRIBUTION LPN = (.019 + O.OO4FANIMAL(3,TYPE,3))FFLEADFMILK C C ABSORBED PROTEIN REQ. APREO = FPN + MNTP/.67 + RPN/.65 + YPN/.5 + LPN/.7 + DPA G G SET CONSTRAINT TYPES IRHTY(1) IRWTY(2) IRHTY(3) IRHTY(4) IRWTY(5) IRHTY(6) II H H H H II dUOUJNUJ-A C C ZERO OUT LP VARIABLES DO 80 I = 1,6 RHS(I) = 0. DO 60 J: 1, 20 DPM(I, J) : OBJ(J)= 0.0 60 CONTINUE 80 CONTINUE C C SET OBJECTIVE FUNCTION, LP MATRIX AND RHS OBJ(1) : -COSTF DPM(1,1) : NDFF DPM(2,1) = RVF - 0.75'NDFF IF(TYPE.LE.4) THEN C DPM(4,1) : 0.046’NELF + 0.95*ESCPF ELSE DPM(3, 1) = 1. 65’NELF C DPM(4, 1) = 0. 02345*(1. 65*NELF+. 45) + 0. 95*ESCPF ENDIF DPM(4,1) : NH3F + 0.15*CPF C DPM(591) : 0.64*0.9*(NH3F+0.15*CPF) + 0.95*ESCPF DPM(5,1) = 0.95*ESCPF DPM(6,1) = CPF DO 100 I = 2,5 J = I+2 OBJ(I) = -COST(J) DPM(1,1) = NDF(J) DPM(2,I) RV(J) - 0.75FNDF(J) IF(TYPE.LE.4) THEN 00 00000 243 DPM(3,I) = NEL(J) DPM(4,1) = O.O458FNEL(J) + O.95FESCP(J) ELSE DPM(3,I) = 1.65FNEL(J) DPM(4,1) = 0.02345F(1.65FNEL(J)+.45) + O.95FESCP(J) ENDIF DPM(4,1) = NH3(J) + 0.15FCP(J) DPM(5,I) = O.64FO.9*(NH3(J)+O.15FCP(J)) + O.95FESCP(J) DPM(5,I) = O.95FESCP(J) DPM(6,I) = CP(J) 100 CONTINUE RHS(1) = IC RHS(2) = O. IF(TYPE.LE.4) THEN RHS(3) = NELREQ RHS(4) = APREQ + 0.1237 ELSE RHS(3) = MEREQ RHS(4) = APREQ + 0.1274 ENDIF RHS(4) : RAPREQ RHS(S) = APREQ RHS(S) APREQ - 0.576FRAPREQ RHS(6) 100. CALL LP ROUTINES TO COMPUTE RATION NCOLS : 5 DO 120 I = 1,6 CALL ROWSET(I,NCOLS) 120 CONTINUE CALL LPSOL(6,NCOLS) IF INFEASIBLEzTRUE, IT COULD NOT BALANCE RATION, THEREFORE, IF LACTATING COW, TAKE CORN SILAGE OUT OF DIET THEN REDUCE MILK PER DAY IF NECESSARY. IF(INFEAS) THEN IF(TYPE.LE.3) THEN IF(FAIF.LT.1.) THEN FAIF : 1. ELSE MILK = MILK'0.985 ENDIF GOTO 50 ELSE ‘ WRITE(*,*) 'INFEASIBLE RATION FOR GROUP',TYPE ENDIF ENDIF ADMI : 0. DO 140 N = 1,5 FPD(N) : 0. 140 CONTINUE DO 160 N = 1,6 C C WITH NUTRIENT DENSITY RICHER THAN NEEDED FOR THE AVERAGE ANIMAL, FOLLOW C FEED DISAPPEARANCE FOR AVERAGE ANIMAL IN GROUP. I.E., REMOVE LEAD FACTOR C EFFECT FOR LACTATING ANIMALS TO DETERMINE FEED USE. C 0000000 244 IF(INACT(N).LE.5) THEN FPD(INACT(N)) = RHS(N) ADMI : ADMI + FPD(INACT(N)) ENDIF 160 CONTINUE 170 ATION INFORMATION FOR OUTPUT -- ARRAY "OTPT" R (1) (2) 13) (4) 200 IF(TYPE.LE.3) THEN NELR2 : NELR - NELL + NELL/FLEAD NELR2 * 0.92/(1.-0.04*(MMNT-1.)) APREQ - LPN/.7 + (LPN/FLEAD)/.7 AMAX1((NELRE2/NELREQ),(APREQ2/APREQ)) NELRE2 APREQ2 FACTOR DO 170 I : 1,5 FPD(I) = FACTOR*FPD(I) CONTINUE ADMI : FACTOR’ADMI ELSEIF(TYPE.EQ.4) THEN NELRE2 : NELREQ APREQZ = APREQ ELSE APREQ2 = APREQ ENDIF MILK/DAY NEL CONTENT OF DIET NDF CONTENT OF DIET CP CONTENT OF DIET DO 200 I = 1,11 RATION(I) = 0. CONTINUE RATION(HLGUSE) RATION(S) = FPD(1)F(1.-FAIF) NELID = FPD(1)FNELF NDFID = FPD(1)FNDFF CPID = FPD(1)FCPF ESCPID = FPD(1)FESCPF NH3ID : FPD(1)FNH3F IF(FPD(2).GT.O)THEN RATION(6)=FPD(2) ENDIF IF(FPD(3).GT.O)THEN RATION(HAXO(7,CRNUSE)):FPD(3) ENDIF RATION(8)=FPD(4) RATION(11)=FPD(5) DO 220 K = 2,5 L : K+2 FPD(1)*FAIF*(1.-FHAYIA) RATION(HAYUSE) - FPD(1)FFAIF*FHAYIA CICICDCICDCIC) 245 NELID = NELID + FPD(K)FNEL(L) NDFID = NDFID + FPD(K)FNDF(L) CPID = CPID + FPD(K)FCP(L) ESCPID = ESCPID + FPD(K)FESCP(L) NH3ID = NH3ID + FPD(K)FNH3(L) 220 CONTINUE OTPT(1) = MILK OTPT(2) = NELID/ADMI OTPT(3) = NDFID/ADMI OTPT(4) = CPID/ADMI ANIMAL(3,TYPE,7) = MILK RETURN END NMNNNNQNNNHNNNHN*Nifi*0!NNH*NHN'NHNHNNNNNNHNHNNNNNNNONNNNNMNNNNNNNNNN LINEAR PROGRAMMING ROUTINES -- Version 4.1 AUTHOR: S. B. Harsh, Mich. State Univ. LAST DATE OF REVISION: April 24, 1986 REVISED TO MEET FORTRAN 5 ABILITIES JANUARY 1987 BY D. BUCKMASTER C Nfifiiifiifii***§**********Nii*fiINNNNNNNNNNNKHNNNHNNNNNNNNHNHNNNNNNNNNNN CH C. CH C! CH CH C! C. CC C’ COMMENT LINES ADDED BY D.R.B. 1/87 FOR CLARITY OF THE PROGRAMS SUBROUTINE ROWSET SETS UP AUXILIARY MATRIX SUBROUTINE LPDMP PRINTS THE MATRIX EACH ITERATION IF DESIRED SUBROUTINE LPSOL SOLVES THE PROBLEM AND PRINTS THE FINAL SOLUTION IF PROBLEMS OCCUR (I.E., NO FEASIBLE SOLUTION, EXCEEDS MAXIMUM NUMBER OF ITERATIONS, OR UNBOUNDED PROBLEM) LPSOL WILL POINT THIS OUT. SOLUTION IS DONE USING SIMPLEX METHOD. PARTIAL GLOSSARY: DPM(I,J) : DOUBLE PRECISION MATRIX OF COEFFICIENTS (COMMONLY REFERRED TO AS A(I,J) INACT(I) - ACTIVITIES IN THE SOLUTION INFEAS = INDICATES WHEN PROBLEM IS INFEASIBLE (INFEAS=TRUE) IRWTY(I) = TYPE OF INEQUALITY CORRESPONDING T0 ROW I IRWTY(I) : 1 IMPLIES LESS THAN OR EQUAL TO IRWTY(I) = 2 IMPLIES EQUAL TO IRWTY(I) : 3 IMPLIES GREATER THAN OR EQUAL TO ISACOL(J) : ACTIVITY RECENTLY REMOVED FROM THE SOLUTION JCOL : NUMBER OF COLUMNS IN ORIGINAL FORMULATION (CHANGES WHEN AUXILIARY MATRIX IS FORMED MXITER : MAXIMUM NUMBER OF ITERATIONS TO CONSIDER DOING NOROW : NUMBER OF ROWS IN THE PROBLEM FORMULATION NOCOL = NUMBER OF COLUMNS IN THE AUXILIARY FORMULATION OBJ(J) = COEFFICIENT IN THE OBJECTIVE FUNCTION FOR EACH VARIABLE OBJV : OBJECTIVE FUNCTION VALUE RHS(I) = RIGHT HAND SIDE CONSTRAINTS FOR ROW I IN THE SOLUTION RHS(I) : LEVEL OF ACTIVITY FOR INACT(I) C CHNNNNHHNNNHiNRCCNNNNNHKN}!NNfiii*NNNNNNNNNN§§§§iifiNNNHKNKNNNNNNNNNKN SUBROUTINE ROWSET(I,JCOL) 246 C N§§N§*§§************§***NNH-N‘I'K!Kifliiflfiiiifiiiifl’fl’iif§*****§*§*****I*** C Sets up rows, slacks, and artificials COHHON/LPDT/DPH(6,20),RHS(6),OBJ(20),ZCF20),INACT(6),ISACOL(6), + IRWTY(40),INFEAS DOUBLE PRECISION DPM,RHS,OBJ,ZC INTEGERF2 INACT,ISACOL,IRWTY LOGICAL INFEAS JCOL:JCOL+1 INACT(I):JCOL ISACOL(I):JCOL IF(RHS(I).GE.0.0) THEN IF(IRHTY(I).LT.1) THEN WRITE(*,890) I ELSEIF(IRWTY(I).EQ.1) THEN DPM(I,JCOL) : 1. OBJ(JCOL) = 0.0 ELSEIF(IRWTY(I).EQ.2) THEN DPM(I,JCOL) : 1. OBJ(JCOL) = -9.*10.**9 ELSEIF(IRWTY(I).EQ.3) THEN DPM(I,JCOL) : 1. OBJ(JCOL) = -9.*10.**9 JCOL = JCOL+1 DPM(I,JCOL) : -1. OBJ(JCOL) : 0.0 ELSE WRITE(*,890) I ENDIF ELSE WRITE(*,890) I ENDIF RETURN 890 FORMAT(1X,//,1X,'ERROR -- Wrong RHS values for row, ',I4, + ', Program stopped in subroutine ROWSET.') END C NHHNHHNNHHHHNNHaNHHNHHXHXHNHHNHNHHNHHHHNHHNHNHNHNHHHNHNHMCHHHHHHHNXH SUBROUTINE LPSOL(NOROW,NOCOL) C HHNNNNNNHNNHNHUNNHNHHNHNHCHNHHHHHNHNHHXXNTHNHHNHHNHNHNHNHHHNXHNHNHHN C *** Solves the LP problem COHMON/LPDT/DPH(6,20),RHS(6),OBJ(20),ZC(20),INACT(6),ISACOL(6), + IRWTY(40),INFEAS DOUBLE PRECISION DPH,RHS,OBJ,ZC INTEGER*2 INACT,ISACOL,IRWTY LOGICAL INFEAS C DOUBLE PRECISION COLKEY(40),X,Z,ZCHX,OBJV,EPISP,RHIN,R,R1,R2 INFEAS = .TRUE. C HRITE(*,980) C 980 FORMAT(1X,8(/),1X,'START L. P. SOLVE ') X=10.**8 EPISP:1./X MXITER:4*NOROW + 1 00000 247 DO 200 NOITER : 1,MXITER JZCMX : O ZCMX : 0.0 DO 50 J:1,NOCOL 40 Z = 0.0 DO 40 I : 1,NOROH z = z + DPM(I,J)FOBJ(INACT(I)) CONTINUE ZC(J) : Z - OBJ(J) IF((ZC(J).LT.0.0) .AND. ((ZC(J)-ZCHX).LT.0.0)) THEN ZCMX : ZC(J) JZCMX : J ENDIF 50 CONTINUE IF(JZCMX.LE.0) THEN 910 FORMAT(' 960 FORMAT(' 60 70 IF(NOITER.LE.1) THEN HRITE(F,91O) BAD MATRIX') RETURN ENDIF X = -9.F1O.FF9 DO 60 I = 1,NOROW J = INACT(I) IF((RHS(I).NE.O.O).AND.(OBJ(J).EQ.X)) THEN HRITE(F,96O) THERE ARE NO FEASIBLE SOLUTIONS’) RETURN ENDIF IF(IRWTY(I).NE.1) THEN J : ISAGOL(I) ZC(J) = ZC(J) + X ENDIF CONTINUE OBJV = 0.0 DO 70 I = OBJV CONTINUE HRITE(*,990) NOITER,OBJV 1,NOROW : OBJV + RHS(I)*OBJ(INACT(I)) 990 FORMAT(1X,//,1X,'+',19('--'),'+',/,1X,'l',38X,'l',/, A 1X,'| OPTIMAL SOLUTION (',I3,‘ Iterations) l',/, B 1X,'| OBJECTIVE FUNCTION = ',F15.4,' I',/, C 1X,'|',38X,'I’,/,1X,'+',19('--'),'+') ELSE INFEAS : .FALSE. RETURN RMIN = 99.*10.**8 NKR = 0 DO 90 I = 1,NOROW IF(DPM(I,JZCMX).GT.0.0) THEN R : RHS(I)/DPM(I,JZCMX) IF(R.LT.RMIN) THEN RMIN = R NKR = I 248 ELSEIF(R.EQ.RMIN) THEN DO 80 J = 1,NOGOL R1 = DPM(NKR,J)/DPM(NKR,J2CMX) R2 = DPM(I,J)/DPM(I,JZCMX) IF(R2.LT.R1) THEN NKR = I GO TO 90 ELSEIF(R2.GT.R1) THEN GO TO 90 ENDIF 80 CONTINUE G HRITE(F.930) NKR,I C 930 FORMAT(' CYCLING HAS OCCURED AT',2X,I4,2X,15) RETURN ENDIF ENDIF 90 CONTINUE IF(NKR.LT.1) THEN C HRITE(F,950) JZCHX C 950 FORHAT(' UNBOUNDED SOLUTION,ACTIVITY',2X,IS) RETURN ENDIF INACT(NKR) = JZCHX DO 100 I = 1,NOROW COLKEY(I) = DPM(I,JZCMX) 100 CONTINUE DO 110 J = 1,NOGOL DPM(NKR,J) = DPM(NKR,J)/COLKEY(NKR) 110 CONTINUE RHS(NKR) : RHS(NKR)/COLKEY(NKR) DO 130 I : 1,NOROW IF(I.NE.NKR) THEN RHS(I) = RHS(I) - RHS(NKR)FCOLKEY(I) IF(GOLKEY(I).NE.0.0) THEN DO 120 J = 1,NOGOL IF(DPM(NKR,J).NE.O.O) THEN DPM(I,J) = DPM(I,J) - DPM(NKR,J)FCOLKEY(I) IF(ABS(DPH(I,J)).LE.EPISP) DPM(I,J) = 0.0 ENDIF 120 CONTINUE ENDIF ENDIF 130 CONTINUE ENDIF 200 CONTINUE C HRITE(F,920) G 920 FORHAT(' NO. ITERATIONS EQUAL MAXIMUM') RETURN END APPENDIX C Formulated rations and feed use data used to determine alfalfa value as presented in Chapter 6. 249 250 Formulated rations with varying alfalfa quality, forage type and cow size. Table C.1 RATION (kg dm/day) AHAY GS CG SBM DST ASIL STG OF MPD LACT. CP NDF CASE MIXED FORAGE DIETS (600 kg COW): 8873029382866073100889 17u72725u3172809876u32 O O OOOOOOOOOOOOOOOOOOOO 1002211033322132222222 9560576800094700000000 689680260001u600000000 1110011100000000000000 10707uu6u290777u052268 859208526521851107u‘76u ...................... 78867789456778u5667u56 953u605385560669657707 876209766u209796u2097u ...................... 2223322233332233333333 054..“606385571660668818 976209766u209797u2097u 2223322233332233333333 OSHH606385571660668818 976209766u209797u2097u 2223322233332233333333 2292222122222222222222 55u5555555555555555555 3333333333333333333333 1| 11111111 11111 11111 111 1&7581H792581u69258692 SSSHMH5553MHMHH5533MHHMHBBH 0000000000000000000000 2225555588888811111444 1111 11111 11111122222222 0 o o o o ooooooo o o o ....... 00000000000000 uuH01181770u086839 9H05260620976475u2 ...... O O O O O O O O O O O 0 111332213322222222 13.4 0358000 00000 1I..!el0000nU0nUnunannUnunvnv0 183u8526230855u637 1962I1077886u113u3 .................. 667” 567723n56723u5 3506“”592375u673u9 6u3086u263086u9630 O OOOOOOOOOOOOOOOOO 333.19 3333nuu3334un4 3507“”603u85577u59 6H3086n363086u9630 ........ O O O O O O O O O 0 333u3333uuu333uuuu 3507“”603” 5577.“.59 6u3086n363 86u9630 111111111111111111 333333333333333333 22222222222229.2222 1.47581471925814ng5 000000000000000000 22255555888888.1111 11111111111111.2222 o o o o ccccc o o o o OOOOOOOOOOOOOOOOOO 1 2 3 u 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1.89 1.78 0.00 3.86 3.86 3.86 6.22 0.00 2.17 4.99 4.99 4.99 2.83 0.00 2.02 4.66 4.66 4.65 4.01 4.36 4.36 4.36 5.04 0.00 251 31.8 31.8 31.8 31.8 0.48 0.24 0.36 0.24 0.39 0.24 0.42 0.21 W m N & 3156778000770500527009 1955642100198600842007 O O O O O O O O O O O O ..... O O O O 3221222200211100011000 0097000043000075700720 fi£080£0065000021400760 . C O ..... C .......... 0000000011000011000000 5700986600851600082006 3870554200122000089004 ........ O O O O O O O O O O O O O 0120012300012300001000 6440275598138699434916 A200742002074202307253 ..................... 4445444455544455554555 7451385599249600535027 4200742002074213307353 ...................... 4445444455544455554555 7&513855992u9600535027 4200742002074213307353 u u u 5 u u u u 5 5 5 u u u 5 5 5 5 u 5 5 5 55u5555555555555555555 OOOOOOOOOOOOOOOOOOOOOO 9999999999999999999999 11 11111111111111111111 3333333333333333333333 1u7581u792581u69258692 555nu5553uuu5533uu‘u33u OOOOOOOOOOOOOOOOOOOOOO 2225555588888811111uuu 1111111111111122222222 OOOOOOOOOOOOOOOOOOOOOO 123u56789 W H 12 B m m w W m w 20 95.2018u0002862007901 8698815151091098155“ O O O O O O O O O O O O O O O O O O O O 00100033200033221222 94687909951200008000 891769O1u99200009000 oooooooooooooooooooo 11111100011100000000 7012u1725u87367825u3 5841893221.“. 5287523320 ...... O O I O O O O O O O O O O I 88888967899956789678 u395u31905n382015931 87098752198775319753 oooooooooooooooooooo 22322233322233332333 “306u32006u3831169u2 87198753198775319753 .................... 22322233322233332333 “306u32006u3831169u2 87198753198775319753 658765888766 887 333333333333 333 1u581u92581u69258692 OOOOOOOOOOOOOOOOOOOO 22555588888811111uuu 11111111111122222222 ooooooooooo o o o o o o o o o OOOOOOOOOOOOOOOOOOOO 1.70 1.34 1.29 1.40 0.24 3.18 3.75 3.75 3.75 6.40 0.43 2.69 3.59 3.59 3.58 6.74 3.44 3.44 3.43 7.22 3.94 3.94 3.94 5.71 32.9 32.2 34.5 $9 0.12 0.51 0.12 0.54 0.15 0.45 0.15 0.48 8 w 5 fl 252 2323606036185198 3931986586532198 2133222222222211 59000000000 0000 oooooooooooooooo 86072637u26720uu‘ 831117.]“57763233 67" 566773u567u56 8380558319165312 5nu2975u8u297852 oooooooooooooooo 33au3333nun33uuu 9u80559u20176u13 5nu2975u85297852 33uu3333uuu33uu‘u 9u80559u20176u13 suu2975u85297852 33uu3333uuu33uuu 1..“55513555552555 32uuuu32uuuuuuuu 3333333333333333 2222222222222222 OOOOOOOOOOOOOOOO 83383109098900u71025 1907530630860075u089 32322201221100111000 0000005300007u000u70 00000075000032000810 00000010000011000000 3882910061630015800” 29118600885200u5u001 11011200012300012001 3326u3uu37fl-‘uu5658637 ”296u23296u235296562 oooooooooooooooooooo unuuuussuunusssuusss u327uuu54855566596u8 “296u23296u235296562 uuuuuussuuflflu‘sssuusss u327uuu5u855566596u8 ”296u23296u235296562 33333333333333333333 1&581u92581u69258692 55nu553uuu5533uuu33u OOOOOOOOOOOOOOOOOOOO 22555588888811111uuu 11111111111122222222 .................... 00000000000000000000 23 2a 25 26 27 28 29 3O 31 32 33 3Q 35 36 37 38 39 H0 u1 “2 95990256089 519696315n~uh 01010001001 15720996uu6 06507660676 21111111110 9u3782nfi3200 758671.“.2373 88888999999 8728nfl728u72 03205320532 ........... 33333333333 982958295 2 032053205 2 33333333333 98295829582 03205320532 00000000000 0.35 0-39 O “2 0.15 0.18 O 18 ’43 an "5 253 759397fl2 98653109 ....... 0 22222221 00000000 00000000 0 O O O O C O 0 00000000 94112703 24059171 ........ 65666667 3163326“ 96319631 3uuu3uuu H274u375 96319631 3Huu3uuu “2733375 96319631 ........ 3uflu3fluu “H8 76 58 76 33333333 22222222 Kafiuoaabzafivoao. H.QJQfiuhquqan 00000000 004:.I.I.INHMHHH .Iadndadndndnzad 5682052083”. £1530u86020 .......... 30220111011 0u008900700 nU.70nN-“.300800 ......... 01001000000 “0250098095 20090076055 ........... 00000001001 120273131u2 95296529952 “554555“.555 231383.24153 95296529952 ussusssusss 23138u2u153 95296529952 “SSH—355.4555 59256925692 u3uu33uu33u 00000000000 58881111uuu 11112222222 ALFALFA HAY BASED DIETS (600 kg cow): 0005901001uu643u621121 20006u1037387u0u195813 0001000011100001100000 36379770786193u000308m 1110111100000010000000 0382u3097126u958506700 “66u7922u69235u0u7983u 9999990099900000000011 11 11111111111 00000000000000 000 0 0000000000000000000000 00000000000000000 0 0 0000000000000000000000 7197:“7092377601237712“. 39“.“. 39u91u839891u8891 8779887700988710098100 11 111 111 1u7581u792581u69258692 0000000000000000000000 2225555588888811111uu.“ 1111111111111122222222 ooooooooooo o o o o o o o o 0000000000000000000000 “567890123n567890123u5 5555556666666666777777 5206238 7300505 ....... 0002110 7.110222 2u502u6 o o ccccc 1110000 1059307. 0602061 8897889 00 0 00 0000000 0000000 0000000 0000000 2839539 5830258 ....... 0992109 1 111 1..“7581“. 555.4,“.55 0000000 2225555 1111111 ....... 0000000 ”567890 5555556 0000000000000000000000 000000000000000 ALFALFA SILAGE 76 77 78 79 80 81 82 83 84 85 86 87 000000000000 .12 .12 .12 .15 .15 .15 .15 .15 .18 .18 .18 .18 000000000000 0000000000000000000000 000000000000000 BASED DIETS (600 .51 .54 .57 .15 .48 .51 .54 .57 -39 .42 .45 .48 NNNNNNNNNNNNNNN wwwwwwwwwwwwwwwwwwwwww _b-D-hd-D-l-‘d—D—D-‘d N .3 o o o o o o o o o o o o o o o o o o o ‘Oomomflfiom-‘mO‘O‘O‘QWQQU‘m—id “CONWOO‘NQ‘QONQ‘A’: 35.4 34.3 33.2 38.3 36.9 35.6 34.5 33.4 112.0 110.2 38.5 37.1 254 9.34 14.27 13.09 12.11 11.26 10.54 9.90 15.69 14.31 13.12 12.13 11.28 15.66 14.25 13.08 12.95 12.10 11.37 15.13 13-97 12.99 12.121 11.40 18.22 16.56 15.18 14.00 13.01 12.111 19.45 18.21 16.53 15.15 13.98 19.54 18.18 16.51 KG COW): 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0000000000000000000000 000000000000000 o O C 0 0000000000000000000000 000000000000000 000000000000 9.64 ommmooommomm O I O O C O O C O O O O AOOO‘m-BW-QOOSDQ szmmd-fl-‘OWNO 0000000000000000000000 000000000000000 O O O O O O O O O O O O O O O O O O O O O O C 0 C O O C O O O O O O O O d-‘AN—b-ON-l-D—Du-DN O O O O O I O O O O O oooooooooooodooooo»oaa 00000000000—b-I—O0 d—b-b0000—D0000 .22 .65 .69 .97 .24 .06 -33 .41 .45 .17 .23 0.18 0.18 0.21 0.21 0.21 0.21 0.21 0.24 0.24 0.24 0.12 0.12 0.12 0.15 0.15 0.15 0.15 0.15 0.18 0.18 0.18 0.18 0.18 0.18 0.21 0.21 0.21 0.21 0.21 0.24 0.24 0.24 0.12 0.12 0.12 0.15 0.15 0.15 0.15 0.15 0.18 0.18 0.18 0.18 mm 0.18 0.21 0.21 0.21 0.21 0.21 NNNNNNNNNNNNNNNNNNNNNN wwwwwwwwwwwwwwwwwww w 0‘ 0 o o o o o o o o o o o o o o o o o o 0 o o o o o o o o o o o o o o m—D'QUINZCO‘ NWNQ‘OW—D—DNNWflWOd—D—hwmooo #50“,szsz 255 'bbooooooooooo gooooooooooooog 0000000000000000000 0000000000000000000000 0000000000 0 0 15.64 14.26 13.07 12.91 12.06 11.33 15.05 13.91 12.93 12.09 11.35 17.04 16.44 15.09 13.94 12.97 12.12 17.51 17.95 16.49 15.13 1&W 0000000000000000000 0000000000000000000000 0000000000 0 O O O O O O O O O O I O O O O O O O O O I O O ~10~u1~a~lcnu1:=oo~a~aonu1:=~a~aanonu1~:~aa\ . C O O O O O I O C C O 0 U1 c: 075 WN0003w~—&00#‘WN-§OWN—b 0U|~l ~10t00£t~®803~ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.86 0.28 0.00 0.00 0.00 0.00 1.44 1.18 0.00 0.00 0.00 dU‘NNw‘leQO‘ woww-q 301031:- 0 O O WU‘GDOUJUIN‘IDNUTNQW sz—b 040001 20000300030—bm d—b—D—DNNNNNNNNWUNNNUUNNU O-l—b-D—I—Ioo-D-D o o o o o o o o o o o o o o o o o o o o o o o o o o o o 000 '4-0 3:00 £11000 ...—nOO-n-a—nNNO—a—om O O O O O O O . ... 0 95 96 97 0.24 0.36 0.24 0.39 0.24 0.42 3 3 3 24.7 23.5 22.6 MIXED FORAGE DIETS (660 KG COW): 98 99 100 101 102 103 1021 105 106 107 108 109 110 111 112 113 1111 115 116 117 118 119 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 98 0 0000000000000000000000 0000000000000000000000 .12 .12 .12 .15 .15 .15 .15 .15 .18 .18 .18 .18 .18 .18 .21 .21 .21 .21 .21 .24 .24 .24 .12 .12 .12 .15 .15 .15 .15 .15 .18 .18 .18 0.51 0.54 0.57 .45 .48 .51 .54 .57 -39 .42 .45 .48 .51 .54 .36 -39 .42 .45 .48 .36 -39 .42 .51 .54 .57 .45 .48 .51 .54 .57 .39 .42 .45 .48 .51 .54 00000000000000000 0000000000000000000 NNNNNNNNNNNNNNNNNNNNNN D) 110.5 39.6 38.8 112.7 41.6 110.7 39.8 39.0 115.2 1111.0 42.8 41.8 110.9 40.0 46.7 115.3 1111.1 113.0 112.0 46.9 115.5 1121.3 36.5 35.7 35.0 38.5 37.5 36.7 35.9 35.2 40.8 39.7 38.6 37.7 36.9 36.1 42.1 40.9 39.8 38.8 37.9 42.3 41.0 40.0 22.5 UIGJONO-IWO‘QQ meNwmQOOANWU‘INWO—DNWEDO-b mowmmwwwmm UINOU'IOWNOONUIOUINOONUIOO—bw .34 1: zzmnzzzmwwzzzzwwwzzwww wwwwwwwwwwwwwwmwwwwmww O O O O I O O O O O O O O O O O O O O O O O O O O O I O I O O O O O O 18.46 18.20 _.s 0‘ U1 U3 \O-AUJO‘QO U'I-QONWU'I‘IOO-DNWUINNO—DNWNO—b mczzzmwwzzzzwwwzzwww wwwwwwwwwwwwwwmwwwwmww ozzmaoooomwzulowoomwwwoom mmomommoommommcommooaw O-hWUIWOQO-hwmma‘fl 0.00 0.00 g czmlzgzlzmwwzzzgwwwzzwww wwwwwwwwwwwwWWNWWWWNww o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o 0 o “modwmmOQo-‘wmfla‘d‘O-fiwmflo mdomwm~1~oc>an9wm~noo04mwoood co co mamwwmooooozwwmomoozwmwooz b—bOWO-B-bOONU‘Ok—DOONW0‘OON 0.00 0.24 909" 10.22 10.30 9.88 .64 .13 .42 .67 .11 .67 .18 .75 .81 .51 .25 .76 .26 .02 .42 .08 .56 .03 .12 .67 .24 .72 c—I—D-b-uhc-D ._.—Dd —h ._.—._. oooooooooooooooooo on -4 -a o c o .12 .85 .89 -39 .87 .08 .82 .51 .11 .57 .85 .56 dommdoome-qosmxooooodaaooooo J: -b on N N 1.58 0 0 0 .nbO—D—l—b—b—bN—D—D—h-‘NN—l—D—de O O O O O O O O O O O 0 00000000000000-b-0000—b-b—n .71 .00 .00 md=~awmoow NO‘W-‘OO‘OON o o o o 0 #:0010000 «I‘lUlz—IN d—b00—I-b-110000-000000-50000 \O dummy—- 0‘0 00 0~I~IJ=0 Uiz zones \00’: wawo—i—INNOCO o o o o o o o o o CWVONO‘O towmmfl 257 6180319669u2209289356 9&08530u52086077u2308 OOOOOOOOOOOOOO O I O O O O O 223222202221101111010 2100000u0m00031000000 020000050 ooouclOOOrDoo I O O O O O O O O O O O O O ....... 000000010000010000000 83720670uu8260085u0u1 32“.“30702210700009089 ..... O O O O O O O O O O O O O O O O 230123300123300122001 5513966832u9709u35116 6“”186u07u186307u1517 unssuuu‘usssu‘urb'bsss’bros 662u9769u3508105u6227 6””186407u196317u1517 unssuuurbsssuutbesss’brbs 662u9769u350810546227 6""186u07u196317u1517 “ussuuu6555uu66555665 333333333333333333333 475814792581469258692 554455534445533444334 ..................... 000000000000000000000 2255555888888 11111 4.44. 111111111111122222222 OOOOOOOOOOOOOOOOOOOOO 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 90123u567890123u56789 900000000001111111111 11111111111111111111 258 Table 0.2 Simulated annual milk production (AMP), annual supplemental feed costs (ASFC), annual corn silage use (ACS) and annual alfalfa use (AA) data used to determine alfalfa value. CASE CP NDF AMP ASFC ACS AA (kg/y) ($ly) (kg/y) (kg/y) MIXED FORAGE DIETS (600 kg COW): 1 0.12 0.51 7939.3 274.0 1220.7 2441.4 2 0.12 0.54 7939.3 290.4 1160.0 2319.9 3 0.12 0.57 7872.7 302.1 1107.5 2215.0 4 0.15 0.45 7939.3 223.9 1368.0 2735.9 5 0.15 0.48 7939.3 240.1 1291.3 2582.6 6 0.15 0.51 7939.3 259.1 1223.5 2447.1 7 0.15 0.54 7939.3 276.2 1162.5 2325.0 8 0.15 0.57 7917.7 290.9 1108.1 2216.3 9 0.18 0.39 7939.3 185.0 1454.1 2908.2 10 0.18 0.42 7939.3 191.1 1450.1 2900.2 11 0.18 0.45 7939.3 204.9 1371.2 2742.4 12 0.18 0.48 7939.3 226.2 1294.7 2589.5 13 0.18 0.51 7939.3 245.2 1226.5 2453.0 14 0.18 0.54 7939.3 262.4 1165.1 2330.2 15 0.21 0.36 7939.3 153.4 1499.9 2999.9 16 0.21 0.39 7939.3 161.0 1490.3 2980.7 17 0.21 - 0.42 7939.3 169.6 1462.0 2924.1 18 0.21 0.45 7939.3 191.4 1375.2 2750.5 19 0.21 0.48 7939.3 213.2 1298.3 2596.5 20 0.24 0.36 7939.3 128.4 1536.5 3073.1 21 0.24 0.39 7939.3 135.5 1529.4 3058.9 22 0.24 0.42 7939.3 152.6 1466.3 2932.6 23 0.12 0.51 8232.2 289.7 1208.7 2417.3 24 0.12 0.54 8052.4 296.3 1155.6 2311.1 25 0.15 0.45 8638.2 257.2 1335.1 2670.2 26 0.15 0.48 8480.1 270.4 1267.9 2535.9 27 0.15 0.51 8277.1 277.9 1209.7 2419.4 28 0.15 0.54 8097.4 285.6 1156.5 2312.9 29 0.18 0.39 8638.2 211.2 1474.9 2949.8 30 0.18 0.42 8638.2 221.8 1423.0 2846.0 31 0.18 0.45 8638.2 242.4 1338.8 2677.6 32 0.18 0.48 8525.0 259.6 1269.4 2538.7 33 0.18 0.51 8323.7 267.5 1210.8 2421.6 34 0.18 0.54 8120.7 272.5 1158.0 2316.1 35 0.21 0.36 8638.2 179.6 1519.2 3038.3 36 0.21 0.39 8638.2 186.5 1512.8 3025.5 37 0.21 0.42 8638.2 204.7 1427.0 2854.0 38 0.21 0.45 8638.2 228.4 1342.6 2685.2 39 0.21 0.48 8548.3 246.8 1271.6 2543.2 40 0.24 0.36 8638.2 153.9 1557.4 3114.8 41 0.24 0.39 8638.2 163.4 1527.9 3055.9 42 0.24 0.42 8638.2 190.5 1431.5 2862.9 43 0.15 0.45 8706.4 262.5 1332.1 2664.3 44 0.18 0.39 9224.8 237. 45 0.18 0.42 8976.8 239. 46 0.18 0.45 8728.0 248. 47 0.21 0.36 9541.0 217. 48 0.21 0.39 9269.7 218. 49 0.21 0.42 9021.8 228. 50 0.21 0.45 8773.0 237. 51 0.24 0.36 9585.9 192. 52 0.24 0.39 9314.6 203. 53 0.24 0.42 9045.1 214. ALFALFA HAY BASED DIETS (600 kg cow) 54 0.12 0.51 8029.1 282. 55 0.12 0.54 7782.9 292. 56 0.12 0.57 7534.1 301. 57 0.15 0.45 8728.0 232. 58 0.15 0.48 8413.5 248. 59 0.15 0.51 8097.4 259. 60 0.15 0.54 7849.4 271. 61 0.15 0.57 7600.6 280. 62 0.18 0.39 9585.9 168. 63 0.18 0.42 9179.9 192. 64 0.18 0.45 8796.3 212. 65 0.18 0.48 8480.1 231. 66 0.18 0.51 8165.6 245. 67 0.18 0.54 7917.7 259. 68 0.21 0.36 10172.5 140. 69 0.21 0.39 9654.1 157. 70 0.21 0.42 9248.1 185. 71 0.21 0.45 8865.4 206. 72 0.21 0.48 8548.3 226. 73 0.24 0.36 10240.7 135. 74 0.24 0.39 9744.0 159. 75 0.24 0.42 9314.6 185. ALFALFA SILAGE BASED DIETS (600 kg cow): 76 0.12 0.51 7984.2 298. 77 0.12 0.54 7735.4 305. 78 0.12 0.57 7487.5 311. 79 0.15 0.45 8638.2 262. 80 0.15 0.48 8323.7 274. 81 0.15 0.51 8029.1 285. 82 0.15 0.54 7782.9 293. 83 0.15 0.57 7534.1 300. 84 0.18 0.39 9472.7 235. 85 0.18 0.42 9066.7 233. 86 0.18 0.45 8683.1 247. 87 0.18 0.48 8368.6 260. 88 0.18 0.51 8052.4 269. 89 0.18 0.54 7804.5 279. 90 0.21 0.36 10014.4 212. 91 0.21 0.39 9517.6 203. 92 0.21 0.42 9113.3 215. 259 oooazmoszachm mdmdommaz~za~o~zo~wm~o==m~o~l wgzmommc~1w~ozmw<~o—. 1488. 1406. 1334. 1544. 1489. 1408. 1336. 1584. 1492. 1411. 0000000000000000000000 00000000000000000 00000000000000000 2 NO-QU'IUOQO-Qd 2976. 2812. 2669. 3089. 2979. 2816. 2673. 3169. 2984. 2822. 3536. 3314. 3120. 4096. 3798. \OON—DQ—AO—IOWWQQJ-i'N-bw-DSO-BOQ UIOUJOU'I #487901 93 94 95 96 97 MIXED FORAGE DIETS 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 0.21 0.21 0.24 0.24 0.24 .12 .12 .12 .15 .15 .15 .15 .15 .18 0000000000000000000000 u—ul oo 0000000000000000000000 kg 8728. 8413. 10059. 9562. 9179. con): 9143. 8931. 8751. 9630. 9382. 9179. 8976. 8796. 10195. 9924. 9654. 9427. 9224. 9021. 10533. 10217. 9956. 9699. 9472. 10578. 10262. 9992. 0 5 \OO‘LU mq—amzoxoooooo—amoowooosooozow 3 8 260 232. 247. 176. 175. 201. 314. 321. 329. 283. 290. 300. 307. 315. 254. 259. 266. 277. 287. 295. 232. 230. 243. 254. 266. 205. 215. 230. (DUIGJNO d-qzzoxzmwwoomoxoommaoswmow 1330. 1272. 1218. 1467. 1396. 1331. 1273. 1219. 1638. 1548. 1470. 1398. 1333. 1274. 1709. 1641. 1551. 1472. 1400. 1745. 1644. 1554. 00000 00000 sz-Iw‘leB—ON—DOQWWQO‘ZO‘N—b 4096 3797. 5088. 4878. 4452. 2660. 2544. 2437. 2934. 2793- 2663. 2546. 2439. 3277. 3097. 2940. 2796. 2666. 2548. 3418. 3283. 3103. 2944. 2800. 3490. 3289. 3109. .8 70th 21000—- mmunooomw maoaom—smwmw APPENDIX D DAFOSYM farm and machinery input files used in the simulation experiments. 261 FILE: FARM.ME BRIEF DESCRIPTION: base medium sized farm (see Chapter 7) ELANWTHR D:\DENNIS\SIMUL\HACHHMEC.SM2 3 1 1 1 4 2 2 3 91 65 41 22 143.00 0 15.00 5 1 8.00 0.08 8.00 260.00 20.00 . 10.00 0.25 100.00 0 0 0 50.00 0 20 0 1.00 0.00 0-33 0-33 0.00 0.00 0.00 1.00 50.00 0 20 0 1.00 0.00 0.75 0.75 0.00 0.00 0.00 1.00 50.00 0 20 0 1.00 0.00 0.75 0.75 0.00 0.00 0.00 1.00 50.00 0 20 0 2 1 5 3 335 183.00 140 20.00 8.00 0.08 0.31 130.00 0.00 0.10 220.00 85.00 000-. 3 NOB-10 0 mowoo - 000-0 05' ”0008 00 ‘00 0.30 0.00 0.21 0 2 0 0 3 2 1 2 1953 1 70.00 95 1 2 1 20 41 21 228.00 15.00 4.00 0.32 250.00 1.00 55.00 154.00 65.00 150 1.86 0.41 0.00 1.00 170 0.25 0.41 0.00 1.00 170 0.25 0.41 0.00 1.00 150 262 ELANCORN 0.08 190.00 84.00 106.00 48.00 150 1.86 14.00 0.00 1.00 170 0.25 14.00 0.00 1.00 170 0.25 14.00 0.00 1.00 150 170 0.25 0.80 0.00 1.00 170 0.25 0.80 0.00 1.00 170 0.25 0.80 0.00 1.00 170 26 1 1 1 1.00 100.00 69.00 100 0.00 4.00 0.00 0.00 100 0.00 4.00 0.00 0.00 100 0.00 4.00 0.00 0.00 100 00-50 0000 00-50 0000 0 00000 00000 00000 00-50 0000 263 1.00 0.00 1.00 0.00 1.86 1.86 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 4 1 6 1 0.00 17000.00 300.00 0 0 0 1 100.00 13490.00 26.00 622.00 30.00 36.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.22 0.10 0.00 0.16 Operation Information: 1 281 4 1.00 4.80 4.60 0.00 0.00 190 290 5 1.00 5.50 1.50 0.00 1.00 190 300 5 0.05 1.00 0.00 0.00 0.00 190 250 2 2.00 15.00 3.00 0.00 0.05 190 250 2 2.00 . 15.00 3.00 0.00 0.05 190 240 4 1.00 22.00 0.00 0.00 0.00 20 45 4 1.00 8.00 3.70 0.00 0.00 40 70 2 1.00 7.00 2.70 0.00 0.00 170 101 5 1.00 6.00 3.70 0.00 1.00 170 170 5 0.05 1.00 0.00 0.00 0.00 170 180 2 2.00 8.00 3.00 2.00 0.10 170 180 2 2.00 8.00 3.00 2.00 0.10 170 230 20 1.00 0.00 0.00 0.00 0.00 150 131 5 1.00 5.00 3.70 0.00 1.00 150 150 5 0.05 1.00 0.00 0.00 0.00 150 250 2 2.00 15.00 3.00 0.00 0.05 150 250 2 2.00 15.00 3.00 0.00 0.05 150 240 4 1.00 22.00 0.00 0.00 0.00 140 131 5 1.00 4.00 1.50 0.00 0.40 140 141 5 0.05 1.00 0.00 0.00 0.00 140 250 2 2.00 15.00 3.00 0.00 0.05 140 250 2 2.00 15.00 3.00 0.00 0.05 140 240 4 1.00 22.00 0.00 0.00 0.00 100 131 5 1.00 6.00 3.70 0.00 0.00 100 150 5 1.00 6.00 3.70 0.00 0.00 FILE: FARM.LG BRIEF DESCRIPTION: 70 ha alfalfa, 70 ha corn” 150 cows, larger storage structures and machinery, identical harvest information ELANWTHR ELANCORN D:\DENNIS\MACHHMEC.SM2 26 3 1 2 2 2 1 1 1 1 0 1 1 1 1 1 2 4 2 4 0 2 6 4 4 6 2 3 3 3 91 335 1953 1978 0 1 1 0 175.00 175.00 70.00 1.00 1.00 4 1 95 2 1 1 2 0 2 2 0 2 1 1 2 65 20 20 65 41 41 41 41 22 21 21 0 143.00 183.00 228.00 288.00 0 140 25.00 25.00 25.00 7 1 5 8.00 8.00 4.00 0.08 - 0.08 8.00 0.31 0.32 .08 260.00 130.00 250.00 190.00 20.00 0.00 1.00 10.00 0.10 55.00 84.00 100.00 0.25 220.00 154.00 106.00 69.00 100.00 85.00 65.00 48.00 0 0 0 75.00 0 20 0 0 0 150 150 170 100 0 1.00 0.00 1.00 0.00 1.86 1.86 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1 00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.22 0.00 1.00 1.00 1.00 0.00 0.00 75.00 0 20 0 40 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 75.00 0 20 0 40 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 75.00 0 FILE: FARM.LC BRIEF DESCRIPTION: 70 ha alfalfa, 70 ha corn, 150 cows, larger storage structures and machinery, identical harvest information ELANWTHR ELANCORN D:\DENNIS\MACHHMEC.SM2 26 3 1 2 2 2 1 1 1 1 0 1 1 1 1 1 4 2 4 0 2 6 4 4 6 2 3 3 3 91 335 1953 1978 0 1 1 0 175.00 175.00 70.00 1.00 1.00 4 1 95 2 1 1 2 0 2 2 0 2 1 1 2 65 20 20 65 41 41 41 41 22 21 21 0 143.00 183.00 228.00 288.00 0 140 25.00 25.00 25.00 7 1 5 8.00 8.00 4.00 0.08 - 0.08 8.00 0.31 0.32 0.08 260.00 130.00 250.00 190.00 20.00 0.00 1.00 10.00 0.10 55.00 84.00 100.00 0.25 220.00 154.00 106.00 69.00 100.00 85.00 65.00 48.00 0 0 0 75.00 0 20 0 0 0 150 150 170 100 0 1.00 0.00 1.00 0.00 1.86 1.86 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.22 0.00 1.00 1.00 1.00 0.00 0.00 75.00 0 20 0 40 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 75.00 0 20 O 40 40 170 170 170 100 O 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 75.00 0 6 20 1.00 0-33 0.00 0.00 #000 came zoowoo 1 0 0 0 0 0.22 190 190 190 190 190 40 170 170 170 170 170 150 150 150 150 150 140 140 140 140 140 100 100 150.00 0.00 0.00 Operation Information: 281 290 300 250 250 240 45 70 101 170 180 180 230 131 150 250 250 240 131 141 250 250 240 131 150 0 00‘3NNO‘O‘3NNO‘O‘0NNJ:I—’Nt-ENNO‘O‘J: 000—- 4 88380 13490.00 10 0.00 0.16 1. 1 0 2 2 1 1 1 1 0 2 2 1 1 0 2 2 1 1 0 2 2 1 1 1 0—900 00 00 05 00 00 00 00 00 00 05 00 00 00 00 05 00 00 00 00 05 00 00 00 00 00 0 .00 -33 .00 .00 0 150 1.86 0.41 14.00 0.00 0.00 1.00 .00 22000.00 1.00 26.00 0.00 4.80 5.50 1.00 15.00 15000 22.00 8.00 7.00 6.00 1.00 8.00 8.00 0.00 5.00 1.00 15.00 15.00 22.00 4.00 1.00 15.00 15.00 22.00 6.00 6.00 265 150 1.86 622. 0 170 00 .00 #40000U‘1000040000~I~1~10000010 0000000000000000000000000 wwowwo-aowwowowwowmwowwOa4:.- w o 0000000000000NN0000000000 100 0.25 0.00 0.80 14.00 0.00 0.00 1.00 0.00 400.00 45.00 0.00 O O O 000000000000000000 oooooogooobbboooooooooooo 000000 54.00 0.00 00000000000-90000-b000 0.10 266 FILE: FARM.SM BRIEF DESCRIPTION: 30 ha alfalfa, 30 ha corn, 60 cows, smaller storage structures and machinery, identical harvest information ELANWTHR ELANCORN D:\DENNIS\MACHHMEC.SM2 26 2 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 2 2 3 0 2 4 3 3 4 1 1 1 1 91 335 1953 1978 0 1 1 0 175.00 175.00 70.00 1.00 1.00 4 1 95 2 1 1 2 0 2 2 0 2 1 1 2 65 20 20 65 41 41 41 41 22 21 21 0 143.00 183.00 228.00 288.00 0 140 10.00 10.00 10.00 4 1 2 8.00 8.00 4.00 0.08 0.08 8.00 0.31 0.32 0.08 260.00 130.00 250.00 190.00 20.00 0.00 1.00 10.00 0.10 55.00 84.00 100.00 0.25 220.00 154.00 106.00 69.00 100.00 85.00 65.00 48.00 0 0 0 30.00 0 20 0 1.00 0.00 1 0.33 0.33 0 0.00 0.00 0. 0.00 1.00 0 30.00 0 . 20 0 40 40 170 170 170 100 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 30.00 0 20 0 40 40 170 170 170 100 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 30.00 0 0 150 150 170 100 00 1.86 1.86 0.25 0.00 .33 0.41 14.00 0.80 14.00 00 0.00 0.00 0.00 0.00 00 1.00 1.00 1.00 0.00 00—1-0 0000 00000 00—30 0000 00000 00000 00-b0 0000 5 0 0 0.22 190 190 190 190 190 40 170 170 170 170 170 150 150 150 150 150 140 140 140 140 140 100 100 20 1.00 m3 0.00 0.00 —-000 006110 doowoo 1 0 0 60.00 0.00 0.00 000—: 00000 000000 0—500 13490.00 0.10 0.00 0.16 O .00 .33 0.41 14.00 .00 .00 0 2m 150 150 1.86 1.86 0.00 0.00 1.00 1.00 .00 11000.00 26.00 622. 0.00 0. Operation Information: 280 290 300 250 250 240 40 70 100 170 180 180 230 130 150 250 250 240 130 140 250 250 240 130 150 N zzwmwczwmmzzommwwmmwmm1:1:1» 1.00 1.00 0.05 1.00 r 1.00 1.00 1.00 1.00 1.00 0.05 1.00 1.00 1.00 1.00 0.05 1.00 1.00 1.00 1.00 0.05 1.00 1.00 1.00 1.00 1.00 4.80 5.50 1.00 15.00 15.00 22.00 8.00 7.00 6.00 1.00 8.00 8.00 0.00 5.00 1.00 15.00 15.00 22.00 4.00 1.00 15.00 15.00 22.00 6.00 6.00 NNONNOOONNONONNONNNONNOdw 170 00 00 &40000&000040000dd<000000 ooooooooooooooooooooooooo N 0000000000000NNOUI00000000 100 0.25 0.00 0 0.80 14.00 1 0.00 0.00 0. 1.00 0.00 0 200.00 18.00 0.00 oooobbboboboooooooooooooo ooooooooooooooooooooooooo 22.00 0.00 00000000000-b0000-9000000-40 000000#000000-A—90000000000 ooommooommoooooooooommooo 0.10 ' L4)!“ 268 FILE: FARM.DRY BRIEF DESCRIPTION: medium sized farm with hay harvest at 151 moisture, silage harvest at 60% moisture (5 points drier than in FARH.HE) 50.00 0 ELANWTHR ELANCORN D:\DENNIS\SIMUL\MACHHHEC.SM2 26 3 1 2 2 2 1 1 1 1 0 1 1 1 1 4 2 5 0 2 5 4 5 2 3 3 3 91 335 1953 1978 O 1 1 0 175.00 175:00 70.00 1.00 1.00 4 1 95 2 1 1 2 O 2 2 O 2 1 1 2 60 15 15 6O 41 41 41 41 22 21 21 0 143.00 183.00 228.00 288.00 0 140 15.00 20.00 15.00 5 1 4 8.00 8.00 4.00 0.08 0.08 8.00 0.31 0.32 0.08 260.00 130.00 250.00 190.00 20.00 0.00 1.00 10.00 0.10 55.00 84.00 100.00 0.25 220.00 154.00 106.00 69.00 100.00 85.00 65.00 48.00 0 0 0 50.00 0 20 O O 0 150 150 170 100 O 1.00 0.00 1.00 0.00 1.50 1.50 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.22 0.00 1.00 1.00 1.00 0.00 0.00 50.00 0 20 0 40 40 170 170 170 100 O 1.00 0.00 1.00 0.00 0.18 0.18 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 50.00 0 20 0 40 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.18 0.18 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 0.22 190 190 190 190 190 40 170 170 170 170 170 150 150 150 150 150 140 140 140 140 140 100 100 o-. 0.00 0.00 O. 000- 00000 000000 0—‘00 13490.00 10 0. 0. 00 16 0 .00 .33 0.41 14.00 .00 .OO 0 269 150 1.50 150 1.50 0.00 0.00 1.00 1.00 .00 17000.00 26.00 0.00 Operation Information: 281 290 300 250 250 240 45 70 101 170 180 180 230 131 150 250 250 240 131 141 250 250 240 131 150 mmzmmmmzwmmmommmmmzzmmmmr.- 1.00 1.00 0.05 2.00 2.00 1.00 1.00 1.00 1.00 0.05 2.00 2.00 1.00 1.00 0.05 2.00 2.00 1.00 1.00 0.05 2.00 2.00 1.00 1.00 1.00 mm 5.50 1.00 15.00 15.00 22.00 8.00 7.00 6.00 1.00 8.00 8.00 0.00 5.00 1.00 15.00 15.00 22.00 4.00 1.00 15.00 15.00 22.00 6.00 6.00 170 622.00 0.00 00000000000000 0 O O O O O O O O O 0000 WWOWWOHOWWOWOWMOWNWOWWO-bc: 0000 w 0000000000000“)N0000000000 100 0.25 0.00 0.80 14.00 0.00 0.00 1.00 0.00 300.00 30.00 0.00 'boooooooo 000000000 000000000000000000 ooobooo ooooooo 36.00 0.00 gggooocooooobadoooooooooo WWOOOUIUI000000000001U|000 0000000000040000d000000-b0 0.10 FILE: FARM.RND BRIEF DESCRIPTION: rectangular baler. ELANWTHR medium sized farm with round baler 270 Bales stored inside. D:\DENNIS\SIMUL\HACHHMEC.SM2 3 1 1 1 4 2 4 3 91 65 41 22 143.00 0 15.00 S 1 4 8.00 0.08 - 8.00 260.00 20.00 10.00 0.25 100.00 0 0 0 50.00 0 20 0 1.00 0.00 0-33 0-33 0.00 0.00 0.00 1.00 50.00 0 20 0 1.00 0.00 0.75 0.75 0.00 0.00 0.00 1.00 50.00 0 20 0 1.00 0.00 0.75 0.75 0.00 0.00 0.00 1.00 50.00 0 8 1 5 3 335 1 183.00 140 .00 8.00 0.08 0.31 130.00 0.00 0.10 220.00 .00 2 1 11 3 2 0 2 1953 1 70.00 95 1 2 1 20 41 21 228.00 15.00 4.00 0.32 250.00 1.00 55.00 154.00 65.00 1.86 0.41 1.00 2.00 80 0.25 0.41 1.00 2.00 80 0.25 0.41 1.00 2.00 ELANCORN 0.08 190.00 84.00 106.00 48.00 150 1.86 14.00 1.00 2.00 80 0.25 14.00 1.00 2.00 80 0.25 14.00 1.00 2.00 14.00 1.00 100.00 69.00 100 0.00 14.00 1.00 0.00 100 0 00 14:00 1.00 0.00 100 0.00 1.00 0.00 130 0.00 1.00 0.00 0.00 130 0.00 1.00 0.00 0.00 130 0.00 '1.00 0.00 0.00 in place of 20 O 0 0 150 150 80 100 130 1.00 0.00 1 00 0.00 1.86 1.86 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00 0.00 2.00 2.00 1.00 0.00 0.00 4 1 6 0.00 17000.00 300.00 0 0 0 0 100.00 13490.00 26.00 622.00 30.00 0.00 0.00 0.00 0.00 0.00 0.22 0.10 0.00 0.16 Operation Information: 1 281 4 1.00 4.80 4.60 0.00 190 290 5 1.00 5.50 1.50 0.00 190 300 5 0.05 1.00 0.00 0.00 190 250 2 2.00 . 15.00 3.00 0.00 190 250 2 2.00 15.00 3.00 0.00 190 240 4 1.00 22.00 0.00 0.00 20 45 4 1.00 8.00 3.70 0.00 40 70 2 1.00 7.00 2.70 0.00 80 111 5 1.00 6.00 3.70 600.00 130 200 4 1.00 6.00 0.00 600.00 130 210 2 1.00 8.00 0.00 600.00 150 131 5 1.00 5.00 3.70 0.00 150 150 5 0.05 1.00 0.00 0.00 150 250 2 2.00 15.00 3.00 0.00 150 250 2 2.00 15.00 3.00 0.00 150 240 4 1.00 22.00 0.00 0.00 140 131 5 1.00 4.00 1.50 0.00 140 141 5 0.05 1.00 0.00 0.00 140 250 2 2.00 15.00 3.00 0.00 140 250 2 2.00 15.00 3.00 0.00 140 240 4 1.00 22.00 0.00 0.00 100 131 5 1.00 6.00 3.70 0.00 100 150 5 1.00 6.00 3.70 0.00 271 36.00 0.00 0 00000000000-b—b-b-9000000-b0 00000030000000000000000 000L11U'IOOOU'IL1100000000UIU1000 0.10 FILE: FARM2.RND BRIEF DESCRIPTION: rectangular baler. ELANWTHR medium sized farm with round baler 272 Bales stored outside. D:\DENNIS\SIHUL\MACHHMEC.SM 3 1 1 1 4 2 4 3 91 u.2857.229...88882:...881'2182: 000- o o 0 o. o o c: 64000 Sacco o-ooo 000- o o 0 O. o o 0 00-40 00-I0 00000 000 000U’10000'00100000 8 2 2 1 0 4 2 3 1953 1 70.00 95 1 2 1 20 41 21 228.00 15.00 4.00 0.32 250.00 1.00 55.00 154.00 65.00 0 0 1.86 . 3 0.41 0 1.00 0 2.00 80 0.25 0.41 1.00 2.00 80 0.25 0.41 1.00 2.00 ELANCORN 26 288.00 0.08 190.00 84.00 106.00 48.00 1 1 4 4 1.00 100.00 69.00 150 1.86 14.00 1.00 2.00 80 0.25 14.00 1.00 2.00 80 0.25 14.00 1.00 2.00 80 0.25 0.80 1.00 1.00 80 0.25 0.80 1.00 1.00 80 0.25 0.80 1.00 1.00 100 0.00 14.00 1.00 0.00 100 0.00 14.00 1.00 0.00 100 0.00 14.00 1.00 0.00 130 0.00 1.00 0.00 0.00 130 0.00 1.00 0.00 0.00 130 0.00 1.00 0.00 0.00 in place of 20 1.00 0-33 0.00 0.00 4 1 0 0 0 0 100.00 0.00 0.22 0.00 #000 6.888180 000—- OOUJO 000000 0—500 ooowo 13490.00 0.00 0.10 0.16 150 1.86 0.41 1 1.00 2.00 00 26.00 0.00 Operation Information: 1 281 290 300 250 250 240 20 45 4O 70 80 111 200 210 131 150 250 250 240 131 141 250 250 240 131 150 U1UIJ=NNU1U1 ammmmmzmmzzwmmmr: 1.00 1.00 0.05 2.00 2.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.05 2.00 2.00 1.00 1.00 0.05 2.00 2.00 1.00 1.00 1.00 4.80 5.50 1.00 15.00 15.00 22.00 8.00 7.00 6.00 6.00 8.00 5.00 1.00 15.00 15.00 22.00 4.00 1.00 15.00 15.00 22.00 6.00 6.00 273 150 80 100 1.86 0.25 0.00 4.00 0.80 14.00 1.00 1.00 1.00 2.00 1.00 0.00 0.00 0.00 622.00 30.00 0.00 0.00 4.60 0.00 1.50 0.00 0.00 0.00 3.00 0.00 3.00 0.00 0.00 0.00 3.70 0.00 2.70 0.00 3.70 600.00 0.00 600.00 0.00 600.00 3.70 0.00 0.00 0.00 3.00 0.00 3.00 0.00 0.00 0.00 1.50 0.00 0.00 0.00 3.00 0.00 3.00 0.00 0.00 0.00 -3.70 0.00 3.70 0.00 130 0.00 1.00 0.00 0.00 36.00 0.00 0000:0000000000000000 0UIU'1000U'IU100000000UiUl000 .00 .00 00000000000—I-fi-b—n000000-00 00 10 274 FILE: FARM.SEA BRIEF DESCRIPTION: medium sized farm with oxygen limiting alfalfa silos in place of stave silos 50.00 0 ELANNTHR ELANCORN D:\DENNIS\SIMUL\MACHHMEC.SM2 26 3 1 2 2 2 1 1 1 1 0 1 1 1 1 1 4 2 5 0 2 5 4 4 5 2 3 3 3 91 335 1953 1978 0 1 1 0 175.00 175:00 70.00 1.00 1.00 4 1 95 2 1 1 2 0 2 2 0 2 1 1 2 60 20 20 60 41 41 41 41 22 21 21 0 143.00 183.00 228.00 288.00 0 140 15.00 20.00 15.00 5 1 4 8.00 8.00 4.00 0.08 0.08 8.00 0.31 0.32 0.08 260.00 130.00 250.00 190.00 20.00 0.00 1.00 10.00 0.10 55.00 84.00 100.00 0.25 220.00 154.00 106.00 69.00 100.00 85.00 65.00 48.00 0 0 0 50.00 0 20 0 0 0 150 150 170 100 0 1.00 0.00 1.00 0.00 1.50 1.50 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.22 0.00 1.00 1.00 1.00 0.00 0.00 50.00 0 20 0 40 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 50.00 0 20 0 40 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 20 1.00 0.33 0.00 0.00 16 0 0 0 0 100.00 0.00 0.22 190 190 190 190 190 40 170 170 170 170 170 150 150 150 150 150 140 140 140 140 140 100 100 0.00 000-0 1 00000 001.000 13490.00 0.10 0.00 0.16 0 0 3 0 0 0‘00 .0 8 .0 .0 0 .00 150 1.50 27 150 1.50 0.41 14.00 0.00 0.00 1.00 1.00 17000.00 26.00 0.00 Operation Information: 281 290 300 250 250 240 45 70 101 170 180 180 230 131 150 250 250 240 131 141 250 250 240 131 150 N mmzwmmmzmwmmommmmmzzmwwmz _.l-JdNNOd—bNNO—Aémmo—bda—I‘NNOA-A .OO .00 .05 .00 .00 .00 .00 .00 .00 .05 .00 .00 .00 .00 .05 .00 .00 .00 .00 .05 .00 .00 .00 .00 .00 d 111—641100000...QO 15. 22. 4. 1. 15. 15. 22. 6. 6. .80 .50 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 00 OO 00 00 00 00 00 00 00 62 5 1 70 1 00 0.25 0.00 0.80 14.00 0.00 0.00 1.00 -0.00 2.00 0.00 300.00 3 4.60 1.50 0.00 3.00 3.00 3m 3m .00 .00 .00 .00 .70 .00 .00 .00 .00 .50 .00 .00 .00 &W 3070 owwo-n owwowowwo 0.00 0.00 w 0000000000000NN0000000000 0 0.00 1.00 0.00 0.00 00 36.00 0.00 oooooboooo 000111010000000000U'101000 08000000-b—b 00000000000-50000d000000-50 ooboo ooomm 0.10 276 FILE: FARM.BUN BRIEF DESCRIPTION: medium sized farm with bunker silos in place of stave silos. ELANWTHR ELANCORN D:\DENNIS\SIHUL\MACHHMEC.SM2 26 3 1 2 2 2 1 1 1 1 O 1 1 1 1 1 4 2 5 0 2 5 4 4 5 2 4 3 3 91 335 1953 1978 O 1 1 0 175.00 175.00 70.00 1.00 1.00 4 1 95 2 1 1 2 O 2 2 O 2 1 1 2 70 2O 2O 70 41 41 41 41 22 21 21 0 143.00 183.00 228.00 288.00 0 140 15.00 20.00 15.00 5 1 4 8.00 8.00 4.00 0.08 - 0.08 8.00 0.31 0.32 0.08 260.00 130.00 250.00 190.00 20.00 0.00 1.00 10.00 0.10 55.00 84.00 100.00 0.25 220.00 154.00 106.00 69.00 100.00 85.00 65.00 48.00 0 0 0 50.00 0 20 0 O 0 150 150 170 100 O 1.00 0.00 1.00 0.00 2.33 2.33 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.22 0.00 1.00 1.00 1.00 0.00 0.00 50.00 0 20 0 4O 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 50.00 0 20 0 40 40 170 170 170 100 0 1.00 0.00 1.00 0.00 0.25 0.25 0.25 0.00 0.00 0.75 0.75 0.30 0.30 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.21 0.00 1.00 1.00 1.00 0.00 0.00 50.00 0 20 1.00 0-33 0.00 0.00 --000 26 0 0 0.22 190 190 190 190 190 20 40 170 170 170 170 170 150 150 150 150 150 140 140 140 140 140 100 100 1 0 0 100.00 0.00 0.00 moowo 0000000 000—. 00030 oowoo 1 13490.00 0.10 0.00 0.16 0 0 3 0 0 0—b00 .0 -3 .0 .0 0 150 2 150 2-33 2-33 0.41 14.00 0.00 0.00 1.00 1.00 77 1 70 1 00 0.25 0.00 0.80 14.00 0.00 0.00 1.00 0.00 .00 17000.00 622. 0. 26.00 0.00 Operation Information: 281 290 300 250 250 240 45 70 101 170 180 180 230 131 150 250 250 270 131 141 250 250 240 131 150 N UH.»zmwmmowmmmommwmmzzmmmmz 1. 1 0 2 2 1 1 1 1. 0. 2. 2. 1. 1. 0. 2. 2. 1. 1 0 2 2 1 1 1 00 .00 .05 .00 ' .00 .00 .00 .00 00 05 00 00 00 00 05 00 00 00 00 05 00 00 00 00 00 4.80 5.50 1.00 15.00 15.00 22.00 8.00 7.00 6.00 1.00 8.00 8.00 0.00 5.00 1.00 15.00 15.00 0.00 4.00 1.00 15.00 15.00 22.00 6.00 6.00 300.00 00 00 0000000000000000000000000 UWCUwo—DOWWOWOWWOWNWOWWO—b3 30.00 0.00 0000000000000000000000008 000000000000000000000000 0000000000000NN0000000000 00-110 0000 00000 36.00 0.00 o o o o o o o o o o o o o o o o o o o o o o 0000003000000—9#0000000000 00000000000-00000d000000-50 000UIU'100001000000000000101000 0.10 278 FILE: FARM.HAY BRIEF DESCRIPTION: medium sized farm with 3 cuttings of hay (no alfalfa silage harvest) ELANHTHR ELANCORN D:\DENNIS\SIHUL\MACHHMEC.SM2 26 3 1 2 1 2 1 1 0 1 1 1 1 1 2 1 1 4 2 5 O 2 5 4 4 5 2 3 3 3 91 335 1953 1978 0 1 1 0 175.00 175.00 70.00 1.00 1.00 3 1 95 1 1 1 1 2 2 2 2 1 1 1 1 20 20 2O 2O 41 41 41 41 22 21 21 0 143.00 183.00 228.00 288.00 0 140 1 15.00 20.00 15.00 5 1 4 8.00 8.00 4.00 .08 .08 8.00 .31 .32 .08 260.00 130.00 250.00 190.00 20.00 .00 1.00 10.00 .10 55.00 84.00 100.00 .25 220.00 154.00 106.00 69.00 100.00 85.00 65.00 48.00 0 0 0 50.00 0 20 0 40 40 170 170 170 100 0 1.00 .00 1.00 .00 .25 .25 .25 .00 .00 .75 .75 .30 .30 .41 14.00 .80 14.00 1.00 .00 .00 .00 1.00 .00 .00 . .00 1.00 .22 .00 1.00 1.00 1.00 50.00 0 1 20 O 40 40 170 170 170 100 0 1.00 .00 1.00 .00 .25 .25 .25 .00 .00 .75 .75 .30 .30 .41 14.00 .80 14.00 1.00 .00 .00 .00 1.00 .00 .00 .00 .00 .00 .00 1.00 .21 .00 1.00 1.00 1.00 .00 .00 50.00 0 20 0 40 40 170 170 170 100 0 1.00 .00 1.00 .00 .25 .25 .25 .00 .00 .75 .75 .30 .30 .41 14.00 .80 14.00 1.00 .00 .00 .00 1.00 .00 .00 .00 .00 .00 .00 1.00 .21 .00 1.00 1.00 1.00 .00 .00 50.00 0 00 .00 A‘m‘T‘TI-rl‘fl I?" " . . ._. Tum-‘JALAI 279 20 0 4O 40 170 170 170 100 0 1.00 .00 1.00 .00 .25 .25 .25 .00 .00 .75 .75 .30 .30 .41 14.00 .80 14.00 1.00 .00 .00 .00 1.00 .00 .00 .00 .00 .00 .00 1.00 .00 .00 1.00 1.00 1.00 .00 .00 1 1 1 1 .00 26000.00 500.00 0 0 0 0 100.00 13490.00 26.00 622.00 30.00 36.00 .00 .00 .00 .00 .00 .00 .10 .00 .16 Operation Information: 1 281 4 1.00 4.80 4.60 .00 .00 190 290 5 1.00 5.50 1.50 .00 1.00 190 300 5 .05 1.00 .00 .00 .00 190 250 2 2.00 15.00 3.00 .00 .05 190 250 2 2.00 ’ 15.00 3.00 .00 .05 190 240 4 1.00 22.00 .00 .00 .00 20 45 4 1.00 8.00 3.70 .00 .00 40 70 2 1.00 7.00 2.70 .00 .00 170 101 5 1.00 6.00 3.70 30.00 1.00 170 170 5 .05 1.00 .00 .00 .00 170 180 2 2.00 8.00 3.00 2.00 .10 170 180 2 2.00 8.00 3.00 2.00 .10 170 230 20 1.00 .00 .00 .00 .00 150 130 5 1.00 5.00 3.70 .00 1.00 150 150 5 .05 1.00 .00 .00 .00 150 250 2 2.00 15.00 3.00 .00 .05 150 250 2 2.00 15.00 3.00 .00 .05 150 240 4 1.00 22.00 .00 .00 .00 14C 130 5 1.00 4.00 .80 .00 .40 140 140 5 .05 1.00 .00 .00 .00 140 250 2 2.00 » 15.00 3.00 .00 .05 140 250 2 2.00 15.00 3.00 .00 .05 140 240 4 1.00 22.00 ‘ .00 .00 .00 100 130 5 1.00 6.00 3.70 .00 .00 100 150 5 1.00 6.00 3.70 .00 .00 Wmiiu‘l um . I“ FILE: FARM.SLG BRIEF DESCRIPTION: medium sized farm with 4 cuttings of alfalfa silage (no hay harvest) ELANWTHR D:\DENNIS\SIMUL\MACHHMEC.SM2 0 N—h-DUJ 0 O 3 65 41 22 143.00 0 15.00 5 1 4 8.00 [0.08 8.00 260.00 20.00 10.00 0.25 100.00 0 0 0 50.00 0 20 0 1.00 0.00 0.33 0-33 0.00 0.00 0.00 1.00 50.00 0 20 888m88&8 999* 02999 88130 o. 0 00000000 990‘ In€3€3 P 0-40.00 00000 o. o 0 0 0 183.00 140 20.00 8.00 0.08 0.31 130.00 0.00 0.10 220.00 85.00 000- N0w0 1000000 000-. 708930 - W00 000—9 mowo dowoo 2 O 0 3 0-500 0 0‘00? O O 88$80 8838c 88$80 0-000 2 0 2 1953 1 70.00 95 2 0 2 65 41 21 228.00 15.00 4.00 0.32 250.00 1.00 55.00 154.00 65.00 150 1.86 0.41 0.00 1.00 150 1.86 0.41 0.00 1.00 150 1.86 0.41 0.00 1.00 280 ELANCORN 26 1 1 1 1 5 4 4 1978 0 1.00 1.00 2 o 2 65 41 0 288.00 0.08 190.00 84.00 100.00 106.00 69.00 48.00 150 150 100 1.86 0.25 0.00 14.00 0.80 14.00 0.00 0.00 0.00 1.00 1.00 0.00 150 150 100 1.86 0.25 0.00 14.00 0.80 14.00 0.00 0.00 0.00 1.00 1.00 0.00 150 150 100 1.86 0.25 0.00 14.00 0.80 14.00 0.00 0.00 0.00 1.00 1.00 0.00 00—40 00—50 C O C O O O 00 0 0000 800 00000 00-30 0 O O 0000 00000 00 w- 281 20 0 0 O 150 150 150 100 O 1.00 0.00 1.00 0.00 1.86 1.86 0.25 0.00 0.00 0.33 0.33 0.33 0.33 0.41 14.00 0.80 14.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 5 1 7 5 0.00 0.00 0.00 0 O 0 0 100.00 13490.00 26.00 622.00 30.00 36.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.22 0.10 0.00 0.16 Operation Information: 1 281 4 1.00 4.80 4.60 0.00 0.00 190 290 5 1.00 5.50 1 1.50 0.00 1.00 190 300 5 0.05 1.00 0.00 0.00 0.00 190 250 2 2.00 15.00 3.00 0.00 0.05 190 250 2 2.00 15.00 3.00 0.00 0.05 190 240 4 1.00 22.00 0.00 0.00 0.00 20 4S 1 1.00 8.00 3.70 0.00 0.00 150 131 5 1.00 5.00 3.70 0.00 1.00 150 150 5 0.05 1.00 0.00 0.00 0.00 150 250 2 2.00 15.00 3.00 0.00 0.05 150 250 2 2.00 15.00 3.00 0.00 0.05 150 240 4 1.00 22.00 0.00 0.00 0.00 140 131 5 1.00 4.00 1.50 0.00 0.40 140 141 5 0.05 1.00 0.00 0.00 0.00 140 250 2 2.00 15.00 3.00 0.00 0.05 140 250 2 2.00 15.00 3.00 0.00 0.05 140 240 4 1.00 22.00 0.00 0.00 0.00 100 131 5 1.00 6.00 3.70 0.00 0.00 100 150 5 1.00 6.00 3.70 0.00 0.00 Vflmn"h2.nv -2 '1, - '4 FILE: HACHHMEC.SM2 BRIEF DESCRIPTION: machine information fileowith HMEC harvested with 282 the forage chopper and a small (2 row) snapper head 1.4 30. 3.0 10. tractor 0.0 0.0 2.0 20.0 0.0 0.0 2.0 35.0 0.0 0.0 2.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 motor 0.0 0.0 3.0 mower 200 6500 2.0 80.0 2.0 100. 2.0 120. 2.0 0.0 0.0 0.0 0.0 mower-conditioner 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SP mow-conditioner 0.0 1.0 2.0 57.0 mat processor 0.0 0.0 0.0 single rake 0.0 0.0 0.0 double rake 0.0 0.0 0.0 square baler 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 round baler 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 forage harvester 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0. 1200. 10000. 9000. 0.0 0.0 0.0 0.0 0.0 2 2100. 17500. 2000. 0.0 0.0 0.0 0.0 0.0 3 3000. 25000. 0.0 0.0 0.0 0.0 4 3900. 32500. 0.0 000000 0 110 0.0 111 0.0 112 0.0 130 0.0 131 0.0 22500. 0.0 29250. 0.0 0.0 0.0 4800. 40000. 0.0 0.0 0.0 6000. 50000. 0.0 0.0 0.0 7200. 60000. 0.0 0.0 0.0 100. 500. 0.0 0.0 0.0 360. 3000. 0.0 7.0 0.0 1360. 10500. 0.0 8.0 0.0 1930. 14700. 0.0 8.0 0.0 4500. 33000. 0.0 8.0 0.0 3000. 28000. 0.0 6.4 0.0 400. 3500. 0.0 7.0 0.0 800. 7900. 0.0 7.0 0.0 1200. 8300. 0.0 6.0 25.0 1450. 10500. 0.0 6.0 30.0 1650. 12500. 0.0 6.0 30.0 0.0 36000. 0.0 45000. 0.0 54000. 0.0 450. 0.0 2700. 0.0 9500. 0.0 13200. 0.0 29700. 0.0 25200. 0.0 3150. 0 O 7100. 0 0 7470. 1.0 9500. ‘00 11250. 1 0 1250. 2000. 2400. 1000. 1400. 9300. 0.0 6.0 300. 14100. 0.0 6.0 600. 16400. 0.0 6.0 800. 8500. 0.0 6.0 0.0 12600. 0.0 6.0 0.0 8400. 1.0 12700. 1.0 14700. 1.0 7600. 1.0 11300. 1.0 .01 O. 2.0 6000. .01 .01 .01 .01 .01 .01 .01 2.0 0. 2.0 0. 2.0 0. 2.0 0. 2.0 7.5 100. 100. 200. 200. 250. 250. 250. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.7 2.7 3.7 3.7 2.7 2.7 5.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.0 12.0 15.0 15.0 7.5 0.0 0.0 6.0 8.0 11.0 6.0 8.5 12.0 11.0 14.0 '. ' Pn!‘ Jam“: S “ti.“- ..L “‘1'“ 283 132 1900. 17000. 15300. 0. 0.0 0.0 18.0 0.0 0.0 0.0 0.0 0.0 0.0 6.0 0.0 1.0 .26 1 SP forage harvester 133 7000. 80000. 72000. 0 0.0 0.0 22.0 0.0 1.0 2.0 186. 0.0 0.0 6.0 0.0 1.0 .06 2 0 row-crop attachment 140 200. 2600. 2340. 0. 0.0 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.05 0.4 .26 1 6 141 400. 3800. 3420. O 0.0 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.05 0.4 .26 1 6 142 900. 7400. 6600. 0. 0.0 2.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.05 0.4 .26 1 6 143 1200. 15000. 13500. O 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.05 0.4 .26 1 6 windrow pickup 150 300. 2200. 1980. 0 0.0 1.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.05 1.0 .26 1 6 151 500. 3800. 3400. 0 0.0 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.05 1.0 .26 1 6 bale ejector 170 250. 3000. 2700. 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05 1.0 .23 1 8 square bale wagon 180 320. 2100. 1900. 0. 0.0 0.0 0.0 4.5 0.0 0.0 0.0 0.0 0.5 8.0 2.0 0.1 .19 1.3 1 round bale mover 1 200 110. 650. 600. 0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 .03 0.2 6.0 0.0 1.0 .19 1 3 round bale wagon 210 700. 7000. 6300 0. 0.0 0.0 0.0 3.2 0.0 0.0 0.0 0.0 0.0 8.0 0.0 1.0 .19 1.3 bale elevator 230 600. 4000. 3600 0. 0.0 0.0 _6.8 0.0 0.0 0.0 0.0 0.0 0.0 22.0 0.0 0.0 .19 1.3 forage blower 240 500. 3500. 3150. O. 0.0 0.0 30.0 0.0 0.0 0.0 0.0 0.0 0.0 22.0 0.0 0.0 14 1.8 forage box 250 1500. 8000. 7200. 0. 0.0 0.0 0.0 9.0 0.0 0.0 0.0 0.0 0.0 15.0 0.0 .05 .14 1.8 bunker compactor 270 6000. 30. 0. 0. 0.0 0.0 0.0 0.0 1.0 2.0 100. 0.0 0.0 0.0 0.0 0.0 .01 2.0 corn planter 280 1700. 10000. 9000. O. 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.8 1.0 0.0 .54 2.1 281 2500. 14000. 12600. O 0.0 4.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.8 1.0 0.0 .54 2 282 3300. 20000. 18000. 0. 0.0 6.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.8 1.0 0.0 .54 2 1 283 5000. 35000. 31500. O 0.0 9.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.8 1.0 0.0 .54 2 corn coublne 290 1000. 8500. O. 0. 0.0 1.5 11.0 0.0 0.0 0.0 0.0 0.0 0.0 5.5 0.0 1.0 .26 2. 291 1400. 12600. O. 0. 0.0 2.3 14.0 0.0 0.0 0.0 0.0 0.0 0.0 5.5 0.0 1.0 .26 1.6 292 1900. 17000. O. 0. 0.0 3.0 18.0 0.0 0.0 0.0 0.0 0.0 0.0 5.5 0.0 1.0 .26 1.6 corn attachment 300 200. 2600. 2340. O. 0.0 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.5 .05 1.0 .26 1.6 special 0 0. 0. O. 0. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05 1.0 0.0 1.0 CORN PLANTING 0.70 4.5 0.0 0.0 0.0 CUTTERBAR MOVING 0.80 1.2 0.0 .01 .75 CUTTERBAR MOW-COND 0.80 3.0 2.0 .02 .75 284 HAT MAKER 0.80 4.0 33.0 .012 .80 SINGLE RAKING 0.80 1.0 0.0 .042 .00 DOUBLE RAKING 0.80 1.0 0.0 .042 {00 TEDDING 0.80 2.0 0.0 .023 1.00 RECT BALING (DROP) 0.80 0.0 5.0 .04 .00 ROUND BALING 0.75 0.0 7.5 .048 .00 LARGE STACK BALING 0.70 0.0 7.5 .136 .00 CHOP TO THE GROUND 0.80 0.0 15.0 .00 .OO AUTO BALE WAGON 0.80 0.0 6.0 .00 .00 LARGE STACK ROVER 0.80 0.0 0.0 .00 .00 ROUND BALE MOVER 0.80 0.0 0.0 .00 .00 CHOP (05) 0.80 0.0 15.0 .05 .00 CHOP (ALF HAYLAGE) 0.80 0.0 15.0 .037 .75 CROP (ALF-DC) 0.80 0.0 18.0 .037 .75 RECT BALING (EJECT) 0.80 0.0 5.0 .052 .00 HANDPICK BALES 0.80 0.0 0.0 .00 .OO R.H. CORN HARVEST 0.80 0.0 13.0 .035 .00 31 40 45 46 50 -M0wER(4) 70 71 -JRAKE(2) 100 101 102 110 111 112 -JBALER(6) 130 131 132 133 -JFHRV(4) 280 281 282 283 -JCRNPL(4) 290 291 292 ‘ -JHMCHV(3) 1 2 3 4 5 6 7 -JTRAC(7) 0. 0 O. O. 88 12.2 63. 15000. 88 15.2 88. 17000. 88 18.3 106. 19000. 49 18.3 142. 23000. .10 18.3 175. 26000. 10 21.3 218. 30000. 10 24.4 265. 33500. 32 21.3 312. 38000. 32 24.4 375. 44000. 14 21.3 485. 55000. 14 24.4 582. 67000. .14 27.4 680. 79000. .88 12.2 63. 40800. 7 u. 11 2.5-7’. . .... {l .14 .10 .14 .14 1.010010110101044mmmmzzzooo-d-qmmmmzzt: O O O O O O O O C O O O O C O O O O O O on on 15.2 88. 18.3 112. 18.3 142. 18.3 175. 21.3 218. 24.4 265. 21.3 312. 24.4 375. 21.3 485. 24.4 582. 27.4 680. 3.05 112. .10 3.05 142. 3.66 175. 5.49 318. 44500. 48300. 53400. 58700. 63500. 68200. 73700. 79400. 89700. 97000. 104000. 20100. 25500. 21000. 39300 - mmommmmwmmwmmmmuzzzzzzzzzzzzz o o o o o o o o o o o o o o a o o o o o o o o o o o o o o 285 15.2 5.49 530. 60900. 6. 15.2 5.49 688. 69000. 6. 15.2 5.49 949. 76000. 6. DAFOSYM output used to determine the value of alfalfa losses. Eliminated losses in Table E.1 correspond to: \OG‘QO‘U‘IcWN—I APPENDIX E Respiration Rain Mower Rake Baler Chopper Hay storage Silo storage Feeding 286 Table E.1 Description of simulated cases. 287 Case Milk Farm Loss(es) Notes Prod. File eliminated 1 13490 FARM.HE none base run 2 13490 FARM.ME 1 3 13490 FARM.ME 2 4 13490 FARM.ME 3 5 13490 FARH.ME 4 6 13490 FARH.ME 5 7 13490 FARM.ME 6 8 13490 FARM.HE 3 4 5 6 all machine losses 9 13490 FARH.HE 1 2 3 4 5 6 all harvest losses 10 13490 FARM.ME 7 11 13490 FARM.ME 8 preseal only 12 13490 FARM.ME 8 fermentation only 13 13490 FARM.ME 8 infiltration only 14 13490 FARM.ME 8 feedout only 15 13490 FARM.ME 8 16 13490 FARM.ME 7 8 all storage losses 17 13490 FARM.ME 9 18 13490 FARM.ME 1 2 3 4 5 6 7 8 9 all losses 19 13490 FARR.ME none optimal allocation 20 8990 FARN.ME none base run 21 8990 FARM.ME 1 22 8990 FARM.ME 2 23 8990 FARM.ME 3 24 8990 FARM.ME 4 25 8990 FARM.ME 5 26 8990 FARM.NE 6 27 8990 FARM.NE 3 4 5 6 all machine losses 28 8990 FARM.ME 1 2 3 4 5 6 all harvest losses 29 8990 FARM.ME 7 30 8990 FARN.ME 8 preseal only 31 8990 FARM.ME 8 fermentation only 32 8990 FARM.ME 8 infiltration only 33 8990 FARM.NE 8 feedout only 34 8990 FARM.NE 8 35 8990 FARH.NE 7 8 all storage losses 36 8990 FARM.ME 9 37 8990 FARM.HE 1 2 3 4 5 6 7 8 9 all losses 38 8990 FARM.NE none optimal allocation 39 13490 FARM.LG none 40 13490 FARM.LG 1 41 13490 FARM.LG 2 42 13490 FARM.LG 3 43 13490 FARM.LG 4 44 13490 FARM.LG 5 45 13490 FARM.LG 6 46 13490 FARM.LG 7 n ,. .i "33""? i" 13490 13490 13490 13490 13490 13490 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 13490 13490 13490 13490 13490 13490 134904 13490 13490 13490 13490 13490 13490 13490 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 13490 13490 13490 13490 8990 FARM. FARM. FARM. FARM. FARM. .LG FARM FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. .LG FARM FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. FARM. .SM FARM FARM. FARM. FARM. FARM. FARM. .SM FARM. FARM. FARM. .SM .SM FARM. FARM. FARM. FARM. FARM. FARM FARM FARM LG LG LG LG LG LG LG LG LG LG LG LG LG LG LG LG LG LG SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM SM DRY DRY DRY DRY DRY 288 8 9 . none optimal allocation 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 none 1 2 5 6 7 8 none optimal allocation 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 none 1 2 5 6 7 8 9 none optimal allocation 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 none 1 2 5 6 7 8 9 none optimal allocation 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 none 1 2 3 4 5 6 hay at 151, silage hay at 151, silage 7 8 bay at 151, silage 1 2 3 4 5 6 7 8 9 hay at 151, silage none hay at 15%, silage at 601 at 60% at 60% at 607 at 60% 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 139 140 141 142 143 144 145 146 147 148 149 150 151 152 8990 8990 8990 13490 13490 13490 13490 8990 8990 8990 8990 13490 13490 13490 8990 8990 8990 13490 13490 13490 13490 13490 13490 8990 8990 8990 8990 8990 8990 13490 13490 13490 13490 13490 13490 8990 8990 8990 8990 8990 8990 13490 13490 13490 13490 13490 13490 13490 13490 13490 13490 8990 8990 FARM.DRY FARM.DRY FARM.DRY FARM.RND FARM.RND FARM.RND FARM.RND FARM.RND FARM.RND FARM.RND FARM.RND FARM2.RND FARM2.RND FARM2.RND FARM2.RND FARM2.RND FARM2.RND FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.SEA FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.BUN FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY FARM.RAY 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 none none none none none none none none none Ana-o O 289 7 8 0000000900 0000090000 0000000000 0000000000 hay at 151, silage at 60% hay at 151, silage at 601 hay at 151, silage at 601 round round round round round round round round round round round round round round bales bales bales bales bales bales bales bales bales bales bales bales bales y bales stored stored stored stored stored stored stored stored stored stored stored stored stored stored sealed alfalfa silos " preseal only " fermentation only " infiltration only " feedout only sealed alfalfa silos sealed alfalfa silos " preseal only " fermentation only " infiltration only " feedout only sealed alfalfa silos bunker alfalfa silos " preseal only " fermentation only " infiltration only " feedout only bunker alfalfa silos bunker alfalfa silos " preseal only " fermentation only " infiltration only " feedout only bunker alfalfa silos all hay system all hay system all hay system all hay system all hay system all hay system all hay system all hay system all hay system all hay system all hay system all hay system inside inside inside inside inside inside inside inside outside outside outside outside outside outside 0 ‘ I . _ ’- ”15.31.1152... h. . 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 8990 8990 8990 8990 8990 8990 8990 8990 13490 13490 13490 13490 13490 13490 13490 13490 13490 8990 8990 8990 8990 8990 8990 8990 8990 8990 13490 13490 13490 8990 13490 13490 8990 8990 13490 13490 8990 8990 13490 13490 8990 8990 13490 8990 13490. 8990 13490 8990 FARM.MAY FARM.MAY FARM.MAY FARM.MAY FARM.MAY FARM.MAY FARM.MAY FARM.MAY FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.SLG FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME FARM.ME 2 3 u 5 7 9 1 2 3 4 5 1 2 3 4 5 7 9 none 1 2 3 6 8 9 1 2 3 6 1 2 3 6 8 9 none 1 2 3 6 8 9 1 2 3 6 1 2 3 6 7 9 none 1 2 3 4 5 6 7 8 9 none 290 1 2 3 4 5 6 7 8 9 none 1 2 3 4 5 6 7 8 9 none 1 2 3 4 5 6 7 8 9 none 1 2 3 4 5 6 7 8 9 none 1 2 3 4 5 6 7 8 9 none 123456789. none 1 2 3 4 5 6 7 8 9 none none 09000000 all all all all all all all all all all all all all all all all all all all all all all all all all all 10$ 10% 10$ 10$ 107 101 10$ 10% 101 10$ 101 101 system system system system system system system system silage silage silage silage silage silage silage silage silage silage silage silage silage silage silage silage silage silage higher higher higher higher higher higher higher higher higher higher higher higher distillers distillers distillers distillers optimal alloc. w/ max forage optimal alloc. w/ max forage hemicellulose breakdown only hemicellulose breakdown only proteolysis only proteolysis only system system system system system system system system system system system system system system system system system system animal weights animal weights animal weights animal weights ingestive capacity ingestive capacity ingestive capacity ingestive capacity protein suppl price protein suppl price protein suppl price protein suppl price unavailable unavailable unavailable unavailable 'J-w . -*‘- .n .. _ . J'.u 291 Table E.2 Feed production and feeds sold (all units T DM/y ~~ negative values indicate feed purchases). Feed production Feeds sold Case RQH LOB HQAS LQAS CS HMEC CG ALF CC SBM DST 1 72 150 104 154 123 98 137 ~10 44 ~18 ~34 2 105 122 105 157 123 98 137 O 47 ~19 ~37 3 91 141 106 157 123 98 137 ~2 49 ~18 ~34 4 84 144 106 157 123 98 137 -5 47 ~17 ~34 5 100 146 104 154 123 98 137 6 48 ~18 ~33 6 92 142 104 154 123 98 137 ~4 46 ~18 ~33 7 72 150 107 161 123 98 137 -5 48 ~17 -35 8 125 140 108 166 123 98 137 30 51 ~18 ~32 9 195 92 112 , 173 123 98_ 137 51 56 ~22 ~34 10 A 74 154 104 154 123 98 137 ~11 35 ~12 ~26 11 72 150 104 154 123 98 137 ~10 45 ~18 ~34 12 72 150 105 155 123 98 137 ~9 47 ~17 ~36 13 72 150 112 163 123 98 137 6 51 ~18 ~43 14 72 150 106 157 123 98 137 ~8 49 ~17 ~37 15 72 150 115 167 123 98 137 16 53 ~18 ~47 16 74 154 115 167 135 98 137 3 59 ~19 -34 17 76 158 110 162 123 98 137 12 46 ~19 ~33 18 211 99 129 197 123 98 137 84 55 ~19 ~34 19 72 150 104 154 123 98 137 ~24 ~4 0 ~11 20 72 150 104 154 123 98 137 ~29 105 ~15 .-31 21 105 122 105 157 123 98 137 ~24 115 ~21 ~26 22 91 141 106 157 123 98 137 ~22 115 ~18 ~26 23 84 144 106 157 123 98 137 ~22 108 ~16 ~29 24 100 146 104 154 123 98 137 ~10 110 ~16 ~29 25 92 142 104 1154 123 98 137 ~20 108 ~15 ~29 26 72 150 107 161 123 98 137 ~22 108 ~16 ~29 27 125 140 108 166 123 98 137 17 118 ~19 ~24 28 195 92 112 173 123 98 137 45 132 ~31 ~12 29 74 154 104 154 123 98 137 ~29 98 ~10 ~21 30 72 150 104 154 123 98 137 ~29 105 ~15 ~31 31 72 150 105 155 123 98 137 ~29 107 ~16 ~30 32 72 150 112 163 123 98 137 ~22 121 ~24 ~24 33 72 150 106 157 123 98 137 ~28 110 ~17 ~29 34 72 150 115 167 123 98 137 ~15 125 ~30 ~18 35 74 154 115 167 123 1 98 137 ~22 124 ~22 ~12 36 76 158 110 162 123 98 137 ~6 108 ~15 ~31 37 211 99 129 197 123 98 137 97 137 ~34 0 38 72 150 104 154 123 98 137 1 42 0 ~2 39 95 225 193 190 191 131 216 ~21 65 ~24 ~62 40 125 204 194 195 191 131 216 ~6 75 ~24 ~71 41 116 219 194 197 191 131 216 ~7 77 ~24 ~62 42 102 228 194 194 191 131 216 ~10 69 ~24 ~61 43 127 229 193 190 191 131 216 4 71 ~26 ~59 44 112 225 193 190 191 131 216 ~10 70 ~24 ~61 100 228 231 225 236 225 175 231 190 225 204 219 228 229 194 193 208 203 193 194 219 193 194 194 194 193 - 193 194 193 208 203 ' 193 194 208 219 152 152 153 152 152 152 153 152 165 160 152 154 165 175 152 152 153 152 152 152 ' 153 152 165 160 152 154 165 175 107 114 118 200 1 90 209 200 190 208 209 24 1 190 195 197 1 94 1 90 1 90 200 190 209 200 1 90 208 209 N 3 0000000000000000000000000000-5 .S—b 0": W“) 156 292 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 83833838333383$338383$$$33$$38§$§“3’5: 216 216 216 216 216 216 - 1 1 ~29 19 12 -39 62 -19 104 ~57 ~49 ~44 ~45 -30 -44 -47 ~58 ~33 ~24 39 ~46 121 11 19 15 14 22 16 14 11 24 19 45 13 ~24 ~ 18 ~26 ~26 -31 ~27 ~28 ~21 -31 ~26 -23 ~25 -23 -23 ~ 15 ~42 ~24 ~48 -30 -49 ~ 12 ~ 12 ~12 ~62 -45 ~77 ~61 -4 ~58 ~48 ~50 ~54 -47 -47 ~50 ~48 ~50 ~51 -36 - 36 ~52 ~17 ~25 -1 ~20 ~24 ~20 ~21 ~21 ~20 -21 ~17 -27 ~21 ~1 -22 ~20 ~20 ~22 ~18 ~18 ~21 ~21 ~21 ~21 ~16 ~14 ~22 ~10 ~10 -1 ~27 ~28 -30 ... 1‘“ "n (”‘3 EVE. 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 358 101 153 95 155 101 131 130 135 152 101 117 135 131 130 135 152 101 117 135 149 149 149 149 149 149 149 149 149 149 149 149 153 153 153 153 153 153 153 153 153 153 153 153 256 229 240 253 257 252 263 269 178 193 132 107 114 118 132 104 104 104 109 0000000000 190 142 165 156 190 153 153 153 161 153 153 161 153 153 153 161 153 153 161 148 148 149 157 150 161 148 148 149 157 150 161 149 150 151 .O-I-D—I-D-bd—D-O GUIUIUIUIsz'IUI gum-500363“ 0000000000 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 293 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 64 -52 ~46 74 ~24 -32 ~67 -55 ~4 -50 -113 -50 ~8 ~80 ~73 ~ 17 ~ 14 ~ 14 -13 ~11 11 -31 -31 -31 ~24 -30 ~19 ~22 ~22 ~22 ~4 ~19 ~41 ~41 ~40 -35 ~40 ~28 -75 ~66 ~66 ~69 -52 -63 -77 -56 - 13 1O 54 86 128 115 137 ~14 ~17 ~8 ~18 ~18 -32 ~17 ~17 ~11 ~18 ~8 ~11 ~14 ~15 ~10 ~16 ~6 ~10 -7 ~16 ~16 ~17 ~17 ~18 ~18 ~15 ~15 ~15 ~22 ~16 ~27 ~16 ~16 ~17 ~18 ~18 ~18 ~14 ~14 ~16 ~22 ~17 ~29 ~17 -13 -14 ~14 ~14 -13 -9 ~16 ~17 ~4 ~25 ~10 ~26 ~27 ~26 ~27 -30 ~29 ~20 ~24 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 142 180 180 155 174 167 woo-... mug-1:- “‘00 .4 N 000000000000000000 256 229 240 253 257 252 263 269 ...-a ‘0" Cum 000000000000000000 .... ... ... ... \OU‘OWQWQU'I 0000001100 150 \O \O 150 99 150 99 150 99 150 150 150 150 150 150 0000000000 ._.-._. U‘lU'lU' 1.1101: 155 155 166 162 155 ' 177 154 155 155 155 155 166 162 155 177 104 129 104 129 104 .129 104 129 104 129 104 129 104 129 104 129 104 104 104 104 104 104 0000000000 154 197 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 294 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 ~1OO -92 125 ~101 23 -57 39 ~85 53 ~11 84 ~29 97 ~11 84 -30 -95 ~121 ~16 -13 -32 ~11 ~15 ~11 ~11 ~12 ~10 ~22 ~6 ~15 ~17 ~16 ~16 ~17 ~28 ~16 ~41 -37 ~36 -34 -32 -34 ~54 -32 ~41 ~11 ~16 ~17 -23 ~16 ~19 ~25 -30 -35 ~41 -23 -34 -39 ~41 -34 -34 ~15 ~10 ~4 ~24 ~14 ~61 Table E.3 Economic information (all units $/y). 295 Case Machine Storage Labor Seed Milk Net Cost Cost Cost Cost Income Return 1 23710 10853 13305 20150 232505 143115 2 23710 10853 13402 20150 235224 146102 3 23710 10853 13449 20150 234826 146507 4 23710 10853 13397 20150 232927 144295 5 23710 10853 13563 20150 233295 145337 6 23710 10853 13433 20150 233071 144432 7 23710 10853 13371 20150 232995 144354 8 23710 10853 13871 20150 235411 149637 9 23710 10853 14196 20150 239162 155288 10 23710 10853 13305 20150 235384 147461 11 23710 10853 13305 20150 232505 143130 12 23710 10853 13305 20150 232775 143548 13 23710 10853 13305 20150 237164 148351 14 23710 10853 13305 20150 233298 144242 15 23710 10853 13305 20150 239087 150550 16 23710 10853 13386 20150 242259 155970 17 23710 10853 13305 20150 232930 145168 18 23710 10853 14196 20150 249664 170196 19 23710 10853 13305 20150 242380 154174 ’ 20 23710 10853 13305 20150 200001 116338 21 23710 10853 13402 20150 200001 116968 22 23710 10853 13449 20150 200001 117747 23 23710 10853 13397 20150 200001 117132 24 23710 10853 13563 20150 200001 117895 25 23710 10853 13433 20150 200001 117180 26 23710 10853 13371 20150 200001 117108 27 23710 10853 13871 20150 200001 120419 28 23710 10853 14196 20150 200001 122764 29 23710 10853 13305 20150 200001 118316 30 23710 10853 13305 20150 200001 116350 31 23710 10853 13305 20150 200001 116495 32 23710 10853 13305 20150 200001 117505 33 23710 10853 13305 20150 200001 116708 34 23710 10853 13305 20150 200001 117986 35 23710 10853 13305 20150 200001 120106 36 23710 10853 13305 20150 200001 118053 37 23710 10853 14196 20150 200001 128786 38 23710 10853 13305 20150 200001 119609 39 25012 14505 19229 30375 351562 227001 40 25012 14505 19381 30375 355569 231515 41 25012 14505 19450 30375 353918 231194 42 25012 A 14505 19396 30375 352921 229324 43 25012 14505 19606 30375 353929 231237 44 25012 14505 19412 30375 352376 228857 45 25012 14505 19349 30375 353247 229556 46 25012 14505 19229 30375 355988 233467 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 25012 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 18112 23710 23710 23710 23710 23710 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 14505 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 7096 10853 10853 10853 10853 10853 19229 19229 19229 20615 19229 20615 19229 19381 19450 19396 19606 19412 19349 19229 19229 19229 19229 20615 19229 20615 8365 8429 .8445 8426 8521 8444 8424 8365 8365 8365 8365 8924 8365 8924 8365 8429 8445 8426 8521 8444 8424 8365 8365 8365 8365 8924 8365 8924 12688 13625 12688 13625 12688 296 30375 30375. 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 30375 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 12150 20150 20150 20150 20150 20150 359701 352463 365723 362071 364266 375790 300001 300001 299911 300001 300001 300001 300001 300001 300001 300001 300001 300001 300001 300001 138670 140191 140287 138829 138746 138854 139128 139982 142351 138538 142789 142576 143777 148666 120000 120000 120000 120000 120000 120000 120000 120000 120000 120000 120000 120000 120000 120000 232074 241810 240114 250393 200001 236515 230230 243846 245519 242958 266236 184447 185380 186518 185594 186691 185579 185553 187376 186870 186914 190143 193782 189968 203135 81107 82780 83197 81699 82151 81802 81911 83389 85121 82052 86882 88024 87509 96533 65829 66117 66526 66232 66777 66348 66200 66966 66654 66867 68108 69311 67868 72698 142398 158715 151381 170166 115995 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 139 140 141 142 143 144 145 146 147 148 149 150 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