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Sisco has been accepted towards fuifillment of the requirements for Ph . D . Jegree in Wehnology fi/fl/W Major professor D afbpfl'JMif‘f A24 /7J’0 0-7639 L MSU LIBRARIES RETURNING MATERIALS: Place in book drop to remove this checkout from your record. FINES wili be charged if book is returned after the date stamped below. A COMPUTER MODEL FOR FEED HARVESTING MACHINERY SELECTION ON A DAIRY FARM By Jesus Antonio Sisco A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1980 ABSTRACT A COMPUTER MODEL FOR FEED HARVESTING MACHINERY SELECTION ON DAIRY FARMS By Jesus Antonio Sisco At Michigan State University, a Dairy Research Group has been formed to study the feed-dairy production system, which stated the need for a computer model for the selection of feed harvesting machinery on dairy farms. A methodology was set up to build the model. Surveys were conducted to identify the different feed production methods; to get information on feed disappearance, cropland distribution and size of dairy farms in.Michigang and to determine the number, size and type of field machinery specifically used in forage, hay and grain harvesting. Feed requirement, feed losses and available time were used to calculate the required system capacity. Effective field capacity and effective material capacity were computed by using alogorithms developed for each machine component of the feed harvesting system. Selection of size and number of machines was made when machine effective material capacity was greater or equal to the required system capacity. Jesus Antonio Sisco Comparison of model output to data from four selected surveys showed a reasonable model behavior in the selection of farm machinery for a feed harvesting system, as well as its ability to handle actual data. J The model output reacts to changes of relevant para- meters such as transport unit travel distance and speed, crop yield, available time, and the harvesting rate. The feed harvesting machinery selection model could be added as a complement to existing farm machinery selection models; to studies evaluating feed quality and quantity losses due to harvesting and handling; to the management of feed harvesting machines in relationship with the rest of the farm, and to studies on the effect of decreased field drying time on a total dairy farm operation. Approved Major Professor Approved iii Department Chairman To my beloved children: Miguel, Vladimir, Lina and Kathleen ii ACKNOWLEDGEMENTS The author wishes to thank Dr. Robert H. Wilkinson for his guidance, understanding and for being his major professor. Appreciations are extended to Dr. Roger C. Brook (Agricultural Engineering) and Dr. J. Roy Black (Agricultural Economics) for their assistance in the development of this study, and for serving as guidance committee members. Sincere gratitude is expressed to Dr. Lynn S. Robertson (Crop and Soil Sciences), Dr. George E. Mase (Metallurgy, Mechanics and Material Science) and Dr. C. Alan Rotz (Agricultural Engineering) for their encouragement, opportune advice, and for being on the guidance committee. The author wishes to express special thanks to the guidance committee members for their crucial decision allow- ing him to continue his graduate work. For facilitating data on weather and dairy farms in Michigan, thanks to Dr. Fred V. Nurnberger (Michigan Weather Service) and Mr. Jim.Mulvaney (Telfarm) respectively. The scholarship granted to the author by Universidad de Los Andes and Nucleo Universitario Rafael Rangel, Venezuela, permitted him to complete his graduate study and this is very much appreciated. For guiding his first walk through at the beginning strange but at the end fascinating world of programming iii and system analysis, the author thanks Mr. Simon Garmendia, Mr. Ardeshir Goshtasby and Mr. Steve Kraus. Finally, thanks to Mrs. Marcia Blackson for her extraordinary diligence in typing this manuscript. iv TABLE OF CONTENTS Page LIST OF TABLES . . . . . . . . . . . . . . . . . . . . viii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . x LIST OF SYMBOLS . . . . . . . . . . . . . . . . . . . xi Chapter 1 INTRODUCTION . . . . 1 1.1. Justification for a Computer Model . 1 1.2. Assumptions to Build the Model . . 2 1.3. Objectives . . . . . . . . . . . . . 3 1.4. Methodology . . . . . . . . . . . . h 2 LITERATURE REVIEW . . . 5 2.1. Applications of System Analysis to Agriculture . . . . . . . 5 2.2. Farm.Machinery Selection . . . 7 2 3 Determination of Available Working Days . . - 9 2.9. Handling and Harvesting of Hay and Forage . . . . 13 5 Some Characteristics of Dairy Farms in Michigan . . . . . . . . . . . . . 14 3 DEVELOPMENT OF THE MODEL . . . . . 19 3.1. Survey of Feed Production Methods . . 19 3.2. Flow Chart Representation of the Feed Production Systems . . . . 19 3.3. Flow Chart for the Selection of Machinery Complement . . . . . . . 26 3.#. Types of Input Data and Parameters . . 29 . Machinery Capacity Parameters . . . . . . 29 3. h. 1. 1. System Capacity . . 29 3.“.1.2. Effective Field Capacity . . . . . . 3O 3.#.1.3. Forage Harvester Capacity . . . . . . 31 3.#.1.#. Transport Unit Capacity . . . . . . 31 Chapter LI. 5 Cycle Time . . Blower Capacity Baler Capacity . Continuous Width Implements . . Non-continuous Width Implements . . . . . equirements . . . Rolling Resistance . Rolling Resistance Coefficient Drawbar Power and Power Take- off Equivalent . . 3.4.2.“. Power- take off Power . 3.“.2.5. Total Implement Power Requirement 3. 4. 2. 6. Tractor Power u) me \O (DVOUI b cbcb H HHHH 3.9.2. e - O l: tears U Who’d U UUUU N NN"! DESCRIPTION OF THE MODEL . . . . A. 1. HHHHHHmefloxU-Hrw N OMPUNHO- - cctctctttccrttp : 17 5.1. Feed Harvesting Machinery Selection Model . . . . . . . . . . . Feed Harvesting Machinery Selection Program . . . . . . . . . Available Work Time . . . . . Counting Dry Days . . Counting Open Haying Da Periods Green Feeding (Chopping) of Forage . . Forage Harvest Machines . . . . . Transportation Units . . . . Harvesting Silage or Haylage . Hay (Dry) Harvest . . . . . . . Conventional Baling . . . . Convention Baling System Rate of Cut for Implements . Round Baler Systems . . Stack System . Cuber System . . . . . . . Grain Harvest . . . . . . . . . . VALIDATION OF MODEL, ANALYSIS AND RESULTS . Model Validation . . . . 5.1.1. Verification of Program and Subroutines . . . . . . . . 5. L 2. Dairy Farm Survey . . 5.1.3. Field Machinery Survey . 5.1 A. Comparison of Four Field Machinery Surveys to Model Output . . . . . . . . . . . vi Page 32 33 3h 35 35 36 Chapter 5.2. Final Analysis . . . . 5.2.1. Effect of Travel Speed on Transport Unit Size and Number . . . 5.2.2. Effect of Travel Distance on Transport Unit Size and Number . . . 5.2.3. Effect of Crop Yield on Machinery Size and Number 5.2.4. Effect of Available Time on Machinery Size and Number . . 5.2.5. Effect of Harvesting Rate on Transport Unit and Blower Size and Number . . . . . 6 CONCLUSIONS AND RECOMMENDATIONS . . . . . 6.1. Conclusions . . . 6.2. Recommendations for Further Research APPENDICES A Nomograph for Determining Loading Time . . B Nomograph for Determining Unloading Time C Dairy Farm Survey . . . . . . . . . . . . . D Dairy Farm Machinery Survey . . . . . . . . E Conversion Factors . . . . . . . . . . BIBLIOGRAPHY . . General References . vii Page 114 114 115 115 119 120 121 121 122 124 125 126 128 130 131 135 LIST OF TABLES Table Page 2.1. Percentage distribution and some character- istics of dairy farms, cows, milk sales and the labor force by economic class, 1964 census of agriculture . . . . . . . . . 15 2.2. Percent of dairy farmers reporting use of specialized equipment for milking, manure handling, forage harvesting and handling and combining, six areas of Michigan, fluid farms, 1971 . . . . . . . . . . . . . 16 2.3. Average number of milk cows, acres of crop- land operated and acres of various crops produced per farm, and feed crops grown per cow, six areas of Michigan, fluid farms, 1971 . . . . . . . . . . . . . . . . 18 3.1. Methods of feed production . . . . . . . . . . 20 3.2 Values of ratio Cn and rolling resistance coefficient CR . . . . . . . . . . . . . . . 36 4.1. Formulas for calculation of tractor number . . ”1 4.2. Input parameters for main program . . . . . . 43 5.1. Output sample, test subroutine GF . . . . . . 87 5.2. Output sample, test subroutine SILHYL (Horizontal silo) . . . . . . . . . . . . . 88 5.3. Output sample, test of subroutine SILHYL (Vertical silo) . . . . . . . . . . . . . . 89 5.4. Test of subroutine BALING . . . . . . . . . . 92 Test of subroutine HAYBl . . . . . . . . . . . 94 5.6. Test of subroutines HAYBZ, STACK and CUBE . . 95 viii Table 5.7. 5.8. Test of subroutine GRAIN . . . . . . . Average number of dairy cows and surface of cropland produced . . . . . . . . . . . Average surface of cropland per cow, ha/cow Average feed disappearance in farms with less than 50 cows, t . . . . . . . . Average feed disappearance in farms with 50 - 75 cows, t . . . . . . . . . . Average feed disappearance in farms with 76- 100 cows, t . . . . . . . . . . . Average feed disappearance in farms with more than 100 cows, t . . . . . . . Percent of dairy farmers reporting use of specialized equipment for feed production Field machinery survey and model output data. One farm with less than 50 cows Field machinery survey and model output data. One farm with 50 - 75 cows . . . Field machinery survey and model output data. One farm with 76 - 100 cows . Field machinery survey and model output data. Effect size Effect unit Effect size One farm with more than 100 cows of travel speed on transport unit and numbe r 0 O O I O O I O O I O O of travel distance on transport size and number . . . . . . . . . of crop yield on machine number and ix Page 97 99 . 101 O 102 . 103 . 104 . 105 107. . 109 . 110 . 112 . 113 116 . 117 , 118 ‘OCDVQU‘C‘WNH c- F? 4: -: c- tr 4: -: c- 4: -P H O 4:- HH NH 4.13. 4.14. 4.15. 4.16. Flow chart for feed production system . Flow chart for the selection of farm LIST OF FIGURES machinery complement . Flow chart Flow Flow Flow chart chart chart Simplified Flow Flow Flow Flow chart chart chart chart Simplified Flow Flow Flow Flow Flow Flow chart chart chart chart chart chart for program FHMS . for subroutine for subroutine for subroutine flow chart for for for for for flow chart for for for for for for for subroutine subroutine subroutine subroutine subroutine subroutine subroutine subroutine subroutine subroutine TIME COUNT COUNTP subroutine GF . FORHAR TRAPUN SILHYL HAY . subroutine BALING . HAYB1 RBC . HAYBZ STACK . CUBE GRAIN Page 22 28 42 46 55 56 58 61 63 66 69 7O 73 75 76 78 8O 83 bu/a ft ft3 ft/s2 gal gal/a gal/h gal/ton ha ha/h hp hp/ft hp - h/gal hp - h/lb hp - h/ton hp/lb kg kg/ha LIST OF SYMBOLS acre acre per hour bushel bushel per acre foot cubic foot foot per square second gallon gallon per acre gallon per hour gallon per ton hour hectare hectare per hour~ horsepower horsepower per foot horsepoWer-hour per gallon horsepower-hour per pound horsepower-hour per ton horsepower per pound kilogram kilogram per hectare xi kg/m3 kg/s km/h kN KW kW/h kW - h/kg kilogram per cubic meter kilogram per second kilometer kilometer per hour kilonewton kilowatt kilowatt per hour kilowatt-hour per kilogram kilowatt-hour per liter kilowatt-hour per ton kilowatt per kilogram kilowatt per meter kilowatt per row liter meter square meter cubic meter meter per square second mile mile per hour dollars per hectare dollars per ton dollars per year metric ton metric ton per hour metric ton per hectare metric ton per year xii CHAPTER 1 INTRODUCTION 1.1. Justification for a Computer Model. On a feed producing dairy farm, the selection of a machinery complement is a complex problem involving many economic, biological and social factors, such as weather uncertainities, timeliness, sequential and parallel oper- ations, soil type and conditions and management practices, not to mention the farmer's preferences for certain machines or agronomic practices. At Michigan State University, a Dairy Research Group has been formed to study the feed-dairy production systems, which stated the need for a computer model for the selection of feed harvesting machinery that could be added to existing computer models, such as the ones developed by Singh (1978) and Wolak (1980). During the past years, several authors have proposed a great variety of methods or procedures to select the feed production system and the related farm machinery complement for agricultural enterprises, including dairy farms. Those methods range from simple hand calculation methods to sophisticated analytical and simulation methods. Computer models have proved to be a very useful tool in the selection and scheduling of farm machinery, the pre- diction of available time for field machinery operations and the economic analysis of farm machinery investments. This study attempts to develop a general computer model to cope with the different feed requirements, field V machinery uses and agronomic practices commonly found in the State of Michigan. The present version of the computer model does not include scheduling of machinery use. Rather it is considered that selection of feed-harvesting machinery is comprehensively treated so as to be used as the basis for further work. 1.2. Assumptions to Build Model. The development of the computer model was initiated under the following assumptions: a) Available time for field operations can be reasonably predicted from probabilistic procedures based on actual weather information. b) Feed requirements and feed losses are previously determined and used as input to the model. 0) Feed production in dairy farms is considered as a problem of processing and transporting material from the field to the storage or feeding areas in the time available for such operations. d) The wide variety of feed production methods are summarized in five large methods, e.g. silage, e) f) s) haylage, hay, green feeding and grain. Silage and grain harvest are considered once over operations, while haylage, hay and green feeding are harvested more than once during the production season. Feed production is treated as a material handling system and, consequently, suitable to application of system analysis methods. The machinery set selected is the result of the working days availability for the years under study, with work days being generated from weather data for a specified period of years. 1.3. Objectives. The objectives of this study include the following: a) b) To develop a computer model for feed-harvesting systems commonly used on dairy farms in Michigan. Such a model will enable comparison between systems with respect to machinery requirements and costs of use. To select the optimal machinery complement for the feed-harvesting systems on dairy farms in Michigan. 1.4. Methodology. In order to accomplish the stated objectives, the study was conducted according to the following steps: a) b) C) d) e) f) 8') h) Review of literature Formulation of an initial model a. Diagram of feed production methods b. Numerical modeling c. Small computer program Data collection a. Feed harvesting systems data b. Machinery data c. Preparation of input data Formulation of detailed model a. Establish mathematical relationships b. Diagram of the model c. Detail model components Programming a. Flow chart of feed production methods b. Translation to FORTRAN c. Debugging programs Validation of model a. Machinery survey in dairy farms b. Comparison of model results to survey data c. Tests of sensitivity Analysis of results Publication CHAPTER 2 LITERATURE REVIEW 2.1. Applications of System Analysis to Agriculture. Pinches (1956) suggested the need for agricultural engineering research with explicit application of management engineering to farm operations; one step forward of hand calculation procedures. Integration of processes, machines, structures and form of products, as they are found in agriculture, constitute a system and as such suitable to application of management engineering. Sammet (1959) stated the use of model building as an alternative to experimental comparison. Definitions and schematizations of system engineering given in this paper facilitated the conceptualization of the model here presented. A planned approach to systems studies at two levels of activity is described. System analysis, compris- ing the study, definition and description of processes, and the establishment of optimum relationships; and system design and development, including research and development oriented to methods improvement and the execution of plans of action based on results of systems analysis. Rockwell (1965) stated the importance of using simulation methods for solution of operational system 5 problems in almost every field of the economic and social activities. An analogy was established between industrial and agricultural production systems to encourage the application of system analysis to agricultural production. Von Bargen (1965, 1966) developed procedures to apply system analysis to alfalfa hay harvesting. Link (1965) stated that decisions about what machines to use arerelated to the crop-production methods in such a way that both have to be considered together. For this purpose, Link proposed the use of techniques of activity network analysis for analyzing crop production systems and machinery selection. Chen and Wensink (1978) illustrated the application of resource planning and management networks (RPM) in agricultural systems analysis, as a means of solving mathe- matical programs. Linear programming models have been presented to build models for forage production systems as a whole and for each of their component sub-systems. Kjelgaard and Quade (1975) developed a model for forage transport and handling, and Tseng and Mears (1975) modeled a system for forage production. Analysis of results from both papers proved to be valuable in determining the harvesting and handling practices in a forage production system. Peart et al., (1963) applied mathematical programming to the optimization of materials-handling systems. Losses in alfalfa dry matter during harvest were determined by Dale et al., (1978) during the development of a harvesting simulation model. Also, to show the effect of handling, harvesting and drying on hay yield, quality, digestability and dry matter, Dobie et al., (1963) established an experi- mental procedure using four harvesting treatments. Hay raked and baled at low moisture content had a lower loss in yield than hay raked and baled too dry. The effect of harvest starting date, harvesting rate and weather on the value of forage for dairy cows are shown by Millier and Rehkugler (1970) through an analysis of simulated harvesting systems. Parke et al., (1978) studied forage conservation methods applying modeling and simulation techniques to determine forage quality and quantity losses. 2.2. Farm Machinery Selection. Hunt (1977) derived formulas to determine annual costs, power requirements and machine size in relation to a timeliness factor, and applied the obtained values to the selection of farm machinery. Recent works of Burrow and Siemens (1974), Hughes and Holtman (1974), Von Bargen and Cunney (1974) approached the problem of farm machinery selection using several techniques from system analysis theory. Wolak and Holtman (1976) designed a computer program to analyze dairy farm design and determine energy requirements in southern Michigan dairy farms. Singh et al., (1978a) developed a computer program for multi-crop farms to determine opera- tions schedule, field machinery requirements and costs. Singh (1978) previously designed a system modeling Michigan cash crop production systems. Wolak (1980) developed a comprehensive computer model for selection and scheduling of farm machinery as a basis for investigation of cropping systems in.Michigan's Saginaw Valley. Bowers (1975) stressed that selection of optimum machinery sets depends on accurate input data for available working time, schedule of field operations, draft require- ments and machine capacity. Fridley and Holtman (1974) pointed out, by using system analysis, the importance of predicting the socio-economic implications of mechanization, such as the effect of this technology on labor lay-off and farm income. Evaluation of potential solutions to that problem, according to the authors, is a previous step to determine which solutions are not realizable and practical. The interaction between machines as components of a system is treated by Hunt (1977) and Kepner et al., (1978). Overall system capacity and reliability is affected by individual capacity and reliability of machines components of that system. Hunt also stated the complexity of a machinery system when sequential or parallel operations are performed, as is the case in many harvesting operations. Hunt recommends the use of cycle diagrams to determine system performance in parallel operations. 2.3. Determination of Available Working Days. Determination of available working days is another problem with a great variety of answers. Several authors have presented different concepts about what to consider a "dry day" available for working a feed production system. Von Bargen (1966) presented the "open haying day" criteria, developed at Missouri, defined as: "...less than 0.1 in precipitation on the date; less than 1.0 in precipitation on the previous date, and more than 70 percent of the possible sunshine on the date." Wiser (1966) explained the application of the Monte Carlo method to a study of the parameters of frequency distributions of amount of precipitation. In this case, a wet or dry day was defined according to whether or not at least 0.01 inch of precipitation occurred. Three different urn models were tested, the Bernoulli model, the Polya model and the Markov model. Results showed that the Bernoulli model was the simplest but the least precise in determining expected values of precipitation and applies only to independent events. The Markov model was not suitable when weather persistence extended over several periods. If this is not the case, the Markov 10 model was superior in getting expected values of precipi- tation. The fit for the dry days was particularly good as compared to observed data. Tulu (1973) and Tulu et al., (1974) applied the concept of tractability developed by Rutledge and McHardy (1968) to design a model to determine available working days from available weather data. This model considers the combined effect of precipitation, evaporation and soil moisture to define a work day. The total number of work days as determined by the model agreed well with the observed work days, but a day by day comparison showed that more than 10 percent of the days are missed. The authors believe that this was due to the fact that the model does not give partial work days, while they are reported in the farm record as full work days. Tseng and Mears (1975) used the precipitation criteria described by Von Bargen and the tractability criteria of Rutledge and McHardy to estimate a "good day". The good day was considered as: "...less than 2.5 mm (0.1 in) of precipitation on the date and soil moisture content of no more than 95 percent of field capacity." Precipitation data was obtained from climatological data and the soil moisture content was computed according to the procedure developed by Thornwaite. Fulton et al., (unknown) based on reports about crop and field conditions from the Iowa Crop and Livestock 11 Reporting Service, developed a model to determine the days suitable for field work at Iowa. A procedure is applied to calculate available work days whether the calendar period of time is within a climatic week, two adjacent climatic weeks or a greater operational period. Four probability levels were chosen (0.24, 0.50, 0.76 and 0.80), 4 the data for each week were ranked, and the minimum number of suitable days was determined under each probability level to permit estimates to be made according to an acceptable risk. An environmental model was developed by Jones et al., (1972) by using past records of daily rainfall, maximum and minimum temperature, and evaporation for State College, Mississippi. The model yields daily values of rainfall, temperature, evaporation and variations of soil moisture content with depth. Feyerhem et al., (1966) developed probabilities of sequences of wet and dry days in.Michigan from past weather records. Two types of probabilities were given: initial probability, used when no information exists on the previous day, and transition probability, computed whether the previous day is known to be wet or dry. For more flexi- bility in calculations of periods of wet or dry days for the different field operations, probabilities were computed that depend on the amount of precipitation that is considered to define a wet or dry day, that is 0.01, 0.10. 0.20 and 0.50 inch of precipitation. 12 Probabilities are grouped for each seven-day period of a year. For initial probability, dry and wet values are given. For transition probability sequences of dry/dry, wet/dry, dry/wet and wet/wet probabilities are also given. Procedures for determining the probability that a parti— cular day or group of days will be dry or wet are clearly explained along with a method for checking computations. Russell (1979) stated that work days for field opera- tions is determined by the interaction of factors such as weather, soil and crop conditions, machinery being used, and the kind of operation being performed. Work day was considered as; "...one in which work takes place and there is no need to precisely identify the underlying relationships." Detailed farm records are used to calculate working days and to derive distributions for particular climatic situations. However, when such farm records are not available, it is suggested to use simulation in deter- mination of working days. Working day criteria as established by Russell are: "...for operations disturbing the soil, ground not frozen, when less than 0.1 inch of precipitation fall in the day in question and when soil moisture is below specified levels. For operations not disturbing the soil, less than 0.5 in of precipi- tation on the previous day and there is less than 0.05 in of precipitation on the day in question. In addition, corn silage harvesting may take place if the ground is frozen." 13 2.4. Handling and Harvesting of Hay and Forage. Bowers and Rider (1974) reported study of hay handling and harvesting systems in Oklahoma farms in relation to conventional hay baling system. Results showed that new hay harvesting systems, such as stacking and round-baling, maintained quality and permitted harvesting and storing increased tonnages when system capacity matched need. A simulation model of forage transport and handling was developed by Kjelgaard and Quade (1975). This model contained variables for machine types, harvesting rates and transport distances. Outputs of the model were the calcu- lated daily capacity (ton), mechanical energy (kcal/ton) and labor requirements (min/ton) for forage transport and handling. Renoll et al., (1974) showed that handling hay from windrow to storage using the stack system reduced labor needs and that hourly capacity was equal or greater than the conventional bale system. Rider and Barr (1976) described the hay and forage harvesting operations and set guidelines for evaluating harvesting systems and for the selection of related machinery. Hay stacking and field cubing data from this work were incorporated to the model here presented. Evaluations of hay and forage harvesting methods were made by Friesen (1978) and Hilmerson and Heir (1978). Parson et al., (1978) presented alternatives for storage and 14 feeding of big-package hay, and Renoll et al., (1978) studied machine systems for handling and feeding round bales. 2.5. Some Characteristics of Dairy Farms in Michigan. Hoglund (1976) and Hoglund and McBride (1970) studied Michigan's dairy industry. Results showed a change in dairy farming such as reduction of number of farms, but an increase in specialization and in the use of mechanization. Farms were divided in five categories according to the gross value of sales per farm. The number of cows averaged 82 in class I, 42 in class II, 27 in class III, 17 in class IV and 11 in class V. The overall average per farm was 35.8 cows. Partial results of this study are shown in Table 2.1. Hoglund and Shapley (1973) reported on a study completed in 1971 to determine the impact of the physical and economic environment on farm organization and the changes that have occurred on dairy farms in Michigan. Results showr: in Table 2.2 indicate that more than 80% of the dairymen interviewed used a hay conditioner or windrower; more than 84% used balers; a third used bale throwers. Forage choppers and blowers were used for more than 60% of the dairymen, except the Upper Peninsula (50.1% and 42.2% respectively). More than 59% of the farmers used pull-type or self-propelled combines. 15 o no hawEmm on» no mumnEmE once 90 0:0 Ho Houmuomo on» vmvaaucfl acumo mans .ohma .ocfinmoz can pnzamom .mflsmcowumaou mowumuumoo “Gunman Ho manna co vmma cw mmosu 0» Show new modem mo usam> mmoum acmam>asvm "mumDOm .oofiuo game dogmas «co co oooom and .mumnfime hawsmM Honuo paw Houmummo mo cowumcfinfioo .paonmmnos m we mumnamfi Ham How m90990m umnuo can acmEhonEm annulmmo Scum mwcwcumm can meooaH on mmm.m: mmm.man mmm.hmn mmm.mm: uc>o one ADV unmac>flswm maoucH oom.mm ooo.n m ooo.vam ooo.m~m ooc.mmw mmoum mwma pcumeunm mom.mw m>¢.m m hvm.m m >m~.m w mvm.¢ m paonmmson\onam> mu an no mw om paosomson mo ucouumm Amy mEoocw Show mmo m m ma hm mm coma: HOQMH «0 accoumm o.H m.H o.a ~.~ v.m muaoao>fisvo no: dd 5H pm me «m snow nod osoo ovm.m ova.» ona.m oqm.oa omm.HH 3oo\mpcaom ooo.~m ooo.oma coo.mm~ ooo.mmv ooo.mmm EMMM\mpcnom mam.~m mom.mm mnm.oam mom.maw mnm.mmm czam> mmouu vaom xawz mmn.mw ohm.hm Hom.vaw mmm.omm nom.¢mm cHOm muuapoum Had Show Hum mmmum>< m ma cm mm mm NH mwamm mmoum mo uncoumm m ma ms .mm mm m m3oo mo unmoumm ma mw on on 5H m maumm «0 unmouom mmm.v: amm.ml Hmuou mmmtman mmm.mm| 99>0 can Enum\moamm mo osam> mmouw oom.mm ooo.mm Insm ooo.cam ooo.o~w ooo.ow% .> >H HHH HH H EmuH mmmau anocoom xawz .m3ou .mEHmm muflmn mo moaumwuouomumnu meow can cowunnwuumwa mmmucmoumm .musuasoflumfi mo msmcmu voma .mmmHU omEocoum mm.oouom Honmq on» can moamm AH. 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N.m¢ m.mm m.mm m.> voaammoum mamm w.mv N.mm m.mH m.mm m.Hm o.~m mama Adam mcwnfiou 5.0H w.ma m.av o.m~ H.m~ H.Hm xcan Hondcmnumz v.5m m.~m v.5m o.mm N.>v w.mv HmpmoHGs Oawm ~.~v w.ao «.mw m.mh «.mo m.mo um3oan mmmuom H.om w.Hm m.om m.mh m.mb «.mm Hmmmono mmmnom m.mm m.MH o.~m m.a~ m.vm ~.hm Hm3ounu mama «.mm m.mm H.¢m N.Hm o.~m H.mm Hoamm m.mm m.mh h.ma m.mm m.vm m.wm H030H©GH3 .umnmsuu .HmmEHHU . mcwapcmz can mcflumm>ums mmmuom m.H v.H m.H m.m m.m h.~ Eoumam chance pwsqu m.mm m.mm m.am H.8m m.av H.Hm Hmcmmao umuuso h.m w.mH «.mm m.va H.mm «.mH Hoaumm mcwxawz m.ma m.h o.o _ o.m m.v m.~ mafia omflo unocoeuoo m.mH m.m m.ma m.> m.mH v.m~ uoam>coo xaflz onwapcmn chance can mcwxawz IIIIII mcwuuommu unmoummllnnll masmcflcom CHOSuHoz sense ouuoz Hmuucoo cumummz momma vacuuwo nusom coma unmemwsvm mumum mo mend .Hhma .msumm waE cflaam .cmmflnoflz mo momma xfim .mcflnwnaoo can mcaapcmc can mcwumm>umn mmmuow .mcfiapcmn chance .mcaxawfi How unoamwavm pmNfiHmwoomm mo om: mcauuommu mumeumw mufimo mo ucmuumm .N.N magma 17 The same report pointed out that the total cropland operated ranged from 84.1 ha (208 acres) to 110.5 ha (273 acres). The most common feed crops were alfalfa and mixtures (30 to 72% of the total area); corn grain (21 to 27% of the total area, except northern and Upper Peninsula) and corn silage (5 to 12%). Less than 10% of the dairymen practiced green chopping and hauling forage daily for summer feeding. Acres of cropland along with some other information is given in Table 2.3. 18 .msma .soamonm oco voodoo: "momsom -.m mo.o ms.~ m~.~ mm.~ mm.~ ooouou Hobos ¢~.H on. om. mm. as. He. ousuooo SH. mo. «a. om. Ha. ma. mono coouo Hm.m ms.m mm.H oa.a ~H.H ms.a oooaum oao ouaouaa Hm. om. ms. so. oo. oo. oooauo :uoo mm.H sm.H m~.~ Hm.a sm.a oo.a afloum Hobos om.a as. mm. «m. no. mm. soauon oco mono mo. mo. mv.H sm.H we.H mm.a aaouo auoo 300 Hum mound o.¢ ~.o ~.oH m.o# m.o~ m.oa ode“ oco oouuo>flo a.~ . m.m~ m.mH H.5H m.~ moouo noon Honuo ~.H m.m m.oH ~.vH m.mH ~.o boos: ~.mv m.¢~ m.om m.m~ H.oH m.oa soauon one 8980 I I ¢.H H.m m.N v.H amusmlcmwanm .cmonm o.~o m.ma s.ma m.o m.mH m.ma ousumoo o.m s.~ o.m m.m o.m m.m mono :oouo 8.5HH o.¢~H o.mo m.s¢ H.oo m.Hs oooaflo coo so: .~.moav AH.ooHV 1o.mmv 1m.mov Ao.amv 1o.~mv mousuxfls one ouaomaa o.HH H.o~ o.om o.h~ m.om m.¢~ omoaflm :uoo s.~ o.H~ m.om o.mm H.oo “.mm afloum cuoo couscoum mmouu mmu0¢ omm NNN mum ham mmm mom oououooo ocoaoouo Hobos anon Hum nomad m.mm «.mm m.ao o.mo o.so o.aq sumo moo msoo AHA: MHSmGHGmm Chmsuhoz Q5539 CRUDE HMHHGTU CHmummba momma uwouboo ausom mumum mo comm .Hhma .mEHMm xHHE Ufisam .cmmw50Hz mo moons xflm .300 mom :3oum macho poem was .Eumw Hon couscoum mmouo mSOflHm> mo mmuom paw pmumuwmo pcmamouo mo mouom .m3oo xHHE mo Hones: ommnm>4 .m.~ manna CHAPTER 3 DEVELOPMENT OF THE MODEL 3.1. Survey of Feed Production Methods. A survey to determine the methods of feed production commonly used was the first step to develop this model. The types of machines used in each one of the production methods were established and, at the same time, a clearcut classification of the machinery facilitates comparisons. A summary of such feed production methods is presented in Table 3.1, based on the work of Tseng and Mears (1975). 3.2. Flow Chart Representation of the Feed Production System. Based on the same work of Tseng and Mears (1975), a flow chart is presented in Figure 3.1, to describe the overall structure of the feed production system. The flow chart presented by Tseng and Mears for the forage produc- tion system is completed by the inclusion of other forage production methods such as cubing from green chopped, partially field cured and complete field cured materials, haylage from partially field cured and chopped material, stack building from complete field cured and chopped material, and round baling from complete field cured material. 19 Table 3.1. 20 Methods of Feed Production g 1. Pasturing 2. Green Chopping 2.1 Green feeding 2.2 Silage 2.3 Dehydration 2.3.1 Market Storage Pelleting 2.3.3.1 Market 2.3.3.2 Storage Cubing 3. Mowing and conditioning long loose wet forage 3.1 Partially field curing 3.1.1 Transfer to mow/drying 3.2 3.1.2 3.1.4 Chopping 3.1.2.1 Transfer to mow/drying 3.1.2.2 Transfer to dryer/feeder 3.1.2.3 Silage 3 1.2.4 Haylage Conventional Baling 3 1.3.1 Wagon drying 3.1.3.1.1 Storage 3.1.3.1.2 Market 3. l. 3. 2 Transfer to mow/drying 3.1.3.2.1 Storage 3.1.3.2.2 Market Cubing Complete field curing 3.2.1 wwww o o o o NNNN o o o o 01th 3.2.6 Conventional Baling 3.2.1.1 Storage 3.2.1.2 Market Round Baling Transfer to long loose forage storage Stacks on the field Chopping 3.2.5.1 Storage 3.2.5.2 Wafering 3.2.5.2.1 Storage 3.2.5.2.2 Market 2.5.3 Stacks eld Wafering 2.6.1 Storage 2,6. 3. F1 3. 3. 2 Market 21 Table 3.1. (cont'd.). 3.2.7 Cubing 3.2.7.1 Storage 3.2.7.2 Market 4. Stover 4.1 Silage 4.1.1 Storage 4.2 StaCks 4.3 Round baling 5. Grain production 5.1 Low-moisture grain 5.1.1 Combining 5.1.2 Ear corn snapper 5.1.3 Pick-up harvesting 5.2 High-moisture grain 5.2.1 Combining 5.2.2 Ear corn snapper 22 LAND pLANTEC‘ . LAND READY PERENNIAL FOR SEED BED LAND READY CROPS PREPARATION FOR PLANTING PLANT GROWN \----—-’ ADJUSTING SOIL PH SEED PLANTING FERTILIZATION @ REGULATING SOIL PREPARATION FERTILIZATION CONTROL OF NEEDS WISIURE CONTROL (3r DISEASES APPLICATION OF MANURE CONTROL OF INSECTS APPLICATION OF HA‘IL'RL LAND IS EMPTY OR GRAZLD K! c s .s a N0 L 0 m A K s ,0 o 0 5 IS nus LEGEND .. HARVESTING or cnop AunuAL, 22 PERENNIAL ' b’ CROP? o a W— D HANURE YES YES 0 _ cou HILL vrs N0 ““1 caops (PLANTS) PERENNIAL ANNUAL _ BE PLANTED 8E PLANTED HERE NEXT HERE NEXT ? '? ___________ LAND LAND FOR NO YES OTHER USE NODE DECISION FOR ANNUAL CROPS j —- LAND READY _. 0 FOR SEED BED LAND READY ' PREPARATION FOR PLANTING PLANT GRONTH ' LAND I PLANTED I \ ,1 u o .. -o \ I I ADJUSTING SOIL PH SEED BED PLANTING FERTILIZATION I REGULATING SOIL MOISTURE PREPARATION FERTILIZATION CONTROL OF NEEDS I APPLICATION OF MANURE CONTROL OF DISEASES I_- 0 CONTROL OF INSECTS Fig. 3.1 Flow chart for feed production system. Adapted from Tseng and Nears, 1975. 23 cans IILKIIG All . -? _Im"L-_O—O—I no Wt'Tmm'_Lo . /. ---------------------- \ .. IsrnImrLoIncHI'amnr no \ mnsm T cnovvm '° ‘/ TO SILO . rout: In mo www.mm In SILO “‘9' I R T MMWYIO KNYNATED ;.. I 1 m. o.;. . I... TO “I"WTOC . LAN: . '1 .4 F R TI . ' . . PILLITIIG ‘ LLITIIG STORAGI m cnomo '1‘" :msru T0 mm m m alumna: 1mg"; 10 C 3‘5 II MNYWIN ; p T _. . T 5.10 STORAGE sxn stone: . C C 0"“ roam In mnsru TO mm PLANT “I !I m1. (unto Lona Loos: =I rouar In mun-tom: Lg. mnsrrn T0 m to: . Ion mu onvIna . In STOIAGE 3I 1!: fit I I I I I . m . cum I mm to SILO row-r W In mum I ‘ In sno MVLAGINC P. (MO 00'. PM r: I VIIG INSI'E' 70 STORAGE .‘ WIS ”ISO 70" ours In :msrra . ° cons m to on In: T0 mm: IAN (LII 5 51m OMS?" ‘ - COVERT I“ ~ I6 MD ”US mo BALING . LEAVE I... on FIEL M INS! rm THIS?“ TO STORM! 5 STACKIIO I STACIS ill’tll‘ urns TRANSFER TO STORAGE MAKERS II SW4 TRMS'ER TO III‘ERIK. PLANT '\'J L,‘ STICKS CWLETEU (LIED LNG LNSE FOMH 10A ‘1 m1!“ IA? R ‘ . . . . .I TRANS“! T0 SIACIS I inns FIELD “FIRING FIELD CIIIIG Fig. 3.1 (cont'd.). 24 «meg: c» 5325:. oz< 9:5 A.s.o=oov F.m .awa > 5 I’d O.» Y \\ 56: 5.8 a: a 8555 E was 58 8585 E 3:25: 23 5.8 .1 222—989 I "5:332: ass; as... 33.; #229. :25 5:. 83% I «S i) > I >93“ 885 5 8555 S 51ng as: 55: 5.8 a: I was a: 5.8 :8 \I I < E z a. 833. Loss; 8 5:25: :25 23:9. 2: 85:65... \\ 85.; >4: 3“: e: go as: 38a “:3 m5: 3:31.282. \«WV 95$. \94/ 3“: e: .5 9.25 ea: c258; .. . . z < S 9:de S; S; 8 5.193: 1‘ 855; E 85: E 585 $85 5:95 a wage: a unmet”. 25 MILKING ///’ TRANSFER ' T0 MANURE STORAGE MANURE IN STORAGE FEEDING TRANSFER BACK TO THE LAND I I l FEEDER Fig. 3.1 (cont'd.). 26 Also, particular consideration is given to the use of stover as a source of animal feeding. In many dairy farms, it is not a current practice to use grain crop residues to produce stover silage and stover in stacks or round bales. Grain production is included describing three main production methods; Harvesting low-moisture grain, high-moisture grain and ear corn. The description of this flow chart is the same given by Tseng and Mears. Links represent activities or operations, the nodes describe the state of material flow, land flow, crop flow, animal flow and the manure flow. Each one of these is shown by different kind of lines. At the right-hand side of the flow chart, the methods of making feed can be identified following the land and crop paths. The use of the land is defined by the sequential decision diamonds at the left-hand side of the flow chart. At nodes B, C, D, E, F, G and H the crop is either pastured by the animal or harvested and the land is ready to continue in production with the same crop or a new crop. 3.3. Flow Chart for the Selection of Machinery Complement. The selection of machinery complement is the result of considerations of many factors. The first step is to consider the size of the agricul- tural firm(in our case, the size of the dairy farm)since this is going to directly determine the size of the 27 machinery complement. The first two blocks, in Figure 3.2, state this problem. The decision is basically an economic one and it is the.result, in the best of the cases, of a feasibility study or, as in most cases, a personal decision. Either case is out the scope of this study, and will therefore be assumed as previously determined. The second step considers technical aspects related to the environment, that is, the local weather conditions and types of soils, as stated in the next 2 blocks of the flow chart. These four blocks of information are used to determine if the dairy, or any other kind of farm activity, can be established following the sequential decision diamonds and, if the answer is positive, to arrive at the types of crops to be used. In order to select the crop production sub-system, it is necessary to generate more information and this is indicated by the blocks corresponding to feed requirements and feeding method, crop management decisions and land allocation. Data on available time, machinery and labor require- ments, energy requirements and cost analysis is required for the selection of the machinery complement. This information will be determined in the model by calling subroutines or furnished as parameters or given values. 28 START N0 . SELECT NUMBER DA;RV Ifihfifil 3 OF COHS yfs ONLY. LAND USES I» DETERMINE LAND SIZE 2 ”O _ ozgngggg? cur FEEDING I 7' YES DETERMINE LOCAL HEATHER CONDITIONS IS THIS I DETERMINE TYPES THISSCROP T”E 1‘57 NO AEESNURIL or SOILS ' , “ARVEST 0‘ PRACTICES _ YES ANNU‘L- PERENNIAL CROP? l ALLOCATE LAND HILL hILL DETERMINE FEEDING ANNUAL NO PERENuIAL no REQUIREMENTS BE PIANTED BE PLANTED AND FEEDING METHODS HERE NEYT? YES YES SELECT SELECT SELECT caon . . I MANAGEMENT PRACTICES “""U‘L p‘R‘“”I“L CROPS CROPS SELECT CROP PRODUCTION SYSTEM DETERMINE AVAILABLE DAYS AND DESIGN PROBABILITIES MACHINE DATA AND LABOR REQUIREMENTS SELECT MACHINERY COMPLEMENT DETERMINE ENERGY REQUIREMENTS SCHEDLLE USE OF MACHINERY DETERMINE FORAGE QUALITY AND QUALITY LOSSES COST ANALYSIS -III-I Fig. 3.2 Machinery complement selection flow chart. 29 3.4. Types of Input Data and Parameters. Input data and parameters used in this model belong to one of the following groups: a) b) e) d) 3.4.1. 3.4.1.1 Constants and logical statements controlling methods of feed production, use of multivalue parameters (arrays) and selection of machines and storage methods. Machinery data such as field efficiency, speed, rate of material feeding, sizes, types, field capacity. tractive efficiency and rolling resistance coefficients. Data related to farm management decisions: type of crops, crop yields, feed requirements, feed losses, number of cuts, farm size, feeding methods and type of transport units. Data on available time: harvesting dates, hours or work per day and weather data and probabilities. Machinery Capacity Parameters. System capacity. The first step is to determine the system capacity of each one of the feed production methods, since feed harvesting is considered as a problem of material handling in the available time. The general formula to calculate system capacity is: so = (FR + FL) / AH (3.1) 30 where: SC system capacity, t/h FR feed requirement, t FL = feed losses, t AH = available time, h. Available time (AH) is calculated by: AH = WD * HPD (3.2) where: WD = working days HPD = working hours per day. Therefore, in order to select the set of machinery for the feed production method, the set must have a capacity greater or equal to the system capacity. This condition is maintained through every and each machinery calculation, to help assure a flow of material at the rate specified by the system capacity. 3.4.1.2. Effective Field Capacity. It is the actual rate of performance of land or crop processed in a given time, based upon total field time. This parameter can be calculated from field speed, machine working width and the field efficiency or by using values of material capacity and crop yield: EFC = w * s * EFF / 1D (3.3) 31 where: or; EFC = effective field capacity, ha/h S = working speed, km/h W = working width, m EFF = field efficiency, decimal EFC = EMC/CY (once over harvest) (3.4) EFC = EMC/(CY * FRY) (more than one cut) (3.5) where : EMC = effective material capacity, t/h CY = crop yield, t/ha FRY = fraction of yield in each cut, decimal 3.4.1.3. Forage Harvester Capacity. For this machine the total system capacity is: SCH = EFF * FHC * HANUM (3.6) where : SCH = total forage harvester capacity, t/h EFF = forage harvester efficiency, decimal (0.65) FHC = forage harvester capacity, t/h HANUM = number of forage harvesters. 3.4.1.4. Transport Unit Capacity. The transport unit capacity was calculated using the formula: TRACAP = 60. * TUC * TANUM/CYT (3.7) 32 where: TRACAP = total transport unit capacity, t/h TUC = size of transport unit, t TANUM = number of transport units CYT = cycle time, min. 60 = dimensionality constant 3.4.1.5. Cycle Time. Cycle time is a very important factor in the calcula- tion of the transport unit capacity, since it reflects the effect of the distance between the field and the storage or feeding area, the time of loading and unloading the transport unit, the blower support time and, when wagons are used, the time of hitching the wagon to the forage harvester and to the shuttle tractor: CYT = ALT + TT + BST + UT + HWFHST + HWTST (3.8) where: CYT = cycle time, min. ALT = loading time, min. BST = blower support time, min. TT = travel time, min. UT = unloading time, min. HWFHST = hitching wagon to forage harvester time, min. HWTST = hitching wagon to the tractor time, min. Elements of cycle time were calculated by using: ALT = 60. * TUC / (EFF * FHC) (3.9) TT = 60. * AD/AS (3.10) 33 UT = 60. * TUC/BLCAP (for vertical silos) (3.11) UT = FT (for green feeding) (3.12) UT = UTH (for horizontal silos) (3.13) where: AD = average travel distance, km AS = average transport speed, km/h BLCAP = blower capacity, t/h FT = feeding time, min. UTH = time unloading transport unit in horizontal silo, min. 60 = dimensionality constant. Blower support time and the time for hitching wagon to the forage harvester and to the tractor are measured or estimated values. 3.“.1.6. Blower Capacity. In order to avoid bottlenecks at the blower, the capacity of this machine must relate to the rate of material harvested and transported to the blower. A formula was devised that gives the blower capacity as a function of the efficiency, capacity and number of forage harvesters: BLCAP = EFF * FHC * HANUM (3.14) A provision is taken for the case when only one forage harvester is required to insure that the blower capacity is at least twice the forage harvester capacity. In addition, 10 percent of reserved capacity is added to the calculated 34 blower capacity. These precautions are used to reduce the risk of bottlenecks at the blower. 3.4.1.7. Baler Capacity. Again, the total baler capacity is expressed as a function of the field efficiency, the baler capacity and the number of balers: BALTPH = EFF * BALCAP * BALRN (3.15) where: BALTPH = total baler capacity, t/h EFF = baler field efficiency, decimal BALCAP = baler capacity, t/h BALRN = number of balers 3.4.1.8. Continuous width implement. For this type of implement, effective field capacity is calculated first and then the equation is solved by width: w = EFC * 1o. /KEFF *‘S) (3.16) where: W = total width required, m EFC effective field capacity, ha/h EFF field efficiency, decimal S = working speed, km/h The next step is to calculate the number of implements of commercial size that satisfy the total width, that is, w = SIZE * NUMBER (3.17) 35 This procedure applies to mowers, conditioners, mower- conditioner, windrowers and rakes. 3.4.1.9. Non-continuous Width Implements. This is the case of implements designed to work in rows, such as pickers, picker-shellers, combines and forage harvesters. The general formula is: W = ANR * RWWD * NUMBER (3.18) where: W = total width required, m ANR = number of rows RWWD row width, m NUMBER = number of implements Effective field capacity is previously calculated in the usual way. 3.h.2. Power Requirements Values of power requirement for each implement were calculated following procedures and formulas from the A.S.A.E. Yearbook (1980), standards EP 391 and D230.3. 3.4.2.1. Rolling Resistance. Defined as the opposition offerred by the soil and crop residues to the wheels of moving implements. RRF = WT * CR * ACC/IOOO (3.19) where: RRF = rolling resistance force, kN 36 WT weight of implement, kg CR coefficient of rolling resistance, decimal ACC = acceleration of gravity, (9.8 m-s'z) 3.4.2.2. Rolling Resistance Coefficient. This value was calculated applying the formula: CR = (1.2/Cn + 0.04) (3.20) where: CR = rolling resistant coefficient Cn = dimensionless ratio equal to the product of the cone index for the soil, the unloaded tire section width, and the unloaded overall tire diameter divided by the dynamic wheel load normal to the soil surface. Values of On and CR, under specified conditions, are given in Table 3.2. Table 3.2 Values of ratio On and rolling resistance coefficient CR. Type of Soil Cn CR Hard soils 50 0.060 Firm soils 30 0.080 Tilled soils 20 0.100 Soft, sandy soils 15 0.160 3.#.2.3. Drawbar Power and Power Take-off Equivalent. Values of drawbar power are obtained from the rolling resistance force (RRF) and the implement working speed (S). DBKW = RRF * s /3.6 (3.21) 37 where: DBKW = kilowatts, kW RRF - kilonewtons, kN S = km/h 3.6 = dimensionality constant This valueiis converted into value of equivalent power at the power take-off in the following way: PTOKWE = DBKW/(TR * TE) (3.22) where: PTOKWE = power take-off equivalent, kw. TE tractive efficiency, decimal (0.72) TR transmission coefficient, decimal (0.96). 3.4.2.4. Power-takeoff Power. It is the power required by the implement from the power take-off shaft of the tractor or engine: PTOKW = UNP * w (3.23) where: PTOKW = power—takeoff power, kW UNP = unit power requirement, kW/m W = machine width, m 3.9.2.5. Total implement power requirement. The addition of implement power components is the total implement power requirement: TIKW = PTOKWE + TPOKW (3.2)) 38 where: TIKW = total implement power requirement, kW 3.4.2.6. Tractor Power. It is the implement total power requirement plus an estimated reserve of 20 percent to overcome any changes in normal conditions of operation and any optional tractor attachments: TPTOKW = TIKW/O.8 (3.25) where: TPTOKW = tractor power at PTO, kW. CHAPTER # DESCRIPTION OF THE MODEL 4.1. Feed Harvesting Machinery Selection.Model. The model consists of a main program and sixteen subroutines. It calculates the effective material capacity (EMC) of every machine and its comparison to the system capacity (SYSCAP), which is determined from the feed requirements, feed losses and the available time. When the effective material capacity of the machine under consideration is greater or equal to the system capacity, that machine is selected, in both number and size. This is achieved by an iteration of the calculation of machinery size and number and the associated effective material capacity, with a continuous comparison to the system capacity. Both effective material capacity and system capacity are expressed in ton per hour. In order to minimize the investment in machinery, first the model tries to satisfy the system capacity by increasing the machinery size up to the maximum size available in the market. If this is not possible, then the number of machines is increased by one unit at a time, progressively from the smallest to the largest size. 39 40 The next step is a screening of these machines that have common use when corn silage and haylage are produced on the same farm. As an example, the same forage harvester can be used in both with just a change of heads. A similar situation is found with the use of tractors. The model calculates the number and power of the tractors required by every pull or mounted type machinery selected, and then reduces the number of tractors to the strictly necessary by means of conventional agreements according to the feed production method and the type of transport unit to be used. Formulas used in calculation of tractor number are given in Table 4.1. 0.2. Feed Harvesting Machinery Selection Program. The main program of the model controls the operation of subroutines by means of indexes (integer constants), logical statements and by direct calls to the proper subroutines. The flow chart for this program is presented in Figure 4.1. The data required by the program consist of specifica- tions of farm size, management practices, field machinery data and available time. Data input to the main program are summarized in Table 4.2. Parameters defining the number of cuts, the method of feed production, initial and final dates of harvesting season and number of working hours per day along with data 41 .hammm no: mmO© u 42o muouomuu Hmaamnmlumxoflm no meoam n wme muoucmnu Hmaon u momma muouomuu Hmsouccwz can HmcowuflUGOOI3oe u.320¢ma muouomnu Hmumm>umn mmMH0u I moumm mo mama Begum: GOflfiUgfiOHm Ummh .umflEd: uouomnu mo cofiumazoamo How mmassuom .H.e manna (#2 START READ FARM DATA MCHINERY DATA ECONOMIC DATA I“ CALL TIME 1 .®. 2 4 3 READ READ READ READ READ PARAMETERS PARAMETERS PARAMETERS PARAMETERS PARAMETERS CALCULATE CALCULATE CALL CALCULATE [CALCULATE SILAGE HAYLAGE HAY GREEN GRAIN MACHINES MACHINES FEEDING MACHINES MACHINES F I , CALCULATE CALCULATE CALCULATE CALCULATE CONVENTIONAL ROUND STACKING CUBING BALING BALING MACHINES MACHINES MACHINES MACHINES _ l ADJUST NUMBER OF TRACTOR _] J [_ADOUST NUMBER OF IMPLEMENngl L PERFORM COST ANALYSIS ] Fig. 4.1 Flow chart for program FHMS. 43 Table A.2. Input parameters for main program. Farm size total surface and distribution by crops, SURF, HECT ha number of dairy animals feed requirements, FR, t/yr feed losses, FL, t/yr Management practices type of storage facility, ISW type of crop, K, KA number of cuts, NC, JA, J feed production method, MI crop yield, CY, t/ha use of mower logical, MOWER, M type of transport unit, BI, BMTI, logical WAGON use of conventional baling logicals, CONBAL, A hay partial field curing logicals, PAEFC, B hay mow drying logicals, MOWDRY, C use of mower-conditioner logical, MC production of shelled grain logical, SHC rowcrop spacing, I use of self-propelled combines, logical SPCO transportation travel distance, AD, km Field machinery data machine size, m, t/h, kw machine weight, kg, t power requirement unit, kW/m, kW—h/t, kW/row specific fuel consumption, L/kW-h row width RWWD, m number of rows, ANR effective material capacity, EMC, t/h effective field capacity, EFC, ha/h field efficiency, EFF, decimal working speed, S, SP, km/h Available time harvesting season initial and final dates number of working hours per day.HPD.h daily precipitation data 44 on daily precipitation for an extended period of years are furnished before calling the subroutine TIME. This subroutines calculates the available working days and transfers this information to the following subroutines. The subroutines for the different methods of feed production are successively called and in that way the number and size of machines are calculated by an algorithm specifically designed for every machine. The next section of the program makes the final selection of the machinery set by reducing the number of tractors, calculated for every pull or mounted type imple- ment, and the machines which have common use in silage, haylage and green feeding, if these feed production methods are simultaneously utilized in the farm. The program output is arranged in machinery sets or systems. Every set is identified with the system name, the system capacity, number of working days and working hours per day. Other information printed is the number, size and type of machine components of the selected system. If desired, extra information is available regarding excess forage harvester capacity and transportation capacity, both in percentage. 4.3. Available Work Time. Subroutine TIME calculates the available time for each operation in forage, hay and grain production, based on 45 weather probabilities and management decisions, according to the algorithm presented in Figure A.2. Input data for subroutine TIME consist of indication of feed production method (MIA), the number of working hours per day (HPD), the initial and final date of the harvesting season (MDI, MDF), daily precipitation for an extended period of time (PR), the number of years in this period (NP, ANY, NY), the criteria defining a dry day, as a function of amount of precipitation, (A1, A2 upper and lower limits respectively), the number of cuts (NC), the total number of days in the cutting period of specified years of daily precipitation (TD) and, finally, the total number of days in the cutting period when the crop is only harvested once (DC) and when the crop is harvested more than once (TDP). According to the type of feed production method, the subroutine proceeds to the calculation of dry days, so that for green feeding, silage and grain production, considered as once over crop harvesting methods, the number of dry days in the cutting period is calculated by calling sub- routine COUNT, then with this value and the number of days in the cutting period, the.probability of occurence of dry days in that period is calculated, so: TDG = DG * ANY (H.1) PDD = DD/TDG (L: . 2) where: PDD = probability of occurence of dry days, decimal Th6 READ WEATHER DATA HARVESTING DATES WORKING HOURS PER DAY FEED PRODUCTION METHOD YES (32} [ DO J = 1, NCI _ l _ rCALL COUNT I o I _ i l 37 POH = DD/TD(J) FWD = PDD ,, DGJ OHDN = POHD * TDP(J) ' * W_ I - 1 [CALL COUNTP J , “4 , rAH=wD*HPD C} ‘ C [ TNDP(N) = FLOAT(N) * FLOAT (NP(N)L1 ERMN) = TNDP(N) / on] Fig. 4.2 FTOw chart for subroutine TIME. 47 r EOHD(N) = PRN(N) VT pr = [0HD(N) / FLOAT (N)I I‘ IPNP(N) = PNP + 0.5 I (9 r <9 I N = N - I I ITPRD = 0.0 ] _ ;] , q "1 [DO L= 1. N I hpRD = TPRD +_ FLOAT (NP(LM _ TI - [:DOI.= 1. N I [ ppN(L) = FLOAT (NP(L)) / TPRD I _ ‘i IBLD (J) = 0.0 I - '“’I 7 I BALD(L) = (L91) * IPNP(L)I IflBLD(J) = BLD(J) + BALD(L)1I 11 d) Fig. 4.2 (cont'd.). 1+8 9 DODP = NP(I) CUTD(J) = OHDN - DODP - BLD(J) ITRAD(O) = BLD(O) - II I TRAD(J) : BLD(J) J ‘ L _ F _ EAKDN) = CUTD(J) - 11 ® J Fig. 4.2 (cont'd.). “9 DD = total number of dry days in cutting periods TDG = total number of days in period of years of observed precipitation DG = number of days in cutting period ANY = number of years in period when observed precip- itation occurs Then, the number of working days and the available time in hours are determined by: WD = PDD * DG (4.3) AH = WD * HPD (4.4) where: WD = working days AH = available time, h HPD = working hours per day If the crop is going to be harvested more than once, as it is the case of haylage and hay production, the procedure to determine available time is more elaborate. The criteria of "open haying day" presented by Von Bargen (1966) is used to determine the time available for each operation at every cut. Therefore, the first step is to calculate the number of dry days in each cutting period. This is achieved by calling subroutine COUNT and running it as many times as the number of cuts (NC) that has been chosen. The probability of occurence of open haying days at each cut is then calculated by: POHD = DD/TD(J) - (4.5) 50 and the number of open haying days is: OHDN = POHD * TDP(J) (4.6) where: POHD = probability of occurence of open haying days, decimal DD = dry days in cutting periods in observed number of years J = index for number of cuts TD = number of days in cutting periods in observed number of years OHDN = number of open haying days TDP = number of days in the cutting period The number of periods (NP) of N consecutive open haying is calculated by calling subroutine COUNTP and running it as many times as the number of cuts (NC). Then the probability of occurrence of a period of N consecutive open haying days is computed by dividing the number of such periods by the total number of periods occurring during the observed number of years. So, PPN = NP(L)/TPRD (4.7) where: PPN s probability of occurrence of a period of N consecutive days, decimal NP = number of periods of N consecutive number of Open haying days 51 TPRD = total number of periods in observed number of years. The periods of N consecutive days are considered as mutually exclusive events. Therefore, an open haying day occurs in only one period. The expected number of open haying days for a cutting period can be computed by previously calculating the probability of a given open haying day occurring in a period of N consecutive open haying days and multiplying this value by the number of open haying days in the given cutting period. Thus, TNDP(N) = N * NP(N) (4.8) PRN(N) = TNDP(N)/DD (4.9) and EOHD(N) = PRN(N) * OHDN (4.10) where: TNDP = total number of days in a period of N consecutive open haying days N = number of days in the period NP = number of periods PEN = probability of occurrence of an openning haying day in a period of N consecutive open haying days. DD = total number of dry days in a cutting period EOHD = expected number of open haying days OHDN = number of open haying days. 52 Finally, a prediction for the number of days for every operation, in haylage or hay production, can be made by using the formulas given below. For baling operation, assumption is made that at least two consecutive dry days are necessary to perform such an operation. Thus: BALD(L) = (L-1) * NP(L) (4.11) eliminates the periods with only one open haying day, and BLD(J) = BLD(J) + BALD(L) (4.12) calculates the total number of days for baling. New terms in the formulas are: BALD = baling days in every period of N consecutive open haying days BLD = total number of baling days For computation of number of cutting days: CUTD(J) = OHDN - DODP - BLD(J) (4.13) new terms: CUTD number of cutting days DODP total days in one-day periods Days required for transportation in hay production: TRAD(J) = BLD(J) (4.14) but for haylage: TRAD(J) = BLD(J) (4.15) new term: TRAD = number of days for transportation 53 The number of days for raking is calculated by using: RAKD(J) = CUTD(J) - 1 (4.16) new term: RAKD = number of days for raking The output of this subroutine is arranged to yield the results for haylage and hay production separately from green feeding, silage and grain. For hay and haylage, values of the calculated parameters are presented in tables, one for every cut. At the bottom of the table, the information on the number of days avail- able for each operation is summarized. For green feeding, silage and grain, the calculated parameters are only summarized. 4.4. Counting Dry Days. Subroutine COUNT finds the number of dry days in every cutting period and has been specially prepared to read daily precipitation data in such periods, according to the new format of daily climatological data designed by the Weather Service of the Michigan Department of Agriculture (1980). The input data are the daily precipitation for an extended period of time, the initial and final dates of the harvesting period and the upper and lower limits of pre- cipitation that, according to the criteria applied, define a dry day for field machinery operations. 54 The subroutine counts the number of dry days just in the range set by the initial and final date of the harvesting period, by comparing the amount of precipitation in those days to the selected dry day criteria. A flow chart for this subroutine is given in Figure 4.3. 4.5. Counting Open Haying Day Periods. Subroutine COUNTP calculates the number of periods of N consecutive open haying days in every cutting period. Data input to this subroutine are the same as for sub- routine COUNT and the number of years of weather data. The subroutine counts the number of periods of N consecutive open haying days applying the same procedure used in subroutine COUNT, but since the periods of N consecutive open haying days are considered mutually exclusive events, care is exerted to avoid the counting of a given day in more than one period. The flow chart for this subroutine is presented in Figure 4.4. 4.6. Green Feeding (Chopping) of Forage. The subroutine GF selects the size and number of machines used in the production of green feeding forage. A flow chart for this subroutine is in Figure 4.5. Data input for this subroutine are the available time, transferred from subroutine TIME, and the feed requirement and feed losses, transferred from the main program. 55 READ HARVESTING DATES WEATHER DATA ,AZ, 0N I_DD = 0.0 I 1e 0 ITON= FALSE‘I H I READ CARD I ‘ YES NO IF RETURN - YES 0N = TRUE IDECODE CARD PR > A2 YES NO IDD = DD + 1I IPR- = O. 01 * FLOAT (IP)I Fig. 4.3 Flow chart for subroutine COUNT. 56 START READ HARVESTING DATES HEATHER DATA A1, A2, 0N RETURN .kFYES E0“ 1 0 NO I DECODE CARD_I {D I ON= FALSE I - JI , I ‘ 0 J JIE f READ CARD] N0 Fig. 4.4 Flow chart for subroutine COUNTP. G 57 IDECODE CARD] [_’PR = 0.01 * FLOAT (IPI_I N0 J >0\ YES YES NO 0 YES r@ YES OYES 6 N0 0 [NP(J)=NP(J)+iI =TRUE =0 0N1J+ Fig. 4.4 (cont'd.). FARM DATA AVAILABLE TIME MACHINERY DATA MANAGEMENT_PRACTICES [ICALCULATE SYSTEM CAPACITYI HARVESTER NUMBER - CALCULATE FORAGE I AND SIZE YES NO 0F FOR. HARV. TRACTORS l .1 CALCULATE NUMBER I CALCULATE NUMBER AND SIZEI AND SIZE OF TRANSPORT UNITS I . ‘ CALCULATE NUMBER AND SIZE OF TRANSPORT UNIT TRACTORS L RETURN Fig. 4.5 Simplified flow chart for subroutine GF. 59 The system capacity is computed and its value trans- ferred to the subroutine FORHAR. Formulas used in this computation are: FL = PCL(MI) * FR(MI) (4.17) SC = FR(MI) + FL (4.18) where: FL = feed losses, t PCL = fraction of feed losses, decimal FR = feed requirement, t SC = system capacity, t. Values of FL, PCL and FR are kept in arrays controlled by the index which defines the method of feed production. To GF corresponds the index MI = 4. The subroutine GF calls subroutine FORHAR for the calculation of the number and size of transport units, when wagons are used, the power required for pulling the wagon along with the number, and power in kW, of the tractors required for the wagons are also computed. It may happen that the number of transport units is not calculated because the capacity of the transportation subsystem is exceeded. When this occurs the calculation stops and the word "COMMENTS" is printed to point out this situation. 4.7. Forage Harvest Machines. Subroutine FORHAR, as stated above, calculates the number and size of machines utilized in harvesting forage 60 for green chop, silage and haylage. According to the flow chart of this subroutine, presented in Figure 4.6, the effective material capacity required for the harvesting system (RTPH) is first calcu- lated in ton per hour unit, as explained in Section 3.4.2.1. Next, the total forage harvester capacity (SCH) is computed using the formula of Section 3.4.2.3, and both values are compared. If SCH is greater or equal to RTPH, the number and size of forage harvesters is obtained and the subroutine TRAPUN is called to perform the calculations related to the transport units. If this condition is not achieved, then the capacity of the forage harvester (FHC) is increased up to the set limit of 60 ton per hour (rated capacity). If this limit is exceeded, then the number of forage harvester (HANUM) is increased by one at a time and again the forage harvester capacity is increased until the combina- tion size and number of forage harvesters yields a total capacity that satisfies the condition: SHC . GE . RTPH There is also another limit to the number of forage harvesters (HANUM = 10). If this limit is exceeded then the calculation stops and the word "COMMENTS" is printed. This means that the available time for harvesting is scarce, or that the system requirement is too large, or a combination of these two factors. 61 START CALCULATE EFFECTIVE MATERIAL CAPACITY (RTPH) IHANUM = I I I .l , fl - ~ I CALCULATE FORAGE I’FHC = FHC + 0.5 HARVESTER CIPACITY ‘ SCH SCH z_RTPH YES I CALCULATE FOR. HARV. TRACTORS l CALLTRAPUN HANUM = HANUM + I f 7 RETURN Fig. 4.6 Flow chart for subroutine FORHAR. 62 Provisions are taken to care for the calculation of the harvesting sub-system capacity when the crop is harvested once or more than once. Also, the number and power of tractors is not computed when self-propelled forage harvesters are used. Power requirement is determined for both self-propelled and tractor driven forage harvesters. 4.8. Transportation Units. Subroutine TRAPUN calculates the number and size of transport units and their power requirement in accordance to the size and number of forage harvesters, combines, picker or picker-shellers.1\flow chart for this subroutine is given in Figure 4.7. A logical parameter (WAGON) is used to separate calcu- lation of wagons from trucks, as the selected means of transportation for forage and grain. The first step is the calculation of cycle time following the procedure described in Section 3.4.2.5. Three options are considered: 1) the transported material is going to be directly fed to the animals, 2) placed in a horizontal silo or 3) in a vertical silo. In the first option, the unloading time (UT) is made equal to the feeding time (FT) and in the second to the time of unloading the transport unit, (UTH); In the third option the unloading time is calculated by dividing the transport unit capacity (TUC) by the blower capacity (BLCAP). 63 START CALCULATE TRANSPORT CAPACITY (RTPH) L 2 L [TANUM = TANUM + I YES NO YES _I h. 2: AL ll n-ILE n—- 1 7 CALCULATE TRANSPORT CYCLETIME CALCULATE TRANSPORT UNIT CAPACITY (TRACAP) N YES ‘3 YES ~ RACAP _>_ RIP 0 YES SELECT NUMBER AND SIZE OF TRANSPORT UNITS WAGON = TRUE _ I SIZE OF TRANSPORT CALCULATE NUMBER AND UNIT TRACTORS N0 L RETURN Fig. 4.7 Flow chart for subroutine TRAPUN. 64 The required transport capacity (RTPH) is computed in ton per hour unit according to the feed production method. The value for silage and green feeding transportation is based on the number and capacity of forage harvesters. For haylage it is based in the amount of material obtained from each cut and the available time for harvesting and, finally, for grain it is based on the required capacity of the grain harvesting subsystem. The transport unit capacity, the number of transport units and the cycle time are used to determine the total capacity of the transportation subsystem. This value is compared to the required transportation capacity and by following a procedure quite similar to the one described in FORHAR, the number and size of transport units are selected. Top limits for wagon and truck capacities are 12 ton and 20 ton respectively. If wagons are used, the number and power of tractors required for pulling them is determined by using the pro- cedure explained in section 3.4.3. 4.9. Harvesting Silage or Haylage. Subroutine SILHYL computes the number and size of machines used in silage and haylage. It also calculates the capacity of the blowers in ton per hour unit, when they are used for filling vertical silos, the power of the tractor required by the blower and the power requirement of 65 the implements as well as the number of tractors used for pulling them. As illustrated in Figure 4.8, after reading all of the input data, the subroutine divides the calculations in two branches, one corresponding to silage and the other to haylage, by using the control index MI (MI = 1 for silage and MI = 2 for haylage). When silage is the option, then the required capacity of the system is obtained first and it is transferred to FORHAR which proceeds to select the machines used in harvesting, as it was explained in section 4.7. Subroutine TRAPUN, called by FORHAR, performs the calculation of machines utilized in transportation of chopped material to the silos, as described by section 4.8. If the time allowed for transportation is limited or the amount of material to be transported is too large as to exceed the transportation capacity of the system, the calculation stops and the word "COMMENTS" is printed to indicate this situation. A different procedure is applied to determine the required system capacity for haylage. Since harvesting is done in several cuts, the rate of harvesting in ton per hour unit is calculated for each cut and the maximum of these figures is selected as the required system capacity for haylage. This is possible by repeating the computa- tion of the tonnage as many times as the number of cuts (NC) 66 START READ FARM DATA AVAILABLE TIME MACHINERY DATA MANAGEMENT PRACTICES l CALCULATE SYSTEMCAPACITY - 1 , I CALL FORHAR _I - 1 YES NO CALCULATE HINDROMER , AND RAKES CALCULATE NUMBER CALCULATE MONER OR * AND SIZE OF FORAGE MON. - CONDITIONERS HARVElSTERS AND RAKES CALCULATE NUMBER _ IV - AND SIZE OF TRANS- [CALL FORHAR] PORT 1UNITS * + CALCULATlE NUMBERI 0F TRACTORS AND [POWER REQUIREMENTSI 3 E CALCULATE I BLOHER CAPACITY - CALCULATE NUMBER OF TRACTORS I POMER REQUIREMENTS RETURN Fig. 4.8 Flow chart for subroutine SILHYL. 67 and calling subroutine RBC, which will be described later, to calculate the rate of cut in ton per hour. . A management decision has to be made in advance, whether a mower and conditionercnra mower-conditioner or a windrower is going to be used in order to select the proper implement. Selection of harvesting and transportation machines is the same as with silage but with the addition of calcula- tions for blower capacity, in ton per hour unit, and the power of the tractor required to operate the blower. The number of these units is also computed. 4.10. Hay (Dry) Harvest. Subroutine HAY calculates the tonnage per out based on feed requirements, feed losses and the fraction of yield obtained in each cut. The total amount of cut material is first obtained by: FL = PCL * FR(MI) (4.19) SYSCAP = FR(MI) + FL (4.20) where: FL = total feed losses, t PCL = fraction of feed losses, decimal FR = feed requirements, t MI = index for feed production method SYSCAP = total cut material, t 68 The tonnage per cut is calculated by: TONCUT(J) = FRY(J) * SYSCAP (4.21) where: TONCUT = tonnage produced at cut J, t FRY = fraction of crop yield at out J, decimal Values of TONCUT are transferred to the subroutines dealing with calculations of machines used in hay produc- tion. A flow chart for subroutine HAY is presented in Figure 4.9. 4.11. Conventional Baling. Subroutine BALING selects the type of balers, bale movers and wagon dryers used in conventional baling. It also finds the size and number of round baler and round-bale movers, and the number of tractors and power requirement for each of those machines. As shown in Figure 4.10, subroutine BALING takes into account two methods of baling, small rectangular bales and large round bales: two agronomic practices, partial and complete hay field curing; connected with partial field hay curing, two hay drying methods, mowdrying and wagon- drying; two storage practices, bales stacked on open-level ground and bales stacked in barn with bale elevator: and eight transportation alternatives for conventional baling and three for round baling. Conventional baling, partial field during and 69 START READ FEED REQUIREMENT FEED LOSSES FRACTION 0F YIELD FEED PRODUCTION METHOD > "1 [/3,/"YES NO [ , FL = FR(MI) * PCL(MI)_] I SYSCAP = FR(MI) + FL .I DO J: I: NC 1(a) = FRY(O) * SYSCAPII ¥_J . RETURN Fig. 4.9 Flow chart for subroutine HAY. READ MACHINERY DATA SYSTEM CAPACITY LOGICALS TRANSPORT INDEX ONBAL = TRU NO YES CALCULATE NUMBER AND ICALCULATE NUMBERI D AND SIZE OF ROUN BALERS l AND SIZE OF ROUN CALCULATE NUMBERI D BALE MOVERS CALCULATE NUMBER , » AND SIZE OF CALCULATE NUMBER RECTANGULAR BALE AND SIZE OF WAGON MOVERS DRYERS J- ~ J _ T CALCULATE NUMBER OF TRACTORS AND POWER REQUIREMENT RETURN Fig. 4.10 Simplified flow chart for subroutine BALING. 71 mowdrying are handled by the logical statements CONBAL, PARFC and MOWDRY respectively. Machines used for transporting bales range from simple flat wagons to automatic self-propelled bale wagons and trucks. The use of one of these machines is controlled by assigning digits from one to eight to the indexes BI and BMTI in a pre-established sequence. After determining the available time for baling and the required baling capacity, a decision is made with respect to the use of rectangular or round balers. In either case, the number and size of balers are calculated following the general procedure explained in previous sections. For the selection of the bale movers, the cycle time is individually calculated, since each one has character- istics which cannot be treated in a general way. The number and size of bale movers for rectangular balers and round balers are separately calculated. There is a section of this subroutine exclusively devoted to the calculation of number and capacity of wagon dryers, based on the baler effective capacity and the rate of drying of the equipment used for that purpose. The number of tractors and the power used by each of the machines selected in this subroutine are computed following the procedure established in the A.S.A.E. Yearbook (1980). 72 4.12. Conventional Baling System. Size and number of machines complement to the conven- tional baling method are selected by subroutine HAYB1. A flow chart for this subroutine in Figure 4.11 indicates that after obtaining the required baling capacity, a decision has to be made between the use of mower and conditioners and mower-conditioner. Once the decision is made, the alternative machines are selected in number and size. Next steps are the computation of the size and number of single rakes and the number and power of tractors utilized in this subsystem, alOng with the power require- ment of each implement selected. By calling subroutine BALING, the calculation of balers and bale movers completes the selection of machines for the conventional baling method. Subroutines SILHYL and STACK partially utilize to subroutine HAYB1. Controls are set to limit the extension of this usage and consequently to stop the subroutine operation. For SILHYL,MI = 2 and for STACK, BIC = 0.0 accomplish that prupose. 4.13. Rate of Cut for Implements. Subroutine RBC was designed to calculate the maximum rate of cut in ton per hour. This subroutine is called at any momentthat this value is required, which happens very frequently in subroutines BALING, HAYB1, HAYB2, STACK, CUBE, FORHAR and TRAPUN. 73 START READ MACHINERY DATA TONNAGE REQUIRED LOGICALS TRANSPORT INDEX , '1 . |DOL= I. N£I _ I _ I AHM(L) = CUTD(L) * HPDII I , ICALL RBC I N0 CALCULATE NUMBER AND SIZE OF SINGLE RAKES YES CALCULATE NUMBER AND I SIZE OF MOWERS _ _ I ICALCULATE NUMBERI OF TRACTORS AND ._ POWER REQUIREMENTS SIZE OF CONDITIONERS CALCULATE NUMBER AND I CALCULATE NUMBER AND SIZE OF MOWER-CONDITIONERS YES NO ICALLBALINGI _IJ RETURN Fig. 4.1] Flow chart for subroutine HAYB1. 74 A flow chart for subroutine RBC is given in Figure 4.12. 4.14. Round Baler Systems. Subroutine HAYB2 was prepared to select the machines used in large round baling method, as shown in Figure 4.13. Windrowers and tandem rakes are calculated in size and numbers by this subroutine, as well as the power required to operate the tractors. Round bales and round—bale movers are selected by subroutine BALING in the way explained in section 4.11. Indexes are assigned to control the usage of subroutine HAYB2 for other subroutines. So, MI = 2 for SILHYL and BIC = 0.0 for STACK and CUBE stops the operation of HAYB2. 4.15. Stack Systems. Size and number of stackers, stack movers and their power requirements are computed and selected by subroutine STACK. Information concerning machine capacity, size, weight, working speed, and tractive efficiency is furnished to this subroutine along with data related to the type of crop, crop yield and fraction of crop yield in each out. In this case, the parameter set for the selection of stacker is its effective field capacity (EFC), which is compared to the effective field capacity required by the stacking system (EFCS). 75 START Z/R, B,REéDI, NC/Z f A 30.03 IBz = A(L)/B(LII Bz>m NO YES I_ BI = B2 I] r LI I C FIB] I Fig. 4.12 Flow chart for subroutine RBC. 76 START READ MACHINERY DATA TONNAGE REQUIRED LOGICALS TRANSPORT INDEX [:' DO I: = 1. NC 7] [_AHN(I)= CUTD(I) * HPDI [_ CALL RBC I ICALCULATE NUMBER‘ AND SIZE OF MINDROHERS BIC = 0.0 . - M1 = 2 YES YES ”0 NO I00 J = 1, NC ICALL BALING] _ I . J_ I_AHTR(O)= RAKD(J) * HPD CALCULATE NUMBER ’ OF TRACTORS AND [P'CALI_ RBCJJ POWER REQUIREMENTS . .1 , CALCULATE NUMBER AND SIZE OF RETURN TANDEM RAKES Fig. 4.13 Flow chart for subrOutine HAYBZ. 77 Values of stacker field capacity in hectare per hour are kept in one to one correspondence to the stacker size in ton, so that the stacker size is indirectly selected at the moment that the effective field capacity of the stacker under consideration be greater or equal to the effective field capacity required by the system. In order to keep a reasonable size-capacity relationship, mower-conditioners are selected when the calculated stacker size does not exceed 0.90? t (1 ton), but when the stacker size is larger than that figure, then windrowers are selected because their large capacity are a better match to medium and big stacker size. The index I = 1 is set to control the alternative of using HAYBl for calculation of mower- conditioners and HAYBZ for calculation of windrowers. A section of this subroutine selects the number and size of stack movers used only in farms. No highway-type stack mover is considered. The size of stack mover is obtained by a procedure similar to the one used to deter- mine the stacker size, that is, the correspondence one to one between the stack mover capacity in ton per hour and the stack mover size in ton. The last section calculates the number of tractors and the power required by the stack wagon and stack mover. A flow chart of subroutine STACK is presented in Figure h.1h. 78 START READ MCHINERY DATA TONNAGE LOGICALS TRANSPORT INDEX [I DO J = ET, NC I - IAHS(J)= BLD (J) * HPDI I , I CALL RBC I CALCULATE NUMBER AND SIZE OF STACKERS I [—810 = 0.0 I I—BIC =o.oII I—M= FALSEI I—CALL HAYBZ ICALL HAVE] I ' L J _ F , CALCULATE NUMBER AND SIZE OF STACK MOVERS ICALCULATE NUMBER OF TRACTDRsI AND POWER REQUIREMENTS RETURN Fig. 4.14 Flow chart for subroutine STACK. 79 n.16. Cuber Systems. Subroutine CUBE selects the size and number of machines used in hay-cubing systems. As it is shown in Figure #.15, subroutine HAYBZ is called to select the number and size of windrowers. The selection of this machine is done because its capacity matches the requirement of a high volume operation such as cubing. As usual the required cubing capacity is calculated from the available time and the maximum rate of cut as determined by subroutine RBC. Comparison between the calculated field capacity (EF) and the cuber field capacity (EFC) allows the selection of field cuber number and size. A high-dump wagon is selected by matching its number and capacity to the number and size of field cuber respec- tively. The cycle time for cube hauling by trucks is computed by adding the travel time, the loading time and the unloading time. The time for dumping a load is a variable in the calculation of the loading time. The required truck capacity is calculated by using: TRC = 60. * TC * TEN/CYT (4.22) where: TRC = total truck transportation capacity, t/h TC = truck size, t 80 START READ MACHINERY DATA TONNAGE LOGICALS TRANSPORT INDEX BIC =0.0 I CALL HAYBZI 7 J _ 'I [Do J = I,NCI I AHC(J)= BLD(J) * HPD I I CALL RBC I A CALCULATE NUMBER CALCULATE NUMBER AND AND SIZE OF FIELD CUBERS SIZE OF NATER I * NURSE TRUCKS CALCULATE NUMBER AND 1 7 SIZE OF HIGH-DUMP CALCULATE NUMBER NAGONS 0F TRACTORS AND 1 POWER REQUIREMENTS CALCULATE NUMBER AND ’ SIZE OF TRUCKS FDR CUBE HAULING f RETURN do Fig. 4.15 Simplified flow Chart for subroutine CUBE. 81 TRN = number of trucks CYT = cycle time, min. 60. = dimensionality constant Successive comparison of this value to the required cubing capacity (RPH) yields the size and number of trucks at the moment that TRC . GE . RPH Water nurse trucks are required to carry the water used in field cubing operations. The number and size of trucks used for this purpose are calculated from the requirement of water per ton of hay and the total tonnage of hay per day. So, FCMC = EF * CY * FRY(J) (4.23) THPD = FCNEB * HPD (4.2M TRCP = 85.851 * THPD/1000 (4.25) where: FCMC = field cuber material capacity, t/h EF total field cuber capacity, ha/h CY crop yield, t/ha FRY = fraction of crop yield in cut J, decimal THPD = tonnage of hay per day, t HPD = working hours per day, h 1000 = dimensionality constant 85.851 = kg of water per ton of hay TRCP = truck size, t 82 4.17. Grain Harvest. Subroutine GRAIN calculates the number and size of machines used in the production of low moisture and high moisture grain. Once the required grain production capacity is calculated, the flow chart in Figure #.16 indicates that a management decision is required, that is, if the grain is going to be shelled or not. This decision is excuted by the logical statement SHC (shelled corn). Immediately after, another decision is called for: use of self-propelled combine or picker-sheller, if shelled grain is going to be produced. The logical statement that directs the control of this decision is SPCO (self-propelled combine). According to the alternative selected, either self- propelled combines or picker-shellers are selected in number, capacity, number of rows and row width. If the decision is the production of corn ears, then the same parameters are computed for the picker. Subroutine TRAPUN is called to take care of the selection of transport units required by the grain systems. Either wagons or trucks may be selected by using the logical statement WAGON. The size of the blower in ton per hour and the power of the tractor to operate the blower to fill the silo are determined according to total capacity of the harvesting 83 START READ MACHINERY DATA AVAILABLE DAYS FEED REQUIREMENTS LOGICALS [AHC = ND * HPD ' I I 506 = (FR(MI) + FL) / AHG I CALCULATE NUMBER AND SIZE OF PICKERS I I _ CALCULATE NUMBER AND SIZE OF SELF- 'PROPELLED COMBINES CALCULATE NUMBER AND» SIZE OF PICKER- SHELLERS 3%. I CALL TRAPUN _ I _ CALCULATE NUMBER OF TRACTORS AND POWER REQUIREMENTS RETURN Fig. 4.16 F10w chart for subroutine GRAIN. 8h machines and the type of material (low or high moisture grain, ground or complete material). In order to cope with any peak condition, the capacity of the blower is increased by 10%. At the same time, to guarantee enough power in the tractor used for the blower, a provision is taken so that the tractor power is never less than that required by the silo height. The last section of the subroutine calculates the power requirements and the tractors used with pull type implements. CHAPTER 5 VALIDATION OF MODEL, ANALYSIS AND RESULTS 5.1. Model Validation. One important step in modeling is to verify that the model is an acceptable representation of the actual system under study, which is referred to as model validation. Traditionally, model validation is conceived as a two-stage process. First, the verification of the program and its components subdivisions (subroutines) to make sure that each one worked the way it was intended to do and second, the comparison of program results with actual data. 5.1.1. Verification of Program and Subroutines. The verification procedure consisted of detection and diagnosis of errors in sintaxis and program logic, successive runs and testing of subroutines, examination of the output produced and correction of the anomalies detected. This procedure was followed with each one of the subroutines before their final assembling and in order to accomplish that, the initial version of subroutines were designed to simulate the actual work of the final version by using input data obtained from the literature reviewed 85 86 and actual farm data. The method proved to be very useful in understanding the behavior of each component of the feed harvesting system model. This facilitated its con- struction since better and more logical relationships could be established among the model components. Subroutines GF (green feeding) and SILHYL (silage and haylage) were tested by varying three parameters simultaneously: tonnage cut per year, values ranged from 116 to 2320 tons per year, number of working days from one to eighteen and number of working hours per day, from one to eight. An output sample for subroutine GE is presented in Table 5.1, corresponding to h6fi tons per year and 5 working hours per day. There it can be observed how the number and capacity of forage harvesters and wagons are affected by the available time. Also, the excess of harvesting capacity and transportation capacity, given in percentages, were used as a check for calculation of those parameters. Excess of harvesting capacity ranged from 0.02% to 25.5%. It is convenient to point out that the upper range limit appeared only once in the complete test, and that the most common values ranged from 0.8% to 5.9%. Excess of transportation capacity ranged from 0.03% to 11.2%. No concentration of values at any particular range was observed. fi\ex oH u ummgm mmmum>m pHcs whommcmup ex m u moCNpmHv Hm>mnp owmhm>m chs phommsmup n m u haw pom mason mcHxhos # do: n hawk Mom 950 mmmccov 87 mH.H 0.: N mm. o.m H NH mm.: o.m N HN.H m.w H NH mm.m o.m N ow. o.m H wH mH.H o.m m wo.m o.oH H mH Nm.: 0.0 N om.N m.oH H :H nN.m 0.0 N 0H. o.HH H mH mH.H 0.0 N om. o.NH H NH mm.N o.n N 0H. o.mH H HH Hm. 0.5 m mm.H m.sH H oH mH.H o.m N mm. o.wH H m wo.m o.oH N om. o.mH H m mm. o.oH N Hm. m.0N H u NH.H o.NH N ow. 0.:N H 0 No.3 o.m m mm.H o.mN H m mn.m 0.0 m cm. 0.8m H : mm.N o.m m cm. o.m: H m «0.0 o.oH m cm. o.wm m m :o.N o.NH m cm. o.m: m H ARVzHHommmo Apvszommmo mpHCD Asv zPHommmo A£\thpHoQOo mumpmm>nmm mhma whommcmue pHcD whommcmna wchmm>pmm umpmw>nmm mmmgom wcHxnoz mmmoxm phommsmue amnesz mmmoxm mNMhom nmpszz .mw mchsohpsm mo pmop .oHQEMm 939950 .H.m mHnme n\ax OH R vommm mwmum>m pHcs ppommcmpp ex m u mosmpch Hm>mpp mmmpm>m Pch ppommcmpp n o u haw pom mason mcpros p :0: u hawk Mom #30 mNNGCop 88 Nm.: 0.: N om.m o.m H NH Nm.: o.: N No. 0.5 H NH NN.N 0.: N mm. m.N H SH NH.H 0.: N ww. o.m H mH mm.: o.m N No. m.m H :H H:.N o.m N om.m m.m H mH NH.H o.m N mm.- o.oH H NH mN.m 0.0 N ou.H o.HH H HH NH.H 0.0 N mm. o.NH H oH mo.N 0.5 N NH.N m.mH H m NN.N o.w N wm. o.mH H m no.3 o.oH N No. o.NH H m NH.H o.oH N mm. 0.0N H o NH.H o.NH N ow. 0.:N H m mm.N o.m m mm. o.om H : Nw.: 0.5 m mm. 0.0: H m om.N o.oH m No. m.mm H N om.N o.oH o No. m.mm N H ARVzPHommmo Apvthommmo mpHcm ANthHoQOo A;\thPHommwo whopmm>hmm mama whomeMHa PHGD pponmcmhe mchmm>hmm Hmpmm>nmm mmmuom mcHxnoz mmmoxm phommsmue Amnesz mmmoxm mwmhom gmnsdz .AOHHm Hmpcoquomv HMIHHm maHPSOMDSm mo pmmp .mHmemm pampso .N.m mHnme 89 :\Ex cH u cmwam mmmuw>m uHc: Ex m n vacuuch H0>muu mvmuw>m uch uuoamcmuh uuoamcmub : h u >m© hon muse: mcquoz a ewe u umma umm uso wan::0& ww.oH om.m NH.H o.m N oa.m o.w H NH ww.OH om.m NH.H o.m N No. o.w H NH mm.HH mN.m hm.m o.v N m¢.H m.w H wH vv.NH Ho.OH Nm.v c.v A N mm.N c.h H mH mm.NH NN.OH Nh.N o.« N mm.N m.h H .VH NN.¢H vv.HH NH.H o.e N mm.H 0.x H .mH cH.mH mH.NH hm.v o.m N No. m.m H NH NN.¢H Nm.nH Hv.N o.m N n¢.N m.m H HH ww.NH Hc.mH Nm.v o.w N ma.N m.OH H OH vv.oN vv.wH cN.N o.w N av.H m.HH H m OH.mN mm.NH mm.N o.h N mm.H c.mH H m ww.wN mv.HN Nh.N o.m N om.N o.mH H b HN.cm Hn.vN hm.v o.OH N No. c.5H H o mv.wm Hm.aN hm. o.oH N Hm. m.ON H m Nm.mv mv.om no.oH o.m M No. m.mN H e mv.co Nw.ov vN.m o.w N No. c.vn H n mw.ca ma.Nh hv.cH o.oH N No. o.Hm H N mw.om mm.Nh hv.oH o.cH w No. c.Hm H H Hzxvum3om Hzxv A». auHommmu A». quommmu uch A». zuHUmmmu .£\u. >UHommmu nuwuno>um= mama uOuumua uuHomamU uuoamcmua uHca uuoamcmus ucHumw>um= noumw>umz wuuuom OCquoz uwson uw3on mwmoxm uuommcmua nonsuz mmmuxm mvmuom uwnfisz .HOHHm HmoHuum>. A>=4Hm wcHusouasm mo umwa .mHQEmm usmuso .m.m mHnt 90 Lines for one and two working days in Tables 5.1 and 5.2 reflect one of the characteristics of this model, that is, the purpose to cope with the cutting requirements using the least number of forage harvesters by increasing the machine capacity if available time allows it. Output samples for subroutine SILHYL are given in Tables 5.2 (horizontal silo) and 5.3 (vertical silo), corresponding to six and seven working hours per day respectively, and the same tonnage, #64 tons per year, for both. For vertical silos there is extra information on blower capacity and the tractor power for blowing operation. Observations of values for blower capacity and for tractor power conducted to the establishment of provisions, as cited somewhere before, to avoid bottlenecks at the blower and make sure that tractor power matches the power required in relation to silo height. Observations of components of cycle time for trans- port units, particularly the loading time and its effect on forage harvester efficiency led to the setting of the transport unit number at a minimum of two. This provision guarantees the forage harvester work with an efficiency close to the maximum that can be achieved with this machine by reducing the time waiting for the transport units. This is shown in Tables 5.1, 5.2 and 5.3, where the number of transport units remains equal to two even though the available time becomes greater. The model 91 response is to reduce the individual capacity of the transport units to keep pace with the required trans- portation capacity. Subroutine Baling was tested using six methods of transportation for conventional baling and two for round baling. These included two baling procedures: conventional and round baling, two agronomic practices: partial and complete hay field curing, and mow—drying or wagon drying when partial hay field curing was used. Results given in Table 5.4 correspond to a system capacity of 280 tons per year, to an available time of 66 effective working hours for baling and four cuts per season. Observations on these results led to adjust the maximum capacity of the conventional baler to 10 tons per hour, and for the round baler to 1k tons per hour, to keep calculations of this parameter inside the limits of actual machine capacity as reported by manufacturers and the literature reviewed. Also, calculation of wagon dryers number and size were isolated from the other transportation units, since this proved to be more appro- priate due to the characteristics of wagon-drying operations are quite different of those of other bale transport units. When using conventional a baler with a trailing wagon, it was necessary to separate calculation of trailing wagon taking into consideration if the baler was furnished with bale ejector or bale chute. So, the capacity of trailing 92 Table 5.#. Test of subroutine BALING. Machine Number Capacity Units round baler 1 3.0 t/h round multi-bale mover 1 5.0 bales round one-bale mover 2 '1.0 bales conventional baler 1 2.5 t/h trailing wagon 1 12.0 t flat wagon w/bale loader 2 2 . O t self-propelled bale handler 1 12.0 t automatic bale wagon, pull type 1 6.0 t automatic bale wagon, SP 1 6.0 t trucks w/bale loader 1 12.0 t wagon dryers 2 1.0 t tonnage cut per year = 280 t number of cuts = h available baling time = 66 h transport average distance = 3 km transport average speed = 10 km/h 93 wagon equipped with bale ejector was considered reduced by 10 percent due to the bale ejector random stacking pattern. Similar consideration was applied to the loading time when the transport cycle time for this unit was computed. For conventional baling (HAYB1) two options were tested, the use of a mower and conditioner or mower- conditioner. The transport unit selected was a flat wagon with bale loader. For a hay production of 702 tons per year, four cuts per season and an available time for baling of 66 effective working hours, the results are presented in Table 5.5. Under the same conditions as above, subroutines HAYB2, STACK and CUBE were tested and the output presented in Table 5.6 corresponds, in the same order, to round baling, stacking and cubing as the hay production methods. Analysis of results of these tests determined the need for correction of a formula used for calculation of field capacity taking into account the fraction of yield obtained in each cut. Thus, the corrected formula is: EFC = RTPH / [CY (K) * FRY (J)] (5.1) where: EFC = effective field capacity, ha/h RTPH = required material capacity, t/h CY = crop yield, t/ha 94 Table 5.5. Test of subroutine HAYB1. Machine Number Size Units Using mower conventional baler 1 7.00 t/h mower 1 2.44 m single rake 1 2.74 m flat wagon w/bale loader 2 7.00 t Not using mower conventional baler 1 7.00 t/h mower-conditioner 1 3.66 m single rake 1 2.74 m flat wagon w/bale loader 2 7.00 t tonnage cut per year = 702 t available baling time = 66 h number of cuts = 4 transport unit average travel distance _ 3 km transport unit average speed = 10 km/h 95 Table 5.6. Test of subroutines HAYB2, STACK and CUBE. Machine Number Size Units Round baling windrower 1 2.13 m tandem rake 1 3.66 m round baler 1 7.00 t/h flat wagon w/bale loader 2 7.00 t Stacking stacker 1 0.90 t mower-conditioner 1 3.66 m stack mover 1 2.72 Cubing windrower 1 2.13 m field cuber 1 1.62 ha/h high-dump wagon 1 4.26 t cube hauling truck 1 2.0 t water nursing truck 1 2.2 t tonnage cut per year = 702 t available baling time = 66 h number of cuts = 4 transport unit average travel distance = 3 km transport unit average speed = 10 km/h 96 FRY = fraction of yield in cut J K = crop index Also, the calculation of rolling resistance force (RRF) in trailing machines was corrected by adding the weight of material carried to the machine weight, wherever this situ- ation was found. In general, the corrected formula is: RRF = (WCM + WM) * CR (IS) * ACC / 1000. (5.2) where: RRF = rolling resistance force, KN WCM = weight of carried material, kg WM = machine weight, kg OR = rolling resistance coefficient IS = rolling resistance coefficient index ACC = acceleration of gravity, m . s-2 1000 = dimensionality constant. Grain production subroutine (GRAIN) was tested using management practices such as production of shelled corn or ear corn, use of a combine or picker sheller when the option was shelled corn, and two transportation units wagons and trucks. Results given in Table 5.7 correspond to the production of shelled corn, harvesting with picker-sheller and wagons as transport units. Grain production is 330 tons per year (12990 bu/yr) and the available time for harvesting is 66 effective working hours. 97 Table 5.7. Test of subroutine GRAIN. Machine Number Size Units picker-sheller 2 3.17 t/h row width 1.02 m number of rows 2 power requirement 32.38 kW picker-sheller tractors 2 40.47 kW wagons 3 3.0 t wagon tractors 11.75 kW (1) blower . 1 6.97 t/h blower tractor 1 44.05 kW tonnage harvested per year = 330t available harvesting time = 66 h transport average distance= 3 km transport average speed = 10 km/h silo height = 1 (1) power used to pull the wagon 98 5.1.2. Dairy Farm Survey A dairy farm survey was conducted to obtain the production records of 40 dairy farms selected at random among those participating at Telfarm, a computarized accounting project offerred by the Cooperative Extension Service at Michigan State University. The objective of this survey was to get information about the size of the farms expressed in number of dairy cows and the feed disappearance, and the surface cropland distribution. Ten farms were selected from each one of the following groups: less than 50 dairy cows, 50 to 75 dairy cows, 76 to 100 dairy cows and more than 100 dairy cows. A brief analysis of the survey data showed that the different methods of feed production were reported by the farmers in the following percentages: silage 87.5%. hay and haylage 100.0%, grain 100.0% and pasture 50.0%. Other cash crop production was reported by 75.0% of the farmers. The average number of dairy cows and surface of cropland produced in each group are given in Table 5.8. All of the dairy farms reported rented land to produce at least one crop used to feed animals. The total tillable land is presented as owned, rented and a combined total for both. Hay and haylage production is given as a whole figure under the designation of hay equivalent. 99 o.o N.h m.c h.H 5.0 a.o m.H v.H 0H0H 0:: 00uum>H0 m.N N.m m.v m.c N.v m.H N.N 0.0 nacho :muo umsuo 0.0 c.n N.H o.h m.o h.m m.o a.N mumo m.N N.o H.H m.N m.n m.m m.H H.m anon: o.o h.c c.° w.H 0.: 0.0 0.0 c.o omMHuoo o.m v.p 5.0 m.n 0.0 H.H c.o h.N ouauuom m.hN o.ov v.m m.mm m.h 0.0m N.m N.HN acon>stu an: m.mv o.mm v.mN v.nn o.NN v.mN v.mH N.NH :Hnuo :uoo n.mH m.nn m.H H.HH v.m H.hH m.v n.a monHHu :uoo 000500um 0:MHQOHU momuusm H.00N m.th m.ovH H.om HmuOu 00:HQEOU m.ooH m.mcH m.mv c.NoH «.mv H.No N.NN «.mm 0:mH oHnaHHHu Hmuoe sham non mmumuowz n.va N.No p.00 m.mv msoo auHm0 mo noses: 0ou:om 00:30 0ou:om 00:30 0ou:om 00:30 0ou:om 00:30 ocH :mzu mac: coH I 05 mp I am cm can» mmmq mzoo mo “025:2 >n oNHm Show .0mos0oum 0:6Hmou0 mo oomuusm 0:: m3ou auHma no umnesz mmmuo>< .w.m mHnme 100 The average surface of cropland per cow devoted to feed production is presented in Table 5.9. There it can be observed that the combined total for forage and hay is 0.76 ha/cow for the group of 76-100 cows and about 0.9 ha/cow for the other groups. The average for all groups is 0.88 ha/cow. The combined total for grain is 0.7 ha/cow for the groups of 76-100 cows and more than 400 cows, 0.82 ha/cow for the group of less than 50 cows and 0.92 ha/cow for the group of 50-75 cows. The average for all groups is 0.81 ha/cow. Feed disappearance was calculated based on farmer's estimates of feed production, sales, purchases, and begin- ning and ending inventories, as reported to Telfarm: FD = BI + PR + PUR - EI - SAL (5.3) where: FD = feed disappearance, t BI = beginning inventory, t PR = production, t PUR = feed purchase, t E1 = ending inventory, t SAL = sales, t Reported information and calculated feed disappearance for each one of the farm groups are given in Tables 5.10, 5.11, 5.12 and 5.13. The survey form is presented in Appendix C. 1(31 m5.0 Hm.0 N0.0 00.0 no.0 Hn.0 0m.0 H0.0 0n.0 50.0 00.0 00.0 0H.0 0~.0 00.0 0N.0 55.0 N0.0 «0.0 Hn.0 00.0 N0.0 00.0 0m.0 00.0 «0.0 00.0 H0.0 00.0 H0.0 50.0 0N.0 0m.0 H0.0 00.0 0m.0 mn.0 05.0 00.0 00.0 0H.0 «0.0 5H.0 55.0 0H.0 05.0 500.0 00.0 00.0 00.0 00.0 00.0 HH.0 00.0 NH.0 00.0 50.0 00.0 No.0 MH.0 00.0 0N.0 00.0 0H.0 H0000 00:HQEOU :Hnum H0008 0000 :Hmum :uoo H0000 00:Haeou >0: 0:: 000000 H0009 000000: 0:0H0>H:00 an: 000HHO :uoo 600:0: oocso ooH cozu mac: 000:0: 00:30 000:0: 00:30 000:0: 00:30 00H I 05 05 I 0m 0m :0:0 0000 @300 no 000552 >0 0uHm Bumm .300\0: .300 000 0:0Hmouo no 0000000 0mmu0>< .m.m 0Hnma 102 m.mH :.n a.m 0.0 a.mH m.m mpmo 0.05 0.5 H.50H m.H: m.M5H 0.00 :Hmhw 0006 0.0 0.0 0.0 0.0 0.0 0.0 mmepmo 5.HH 0.0 0.0 0.0 5.HH 0.0 mNSpmwm m.NoH 0.H: H.Nom o.m: m.:HN m.oHH PcBHm>Hsum Hm: N.mo: 0.0 m.omN o.o m.m0m o.NNn mmBHHm choc mocmpmmeMmHQ 00mmnomzm myopcm>cH mmHmm :OHpos0opm muopsm>cH 0000 0000 mchcm wchcmem .0 .m306 00 :030 mmmH :pH3 mshmm :H mosmhmmmmmmHU 000% mmmum>< .0H.m mHnwa 103 0.mH N.H 0.0 0.H N.HH H.oH mpmo 0.00N 5.0 H.0HN N.mm o.Hmm 0.00H :Hmpw :uoo 0.0 0.0 0.0 0.0 0.0 0.0 mmmHvMO 0.0 0.0 0.0 0.0 0.0 0.0 0059mmm 0.5Nm 5.0 0.00N 0.0 5.0H: 0.05H #:0H0>H500 hm: H.Nm0 o.o s.mH0 o.o m.uom 0.N00 mmmHHm zuoo mozmnwmQQMmHm 0000:6050 zpopcm>cH mmem :OHposconm huop:m>:H 0000 000m wcH0cm mchcmem .P .m3oo 05 I 00 APHB menmm :H mo:mnmmmmmmH0 0000 mwmpm>¢ .HH.0 meme 104 m.mH m.o n.3a m.o 3.0N N.NH mpmo :.mwm :.HH m.mmm N.mm m.mnm a.:mm :flmum cuoo m.ma 0.0 m.:H o.o m.:m :.m mwmapmo m.wm 0.0 0.0 0.0 m.mm o.o mHSPmmm m.Hmm m.oH m.mm: m.: m.mmm N.omm pcmam>flsum mm: m.:wn o.o o.Hmm o.o m.Hm: m.mo: mmmaflm Choc mo:MMMmQAMmflQ ummmnohsm hQOPCm>cH mmamm :0fl90360hm hQOPCm>nH vwmm vmmm mcficcm wzaccflwmm .P .msoo ooH a on spa; meumm CH mocmhwmmmmmflc cmmm mwmpm>< .NH.m mamas 105 N.“ m.o m.m o.o o.oH m.o memo H.amm m.m n.0mm o.mm m.m:w w.mom sawhm cgoo :.m 0.0 0.0 0.0 d.m o.o mmmvao m.om o.o o.o o.o n.0m o.o mgSPmmm m.onw o.o n.mH: 0.0 :.:mm o.mwm pcmam>flzuw am: N.H«ofi H.mm N.mm:H o.o w.moma n.0NNH mwmawm Chou mozmuMmeMmfla cmmmnomsm hpopcm>cH mmamm nowposUOHm hp0#2w>cH uwmm wmmm wcficcm wcwzcfiwmm .P .mSOO ooH cmcp macs nufls meumm Ca mocmnmwQQMmHU Ummm wwmnm>< .mH.m manma 106 5.1.3. Field Machinery Survey. In order to get information on farm machinery specifically used in feed production, a survey was con- ducted among the same farms selected in the dairy farm survey, but only sixteen farmers answered it. Percent of dairy farmers reporting use of specialized equipment for feed production is given in Table 5.14. It is observed that the use of forage choppers, forage wagons and rakes was reported by all of the farmers. Mower- conditioners by 81.2% against 18.2% for mowers. Conventional baling was used in a proportion of 2 to 1 with respect to round baling. Self propelled combines were used by 81.0% - of the farmers in comparison to #3.7% for corn pickers and 12.5% for corn picker-shellers. Three of them reported the use of combine and picker simultaneously. None reported the use of hay stacker, stack mover or hay conditioners. The use of blowers and bale elevators was indicated by 93.7% and 68.7% of the farmers respectively. Wagons were the most common machine used for material I transportation. The survey form is given in Appendix D. 5.1.4. Comparison of Four Field Machinery Surveys to Model Output. The number of returned field machinery surveys was lower than expected, and incomplete information on machinery size and number was furnished on the surveys that were received. For these reasons, the idea of an all survey 107 Table 5.14. Percent of dairy farmers reporting use of specialized equipment for feed production. Machine % forage chopper 100.0 forage wagon 100.0 flat wagon 75.0 truck 56.2 mower 18.7 hay conditioner 0.0 mower—conditioner 81.2 windrower 18.7 rakes 100.0 conventional baler 68.7 round baler 31.2 bale wagon 50.0 round bale mover 25.0 high-dump wagon 12.5 grain wagon 75.0 combine 81.0 corn picker 43.7 corn picker-sheller 12.5 hay stacker 0.0 stack mover 0.0 blower 93,7 bale elevator 68.? 108 testing was dropped and, instead of that, one survey was sampled at random from each dairy farm size group, the relevant information supplied to the feed harvesting model and its output compared to the information given by the farmer. Results from this comparison are not to be considered as a complete validation of the model due to the reduced number of samples, but rather to show the model ability to handle actual data and the possibility of a full validation based on an acceptable sample size. Model output and data from a farm with less than 50 cows are presented in Table 5.15. Results in this table show a good agreement with respect the number of machines reported and the one calculated by the model. With respect to the size, the values calculated by the model were below the reported sizes. This is so because the model calculates the minimum size that guarantees the completion of operation in the available time. An in- compatability exists between the size unit the forage chopper was reported and that given by the model, perhaps due to the fact that the survey was not clear enough requesting this particular information. No information was reported on size of conventional baler, bale wagon and grain blower. Data from a farm with 50-75 cows are in Table 5.16. Exception made of single rake, bale wagon and grain wagon, 109 n so u mcHHmn n me u mOHHm mcHHHHH : em u mcmeo n me u wsHHSM£ :Hmuw n 2N u wsflcOHpHosooasoe : me u msHpmm>Hmn :Hwnw c mm u mcHHsm: mmmnom : ow u wswasmz mama : ow n wcHPmm>nwn mwmuom .onpspHupmHe msHe p o.mmH u :Hmom P 0.5mm u Pson>H3dw mm: H o.mmm u .mMHHm “mama pom cmosvohm mwmccoa :\u 0.5 H - H umsoHp :Hmum p o.m m p o.m m comm; aHmum msop o.N H mzon o.m H mGHnEoo p o.H N - m comm; mHmn c\P m.m H a H pmHmn HanoHPCm>Coo e :H.m H e sm.m H mxmp mHmsz e :H.N H s dm.m H anoHpHvsoonhoSos 3x N.H: H 3x m.mn H popompp nommoao owmuom # 0.: N P 0.: N Gowns ommuom n\v 0.:H H mzon o.N H nommono owmhom mpHsD mNHm Honssz mpHGD muHm popssz ocHnomE psopso kuos upon hm>hsm Spam one .msoo on swap mmod :PHB .mvmc pampso Houos cam hm>u3m hsocHnows cHon .mH.m mema 110 n 66 u wcHHmn : me u mOHHm mcHHHHH : :N n mcHHmH a me u wzHHsmn CHNHm a 2N u mchoHpHUCooasoE a we u msHpmo>umn :Hmnm : ow u mcHHsmn mmmhom A ow u wCHHsms onn n me u msHPmm>Hms onpow ”soHpanppmHo oEHe H o.mmm u chcm P :.mww u psofim>Hsuo mm: H m.mwNH u mmeHm sumo» pom couscoum owmccoe n\p m.oH H - H umsoHp :Hmpm p o.m N p mm.m : msowmz QHMpw msoy o.m H mson o.N H HmHHonmnHoxOHm H o.N m p mn.m m comm; mHmp g\p n.o H n\» m.: H pmHmp HchHpcm>coo s :H.N N s 3:.N H oxmn mHmch a ow.m H s :N.N H sosoHpHusoonnmsos 3x m.mm H 3x 0.50 H sopoMHp someone mmmuom p o.m N p o.m N Comm: ommuom n\P m.mN H mzoh o.N H nonmono ommnom mpHsD muHm nonssz wPHCD ouHm Honssz oszomz psdpso Hmcos mama hm>psm 89mm oso .msoo mm n on .mwmo Pampzo Hmcoe cam mm>h3m zuoanome UHmHm .oH.m mHnme 111 the number of reported machines agrees with the number calculated by the model. Machine sizes were below the reported sizes with the exceptions of mower-conditioner and picker-shellers. The size of grain blower was not reported. The information from a farm with 76-100 cows is in Table 5.17. There is a coincidence in some values of machine number on this farm and in the model output, excepted the number of mower-conditioners, single rakes, and bale wagons. Sizes of bale wagons, combines and grain wagons, were larger than reported sizes of these machines. Grain blower size given in the survey is the maximum capacity while the size given by the model is the used capacity. Similar reasoning applies to the power of the forage chopper tractor. No information was reported on sizes of forage wagon and conventional baler. Data from a farm with more than 100 cows and the output model appear in Table 5.18. The number of mower- conditioners and single rakes calculated by the model exceeds the one given by the farmer. Bale wagon and grain wagon number are below the reported number; the number of other machines coincides with the number computed by the model. There was not information on sizes of single rakes, conventional balers, bale wagons, grain wagons and grain blowers. The available information on machine sizes 112 : 00 u mcHHmn g 00 u mOHHm mcHHHHm n :N u mcHHmp : 00 u wsHHsmn chpm 3 :N u wGHCoHpHccoonSos a 00 u wsHpmm>nmn chnm a 00 u wcHHzmn ommpom n 00 u wcHasmg mama : 00 u mchmm>nms mwmpom ”soHPSQprmHu mEHe p o.mHm u :Hmpw P N.mmm u psmHm>Hsvm mm: P n.0Nm u mmmHHm .umoh Hon coosuonm wmmcsoe :\p :.NH H :\p 0.00 H nmonn chpm 9 0.0 N P 0.n N comm: :kuw mach o.m H mzoh o.m H oCHnEoo p o.m N p N.H H comm; mHmn n\v m.m H u H HmHmn HmsoHpso>zoo a :N.N N s o.m H mxmn mesz s :m.N N s :m.N H nonoHpHusoonhosos 3x N.H: H 3x m.wm H sopomnp ummmono mmmuom P 0.: N n N mCowms mwmpoH n\9 m.NH H mzoh o.N H pmmmoso ommpoN mPHSD mNHm nonesz mpHsD mNHm umpssz mcHnoms pampso Hmcos mema hm>hsm 880% 0:0 .msoo ooH . 0N .mvmc psapso H0008 0cm am>usm zhmcHnome UHmHm .NH.m mHnms 113 : 00 u w:HHmp : 00 u moHHm w:HHHHm : :N u m:me: : 00 u w:HH30: :Hmnm : :N u w:H:oHPH0:oousoE : 00 u w:HP00>:0: :Hm:w : me u m:HH:m: 0wmnom : 00 u m:HH30: 0H0: : 00 u m:HPm0>:m: 0mmuom «:oHPan:PmH0 meHe P N.uH: u :Hmnw P m.m:m u P:0H0>H:v0 mm: P 0.5:H n 0w0HHm .nm0z :0: 00oscopm 0mm::o9 .s\P a.mH H - H :mSOHp chpm P 0.m N u : :ommz :Hmnm 03o: 0.: H 03o: 0.0 H 0:Hnaoo P 0.: N u m :ommz 0H0: :\P 0.0 H n H :0Hmn Hm:oHP:0>:oo s :H.N N - H 0:00 mech a :H.N N e mo.m H :0:oHPH0:oon:03os 3x o.ooH H 3: o.NHH H :OPompp umgmono 000:0: P o.oH N P o.oH N comm: 000:0: :\P m.:n H 030: o.N H 900:0:0 0wwnom wPH:D 0NHm :0nssz mPH:D 0NHm :0nssz 0:H:002 Pampso H0002 0P0: 00>:3m 8.80m 0:0 .mzoo 00H :0:P 0:08 .0Pmc Pngso H0008 0:0 00>:Sm 0:0:Hzoms 0H0Hm .mH.m 0Hnma 114 reveals that the model sizes are below the reported ones with exception of forage wagon size.' The time data used in this comparison were eleven working days and six working hours per day for a three- week harvesting season. The time distribution per oper- ation is given, along with other relevant information, at the bottom of tables related to this section. 5.2. Final Analysis. A final analysis was conducted in order to determine how the feed harvesting model output reacts to change in selected model parameters, and to show the importance of how precise they have to be estimated or selected. Five parameters were chosen and the results of the tests are discussed in this section. 5.2.1. Effect of Travel Speed on Transport Unit Size and Number. ‘ Three values of speed were used in this test at a fixed average travel distance of 3.0 km on a farm with the following feed production: tZyear silage 1263.6 haylage 657.7 hay 663.4 grain 295.0 The time used for transportation was 66 effective hours for silage, grain and hay in small bales and cubes. 115 For hay in big bales 32 effective hours and for haylage 60 effective hours. Results in Table 5.19 show that for increasing speeds the number and/or size of transport units is reduced. This is particularly more evident when trucks are used in hauling big round bales and cubed hay. 5.2.2. Effect of Travel Distance on Transport Unit Size and Number. Three average round-trip distances were used in this test at a fixed average speed of 10.0 km/h. The conditions of this test were the same as for the travel speed test, and the results are presented in Table 5.20. There it can be observed that for increasing travel distance there.is also an increase in number and size of transport units. That is, a completely reverse effect to that caused by increasing speeds. Again, the effect is more clearly noticed on trucks. The combined effect of both factors is determinant in the calculation of size and number of transport unit, therefore, particular care should be placed in the selection or estimation of both parameters. 5.2.3. Effect of Crop Yield on.Machinery Size and Number. The effect of crop yield on number and size of machines was demonstrated by using two values of alfalfa yields, 7.0 and 15.0 ton per hectare, on a farm with a required annual hay equivalent production 748.8 ton, and four cuts in the harvesting season. Results in Table 5.21 show that low yield causes an increase in number and size vmzoP NosuP Hmv :oP60H0 0H0: :PHz :0Hmp A:v 0m0Hzm: m:HP:omm:0:P Amy 0w0HHm m:HP:omm:0:P ANV ex 0.m u 00:0P0H0 H0>0:P .>0 AHV P o.H H o.N H o.m H gospP wcHHzm; 09:6 P o.m N o.n N 6.0 N 26063 :Hmnm monp o.m m o.m : o.m 0 Hmv pm>oe mHmn 6:36: P o.N N o.N N o.m N “:0 comm; mcHHHmuP P 0.m N 0.: N 0.0 N Amy :owms 0wmnom P .0.m N 0.0 N 0.5 m ANV :ommz 0mmuom mPH:D 0NHm :09852 0NHm :0pasz 0NHm :09832 0:H:oms n\sx .mHuemmmm .>< :\sx o.oHuemmmm .>< n\3x o.nucmmmm .>< .AHV 90:85: 0:0 0NHm PH:: Pnomm:0:P :o 000mm H0>0HP mo Po0mmm .mH.m 0Hpma 117 umsoP xos:P Amv :0P00m0 0H0: :PHs :0H0: A:v 0w0Hz0: w:HP:omm:0:P Amv mw0HHm m:HP:omm:0:P ANV :\ex 0.0H u 000nm H0>0HP .>0 AHV P o.m H o.N H o.H H gospP mcHHsms 00:6 P 0.0 N o.m N o.N N comm: :Hmpm 00H0n 0.m m 0.m : 0.: m Amy n0>oe 0H0: 0:5o: P o.m N o.N N o.H N H:0 comm; mcHHHmnP P 0.0 N 0.: N 0.m N “my :om03 000:0P P o.N m 0.0 N 6.0 N HNV 06003 000:6: mPH:D 0uHm :0nssz 0NHm :09852 0uHm pmnesz 0:H:o02 exo.0umochmH0 .>< exo.mumocumHe .>< exm.HumocumH6 .>< .AHV H0985: 0:0 0NHO PH:: Pnomm:0:P :o 00:0POH0 H0>0hP mo Pommmm .0N.m 0Hn0a 118 P N m u :H0:w P m.m:n n P:0H0>H:00 >0: P N.Hn:H u mmmHHm ex o.m : 0% :09 00000099 000::oa :\8x 0 n 00:0P0H0 H0>0:P..>0 .0H u 000mm H0>0:P .>0 P 0.m H 8.: H xosnP 00:3: HOP03 P o.N H o.N H x65:P mcHHsmn mnso n\mg N0.H H N0.H N 000:6 eHmHP P ::.m H ::.m H :0>o8 xo0Pm P Hm. H Hm. H :mxoum 80: e NN.: H NN.: N 0:00 amccmP 8 00.m H 00.m N :0sop0:H3 P o.N N o.N N 26003 wcHHHmuP e :H.N N :N.N m 0:00 mHmsHm 8 :H.N N ww.m m 90:0HPHO:oou:03o8 8\P o.N H o.N H pmHmn mPH:s 0NHm :09832 0NHm M09852 0:H:o02 mg\Po.mHu6HmH» 06:0 mn\Po.NneHmHP mayo .0NH0 0:0 “098:: 0:H:o08 :o 0H0Hh moho mo Pommmm .HN.m 0Hn0e 119 of machinery. This is so because in order to maintain the required production with low yield, more land has to be cropped and, consequently, more machinery is needed to cover the increased surface if working speed and field efficiency of the machinery remain the same. 5.2.4. Effect of Available Time on Machinery Size and Number. Available time is definitively one of the most import- ant parameters affecting the number and size of a machinery system. This effect can be observed in Tables 5.1, 5.2 and 5.3. Values contained in these tables belong to the same farm with a silage production requirement of 464 ton per year, a hauling speed 10 kilometers. Values in Table 5.1 correspond to 5 effective working hours per day. It can be observed there that just increasing the number of working days from one to two days the number of forage harvesters is reduced in one and the required capacity of this machine is also reduced in 25.0%, which means a lower investment in this type of machinery. Increasing the number of effective working hours per day has a notable effect. As an example, by just increasing one hour a day to the working time of one day, as it is showed in the first line of Tables 5.2 and 5.3, the number of forage harvesters is reduced to one machine, and the required capacity is also reduced in 12.2.%. 120 5.2.5. Effect of Harvesting Rate on Transport Unit and Blower Size and Number. Harvesting rate directly affects the number and size of transport unit number and size. This effect was used in the model by relating the total harvest capacity of forage harvesters, combines, pickers and picker-shellers to the total transport capacity when the number and size of their corresponding transport unit were calculated. Similarly, the harvesting rate affects the blower capacity. They have to be closely related or otherwise the flow of material harvested and blowed is not guaranteed and bottlenecks may occur at either one of these machines. Both situations can be observed in Table 5.3. 6.1. CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS Conclusions. Output data of this model are to be interpreted as the minimum size and number of machines that will satisfy a defined set of conditions related to the required feed production and to the available time. Miscellaneous equipment such as bale elevators, harvesting heads, manure spreader and the like, were not included in the model. The available time is used as effective working days and effective working hours per day. Consequently, the ability of the farmer in using his time will also affect the final machinery selection. The present version of the model can be applied to selecting the machinery used in feed harvesting oper- ations on dairy farms. Comparisons to selected dairy farm surveys indicate an acceptable model behavior in the selection of feed harvesting machinery and its ability to handle actual data. 121 122 The model could be added to a more comprehensive machinery selection model; to studies evaluating feed quality and quantity losses due to harvesting and handling; to models dealing with the management of forage, hay and grain harvesting machines in relationship with the rest of the farm; and to studies of the effect of decreased field drying time on a total dairy farm operation. The model reacts to changes in relevant factors such as transport unit travel distance and speed, crop yield, harvesting rate and the available time for field operations. The present version of the model does not locate the sequence of periods of consecutive dry days over the harvesting season. As a consequence of the inability stated above, scheduling of use of the selected machinery is not performed by this model. Recommendations for Further Research. Further work is recommended to establish an algorithm to predict the location of periods of consecutive dry days and to develop the scheduling of use of feed harvesting machinery. Field time studies are also recommended to determine more precise values for cycle time components of 123 machines such as automatic bale wagons, wagon-dryers and automatic bale wagons. Another area that needs further work is related to the study of parallel mechanized operations and the cal- culation of cycle time in forage harvesting operations. APPENDICES APPENDIX A NOMOGRAPH FOR DETERMINING LOADING TIME 124 com 00H ow— ;\P .xPPomamo L0Pm0>gog 000000 0'0l cup 00H omp HUI; . mo.o0 \ usP P om S'Zl HPsav .cPs .mEPP acPumoH OPH omH ONH oHH ooH om om oN .I. ll 7v 2 2 C... I. 0 2 C.— o o o O O 0 C4 0 g 0 0:0 msPP ch0moH chcwsngmo 000 gaogmosoz < xHozmaa< 0'08 00 om O'SE 0'0? 0: 0'05 0'09 om ON 0— moonwmemmr- ommNQLOQ’MNl—o Nr-I—F-u—r-r-F-P-F-r— (301) 1 ‘Kfigoedeo 11un quodsueaq APPENDIX B NOMOGRAPH FOR DETERMINING UNLOADING TIME 125 AA 0'01 o—— 00— ;\P .HPPuwamo gmonn a