A . ENERGY CONSUMPTION IN BEEF PRODUCTIGN . E ISYSTEMS As INFLUENCED BY TECHNOLOGY AND; Dissertation for the Degree of Ph D ‘ MICHIGAN STATE UNIVERSITY 7.:._;§;I;;;5Z;;;_j; ;;;;:_;;;.; " HAROLD ARTHUR HUGHES eeeeee _ g; jiff;fif._f’._g:;,-i—v.}gi. \IflllllllfllflllLllfllljllllljllllllllfllflll '3 ,....._. _. WW . 5 ‘ ’ . “—‘d J ‘ —, Ls. ; Mich i ga- . u. S t2; t3 University -—w__‘-q. ——‘._ «- This is to certify that the thesis entitled ENERGY CONSUMPTION IN BEEF PRODUCTION SYSTEMS AS INFLUENCED BY TECHNOLOGY AND SIZE presented by Harold Arthur Hughes has been accepted towards fulfillment of the requirements for Doctoral degree in Agricultural Engineering // Zr {1/2 m1 4 (.2M// ajor professor y/V/Z? C. J. Mackson / Date / 0-7639 ABSTRACT ENERGY CONSUMPTION IN BEEF PRODUCTION SYSTEMS AS INFLUENCED BY TECHNOLOGY AND SIZE by Harold Arthur Hughes The objectives of this study were to develop a system model of a beef farm, develop subsystem models for some parts of the system and to evaluate the energy costs of producing beef in systems using various technologies over a range of capacities. The system model was used to determine the flows of materials into and out of the system as well as between components of the system. The material flows were all related to the flow of beef produced by the system by a set of technical coefficients. Cost of beef produced by the system*was measured in terms of six energies: capital, land, fossil energy, electrical energy, labor and dollar cost. The energy cost of beef was related by the technical coefficients to the energy cost of each input material and the processing energies required for the system to operate. Three subsystem models were used to estimate the processing energies. The models were for the field machinery, farmstead, and transportation systems. The field machinery model was used to select a set of tract- ors and field machines required for the field operations involved in producing and harvesting the crops. The set of field operations and Harold Arthur Hughes the land area depends on system technology and capacity as well as crop yields. After the system was designed, the model estimated the amount of each of the six processing energies needed for the system to operate. The farmstead was taken to include the feedlot, feed stor- ages and other components concerned with confining and caring for the cattle. The model specified the size of each structure, calculated the price of the farmstead equipment and estimated the quantity of each of the six processing energies required. Transportation was the link between the field crop production model and the farmstead model. The transportation model determined the number of each kind of transportation equipment needed and eval- uated the processing energies required for transportation. The quantity of each energy required per unit of beef out- put could be varied by altering the syStem technology or by changing the system capacity. To evaluate the influence of various parts of the system on the energy costs, a number of systems were compared. The set of technologies analyzed included four feedlot types, two ration, four feed storage systems, two waste handling systems and two animal types. The same crop production and transport technology was used throughout the study. System capacities ranged from 100 to 1000 head. Conclusions from the study included the following: 1. There is little reduction in dollar cost per hundred pounds of weight gain by the animals, produced in any of the systems analyzed in this study, if the system is in- creased past about 500 head. Harold Arthur Hughes Labor requirements in systems using the same technology, decrease to a minimum then begin to increase as system capacity is increased. The system capacity at which the minimum is reached depends on the size of the farm and the capacity of the transport and feeding equipment. Restricting the size of tractors used for field crop production increases the capital and labor required for operating the crop production system. Peak labor requirements occur during the silage harvest season. Approve r P fessor Approved: fiépartment_ahairman ENERGY CONSUMPTION IN BEEF PRODUCTION SYSTEMS AS INFLUENCED BY TECHNOLOGY AND SIZE by Harold Arthur Hughes A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1973 av? This work is dedicated to my wife Beatrice and to our children Christopher, Trevor and Hal, for their love, understanding, encouragement and sacrifice. ii A CKNOWLEDGEMENTS The author gratefully acknowledges the support of the many individuals and organizations who contributed to this study and partic- ularly wishes to express his appreciation to the following: To Dr. C. J. Mackson, my major professor, for his counsel and friendship for many years. To Dr. J. B. Holtman, my thesis advisor, for his assistance and guidance in setting up and conducting this study and preparing this thesis. To Dr. L. J. Connor and Dr. J. R. Black, of the Agricultural Economics Department, who also served on my guidance committee for their helpful suggestions and continued interest. To the other members of the Agricultural Engineering Depart- ment for suggestions and assistance in locating data for the study. To Mr. David Filpus for his assistance in programming and running the computer. To the National Science Foundation who provided financial support for the study through the project on Design and Management of Environmental Systems. To the typist, Beatrice Hughes for being willing to work from a continually changing manuscript. iii TABLE OF CONTENTS Page ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . iii LIST OF TABLES . . . . . . . . . . . . . . . . viii LIST OF FIGURES . . . . . . . . . . . . . . . . x Chapter 1. INTRODUCTION . . . . . . . . . . . . . . . l 1.1 SOCIAL IMPLICATIONS . . . . . . . . . 2 Waste Problems . . . . . . . . . . . 2 Power Over Markets . . . . . . . . . . 3 Purpose of the Study . . . . . . . . . . 4 1.2 MEASURES OF EFFECTIVENESS . . . . . . . . . 5 1.3 OBJECTIVES OF THE STUDY . . . . . . . . . . 7 2. SYSTEM MODEL . . . . . . . . . . . . . . . 8 2.1 FORMULATION OF THE MODEL . . . . . . . . . 9 2.2 COMPONENT MODELS . . . . . . . . . . . . 9 Feed Production Subsystem . . . . . . . . . 9 Feedlot Component . . . . . . . . . . . . 14 Waste Storage . . . . . . . . . . . . . 18 Waste Transport . . . . . . . . . . . . 20 Commercial Fertilizer . . . . . . . . . . 21 2.3 BEEF FARM MODEL . . . . . . . . . . . . 21 2.4 TECHNICAL COEFFICIENTS . . . . . . . . . . 26 3. FIELD MACHINERY . . . . . . . . . . . . . . 27 3.1 POWER REQUIREMENT . . . . . . . . . . . . 27 Field Operations Required . . . . . . . . . 28 Subsets of Field Operations . . . . . . . . 28 Theoretical Energy . . . . . . . . . . . 30 iv Chapter Page Effective Energy . . . . . . . . . . . . 31 Subset Time . . . . . . . . . . . . 32 Self-propelled Machines . . . . . . . . . . 35 3.2 TRACTOR SELECTION . . . . . . . . . . . . 35 3.3 MACHINERY SELECTION . . . . . . . . 38 Allocation of Tractor Energy in Subsets . . . . 39 Allocation of Tractors to Field Operations in a Subset . . . . . . . . . . . . . . 41 Machine Selection . . . . . . . . . . . . 42 Machine Costs . . . . . . . . . . . . . 43 3.4 PROCESSING ENERGIES . . . . . . . . . . . 44 Capital . . . . . . . . . . . . . . . 45 Labor . . . . . . . . . . . . . . . 45 Fossil Energy . . . . . . . . . . . . . 45 Land . . . . . . . . . . . . . . . . 45 Electricity . . . . . . . . . . . . . . 45 Dollar Cost . . . . . . . . . . . . . . 46 FARMSTEAD SYSTEM . . . . . . . . . . . . . . 47 4.1 COMPONENT DESIGN . . . . . . . . . . . . 47 Feedlot . . . . . . . . . . . . . . . 47 Feed Storage . . . . . . . . . . . 49 Sealed Tower Silos for Moist Corn . . . . . . 50 Tower Silos for Corn Silage . . . . . . . . 51 Bunker Silos for Silage . . . . . . . . . . 52 Liquid Waste Tank . . . . . . . . . . . . 53 4.2 PROCESSING ENERGIES . . . . . . . . . . . 53 Capital . . . . . . . . . . . . . . . 54 Labor . . . . . . . . . . . . . . . 54 Fossil Energy . . . . . . . . . . . . . 56 Land . . . . . . . . . . . . . . . . 57 Electricity . . . . . . . . . . . . . . 57 Dollar Cost . . . . . . . . . . . . . . 57 TRANSPORTATION . . . . . . . . . . . . . . 58 Assumptions . . . . . . . . . . . . . 58 Discussion of Assumptions . . . . . . . . . . . 59 5.1 WASTE TRANSPORT . . . . . . . . . . . . 60 Time Per Load . . . . . . . . . . . . . 60 Number of Spreaders . . . . . . . . . . . 61 5.2 FEED TRANSPORT . . . . . . . . . . . . . 61 Number of Blowers . . . . . . . . . 62 Silage Wagons for Tower Silo Systems . . . . . 62 Silage Wagons for Bunker Silo Systems . . . . . 63 Chapter Page Wagons for Corn Transport . . . . . . . . . 63 Transport Tractors . . . . . . . . . . . 63 5.3 FEEDLOT TRACTORS . . . . . . . . . . . . 64 5.4 ENERGY COSTS . . . . . . . . . . . . . 64 Capital . . . . . . . . . . . . . . . 64 Labor . . . . . . . . . . . . . . . . 64 Fossil Energy . . . . . . . . . . . . . 65 Land . . . . . . . . . . . . . . . . 65 Electricity . . . . . . . . . . . . . . 65 Dollar Cost . . . . . . . . . . . . . . 65 6. IMPLEMENTATION OF THE MODEL . . . . . . . . . . 66 Comparisons . . . . . . . . . . . . . . . 7O 7. RESULTS . . . . . . . . . . . . . . . . 72 7.1 EFFECT OF FEEDLOT TYPE . . . . . . . . . . 73 Capital . . . . . . . . . . . . . . . 73 Local Irregularity . . . . . . . . . . . 76 Electrical Energy . . . . . . . . . . . . 77 Fossil Energy . . . . . . . . . . . . . 77 Labor . . . . . . . . . . . . . . . . 81 Land . . . . . . . . . . . . . . . 84 Dollar Cost . . . . . . . . . . . . . . 84 7.2 EFFECT OF RATION AND FEED STORAGE . . . . . . 87 Capital . . . . . . . . . . . . . . . 87 Land . . . . . . . . . . . . . . . . 89 Electrical Energy . . . . . . . . . . . . 89 Fossil Energy . . . . . . . . . . . . . 89 Labor . . . . . . . . . . . . . . . . 92 Dollar Cost . . . . . . . . . . . . . . 92 7.3 EFFECT OF WASTE HANDLING SYSTEM . . . . . . . 92 Capital . . . . . . . . . . . . . . . 95 Fossil Fuel . . . . . . . . . . . . . . 95 Labor . . . . . . . . . . . . . . . . 95 Dollar Cost . . . . . . . . . . . . . . 100 7.4 EFFECT OF ANIMAL TYPE . . . . . . . . . . 100 7.5 EFFECT OF ALLOWABLE TRACTOR HORSEPOWER . . . . . 108 7.6 DISTRIBUTION OF LABOR REQUIREMENTS DURING THE YEAR . 112 8. CONCLUSIONS . . . . . . . . . . . . -. . . 115 9. SUGGESTIONS FOR FURTHER STUDY . . . . . . . . . 119 vi Page APPENDIX A . . . . . . . . . . . . . . . . . . 122 APPENDIX B . . . . . . . . . . . . . . . . . . 12 6 APPENDIX C . . . . . . . . . . . . . . . . . . 140 APPENDIX D . . . . . . . . . . . . . . . . . . 149 REFERENCES . . . . . . . . . . . . . . . . . . 152 vii Table 6.1 6.2 7.1 7.2 A.1 A.2 B.l B.2 B.3 B.4 B.5 B.6 B.8 B.9 B.10 8.11 B.12 B.14 LIST OF TABLES Components Included in Technology Definitions . . . Set of Technologies Selected for Analysis . . . . . Land Requirements for 1000 Head Capacity Systems Labor Required During the Year by a 500 Head Capacity Beef Farm Using Technology 119 . . . . . . . . . Material Flow Units . . . . . . . . . . . Technical Coefficients . . . . . . . . . . Power Requirement Analysis for Field Operations Allocation of Tractor Energy to Subsets . . . . . Tractor and Machine Schedlue . . . . . . . . . Annual Machine Use . . . . . . . . . Summary of Farm Machinery Costs . . . . . . Energy Costs for the Field Machinery System Parameters for Feedlot Design . . . . . Report of Feeding Component Selection . . . . . Report on the Feed Storage System for a Beef Feedlot . Acreage Determination Report on Feeding and Waste Handling . . . . . Report of Fossil Energy Consumption in a Farmstead Electrical Energy Use . . . . . . . . . . Silage and Corn Transport and Feedlot Tractor Size viii Page 67 68 85 113 122 124 126 127 128 129 130 131 132 132 133 133 134 135 135 136 Table B.15 Technical Coefficients and Material Flows for a 500 Head Capacity Feedlot . . . . . . . . . . . . B.16 Energy Costs for a Complete System . . . . . . . . C.1 C02 C.3 C.4 C.5 C.6 C.7 D.1 Constant Values Used in the Program . . . . . . . . Feedlot Area Requirements . . . . . . . . . . Relationship Between Animal Type, Housing System and Ration Assumed Field Machine Data . . . . . . . . . . . Percent Useable Work Days for Subsets . . Functions for Determining First Cost of Field Machinery and Tower Silos for Corn and Silage . . . . . Input Material Requirements and Costs . . . . . Selected Results from the Analysis of Nine Systems . . . ix Page 137 139 140 143 144 145 146 147 148 149 --I-ev~ v Figure 1.1 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 3.1 3.2 3.3 4.1 7.1 7.2 7.3 7.4 7.5 7.6 LIST OF FIGURES Beef Farm as a "Black Box" . . . . . . . . . Corn Production Component . . . . . . . . Silage Production Component . . . . . . . Ration Combination Component . . . . Transport Components for Feed . . . . . . Feed Production Sub-system . . . . . . . . . Beef Feedlot Component . . . . . . . . . . Typical Waste Storage Component . . . . . . . Waste Transport Component . . . . . . . . . Commercial Fertilizer Requirement . . . . Material Flows in a Beef Production System . Procedure for Estimating Horsepower Requirement Tractor Horsepower Determination . Allocation of Tractor Energy in Subsets . . . . Assumed Feedlot Layout . . . . . . . . . . Effect of Feedlot Type on Capital Requirements . Effect of Feedlot Type on Electrical Energy Consumption Effect of Feedlot Type on Fossil Fuel Consumption Breakdown of Fossil Fuel Consumed by Parts of a System Effect of Feedlot Type on Labor Requirements Breakdown of Labor Required by Parts of a System . Page 11 11 12 15 16 17 19 19 22 23 29 37 40 48 74 78 79 8O 82 83 .... V‘ "“"V .. —--. Figure 7.7 Effect of Feedlot Type on Dollar Cost of Beef . . . . 7. 8 Effect of Ration and Feed Storage Systems on Capital Requirements . . . 7. 9 Effect of Ration and Feed Storage Systems on Electrical Energy Consumption 7.10 Effect of Ration and Feed Storage System on Fossil Fuel Consumption . . u u - n o n a o 0 Effect of Ration and Feed Storage System on Labor Requirements . o . o o . o Effect of Ration and Feed Storage System on Dollar Cost of Beef Effect of Waste Handling Effect of Waste Handling Consumption . Effect of Waste Handling Effect Effect Effect Effect Effect Effect Effect of Waste Handling of Waste Handling of Animal Type of Animal Type of Animal Type of Animal Type of Animal Type Weight Gain . Effect of Animal Type Total Body Weight on on on on System on Capital Requirements System on Electrical Energy System on Fossil Fuel Consumption System on Labor Requirement . . System on Dollar Cost of Beef Capital Requirements . . . . Electrical Energy Consumption Fossil Fuel Consumption . . . Labor Requirements Dollar Cost Per Hundred Pounds of Dollar Cost Per Hundred Pounds of o - u n a . o a o o . Effect of Maximum Allowable Tractor Power on Capital Required for Field Machinery . . . . . . . . . Effect of Maximum Allowable Tractor Horsepower on Labor Required for Field Operations . . . . . . . xi Page 86 88 90 91 93 94 96 97 98 99 101 102 103 104 105 106 107 110 111 1. INTRODUCTION Total beef production in Michigan is small in comparison to U. S. production. Less than two percent of the nation's supply of beef comes from Michigan. In fact, Michigan.feedlot operators do not produce enough to satisfy the demands of people residing in the state (17). Michigan beef production systems are unlike the better known western systems in several important ways. Western beef is most often finished on all grain rations in large open lots. -Feedlot volumes are large, frequently exceeding 10,000 head per year (10). Grain for the ration is purchased from local growers or feed dealers who import it from grain growing areas. waste disposal is complicated by sheer volume and by the lack of cropland for spreading. As a consequence, many other approaches to waste handling are being used, such as lagoons and driers. In Michigan, a variety of feedlots exist. Feed is usually Produced on the same farm and often includes a large proportion of corn silage. Waste from the feedlot is recycled back onto cropland. Afichigan feedlots tend to be considerably smaller than western units. While there was an average of 210,000 cattle on feed in the state in the period 1969-1971, the average feedlot turned out 175 head per Year and only one percent of the feedlots in the state produced over 5000 head (16). 2 Henderson, 23 31. (16), using projections of increased per capita consumption, population projections and extrapolating from past production figures, predict that beef production in the state will increase and that individual systemSAwill get larger. Specif- ically, they predict that by 1985, Michigan feedlot operators will market 532,000 head from 1000 installations. Thus, by 1985, the average feedlot will be as large as the largest one percent is at present. This expansion will require that many new facilities be built and that many existing facilities be altered and expanded. 1.1 SOCIAL IMPLICATIONS The type and size of feedlots to be built is obviously of concern to present or potential feedlot operators. Less obviously, the public at large also has concern about these same two factors. Feedlot size is an issue because many people feel that large opera- tors try to exert control over markets, and technology is an issue because of the impact it can have on the environment. Waste Problems Koenig, gg a1. (25), discussing labor efficiency in agri- cultural operations observes that concentration of agricultural ' production into large units, such as the western type feedlots, has caused urban waste disposal problems to increase. They state that: "...since in agricultural processes the size of machines is specific to the size of the land holdings on which they operate, the drive for labor efficiency through the use of larger mach- ines has a partidularly high social and environ- mental impact. Indeed it has all but eliminated the "family farm" and with it the relatively small ...villages and cities distributed rather 3 uniformly over the landscape where their wastes were reasonably well matched to the carrying capacity of the landscape." They go on.to note that all of the food necessary for the people who have, as a.consequence, migrated to the cities must be moved to the cities. The resulting wastes are concentrated in small areas with the attendant problems of treatment and disposal. In fact, after whatever processing is available has been completed, the wastes are usually discharged into the nearest lake or'stream. Increased labor efficiency also leads to increased special- ization and spatial concentration of agricultural production units, such as beef feeding operations. The resulting agricultural waste control problem for beef is well recognized. Maddex (l6) observes that: "...manure handling and disposal is today's greatest challenge facing the beef industry. With increased urbanization and concern for environmental quality, the challenge will be- come greater. Changes will have to be made in accepted ways of housing and managing cattle." nger Over Markets Discussing the issue of control over markets, Sundquist and Guither (36) state that: "Many nonfarmer residents of rural commun- ities are concerned that any takeover of farming by large scale production units will squeeze out small farmers and small farm supply and market- ing businesses. They also feel that large cor- porations will be less inclined to support high- quality public services such as schools, health care facilities and roads and recreational facili- ties. 4 Concerns of the general public, including consumers and taxpayers, center on at least four broad issues: 1) they want dependable supplies of low cost and high quality food, 2) they want to curtail agricultural practices that adversely affect environmental quality and the availab- ility of open spaces, 3) they want tax costs of any policy to be in line with the benefits realized, 4) they want a fair share of the benefits of farm programs to accrue to smaller (as contrasted to large scale) producers. Though some think that large scale farming will be low cost and efficient others think big farm corporations will try to gain monopoly controls and raise food prices." Purpose of the Study From the foregoing discussion, it can be seen that both rural and urban residents have concerns regarding the type and size of beef systems to be used. Present systems, as noted in the quote by Maddex (16), are not likely to be satisfactory. Changes are needed. Maddex (16) went on in the same article to state that research in- formation and analytical techniques are not available to predict the direction the changes will take. I Society determines the direction of change in accordance with societal goals. The decision, made by trading off desirable and undesirable features of candidate systems, is implemented by the . use of economic or legal controls or by the use of some other approp- riate social mechanism. The purpose of this study was to expedite this process by developing a means of analyzing and comparing beef production systems in terms of certain physical characteristics for a selected set of technologies and sizes. Since the variations in technology are practically unlimited, the set of technologies had to be limited. The technologies chosen 5 for evaluation will be described along with the design procedures in Chapters 3, 4 and 5. 1.2 MEASURES OF EFFECTIVENESS A beef farnlcan be conceptualized as a "black box" wherein input materials of various kinds are transformed into finished beef animals and waste materials, as shown in Figure 1.1. Energy is needed to effect the material combination. Figure 1.1 shows a vector of the required energies. Monetary energy (capital) is required to initiate the system and to supply input materials and replacement equipment. Fossil and electrical energy are used to operate machines. Solar energy is necessary for crop growth. Human energy is used to manage and control the system, operate machines and to perform various unmechanized functions. Dollar cost is an economic "weighted" func- tion over all the other energy requirements. Systems, distinguished by size and technology, require different quantities of each kind of energy. For a particular situation, it may be desirable or necessary to minimize or limit consumption of one or more of the energy types. For example, the Present "energy crisis" in this country demands that fossil energy consumption be held to a minimum. Consumption of each energy type, quantity of beef produced and cost of beef produced are the measures of effectiveness of a system (20,42). The "best" system for a part- icular situation depends on the ordering placed upon these measures of effectiveness as well as others not considered in this study. :xom xomHm: m mm spam womm .H.H ouswfim ucoEmHmasm muowoom umumz ummaaanuma mamaumumz mums: , meaoanumm mmwm emnmacam emmm mammabo mesmzH A 53.3 zofiopaomm mmmm A “woo uaaaom xv uonmq 1 umHom Hmowuuomam Hammom Hmuaamo summZm 7 1.3 OBJECTIVES OF THE STUDY In consideration of the preceding discussion the following objectives for the study were formulated: 1. Develop a model of the beef production system that will allow evaluation of the cost of producing beef and the energy consumed by the system. Develop subsystem models for selecting the components needed for a system of a given size and technology. Evaluate the energy and dollar cost of producing beef in systems using certain technologies over a range of sizes. “'___ _ 2 . SYSTEM MODEL The objective of the study was to evaluate systems over a range of capacities for a selected set of technologies. There are tun: ways to approach such a study (20). Actual operating systems naming each technology, of sizes distributed over the desired range, can be located and analyzed from information gathered on site. This approach is difficult because comparable systems using the same tech- ncilogy and similar management over a suitable range of sizes must be lcnsated. This difficulty is compounded by the necessity of obtaining Ctr-operation from the operators after the suitable systems have been fcnxnda Accordingly, the alternative, a modeling approach was adopted. The next problem to be considered was whether to use a static or dynamic model. A dynamic model is useful for analyzing the operations of an individual system to find information such as Peak labor useage or the effect of a disturbance such as bad weather on.the system (20). This study, however, was designed for compar- ison of a large number of systems. Static models are simpler and more suitable for an application of this type. Therefore, static models'were used for the studY- 2.1 FORMULATION OF THE MODEL Koenig and Tummala (24) discuss a modeling technique in Which a system is decomposed into a set of components described in 9 terms of their mass-energy characteristics. When each component is assumed to operate independently of the remainder of the system, alternative system designs can be evaluated by replacing components. For the beef model, components are of two generic types: [material transformation and transportation. A material transforma- tixan.component is one that transforms input material into output materials. The mix of input and output materials achieved by the ccnnpoment is described by the technical coefficients for the com- ponent. An alternate component, of a different technology, performs the: same material transformations, but might have a different set of txeclinical coefficients. The transformations are effected by the application of processing energy. Transportation components do not perform a material trans- fornnation. There is, however, a processing energy cost (24). A notational scheme has been adopted for the formulation (of component models. Material flow rates of material i into or out of component j are denoted Yij (i.e., Yij has units of quantity of material per unit time). The amount of energy m associated with this sanwznmterial is denoted XYj (i.e., ij has units of quantity then 0f energy m per unit quantity 0f material). The prOdUCt XY‘.Yij 1] denotes an energy flow rate. 2.2 COMPONENT MODELS -E§§§_Production Subsystem The feed production subsystem includes shelled corn pro- ‘3UCtion and transportation components, silage PrOdUCtiOn and transportation components and component for mixing the ration to be fed to the cattle. fit 10 The corn and silage production components are shown in Figures 2.1 and 2.2, respectively. The two components have similar inputs, but the units of output are different, being bushels of shelled (yarn per year and tons of corn silage per year, respectively. The material flow model for corn production takes the form: Y11 = K11 Y01 i = 1,2...6 (2'1) uflaere Kil is the quantity of material i1 required to produce one unit (bushel) of shelled corn. The output, determines the quantities of the other Y01’ flxows and is called the stimulus variable. The flow other than the stzimulus are called response variables. The Kil's are the technical coefficients for the component. Similarly, the material flow model for silage production H. (D YiZ = K12 Y02 i = 1,2...7 (2-2) Ration production, another material combination component is shown in Figure 2.3. Ration is produced by combining, in the PrOPer proportions, corn and silage, which has had protein supplement added to it before storage. The mathematical expression for the material Combination is: Y i = 1,2 (2-3) 13 = K13 Y03 Associated with each edge (material flow) is a vector of values representing the energy per unit of the material required to PUt the material into its current state. Elements of the energy cost vector are denoted by XYj (m indicates the energy type) where: ll 1 : capital ($) 2 : labor (man hours) 3 : fossil energy (horsepower hours) 535 ll ll 11 N 11 P .0 ‘ 21 .“ ‘.'-. "*‘\ . x, \ K 31. /' 01' l_. .; {fi'. ‘4. \ R ' Shelled Corn 41 ;, \. I ' ' ..- \ - Seed ,.‘ . 51 " . . , Chemicals 6 I ’ Water Figure 2.1. Corn Production Component N 12 . P “ 22 A.“ o. ,, _4. K 32 ‘3' f R bVESilage 42 «'92 .__,./ 4% ,w 366(1 52 " 4' t, ‘ . 6 . 72 Protien Chemicals "Supplement Water I» Figure 2.2. Silage Production Component 12 unocanoo sowummwneoo cowumm .m.N ousmwm emanam coauem mo euoo eeHHSEm ma 13 m - 4 : electrical energy (kilowatt hours) m = 5 : land (acres) m = 6 : dollar cost ($) Land is a measure of the solar energy needed to produce the crop. ij, the unit dollar cost of material i for component j, is, among other things, a scalar function of the energy costs of the (Ither five energy forms. The value of xgj depends on the relative axnailability of the five forms of energy and the preference society pfilaces on each of the input materials. Examples of costs are: X21 is the land required to produce cune:bushel of seed corn and X32 is the labor in man hours needed to plmoduce one ton of silage, including the labor required to produce true quantity of each of the input materials used to produce one ton of silage. Employment of the conservation of energy principle implies tiuat the net energy flow into the component plus the applied process- ing energy must equal zero. The expression: 6 m = ._{Nil xi1 m 1,2...6 1-1 is an accumulation of the amount of energy m in the input materials required to produce one bushel of shelled corn. Processing energy costs include the cost of machinery, buildings, labor, fuel, taxes, depreciation, etc. The processing energy cost function is typically a non-linear function of the pro- duction level. The amount of processing energy m required for one bushel of shelled corn is: m E1(Y01) m - 1,2...6 14 Energy relations in each of the m types are expressed below for the shelled corn, silage, and ration components, respectively: 6 _ _ m _ m = - XOl _ iggkilxil f1(YO1) m 1.2---6 (2 4) x = -{k. xm - fm(Y ) = 1 2 6 2 5) 02 1:1 12 12 2 02 m ’ --- ( X = “%k Xm fm’Y ) = l 2 6 2 6 03 .g? 13 i3 ‘ 3- 03 m » --° ( - ) After each crop has been harvested, it is transported to stuorage. The model contains components for the transportation of txotfln feeds, of the type shown in Figure 2.4. Since the same material :fltnws into and out of a transportation component, and it is assumed trust no losses are incurred, only processing energy costs need be con- sirhered. The cost models for the transportation components for shelled corn and silage are: m _ _ m = .. m -' m = - The combination of components that make up the crop pro- duction subsystem are connected as shown in Figure 2.5. Energy costs of crop production, transportation of each crop, and the energy costs of the ration can be evaluated from the model of the sub-system. .Efigélot Component After the ration has been produced and transported to eatorage, it is moved to the animals in the feedlot, as shown in Figure 2.6. The mathematical form of the feedlot component model is: 15 comm now monocanoo uuoamcmuH .q.m ouswflm mmmaam now Hm ucocoaeoo ADV m@ A /AK J-f.‘ Chou ooHHmnm pom HN ucocanoo Amv 3 /A..\ . .0 Hm Emumzmunsm soguosvoum pooh .m.N mesmHm 16 \‘,.2 ll ‘ .. mo 1'1] 1 i ll I‘ll'l Y I. : l . l . till I l 17 Q nouns so one mm s c m an ' .P do .uf do ‘-‘I‘ / cowouumz » «H monocawon. EsHmmouom ,uqm unocoaeoo uoHooom «mom we... .o.~ ouswflm momma as u. 18 d xm = HEN xm - fm(Y ) = 1 2 6 2- o a“ 04 .=1 14 14 4 04 m ’ ( 1) The waste material flow from the feedlot is decomposed into Y , a d Y . ’ - the flows Y14’ 24, Y34 n 44 The first three are re cyclable nutrients N, P, and K (Nitrogen, Phosphorus, and Potassium) respect- ively. The flow Y represents the flow of waste water and other 44 inert or non-nutrient portions of the animal manure. An alternative description of this component would show a single output of waste material. The four-output form was selected because changes in animal type, ration, or feedlot type are expected to affect both the composition and quantity of waste material (i.e., technology change alters technical coefficients). Waste Storage As waste material is held in a manure pack, liquid tank, or elsewhere, there are losses to the environment. Waste water runs off, evaporates, or infiltrates into the soil, carrying some of the nutrients along. Volatilization and other losses occur which also reduce the quantity of each nutrient to be re-cycled. There are similar components to account for the loss for each of the waste material flows of the type shown in Figure 2.7. The mathematical form of the model is: = K ' = - = _ YiL iLYOL l 1,2, L 5,6,7,8 (2 11) d m 2 m m an 2 _ _ = - XOL ZKiLXiL fL(YOL) m 1,2...6 (2 11) l9 1L Leakage to Environment O2L Applied to Crop Land L = 5,6,7,8 0L From Animals Figure 2.7. Typical Waste Storage Component_ o 4 HONigrogen 0* fl --' 0 Phosphorus Waste Applied to Soil 3‘ fl-..” — --------- ~43 Potassium t lurfl ' waSte QDR Figure 2.8. Waste Transport Component 20 non-nutrient material potassium phosphorus nitrogen r‘t‘F‘t" ll (DNO‘M where: Waste Transport The non-nutrient waste and fertilizer equivalents of N, P, and K that remain after storage losses have occurred must be transported to the field for application to the soil. The waste transport component is shown in Figure 2.8. The energy costs for the component are evaluated using the "common carrier" concept discussed by Koenig and Tummala (24). The energy costs are allocated according to the relation: m __ i = 1,2,3s4; _ X1T CiFlT(YlT) m = 1,2...6 (2 13) 4 where: Y1T = .: CiYi 1-1 The ci are factors which convert all material flows to the same Iveight base. Y1T is the total weight of the four materials which are transported together. In this case, the nutrient flows are stated in units of fertilizer equivalents (equivalent to commercial fertilizer), and the actual weight of N, P, and K is not important. Only total weight of nutrient and non-nutrient material is important relative to transport cost. Therefore, the ci's for the flows of N, P, and K were arbitrar- ily set to zero and the c; for total waste mass flow was set to one. In effect, for the purpose of evaluating transport costs, all mass is viawed.as passing through the flow of waste material with the HUtrietn: flows being massless fertilizer equivalents. 21 Commercial Fertiligg; The quantity of each fertilizer constituent (N, P, K) required is determined by the crop production components. A steady- state nutrient equilibrium was assumed (nutrients applied equal nutrient uptake by plants plus losses). The amount of each nutrient to be applied as commercial fertilizer is the difference between the quantity required by both crops and the quantity available from the waste, as shown in Figure 2.9. The commercial fertilizer require- ments are: YON = Y21 + Y22 ' Y18 (2'14) YOP = Y31 + Y32 - Y17 (2-15) Y0K = Y4l + Y42 ‘ Yl6 (2'16) The cost x31 1 = N, P, K: m = 1,2...6 of the commercial fertilizer purchased is the cost of the material supplied to the farm. 2.3 BEEF FARM MODEL The diagram in Figure 2.10 shows the entire system. Each material flow in the system depends on the quantity of beef produced. The energy costs per unit of beef also depend on the system size. The method advocated by Koenig and Tummala (24) and illus- trated by Holtman, t al., (22) was used in deriving the following SYstem material flow relations. The flows are: Yea = K64Y04 Y74 = K74Y04 Y=KKKY 52 52 23 54 O4 Y62 = K62K23K54Y04 22 (a) Commercial Nitrogeanequirement 0P 31 (b) Commercial Phosphorus Requirement (c) Commercial Potassium requirement Figure 2.9. Commercial Fertilizer Requirement 23 swuwmw cowuosvoum mmwm m an macaw Hmwuoumz .oH.N madman . m. Ne. . N . e \\l/ k MO». .4 \m . i S . xii... ( m m a - he... ~ m .qm ' n so . .. A e. S .. x. S \x . mo .3 .. .. D t: x a S . t E 1‘ / A. Hm I... .. "\IMm/J b c we. 3 SN mo nor a. 2 H t . t) \. t A a no .6 .s A ”a. wH. w. . 0H .th DMMHU an mm A. HH My; 1. \\I...NH Y . , fiH . a A. - C t. Jul A N e < _ . mm _ 24 Y72 = K72K23K54Y04 Y82 = K82K23K54Y04 YSl = K51K13K54YO4 Y61 = K61K13K54YO4 Y71 = K71K13K54Y04 15 K15K44Y04 16 K16K34Y04 17 K17K24Y04 18 K18K14Y04 YON = (K54(K12K23 + K11K13) ’ K28K14)Y04 = + - YOP (K54(K22K23 K21K13) K27K24)Y04 = + - '- YOK (K54(K32K23 K31K13) K26K34)Y04 (2 17) In effect, the set of equations above transforms the entire system model into a component model with seventeen material flows to and from the environment. A consistent set of flows and coefficient units are shown in Appendix A. The flow Y04 (output of beef) is the :stimulus variable that dictates the remaining flows. The energy cost per unit of output beef is expressed in the following relation- ship: X64 = (K11K13K54 + K12K23K54 " K28K14)XUN + (K21K13K54 + K22K23K54 ' K27K24)XUP + (K31K13K54 + K32K23K54 ‘ K26K34)XUK m m m - + K13K54(K51X51 K61x61 K71X71) 25 m m m + + K23K 54(K52X 52 K62K62 + K72K72 Kszxsz) m m ' K64K64 ” K74K74 + K K Xm +K + K m (K15K44X 15 16 34 16 17K24Kl7 ) 18K 14 X18) K13 K54 f1(K13K 54 YO4 m K23 K54 f2(K23K 54 Y04) ‘ K54 f3(K54Y 04) ’ f4(YO4) (K K44 f5(K44Y04) ‘ K34 f6(K34 Y04) ' K24Y7 24 Y04) K14 f8(K14Y 04) ’ K25 K44 f1T(K25K 44 Y04) K K Y ) - (2-18) K13K54f;T( 13 54 o4 K23 K54 f3TKK23K 54 Y04) The first three terms are the cost of commercial fertilizer added to crop land. The following two terms are the costs of other materials used for crop production. The next two are the costs of inputs to the feedlot component (water and feeders, respectively). The following term is the cost of the materials leaked to the environ- ment. The remaining terms represent the processing energies used to convert the input materials into finished beef. The model expresses all input and output material flows, for a given system, and the cost of beef produced by the system as flnuztions of the technical coefficients (technology) and the amount of beef produced by the system (size). Given the set of technical co- efficients, the processing energy function, and system capacity, beef production systems can be analyzed to find the quantity of material flows to and from the environment and the cost of beef produced. 26 2.4 TECHNICAL COEFFICIENTS The technical coefficients and the processing energy func- tions are not all readily available. Some, like K13 and K23 (amounts of silage and corn fed per animal/day) can be found in the literature. Others must be evaluated by different means. The pro- cessing energy functions are universally unknown. The estimates of energy consumption that are available (1,11,32,37) often do not consider variations with farm size. They are usually averaged over. whatever farm sizes that were available when the information was collected. It was necessary, therefore, to analyze each component to find some of the energy costs. Discussion of the approach is divided into three chapters 1) crop production, 2) farmstead, and 3) trans- portation. " V" 3. FIELD MACHINERY The field machinery system includes only those operations tvtIich take place in the field and are needed for tilling the soil c>rrplanting and harvesting the crops used for the ration. Referring tr) Figure 2.10, this field machinery system model estimates the twaquirements for each of six processing energy types for field crop prroduction. Energy associated with the input materials will be discussed below. The machinery model selects a machinery complement, in- clJJding power units, which is capable of performing all field opera- tixans at a rate sufficient to achieve a successfull crop. The model begins by estimating horsepower needed for the cropping operations. TTuen the tractors and field machines are selected and the processing ene rgies are calculated . 3.1 POWER REQUIREMENT Power required for field operations depends on the amount Ofxvork to be done and the time available. Work to be done depends on: 1) the acreage of each crop to be grown, 2) soil characteristics, 3) field operations required for planting, cultivating, and harvesting the crops and 4) the technology (kinds of machines) being usedo The time available for completion of field work depends on 1) weather and soil characteristics, 2) hours worked per day, 3) number of days 27 28 allowed for completion of field operations, 4) scheduling efficiency, 5) machine reliability and 6) field efficiency. The method described in this section is taken from Hughes, _;§.al. (23) and is shown in Figure 3.1. Ffiield Operations Required The set of field operations to be performed depends on the crrops to be produced and the production technology adopted. In its e;implest terms, production technology refers to the particular set of ifield operations used to grow the crop. Zero tillage, where the seed 113 planted in the soil without any preliminary tillage, is an example ()f a production technology. Stflasets of Field Operations The set of field operations are organized into subsets. Ikech.subset is a group of field operations that must be performed (either simultaneously or sequentially during a specific time period. P“) particular order of operations within the subset is assumed. A subset can consist of a single field operation, such as conflxining grain corn or it may include several operations such as the thving, tilling, and planting that occurs in the spring. Starting and ending;dates for the subset may be dictated by cropping characteristics, bY‘weather and soil conditions, or by management requirements. The Spring operation subset, for example, cannot begin until soil condi- tions are suitable for tillage. The ending date for the subset is the target date for completion of planting, a management decision based on CrOP growth characteristics and the prevailing climate. W n-, ‘ 29 List set of field operations for gggrowing all crops List of crops to be grown List of field operations for each crop ()rganize field operations into subsets with specific starting and ending dates I)e:termine theoretical energy per acre for each field operation Weather information Crop growth pattern Management requirements Ckalculate total theoretical energy for each field operation Ckalculate total effective energy for each field operation in each subset (kilculate total effective energy for each subset Calculate total time in hours for c0mpleting each subset l [Calculate horsepower for each 4] subset ,/’ Can\\“‘ Yes peak horsepower be reduced? No Calculate horsepower needed for S stem Calculate design horsep0we21 (43.3 Figure 3.1. Soil factors, crop yields Machine characteristics Area of each field opera-' tion in each subset lField efficiency for each field operation Hours per day (nominal) Scheduling efficiency Useable days System reliability Modify operations Safety factor Procedure for Estimating Horsepower Requirement 30 Theoretical Energy Theoretical energy consumption is a familiar subject which has been discussed by many authors (1,3). It is the energy, in horse- [power hours, that would be consumed in performing a particular field (aperation if there were no field inefficiencies such as wheel slippage, crverlap, etc. Theoretical energy consumption by a field machine is ssituation specific and depends on factors wuch as soil characteristics, crrop yields and machine characteristics. The assumption is often made (3) that theoretical energy arequired per acre for a particular machine is speed invariant. This :is not always true, particularly for tillage machines such as mold- laoard plows for which energy consumption increases as some power of speed (3). However, if machine speed is restricted to a narrow range, tflne theoretical energy can be assumed to be constant. This procedure was selected. Theoretical energy can be calculated from Equation 3-1, inhere the first bracketed term is the horsepower requirement and the second term is the theoretical time per acre: U.K.S. 8 E . = (.....__..._J J J><—=-2—-5-) j = 1 2.... (3-1) 1‘3 KS ’ 375 j j where: ETj = theoretical energy for field operation j (hp-hr/acre) Uj = unit draft of machine j (pounds/unit of size) Kj = size or capacity of machine j (units of size) Sj = assumed speed of machine j (mph) n = number of field operations For example, a harrow will have Uj given in pounds per foot Of width and Kj given in feet. It can be seen in Equation 3-1 that Kj \O 31 and Sj both cancel out of the relation. Thus, theoretical energy is not a function of machine size or speed. It is, rather, a function of the unit energy which depends on an interaction between soil conditions and the design of the machine. Total theoretical energy required for a field operation (UTOTETj) is the product of the theoretical energy per acre (ETj) arnd the acreage for which the field operation is required (aj), as shown in Equation 3-2. TOTE . = E ,.a, ' = 1,2...n 3-2 T3 T33 J () E ffective Energy Field efficiency is a measure of time losses in the field dine to overlap, wheel slippage, turning, plugging, etc. Several of tflnese factors increase the energy that must be expended before a field operation on a particular area is completed, and all of them require tflmat the rate of energy expenditure (power) be increased if the oper- aition.and others in the same subset are to be completed without *violating the subset time constraints. Field efficiency tends to decrease as machine size increases (1) and to increase as field size gets large. Usually, large machines are located on large farms which tend to have large fields. This baLancing effect is the justification for the assumption that field Efficiency is independent of machine speed. It is felt that the in- accuracies introduced by the assumption are negligible. The total effective energy required for each field operation (TOTEEj) is the total theoretical energy divided by the field efficiency (Bffj) 0f the operation as shown in Equation 3-3. 32 TOTEEj = TOTEEj/effj J = l,2...n (3-3) The total effective energy for a subset (EFENi) is found by adding the total effective energies for all field operations in the :subset, as shown in Equation 3-4 where i is the subset number and n is the number of operations in the subset. n EFEN. = {TOTE 1 j=1 Ej <3-4) S ubset Time Time available for completing the operations in a subset <1epends on 1) hours the machines work per day, 2) number of working (iays for completion of the subset, 3) percentage of working days tflnat are useable and 4) machinery system reliability. The hours of machine use per day equals the operator's ncnninal work day less the time spent in activities such as road travel, lritching, and others that take place outside the field. Scheduling (efficiency, the percent of scheduled work time that the machine and operator are in the field, is a measure of this-type of time use. Tulu (32) developed a method for determining the percent useable work days, at a specific location, based on a soil moisture budget and a tractability criterion. The model was evaluated, by com- Paring predicted tractability with actual tractability conditions which occurred on certain farms, and found to be an effective predictor 0f tractability. The percent useable days for each subset, in this study, were determined by Tulu's method (using sixteen years of weather data from the Detroit City Airport), using his combine tractability 33 criterion for the shelled corn harvest and his tillage criterion for all other operations. The percentages will be presented in Chapter 6. The number of working days is the number of days from the luaginning to the ending dates for the subset, less days when no work is done, such as Sundays and holidays. Machine reliability is a measure of the percentage of time, when the machine is in the field, that it is in operating condition. Urine lost because of repairs reduces the time available for completing tflne operation. Machine system reliability is an overall figure zipplied to all machines in the system. Time for completion of a subset can be calculated from Equation 3-5: ‘ SST = (BDAY-FDAY-HDAY)(HRSDA)(USEDA)(RE)(SCED) (3-5) where SST = subset time (hours) BDAY - beginning day number (day) FDAY = final day number (day) HDAY = non-working days (day) HRSDA = nominal hours worked per day (hours/day) USEDA = percent useable days (decimal fraction) RE = system reliability (decimal fraction) SCED = scheduling efficiency (decimal fraction) The total effective horsepower needed for the operations in each subset is found by dividing the effective energy required for the Subset by the subset time as shown in Equation 3-6. This calculation must be made for each subset. _ EFEN. EHPi - SSTil 1 = 1,2...n (3-6) 34 where: EHPi = effective horsepower needed for subset i (hp) EFENi = effective energy for subset i (hp-hr) SSTi = time for subset i (hours) n = number of subsets The set of effective horsepower requirements by subset ggives the distribution of horsepower requirements throughout the year. — ()ften power reductions are possible by modification of the order of og>erations, time constraints or the production technology. If any rteduction can be made, the process should be started over, as shown irl Figure 3.1, with the new input information. The effective horsepower required for the system (ESHP) is '1 tlie: maximum of that required for any one subset. It should be noted, hCNvever, that effective horsepower is the minimum that is capable of CCanleting the field operations under the assumed conditions. If C(Driditions are such that more power is required than has been estimated, tile: effective horsepower would not be adequate and either the work Stzliedule would have to be extended or the quantity of work reduced. To circumvent this possibility the effective system horse— PCerr (ESHP) is increased by a factor of safety (MFAC) to a design hcrrsepower (DHP) as shown in Equation 3—7. DHP = ESHP/MFAC (3-7) Iv[FAG is normally in the range of 0.7 to 0.8 (6). The additional horse— pCWVGEr is available for handling unexpected situations such as extra fiefild operations, modified soil conditions, etc. Another effect of increasing power to DHP is that the average loading rate of the tractor is lfeduced to some percentage of its rated power. The reduction also prennotes engine life and reliability. 35 The power determination has been made by use of anticipated aloads on the tractor. Thus, the power requirements that have been estimated are all drawbar horsepower. Self-propelled Machines The preceeding discussion was for tractor powered machines. Obviously, all operations do not require input of tractor power. The selection model can also be used for self-propelled machines. The only restriction is that self-propelled machine operations must be placed in subsets separate from tractor powered operations. The sub- set dates can overlap the tractor powered subsets, since separate equipment is to be used in each. The self—propelled machine power is then calculated via Procedures parallel to that used with the tractor powered operations. AS many "parallel paths" as needed may be defined. 3.2 TRACTOR SELECTION The size and number of tractors needed depend on the design horsepower for the system (DHP) and the size range of tractors avail- able in the marketplace.‘ The number of tractors (NTR) must, of necessity, be an integer. The minimum number of tractors needed is found by taking the ratio of design horsepower to the maximum tractor drawbar horsepower (MAXHP) and rounding up to the next integer. Many combinations of NTR tractors with total horsepower equal to DHP are usually available. Two courses can be followed at this point to select a Particular set of tractors: 1) use all possible combinations of tra(:tors with total horsepower equal to DHP, select a machinery system 36 for each, and choose the combination which best satisfies some preset criteria, or 2) select a particular set of tractors which is assumed to be best for a particular situation. Initially, the first approach was used. An algorithm was prepared which would produce all combinations of tractors of integer sizes with a total horsepower equal to DHP.. machinery was then select- ed for each tractor combination, using the method which will be de- scribed in the following section. The complete system with lowest total annual operating cost was then selected. The best system, including both tractors and machinery, was observed to consistently be of one characteristic type. It had all but two of the tractors as large as possible with the remaining horsepower distributed between the other two tractors. One of these was as large or as small as possible. Accordingly, the original algorithm was modified to the form shown in Figure 3.2 which produces a single combination of NTR tractors with horsepower distribution as described above. An exception to the preceeding rule can occur if the power from the tractors can not all be used because of field machine operat- :ing limits. For example, harrowing 1000 acres in five hours, with a unit draft of 150 pounds per foot of width and MFAC equal to 0.75, Ifiaquires 73.4 effective system horsepower, 98 design horsepower and all effective field capacity of 20 acres per hour. The algorithm in Ffiigure 3.2 would select one 98 horsepower tractor for this situation. From Table C.4, maximum width and operating speed for a harrow are, respectively, 30.0 feet and 6.0 miles per hour (reasons fC>r these limits are discussed below). To operate at 20 acres per 37 C D l NTR = IFIX(DHP/MAXHP)+1 _. DHP, MAXHP I FHP = DHP-(NTR-2)(MAXHP) ”W‘fi TRAC(NTR) = MINHP v MINHP I [TRAC(NTR-l) = FHP-MINHP _ I .___ TRAC(NTR-l) : MAXHP TRAC(NTR-l) = MAXHP . TRAC(NTR) = DHP-(NTR-l)(MAXHP \V J = 1 44 (:T Stop :> 7 I g..-" -—__._......_.———.. -- . TRAC(J) = MAXHP L" J = J-l Figure 3.2. Tractor Horsepower Determination 38 hour, the tractor can pull a 30 foot harrow at 6.2 miles per hour or a 30.5 foot harrow at 6.0 miles per hour. Neither of these solu- tions is acceptable because one of the constraints is violated in each case. The effective system horsepower cannot be used because of the operating limits on the field machine. The solution to problems of this type is to increase the number of tractors. For the example, two tractor-harrow combinations, ‘with a total of 98 horsepower, could be used so that the limits on harrow size and speed are not exceeded. For this study, since enough time was alloted for the completion of each subset of field operations, problems of the per- <:eeding type did not arise. Therefore, no modification was ever made to the set of tractors selected by the use of the procedure of Figure 3.2. The effect of restrictions on power level can be evaluated, simmly by changing MAXHP. It should be noted that the system chosen by the method of Figure 3.2 may only be best for the set of crops and.the production technology being used in this study. Inclusion of other field operations, or a change in production technology might alter the best system characteristic form. 3.3 MACHINERY SELECTION The method used for selecting field machines is a modifi- Cation of the process presented by Connor, _E__l., (12) which was also developed by this author. Before selecting the field machines, a work schedule must be constructed for tractor use to determine Which tractor powered each machine. 39 Several procedures can be used, and were evaluated for allocating field operations to be powered by each tractor. The method chosen, which is presented below, was considered to be the most realistic. The work to be done by each tractor in each subset of (operations is first determined. lkllocation of Tractor Energy in Subsets The method for assigning a quantity of work from each sub- sset to be done by each tractor is shown in Figure 3.3. Before this [arocedure is initiated, the number of tractors (NTR), the horsepower (of each tractor (TRAC(J), J - 1,2...NTR), the number of subsets CNSETS), the work time for completing the subsets (SST(I), I = 2... PMSETS) and the total effective energy required for each subset (EFEN (I), I = 1,2...NSETS) will all be known. For each subset I, the maximum amount of energy (TEN(I,J)) that tractor J can be expected to develop in time SST(I) is calculated. It is assumed that the tractor operates at an effective horsepower Vfliich is its rated horsepower (TRAC(J)) multiplied by the factor MFAC. Thus: I J 1,2...NSETS Ten(I,J) = MFAC 7* TRAC(J) 7" SST(I) 1 3 NTR ’ CO. For the subset, the tractors are all checked, from smallest to largest in order, to determine if any one can develop enough energy to Power all the field operations in the subset. If one tractor is caPable of powering all the work in the subset, then it is assigned to SUPPIY all the energy required for the subset of operations. If no single tractor can power all the operations, then the largest tractor is assigned to work at capacity, for the time SST and 4O (:_ Start ) s...' ‘ TRAC(J), J % 1,NTR SST(I) and EFEN(I), I = 1,NSETS' -i-.--———--.~_. ....—-.——....--._.-..-..-.. ._——-_--_._.____._- .v... ... . 4 < a- . . . . . . .. ._ - _ v. ,_ . - -..,-, .— no—v-~—-—-...-.-1 Calculate max energy output from each tractor in Subset (I) TEN(I,J) = TRAC(J).SST(I).MEAC,WJ_= 1.NTR I 1w... J = NTR 7“] = Stop Tractor J does EFEN(I) work _a-.--m- in Subset I ,', I = 1+1 Calculate working time TEN(I,J) : EFEN(I) V \\ Tractor J assigned\\\» J = J+l 1 Tractor J does TEN(I,J) work in Subset I L—-_cc_. EFEN(I) = EFEN(I)-TEN(I,J) Figure 3.3. Allocation of Tractor Energy in Subsets 41 develop energy equal to TEN(I,J). The procedure is then repeated in allocating the remaining work to the remaining tractors. Table B.2 is an example of the energy allocation for the field operations and subsets shown in Table 3.1. After energy to be supplied from each tractor for use in each subset has been allocated, specific field operations in each subset must be assigned to each tractor. Several methods are avail- able for the assignment process. The one used in this study is des- cribed below. Of course, if only one tractor is used, the allocation problem is non-existent, since the one tractor powers all operations. Allocation of Tractors to Field Operations in a Subset If subset I has field operations requiring energy from more than one tractor, tractor J (the largest tractor scheduled for use during the subset) is assumed to power the largest possible portion, 0f the earliest field operation, that has an energy requirement less than or equal to TEN(I,J). If TEN(I,J) exceeds the energy required for the earliest operation, subsequent operations and/or a fraction of an Operation are assigned to tractor J until the total energy re- QUired fiar the assigned operation equals TEN(I,J). Usually this process twill leave a fraction of one of the operations unassigned, WhiCh is then taken as the starting point for the assignment of field Operaticuns for the next smaller tractor scheduled to power operations in the smibset. The procedure is continued until all field operations in the smibset are assigned to tractors and is then repeated for the Other subsets . 42 Since the power rating of each tractor and the energy requirement for each field operation are known, the time required to complete each operation can be calculated. Dividing the energy requirement in horsepower hours by the product of tractor horsepower and MFAC yields the time to complete the operation in hours. Thus, the acreage and time for each field operation can be calculated. The effective field capacity of each field operation powered by each tractor can be found by dividing the acreage by the required time. Table B.3 shows the assignment of field operations for the example in Appendix B. Disking in Subset l and plowing in Subset 5 are examples of field operations divided between two tractors. Machine Selection Machine selection begins by establishing limits on the size 0f machine that can be used for each machine-tractor combination specified in the previous section. These limits are established by using size and speed constraints. Each particular machine is assumed to be available in a range of sizes from a minimum to a maximum‘width, and to Operate properly over a range of speeds from a minimum to the maximuu1:allowable. For a particular technology, these are obviously realistxic assumptions. Table C.3 shows the size and speed limits for eacli of the machines in the example. When an operation in a subset is divided between tractors, or whet; the same operation is performed in more than one subset, the use tiuma and size limits should be checked to determine if the same machine <:an be applied to more than one of the uses. 43 If an operation in a subset is performed by more than one tractor, if the size limits overlap, and if the total time for the field operation is less than the subset time, then one machine will satisfy the need. If, on the other hand, time for the field operation exceeds the subset time, multiple machines will be needed. Similarly, if the same operation is performed in more than one subset, and the size limits overlap, one machine will suffice. If the size limits do not overlap, multiple machines will be needed. After all possible combinations of this type have been found, the annual time each machine is used is found as the sum of the several times the machine is used, and the size limits are reset to the widest pair that satisfies all of the limits. Table B.4 shows the results of this procedure for the example. Machine Costs Since the entire selection procedure was based on energy consumption, it was considered that service life should also be based on energy consumption. After machine life was expressed as quantity of energy, the service life would be found by dividing the service life energy by the annual energy consumed for operating the machine. For example, if the average horsepower to pull a harrow was known, the machine life in horsepower hours could be calculated by multiplying by the average harrow life in hours (1,6). This method is advantageous because it takes into account the severity of machine use. Two similar machines used under different conditions for the same amount of time would be found to have different service life periods. Also for this study, where the soil is assumed to be 44 the same for farms of all sizes, changes in energy consumption due to differing soil types would not have to be considered. However, reliable estimates of machine life in energy terms could not be readily made, so machine life was determined from the annual hours of use. A service life in hours was assumed for each machine. Then after the annual hours of use were determined, the service life was calculated by dividing machine life in hours by annual use. The original cost of each machine and tractor was calculated from a regression relationship relating cost per unit of size to machine size. The regressions were developed from information supplied by a machinery manufacturer (13). These regression relationships are shown in Table C.6. Depreciation was calculated by the straight line method. Repair costs over the life of the machine were taken as a fixed per- centage of the original cost of the machine and were prorated accord- ing to annual machine use. Annual cost for interest, housing, taxes, and insurance were calculated as annual percentages of the purchase cost of the machine. The self-propelled machines, if any, are selected by the same procedure discussed for the pull type machines. Costs for self- propelled machines and the tractors were evaluated similarly to the machine costs. 3.4 PROCESSING ENERGIES The preceeding portions of this chapter have explained the selection and assumed mode of operation of the field machinery system. 45 From the information that has been developed totals of each of the six processing energies can be found. If the analysis is repeated several times using the same technology and over a range of sizes, the processing energy function In f1(YO3) can be developed. Capital Capital requirement in dollars is the sum of the initial costs for the tractors, field machinery, and self-propelled machines. m Total labor needed in the field equals the sum of operating times for each tractor and the self—propelled machines plus an allow- ance for scheduling inefficiencies, and an additional allowance for administration. Fossil Energy Fossil energy in horsepower hours is the sum of the energy requirements for each of the subsets. Land Land cost is equal to the area required for all crops. may There is no electricity consumed by the field machinery System, 46 Dollar Cost Dollar cost per year is the sum of the operating cost for eeach machine, tractor, and self-propelled unit plus the dOllar cost of each of the other energies used. Table B.6 shows the energy costs for the example system. 4. FARMSTEAD SYSTEM The farmstead system includes the equipment and opera- tions connected with feeding and handling the cattle. Referring to Figure 2.10, this model is used to estimate the requirements for each of the six processing energies for the farmstead operations. Ener- gies associated with the input materials will be discussed later. The farmstead system components were first designed; then the processing energies were estimated for the system. A straight forward design process was used in all cases. Components to be de- signed included the feedlot, the feed storages, and the liquid waste tank, if needed. In addition, equipment such as waterers, silo un- loaders, and liquid waste pumps were specified. 4.1 COMPONENT DESIGN Feedlot The four types of feedlots considered were: 1) completely Open, unpaved lot, 2) completely covered, 3) partially covered, VVith hard surfaced lot, and 4) partially covered, with unsurfaced 10t (26). The floor in the completely sheltered feedlot was either SOlid concrete or slotted, depending on the waste handling system (11). The feedlot layout can be completely specified by five Parameters if the basic layout shown in Figure 4.1 is assumed. The Ilecessary data are 1) inches of feedbunk per animal (INHD), 2) final Vveight of the animals (ENDWT), 3) area (square feet) of open lot and 47 48 SHELTER AREA OPEN LOT AREA 7 Shelter - Width—- 4 Lot Width FEEDBUNK rr~-—~»~~—wwnLot Length-——«-—w«~~~5>4 Figure 4.1. Assumed Feedlot Layout 49 shelter per 100 pounds of final animal weight (CLOT and CSHEL respec- tively) and 4) feedlot capacity (HDFB). Feedlot length equals feedbunk length which is the product of INHD and HDBF. Open lot area per animal is the product of CLOT and total final hundred weight of animals. Shelter area per animal is similarly calculated from CSHEL. Width of open lot (WLOT) and shelter (SHEL), respectively, can be found from the following re- lations: (CLOT)(ENDWT/100) WLOT = (INHD/12) (feet) (CSHEL)(ENDWT/100) WSHEL = (INHD/12) (feet) The size of the constants CSHEL and CLOT depend on the technology being used. They can be zero in some cases. CSHEL for the fully sheltered feedlot also depends on the waste handling system being used. A slotted floor liquid waste system will usually have less area than a solid waste system. The final animal weight (ENDWT) is the average for all eanimals in the lot. It depends on the kind of animal being fed in tflne lot (calves or yearlings). Feedlot capacity is the largest number of fully grown aniumls that can be housed simultaneously in the feedlot. Feedlot \nilume, or the number of animals produced per year is proportional to CaPacity. The constant depends on days on feed, which varies with animal type, ration and feedlot type. Efied Storage Two feeds were required for the two assumed rations (4). Corn silage, which is in both rations, can be stored in tower or 50 bunker silos. Grain corn, which is in one ration, was assumed to be handled as "high moisture" corn stored in sealed tower silos. Other handling and storage systems were not evaluated. Sealed Tower Silos for Moist Corn Tower silos for moist corn were selected under a minimum capital criterion. These units are available in a finite set of diameters, and in multiples of a height increment up to a maximum height for each diameter. Cost varies with diameter and height (37). The quantity of corn to be stored depends on feedlot cap- acity and average daily corn feeding rate (4). For the conversion to volume, moisture content of the corn was needed since corn weight and volume per bushel both depend on moisture content. The procedure began by assuming a diameter for the silos. Total silo height was easily calculated from volume and diameter. If the total height exceeded the available maximum height for the diameter (37), multiple units were purchased until the average height wvas less than the maximum. The height of individual silos was adjus- ted to the next higher or lower multiple of the height increment until total height was Within one height increment in excess of the total lieight required. The cost of the silos was then estimated from a price function. The procedure was repeated for each of the available (iiameters and the lowest cost combination was selected. Additional equipment included a silo unloader for each unit, permanently installed blower pipe and a roller mill for pre- Paring the corn to be fed. 51 Tower Silos for Corn Silggg Concrete tower silos are available in diameters ranging in incremented steps between a minimum and maximum diameter. There is a maximum height available for each diameter. Silo heights are also available in increments. Silo cost is a function of diameter and height. Silage must be removed from a tower silo at a minimum removal rate (TSRR) to retard spoilage. This minimum removal rate was used to determine the maximum allowable diameter of the silos (34). The following function, where TSDEN is silage density in storage in pounds per cubic foot, LBS is daily silage fed per animal in pounds and TSRR is expressed in inches/day yields the maximum diameter: _ (48)(LBS)(HDEF) MAKD — (3.14)(TSDEN)(TSRR) The silo diameter selected was the next smaller available diameter. The quantity of silage to be placed in storage depends on 1) feedlot capacity, 2) daily silage feeding rate and 3) the amount of silage expected to be lost because of spoilage (19). The required 'volume depends on the density of silage. Total height was then cal- culated from the diameter and volume, the number of silos was deter- mined using the maximum height for the selected diameter, and the average silo height was calculated. The height of individual silos was then adjusted up or down frxnn the average height to an adjacent multiple of the height incrememit until the total height of the individual silos was within one incrnamental unit greater than the total height needed. 52 No further evaluation of alternatives was made. Study of the cost function clearly indicated that tower cost per ton of mater- ial stored increased as height increases and as diameter decreases. Another reason for holding the silos to approximately the same height was to avoid the high energy cost for blowing silage into a tall silo. Additional equipment included with the tower silos were permanently installed blower pipes and a silo unloader for each unit. Bunker Silos for Silage The quantity of silage to be placed in the bunker silo is determined the same way as for tower silos. Typically, loss in storage is greater with a bunker silo than in a tower silo. There is also a minimum removal rate (MRR) for bunker silos. That much silage must be removed from the face of the silo per day to retard spoilage. If MRR is removed each day, then the minimum bunker silo length is(MRR)(365). Bunker silo costs are expressed as the sum of two figures, dollars per Square foot of floor and wall (19). Preliminary analysis showed that if endwalls are neglected, and depth and length kept constant, silo cost per unit of volume decreases as width increases up to some maximum width where the cost becomes essentially constant. Until this maximum (WMAX) is reached, the silo cost per unit volume increases with increased depth. However, once the width reaches the maximum, increasing depth decreases cost per unit volume. Following the preceding guidelines, bunker silos were designed by the rule: -—..._ ‘ 53 Set minimum length defined by the minimum inches removed per day. Set depth at the minimum. Then increase width from zero until it either reaches WMAX or the structure volume equals the required storage volume. Once the Width reaches WMAX, increase height by one increment and re- peat the process. Length is only increased after both width and height have reached the maximum. The only additional equipment needed is a front end loader for removing silage. Liguid Waste Tank If a liquid waste handling system is used, a tank and a slotted floor must be included (11). Weight of waste (flow Y44) was calculated as a percentage of body weight. Required tank volume was determined by dividing by the waste material density. Slotted floors are made up of units of a particular length. The ends of the floor units are supported on the outside walls or on internal walls included for the purpose. The tank width is taken as an integer multiple of the slotted floor unit length. The tank length is assumed to be equal to the length of the building. Depth is determined by dividing the volume by tank width and length. The tank contains internal walls, as needed, to support the ends of the slotted floor units. 4.2 PROCESSING ENERGIES There are many ways a feedlot can be operated after the basic components have been specified. The operation depends on the physical layout of the system and managerial preferences, and can affect some of the processing energies. Therefore, the operation assumptions that were made for analysis of the system, are incor- Porated into the following discussion of processing energies. 54 Capital Because of the variation of interest rates with length of investment, two classes of capital were considered. Long term capital included the initial cost of the feedlot, shelter building, fence, feedbunk, concrete slabs, liquid manure tank, well and feed storages. Short term capital included the initial cost of waterers, gates, silo unloaders, roller mill, blower pipe, silage blowers, front end loader, liquid manure pump, feeding wagon and feedlot tractors. The number of blowers, and the number and horsepower of the feedlot tractors had to be designed in conjunction with the transportation system and thus are not discussed in this chapter. The energy costs for the blowers and tractors, however, were assigned to the feedlot. labs; Total feedlot labor includes the labor required for feed- ing the cattle each day, for packing the bunker silo, and for clean- ing the lot and loading the waste plus allowances for scheduling efficiency and administration. Feeding time is made up of the time for removing the feed from storage, feed wagon travel and unloading the wagon. It was assumed that silage was removed from storage and loaded immediately into the feed wagon. Corn was removed from silage, passed through a roller mill, then loaded directly into the feed wagon. Time delay involved in the grinding operation was considered to be negligible. When tower silos were used for silage, the time for un- loading corn and silage were calculated and the larger was taken 55 as the feed wagon loading time, since the two operations were con- sidered to be done at the same time. For systems using a bunker silo, the feed wagon loading time was the sum of the corn and silage un- loading times, since it was assumed that the two operations must, because of the necessary layout, occur sequentially. Feed storages were assumed to be located near one end of the feedbunk. The loaded wagon was pulled to a point on the feed- bunk where unloading was commenced. The wagon was then pulled along a section of the feedbunk while the ration was unloaded. After completion of unloading, the empty wagon.was pulled past the rest of the feedbunk, turned around and returned to the feed storage area. Travel around the feedbunk area was assumed to be at a preset speed, and unloading was at a preset rate. Unloading time was taken as a constant for a load unless feed wagon capacity was changed. The length of feedbunk filled by each load was determined from the number of times the animals were fed per day, the total quantity of feed and feed wagon capacity. Round trip distance equalled txvice the length of the feedbunk plus the distance for turning at the: end. For each trip, the travel distance equalled the round trip disztance less the length of feedbunk filled by each load. Travel tiuua per load was calculated from travel distance for each load and travel speed. The number of loads per day was the total weight of feed per (hay divided by feed wagon capacity and the number of trips along the feedbunk equalled the number of loads rounded up to the next integrar; Daily feeding time equalled the number of loads per day 56 multiplied by the sum of loading and unloading time per load plus the number of trips per day multiplied by the travel time per day. Bunker silo packing rate is determined from the quantity of silage divided by the time required for harvest or by a preset minimum packing rate, which ever is larger. Silo packing time was determined from the quantity of silage and the silo packing rate. It was felt that solid manure loading rate was more depend- ent on operator skill than on tractor horsepower. Loading rate was, consequently, assumed to be independent of tractor size. Waste loading time was taken as a quantity of waste divided by the load- ing rate. Fossil Energy Depending on the technology, fossil energy was consumed in the feedlot for some of the following operations: 2) feeding the cattle, 2) blowing silage and grain corn into tower silos, 3) packing the bunker silo, 4) unloading the bunker silo, 5) pumping liquid manure and c0 loading solid manure. Energy for feeding was consumed for loaded travel, unload- iJng the feed wagon and unloaded travel. The average distance a llbaded feed wagon was transported equalled one—half the length of tile feedbunk. Force to pull the wagon equalled the combined weight Cflf the wagon and load multiplied by the coefficient of rolling reasistance for the farmstead (l). Loaded travel energy per load was tliéi product of loaded travel distance and force. Unloaded travel diJStance equalled one and one-half the length of the feedbunk plus tultn around distance. Unloaded travel energy per load was calculated 57 the same way as the loaded energy. Unloading energy per load equals the unit energy for unloading multiplied by feed wagon capacity. Total feeding energy per day was the sum of these three energies multiplied by the number of loads per day. Energy for blowing silage and corn, packing the bunker silo, unloading the bunker silo, pumping liquid manure and loading solid manure was determined from the unit energy and quantity of each material. The energies were preset except for the unit blowing energy which was a function of the height of the silos. Land Land required for the farmstead was equal to the area of the feedlot, feed storages and roads plus an allowance for easy access to these components. Electricity Electricity was consumed for lighting, pumping water, un- loading corn and silage from tawer silos and grinding corn. The energy required for each operation was determined from the quantity (Df each material and the unit energy for each operation. Do 1 lar Cost Dollar cost per year was the sum of the fixed and variable CC>s:ts for all components plus the dollar cost of each of the other energies used in the farmstead. 5. TRANSPORTATION The transportation system is the link between the field crop production and the feedlot and other farmstead components. On a beef farm, the transportation system moves feed to the farm- stead, and returns waste to the field for disposal. For the technologies being used in this study, the transportation system consisted of a subset of the following five kinds of components: 1) corn wagons, 2) silage wagons, 3) liquid manure Spreaders, 4) solid manure Spreaders and 5) transport tractors. This chapter describes the way a set of components was selected to form a system capable of fulfilling the necessary transport functions on a beef farm of a particular size and technology. Assumptions The following assumptions, discussed below, were made: 1. The transport distance between field and farmstead is strongly influenced by farm layout. An accurate assessment of this phenomenon would require information on the variation of transport distance with farm size. Since such information was unavailable, it was assumed that all farms were square and that the average trans- port distance was equal to the length of one side multiplied by 0.707. 58 59 2. Weights, capacities and all other relevant informa- tion about transport system components are known. 3. A maximum average speed exists which a unit can not exceed. 4. Variations in speed do not affect the energy required for a trip of a particular distance. Discussion of Assumptions The distance from the center of the farm to one corner was the assumed average transport distance for each load. Implicit in this first assumption are the additional assumptions that 1) production of each crop is distributed over the farm and 2) waste is applied to all parts of the farm. It does not require that waste be applied to all parts of the farm every time the feedlot is cleaned or that each crop has to be uniformly distributed over the farm. It does mean that ”the center of gravity" of the application area and the crop area coincide With the geographical center of the farm. Location of the farmstead on one corner is arbitrary. A similar analysis can be made using any layout, as long as the average distance the loads are transported can be determined. It is probably true that transport distances are underestimated for the large units relative to the smaller ones. Component capacities are determined externally and used as input parameters to the model. A system is specified by determining the number of each type of component needed. A model capable of selecting component capacity as well as the number needed would have to have a more complicated logical structure. This was considered to 60 be unnecessary, since the object of the study was analysis, not design. If desired, the effect of component capacity can be measured by using a different set of parameters and re-evaluating the number needed. It is reasonable to assume that an upper limit on speed exists. The speed is limited by ground conditions as well as tractor power. Most transportation systems are more efficient users of energy at lower speeds. On a farm, however, the transport speed range is limited. The highest speeds are seldom over ten miles per hour with a loaded unit pulled by a tractor and these speeds are atainable only under good conditions where the rolling resistance is low. Under these conditions, it is reasonable to assume constant energy requirements 5.1 WASTE TRANSPORT The quantity of waste to be transported depends on the waste handling technology in use. Liquid systems collect and store all animal waste material and a liquid spreading system must trans- port this entire quantity of material. A solid system has less to transport since runoff, evaporation and infiltration of the liquid fraction reduce the weight of material by about 85 percent (10,11). Time Per Load Time per load, or round trip time, has to be determined before the number of spreaders can be calculated. It can be de- composed into four parts; loading time, loaded travel time, unload- ing and spreading time and unloaded travel time. Loading and un- loading times are determined by the loading and unloading 61 rates, respectively. Loaded and unloaded travel time depend on average speed and distance traveled. Unloaded travel is assumed to occur at the maximum allowable travel speed. Loaded travel speed is determined by transport tractor horsepower as long as the upper limit on transport speed is not exceeded. Number of Spreaders The number of loads to be transported depends on the total quantity of waste and spreader capacity. The average time between loads depends on the number of loads and the allotted time for clean- ing and spreading. If the time required for one spreader load exceeds the average time between loads, multiple spreaders are needed. The number of spreaders can be found from the ratio of time per load to the average time between loads rounded up to the next integer number. 5.2 FEED TRANSPORT All of the systems being analyzed use silage in the ration and require silage transport. Corn transport is only needed for those systems using corn in the ration. Silage is handled in self unloading wagons. The unloading function can be powered by the transport tractors or by an output shaft on the silage blower. For this study, the wagons are assumed to be powered by the tractors. Corn is assumed to be handled in side unloading "gravity boxes". The same tractors are used to transport silage and corn as well as waste. The number of loads, time between loads and travel time per load can be determined the same way as for waste transport. 62 For silage, loading time need not be considered since the wagons are pulled by the field machinery tractors while being filled. However, time for unhitching from one wagon and hitching to another must be included in the time per load. Corn loading time depends on harvest rate. Unloading time is governed by blower capacity as the corn is blown into the storage. Number of Blowers Blowers are used for elevating corn silage and shelled corn into tower silo storages. If the system uses a bunker silo rather than tower silos, one blower is still required for those systems which include corn in the ration. An all silage ration, with bunker silo storage, eliminates the need for blowers completely. When putting silage into tower silos, total blower capacity must be equal to or greater than the total forage chopper capacity. Lower capacity will cause wagons to queue up to unload and force idle time on the forage harvesters which, as a consequence, will not be able to complete the harvest on schedule. The average time between loads (TBL) filled by the choppers is found by dividing the total harvest time by the number of loads. The minimum time (MINTIM) a blower can elevate a load of silage into a particular silo depends on the maximum tractor horsepower, silo height and MFAC. The number of blowers needed was taken to be the ratio of MINTIM to TBL, raised to the next.higher integer. Silage Wagons for Tower Silo Systems One wagon is assumed to be required with each chopper and blower plus one or more in transit. The number in transit (NIT) can 63 be calculated from Equation 5-1, where HTIM is the field hitching time, OUTIM is the time to make the unloaded trip and INTIM is the time for travel with a loaded wagon. NIT = (HTIM + OUTIM + INTIM)/TBL (5'1) NIT is rounded up to the next higher integer. The maximum time available for unloading a wagon into a blower (ULTIPD in a system with IBL blowers is: ULTIM = (IBL)(TBL) The unloading time affects the horsepower of the tractor used to power the blower. Silage ngons for Eppkgr Silo Systgmg Wagons are assumed to be unloaded as fast as possible into a bunker silo. Unloading time (ULTIM) is determined by wagon capacity and transport tractor horsepower. The time per load (TPL) is the sum of HTIM, INTIM, OUTIM, and ULTIM. The number of wagons required is found from the ratio of TPL to TBL raised to the next integer plus the number of forage harvesters working in the field. Wagons for Corn Transport The number of corn wagons required can be determined from the ratio of time per load to time between loads as discussed in the previous section. Transport Tractors TranSport tractors are usually small used tractors. Their only function is supplying drawbar and PTO power for wagons of the type previously discussed. The number of tractors needed is the 64 maximum of the number needed for transporting waste, silage or corn. Usually, for the systems considered, the maximum is the number re- quired for silage transport. 5.3 FEEDLOT TRACTORS Feedlot tractors were sized by the power requirements for putting silage into storage. Time for packing a load of silage has been discussed pre- viously. The packing horsepower was determined from the quantity of silage per load, the unit energy for packing and the packing time as long as the power required did not exceed the maximum allowable power. It was assumed that this tractor was adequate for other feedlot oper- ations. When tower silos were used, each feedlot tractor (there could be several) size was determined from the silage blowing require- ments. The horsepower was determined from wagon capacity, unit blow- ing energy and unloading time. The number of tractors was equal to the number of blowers. Energy costs for the feedlot tractors were included with other feedlot equipment. 5.4 ENERGY COSTS Capital Capital requirements equal the purchase price of spreaders, silage wagons, corn wagons and transport tractors. Labor Labor requirements can be found by multiplying the time per load by the number of loads for each material which is trans- ported plus an allowance for scheduling efficiency. 65 Egsgil Energy Fossil energy for transportation is the product of the average force of pulling the wagons and the average travel distance. Fossil energy for unloading silage and waste can be found from the unit unloading energies and the total quantity of each material. Land There is no land required specifically for transportation. Electricity There was no electricity required for transportation. Dollgr Cost Dollar cost per year was the sum of the operating cost for each wagon and tractor plus the dollar cost of each of the other energies used. 6. IMPLEMENTATION OF THE MODEL The definition of systems to be analyzed included the type of feedlot, feeding storage, waste handling, animal and ration. Table 6.1 shows the set of component types used. Table 6.2 shows a set of forty feasible combinations and the identification number assigned to each. All other aspects of the technology used for the systems were fixed. The same soil, climate, field operations, time constr- aints and machine characteristics were applied to all systems, and thus were assumed to have no influence on the comparisons to be made between systems. It was originally intended to perform the calculations needed for making the analyses manually. However, the experience gained in designing and analyzing the assumed operations for one system clearly indicated that the volume of work involved would be excessive. Accordingly, the procedures involved, which have been discussed in Chapter 2 through 5 were automated by use of a computer program. Using the computer with appropriate data for each size- technology combination, allowed the analysis to be carried out. The use of the computer had the added advantage of insuring a consistent application of procedures and assumptions without the probability of human error. 66 Table 6. Type Type Type Type Type 1. of 01 02 O3 04 of 01 02 03 04 of 01 02 of 01 02 of 01 02 67 Components Included in Technology Definitions Feedlot Partial shelter, unpaved lot Partial shelter, paved lot - Open unpaved lot Completely covered Feed Storage - Moist corn storage, tower silos for silage - Moist corn storage, bunker silo for storage - Tower silos for silage - Bunker silo for silage Animal - Calves - Yearlings Ration - All silage - Corn and silage Waste Handling - Liquid - Solid 68 Set of Technologies Selected for Analysis Table 6.2. FEED STORAGE TECHNOLOGY NUMBER RATION ANIMAL HANDLING FEEDLOT 24 25 26 27 ll 28 29 3O 31 15 88 89 9O 91 75 92 93 11.111111111122222 1.1111111111111111. 222212222122221 333334444433333 123441234412344 94 95 79 48. 49 50 51 35 52 53 54 55 22222111111111 11111222222222 22221222212222 44444111112222 12344123441234 39 112 113 114 115 99 1234/... 116 117 118 119 103 22222 22222 22221 22222 123/44 69 Many input values are dependent on the technology being used. Others are constant for all systems. Appendix C shows the complete set of data used. Table 0.1 is a listing of parameters taken as constants for all the systems. Only those constants required were used for each technology. For example, CLOAD which is the load capacity of the shelled corn wagon in pounds is only needed for systems using ration 2 (corn and silage). Table 0.1 gives the name and definition for each variable, the value assigned and the reference, if any, where the value was found. Reliable estimates of some parameters could not be found, so estimates were made by the author, using any information which could be found. The values so estimated are noted. Table C.2 gives the areas of shelter and open space required for each type of feedlot, per hundred pounds of final animal body weight (26). Table C.3 shows expected feed consumption and rates of gain for each animal ration and feedlot combination (4). Data for field machinery characteristics are shown in Table C.4 (1,3,6,l3)o Percent useable days are given in Table C.5 (33,38)° The five time periods given in the table correspond to the dates for the five subsets of field operations given in Table B.1. Initial cost of the field machinery was calculated from functions shown in Table C.6. Table C.6 also contains functions for estimating initial cost of tower silos for silage and corn (5,37). The cost functions for field machinery were developed from cost data made available by a manufacturer (13,35). The required amount of each input material is shown in Table C.7 (5,37). The dollar cost of the materials is also given (5,37). 70 These values are not of themselves the technical coefficients for the system, but a number of the coefficients can be derived from the values. All other energy costs for input materials were set to zero. The energy requirements determined were strictly those internal to the system. It was felt that determining the total energy consumption was outside the scope of this study. Unit dollar costs for the mater- ials are shown in Table C.7. To the labor identified by the various parts of the system, 30 percent was added for miscellaneous labor. An additional 1000 hours of administration time was assumed for all systems. Comparisons Using the data in Appendix C and the computer program, discussed previously, analyses of systems were made to determine the following: 1. The effect of feedlot type on energy cost of beef 2. The effect of ration on energy cost of beef 3. The effect of type of feed storage on energy cost of beef 4. The effect of waste handling system on energy costs of beef 5. Breakdown of fossil energy consumption by the field machinery, farmstead and transportation systems as influenced by system capacity 6° The effect of animal type on energy cost of beef 7. Breakdown of labor requirements by the field machinery, transportation and farmstead systems as influenced by system capacity. 71 8. The effect of maximum allowable tractor size on capital and labor required for the field operations. 9. Breakdown of labor requirements during the year. 7. RESULTS Fossil energy consuption for systems using tower silos was found to be strongly influenced by silo height. Dollar cost and con- sumption of other energies were observed to change rapidly for smaller (100 to 300 head capacity) systems. For these reasons, and since the computer was available to make the computations, more systems were‘ analyzed than had been originally intended. Systems using tower silos for silage storage were analyzed at 25 head intervals from 100 to 1000 head capacity. Other systems were analyzed at 25 head intervals from 100 to 300 head and at 100 head intervals from 400 to 1000 head capacities. Appendix D, Table D.l, is a set of selected results from the analyses that were performed by the computer. In addition to the in- formation in Appendix D, printouts showing component selection, dollar cost of components and energy consumption by various parts of the system could be produced, if desired. Appendix B was derived from such a complete printout. It has been reorganized somewhat to improve readability. Information of this type was used to find explanations for differences between systems which were found. The curves presented in this chapter were developed from the information in Appendix D or other computer printouts. The curves showing energy costs for systems were smoothed and plotted on an X-Y plotter controlled by the computer. 73, The curves in the figures are identified by the technology number, as explained in Chapter 6. In the discussion of the figures, systems are identified by both the technology number and by some characteristic feature which distinguishes the system from others be— ing discussed. This is done to reduce the amount of "decoding” necessary to understand the figures. 7.1 EFFECT OF FEEDLOT TYPE The effect of type of feedlot on quantity of beef and the cost of beef produced were found by analyzing systems which were similar except for the feedlot. The systems being compared all used yearlings, one percent concentrates ration, bunker silo storage, and solid waste handling. The feedlot types were 1) partial shelter with unpaved lot, 2) partial shelter with paved lot, 3) open and 4) completely sheltered, corresponding to technology numbers 116, 117, 118 and 119, as shown in Table 6.2. Capital Figure 7.1 shows the variation with system capacity of capital per hundred pounds of weight gain for the four systems. Hundred-weight of gain was used as the basis of comparison because weight gain accounts for the variation in productivity between systems. Feed efficiency of the animals is affected by the type of housing, by the ration and by the type of animal. Those combinations which cause the animals to gain weight faster will have greater productivity than other systems of the same capacity. mquEmuHsva HNuHamo so mmmH uoHUmmm mo uomwmm .H.m muswfim Aemmnv wHHoameu Hoanmmm oooH ooH . J 4 a d 14 J 11 4 . omH oHH.Illlllllll! oHH n: a mad 4. 7 a a . oNN (Utes nMO/s) TVLIJVO 75 Capital is defined to include all initial investments for building, land, and equipment plus the annual investment for animals, fertilizer, chemicals, seed and supplement. Small systems used small field equipment and small feed storage which tend to have higher in- itial cost per bushel or per ton of feed handled than larger units. This higher initial cost is part of the reason for the high capital per hundred weight of gain shown in Figure 7.1 for the small system. In addition, certain pieces of equipment must be used for the system to conform to the specified technology. In some cases, even the smallest available units are not used to capacity. Equal capital is required Whether or not the machine is used to capacity. A notable example is the self-propelled combine used to harvest shelled corn. The capital for this and other pieces of equipment are spread over greater total weight gain in larger systems. The net effect of these two factors on capital per hundred-weight of gain is shown by the shape of the curves in Figure 7.1. The highest and lowest capital per hundred weight of gain were required for system 118 (the open feedlot) and system 119 (the completely covered feedlot), respectively. The partial shelter systems, numbers 116 and 117, had capital requirements midway between the other two systems. The differences are due to two factors, total capital and total hundredrweight of gain. From Table D.l, it can be seen that even though system 113 had the lowest total capital require- ment, the low feed efficiency brought about by lack of shelter caused the hundred-weight of gain to also be the lowest of the four systems. The total hundred weight of gain was low enough to cause the capital per hundred-weight of gain to be higher than for the other systems. 76 So the differences between systems are affected by the feed efficiency of the animals, which is a result of housing type. Local Irregularity A "local" irregularity in the capital per hundred-weight of gain curves occurs at 250 or 275 head capacity for all of the systems. The temporary increase is the result of the addition of equipment to the field machinery and transportation system. Specifically, system 119 requires 118 horsepower at 250 head capacity. For 275 head, the power requirement is 130 horsepower which exceeds the present maximum allowable horsepower. Two tractors (100 hp and 30 hp) are needed. Both of these cost more per horsepower than the 118 horsepower tractor used for the 250 head capacity system, which increases the capital per hundred-weight of gain requirement slightly. Of greater importance, is the fact that a second forage harvester and an additional silage wagon are required. The net effect of these factors is to cause a "bump" on the capital curve at 275 head for system 119. The "bump" occurs at 250 head for system 118, the open feed- lot. From Table C.3, it can be seen that each animal in the open feedlot consumes an average of 1.4 pounds of silage per day more than it would in the covered feedlot. For the 250 head system an extra 22.6 tons of silage are required for feeding the animals in the open feedlot per year. This extra feed production is sufficient to cause the local increase to occur for the 250 head system with tech- nology 118 rather than at 275 head for the others. Irregularities of this type are present throughtout the results. In all cases they are due to causes like the one previously 77 discussed. Unless some particular significance is involved, there will be no further discussion of the irregularities. Electrical Energy Electricity consumption per hundred-weight of gain for the same four systems is shown in Figure 7.2. A pattern similar to the capital requirements is exhibited, with high per unit consumption in small systems. Electricity cost is small When compared to other costs in the system. For example, Table B.16 shows that the electri- city cost for the example system is only 23 cents per head. Reduction in consumption with an increase in system capacity is due to the lighting energy being spread over more animals. The lights specified were large units, and were assumed to be adequate to light one acre of feedlot. One light was required for each complete acre or fraction of an acre. Other electricity consumption for water pumping and feed handling is directly related to the amount of each material used. Differences in energy consumption between systems shown in Figure 7.2, result from the higher feed efficiency of the animals in the sheltered systems. Fossil Energy Fossil energy consumption per hundred—weight of gain for the four systems is shown in Figure 7.3. Fossil energy is consumed for the field operations, in the feedlot and for transportation. Variation in energy consumption with system capacity is mostly attributable to transportation. Figure 7.4 shows a breakdown of fossil energy con- sumption by the three parts of system 119 as an example. Fossil energy consumption per hundred-weight of gain in the feedlot is a small coauaasmcoo zwuomm Hmowuuome so mama uoHcoom mo uoommm .N.n ouswwm Acmonv wHHo r r 00.0 00.N (uieg nus/suonefi) "Inna ”11880.1 81 percentage of the total and is nearly constant. Fossil energy per hundred-weight of gain for field operations is independent of capacity. Transport energy increases monotonically, but at an ever decreasing rate, because the required fractional increase in average transport distance is smaller than the fractional increase in system capacity. For the technology shown, the smaller system requires about 10 percent of total fossil energy consumption for transportation. For the largest system, the transportation system requires about 18 percent of the total. Differences in fossil energy consumption between the systems is attributable to differences in feed efficiency as has been explained. Lauri Labor per hundred weight of gain required for the system is shown in Figure 7.5. Labor requirements, similar for the four systems, were again lowest for the fully sheltered and highest for the open systems primarily because of the feed efficiency of the animals in the systems. The shape of the labor requirement curves results from the use of labor by various parts of the systems, as shown in Figure 7.6. For systems with low capacity, the administration time per hundred- weight of gain is high, since total administration time is constant (1000 hrs) for all systems. Field crop production labor per hundred~ weight of gain is high for small systems because small machines are used which tend to spread machine use over the allowable subset time (see discussion in Chapter 2). 82 muooemumsvom uonmg so mama uonoom mo uoommm .m.m ouswwm Ammwnv wHHoH uoflvoom mo uoowmm .m.m ouswflm 985 @523 980mm. 000a 00H . 0.02 a: 213111 w: 86 a 0.0mH (11193 3140/5“) 1.80:) TVHNNV 87 7.2 EFFECT OF RATION AND FEED STORAGE Four systems were selected to illustrate the effect of ration and feed storage on energy costs and system productivity. The systems used solid waste handling, yearlings and the completely covered feedlot. Ration and storage type for the silage for each system are: 91 - all silage - tower silos 95 - all silage - bunker silo 115 - corn and silage - tower silos 119 - corn and silage - bunker silo The shelled corn for systems 115 and 119 is as usual, stored in sealed tower silos. Shape of the energy curves results from the same factors as discussed in section 7.1. So, for simplicity, only differences be— tween systems are discussed. Also, mention of an energy can be taken to mean energy per hundred weight of gain unless otherwise specified. Capital Capital requirements are shown in Figure 7.8. Systems 95 and 119, using bunker silos, require less capital than the two systems using tower silos. For the systems using tower silos, system 91, with the all silage ration, requires less capital than system 115 if the capacity is smaller than 400 head. Above 400 head, the situation is reversed and the all silage system requires more capital. The all silage sys- tem has lower capital requirements for all sizes when bunker silos are used. 1 1 88 muooaomwsvom Hmuflamo so mEoumkm owmuoum room vow coaumm mo Hoommm .w.m ouswwm flammav waHo1 r r r (“198 ans/Sis) uoavm 94 moon mo umou unflaoa no Eoumhm owmpoum woom vow soaumm mo uoommm OOOH “odomv wsHowuom uuomwamuu 0cm macaw ou wofiammm mace ammo Manson: + mafia 0>Humuumwcwavm owdaoofl uoc moon * mGOfiumuomo msooomHHoomHE mam momoflowmmo moflaswofiom mom moomsoflamcm wovsaoaH % mm.m 1111 mm.m ¢.om 1111 11111 ¢.om 0H Hm\NH 1 mH\NH «0.0N NmH. wo.m ~.Hom ¢.wn H.q0H N.aNH Hm ¢H\NH 1 mN\0H mm.qw soc. mm.mH m.HNm o.¢NN 0.HOH n.m0a 0m ¢N\0H 1 mN\oo an.oo mum. wN.m¢ N.wm0H H.Noo 0.Hmm m.¢w em ¢N\oo 1 H0\mo mm.m 1111 mm.m 0.0mm 1111 11111 0.0mm we Hm\mo 1 mm\oo 00.5 000. mm.m m.mHH 1111 0.m¢ m.0m 0N ¢N\oo 1 mo\oo mm.m 1111 mm.m n.5H 1111 11111 m.mH m ¢0\oo 1 Hm\m0 om.NN mwm. 00.0H n.mmq 8.55 m.HmN o.mmH as om\mo 1 NH\¢0 mm.m 1111 mm.m m.mnm 1111 11111 m.mnm 00H 0H\q0 1 H0\H0 02.393 .85 22:83 31:: A35 A85 85 Ga: was mafia HZMEMMHDOMM mw< sgmwoa sqHuH=o GHH N 0.0m o.om o.oH H.Nm o.mNm m.msw~ namHm SHH H o.om 0.0m H.m~ H.~N o.m~m H.mNaH septum SHH H 0.0m o.NN N.sH e.sH o.mo~ H.omNH mea SHH H 0.0m o.m~ H.¢H S.HN m.aHm a.HmAH mem ONH H o.oH o.w s.m o.~oH a.aqm o.oqNa son omH H :HaHz mHmHz sHHoHuHso 0.0m 0.0m m.m¢m~ o.~m s.s~m o.H uamHm m 0.0m o.om H.mNaH H.NN o.mmm o.H sesame o.om o.m~ o.momm H.om o.m~m o.H mea o.oH 0.x a.mmme s.smH m.st o.H onm Aumv Aumv Aua1aav Amunv mmmo< sunspz maHnomz mosaH HHaZm swamzm mzHH mszoaz mazes mmHm mszoaz mszoaz mszoaz mm: mcHaUmz Hmsca< .a.m mHan 130 00.00am 0N.5HOH mN.HmH 00.0 NH.wNm 0N.mH0 m0.000H 00.0000H mAHuH=o Nm.me 0N.mHH 00.0H 00.0 0H.00 00.00 0N.mHH 0.0m mN.m0mN 0.0m uomHm 05.0mm mw.05 0N.0H 00.0 H0.m~ m0.w0 00.00 0.0m 50.00NH 0.0m souumm 0N.ma0 0N.HOH mw.wH 00.0 00.50 Hm.ww 05.5HH 0.0m mm.0mmw 0.mN mea 0N.N50 00.00H Hm.NH 00.0 0N.Nm mm.00 5m.0oH 0.0 mm.0HOH 0.0 son 338 338 338 338 0.38 233 £33 Cb $0 FEE $303; 8000 HOB mmm mZH N09 Mmeqmmm MmHZH ommmma mmHA Hmoo AS1220. 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NNOHONONS O on DA» Hmmm o¢mz nmum MDom mowm mUDUm HHAOM H<36m Awam Dawwm mbwom meum ADWUM aflwom H¢UUD 02mm: H< 00.00 00.00 00.00 00.00 00.00 00.N0 06000 606000 00060 00.0 00.0 00.0 00.6 00.0 00.6 06000 6660 00060 00 660060 00.0 00.0 00.0 00.0 00.0 00.0 0066\6000 oflmu 00069 owmuo>< 00.00 00.00 00.00 00.00 00.00 00.00 06000 606000 00060 ......................... ----- Am00v 6060 00060 00 660660 0000 0000 0000 0000 0000 0000 000063 06600 000 000 000 000 000 000 600062 0606060 HOUHwSm Hmufimfim UOH Gmao .Hmuawfim umuflwfim “01H dmaO ouofiaaoo Hmfluumm 00000800 Howuumm 6008 uonoom ome uonomm 0020000000 00505 GOHUmM cam Emumzm wcflmsom .0000 HmE0o< cooBuom aflsmGOHuoHom .m.o maan 145 mm 00033 630002 03650 06 0002068 mo uswwos v Ammvao.fiov+sn\uznmn ma.o .oocoumMmou woflaaou mo ucofio0mwooo 0:0 00 am vaowm Mom muouUMM 00650000 scansoo mo ammuv 00a: 666% mzou mo “@0550 G0 ooHMHanm £0003 o>m£ 00000068 36m 666 000m 50 co>0w 006 030003 0000 owMHHMH 66 500000 5000 00 mo 200550 00 ao>0m mum 00000 300m 6 0000 06a\00\00-60 0.0 00.0 00 0.6 0.0 00. 660 600600 6600 OONH 6666 00.0 ONH 0.0 0.0 w 666 0000 Haosm 0000 660\00-60 0.0 06.0 00 0.0 0.0 N 666 606000 6660 0000 36:600 000 00.0 000 0.0 0.0 0 6660 606600060 0000 360\600 000 06.0 000 .0.6 0.0 N0 6660 06600 0000 00\600 000 00.0 000 0.6 0.0 00 660 360060 0000 00\600 000 00.0 000 0.6 0.0 00 666 0600 0000 ~60\600 00 00.0 000 0.0 0.0 0 60 3600 0606600 000020 0020 000 00666 060 00 00660 00660 200000000 0000 0020000000 0000000 0660 3600 60060 00660 0200002 00000 02000000 00200 00000 00200 0000 6060 6600662 60600 6666660 .0.0 60060 146 I Table C.5. Percent Useable Work Days for Subsets PERCENT SUBSET BEGINNING ENDING USEA BLE NUMBER DATE DATE DAYS 1 04/17 05/30 0.385 2 06/05 06/24 9.606 3 09/01 09/24 0.630 4 09/25 10/24 0.665* 5 10/25 12/14 0.159 . i Percentage holds only for harvesting opera- tion. ‘ 0000 000060 6006 06360 AuMV 0000E00w 0000 00300 *0 000 now 05:0o> 0m00oum C000 000oz * 00* Ammmov 0000 0000000 0000 00003 6600662 66 A00 00000 00000030 00 0000 00000 0 «00 000000600006060000.0+ 000060000.0-000006000000.0000000+ 060600000.0-02000006600000.0000z<0o+00600600.00 u 00 06000 06660 0w0000m 000030000000000.0-000606006000003 n 00 6060 00060 000 0000 00 000000066600000600.-000.0600 u 00 0606600 0.00 n 00003 00060 n 00 0.00 n 00003 00000 n 00 0.00 n 00003 00000 u 00 0.0 n 00003 00600 n 00 6600660 6060 m 00000300000003000.0+00.0000 u 00 0666600 00600 1 00.0000 u 00 0666600 60600 00.00000030000.0000003000.0-60.0600 u 00 606600060 00.000000300000000003006.-00.0000 u 00 0606600 3000mm 00000300000003000.+60.000 n 00 00660060060 0.00 00003 000003000000030000.0+66.000 u 00 650060 0.00 00003 0000030000000300.0-00.000 u 00 0000 6.0 00003 000.0\000030A000.0\00003000.0-00.6000 u 00 3600 6.0 00003 000.0\0000300000.0\6600003000.0-0.6600 "600 60660606: 000000 0000 0600020 0200062 0w000m 0:0 0000 000 0000m 00300 0:0 000C0£omz @0000 00 0000 00000 m00c0e00u0o 000 000000030 .0.0 0Hn08 148 Table C.7. Input Material Requirements and Costs Input Flow Material Requirements Cost ($) 21 Nitrogen 1.113 lb/bu .045/1b 31 Phosphorus .300 lb/bu .206/1b 41 Potash .727 1b/bu .060/1b 51 Seed Corn .0022 lb/bu 14.000/bu 61 Chemicals .009 (1)/bu 6.550/(1) 71 Water -------------- 22 Nitrogen 10.00 lb/ton .045/1b 32 Phosphorus 2.125 lb/ton .206/1b 42 Potash 10.00 lb/ton .060/1b 52 Seed Corn .0152 bu/ton 14.000/bu 62 Chemicals .0625 (1)/ton 6.550/(1) 72 Water -------------- 82 Supplement 170.2128 lb/(2) ------ 64 Water -------------- 74 Feeders (3) (4) (1) Unit of chemicals is 2 qts. LASSO, 1.5 lb. ATRAZINE and 2.5 lbs. of SEVIN. (2) Ration consumed by one animal in a cycle through the feedlot (3) Depends on animal type and amount of weight to be gained (see Table C.3) (4) Calves cost $49.00 per cwt. Yearlings cost $43.00 per cwt. 149 “199 :uSIaM o.m~¢~ ¢o~oo~ «.N¢o ¢om~o o.hph m.o~o., m.~m¢ “cOC¢ ¢.m~m N.-m o.o~¢~ m.m-~ N.mfio~ ocmwo m.h>h o.o~o h.mw¢ N»? AOqE'} .—.4 m r". 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REFERENCES American Society of Agricultural Engineers, 1971. Agricultural Engineers Yearbook° St. Joseph, Michigan. Asimow, Morris, 1962° Introduction to Design. Prentice-Hall, Inc., Englewood Cliffs, New Jersey. Bainer, Roy, R. A. Kepner and E. L. Barger, 1965. Principles of Farm Machinery. John Wiley and Sons, Inc., New York. Black, J. R. and H. D. Ritchie, 1973. Average Daily Grain and Daily Dry Matter Intake of Various Kinds of Cattle Fed Three Different Rations Under Several Environmental Situations. Staff paper 1973-1, Agricultural Economics Department, Michigan State University, East Lansing, Mich- igan. Black, J. R., 1973. Assistant Professor of Agricultural Economics, Michigan State University, East Lansing, Michigan. Personal communication. Bowers, Wendell, 1970. Modern Concepts of Farm Machinery Manage- ment. Stipes Publishing Co., Champaign, Illinois. Boyd, J. S., 1970. Alternatives for Handling Manure. 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Rutledge, P. L. and F. V. Mc Hardy, 1968. The Influence of the Weather on Field Tractability in Alberta. Canadian Journal of Agricultural Engineering, 10:70-73, Ottawa, Ontario, Canada Shook, G. E., §£_al.,,1972. Determine Silo Size. Teleplan Program 38, University of Wisconsin, Madison, Wisconsin. Steele, R. G. D. and J. H. Torrie, 1962. Principles and Prosed- ures of Statistics. Mc Graw-Hill Book Co., New York, NEW York. Sandquist, W. B. and H. D. Guither, 1973. The Current Situation and the Issues. North Central Regional Extension Publica- tion No. 32-1, University of Illinois, Urbana, Illinois. Trimble, R. L., L. J. Connor and J. R. Brake, 1971. Michigan Farm Management Handbook. Agricultural Economics Report No. 191, Michigan State University, East Lansing, Michigan. 38. 39. 40. 41. 42. 155 Tulu, M. Y., 1973. Simulation of Timliness and Tractability Conditions for Corn Production Systems. Unpublished Ph° D. Thesis, Department of Agricultural Engineering, Michigan State University, East Lansing, Michigan. United States Department of Agriculture, 1962. Machanical Silo Unloaders. Farmers Bulletin No. 2188, Washington, D. C. University of Nebraska, 1970. Nebraska Tractor Test Data. Agricultural Experiment Station, Lincoln, Nebraska. White, R. G., 1972. Selecting Machinery for Forage Harvesting. AEIS No. 277, Agricultural Engineering Department, Michigan State University, East Lansing, Michigan. Wymore, A. W., 1970. A Notebook of Systems Engineering Method- ology. Department of Systems and Industrial Engineering, University of Arizona, Tucson, Arizona. "ill11111111“