($58. m .If 95;) i‘ 2 h 1.33 r19 hrs. Pu 6.1:. F... 32.; u. a}: ... Lia-Evy‘rfi . ~Ve.: E. \»:.....-\..I 32%! $5.2... .- 1 his. . , 31.5. .... cc 7“ WWW)“; x In. .1 c) r (to c vol . . I . ...! rflfluDflvt.eI(i tilt-c E v.3: 1.719;; 9...!!! :lviv: .. i)al!.:a ' [It-t. nut. in 5.. ..5 ,5 7...}... ..DL...) VI... (...union. 5 3:10;! bani). . c .I ELL v Q. I... I \ ‘1... I: : ...»). ... : , - ... . ...: #14... ..v ‘ ‘3 . .. , .0..taf.!.ulo.VL‘an)|‘.o.. .v . «v1.1 wily It. .l {‘1‘} It-lfifits TATE IVERSITY LIBRARIES Illllllll ll! l l m I: m 3 1293 00891 0329 This is to certify that the dissertation entitled TILLAGE MACHINERY MANAGEMENT MODELS FOR RICE FARMING PROJECTS IN KENYA presented by Kunihiro Tokida has been accepted towards fulfillment of the requirements for PhD Agricultural Technology degree in and Systems Management %4 H Mdt4? Major professor Dateflt’t‘c an 53’)“ Jflf/Q 71 M5 U is an Affirmative Action/Eq ual Opportunity Institution 0-12771 LIBRARY M'Chlgan State University PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. " DATE DUE DATE DUE DATE DUE ll _ — MSU I: An Affirmative Action/Equal Opportunity Institution chS-on TILLAGB MACHINER! MANAGEMENT MODELS FOR RICE FARMING PROJECTS IN KENYA BY Kunihiro Tokida A DISSERIAIION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPK! in Agricultural Technology and Systems Management Department of Agricultural Engineering 1992 ABSTRACT TILLAGE MACHINERY MANAGEMENT MODELS FOR RICE FARMING PROJECTS IN KENYA BY Kunihiro Tokida The machinery selection program, "TILLAGE PLANNER" developed during this study is able to evaluate tillage machinery systems for rice farming projects in Kenya for different soil conditions, farm sizes, operation speeds, tillage depth, weather situation, and operator experiences. The machinery selection model is able to recommend tractor sizes and numbers that are able to complete the land preparation operations under different conditions within specified time constraints. The reliability analysis model is able to estimate system reliability when the machinery system has machinery "backup" units. The machinery cost analysis model is able to estimate machinery costs considering timeliness costs other than initial costs and running costs. The machinery utilization model, ”TILLAGE MANAGER" was also developed to maximize the utilization of machinery system capacity. This program is able to estimate tillage completion days under different operation conditions and to estimate costs and returns from the tillage operation. The tillage operations of Mwea Irrigation Settlement Scheme (MIS) were simulated and. compared to the actual operations of 1989 and 1991. The "TILLAGE PLANNER" and "TILLAGE.MANAGERF models performed simulations satisfactorily based on comparison with actual operations. The implementation of the models showed that an increased number of small tractors with narrow rotary tillers was more cost effective than large tractors with wide rotary tillers. A minimum machinery cost was encountered by changing the number of machine sets. Front wheel assisted (4WD) tractors are :more expensive to own. compared. with standard (2WD) tractors for the same tillage. System reliability can be increased by increasing the number of spare machine sets. Higher machine availability had higher cost effectiveness, but the overall system.reliability was reduced by lowering system redundancy. Higher performance operators are essential to improve tillage productivity. Approved by: ggaébupd A/62L4jéééL497\ Major Professor gym/W Department Chairperson ACKNOWLEDGEMENTS The author wishes to express his sincere gratitude to the following: Dr. Thomas H. Burkhardt, the author's major professor, for his professional guidance and endless support throughout my study. Dr. John B. Gerrish, Dr. Charles Cress and Dr. Eric W. Crawford who served on the author's guidance committee, for their advice and continued supports. Japan International Cooperation Agency (JICA) for providing financial support. Jomo Kenyatta University College of Agriculture and Technology (JKUCAT), National Irrigation Board (NIB) and the Government of Kenya for their courtesy to the study. iv TABLE OF CONTENTS Page LISTOFTABLES.............................................ix LISTOFFIGURES...........................................xii GLOSSARY...xJ.v 1. INTRODUCTION............................................1 1.1 Food Security and Rice Farming in Kenya ..........1 1.2 Mwea Irrigation Settlement Scheme (M18) ..........5 1.3 Systems Approach to Project Management ...........8 1.4 ProblemIdentification...........................9 1.5 Objectives ......................................10 2. REVIEWOFLITERATURE...................................12 2.1 MachinerySelection.............................12 2 . 1 . 1 Possible Work Days and Weather Simulation . . . . . 13 2.1.2 Power Requirement for Tillage ................14 2.1.3 MachineProductivity..........................15 2.1.4 SimulationLanguages .15 2.2 SystemReliability..............................17 2.3 Cost/BenefitEstimation.........................18 2.3.1 MachineryCost................................18 2.3.2 LaborCost....................................19 2.3.3 Timeliness Cost...............................20 3. METHODOLOGY 0......0.000000000000000.......OOOOOOOOOOOOOZI 3.1GeneralProcedure................................21 3.2Problemformulation...............................24 3.3 Data Collection and Systems Analysis .............27 3.4 Mathematical Model Construction . . . . .. .. .. . . . . .. . .28 3.4.1 Computer program elaboration 29 3.4.2 Verification..................................30 3.4.3 Validation....................................30 4. SYSTEMMODEL............................................32 4.1 Machinery SelectionModel .......................33 4.1.1 Weather Simulation and Possible Work Hours . . . .35 4.1.2 Tillage and Power Requirement 39 4.1.3 MachineProductivity..........................51 4.2 SystemReliability..............................61 4.2. 1 Reliability Calculation of Redundant System . . . .61 4.2.2 EstimationofReliability......................73 4.3 Cost/BenefitEstimation.........................76 4.3.1 MachineryCost ................................‘76 4.3.1.1 List Price ............................78 4.3.1.2 Capital Cost ..........................80 4.3.1.3 Repair and Maintenance Cost ...........85 4.3.1.4 Fuel and Oil Cost .....................90 4.3.1.5 Labor Cost ............................92 4.3.2 Cost for Agricultural Inputs 92 4.3.3 Timeliness Cost...............................93 4.4 Summaryof TILLAGE PLANNERModel ................96 4.5 TILLAGEMANAGERMOdel ......OOOOOOO0.0.0.0000000108 vi 4.6 Verification .......................... ......... 115 5. IMPLEMENTATIONOFTHEMODEL......OOOCOOOOO0.0.00000000117 5.1 5.2 Observation from the MIS Project in 1989 and 1991 0.00.000.000.000...00.0.00...00.0.0.0..117 Comparison of Simulated Results ................123 6 . SENSITIVITY ANALYSIS AND EXPERIMENTATION . . . . . . . . . . . . . . 128 6.1 Effect of Randomization for Uncertainty .....................................129 6.2 Effect of Rotary Tiller Width ..................130 6.3 Effect of Number of Machine Sets ...............l35 6.4 Effect of Machine Availability .................137 6.5 Effect of Ground Speed and Bite Length .........138 6.6 Effect of Operator Performance ........ ....... ..140 6.7 Effect of Plot Size and Field Length ...........142 6.8 Effect of Tractor Drive System..................144 7. DISCUSSIONS ................................. ........ ..146 7.1 List Price of Machines .........................146 7.2 Utilization of Tractor Power ...................149 7.3 Soil Condition anleillage .....................150 7.4 Number and Size of Machine Sets ................152 7.5 MachineryManagement 152 7.6 Plot Size ..154 7.7’ Tractor Drive System ...........................155 8. CONCLUSIONS O......OOCOOOOOOOOCOOOOOO0.0.0.000...0.0.0.156 8.1 Tillage.Machinery'Management.Models ............156 8.1.1 The TILLAGE PLANNER Model ....................156 vii 8.1.2 The TILLAGE MANAGER Model ...... .157 8.2 Tillage Machinery Management at M15 ............157 8.2.1 Evaluation of Machinery Management at M18 ...158 8.2.2 Suggestions for Future Expansion of M18 ......159 9. SUGGESTIONSFORFURTHERSTUDY.........................160 Appendices AppendixA Reference Data ..164 Appendix B Selected Verification Calculations . . . . . . 182 Appendix C Output Data for Analysis ................193 AppendixD ProgramList ............................205 ListOfReferences..........OOOOOOOOOOOOOOOOOO. ..... 235 viii Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table LIST OF TABLES Page 1 . 1 Distribution of Population in Kenya, 1992 . . . . . . . . 1 1.2 Rice Supply and Consumption in Kenya ............3 1.3 NIB Irrigation Schemes in 1984/84 ...............4 3.1 Types of Economic Simulation ...................22 4.1 Specific Power Requirement from Catalog Data ...43 4.2 Estimated Coefficient of Rolling Resistance ....47 4.3 Rotary Tillage Survey in Western Kenya .........57 4.4 Field Time Share of Tillage Operation ..........58 4.5a Tillage Operation Speed ......................59 4.5b 'Tractor Speed.on Road .........................59 4.6 Example Agricultural Machinery System by'Shoup .......................................66 4.7 Combinations of Four-Component Models ..........69 4.8 Comparison of System Reliability Calculations ..................................115 5.1 Specifications of Tractors Used in M15 ........118 5.2 Specifications of Rotary Tillers Used jJIMIS ........................................118 5.3 Frequency Analysis for Machine Availability ............OOIOOOOOOOOOOO00......120 ix Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table 5.4 6.3 6.4 6.5 6.6 6.7 6.8 6.9 A.1 A.2 A.3 A.4a A.4b A.4c A.4d A.5a A.5b A.6 A.7 A.8 A.9 Frequency Analysis of Daily Tillage Productivity .......................... ....... .122 Simulation Results of Tillage in 1989 .........124 Simulation Results of Tillage in 1991 .........126 Effect of Random.Simulation ...................130 Effect of Tiller Width by TILLAGE PLANNER .....131 Effect of Tiller Width by TILLAGE MANAGER .....134 Effect of Number of Machine Sets ..............135 Effect of Machine Availability ................138 Effect of Ground Speed and Bite Length ........140 Effect of Operator Performance ......... ..... ..141 Effect of Plot Size and Field Length ..........143 Effect of Tractor Drive System.................145 Tillage Power Requirement by Fujisawa et al....165 Extracted Rotary Tiller Catalog Data ..........166 Tillage Power Requirement by Beeny and Khoo ...167 Cone Index at M9 (1 hour after flooding) .....168 Cone Index at M9 (8 hours after flooding) ....168 Cone Index at M9 (24 hours after flooding) ...169 Cone Index at 86 (dry) .......................169 2WD Tractor Weight Distribution Data .........170 4WD Tractor Weight Distribution Data .........171 Tillage Depth Data at M9 ......................172 List Price of Tractors in Nairobi .............173 Tractor List Price Comparison .................173 Effect of Tillage Duration on Rice Yield ......174 Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table .A.10 Load Cell Calibration ........................174 A.11 Precipitation Data at Mwea ...................175 A.12 Daily Tillage Productivity in 1989 ...........176 A.13 Daily Tillage Productivity in 1991 ...........178 A.14a Tractor List Prices in Japan (2WD) ..........180 .A.14b» Tractor List Prices in Japan (4WD) ..........181 C.1 Simulation Results of Tillage in 1989 .........194 C.2 Simulation Results of Tillage in 1991 .........195 C.3 Effect of Random.Simulation ...................196 C.4 Effect of Tiller Width by TILLAGE PLANNER .....197 C.5 Effect of Tiller Width by TILLAGE MANAGER .....198 C.6 Effect of Number of Machine Sets ..............199 C.7 Effect of Machine Availability ................200 C.8 Effect of Ground Speed and Bite Length ........201 C.9 Effect of Operator Performance ................202 C.10 Effect of Plot Size and Field Length .........203 C.11 Effect of Tractor Drive System ...............204 xi Figure 3.1 Figure 3.2 Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 Figure 4.5 Figure 4.6 Figure 4.7a Figure 4.7b Figure 4.8a Figure 4.8b Figure 4.8c Figure 4.8d Figure 4.8e LIST OF FIGURES Page Processes of Simulation Model Development. . . . . . . 23 Conceptual Model of MIS Rice Farming System . . . . 25 The TILLAGE PLANNER Conceptual Model. . . . . . . . . . . .34 Soil Cone Penetrometer Resistance in Mwea . . . . . .45 Measurement of Tiller Working Width. . . . . . . . . . . . . 52 Rotary Tillage Operation Patterns . . . . . . . . . . . . . . . 55 Graphical Representation of System Reliability ....................................68 Four-component System Reliability . . . . . . . . . . . . . . 70 Reliability of System Model 1..... . . . ...72 Reliability of System Model 2.... ....... .. ..72 Simplified TILLAGE PLANNER Flow Chart. . . . . . . . . . 99 Simplified TILLAGE PLANNER Flow Chart (continued).................................100 Simplified TILLAGE PLANNER Flow Chart (continued).................................101 Simplified TILLAGE PLANNER Flow Chart (continued).................................102 Simplified TILLAGE PLANNER Flow Chart (continued).........0......OOOOOOOOOO0.0.0.0103 xii Figure 4.8f Figure 4.89 Figure 4.8h Figure 4.8i Simplified TILLAGE PLANNER Flow Chart (continued).................................104 Simplified TILLAGE PLANNER Flow Chart (continued).................................105 Simplified TILLAGE PLANNER Flow Chart (continued)........................... ...... 106 Simplified TILLAGE PLANNER Flow Chart (continued).............0.000.000.0000...00.107 Figure 4 .9 Conceptual Model of TILLAGE MANAGER Program. . . . 109 Figure 4 . 106 Simplified TILLAGE MANAGER Flow Chart. . . . . . . . 111 Figure 4.10b Simplified TILLAGE MANAGER Flow Chart (continued)..l....0.00.00...0.0.00.0...0.0.0112 Figure 4.10c Simplified TILLAGE MANAGER Flow Chart (continued).............OOOOOOOOOOOOOOOOO0.0113 Figure 4.10d Simplified TILLAGE MANAGER Flow Chart (continued)......OOOOOOOOOOO00.0.00000000000114 Figure 5 . 1 Daily Tillage Productivity Frequency. . . . . . . . . . . 122 Figure 6.1 Effect of Number of Machine Sets on Machinery costOOOOOO......OOOOOOOOOOO00......0.0.0.0....136 xiii Amuu Alt EFC FC FCN E3 13 :3 GLOSSARY plot size, ha total field area, ha altitude, m bite length, cm wheel width, cm coefficient of front axle load coefficient of rear axle load coefficient of altitude effect coefficient of rolling resistance coefficient of rolling resistance for front wheel coefficient of rolling resistance for rear wheel wheel diameter, cm unit draft, N/cm2 base of the natural logarithm effective field capacity, ha/h frequency of sinkage trouble, times/ha fuel cost for one operation, sh fuel consumption, L/kW.h field efficiency fuel price, sh/L field time of operation, h xiv H .0“ = total net work time, h i, 8 real interest rate in = the nominal interest rate i1 = the rate of inflation k = minimum required number of operational components LP list price of tractor or implement, sh LPR 8 list price of rotary tiller, sh LPRy 8 list price of rotary tiller, yen LPT - list price of tractor, sh LPTnm = 2WD tractor list price, sh LPTflm = 4WD tractor list price, sh m 8 number of components or units (general use) MA 8 target season machine availability MArad - randomized machine availability for a given day MR a motion resistance, N n = number of components or units (general use) n = number of components in system N = number of machine sets CC = oil cost for one operation, sh 8 2 II oil consumption, L/h OP = oil price, sh/L Put 8 actual power used for tillage and motion resistance, kW 8 maximum power requirement, kW/m Ru, 8 minimum power requirement, kW/m P‘n = power requirement for motion resistance, kW P m“ = rated power output, kW XV Ptil 91 TFC USE USEN specific power requirement for tillage, kW/m/lOcm unreliability of component i reliability of an individual component reliability of component i reliability of component n reliability for time t repair cost of tractor or implement in n th year, sh R‘& M factor for tractor or implement R 5 M factor for tractor or implement random figure between 0 and 1 rotary tiller remaining value, sh tractor remaining value, sh ground speed, km/h tillage duration, day time lost due to other activities, h/ha = time lost by sinkage trouble, h/ha theoretical time for tillage , h/ha time for turning and finishing, h/ha = total accumulated repair and maintenance cost at end of nth year, sh theoretical field capacity, ha/h total machine weight, N annual use, h total use of a tractor or implement at the end of nth year, h xvi USE(N-1) = accumulated use of tractor or implement by (n-l) th w = w a: was ' was 3 w, s x =- y = z = z o = a: n = A«yum. = year, h rotary tiller width, cm effective rotary tiller width, cm 2WD tractor weight, N 4WD tractor weight, N field width, m ratio of actual power used from rated power considering the altitude effect, x = Put/(Prudl Cut) rice yield, kg/ha dummy variable in generating function maximum sinkage, cm failure rate failure rate of component n system failure rate xvii 1 . INTRODUCTION 1.1 Food Security and Rice Farming in Kenya Food shortage has become almost a perennial crisis in Kenya because of the ever increasing population. The United Nations (1985) estimated that the population of Kenya will reach 30 million by the year 2000. The increasing number of people will cause a need for more food. The age distribution of population (Table 1.1) demonstrates that Kenya has an excess of people too young to help produce food. Table 1.1 Distribution of Population in Kenya, 1992 Age Thousands Percent ire-school age (0-5) 6054 23.1 Primary school age (6-13) 6282 23.9 Secondary school age (14—17) 2534 9.7 Productive age (15-64) 12695 48.1 Potential labor force 10202 38.9 Total 26247 m Mo 1 ied fromTDevelopment plan (firm-1993) Partly as a result of the temporary food supply problems faced. by the» country during’ the 1979-1980 drought, the government formulated the National Food Policy (Republic of Kenya, 1981). Kenya has since implemented the policy with the aim of attaining self-sufficiency in food production through 2 provision of incentives to farmers and avoidance of consumer subsidies. In 1984, the country experienced severe drought, and as a result food imports rose and the balance of payments position worsened considerably. Kliest (1984) reported the instability of food production and supply. The fifth development.plan for 1984-1988 (Republicrof Kenya, 1984) which has given the highest priority to the agricultural sector with emphasis on attaining self-sufficiency in food supply; In the sixth development plan, Kenya gave the first priority to creation of productive employment primarily coming from agriculture (Republic of Kenya, 1989). Irrigation development ‘will make a major contribution towards the attainment of the objectives in the policy. The major crop in the irrigated areas is rice. Rice is a "scheduled” crop and.is marketed under the sole aegis of the National Cereals and Produce Board (NCPB) . The domestic output of milled rice sold to NCPB averaged 21328.5 tons annually for 1981-86 as shown in Table 1.2. Kenya's rice supply largely depends on imports, and this causes trade imbalances and uses Kenya's limited hard currency. The amount of imported rice fluctuates greatly according to the availability of hard currency for rice import and/or the availability of foreign food (rice) aid. The National Food Policy (Republic of Kenya, 1981) predicted the domestic rice requirement in 1989 to be 90,000 tons; to meet it, the annual production growth rate for self-sufficiency during 1980-1989 3 would have been 16.4 percent. The Kenya government (1989) estimated in its sixth development plan that the annual rice consumption per capita would be 3.0 kg, the projected demand in 1993 would be 84,000 tons, and that the domestic production would be 41,000 tons in 1993. To meet the national demand, Kenya should continue to import more rice than the amount produced domestically. Table 1.2 Rice Supply and Consumption in Kenya (tons) Year Marketed Imported Total Consumption/capita 1931 22,200 4,573 26,773 1.7 (kg) 1982 19,427 11,880 31,307 1.8 1983 21,818 44,768 66,586 3.6 1984 20,745 507 21,252 1.0 1985 23,932 562 24,494 1.2 1986 19,849 61,745 81,594 3.6 The important strategies for increasing rice production are: 1) the rehabilitation and improvement of existing irrigation systems, 2) the expansion of irrigated areas, and 3) the improvement of rice productivity per unit area by efficient mechanical power. The Ministry of Water Development (1979) estimated that the total potential irrigation area was 540,000 ha. However, implementation of irrigation projects is not easy. The major constraints are: 1) requirement of highly specialized agronomic and water management technology, 2) large funds for initial investment, and 3) high maintenance cost. 4 The National Irrigation Board (NIB) was established under the Irrigation Act (CAP347) in 1966. NIH has been responsible for the development, control and improvement of all the national irrigation projects in Kenya. From its establishment until 1987, NIH performed its functions under the jurisdiction of the Ministry of Agriculture and Livestock Development. In 1987, NIB was moved from the Ministry of Agriculture and Livestock Development to the Ministry of Energy and Regional Development. The National Irrigation Board reported that in 1984/85 the total irrigated crop area was 10,732 ha (Rice 7,564 ha, others 3,167 ha) and the number of plotholders (households) was 7,072 as shown in Table 1.3. The total irrigated area under the NIB projects is only two percent of potential area which was estimated in the National Master Water Plan. Table 1.3 NIB Irrigation Schemes in 1984/85 Projects Area Holders Paddy yield Payment to holders (ha) (ton) (K5) Mwea 5,825 3,234 27,553 2,065,890 Ahero 1,073 519 3,777 250,693 west Kano 615 164 2,059 160,401 Bunyala 215 131 1,094 71,526 Perkerra 143 377 - 127,338 Hola 850 632 2,374 220,378 Bura 2,010 1,626 - 509,645 Total 10,732 7,072 36,857 3,405,871 5 The expenditures of the NIH. exceeded income by K2 1,187,056 in fiscal year 1984/85, according to the NIB annual report and accounts (1985). A. government grant of K5 1,648,287 in 1984/85 wiped out the deficit and resulted in an operational surplus of RP 461,230. This was a substantial improvement.over the operational deficit.of K£ 932,632 for the previous fiscal year. The government.may require international cooperation for the initial development. of the irrigation projects, but eventually operational costs should be fully covered by the revenue from the projects. 1.2 .Mwea Irrigation Settlement Scheme (MIS) The Mwea Irrigation Settlement (MIS) is situated on the foot-hills of Mount Kenya, at an altitude of 1,159 m above sea level and latitude 0 degrees 40 minutes South (Mohdhar, 1991) . Today, MIS covers an area of 12140 ha, and is divided into five sections for paddy production: Tebere 1266.7 ha; Mwea 1218.9 ha; Thiba 1146.5 ha; Wamumu 1119.4 ha and Karaba 1068.4 ha, The total area under paddy production is 5819.9 ha leaving 6320.8 acres to be utilized for villages, schools, dispensaries, business plots, church plots, roads, swamps and red soil patches for rainfed subsistence farming. There are 3,240 farmers with their families settled in the Scheme, each growing rice on a basic holding of 1.6 ha (4 acres). .All the farmers live in 36 villages on the settlement 6 and each village is located as centrally as possible in relation to the farmers' holdings. The majority of the farmers have been recruited from Kirinyaga District in the Central Province with varying percentages from Nyandarua, Nyeri, Muranga, Kiambu and Embu Districts. To qualify for admission to the Settlement, One must be landlessness and unemployed. Agricultural competence has never been used as a criterion for selection. None of the farmers had any previous experience in rice cultivation. The MIS provides technical information and guidelines to farmers to improve rice production. Seeds, fertilizers and insecticides are provided to farmers, and the amount is determined by MIS. MIS charges 4469 sh/ha for land preparation fee and water supply fee including repair and maintenance of equipment and facilities. The annual rainfall over the past 20 years has varied from a maximum of 1,626 mm to a minimum of 356 mm (averaging 950 mm/year). The two rainy seasons take place in April/May (long rain season) and October/November (short rain season). The average maximum temperatures range from 16°C in the cooler months (June, July, August) to 26.5°C in the warmer months (December through March). Relative humidity varies from 52 - 67%. Radiation is especially low in July and August (about 390 cal/cmzlday) and Sunshine duration ranges from 3.7 hrs/day in July/August to 10.1 hours/day in January/February period. All rice is manually transplanted. The first variety 7 used was high yielding Sindano, a medium to long grain variety; Basmati (an aromatic) variety also know as 'Pishori' was introduced for commercial production in 1972. Basmati's yields are consistently lower than Sindano's, although the higher market price more than compensates for Basmati's lower yield. Other varieties which are grown commercially include BW 196, IR 2793 and IR 2035 to a lesser extent. The Irrigation water is tapped from the Nyamindi and Thiba rivers and fed to the Scheme by gravity. The run-off from both catchments is relatively high with a minimum flow in each river seldom falling below 2.83 cubic meters per second. The soils consist of (a) free draining'reddish.brown lateritic clay loam (Red Soils) and (b) impermeable montmorillonitic clays, with about 80% clay (Black Cotton Soils). Rice is solely cultivated on the black cotton soils. Land preparation is one of the most labor-intense operations in paddy farming. In 1960, 6 tractors were introduced to the Mwea irrigation project, and, thereafter, mechanical rotary tillage has been carried out in all large irrigation projects. However, several critical mechanization problems have occurred in the irrigation projects. Land preparation is carried out by flooding the fields to a depth of about 10 cm.and then tillage is performed by using tractors about 45 kW in size equipped with rotary tillers. If the soil is flooded for more than 4 days, the soil's capacity to support tillage machines is lowered so that tractors sink 8 in the mud. Generally, the rotary tillage begins in February, and.it continues throuthAugustm This land preparation period is too long and delays transplanting and harvesting which results in loss of yield. Tractors operated by government tractor drivers whose average salary is 2960 shillings per month including house allowance of 460 shillings per month. Their experience is about 10 years on the average. They tend to change their jobs to get better incomes. This may result in a shortage of skilled tractor operators; definitely it leads to lower field efficiency and shorter machine life. A major cause of low tractor productivity is repair and maintenance of machines. Some mechanical troubles take time to repair and cause delay of operation, especially when spare parts are unavailable. 1.3 Systems Approach to Project Management Simulation in agriculture has grown in parallel with the popularity of systems analysis and with the demand for a more exact understanding of the real agricultural system. The importance of information in agriculture has increased as more realistic modeling is demanded. Farming is dynamic in nature, influenced by random effects, and based upon biological and economic principles. This means that the systems approach based on simulation is suitable for the study of agricultural problems. 9 The essence of systems simulation implies the reconstruction of a certain part of reality, and the execution of an experiment on the basis of a.model in order to obtain a fuller understanding of a phenomenon or a problem. Kinsey (1976) focused on the relationships between economic and technical research for agricultural mechanization. Gibb et al. (1986) presented a checklist of non-engineering factors to assist in planning agricultural mechanization projects. Willcocks (1986) discussed the need for the development and application of a diagnostic approach to direct the management of agricultural engineering strategies. Pingali et a1. (1987) noted that appropriate agricultural mechanization should be developed based on a farming systems model . 1.4 Problem Identification Kenya has food shortage problems mainly due to its ever increasing population and low agricultural productive farming systems. Rice has become a staple food in Kenya with demand increasing every year. Irrigated agriculture is considered as one of the solutions to improve food security in Kenya. Strategies are either to expand the irrigated area or to increase production intensity at existing sites irrigated including large scale projects. However, the nation has invested. timidly in irrigation due to the high capital required for construction and maintenance. Thus, expansion of 10 irrigated area is not an easy task in Kenya. One of the practical strategies to obtain high productivity in large irrigation projects is utilization of mechanical power. However, machinery productivity in these projects is low because machinery has not been well-managed due to some technical problems. 'Therefore, a systems approach to machinery management and a simulation model should be developed to improve machinery management. 1.5 Objectives The purpose of this research is to analyze machinery management at large scale irrigation projects in Kenya using system analysis techniques. To design a field machinery system which satisfies specified physical performance characteristics and probablistic calendar date constraints, a large number of calculations is required. Therefore, a mathematical model using a computer program was considered for designing tillage machinery systems. The following objectives of the study were formulated: 1. To develop a computer model to select tillage machinery systems for irrigated large scale rice farming that will enable comparison of different work patterns with respect to costs and requirements for machinery, labor, and fuel. 2. To develop a computer model to estimate job completion time and cost with a particular machinery system. 3. To consider system. reliability as a parameter for 11 redundant machinery systems in the models. 4. To evaluate tillage machinery management at MIS and to give suggestions for future expansion of MIS. 2. REVIEW OF LITERATURE 2.1 Machinery Selection Edwards and Boehlje (1980) discussed the simulation of total net machinery costs and they found that improved field performance increases crop yields by allowing crops to be planted and harvested on more nearly optimal dates. Rotz, Muhtar and Black (1983) developed a time constraint algorithm to include a cost analysis to determine a machinery complement for maximum profit of a given farm and tillage system for various crop rotations on different soils. Danok et a1. (1980) used a mixed integer programming technique for maximizing the profit considering crop related constraints. Whitson (1981) used a linear programming model to maximize returns to the limited farm resources including machinery characteristics. Al-Soboh et a1. (1986) developed a mixed integer-linear programming (MILP) model to select the optimum harvesting method and machinery system. Singh and Holtman (1979) developed a heuristic algorithm to evaluate and compare selected crop production systems over a range of farm sizes with respect to costs and requirements for machinery, labor and fuel for field operations. Krutz et a1. (1980) used a linear programming model to evaluate alternative farming 12 13 situations. in order to provide general guidelines for machinery selection and indicated its usefulness for evaluating machinery-sizing decisions for a particular farm situation. Doster et a1. (1990) used linear programming to test ways to improve crop mix, machine size, tillage system and farm size. Kotzabassis et a1. (1990) developed an integrated software package (expert system) to assist in decisions for cost effective farm.machinery selection and management. 2.1.1 Possible Work Days and Weather Simulation ASAE D497 (1991) provides probabilities for a working day for both 50 and 90% confidence levels. It also gives some coefficients to adjust for holidays and Sundays if a farmer chooses to not work on those days. Variation in weather and its resultant impact on the number of available days for' field operations is a major uncertainty in making selections. Some researchers reported suitable days for field operations (Persons, 1982; Donaldson, 1968). Rutledge and McHardy (1968) developed a "go", "no-go" criterion based on. moisture distribution in three soil moisture-zones. Fridley and Holtman (1972) developed a model which determined soil temperature based on a heat unit system. Tulu et a1. (1974) extended the model and simulated tractability conditions based on temperature and rainfall data for estimating possible working days. Rosenberg (1982) 14 further developed the work to estimate cumulative probability distributions for suitable field workdays by generating sequences of "go" "no-go” days using weather records of daily temperature, precipitation and pan evaporation. Hargreaves (1991) used temperature and runoff with a model to predict potential crop yield for irrigated agriculture. 2.1.2 Power Requirement for Tillage ASAE D497 (1991) gave an equation to estimate draft and power requirement for rotary tillage considering negative unit drawbar force for a forward turning rotor. Hendrick (1988) noted that power requirements for rotary tillers ranged from ‘12 kW/m to 35 kW/m based on manufacturers' recommendations. Power requirements for garden tillers ranged from 3 kW/m to 14 kW/m according to provided figure. Fujisawa et al. (1953) performed a power requirement experiment using a garden tractor with a rotary tiller in a clay loam soil rice field. As bite length and forward speed increased the power requirement was increased. The same trend was given in a Japanese book (Kawamura, 1992). The book also provided a table of specific torque for different soil types. Gee-Clough (1979) provided a prediction equation of the coefficient of rolling resistance for a rigid wide wheel in sand. Gee-Clough (1985) noted Gupta's equation obtained from experiments in soft clay soil. Manby et a1. (1960) tested diesel engine performance in 15 Kenya and reported that reduction of power due to reduced air density at high altitude was less than 4% per 303 m which was given by British Standard Specification 649. The specific fuel consumption was observed to be higher at high altitude than at sea level. 2 . l . 3 Machine Productivity Upton (1964) investigated factors affecting turning time for various turning methods. Wakui (1964) reported that for a time effective rotary tillage operation, the field width- length ratio should be more than 1:4 for field sizes less than 0.5 ha. Hunt (1983) provided pattern efficiencies for various plowing patterns in square fields, and analyzed field capacity changes for irregular shape fields. Hunt (1983) referred to K. Von Bargen's research on operator performance to show how operators contribute to operator-machine performance. 2.1.4 Simulation Languages A number of computer programs have been developed to select an optimum machinery set for a particular farm. Many different computer languages were used on different computers. Hunt (1966) used FORTRAN in his farm equipment selection program. Wolak (1982) used BASIC to develop programs to incorporate ASAE machinery management data for non-technical 16 users. Freesmeyer (1986) developed a farm machinery selection program using linear optimization techniques written in BASIC. Chen developed a general crop budget model to allow an individual farmer to budget his farm operation and evaluate different farming alternatives by using BASIC. Ismail (1991) used Turbo-PASCAL to develop a simulation model, the Crop Production Machinery System (CPMS) to evaluate machinery system for crop production. Cawich and Slocombe (1991) developed a machinery management model using dBase III‘. Sowell et a1. (1988) developed a farm machinery cost analysis program using dBase III and Clipper. Somerville and Sowell (1992) extended the program.and made it available for MS-Windows. Lal et a1. (1988) developed an intelligent field operations simulator in the form of a knowledge based system written in PROLOG. Artificial Intelligence techniques were employed to evaluate qualitative and fuzzy attributes, and to combine the decision making strategies of the domain experts with technical constraints using PROLOG (Oskoui et al., 1990) Kotzabassis et al. (1990) developed an integrated software package, the Farm Analysis, Research and Management (FARM) program.using QuickBASIC for the database manager and the in— soil tractor' performance prediction, and. PROLOG for the 1 Trade names are used in this dissertation solely to provide specific information. Mention of a product name does not constitute an endorsement of the product by the author to the exclusion of other products not mentioned. 17 machinery selection and cost analysis. 2.2 System.Reliability ASAE (1991) defined reliability as the probability that a part, assembly or system will perform satisfactorily for a specified period of time under specified operating conditions. Two reliability figures of merit were listed in the same ASAE standard, failure rate and.mean time between failures (MTBF). The failure rate is the number of failures of an item.per unit time. MTBF is the total operating time of a population of a product divided by the total number of failures. The standard described the reliability of a product which has a Weibull distribution using the following function. Reliability of redundant systems such as parallel-series, series-parallel, or their combinated systems was analyzed by electrical engineers (i.g. Gonen, 1986; Myers, 1964). Shoup (1982) presented techniques for estimating the reliability of complex agricultural machinery systems. Redundant units in parallel possessing different reliability values were termed as heterogeneous machinery "backup” units, and their management implications were discussed. Barlow and Heidtmann (1984) presented a simple, exact algorithm for calculating the reliability of a k-out-of—n:G system (the system is good if and only if at least k of its n components are good). Hunt (1971) reported that the average Midwest corn and soybean farmer had less than a 50% probability of getting 18 through the season without a breakdown that has a timeliness cost associated with it. Shoup (1986) described a comparison of man-machine system and machine systems and discussed the importance of human factors in system reliability. 2.3 Cost/Benefit Estimation Machinery systems are commonly selected and evaluated by economic terms. Estimation of labor cost and timliness cost were reviewed as well as machinery cost estimation. 2.3.1 Machinery Cost Machinery cost is one of the major components of the total farm budget. ASAE standards (1991) listed ownership or fixedlcosts as depreciation, interest on investment, sales and property taxes, housing and insurance. Variable costs included repairs and. maintenance, lubrication, fuel and operator's labor. Depreciation and interest on investment are the most important items of fixed costs and two methods are commonly used to calculate average annual depreciation and interest over the life of the machine (ASAE, 1991; Hunt, 1983; Bowers, 1987). These are the capital recovery with return method and the straight line depreciation, interest on average investment method. Rotz (1987) compared various reports on machinery 19 lifetime and repair costs and proposed a standard model. Adelhelm.and Steck (1974) estimated that over the lifetime of Kenyan tractors 130% of the purchase price was paid for repairs and parts in comparison to 100% for German tractors, and claimed tractors in Kenya lasted for 67% of the years and for 83% of the hours of German tractors. Mayfield, Hines and Roberts (1981) developed a method of cost estimation of owning and operating farm machinery based on replacement price rather than purchase price. Barthlomew (1981) approached farm machinery costing considering inflation. 2.3.2 Labor Cost For a grower-operator of a farm, the labor costs are the opportunity cost of operator time used for operating machinery. If hired labor is used, the cost may be on an hourly or annual basis. On an hourly basis, total labor cost is directly proportional to machine operating time. When labor is hired on an annual basis, total labor cost is independent of machine operating time. Burrows and Siemens (1974) computed labor cost assuming that each man was hired at an annual salary, full time, only to operate machinery. Hughes and Holtman (1974) assumed hired labor on an hourly basis. 20 2.3.3 Timeliness Cost Timeliness is a measure of ability to perform a job on time which gives optimum quality and quantity of product. Timeliness cost is an economic penalty resulting from a reduction of crop yield in quality and/or quantity which farmers pay indirectly; when.critical field.operations are not completed within an optimum period. Some untimely operations may have zero timeliness costs and others, particularly the harvest of highly perishable products, may have very high timeliness costs. (ASAE standards, 1991) A. timeliness loss curve varies with operation, crop and location. Hunt (1983), Bowers (1987) and ASAE D497 (1991) estimated timeliness factors for some specific crops and operations assuming a linear reduction in value of crop after the optimum date. It is not readily possible to obtain reliable data for timeliness costs of all operations for all crops and locations. Hughes and Holtman (1974) considered timeliness as a constraint rather than a cost factor. Tulu et a1. (1973) developed a model to analyze in detail timeliness costs associated with equipment capacity of the planting operation reflected in yield. Oskoui et a1. (1991) examined the effects of location (latitude), temperature (growing degree days), available moisture (annual precipitation) and crop type (maturity data) on coefficients of timeliness penalty functions. 3. METHODOLOGY 3.1 General Procedure The agricultural system is a set of persons, material and technical means, and plant and animal organisms segregated within the division of labor for the task of farm production. Agricultural systems may form some subsystems such as biological systems, machinery systems and food production systems as well as higher order systems such as the regional food supply or the national economy. Physical models of agricultural systems are not useful for testing the effects of different agricultural operations on economic output unless they are used for material movement. Agricultural systems represent a particular type of economic system in nature, therefore, they can be simulated by using mathematical models rather than physical models. Mathematical models may be classified by several criteria as shown in Table 3.1. Economic applications have been performed mainly with computers because the processes are dynamic. If an economic system is static and deterministic, it can be calculated without a computer. Thus, decision makers (e.g. project managers) usually do not simulate the economic activities of 21 22 the system, but can calculate an optimum directly. Table 3.1 Types of Economic Simulation Category Types of economic simulation Characteristics Deterministic Stochastic Variable type Continuous Discrete Time factor Dynamic Static Dynamic simulation of most agricultural systems is a recurrent investigation of a discrete characteristic which varies with the discrete time units of the processes. For example, the tillage operation takes place daily while rice yield takes place once a year, tractors may be traded every 5000 hours while irrigation systems last for 30 years. Simulations may be done dynamically by year-to-year or may be treated as static by averaging annually. Models constructed in which random effects are taken directly into account are called ”stochastic.” Development of a simulation model depends on how the model describes a real system. Simulation requires a step- wise process including: (a) formulation of the problem; (b) data collection and analysis of the system; (c) construction of the mathematical model; (d) elaboration of the computer program; (e) verification and validation of the model; 23 (f) experimenting with the model; (9) evaluation of the simulation results. Cm? PROBLEM FORMULATION \ CONCEPT MODEL DEVELOPMENT j DATA COLLECTION SYSTEMS STUDY LOGICAL MODEL DEVELOPMENT MATHEMATICAL MODEL DEVELOPMENT ‘ L COMPUTER PROGRAM ELAIORATION IL VERIFICATION VALIDATION I EXTENDED USE OF SIMULATION I EVALUATION OF RESULTS ( ...) Figure 3.1 Processes of Simulation Model Development. 24 Figure 3.1 describes the basic sequence of a simulation process. The steps of simulation are not rigid and some stages of the process can have other feedback paths. 3.2 Problem formulation Problem formulation involves defining the problem and setting the objectives. The systems concept involves a most careful search for interrelationships and interdependencies among interacting components as well as a search for subsystems and components. As stated in the objectives, this simulation model was designed to facilitate machinery management decisions. The first step in the development of the model was to construct a conceptual model of a crop production system reflecting the objectives of the study. The modeled system was to include: (a) the relation between the machine system and the land system when employing rotary tillage, (b) the impact of materials and other inputs on rice production (the major output), and (c) the money flow of the production as an economic activity. A conceptual model is the pictorial representation of the system using block diagrams indicating major components and linkages, as shown in Figure 3.2. 25 Market 4) money money husked rice Fertillzer money chemical salvage machine fuel °" N C P B ~ new machine parts 17 energy rice bran salvage machine money paddy i subsidies ' M I S ‘ _‘ —. 0 management labor new machine repair-maintenance money ”day spare parts operation river fuel water oil management ' Irrigation repair work 1 tr 3 fertilizer : chemical Farmer E VCLCI' need laborl l paddy . l ' u 3 Land , 3 ‘—" ~— Machinery 5 I tillage I 1 rain stalks 02 C02 l d i t so at energy ra ned wa er NOx C02 worn machine parts: used oil 1 ................................................................................................................. Figure 3.2 Conceptual Model of MIS Rice Farming System. 26 The rice production machinery system consists of five subsystems namelyc MIS administration, farmer, land, machinery, and irrigation” The plant component is not considered as a subsystem but it was handled as an input/output variable. The arrows into the building blocks of the components are the inputs and the out going arrows are outputs. Linkages are the discernable paths of flow between the system components. The timing of material flow should be carefully investigated because it affects productivity although time factors are not shown in the conceptual model. Another important process of the systems approach is determination of system boundaries. The system boundary arises from the need to define the system or subsystem. The research objectives should be reflected on the system boundary. This process determines exactly which subsystems must be explicitly represented within the model structure. Across the boundary there is assumed to be no interaction. The farmer' provides manual labor for seeding, transplanting, weeding, chemical application and harvesting. MIS administration provides technical support in irrigation and machinery. In this study, however, special interest was focused on machinery productivity and management. Therefore, machinery and land subsystems were considered to be reasonable for the mathematical model construction as indicated using dashed lines in Figure 3.2. The next step is to select input and output variables. 27 Changes of uncontrollable input variables of the land component, solar energy and C0,, were assumed to have no effects on machine productivity and they were eliminated as input variables. The output variables of the land component such as stalks, drained water, wasted chemicals and Oz, and the output variables of the machinery component, CO2 and NO," may affect the environment and they may be evaluated as environmental costs from a long term view point. However, these output variables were eliminated in the simulation because there is no relevant information which can be used to include these variables. Rainfall and irrigation water availability may affect machinery productivity by limiting daily tillage area such that those variables were further investigated after data collection. The input variable of the machinery component, 0,, was considered in the models because the engine power is lost due to altitude effect. The main output, of course, is produced paddy. The farmer component was treated as a dummy component because they were not paid for their labor but for the amount they produce. Another difficulty was to quantify management activities as inputs from an economic sense. This issue also required further investigations during data collection. 3.3 Data Collection and Systems Analysis To identify the factors which affect the mechanization of the projects, an interview of the senior workshop 28 superintendent was planned. Additional information on viable strategies for better farming and land use were obtained from administrators and technical personnel of the project sites, as 'well as from. officials in the Ministry' of Regional Development, National Irrigation Board, and Ministry of Agriculture. To determine the machine performance, a field experiment was planned. The parameters to be measured were, plot size, machine working width, working depth, cone index changes after flooding and tractor operation speeds. These field experiments are discussed in Section 4.1.3. Tractor sizes can be better determined when power requirement data exists for rotary tillage tools under specified soil types and.conditions, field.ooverage, speed and tillage depth. However, field experiments for energy requirements of rotary tillage in situ were not feasible because instrumentation wasn't available for such testing. Therefore, empirical data from literature were gathered and compared for better simulation modeling. The first part of the research was to determine the soil characteristics of the paddy field before and after flooding. The test for measuring tractor speed was conducted on the farm in Mwea. The field experimental data were compared to the other data provided by literature in section 4.1.3. 3.4 Mathematical Model Construction The modelling phase of simulation consisted of developing 29 a mathematical model of a system suitable for operation on a micro computer. 3.4.1 Computer program elaboration Selection of the most suitable computer programming language was also a major decision. A programming language ‘which.can.be used with.personal computers and easily edited.by a decision maker was desirable. In Kenya, BASIC computer language is widely used in college education. Therefore, BASIC was selected for the computer simulation language in this study. Two programs were produced in this study. The first program was intended for planning a new project from the aspect of machinery selection. This program. should be able: (1) to select machine size and number, (2) to estimate system reliability, and (3) to estimate cost/profit. The second program for machinery management is intended to evaluate the existing machines on a farming operation, and it should be able: (1) to estimate operation completion day, and (2) to estimate system reliability, and (3) to estimate cost/profit. A pictorial representation of a system is useful. Initially, a series of simple symbolic representations in diagrammatic form was drawn. Finally, a detailed diagram was 30 constructed which is readily translated to a computer program. 3.4.2 Verification Verification was done to provide results which are mathematically and logically correct. Its purpose was to check the logical approach of the model and the correctness of the simulation outcome. To verify the results, a hand calculation was carried out for both the machinery selection and the machinery cost analysis model. For system reliability model verification, another logical model was constructed and tested by using computer because the calculation was too complicated to use a manual calculator. A comparison of system reliability by models, a checker program, and a hand calculator is provided in Section 4.6. Some example calculations were made by using a hand calculator to verify the calculation processes in the models as shown in Appendix B. 3.4.3 Validation The model needs to simulate the real system sufficiently well to fulfill the purposes for which it was developed. The use to which this model will be put and the purposes for which it was designed is the framework within which we had to make the assessment of model validity. The machinery selection model was validated using the real farm data which were collected in 1989 and in 1990. The system reliability and 31 cost analysis model were validated by simulations of actual activities at MIS and sensitivity analysis as discussed in Chapter 5 and 6. 4. SYSTEM MODEL Several constraints and assumptions were made to better define the system boundary. A number of system constraints and model characteristics were established at the outset: (a) The model should be developed for an individual farming operation that exists solely for rice production with parastatal ownership and management of the equipment. When considering tractor operators, there should be no competition for men, machines or field time from other farm enterprises. The farmers provide manual labor for rice farming, and the organization supplies materials (seed, fertilizer, chemicals), irrigation water and mechanical tillage. The farmers receive payment for their produced rice from MIS after subtracting fees for supplies. (b) The model should be able to handle tillage operations (0) (d) using rotary tillers under different operating conditions. The model should be able to take into consideration the probablistic nature of weather and its effect on field machinery requirements. The model should be able to select "realistic" field 32 (e) (f) (9) 33 machinery systems and produce a schedule of operations which. is consistent. with normal practices for’ rice production systems with an average of 100 ha or more and the average plot size is 0.405 ha. The technology considered should consist of four wheel tractors with and without front wheel assist and equipped with rotary tillers. The order of tillage operations should only be determined by water supply priority with plots tilled in the same order that they are flooded. The model should provide system reliability and annual costs associated with the machinery set for a rice production system. Machinery Selection Model The "TILLAGE PLANNER" model was intended to be used for planning a new project from the aspect of machinery selection. The model construction is shown in Figure 4.1. There were four major subprograms in the model. First, the machinery selection subprogram determines tractor size. The required power for the rotary tiller was calculated by entering soil conditions, tillage depth, tillage ‘width, bite length, forward speed and altitude. 34 tiller width tractor t e ' yp - Tractor Size tillage depth 7 . Selection ground speed we length Sub program soil condition altitude effect . tractor size i weather condition day for starting tillage tillage duration — work time per day machine availability Number of Machine Subprogram work pattern plot size field length operator experience frequency of sinkage less time total area annual work time number of machine number of .PII'C MIChLIIC _ reliability level System Reliability Subprogram Figure 4.1 system reliability machine lifetime fuel price oil price registration fee insurance cost operator salary paddy price seed cost chemical cost fertilizer cost handling cost fl Cost Analysis Sub program tractor list price tiller list price average repair cost average depreciation fuel cost oil cost annual farm machinery cost annual farm input cost paddy yield gross return net return TILLAGE PLANNER Conceptual Model. 35 Then, the number of machine subprogram calculates required number of machines according to time information of land preparation. The day for starting tillage, duration of the tillage, and work patterns were selected as input variables to obtain available work hours. The field efficiency was determined by’ entering plot size, field length and operator skill levelt Finally, the number of required tillage machines was calculated based on the available work time, effective field capacity and total tillage area. After obtaining required number of machine sets, the system reliability subprogram calculates system reliability of the machine system by using calculation method for the reliability of a k-out-of—n:G system. The cost of analysis subprogram provides information of the cost of minimum point for trading tractors and rotary tillers with processed data from other subprograms, first. Users can enter machine lifetime referring the cost of minimum point, and are required to give cost information. Then, the subprogram provides list prices, depreciation, repair and maintenance cost, fuel cost, oil cost, agricultural input cost, paddy yield, gross return and net return. 4.1.1 Weather Simulation and Possible Work flours JPrecipitation. data. were available but there ‘was no significant relationship between rainfall and work limitation. The reasons were: (a) rotary tillage was done after flooding 36 the field by controlled irrigation, and (b) rotary tillage does not require high traction because of its negative draft effect. The senior workshop superintendent, Kimaru (1992) reported that the rotary tillage operation had no interruption or no slow down by rain, but water unavailability in the rivers during draught periods limited the total work area per day. Therefore, the maximum daily work area was considered as a constraint during dry periods. The average tillage productivity per day from the available machine sets was calculated to be 33.93 ha in 1989. If the amount of river water is down to 5.66nfi/s (2.83nP/s from each river) during dry period and 10% of it is taken from the head works for irrigation with efficiency of 50% (assuming evaporation and leakage), then it will cover 24.5 ha per day with 10 cm depth flooding; In the TILLAGE PLANNER.program, a selection of four weather conditions were given to bracket water unavailability constraints: drought condition, dry condition, normal condition and wet condition. The drought condition limits field‘work.to 50% of the available time in February and.March. The dry condition limits field work to 50% of the available time in February and 75% of the available time in March. The normal condition limits field work to 75% of the available time in February, and there are no constraints for the wet condition. The work was done everyday except Sundays and holidays. ASAE D497 (1991) provided correction factors for probability 37 for a working day to adjust for Sundays and holidays by multiplying possible‘work.days by 0.86, 0.82, 0.78 or 0.75 for months with 0, l, 2 or 3 holidays. If operators have 5 off- duty days (assuming 1 holiday and 4 Sundays in each month) out of 30 days, then 83.3% of the days are available for operation. In the simulation, there were four options. The coefficients were, 1, 0.833, 0.767 and 0.7 to represent working everyday, working everyday except Sundays and holidays (1 holiday per month), working weekdays and half a day on saturdays (1 holiday per month), and working weekdays (1 holiday per month), respectively. The average work time per day for rotary tillage was 10 hours (Kimaru, 1992). However, not all tractors were used for tillage. Some tractors were idle, used for other work, or under repair. The average percentage of tractors used for the tillage operation.on the field in 1989 was 81.0% assuming that they had the maximum number of available machine sets was 30 each work day from the MIS record. The percentage was obtained by dividing the total of 4214 available tractor-days by 5160 possible tractor-days (172 work days multiplied by the maximum number of 30 machines per day). This did not mean that 81.0% of the machines worked a full day when they showed up in the field. The senior superintendent, Kimaru (1992) described average breakdown time as about 20% for tractors and rotary tillers 3 years old and above. Twenty- four ISEKI tractors were introduced in May 1990 and eight of 38 them.were in working condition by August 28, 1992 when rotary tillage was completed. Another factor considered to affect available work hours was transportation time. If the average distance from the station to the field is 4 km and the speed is 20 km/h then the total transportation time will be 24 minutes which is about 5% of work hours. Therefore, the actual time used for field work was estimated as 64.1% (0.833*0.810*0.95*100%) of total work hours. In the simulation, machine availability was selected as an input variable. Machine availability was defined as the ratio of the number of machine sets used each day for the tillage operation.out the total number of machine sets in this study. The uncertain part of machine availability time was simulated by using generated random numbers in Equation (4 .1) . To generate different random numbers for each simulation, the BASIC command RND with negative figures produced by the TIMER command was used. The random number produced is between 0 and 14 Therefore, the randomized machine availability of each day was given as follows: mmfz (l-MA) tRND+ (ZMA—l) (4 . 1) Where: MArad = randomized machine availability for a given day (decimal) MA = target season average machine availability (0.5 5 MA s 1) END = random figure between 0 and l 39 The range of fluctuation is given at RND = 0 and RND = 1 which is expressed as 2MA - 1 and 1. Thus, this equation provides the maximum fluctuation of the randomized machine availability for a given day at target season average machine availability of 0.5. 4.1.2 Tillage and Power Requirement Hendrick (1988) listed factors other than soil type which also affect the required power input: (a) Rotor speed (increasing speed increases power) (b) Rotor diameter (larger diameter requires more power) (c) Type of blade (in descending order of power requirement, ”L,” "C,” ”Pick," and "Spike") (d) Previous soil tillage or soil compaction (e) Soil moisture content (f) Number of blades per flange (increasing number of blades increases power) (9) Ground speed (increasing speed increases power) (h) Type of residue (i) Depth of operation Bite length is determined by rotor speed, number of blades per flange and ground speed. Some papers were published about the power requirement of rotary tillers. However, there have been few papers which reported integrated research on rotary tillage power requirements under flooded soil conditions. The ASAE standard 40 D497 (1991) includes an equation for estimating power requirement. This standard gives the following equation to estimate effective draft per unit cross sectional area of furrow slice for a 450 mm diameter rotor turning forward with a rotor speed in the range of 400-700 rpm. This equation was developed for a 10 cm tillage depth and assumes a negative unit drawbar force under silty loam soil conditions. It is valid for ground speeds from 2 to 7 km/h if the parameters given in another section of the same ASAE Standard apply. D=43.9b'°°“-0.14b (4-2) Where: D = unit draft (N/cm?) b = bite length (cm) The required power is calculated by multiplying unit draft, tillage width, tillage depth and forward speed. The power calculated from this equation was too high because, in this case, these rotary speeds and ground speeds were too high due to the upland use. Fujisawa et a1. ( 1953) performed a power requirement experiment using a tractor with a rotary tiller in.a clay loam soil rice field. ( Table A.1) They reported that the power requirement was increased as bite length and forward speed ncreased. The linear regression equation for this relationship was expressed below with R-squared of 0.99. 41 18.13, - . 4.3a 3.6 o 3591: ( ) Where: IQ“ = specific power requirement for tillage (kW/m/lOcm) S, = ground speed (km/h) b = bite length (cm) This equation was experimentally obtained for standard blades (pick shape). Curved Natazume blades are conunonly used for paddy field operation. The power requirement for Curved Natazume blades was observed by the same researchers to be 34% more than that of standard blades in another experiment under the same conditions. Therefore, the equation was multiplied by 1.34 to obtain the equivalent power requirement for Curved Natazume blades. - .4 4.3b 3.6 0 80b ( ) Pul=0 a 528+ An analysis was made on a manufacturer's recommended tractor size in a Japanese rotary tiller catalog (Nogyo Kikai Chosakai, 1985). Forty-three rotary tillers of two major manufacturers were selected for this linear regression analysis ranging from 1.40 to 2.80 m width as shown in Table A.2. The recommended tillage depth ranged from 12 cm to 18 cm, and the average depth was calculated to be 13.98 cm. The 42 regression equations of required power range for the minimum level and the maximum level were given below: Pun-~16 $2363” (4.4a) 33.0w =- ' +— 4.4b pm 19 o 100 ( ) Where: Pm, = minimum power requirement (kW/m) = maximum power requirement (kW/m) W = rotary tiller width (cm) The Rrsquared value was 0.618 for equation (4.4a) and 0.557 for equation (4.4b). It should also be mentioned that the manufacturer's recommended tractor size includes not only power for tillage but also power for motion resistance and some extra power for other unexpected requirements. Some sample values of specific power requirement are shown in Table 4.1 for easier comparison to other literature. The adjustment was made to obtain specific power requirement per unit cross sectional area dividing by width factor (m/m) and 1.398 (10cm/13.98 cm). Hendrick (1988) reported a similar analysis based on manufacturer's recommendation. The power requirements for a rotary tiller ranged from.12 kW/m to 35 kW/m. It was noted in his literature that 10 cm depth fine tilth or 15 cm depth 43 coarse tilth required 30 kW/m. These same tillage practices required 20 kW/m for soils with coarse particles and 35 kW/m for soils with fine particles. In the same paper, power requirements for garden tillers ranged from 3 kW/m to 14 kW/m according to figures based on the National Tillage Machinery Laboratory (NTML) photo. Table 4.1 Specific Power Requirement from Catalog Data Tiller width (m) 1.50 2.00 2.50 Minimum (kW/m/10cm) 8.89 10.84 12.00 Maximum (kW/m/10cm) 14.54 16.81 18.17 * the specific power (kW/m/10cm) was calculated based on thelequations (4.4a) and (4.4b) by adjusting the depth to 10 cm.from the average depth of 13.98 cm. Beeny and Khoo (1970) studied power requirements of rotary tillage under supersaturated conditions in 60% clay content soil. They provided specific work data under various bite lengths for a range of constant rotor speeds. From graphs presented in their paper, a.data table was developed as shown in Table A.3. A linear regression equation was obtained from their data for power requirement of rotary tillage as follows: I110 Where: 44 Ign_= specific power requirement for tillage (kW/m/lOcm) S, = ground speed (km/h) b = bite length (cm) The power requirement figure for tillage obtained by equation (4.5) is lower than minimum. power requirement recommended by manufacturers. The equation does not include power requirement for motion resistance, and super saturated soil condition reduced power requirement by reducing impact force to cut soil and by reducing frictional force between blades and soil. In the simulation model, Beeny and Khoo's equation (4.5) was used to estimate power requirement for tillage because the soil conditions were close to those in.Mweaa The soil in Mwea has 80% clay content while the soil used for Beeny and Khoo's experiment had 60% clay. The tillage operation was done in super saturated soil condition after flooding at both places. The penetration resistance of soil was measured by using a standard soil cone penetrometer in Mwea, March 1992. The equipment was a hand-operated soil cone penetrometer with 323 um? base area. The test site was plot M9 and measurement were made 1 h, 8 h and 24 h after flooding. The soil surface was so hard that the measurement before flooding was impossible. The result is summarized in Figure 4.2 from the data in Tables A.4a to A.4d. ”Dry" shown in Figure 4.2 was test performed in plot H6 where soil surface was relatively soft in March 1992 45 because the soil in M9 was too hard to measure with a cone penetrometer before flooding. The cone index.of soil measured at 1 h after flooding was more than 2000 kPa at 15 cm and 20 cm. The cone index measured at both 8 h and 24 h after flooding has less than 1500 kPa to the depth of 20 cm which is considered the normal tillage depth. Cone Index (kPa) 51'01'5 2'0 2'5 3'0 3'5 4'0 4'5 50 Depmcm) +1hour +8hours +24hours—a—Dry Figure 4.2 Soil Cone Penetrometer Resistance in Mwea. In this simulation, sinkage of 10, 20, 30 and 40 cm.were assumed to represent firm, soft, very soft and extremely soft 46 soil conditions. Gee-Clough (1979) provided a prediction equation for the coefficient of rolling resistance of rigid wide wheels in sand considering a bulldozing effect. Gee-Clough (1985) noted Gupta's equation (4.6) whichnwas obtained under soft clay soil condition. cn=(|—f19(o.63+o.34-g) (4°5) Where: Cul=coefficient of rolling resistance 2, =maximum sinkage (cm) c = wheel width (cm) d = wheel diameter (cm) Table 4.2 shows sample calculations made by Gupta's equation to estimate coefficient of rolling resistance for tractor wheels used at MIS for the sinkage of 10, 20, 30 and 40 cm. The tire size figures were obtained from ASAE standard 8220.4 (1991). The wheels and tires used in the estimation were for 45 kW class tractors. To represent more general situations, the coefficients of rolling' resistance for’ rear' wheels were averaged as 0.194, 0.275, 0.337 and 0.389 for both 2WD and 4WD tractors because the difference the coefficients between 2WD and 4WD was less than 1%. Coefficients of rolling resistance 47 for front wheels were selected as 0.252, 0.356, 0.436 and 0.503 for 2WD, and 0.224, 0.318, 0.390 and 0.450 for 4WD tractors at respective sinkage assumingwother sizes of tractor have wheels and tires which have similar coefficients of rolling resistance. Table 4.2 Estimated Coefficient of Rolling Resistance Tire size 14.9-28* 16.9-28* 8.3-20** 7.5-16** Tire width (cm) 37.8 42.9 21.1 20.3 Tire diameter (cm) 136.7 143.5 97.8 80.8 sinkage 10 cm 0.195 0.193 0.224 0.252 20 cm 0.277 0.273 0.318 0.356 30 cm 0.339 0.335 0.390 0.436 40 cm 0.391 0.386 0.450 0.503 * Rear tires of the tractors used in MIS ** Front tires of the tractors used in MIS Motion resistance is calculated by multiplying load on axle and the coefficient of rolling resistance as follows: m: (crr£* Cali!“ Crrr*ca1r) TW (4 ° 7 ) Where: MR = motion resistance (N) Cm =coefficient of rolling resistance for front wheel Cm =coefficient of rolling resistance for rear wheel Cm = coefficient of front axle load C“, = coefficient of rear axle load TW = total machine weight (N) 48 The coefficients of axle load were calculated based the data given in Table A.5a and A.5b. which was obtained from Nebraska.tractor test results given in Implement.& Tractor Red Book (1985, 1986, and 1987). Seventeen tractors for 2WD and 20 tractors for 4WD with maximum power ranging from 20 kW to 60 kW were selected. The average weight distribution rate, Cm:C.1, was .342:.658 for 2WD tractors and .398:.602 for 4WD tractors. The load on axle is based on the tractor weight according to the size of power output. The following regression equations were developed from the catalog data (A.5a and A.5b) . The R-squared values were 0.916 for 2WD tractor weight and 0.659 for 4WD tractor weight. 2WD tractor weight: wm=3112 +555 . 29m“, (4 . 8) 4WD tractor weight: wm,=4621+563 . 02m“, (4 . 9) Where: Wm, = 2WD tractor weight (N) 4WD tractor weight (N) P um = rated power output (kW) In the simulation, it was assumed that the static load distribution of tractor weight was maintained during tillage 49 operation. The difference between free rotating front wheel and front drive wheel was neglected because there was no relevant information to estimate a difference. It was assumed that the weight of the rotary tiller was totally supported by soil, and that the axle load was the tractor weight only; The effect of soil disturbance by the first pass of front wheels on.motion resistance of the rear wheels was neglected because there was no relevant information. Therefore, power requirement for motion resistance is estimated below: _ Mia-s, 4.10 PM 3600 ( ) Where: P.» = power requirement for motion resistance (kW) Manby et a1. (1960) tested diesel engine performance in Kenya and reported that reduction of power due to reduced air density at high altitude was less than 4% per 303 m which was given by British Standard Specification 649. The specific fuel consumption was observed to be higher at high altitude than at sea level in their research. A study of Manby's experiment data revealed a 3.5% power loss for every 303 m change in elevation. The coefficient of power reservation against the power loss due to altitude effect, Cm, was obtained using the following equation: 50 IE 03 (4.11) in) 0.151.035 Where: Calt = coefficient of altitude effect, decimal Alt = altitude (m) This equation is included in the simulation model to consider a compensating power requirement because of the power loss due to the altitude effect. The air density is also a function of air temperature and atmospheric pressure. The average maximum temperature was 27.4 °C and the average minimum was 15.7 °C. Therefore, the effect of temperature on air density was neglected. The effect of atmospheric pressure change by weather system was neglected because the duration of tillage is long enough to average the changes. A tractor should have some extra power in reserve because it does not work at the maximum power all of the time and it should be able to overcome some unexpected load. Tillage depth measurement was done during the field experiments in March 1992 as shown in Table A.6. The average depth was 11.26 cm and its standard deviation was 4.02 cm which is 37.37% of the average depth. In the simulation, 37.37% was added to the required power for the rated PTO power calculation as follows: PZICOd=(Pt11+PMC)*1'3737*C81t (4.12) 51 Where: Pam = rated PTO power, kW Pul== power required for tillage, kw Pnot = power required for motion resistance, kW C .1: = coefficient of altitude effect, decimal 4.1.3 Machine Productivity Field efficiency is determined by theoretical field capacity and effective field capacity. Typical field efficiencies are given in ASAE standard D497 (1991) and the figure for rotary tillage is 85 %. In this study, effective field capacity was estimated from the aspects of effective work width, operation speed, operator experience and field length. Effective field capacity was reviewed especially from the aspects of effective machine width and operator skills. The measurement of effective work width of rotary tillage was done in Mwea section in March 1992. Guo (1987) measured effective working width by using three reference posts placed in the tilled soil to obtain three actual working widths for each pass. He measured the distances from each post to the untilled soil before and after each test run and then subtracted to obtain the working width. This method provided the precise working width measurements of a particular pass, but is not considered to provide the average working width of several passes. Therefore, the average working width was 52 measured by the method shown in Figure 4.3. The test field size was 123 m.x 33 m (0.406 ha), and the tillage operation method was a headland pattern from the boundaries. (See Figure 4.4) This operation pattern starts along the boundaries and progresses towards the center of the field, and it provides easier turning at headland. The total tilled width of 6 passes with a 200 cm width rotary tiller was 9.6 m. lst pass 2nd pass W Afi : nth pass w W W-w l W _. r I: 4. nW-(n-l)(W—w) Figure 4.3 Measurement of Tiller Working Width. The average working width was calculated to be 152 cm using the method presented in Figure 4.3. The operator achieved only an 76.0% average working width efficiency for the trial using a 200 cm width tiller. The direct average method which is obtained from the total width divided by the 53 number of passes is an. appropriate method to estimate an operator's performance when the number of passes gets large because a large number of passes compensates the advantage of the first path from the boundaries and the loss due to adjustment for the final pass of the operation. The working width is estimated to be 160 cm at 80.0% efficiency with a 200 cm width rotary tiller, if it is directly averaged. If a 40 cm overlap is also required by the operator with a 150 cm width rotary tiller, theicoefficient will be lowered to 73.3%. Hunt (1983) reported that an experienced operator achieved 93% working width efficiency using a self propelled windrower. The windrower operation is considered to be easy compared to the rotary tillage operation, because the rotary tillage is a rear hitched operation under submerged, slippery field conditions. Operator skill can be used as a factor which affects effective work width. A skilled operator requires less overlap and has a higher effective work width. In this simulation program, working width efficiency was calculated by using overlaps of 50, 40 and 30 cm for less experienced, experienced and most experienced operators, respectively. Field shape was assumed to be rectangular because most plots have rectangular shape. Field length is also included in the model as one of the factors which affect field efficiency. Two to four tractors were assigned to operate in the same field plot, because one tractor can pull another stuck tractor out in a short time when that tractor is bogged 54 down due to a sinkage problem. However, it was impossible to maintain an organized operation pattern because multiple tractors interfered with each other. Wakui (1964) reported that for a time effective rotary tillage operation, the field width-length ratio should be more than 1:4 for field size less than 0.5 ha. The ratio of field width and length for the test field was 1:4.1 and field shape was assumed to be rectangular because irregular shaped fields are rarely observed in large scale irrigated rice fields under NIB. In Mwea the most common patterns were headland pattern from boundaries and continuous turn strips at each end as shown in Figure 4.4. The headland pattern from boundaries method disturbs the saturated soil structure by travelling several times on the same path at headland. Having continuous turn strips at each end is more effective than the headland pattern from boundaries from the aspect of soil structure disturbance. Hunt (1983) described equations to calculate pattern efficiency for the continuous pattern. He suggested that a headland of twice the effective machine width will provide adequate room for high-speed turns yet limits finishing travel to two passes. The time lost for a continuous pattern, using a rotary tiller, involves only the turning time plus two finishing passes at each headland by skilled operators. The extra time required for each turn was assumed to be the same ...“? 55 as the time to travel half of the circumference of a circle with radius equal to the effective machine width without any speed reduction when turning. continuous turn strips at each end headland pattern from boundaries Figure 4.4 Rotary Tillage Operation Patterns. The theoretical time for tillage is expressed by the equation below: 10 Ttils ms: (4°13) Where: Tul== theoretical time for tillage (h/ha} 56 w = effective tiller width (m) S, = ground speed (km/h) The time for turning and finishing depends on an operator's skill level. The number of passes to till a headland is assumed to be two for the most experienced operators, three for the experienced operators and four for the less experienced operators. For the most experienced operators: W’ W}+ - 3.14% 100 (mm—w 2) +4W, Tm" 3600 *s,-vAp (4.14a) For the experienced operators: W' W: _3 14 100 (100 4) 791, T - "‘m 3 6 0 0 *S,*A,, (4.145) For the less experienced operators: “7 W _3.14* 100 (100*—wf+6)+8W, T .. “I" 35 00:5,mp (4.14c) Where: T,mm = time for turning and finishing (h/ha) W = rotary tiller width (cm) W, = width of field (m) .AP = plot size (ha) 57 The most important factor which affects field efficiency is the tractor sinkage problem in Mwea. If the soil is too soft, the motion resistance overcomes the traction, and then the slippage becomes too high to move the tractor forward. ‘When.bogging down occurs, another tractor closest to the stuck tractor interrupts its operation to pull out the stuck tractor. Sinkage troubles were observed four times in the tested field of 0.406 ha in Mwea which has severe sinkage troubles every year where the soil condition was considered to be loose, although the field was tilled only one day after flooding in March 1992. NIB (1979) reported tractor productivity in Ahero and West Kano as shown in Table 4.4 under survey conditions given in Table 4.3. Table 4.3 Rotary Tillage Survey in Western Kenya Ahero 1979 West Kano 1978 Surveyed area (ha) 573.4 #59.1 Number of machine sets * 16 6 Operated days 116 6 Recorded time (h) 5296 145 * tractor with rotary tiller In the simulation, the sinkage trouble*was selected as an input variable. Generally, lost time decreases as frequency of sinkage trouble increases. The effect of frequency of sinkage trouble on lost time was estimated by the equation (4.15) by establishing a linear relationship between data of 58 Ahero and West Kano given in Table 4.4. Table 4.4 Field Time Share of Tillage Operation Ahero 1979 West Kano 1978 Tillage (% of Field Time) 46 66 Sinkage trouble (% of FT) 17 14 Field break down (% of FT) 14 11 Others (% of FT) 23 9 Sinkage frequency (times/ha) 7.66 1.00 Avg. time loss (min/sink) 12.3 20.6 . (21.85-1.25*F5)F, Where: Tun, = time lost by sinkage trouble (h/ha) F; - frequency of sinkage trouble (times/ha) Forward speed affects both field efficiency and field capacity. Tillage operation speed measurement was summarized in Table 4.5a, and ground speed on a smooth surface road at the same gear position was measured as shown in Table 4.5b. Average required time was 23.53 seconds to travel 30 meters on a firmigrass covered road and-the standard deviation was 0.21 which gave a speed of 4.591 km/hr; Eight trials were made to measure time to travel 30 meters in the field when land preparation was done. It took an average of 35.01 seconds with standard deviation of 3.18 s and the average speed was 3.106 km/hr. 59 Table 4.5a Tillage Operation Speed Trial Time* Speed Speed Slippage** (8) (m/S) (km/h) (%) 1 41.1 0.730 2.628 42.7 2 34.3 0.875 3.149 31.4 3 36.7 0.817 2.943 35.9 4 37.4 0.802 2.888 37.1 5 32.6 0.920 3.313 27.8 6 32.9 0.912 3.283 28.5 7 31.5 0.952 3.429 25.3 8 33.6 0.893 3.214 30.0 Average 35.01 0.863 3.106 32.3 * time required to travel 30 meters ** based on the average tractor speed on road in Table 4.5b Table 4.5b Tractor Speed on Road Trial Time* (3) speed (m/s) speed (km/h) 1 23.3 1.288 4.635 2 23.7 1.266 4.557 3 23.7 1.266 4.557 4 23.4 1.282 4.615 Average 23.53 1.275 4.591 * time required to travel 30 meters Assuming zero slippage on firm grass covered road, it gives average slippage of 32.3% and a maximum slippage of 42.7% for the trial 1. In this simulation, speed was selected as a input variable with a limited range from 1 km/h to 5 km/h. 60 The rotor speed was observed to be 235 rpm at a PTO speed of 540 rpm for Niplo in Mwea in 1991. The bite length is calculated to be 5.51 cm if the average ground speed is 3.11 km/h. There are some other activities which interrupt tillage operation” The gross field time was reduced by one and a half hours per day to account for time for transportation to and from the field, lunch break and scheduling. Other lost time due to adjustment, refueling, minor repair and short breaks for the operator was selected as an input variable. The time can be input either per unit area, per unit time or both. In this simulation, lost time‘was assumed to be linear to tillage productivity, and it was given in per unit area. The field efficiency is given as follows: T FE= “1 ( 4 . 1 6 ) Tturn + Tank+ Tether + Ttil Where: FE = field efficiency, decimal Tmmu.= time lost due to other activities (h/ha) The effective field capacity is given below: EFC=FE¢ TFC (4 . 17) Where: EFC = effective field capacity (ha/h) 61 TFC = theoretical field capacity = l/Ttil (ha/h) The number of required tillage machines is given below: A N“ total (4.18) Era-Hm, Where: N = number of machine sets Atotal = total field area (ha) Hmfl;= total net work time (h) Total net work time is obtained.by accumulating daily net work hours as explained in section 4.1.1. In the program, number of tillage machines was rounded up to obtain an integer figure. 4.2 System Reliability First, computation methods of system reliability for redundant systems were described. Then, estimation of reliability for practical system was performed. 4.2.1 Reliability Calculation of Redundant System Two reliability figures of merit, failure rate and mean time between failures (MTBF) were listed in the ASAE Standard EP456 (1991). The failure rate is the number of failures of an item per unit of cumulative time. MTBF is the total operating time of a population of a product divided by the 62 total number of failures, in other words, the inverse of failure rate. Any system can be modeled by combining its elements. Systems with repairable components in series are more typical of agricultural machinery, in which a failure of one component will disable the function of whole system. The failure rate of a system comprised of n components in series is the sum of the component failure rates. 1”,,u=i,+l,+. . .+l,, (4.19) Where: My“. = system failure rate A, = failure rate of component n The failure rate of a system comprised of 2 components in parallel is defined by the following equation: (4.20) The assumptions for these equations (4.19 and 4.20) are that the components and the system are 2-state (operational or not operational) and that the component states are independent. This is, failure of one component does not affect the reliability of the other components. When a tractor is aging, gradual reduction of its power is not considered as a failure as long as the tractor remains operational and productive. Performance of some tractor parts are dependent 63 on other parts, and equations (4.19) and (4.20) may not be applied to these dependent parts. The reliability of a component during its useful life time period is described by the single parameter exponential distribution: R(t)'e'“ (4.21) Where: R (t) = reliability base of the natural logarithm 0 ll failure rate >’ ll time of use (‘1' II System reliability is defined as the probability that a system. will perform its function satisfactorily for a specified period of time under specified operating conditions. The system reliability of a system in series is obtained by multiplying reliability of each component as follows: R ,y,m=R,n-R,t. . JR” (4.22) Where: lg = reliability of component n Reliability of redundant systems such as series-parallel, parallel-series, or some other combination of parallel and series systems is commonly used in electrical and electronics engineering fields. 64 The equivalent reliability of series-parallel system is: 1i',,,,,.,,,,=[1.-(1-Ii‘)"’]n (4.23) Where: m = number of components in a parallel unit n = number of parallel units in series R = reliability of an individual component The equivalent reliability of parallel-series system is: Rum tugl-(I‘Rn)n (4.24) Where: m.= number of series units in parallel n - number of components in a series unit Barrow and Heidtmann (1984) presented a simple, exact algorithm for calculating the reliability of a k-out-of n:G system. Such a system is operational if at least k out of n components are operational. To obtain the probability that at least k components operate, expand and sum coefficients of zj for j = k, ..., n by using the following generating function: 9(2) 11 (Q,+R,z) (4.25) Where: 65 .2“ u reliability of component i unreliability of component i O p ll N II dummy variable in generating function number of components in system k = minimum required number of operational components Their algorithm assumed that the system is good if and only if at least k of its :1 components are operational in additional to the above two assumptions of two state and independence of components. Another assumption that each component in a parallel system has equal capacity must be applied for this calculation. This method can be utilized to estimate machinery system. reliability when tractors are treated as independent components. Shoup (1982) presented techniques for estimating the reliability of complex agricultural machinery systems as a parallel redundant system using the following equation. Il',,y,,..,,,==[1-(1--R)"']n (4.26) Where: m = number of components in parallel n = number of functions the system.must perform This equation is exactly the same as equation (4.12) for parallel-series system reliability if n = number of series applies. The meaning of numbers of functions is considered 66 to be the number of different tasks which can be performed by the system for different purposes. However, there is no relevant rationale to express the system reliability by equation (4.26). Shoup performed a reliability estimation by giving a production system example shown in Table 4.6. Shoup regarded this machinery system as a parallel-series system and estimated the system reliability by multiplying all of the subsystem reliabilities. Table 4.6 Example Agricultural Machinery System by Shoup Subsystem Available machines Reliability (Component) 1 (A, B) 2 100HP tractors* 0.83 each 2 (C, D) 2 peanut combines 0.85 and 0.72 3 (E, F) 2 wagons ** 0.90 each 4 (G) 1 extra wagon to back up 0.90 5 (H) l 40 HP tractor to pull wagon*** 0.85 * either tractor can pull a combine, can back up small tractor or can be used for dairy work ** one for behind combine, another for behind tractor *** this tractor cannot back up a 100 HP tractor Shoup did his estimation of system reliability of this system as follows: Subsystem 1 R“1 = [1 -(1-0.83)’]3 = 0.9158 Subsystem 2 R..2 8 0.85 + (1-0.85) * 0.72 = 009580 67 Subsystem 3 and 4 Run = [1 - (1-0.90)’]’ = 0.9801 Subsystem 5 R“, = 0.85 + (1-0.85) * 0.9158 = 0.9874 Total system R.,.,_, = 0.9158 * 0.9580 * 0.9801 * 0.9874 = 0.8490 For the reliability calculation of subsystem 1, R“1 should be 0.832 = 0.6724 if one tractor is operated for pulling a combine and the other one is used for dairy work at the same time. It is assumed that the assignment of work for each tractor does not affect system reliability when using equation (4.26) . He violated the principle of independence in his calculation of the reliability for the subsystem 5 by multiplying reliability of subsystem 1. In the calculation of system reliability, reliability of each component should appear only once. A graphical representation was made using Venn diagrams for easier interpretation of system reliability including the k-out-of-n:G redundant system in Figure 4.5. The shaded areas in the figures show the probability of the system to be operational. 24xnotannuxn 245 0.05 0.0 0.05 0.00 0.55 0.5 PM 0.05 0.05 50 rd 2:. 5.005 0.3 0.1: 0.0: 0gp 0. 5 5.05 0.55 0.8F «.000 0.50— >(S v.80 0.50..“ 0.000 mg 0.000 0.3 0.00.. via v.5mN #000 5.0: 4.1% 0.05 0.05 0.0— p 0.0 0.05 0. 5 Na 0.0 0.50 0.000 5.3 7% Now 0.0 00' 0.0 0.0 0.00 0.0 00' NN 0.0 5.0 >533 0.05 0.00 00w 0.0 0.? 0.0— 0.0 0.5 50 5.00 0.0 >53 25: 89 map 58« mar gr «8.. 085 Nap pap gr 6032 no case cowusuwmfluwum HH.¢ manna 176 Table A.12 Daily Tillage Productivity MY m M DALYWWW DATE DAYLY m1. AW 10TH. m1 AVG OOVOG§UNd 88 988838 8888 8888 2888288283$8825883225358223656322283536582536; 1312 1402 152 162 172 162 192 242 2.47 257 2.67 2.64 242 226 2.75 29.1 4 2463 31 .1 6 2.75 27.42 225 22 2.12 2.65 2.17 34.40 2.15 2.12 372 2.34 34.91 31 .67 2.35 2.46 41 56 3754 43.10 22 37.94 2.95 2.41 2.76 41.16 2.12 2.76 31 2 224 2.15 3723 37.03 41.79 2.70 2 16 2 17 21 2 21 21 21 24 24 8388 888338 888888 888838 838888 888888 883388 88888 121 12 1.14 12 02 124 12 1.42 12 12 125 124 125 1.2 1.46 12 12 12 1.43 12 12 1.45 12 12 1.34 12 12 12 12 12 12 12 1.44 12 12 12 12 12 1.54 1.43 1.43 12 1.57 12 12 12 12 12 1.43 12 12 1.54 121 1.37 1.43 12 12 1.64 242 47.75 70.2 942 1142 1392 1&2 1912 20.97 2492 200.35 310.71 26.13 26.13 “2.37 477.34 ”2 574.07 61 0.19 64651 23.74 71 32 7472 7472 7792 610.01 24.17 “21 1212 1” 1077.01 112.11 112.11 112.11 112.11 112.11 1122 112.97 1222 1&2 12121 121 21 1333.00 1370.42 1412.91 1422 142.56 127.74 127.74 1242 1m.“ 1222 124.33 172.57 17412 17412 1771.17 1u2 16462 1” 1252 1” 1m 21 2 2 77 97 116 12 12 d O 2 \ 836888 633 £3" m 323?. 833 3333 S85 SEM 831811938 888 I \ 1.16 12 12 12 1.16 1.16 1.17 12 12 12 12 12 12 12 12 125 12 12 12 12 12 12 12 1.31 1.31 1.31 1.31 1.31 1.31 1.2 1.2 12 1.34 12 12 12 12 12 12 12 12 12 12 1.37 1.37 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 1.0 12 12 12 12 12 1.41 1.41 24.04 88338 66388 886988 888338 233323838885E8888588252353553222;33:22:28?r 88 12 2405 110 22 111 272 112 22 113 22 114 m 115 01‘" 116 0507 117 04437 116 M7 31 .77 40.07 31 57 442 41 .2 34.91 41.36 ”.47 2.17 2.33 37.64 272 34.70 2.45 37.64 2.45 31 26 26.61 3754 2.46 34.50 34.40 372 51 .66 34.70 37.94 4229 2.61 2.41 34.70 2.66 0.07 ”.07 251 31 .67 41.16 37.03 2.71 3754 221 2 10 888888 88888 888888 888838 383888 338888 88 888 888888 888888 88883 12 1.33 12 12 1.16 1.46 1.40 127 1.67 1.13 1.75 12 1.47 12 1.45 1.97 1.45 12 1.17 151 1.10 12 154 12 1.2 12 1.17 1.2 12 1.13 1144 1.2 12 12 1.45 1.41 157 12 1.37 12 12 1.2 156 1.17 12 12 12 154 12 12 1.46 12 12 1.42 1.40 1.16 1.44 12 2212 2.14 2122 2146.15 21762 21762 21762 213.61 2251.04 216.43 2.16 2467.19 22 2712 25712 212.44 22.91 2715.41 272.77 272.77 21 5.47 221.56 2.01 22 2172 2.10 276.43 31122 31472 31472 31642 212.43 212.43 301 .55 22 271 .2 “71 2.01 41254 4141 .75 “a “w an an mm as as as an an «M as um mm 1711 33135538333311133985855931888833338533833 1.41 1.” 1.40 1.0 12 12 1.40 1.40 12 1.40 12 1.” 12 12 12 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 12 12 12 1.41 1.41 1.41 1.41 12 12 1.41 12 1.0 1.0 12 12 12 12 1.” 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1.41 12 1.41 1.41 1.41 1.41 1.41 1.41 1.41 1141 1.41 1.41 1.41 1.41 1.41 1.41 1.41 ' 1.41 (contlnued) 119 207 31.06 2 1.2 4172.61 224 1.41 120 mm 37.33 25 1.49 4210.15 2979 1.41 121 207 33.59 24 1.40 4243.74 arm 1.41 207 4243.74 3003 1.41 122 1007 40.07 25 1.00 4263.8) 3023 1.41 12 um 34.91 24 1.45 4316.71 3052 1.42 124 1207 4523 26 1.74 423.93 276 1.42 125 1307 2.01 30 1.17 4393.94 3103 1.42 126 14D7 40.67 25 1.63 442.61 312 1.42 127 1507 $21 26 1.5 44742 31$ 1.42 1607 4474.62 319 1.42 12 17.07 2.52 2 1.40 4511.34 315 1.42 129 1607 41.36 24 1.72 452.72 m 1.42 130 1907 2.07 26 123 45642 3235 1.42 131 2007 272 24 1.15 4612.42 :29 1.42 12 mm 41.46 25 12 m 264 1.42 133 207 2.46 27 1.46 m 3311 1.42 2307 4232 211 1.42 134 2407 3420 25 1.37 4727.55 2 1.42 12 2507 37.13 26 1.43 47642 3:2 12 13 2607 42.19 25 12 m 327 1.42 137 27m 2633 26 1.01 465.20 3415 1.42 12 26‘)? 2.62 22 12 4671.12 3437 1.42 12 207 ”.17 27 1.0 011.19 3464 1.42 207 4911.19 3464 1.42 140 am: 31.47 26 121 49422 3400 1.42 141 012 41.66 23 1.61 424.34 213 1.42 142 m 41.46 24 1.73 51252 337 1.42 143 m 26.63 24 1.19 $54.45 261 1.42 144 04k! 26.31 22 120 2.76 363 1.42 145 0503 3.55 24 127 5111.31 3307 1.42 m 5111.31 3307 1.42 146 072 242 23 1.07 5133.00 sum 1.41 147 m 29.34 22 1.33 512.34 3352 1.41 146 22 2.96 22 1.41 519330 274 1.41 149 102 $24 24 13 5234.54 an: 1.42 150 112 2.02 25 1.44 270.56 3723 1.42 151 122 2.55 24 127 2.12 3747 1.41 132 521.12 3747 1.41 12 142 26.53 26 1.10 was 3773 1.41 153 152 19.43 27 0.72 5349.07 3300 1.41 154 162 2.34 23 12 5376.41 3323 1.41 155 17.06 25.50 17 12 5403.91 240 1.41 156 162 33.79 24 1.41 5437.70 3334 1.41 157 192 332 26 126 54712 am 1.41 am 54712 300 1.41 156 am: 2.62 24 1.49 5503.91 214 1.41 19 222 34.50 23 150 5541.41 237 1.41 18) 2386 312 25 124 5572.47 3932 1.41 161 242 2.46 19 1.71 524.94 ”1 1.41 12 252 2.76 25 123 562.70 4113 1.41 1. 262 332 24 1.41 5&2 402 1.41 272 2.9 40:!) 1.41 164 262 27.42 25 1.10 527.01 425 1.40 12 292 27.72 24 1.16 5724.73 4079 1.40 12 m 26.10 23 1.13 572.64 412 1.40 167 am: 21.35 23 0.93 5772.19 4125 1.40 1C 012 15.36 19 0.61 5767.56 4144 1.40 12 m 14.37 20 0.72 5N1.” 4164 12 m 521.93 4164 12 170 m 1621 16 1.01 520.14 412 12 171 m 162 16 0.91 563.53 mo 12 22 562.53 4200 12 072 m mo 12 172 can 92 14 0.71 5646.45 4214 12 cum 172 172 172 MAX 5166 so 22 1.42 m 92 14 0.71 1.16 TOTAL 563.3 4160 AVG 33.93 24.3 12 177 178 Table A.13 Daily Tillage Productivity in 1991 DALY MM 0“.va mum» DAY DATE W m AVG MI) W AVE 1 m 27.11 24 1.13 27.11 24 1.13 2 010:! 27.32 24 1.14 54.43 48 1.13 3 (2:03 3.05 23 1.70 ”.49 71 1.32 mm ”.49 71 1.3 4 0403 43.51 25 1.74 13.” N 1.43 5 $03 37.54 28 1.44 174.53 122 1.43 8 cam &.38 27 120 218.9) 149 1.3 7 07103 ”.73 27 me 2584 178 1.34 8 mm £58 27 084 25820 203 127 9 09m 27.72 28 089 235.92 231 124 mm 2$R 231 124 10 11m 4128 28 1.47 32720 2% 1.28 11 was 45.73 as 1.58 372m 2” 129 12 13m 381 an 127 “.54 318 129 13 mm 34.” 29 1.18 44284 345 128 14 154” 3.83 E 128 479.47 374 128 15 mm 33.59 29 1.18 513.03 403 127 17103 513m 403 127 18 1803 $21 29 121 54827 4Q 127 17 1903 27R 28 me 5753 458 128 18 2003 3.81 27 1.32 811.50 45 128 19 2103 2782 28 mo 83.43 513 125 20 2203 35.90 30 0.“ ”.33 543 123 21 23:03 2387 29 on 888.3 572 120 24.03 888.3 572 120 22 2503 27.52 27 1.02 71581 599 120 23 m 12.55 25 0.50 728.48 Q4 1.17 24 27m 2489 25 190 753.3 8% 1.18 25 2803 3784 27 1.40 791.19 878 1.17 m 791.19 878 1.17 3003 791.19 878 1.17 31103 791.19 878 1.17 0104 791.19 878 1.17 28 1204 45.33 27 1.88 83.51 703 1.19 27 cam 423 28 1.83 878.91 729 121 28 04.04 27.82 27 am “.53 758 120 29 (504 28.73 28 ms 9328 784 1.19 1!) 03104 28.31 27 0.97 $1.57 811 1.19 07:04 $1.57 811 1.19 31 ow: 25m 27 0.93 908.63 83 1.18 3 mos 18.59 25 0.88 100325 1.18 3 1004 1184 27 0.44 10155 no 1.14 34 11D4 18m 25 087 1&188 915 1.13 5 12D4 19.02 27 0.70 1%.” 942 1.12 3 13D4 1333 an 0.45 1m28 972 ms 1404 1W428 972 1.00 37 1504 13.” 27 0.52 107822 can we 3 1804 1922 28 0.09 1W7“ 1&7 1.07 17“ 1N7.45 1&7 1.07 3 1804 11.53 29 0.40 1103.93 1N8 ms 8) 1904 2185 20 mo 113183 1078 1.5 41 2004 5.87 29 020 113.50 1105 1.03 21:04 113.50 1115 ms 42 2204 11.53 as 0.40 1148.03 1134 1.01 43 2304 14.47 28 0.52 1 1&50 1182 mo 44 2404 12.75 29 0.44 1 17525 1 191 0.” 45 25D4 15.78 29 0.54 1191.03 1220 0.” fl 2804 8.17 29 021 119720 1249 0.” 47 27:04 1781 23 0.78 1215.11 1272 0.3 2804 1215.11 1272 0.” 48 2904 40.57 & 127 125.88 1&4 0.” 49 3004 42.39 as 128 1290.00 1337 0.97 0105 1293.00 137 087 an (2.05 Q81 31 1.70 13189 188 0.99 51 m 47.55 & 1.49 1&24 «no mo R 04113 58.15 31 181 1454.3 1431 me 0505 1454.39 1431 am 53 03.05 ”.07 3 1.” 1&0.“ 1488 1.04 8888! 388888 888988 88888 388338 SSEES22835338588888853858228583 71.53 84.14 81.01 52.31 ”.47 81 .51 $.78 ”.40 54.03 ”.40 $.51 87.” 8425 8425 7527 78.41 mm 7498 57 27 78.77 7n: NM «2: sun «as mm) 5828 58.07 58.1 5 00.00 6.48 84.55 $.79 9.78 54.33 54.53 88888 888888 888888 88888 888888 888888 888888 888888 88 31 1.82 1.51 2.02 1.88 1.58 1.45 184 1.55 1.95 1.85 1.54 1.75 1.58 1.81 224 1.94 1.” 1.97 1.74 1.83 1.83 1.88 1.” 1.” 1.77 1.” m 1.83 2.02 2.00 1.95 1.89 2.51 2.38 2.31 185 2.15 2.12 2.10 2.18 2.01 1.88 1.87 1.81 1.95 2.18 2.15 1.93 187 2.15 1.90 187 1R 1 584.1 0 1‘77 1&25 1747.80 1&81 1&81 15788 190:3: 1“.“ NR 2071 .5 213.95 213.95 2181 .74 237.” @8787 2340.” 2405.44 2478.97 2478.97 253.” zoom 3 284.1 4 2719.79 2772.09 2772“ 2772.“ 2&784 2M8? M785 @1888 W582 31 33m 31m 31 ”.05 ”0.45 3318.” M85 3451 20 3515.44 $15.44 $.72 ”.1 3 373.13 312.1 9 ”.48 34822 401725 4037.38 41 5030 4217.88 401 .1 1 44$.1 8 4515.33 457584 4841 .40 47“” 4705.95 475574 4&574 mm 451 .19 5000.03 1501 1534 1583 1595 1&8 138 159 1&1 1721 .1752 1785 1817 1817 1&0 1N2 1914 1947 2371 38888388353 §§ NV 347 ms 1.07 we 1.10 1.11 1.11 1.12 1.13 1.14 1.15 1.18 1.17 1.17 1.18 1.19 120 120 12 1” 123 124 18 18 1.27 123 m 128 128 129 1.” 1.31 1.31 13 1.3 1.33 1.33 1.34 1.3 1.3 1.37 1.37 1.3 1.3 1.” 1.41 1.42 1.43 1.43 1.44 1.45 1.45 1.48 1.47 1.47 1.47 1.48 1.48 1H 1.“ 150 15 150 1.51 1.51 1.9 13 18 153 179 (contlnued) 1407 506096 3315 1S 112 1507 84.14 29 221 512420 3344 1.53 113 1807 57.47 29 1.98 5181.87 3373 1.54 114 mu 81.92 31 2.00 5243.58 3404 1.54 115 18D7 58.58 31 1.88 $2.17 3435 1.54 118 1907 5929 31 1.91 5&1 .46 3486 1.55 117 2307 87.48 31 2.18 5428.94 3497 1.55 21m 5428.94 3497 1.55 118 2207 «ms 37 1.81 54$.02 3534 1.58 119 2307 87.78 33 2.05 5583.81 3587 1.58 120 2407 $.81 33 2.12 5833.62 3300 1.58 121 2507 75.98 33 2.30 5709.60 333 1.57 122 2807 58.88 3 1.45 57628 372 1.57 2707 28.84 21 1.41 579590 3993 1.57 123 2807 5795.90 m ' 1.57 124 M7 1922 20 0.88 5815.13 3713 1.57 3.107 5815.13 3713 1.57 125 mm 23.47 20 1.17 533330 3733 1.58 comr 125 125 125 W 78.41 as 2.51 1.57 W 5.87 an 020 one 101W. mas AVG 48.71 28.8 1.53 180 Table A.14a Tractor List Prices in Japan (2WD) mwagnanauoprioe PlioakWPliceNVgt Model (kW) (N) (WM1000yenwuwW1w1/N) $5533 39 0 $594 1.9 $50 @634 177.2 85631” 46.3 23095 2.0 3820 82440 165.4 $573007 53.7 23193 2.3 «BO 75058 173.8 8583(1) 61.0 3241 1 1.9 4340 71093 133.9 15000 35.3 22E” 1.6 3&0 $173 148.6 T6010 43.8 23928 1.8 3950 $260 165.1 17010 51.1 27655 1.8 43'!) 84120 155.5 T8010 58.5 30303 1.9 4600 78670 151.8 T9510 69.9 30303 2.3 5310 7552 174.9 M4950 36.4 21575 1.7 3540 97233 164.1 M5$0 43.8 22457 1.9 3840 87747 171.0 M6950 51.1 26380 1.9 4180 81773 158.5 M7950 58.5 26478 2.2 4400 75249 166.2 MQSSO 69.9 30204 2.3 5430 T1713 179.8 MF240 35.3 17976 2.0 3400 m 189.1 MF265 48.5 2457 2.2 4300 88581 191.5 MFZW 58.1 28096 2.1 4990 85880 177.6 F3910 35.3 20349 1.7 3530 9999 173.5 F4610 45.6 21477 2.1 380 87279 185.3 F5610 50.7 25% 2.0 4410 $897 171.6 F6610 58.1 26282 2.2 4620 79512 175.8 MT4N1 F 35.3 17162 2.1 3335 94465 194.3 m1 46.3 27508 1.7 4150 89562 150.9 M17311 53.7 27% 1.9 ' 4450 82881 159.5 M 31.6 14661 2.2 2455 77625 167.5 mm 36.0 2331 1.5 3350 92%4 143.8 W 43.4 24664 1.8 m 82% 146.0 mm 50.7 25154 2.0 3900 76848 155.0 .101 140 43.4 25841 1.7 4&0 99091 166.4 JD1640 48.5 25399 1.9 4550 93731 179.1 JD2040 58.1 26184 2.2 5020 $396 191.7 JM140 64.0 27851 2.3 5520 $36 198.2 J03140 75.8 35794 2.1 7730 10337 216.0 AVG tm $524 170.3 MIN 1.55 710% 133.9 MAX 232 102037 216.0 15' 181 Table A.14b Tractor List Prices in Japan (4WD) Paul “mmmzRfihfla PW») lfideN Pfinwwn Model (kW) (N) (MN) woven (WNW) (yaw/N) 855346 39.0 22555 1.7 4350 1 1 1591 192.9 856340 46.3 25399 1.8 4520 97547 178.0 SE7340T 53.7 25497 2.1 47& 88098 185.5 SE8340 61.0 34716 1.8 5040 &560 145.2 W 35.3 25448 1.4 4035 114293 158.6 1601C? 43.8 25&3 1.7 4750 1W1 184.9 17010" 51.1 29714 1.7 5&0 101727 175.0 T801 (F 58.5 32460 1.8 5500 94(B2 169.4 T951 (F &.9 32460 2.2 6200 88733 191.0 M4950DT 36.4 24419 1.5 4190 1 15&7 171.6 M59500T 43.8 25&1 1.7 4570 104428 1&.6 M&SODT 51.1 29322 1.7 4&0 97423 1&.8 M79500T 58.5 29420 2.0 5Z1) 88931 176.8 M&500T &.9 33343 2.1 6380 91 &9 191.3 MF154-4 38.2 22653 1.7 5450 1424& 240.6 MF174-4 50.0 24026 2.1 5&0 119167 248.1 MF194—4 55.9 W 2.0 6615 118341 237.1 F39104W 35.3 22163 1.6 4320 1223& 194.9 F4610-4W 45.6 24222 1.9 4870 106m &1.1 F5610-4W 50.7 29812 1.7 5330 105026 178.8 F6610-4W 58.1 &499 1.9 5560 95&0 182.3 MT4&1D 35.3 18535 1.9 3685 104379 198.8 MT6&1D 46.3 29224 1.6 4&0 1035& ‘ 164.2 MT7&1D 53.7 29812 1.8 5150 &918 172.7 W 31.6 15153 2.1 2770 87585 184.0 msoooo 36.0 25448 1.4 3950 10&02 155.2 MB 43.4 26723 1.6 41!!) 99091 1&9 YM7(XX)D 50.7 27213 1.9 4650 91627 170.9 J01 1400 43.4 27037 1.6 5&0 1341 18 215.3 JD16400 48.5 28018 1.7 6110 125868 218.1 JD20400 58.1 28733 2.0 6&0 11&33 230.4 J0214OD 64.0 &744 2.1 7120 111270 231.6 JD3140D 75.8 38491 20 9370 12&& 243.4 AVG 1.81 105% 190.9 MIN 1.39 &560 145.2 NMX 2J5 awn 142499 APPENDIX B SELECTED VERIFICATION CALCULATIONS To verify "TILLAGE PLANNER" and "TILLAGE MANAGER" models, manual calculations using a hand calculator were performed. The results are shown in 8.1. to 8.3. 182 183 Some sample calculations were made for power requirement, field efficiency and machinery cost following the flow of the algorithms of the models. 8.1 Power Requirement Calculations Input data: Drive system: 4WD Tiller width: W = 200 cm Tillage depth: d 12 cm Ground speed: 8, = 3 km/h Bite length: b = 5 cm Altitude: 1159 m Sinkage: 30 cm (Very soft) Power requirement for tillage is calculated by equation (4.5) by multiplying rotary tiller width and tillage depth. P,,,=5.341+ -0.6384b (4.5) Where: Igu_= specific power for tillage (kW/m/lOcm) S, = forward speed (km/h) b = bite length (cm) = 9.9073333 (kW/m/IOcm) Therefore, the power required for tillage, Powertn (kW) is 184 obtained by multiplying tiller width and tillage depth Powerm = 8.9073333 * 200 / 100 * 12 / 10 = 21.3776 (kW) The altitude effect is calculated by equation (4.11). All: c,,,=1.o3s 303 (4°11) Where: c;n_= coefficient of altitude effect, decimal Alt= altitude (m) C .1: = 1.035 (1159/303) = 1.1406386 4WD tractor ‘weight. is calculated. by’ equation (4.9) and provisional PTO power is estimated as 140% of the rotary tiller power requirement for tillage to allow sufficient power for overcoming motion resistance. Pp," = 1.4 * Pm.* Cm * 1.3737 = 1.4 * 21.3776 * 1.1406386 * 1.3737 = 46.895044 (kW) Then provisional weight is estimated by equation (4.9) using provisional power instead of rated power output. 185 w,w=4621+563.0p (4.9) Where: an = 4WD tractor weight (N) P = rated power output (kW) w...“ = 4621 + 563.0 * 46.895044 = 31022.91 (N) Now, provisional motion resistance is calculated by multiplying axle load and the coefficient of rolling resistance by equation (4.7). M?" “at" can“ CrrrICalr) T” (4 ' 7) Where: MR = motion resistance (N) can =coefficient of rolling resistance, front = 0.390 Cum scoefficient of rolling resistance, rear = 0.337 coefficient of axle load, front = 0.398 O E II cgu.= coefficient of axle load, rear = 0.602 TW = total machine weight (N) MR1 = (0.390 * 0.398 + 0.337 . 0.602) * 31022.91 =11109.118 (N) 186 Then provisional power requirement is calculated by equation (4.10). .MR*S, =— 4.10 PM 3600 ( ) Where: Pmt = power required for motion resistance (kW) Plot 1 = 11109.118 * 3 / 3600 = 9.2575983 (kW) The tractor size, P rated (kW) is calculated by equation (4 . 12) . P,,,,d=(Pm+Pmc) *1-3737*C.1c (4.12) 1’,“,,1= (21.3776 + 9.2575983) * 1.3737 * 1.1406386 = 48.002146 (kW) Now, the provisional power and calculated rated power is compared. provisional power = 46.9 < calculated power 48.0 (kW) Therefore, replace provisional power by calculated power and repeat this procedure. For second time calculations: W4”, = 31646.208 (N) MR2 = 11332.317 (N) Pu,” = 9.4435978 (kW) Pr...“ = 48.293563 (kW) Pam, = 48.0 < Pr...“ = 48.3 (kW) Go to third round: 187 W,,,,,, 8 31810.276 (N) MR, 8 11391.069 (N) Pm” =- 9.4925574 (kW) Pm,“ = 48.370277 (kW) Pun“:- 48.3 < Pm.“ = 48.4 (kW) Go to fourth round: Wm, = 31853.466 (N) MR, = 11406.535 (N) P,“ = 9.5054459 (kW) P,",,,= 48.390472 (kW) P...... = 48.4 = 9...... = 48.4 (kW) Then tractor size is determined as P = 48.4 (kW) by rounding at second decimal point to the right. All the calculation results were computation results of the simulation significant digits of 7. 8.2 Field efficiency calculations: Input data: Operator skill level: experienced Effective tiller width: 160 cm Plot size: 0.405 ha Field length: 100 m Field width: 40.5 m Sinkage frequency: 2.5 times/ha Lost time: 30 min/ha = 0.5 h/ha consistent with program at the 188 The theoretical time for tillage T.“ (h/ha) is calculated by the equation (4.13). Ttil- ms: (4'13) T 1:11 1000 / (160 * 3) 2.0833333 (h/ha) The time for tuning, T can (h/ha) for experienced operators is calculated by equation (4.14b). 1 *W 3,144.]. (La) +7w: T ‘_ 100 - (4.14b) ...... 100043.44, Ttmm = (3.14*200/100(40.5*100/160+4)+7*40.5)/(1000*3*0.405) = 0.3848415 (h/ha) The time for sinkage trouble, T,hm (h/ha) is calculated by the equation (4.15). = (21.85-2625*F5)F8 (4.15) T,nm = (21.85 - 1.25 * 2.5) * 2.5 / 60 Tank = 0.7802083 (h/ha) The field efficiency, FE (decimal) equation (4.16). is calculated by Has 75“ (4.16) Tturn + Tank" Tether + T811 189 FE = 2.0833333 / (0.3848415 + 0.7802083 + 0.5 + 2.0833333) = 0.5557951 The effective field capacity, EFC (ha/h) is calculated by equation (4.17). EFC=FE* TFC (4 - 17) Where: NC = 1 / (rm EFC = 0.5557951 / 2.0833333 = 0.2667816 (ha/h) All the calculations were consistent with the simulated computation at significant digits of 7. 8.3 Machinery cost calculations Input data: Annual use: USE = 713.9757 h Year to trade for tractor: 5 years Year to trade for tiller: 2 years Tractor rated power: 48.4 kW Tractor actual PTO power: 30.888041 kW Fuel price: FP = 12 sh/L Oil price: OC 8 45 sh/L The list price for front wheel assisted tractors, LPT,m, (sh) is calculated by equation (4.29). 190 Lme=1000(-36.3+15.op) (4.29) = 689700 (sh) The list price for rotary tiller, LPR (sh) is calculated by equation (4.30b). LPR=162.3(-205+4.57W1 (4-30b) - LPR 162.3 (-205 + 4.57 * 200) 115070.7 (sh) The tractor remaining value is calculated by equation (4.34). RVT=LPT*0.60*0.935"’m‘/1°°°’ (4-34) RVT 8 689700 * 0.60 * 0.935 ‘5‘"3-9757’ 1°00) = 325546.08 (sh) Therefore, the average tractor depreciation, ADT is ADT = (LPT - RVT) / y (689700 - 325546.08) / 5 = 72830.783 (sh) The rotary tiller remaining value, RVR is calculated by equation (4.35). RVR=LPR¢0.45*0.660""”‘/n“’ (4-35) RVR = 115070 * 0.45 * 0.660 ‘2'7‘3-9757 "1'" = 39040.278 (sh) Therefore, the average tiller depreciation, ADR is ADR = (115070 - 39040.278) / 2 8 38014.861 (sh) 191 The tractor average annual repair cost, AART is calculated from equation (4.36) dividing by the year of use. _ USEN M0 - * * —— e TERN'LP.RF1 (1000) (4 36) AART (689700 * 0.0450 * (713.9757 * 5 / 1000)’”) / 5 79106.04 (sh) The average annual repair cost for rotary tiller, AARR is AARR (115070 * 0.281 * (713.9757 * 2 / 1000)*°) / 2 32965.927 (sh) The fuel consumption, FCN (L/kW.h) is calculated by equation (4.38). FCN=2.64*X+3.91-0.203flm+—IT3 (4.38) 30.888041 / 48.4 >4 ll 0.6381826 FC =2.64 * 0.6381826 + 3.91 - 0.203 SQR(738 * 0.6381826 + 173) = 0.4433243 (L/kW.h) The annual fuel cost per machine, FC (sh) is calculated by equation (4.39). FC=FGV¢PP¢PT¢FP (4 . 3 9 ) PC = 0.4433243 * 30.888041 * 713.9757 * 12 = 117321.22 (sh/year/tractor) The annual oil cost per tractor, CC is calculated by equation (4.40) and (4.41). 192 OCN=0.00059*PR+0.02169 (4.40) 0C=OCN*FT*OP (4.41) oc (0.00059 * 48.4 + 0.02169) * 713.9757 * 45 1614.349 (sh/year/tractor) It was found to be the same as computation results of the simulation program at significant digits of 6. APPENDIX C OUTPUT DATA FOR ANALYSIS Tables C.1 through 0.11 show the output data from the TILLAGE PLANNER and the TILLAGE MANAGER used for implementation and sensitivity analysis. 193 194 Table C.1 Simulation Results of Tillage in 1989 1%9 SUM Auhbiiy Latin mm (a. 150 150 150 150 Thus dent) (on. 11.3 11.3 11.3 W .9...) (mm) 3.11 3.11 3.11 3 bag!) (an) 5.51 5.51 5.51 Sol candlbn SCI-T SOFT SGT me 2V0 2M) 2M) 1M0 Made ((19 1158 1158 1158 Pow-Hoth- M 14.97 14.97 14.87 Pawn union mu. 4M!) 7.42 7.42 7.42 Tnduslze 81W) 447 44.7 44.7 44.7 Power nonunion ('16) 48.91821 48.91821 48.91&1 00/10!“ In. 13 13 13 13 Du“ ofllqs 1% 181 203 188 0. d Ins caution 208 194 218 211 Nd dim d Me 172 151 1a 185 “but how pet day 4)) 9.5) 8.5 8.5 8.5 W m .817 .8 .7 .8 w 01M pdhm m .833 m3 .833 Rumored tine 01) 1218.9 1:33.129 1340.489 Pu size on) (.415) .41'5 .405 .415 Fina how: (u. 123 123 123 Find “18) («1) 32m 32.” 3.93 Guardrail level WEI-1808) menace: EXPERIBKZD 888.com 2.5 2.5 2.5 Leanne (uh) 80 so 90 m did-no] .583 .583 .53 TFC CM!) .342 .342 .342 EC M) .1m Am .181 Tad-lee M 5835.3 5835.3 5835.3 5835.3 mum Indians 30 24 21 24 may. who 0) 8 0 8 Pet! in (11) 1283.5 1438.5 1412.5 Swan-n may .1113 .125 .001 Mud me (1)) 875.1212 875.1% 1072.375 Tracts H pr'ne (sh) 541540 544540 540540 TI. H price (sh) T135 m5 77985 Yeubttdmm 5 5 5 Yuma-inn til! 2 2 2 Tm avg. M 81 ”x 81mm “.88 Thaw. W 279nm 27978.2 28715.84 Ttfl avg. W ”345.8 ”5.9 91575.52 Lianne Who (away) 1840 1840 1840 Fueled (shin-Iain) 131m3 13188.3 1451514 and (iv/(maths) 21 (3.024 2113.124 2319.371 Tmszpu'md 115845 115845 13863.3 TI. avg. tended «873$ «873$ 5040121 1‘10: oust (dukes) 41551 42284 42117 Wood (dime) 2178 21& 2431 Seed and (sh/M) 278 278 278 Chunks mu (sMn) 442 442 442 Putin: and (sMu) 2310 2310 2310 Who dupe Wu) 1281 1230 1237 Fun inn cost (£14.) 42m 4258 4285 Tu! amml and (IMI) 8488 8440 m Myth) 403) 4321.5 47:!) 4814 4841 Fatty price (sh/lg) 5.5 5.5 5.5 5.5 (In. Mun (dime) 234$ 2&18 25377 25523 Nd nun OM.) 18548 1M7 18827 195 Table C.2 Simulation Results of Tillage in 1991 131 sum Trade Wed 881.8280 “AND Sim-as sumo SHMD mud“ m0 200 200 15043!) 150 200 150431) 150 200 Thad-finch) 11.3 11.3 11.4 11.3 11.3 11.4 11.3 11.3 3.11 3.11 3.1 311 311 31 311 3.11 an- (ad 551 551 55 551 551 5.5 551 551 Solcntulbn SOFT SOFT vain/son SG-T SCI-'1’ VB‘NSOFT 807T SCFT Truman. MD 4WD 4WD m 2WD 4WD m 2WD 4WD “(at 1158 1158 1158 1158 1158 1158 1158 1158 Peach has (11W) 18.3 18.3 14.87 18.3 14.87 18.3 mu mm (11W) 7.47 7.47 7.42 7.47 7.42 7.47 Tm in (WV) 44.7 44.4 44.4 44.7 44.4 44.7 44.4 Panama) 3223 3.233 0.8131 m 0.8131 3.2283 mum-no. a 3 a 3 3 a an as a Dullundlqp 154 154 155 13 13 13 147 148 148 walla-m 182 182 188 183 184 184 175 174 178 “W01” 13 128 1a 113 113 113 13 1a 13 Work has pad-y (N (85) 8.5 8.5 8.5 85 85 8.5 8.5 8.5 m» .817 .8 .8 (0.81) .m .875 m7 .875 want” .812 .83 .83 .83 .83 .83 m .83 .83 Willem) 383077 383077 843” 843353 845.2218 8458158 Push (to) (.405) .415 .43 .415 .43 .415 .43 .43 .415 WWW 123 123 123 123 123 123 123 123 Find nun (n. 3.3 :23 3.88 33 3.3 3.83 383 323 Omani-uel mmmmmmmmmmmm sung-m 25 2.5 2.5 25 2.5 25 5 5 Leah-(nit so co m m 3 Q 3 00 $0M .244 .244 .13 .244 .181 .217 Tunas (II) 5&8 583.8 5M8 583.8 1514.1 4324.5 583.8 1537.5 434.1 mama-ah- 24 24 24 3 8 21 3 8 21 Nam-7018mm (13) 8 8 7 4 3 7 4 3 Hum (h) 1” 1&5 m8 m5 137 1045.5 Swim“ .018 .015 .451 .03 .33 .004 m as (1)) 787.043 787.043 5888a 737.833 64.3843 827.8784 maul-1mm) mm 9870) 54640 85700 541540 3873 Tlsrktpdnsm 115070 115070 77% 115070 77% 115070 Yacht-tum 5 4 5 5 5 5 5 5 Yenbttedhgllu 2 2 2 2 2 2 2 2 Tmm W 3131.3 81187.78 5473.58 ”71.7 5347.3 887233 Thug. M 313.45 3120.45 ““55 334044 2513.3 @1212 Tu W 1072518 1M2 1840 733.14 13812.1 180 812453 1323.5 mums-(m 18¢) 1875 1840 180 1840 1840 Halon-1 (We) 11m 11” 7377.2 1M124 £13.04 1M3 Claw.) 1717.531 1717.531 1&847 153.183 1415.“ 178353 haul-v9 and!“ mu 7200875 4143.44 77153.84 “.3 87158.34 Tlsrevg. (operand 413331 413331 1041.78 3215.3 18787.78 4431.81 mum 451 m 40:52 40052 4:185 0451 Walnut!“ 357 m m 212 254 e341) 201 m swam 278 278 278 278 278 278 278 278 278 mm(m 442 442 442 442 442 442 442 442 442 mums) 2310 2810 2310 2310 310 310 310 310 2310 WWW“ 123 (as 134 134 134 (11!) 1310 137 mewwm 437 4328 432 432 432 483 433 Twmucusfifll) «34 «58 9447) 8584 843 m 873 355 Pedqwdm 4873 4” (4”) m 4m (4m ‘15 Q3 Mathew 55 5.5 55 5.5 5.5 5.5 55 55 Own-WNW m 3771 @7323) 2732: 2733 M 2733 2375 “7.171(er 3418 3413 2378) 3758 ”17 2314) m4 330 Table C.3 Effect of Random Simulation 196 Tlhl 1 2 3 4 5 11811111181 (an 23 23 23 23 23 ”when. 11.3 11.3 11.3 11.3 11.3 81011111 spud (1111111) 311 3.11 3.11 3.11 3.11 an. huh (an) 5.51 5.51 5.51 5.51 551 Sol caution SOFT SOFT SGT SG’T SG-‘T Twain). MD 4WD 4m «MD MD mm 1159 1159 1159 1159 11511 Pouch!” M 18.85 18.85 18.3 18.3 18.3 Pow. 1a "dim unit. 01W) 7.47 7.47 7.47 7.47 7.47 Twat sin m 44.4 44.4 44.4 44.4 44.4 Punt W (96) 3822283 3822283 3M3 3822283 32223 00/10! M19 he 28 28 28 28 28 Dawn ofllqa 13 154 155 155 155 0‘: din. emotion 13 13 183 183 13 Nd 81113011 dingo 127 128 128 128 128 M In: patchy Q1) 8.5 8.5 8.5 8.5 8.5 .8 8 .8 .8 .8 mm 01180111 PU“ .33 .83 .33 .83 .33 Mind 81111 81) 888.377 888.377 38.377 “.377 “.377 thl laugh on 12:1 12:1 123 12:1 12:1 Find m (111) 3.3 32.3 32.3 32.3 32.3 Wold low! m menace m 90m 80m 588090 2.5 2.5 2.5 2.5 2.5 1.1318110 (1181) 3 3 3 3 3 Ft“ M .481 .481 .481 .481 .481 TFC M) .488 .43 .43 .43 .488 30 M) 244 .244 .244 244 .244 Tune. 01.) 58388 5838 583.8 5388 5388 We!“ W 24 24 24 24 24 We!" M10 8 8 8 8 8 Phil in (11) 1078.5 was 103.5 138.5 138.5 swan my .013 .001 3.1034 .001 Anni no 81) 787.043 787.043 787.0482 787.043 787.043 Tudor 81 pic- (:11) 828700 82873 3873 3873 3873 Tluflpficobh) 115070 115070 115070 115070 115070 Youbtid'ngm 5 5 5 5 5 Yawning (In! 2 2 2 2 2 Tudor w. W 88131.3 88131.3 88131.3 3131.3 88131.3 Th! m. M 38120.45 3813.45 3120.45 3120.45 3120.45 Tcfl avg. W 1072518 1072518 1072518 1072518 . 1072518 Lia-mo hum 18. (31M 1840 1840 1840 1840 1840 filled (gm-am) 11m 11m 11801111 11388 1133 on and W) 1717.31 1717.531 1717.531 1717.31 1717.31 Tm avg. npfl 3‘ 8m.“ 900011.44 3118.44 ”3.44 38.44 Tlu «a. nod and 4133.31 4133.31 4133.31 4103.31 4133.31 1110! and (uh/yet) 433 4351 4083 4083 40885 W and (ah/1'.) 237 237 2057 237 237 Seed and (IMII) 278 278 278 278 278 Wood (:MI) 442 442 442 442 442 Wood (8111114) 2310 2310 2310 2310 2310 Minn dune (81.118) 132 123 123 1288 123 Fumimucud (IM'I) 433 437 4328 438 4328 Tfl anal and (IMII) 837 834 833 833 383 MM (an) 4883 4873 4888 4888 4888 Pddy print 0M0) 5.5 5.5 5.5 5.5 5.5 Gm. 188m (IMI) 2838 261100 28771 28771 28771 Nd III-11 W!) 20471 20418 2033 233 2388 197 Table C.4 Effect of Tiller Width by TILLAGE PLANNER May-01m 1mm Man 2400111 2801111 Tluwidh (ad 160 200 240 200 “rundown (at. 11.3 11.3 11.3 11.3 0mm and m») 3.11 3.11 3.11 3.11 3 Dub (an) 5.51 5.51 5.51 5.51 Sol cudlbn SCFI' SOFT Sd-‘T SOFI’ 71.11am). 4M) 4M MD 4m Md: (no 1159 1159 1159 1159 my” M 15.98 19.95 235 27.94 P01001101 11109011 mu. (WI) 8.07 721 8.5 9.49 1mm 01W) 34.5 42.8 50.8 58.8 Tm”! N 24047.33 28578.74 31 $.14 3759752 Solcmdl'nn URY DRY DRY DRY mum-no 29 29 29 29 Dawn altho- 153 153 153 153 W011: how not do] (I) 8.5 8.5 8.5 8.5 W .8 .8 .8 .8 00.170011101100111 pdom .833 .833 .83 .83! mm how 825 :m 825 828 Fill how 1'" 123 123 123 123 Find wflh (m) &.93 32.93 32.90 32.93 Malibu EXPERIEMED menace: mm m WM 2.5 2.5 2.5 2.5 Inuit- (win) so 80 00 80 Phil M w .491 .43 .31 TFC Cum) .373 .400 .822 .748 EC M) 21 244 .271 29 Twas...) 5838.8 5&88 5838.8 5838.8 Milt-rum mm 27 23 21 19 We!" mu 7 8 5 5 Phil in 81) 1133.317 “33.317 1133.317 1003.317 Gym 105-bl] .019 m 027 .049 hand mo 81) 3133002 0245303 -3848 833.8187 Tm Isl pd:- (Ih) 48121!) 002700 moo 342700 Th 81 01100 (:11) 05402 115070 144739 174407 Yacht-abound: 5 5 5 5 Vacuum tint 2 2 2 2 Tm avg. M 338598 85718.78 70910.77 9212223 Thnvg. M 294129 3947228 49728.87 00000.64 Tfl avg. M 8182725 13191 1288378 121229 Lin-Ia Who (am 1840 1840 1840 1840 Pad and Winn) $941.87 1 1W8 “2&2 1859154 Clad W) 1549.191 1737M 1923.741 2110.823 Tam avg. and ad 7258788 921%” 1118543 1317821 TI. «9.1006 0041 32178.13 43905.40 55954:! 88113.8 maroon (uh/ya) 40810 4061s 4031s 4001s Wand m 180 2000 2151 2311 30000001 (1M!) 278 278 278 278 Wood m 442 442 442 442 Wood Wu) 2310 2310 2310 2310 W dune m 1300 1300 1&0 1300 Fm'npuoud (3M!) 4328 4:28 4328 4328 Tainan-lend m Q27 8337 8479 Pumwmaw an an an an M 9110. m 5.5 5.5 5.5 5.5 an. alum (£18.) 28829 28829 28829 20029 Nd m FM.) 20002 204% 20350 20190 198 Table C.5 Effect of Tiller Width by TILLAGE MANAGER Th1“! (and 13 23 240 23 Tluwklh (0111 180 23 240 23 Thy-depth (an) 11:1 11:1 11.3 113 Grand spud (11min) 3.11 3.11 3.11 3.11 an. um (an) 5.51 5.51 5.51 5.51 Solcnndlbn SGT SOFT SGT SOFT Tndoflyp 41M) 41M) 4WD 4N) “do (n. 1159 1159 1159 1159 M101” (KW) 15.98 19.95 23.5 27.94 Paw-1h m mu. (kW) 8.07 722 8.3 9.49 Tatiana. (kW) 345 42.8 3.8 58.8 mm ('16) 3.1404 3821284 3.18424 38.1283 00110101!“ No 28 28 28 28 W011”. 180 13 157 181 Oh dwm 188 188 15 13 MW d“. 133 133 131 134 Wouxmpudq 81) 8.5 8.5 8.5 8.5 “in. wobbly .7941 178 .7931035 37.191387 Waived!” .833 .833 .833 .833 W in. 01) 131.32 1009.025 138.857 1&93 M um (11'. 123 123 123 123 F8011 M (m) 32.3 32.93 3.3 32.93 Maul-val 9035mm EXPENSES 90838105: 903431050 58mm 2.5 2.5 2.5 2.5 mm (nit) 3 80 3 80 M M .582 .491 .45 .31 TFC 410.41) .373 .43 .822 .748 50 M) 21 244 271 .23 Twain 8:) 5838.8 5838.8 5838.8 583.8 Monthly m 27 23 21 19 may.» m 7 8 5 5 Min ()1) 113.5 113.5 1113.5 113 Syahm 10m .03 .018 .017 .37 m mo 8:) 818.302 824.533 39.3848 83.8187 Tm U pvt:- (Ih) 48123 012700 72273 8427!!) Th lst pin- (:11) 8543 115070 14473 174407 Yacht-04191100101 5 5 5 5 Yulatming 1801 2 2 2 2 Twang. dnpnddlon 52385.98 85718.78 7310.77 312223 Thug. 000100881011 234129 3947228 49728.87 833.84 Tdd m.anpncnbn 8162725 15191 1288378 1521229 Lin-mo Mambo (Buys) 1840 1840 1840 1840 M0001 W) 5940.18 11319.7 1425018 185349 G001 W) 1549.191 173733 133.741 2110.33 Tm avg. maid 72587.88 9213.5 11154.3 13173.1 Tllulvg. npu‘cnd 3178.09 43905.49 55954.3 88113.8 Wood (ships) 4351 4351 40751 4385 Want (011nm) 1900 2010 2152 2312 500110001 (IMII) 278 278 278 278 Wad (iv/h.) 442 442 442 442 Wood WI.) 2310 2310 2310 2310 mm (011.410) 123 123 1295 123 Fmiuncud (aMn) 4318 4318 433 4317 Tammi (am) 318 8328 8475 8829 My“! 41g) 4841 4841 457 483 Putt/01100 m 5.5 5.5 5.5 5.5 Gm. Mum (like) 2528 2&8 28713 28597 Nam QM.) 2043 233 2338 133 199 Table C.6 Effect of Number of Machine Sets Ffimwmuhu m 24 28 a x Spa. m 5 3 7 9 9 Thwih (ad 23 23 23 23 23 Tho-MM 11.3 11.3 11.3 113 113 81811111 upset! (11mm) 3.11 3.11 3.11 3.11 3.11 an. knob (on) 5.51 551 5.51 5.51 5.51 Sol comm SOFT SCI-T SGT SG-‘T SOFT TWMI. MD 4M 4M) MD 4WD M910 1159 1159 1159 1159 1159 Powbt In. 81W) 19.5 19.5 19.5 19.5 195 Power ht m mu. 01W) 7.47 7.47 7.47 7.47 7.47 Tudor :1:- M 44.4 44.4 44.4 44.4 44.4 Pawn m (96) 3233 3233 3233 3233 some: 00/ 1111 M19 In. 28 28 28 28 28 W unlu- 178 155 137 127 118 0‘ d w M 204 13 185 155 148 Nd «Minn d In. 147 129 114 13 5 Wk 1101: put day 91) 8.5 8.5 8.5 8.5 8.5 m m .8 .8 .8 .8 .8 w am pan .33 .33 .833 .833 .83 Mind 'bm 81) 115.589 “.377 539781 747238 34251 Fol! m (118 13 13 13 13 13 FM will (m) 3.93 3.93 3.3 3.3 3.93 Opal! dd bvul EXPERIEME) mama) m m m Sid-go 11m 2.5 2.5 2.5 2.5 2.5 1.081 line (uh) 3 3 3 3 3 Fad M .491 .491 .491 .491 .491 EC M) 244 244 244 244 244 T“... M 5838.8 5838.8 5838.8 5838.8 5838.8 mam Indian 20 24 28 3 3 Wotan-1. «who 5 8 7 8 9 FM! in (11) 1249.5 138.5 m 31 33 Syuhm M .39 .015 .35 .032 .045 Amt-I mo 81) 58.4554 797.043 83.135 597.7847 531.341 Tm it 91b (Ill) 3973 3973 3973 3973 3973 Titanic-(uh) 115070 115070 115070 11370 115070 Yacht-aha 11am: 5 5 5 5 5 Yachts-filo 18:1 2 2 2 2 2 Tuna Ivy. dept-(Hm 71148.3 88131.3 5878.3 84127.5 373.79 Th: avg. W 4139.72 3120.45 375883 3935621 5345.51 1’“ m. M 11318.4 1072518 13485 134842 3078.3 Linn. m be (31M 1840 1840 1840 1840 1840 Fallow (3M) 14131.5 1133 10131.1 88550.3 78711.98 on and W) 2081.37 1717.31 1472.17 1288.148 1145.31 1'“! m. and cod 12312.1 W.“ 3128.3 5329.75 433.75 TI. :10. and and 3159.98 4133.31 313.3 3133 1825925 unload (HM 4134 4385 435 373 3452 W and m 238 257 233 2M 2137 8.11am WI) 278 278 278 278 278 W cl m 442 442 442 442 442 Fa” and (31101.) 310 310 310 310 310 W dupe m 1288 123 133 1337 1350 Pam m and QM.) 423 438 451 435 4378 Tfl and and (IMI) 334 8414 8457 8515 Fatty ybu .19) 4757 438 433 5015 503 P0110] 91b w 5.5 5.5 5.5 5.5 5.5 810. 131111 (IMI) 28181 28771 27294 27584 27848 mm m 19777 20388 233 21127 21331 200 Table C.7 Effect of Machine Availability huh-filly (95) so 80 70 mm 27 24 21 8mm 3 8 9 Thwidh (on. 200 zoo zoo Thou»: (an 11.3 11.3 11.3 Ground spud 00M!) 3.11 3.11 3.11 an m (an) 551 551 551 summon SOFT SOFT SOFT Tarantula 4WD 0M) 4M Aid- (ni 1150 1159 1150 Pawn” OMI) 19.95 19.95 19.95 Panda union mil. 0M1) 7.47 7.47 7.47 Turnout. m 44.4 44.4 44.4 mm (9‘) 30mm 3822283 3822283 0.11am” 28 28 28 Manic. 140 154 173 D‘- elm-caution 168 182 201 MM dime 117 128 144 “mp—raw .1) 3.5 0.5 0.5 .9 .8 .7 Walnut m .833 .833 .333 W in. (h) 085.“ “1377 113.837 M m (n. 123 123 123 Find with (m) 3233 32.93 32.93 Out-(mull bval EXPBIEBDED m m Shalom 2.5 2.5 2.5 1.1-11in. (Ni!) 60 00 00 Phil m .401 .491 .491 8’0 M) 244 .244 .244 Tainan (ha) 5830.6 5838.6 5838.6 Wotan-yum!“ 27 24 21 magnum 3 B 9 Film (11) 994.5 1180 1224 System I“ .001 .018 .134 m mo 01) 797.04% 797M 707M Tm H ptbo (Ill) 8297!!) mm m Tlu it print (ah) 115070 115010 "$70 Youbttd‘ngbuch 5 5 5 Youlutming (In: 2 2 2 Tm m. M 881313 68131:” 00131.3 Thaw. M $120.45 39120.45 $120.45 Tau mad-pawn 1072518 1072518 1072518 Minna-bow 1840 1040 1640 Falcon (sh/imam.) 11m "one: "was Gal W) 1717.531 1717.531 1717.531 Tm avg. (opal an 0013.44 90003.44 90003.44 Tlu avg. rand «.1 41133.31 41133.31 41133.31 Lined 4015 41351 41204 Wood m 2155 267 2000 Sudan-1 WI) 278 270 270 Wald (IMI) 442 442 442 Wand m 2310 2310 2310 Minding. m 1319 129 1272 MW“ (9M!) 4347 4&7 4:!!! Tdnmualcod WI) 6412 6384 m Myth NI) 4947 4873 4773 Mutton bMo) 5.5 5.5 5.5 Gm. mum (IMI) 27207 26000 2&49 Nd mu m zooms 20416 1m 201 Table C.8 Effect of Ground Speed and Bite Length W apaad M) 2.5 28 3.1 3.4 3.7 shanty» Ln 33 4s 81 a 1'th (uni 200 200 200 20) 200 magnum 11.3 113 11.3 113 11.3 Gland spa-d mi) 25 2.8 3.1 3.4 3.7 h m (an) 1.72 3.3 4.9 8.4 8 Sol mum SGT SOFT SCFT SOFT SCI-T Twat":- 4WD 4MB 41M) 4MB N10 men) 1159 1159 1159 1159 1159 PW. 10:” 01W) 22.32 21.57 20.78 20.15 19.37 Paw ht "din nail. (WI) 8.01 8.73 7.45 8.17 8.89 Tm aha M 44.4 44.4 44.4 44.4 44.4 m m (%) 321378 382741 3.4342 3621978 3.3551 Dal 10! M19 he 28 28 28 28 ' a W 011”. 170 182 154 148 144 0* d Iaga caution 198 190 182 177 172 Nd am d Iihga 142 135 128 124 120 Well: how pat day .1) 8.5 8.5 8.5 8.5 8.5 mu ”My .8 .8 .8 8 8 W 01 wk pdbm 833 .am 833 .833 .833 Haunted in. 0!) 1133.735 1058.885 n.1248 9482877 913.457 F1081 M (a. 123 123 123 123 123 Fla! M (m) 32.93 3293 &.93 3293 32m Opataht all laud BG‘ERIENCED memes: m m mama) 58mm 2.5 2.5 2.5 2.5 2.5 Lad tins (nit) so 80 so 80 a0 Phi! m .53 .513 .491 .472 .453 TFC M .4 .448 .498 .544 .592 EC M) 215 .23 244 .257 288 'Td an: M 583.8 5838.8 5838.8 5838.8 58388 Mdm Indian 24 24 24 24 24 Wain-u "diva 8 8 8 8 8 PH! 181. (h) 1207 1147.5 1138 1154 «no Spun may .013 .01 .018 .12 .128 m ma (1:) mm 848.932 793.4993 758.8141 725.1% Tm D1 pica (ah) 8297a) 8287!!) moo 8287!!) m Tlarhtptinahh) 115070 115070 115070 115070 115070 Ya-bnuhg m 5 5 5 5 5 Vacuum lid 2 2 2 2 2 Tudor avg. M 71228.18 m.“ 88158.83 87379.” $718.84 Th avg. deposition 404$ 39754m 313923 388188 381873 Tdd avg. dapnchlbn 1107182 1138485 107289 1m 1048$8 Undu- Mm ha (am 1840 1840 1840 1840 1840 Fa out (stdsha) 13894.2 1275858 1201:”.4 114318] 1181 53.4 (I cad W) 164.442 1‘028 1720.” 1834.715 15%.?!» Tom avg. «pi and 1185518 101- M37 81537.83 7451225 Tld avg. and out 531 $.78 4050392 4123328 3721891 3401025 Ldnr and (drips) 41184 41818 41.851 4046 41818 W and (1M!) 2373 22m 2070 1N7 1881 Sad and WI) 278 278 278 278 278 an“ and (1M!) 442 442 442 442 442 Fdfizarcod 0M1.) 2310 2310 2310 2310 2310 Ming dug- WI) 1278 1288 1290 1303 1313 Pam hull and (aMn) 4304 4318 4:27 4334 4341 ‘Tdd animal and min) $77 8519 8:97 8:!)1 :«n MM kg) 4788 4831 4873 4899 4828 Pddy price (3M9) 5.5 5.5 5.5 5.5 5.5 Oman Idum m 2833 28588 26300 2948 27091 Nd nan WI) 19859 2M8 20403 21345 20039 202 Table C.9 Effect of Operator Performance candid!“ LESBMMLBSWWWWWWWM Leah-(m 70 co so 50 50 0 Macadam” 25 3.11 311 3.11 3.11 3.5 Tlandm 1:10 zoo zoo zoo zoo 200 an The 11.3 11.3 11.3 11.3 11.3 11.3 WWW 2.5 311 3.11 3.11 3.11 3.5 a human) 5.51 551 $51 551 5.51 5.51 Solcurdlbn son sa-‘T SG-T SIT son SGT Tm!” MD 4WD 4ND 4M) 00 MD A.8-(m) 1159 1159 1159 1159 1159 1159 mulls-NV) 1805 19.95 19.95 115 11185 21.04 mu manual MN) 452 7.21 7.21 7.21 7.21 can Tmbm 34 42.8 428 0.8 42.8 488 “WWW 2379.6 2578.74 ”7874 57874 257874 315801 Moat-on DRY W [FY CRY W W Baum” a 29 28 a 28 a animal-no- 153 153 153 153 153 153 “model-yo. 8.5 8.5 85 85 8.5 8.5 .8 .8 .8 .8 .8 8 mama-nun m .83 .833 m .833 m Wanna: m m &4 can can Plankton) .45 .415 .15 .15 £5 .45 aunt-111mm) 123 123 123 123 121 1% 58111181019 9.83 283 3293 3.93 3.83 an Opddnrdtllavd mmmmmmmmmmm sung. 2.5 2.5 2.5 2.5 25 25 Lodmmfl 70 co on 50 50 0 Hum .53 502 .01 .512 .509 .507 TFC M .375 .4a .m an .&8 .5“ B’CM .1“ 234 .244 .35 an .3! Todd-a (1') 583.8 58:38 5&8 some 5838 m mam M. 29 24 a 22 21 18 mum-mun 7 8 8 8 5 5 Hana-(11) 1033317 1183317 1W7 1183317 1183.317 1133317 Spinal-bu .01 .023 .03 .04 .127 .0‘ Had ma (N 8185507 881.4”3 824.538 818.244 mm ”.01 TWHWM 473700 m m «2700 1mm m umm 115070 115070 115070 115070 115070 115070 Vanuatu” 5 5 5 5 5 5 Yunnan-u 2 2 2 2 2 2 Tmavg. W 5155.6 $848.84 671878 m mm 751“ Maud-91m “18 3472.3 mas m1 m1 Todavg M11 M18 1W 118181 1M8 1651!) 11431.3 Mimbflm 188) 180 11140 180 180 180 Wood (M) 84421.7 1W3 110320 118455 13842.3 1330714 alumna-an) 1537.62 1751.“ 1737.32 1721171 1758.33 1&7811 Tuscan updrood 714128 ”75355 was: 81010.1! 844333 1013738 Tia-1g. “and w 4470.77 m 4301.6 “94 m 1.“de «18 we «18 4318 «18 «18 Madam.) 213 m m 1&7 1&4 1m 50.11de 278 278 278 m 278 278 Woodwfll) 442 442 4Q 4Q 442 4c Fart-1mm ”10 2310 2310 ”10 310 $10 Hmm(m 1300 1300 1300 13m 1300 1am Farrnb'utcodww‘) 4:28 43 4&8 m m 43a Tchlamdcndum M 8424 83:37 8152 8118 Padayylddnrg) £78 4878 4878 078 4878 4878 WWW 55 5.5 55 5.5 55 55 Gunman-um am am 2am m m m NdeaMa) mm m m 3574 20377 31713 203 Table C.10 Effect of Plot S12e and Fleld Length Flu eh me) .405 .445 .405 .81 .81 .81 F“! m (n. 100 150 200 100 150 200 Tler will: (one 200 200 200 200 200 200 ‘I'hgedephm 11.3 11.3 113 113 113 113 Gmmd speed 0mm) 3.11 3.11 3.11 3.11 3.11 3.11 880 Iendh (an) 5.51 5.51 5.51 5.51 5.51 5.51 Sol caution SOFT SOFT SOFT SOFT SCFT SOFT Tudor type 41M) 0ND 4M) 4WD 4WD 4WD WM 1159 1159 11$ 11$ 11$ 11$ Powhflege (RW) 1885 19.95 18.85 18.5 19.95 18.85 Pow 10: motion we“. 01W) 721 7.21 721 721 7.21 721 Tudor size (kW) 42.8 42.8 42.8 42.8 42.8 42.8 Tide: void! (M 28578.74 28578.74 28578.74 28578.74 28578.74 28578.74 Ween: cmdlien DRY W DRY NY NY DRY 00/ Int ehnhg Due 28 28 28 28 28 28 Dud'nn align 153 153 153 153 153 153 W08: how per day 81) 8.5 8.5 8.5 8.5 8.5 8.5 Hi. ”My .8 .8 .8 .8 .8 .8 w 01 work M .833 .833 .833 .833 .833 .833 m wk hunt 820 834 818 822 &1 824 not an (he) .405 .405 .405 m .01 m M m (118 100 150 200 100 150 200 Fled will: (:11) 40.5 27 2025 81 54 40.5 Opemur ekl level EXPERENCE) EXPERIEMED menace: m EXPERENCED 803888408) sung. 8W 2.5 2.5 2.5 2.5 2.5 2.5 Lost t'me (win) so so 80 50 50 50 F881 m .483 .487 .504 .504 .52 .528 TR: M) .488 .490 Am .4“ .4“ .488 30 M) 24 247 .251 251 258 .283 Tfi evee (he) 5838.8 5838.8 5838.8 5&88 583.8 5&88 Meme! d mind Mile 24 23 23 23 22 22 m 01 eyes mu 8 8 8 8 8 5 Fieu fine (11) 1m.317 «83.317 1133.317 “33.317 1133.317 1003317 Spun My 023 .00 .00 .03 .04 :2 NM 1.8 (h) “.81!!! 814.1“ «2331 802.1927 “.4255 823.6% Tm H ptbe (eh) 8&700 802700 012700 02700 82700 802700 Thhtpdnebh) 115070 115070 115070 115070 115070 115070 You 101 redhg new 5 5 5 5 5 5 Yea Int Inning tlet 2 2 2 2 2 2 Tue! evg. depleddhn 8544727 85527.78 85:18.41 85:95.7 8538421 8570221 Th evg. deptecidim M845 $340.$ 381$.“ $188.5 $24128 39400.39 Tdfl evg. W 1047322 104m3 1044888 IWS 104625.!) “5163.1 Lbnee W be (8m 1640 1840 1840 1840 1840 1840 M0081 (eMned'nhe) "7077.4 1177133 11m2 1158732 11855.1 "$72.8 on and (minutiae) "(3.377 1715.544 180.703 1M2“ 1&8203 173.48 Tieda evg. mi mi 8mm mm 87318.8 87285.2 88188.58 81882.13 'l'ler evg. m and 42411.78 42888.6 4183.3 41815.57 42(558 WIT Ltd and (ships) 41318 “18 44318 «£18 44318 44318 W and m 2040 133 1%3 153 18% 1888 Seed 0081 (eh/h) 278 278 278 278 278 278 W an (anal) 442 442 442 442 442 442 W and (eM1e) 2310 2310 2310 2310 2310 2310 Heading chemo (eMIe) 1300 1:00 1300 1300 1300 1300 Fm m 0081 (3M!) 4:28 4:28 4&8 4328 4328 4328 Tddemudcnd (the) 838 8311 8281 Q81 8224 8188 Peddy ybfl kg) 4878 4878 4878 4878 4878 4878 Pddy price W 5.5 5.5 5.5 5.5 5.5 55 Gtoee mum (eMIe) 28828 28828 2- 28828 28828 2- Ni nun WI) 20481 20518 21548 21548 m m 204 Table 0.11 Effect of Tractor Drive System DIM 2M) 2WD 4WD 4WD Salaam VBIYSOFI' EXTRBEYSG’T VEIYSOFT EXTREIELYSOFT sum may 2.5 5 2.5 5 Tluw'dh (an) 200 zoo zoo 200 quh (at. 11.3 11.3 11.3 11.3 Glomd spud W) 3.11 3.11 3.11 3.11 0|. W11 (c110 5.51 5.51 5.51 5.51 Soicnndiian VBIYSOFT summon VBIYSOFI’ EXTRBEYSOFT Train”). 21M) 2V0 41M) 4N) Mada tn) 11$ 11$ 11$ 11$ Mfuflqam 195 19.95 19.95 10.05 mmmwm 9.29 11.41 9.45 ".50 Tim sin kW) 45.0 49.1 46.1 49.4 Trust \vo'ofl (N) 2$90.00 was $550.54 3242025 Wodhu motion [RY DRY DRY DRY Day hr duthg than 29 29 20 an Dalian d lingo 153 153 153 153 Wed: how pot day or) 05 0.5 0.5 0.5 W .0 .0 .0 0 W 01m pdcm .033 .033 .033 033 Awidiowuk how 017 0:3 025 $2 Flak! length (m) 123 123 123 123 Had widh (I!) 32.93 3293 32.93 32.03 Open»! ski Incl m menace: m m sum 25 5 2.5 5 Led ima (mi!) 30 no so on Hold “my .491 .43 .401 .435 1FC M) .493 .4“ .403 .400 50 M) 244 .217 .244 217 T“ can 0") 503.0 5030.0 503.0 5030.0 limb. :1 wind mm. 23 20 23 20 m d spun "who 0 0 0 7 Field in. (h) 1&317 1133.317 1003.317 1133.317 Spam ninbily .03 .012 .00 1125 Annual use 01) $4.515 042.07 024.53% 010.5527 Tm 51 wine (uh) 553% $4220 5521!) 704700 Tbvflpricooh) 115070 115070 115070 115070 You 1a m m 5 5 5 5 You hr wading Ila 2 2 2 2 Tm no. deposit» $404.12 05111.73 714433 7633333 TI. m. deuterium $47220 3969323 $472.20 300703 T“ avg. W “70.4 104“ "(915.7 "my Liar-o hm 1o- (ah/yuu) 1040 1040 1040 1040 Pulled (sh/Irwin) 1202430 1404435 1290203 1m3 DI and (sum-chin) 1&1404 1010.020 1013.071 1$7N Tm avg. vopair and 0473720 “$0.0 1‘0 11571 0.5 TI. avg. Input and 4305.40 450553 43965.40 431103 Labor and (aw-U) 40610 40818 40318 40618 W and (sh/ha) 1001 2357 2120 2520 Send and (sh/ha) 270 270 270 270 Chem and (skull) 442 442 442 442 Foam cost WI) 2310 2310 2310 2310 Hurting dupe Wu) 1300 1300 1300 1300 Fum but and (sh/M) 4320 4:20 4320 4320 TM and ed (IMI) 0310 00% 0454 0040 M yhfl (In) 4070 4070 4070 4070 Pddy ptico m 5.5 5.5 5.5 5.5 Gm: mun huh.) 20020 am 20020 2m H Mam (3M!) 21510 20144 20375 1M1 APPENDIX D PROGRAM LIST A complete program.list of the TILLAGE PLANNER and a program list of the checker program used for verifying computation of system reliability are attached. 205 206 0.1. Program List of TILLAGE PLANNER 1000 COLOR 7, 1, 1 1010 CLS 1020 LOCATE 1, 1O iiittfittfiififi Oiitfliiifitfififlt ......ti 1030 PRINT litfitfiifi i... .******i ......ttfitfittfll 1040 LOCATE 2, 10 1050 PRINT ' TILLAGE PLANNER Kunihiro Tokidl' 1060 LOCATE 4, 10 1070 PRINT ' DECISION SUPPORT SYSTEN FOR PLANNING TILLAGE NECNANIZATION ' 1000 LOCATE 5, 10 1090 PRINT ' OF RICE EARNING IN KENYA” 1100 LOCATE 6, 10 1110 PRINT aeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeaeeeeeeeeeeeeeeeeeseeeeseseeeeeeeau 1120 LOCATE 15, 10 1130 PRINT ' NIT ANT KEY TO PROCEED!" 1140 A3 8 INKEYS: IF AS 8 “' TNEN 1140 1150 CLS 1160 LOCATE 5, 10 1170 PRINT ' TNIS PROGRAM, TILLAGE PLANNER, NAS FOUR MAJOR SUEPROCRAMS' 1100 LOCATE 7, 10 1190 PRINT '1. TRACTOR SIZE SELECTION 2. NUMBER OF MACNINE SETS ' 1200 LOCATE 0, 10 1210 PRINT '3. SYSTEN RELIABILITY 4. COST ANALYSIS" 1220 LOCATE 10, 10 1230 PRINT I'This prograa calculates remired tractor size and Mr," 1240 LOCATE 11, 10 1250 PRINT “and facilitates your decision froa reliability and cconoaics aspects.“ 1260 LOCATE 14, 10 1270 PRINT "This program has an option to output the results to a printer or“ 1200 LOCATE 15, 10 1290 PRINT 'a disk file.“ 1300 LOCATE 20, 10 1310 PRINT ' HIT ANY KEY TO PROCEED!“ 1320 A3 8 INKEY’: IF A3 I “' THEN 1320 1330 CLS 1340 LOCATE 2, 10 1350 PRINT lfittfitfififitttfiittttittt..fitfiifittti.ttitttiittttitiitOOOOOitiA...I 1360 LOCATE 3, 10 1370 PRINT ' MACHINERY SELECTION ' 1300 LOCATE 4, 10 1390 PRINT IOitiifififitttfitifiittttttttttt9.9!.tttfittififiitfiififitttttOtittfiitttI 1400 LOCATE 5, 10 1410 PRINT "This progr- provides size and rewind MP of tillage " 1420 LOCATE 6, 10 1430 PRINT 'aachine sets for land preparation in paddy fielde.‘ 1440 LOCATE 0, 10 1450 PRINT 'Neccessary inforaation is' 1460 PRINT ' Tractor type" 1470 PRINT ' Rotary tiller width and tillage depth" 1400 PRINT ' ECround speed and bite length“ 1490 PRINT ' Soil condition“ 207 1500 PRINT ' Altitude effect“ 1510 PRINT ' Available uorking hours per day “ 1520 PRINT ' Uork pattern" 1530 PRINT ' Machine availability“ 1540 PRINT ' Heather condition" 1550 PRINT ' Lost time due to sinkage and other activities" 1560 PRINT ' Day for starting tillage and tillage duration“ 1570 PRINT ' Operator skill level" 1500 PRINT ' Field size and length" 1590 PRINT " 1600 PRINT " NIT ANY KEY TO PROCEED!" 1610 A5 - INKEYS: IF A5 8 " THEN 1610 1620 'CLS 1630 'LOCATE 3, 10 1640 ’PRINT ' LAND PREPARATION METHOD“ 1650 'LOCATE 4, 10 1” I PR1"? somenemeseeeeemessmetemeeeeememeemeeeesa 1670 'LOCATE 6, 10 1600 'PRINT “Land preparation method is required to determine” 1690 'LOCATE 7, 10 1700 'PRINT “PTO power of tractorls).' 1710 'LOCATE 0, 10 1720 'PRINT “In this program, ONLY rotary tillage is considered.“ 1730 'LOCATE 9, 10 1740 'PRINT 'Puddling and other work is done by individual farmers.“ 1750 'LOCATE 20, 10 1760 'PRINT ' NIT ANY KEY To PROCEED!" 1770 '08 I INKEYS: IF 00 8 " THEN 1750 1790 REN TRACTOR TYPE 1000 00000 2620 1010 REM TILLAGE NIDTH AND DEPTH 1020 00000 2960 1030 REM GROUND SPEED 1040 00000 3400 1050 REN 0ITE LENGTH 1060 00000 3720 1070 REM SOIL CONDITION 1000 00000 4040 1090 REM ALTITUDE 1900 00000 4670 1910 REM POUER CALCULATIONS 1920 00000 4990 1930 CLS 1940 INPUT "HOULD YOU LIKE TO TRY OTHER VALUES FOR MACHINE SELECTION? (YIN)'; M00 1950 IF N00 t 'Y' OR M00 8 “y“ THEN 00T0 1700 1960 IF N05 8 "N“ 0R N00 8 'n' THEN 00T0 1990 1970 0EEP 1900 0010 1940 1m I WW‘ER OF “C" l “E sifiiflfififiifit*fifitflififl*0**“** 2000 REN HEATHER 208 2010 GOSUB 5500 2020 REM TILLAGE DURATICNI 2030 00518 6290 2040 REM MK TIIE AND ”K PATTERN 2050 GOSLB 6610 2060 REM FIELD EFFICIENCY CALCULATION 2070 GOSLI 7260 2080 REM MER OF MACHINES CALCULATIOI 2090 ROSIE 8540 2100 CLS 2110 INPUT WLD you LIKE TO TRY OTHER VALUES FOR MACHINE PRCDUCTIVITY? (Y/N)"; MPS 2120 IF FPS 8 "Y" on MPS 8 "y“ THEN GOTO1990 2130 IF PS 8 'N" on FPS 8 "n" THEN GOTO 2160 2140 BEEP 2150 GOTO 2110 2160 PRINT 2170 INPUT ”WILD Yw LIKE TO PRINT WT MACHINE SELECTIGI RESULTS? HIM)"; MSRS 2180 IF MSRS 8 "Y" on MSRS 8 “Y“ THEN GOSUB 12980 2190 IF MSRS 8 "N" on MSRS 8 “n“ THEN GOTO 2230 2200 BEEP 2210 CLS 2220 GOTO 2160 2230 o WMREL I AB I L I 1yeneemeenneeeeneeeeeeeenesee 2240 REM RELIABILITY 2250 COSLB 8910 2260 CLS 2270 PRINT 2280 INPUT WLD YOU LIKE TO PRINT GIT SYSTEM RELIABILITY RESULTS? (Y/N)”; SRS 2290IFSRS8'Y'MSRS8'Y“ THENGOSLB13300 23001FSRS8'N'USRS8'1'1" THEN COT02330 2310 BEEP 2320 GOTO 2270 2330 tummy MLVSIseemseeeeaeeeeeeeeeeeeeaeeeeeeeeee 2340 REM PRICE INFMMATICNI 2350 cows 11100 2360 REM TRADE YEAR 2370 ROSIE 10280 2380 REM COST ESTIMATE 2390 009.8 11780 2400 CLS 2410 INPUT WLD YOU LIKE TO PRINT WT COST ANALYSIS RESULTS? (YINI'; CARS 2420 IF CARS 8 "Y' on CARS 8 “y“ THEN 00818 13380 2430 IF CARS 8 ”N" on CARS 8 “n” THEN GOTO 2460 2440 DEEP 2450 “TO 2400 2460 REM SLMARY 2470 0001.8 12200 2480 CLS 2490 INPUT WLD you LIKE TO PRINT WT ALL RESULTS? (YINI': ARS 2500 IF ARS 8 'Y" on ARS 8 “y“ THEN GOSIB 13630 2510 IF ARS 8 "N" on ARS 8 'n" THEN GOTO 2540 2520 BEEP 2()9 2530 0010 2400 2540 CLS 2550 INPUT ~uouL0 100 LIKE 10 SAVE ALL RESULTS 1» A FILE? (1/u)», ARS 2560 IF ARS - ~v~ on ARS = “y“ 105» 00000 14220 2570 1: ARS = “n- on ARS . “n“ 1300 0010 2600 2500 BEEP 2590 0010 2540 2600 PRINT ~1u1s 1s THE 500 or 105 pnoann.u 2610 euo 2620 COLOR 7, 1, 1 2630 CLS 2640 LOCATE 2, 10 2650 PR1“! Itftifiififiitttiitifittttttittttitttt*fittttifitflfiiittttitt*ittttI 2660 LOCATE 3, 10 2670 PRINT - 1040100 1192 - 2600 LOCATE 4, 10 2690 PR!'1 lifiifiiflfifitt*Aiiittiiitififittifi*fiitiitiiitittOtitiiiiiifltiitttu 2700 LOCATE 5, 10 2710 PRINT “You can select from either standard tractor (200) or“ 2720 LOCATE 6, 10 2730 PRINT “front drive assisted tractor (400)." 2740 LOCATE 0, 10 2750 PRINT “Decide your selection and type in the number." 2760 LOCATE 15, 10 2770 PRINT '1. Standard tractor (200)“ 2700 LOCATE 16, 10 2790 PRINT '2. Front drive assisted tractor (400).“ 2000 LOCATE 20, 10 2810 INPUT "YGNI SELECTIINI IS 8", DRIVE 2820 IF DRIVE < 1 011 DRIVE > 3 THEN GOTO 2830 ELSE 2850 2830 SEEP 2840 GOTO 2800 2050 DRIVE 8 INT(0RIVE) 2860 IF DRIVE 8 1 THEN GOTO 2880 2070 IF DRIVE 8 2 THEN 00T0 2920 2000 NOS 8 “200' 2090 ALF 8 .342 2900 ALR 8 .650 2910 RETURN 2920 000 8 II400" 2930 ALF 8 .390 2940 ALR 8 .602 2950 RETlRN 2960 CLS 2970 LOCATE 2, 10 2980 Pu‘ut littfititfltititittttiitttitttfifiitfiitA...tttttttifitttttI 2990 LOCATE 3, 10 3000 PRINT ' ROTARY TILLER UIDTH AND TILLAGE DEPTH" 3010 LOCATE 4, 10 3020 PR1“! littfittflfiittitttttttfitit9t..99....tittitfittfifitittttfiAI 3030 LOCATE 6, 10 3040 PRINT “Rotary tiller width is required to estimate required“ 3050 LOCATE 7, 10 210 3060 PRINT “power and theoretical field capacity.“ 3070 LOCATE 0, 10 3000 PRINT '10 input necessary information, provide" 3090 LOCATE 9, 10 3100 PRINT “rotary width in cm.” 3110 LOCATE 11, 10 3120 PRINT “140 8< Rotary width (cm) <8 280 " 3130 LOCATE 15, 10 3140 INPUT WIDTH OF ROTARY (CHI) 8", ROTARYJIIDTH 3150 IF ROTARY.NIDTH < 140 OR R01ARY.0IDTH > 280 THEN 0010 3160 ELSE 3240 3160 LOCATE 12, 10 3170 PRINT "THE FIGURE IS OUT OF RANGE!“ 3100 BEEP 3190 LOCATE 15, 10 3200 PRINT " " 3210 0010 3130 3220 LOCATE 12, 10 3230 PRINT ' 8 3240 ROTARYJIIDTH 8 INT(ROTARY.UIDTH * .1) 3250 ROTARY.0101H 8 ROTARY.NIDTH * 10 3260 LOCATE 15, 10 3270 PRINT ' YOU SELECTED ROTARY NIDTH (cm) = ', ROTARY.UIDTH 3200 LOCATE 10, 10 3290 PRINT “Average tillage depth in Mwea is 11.3 cm. (0 <8 depth 8< 25)" 3300 LOCATE 20, 10 3310 INPUT "INPUT DEPTH 0F TILTH (cm) 8“, DEPTH 3320 IF DEPTH < 0 OR DEPTH > 25 THEN 0010 3330 ELSE 3390 3330 LOCATE 12, 10 3340 PRINT “THE FIGURE IS 001 OF RANGE!“ 3350 DEEP 3360 LOCATE 20, 10 3370 PRINT ' 8 3300 0010 3300 3390 RETURN 3400 CLS 3410 LOCATE 2, 10 3‘20 P“ I “1' unmeeememmemeeeeeemeeeeeeeeeeeeeew 3430 LOCATE 3, 10 3440 PRINT ' GROUND SPEED” 3450 LOCATE 4, 10 3‘“ PR ‘ '1’ wemnemmneueeemeeenememeteeee II 3470 LOCATE 6, 10 3400 PRINT “Ground speed is required to estimate theoretical“ 3490 LOCATE 7, 10 3500 PRINT “field capacity and required power.“ 3510 LOCATE 0, 10 3520 PRINT ”To input ground speed, type figure in kmlh.' 3530 LOCATE 9, 10 3540 PRINT “Average ground speed for rotary tillage in Mwea is 3 kah.' 3550 LOCATE 10, 10 3560 PRINT I'Higher speed operation requires more power“ 3570 LOCATE 16, 10 121.1 3500 PRINT 8Input forward speed in km/h, 1 8< Speed <= 5 8 3590 LOCATE 10, 10 3600 PRINT 8 8 3610 LOCATE 19, 10 3620 INPUT 80RGJND SPEED (km/h) 8", 01100110 3630 IF 01100110 < 1! 011 GRGJND > 5! THEN GOTO 3640 ELSE 3700 3640 LOCATE 14, 10 3650 PRINT “THE FIGURE IS 001 OF RANGEI" 3660 DEEP 3670 LOCATE 19, 10 3680 PRINT 8 8 3690 0010 3610 3700 SPEED 8 GRGIND I 3.6 3710 RETlRN 3720 CLS 3730 LOCATE 2, 10 37‘0 PR‘uT lifiifiiitiiiiitt*iittfitttititiititttfi*ttiitttitttttttfitttN 3750 LOCATE 3, 10 3760 PRINT 8 BITE LENGTH“ 3770 LOCATE 4, 10 3730 PRINT IttiittttiittttttfittfitttttfitfiitfitittittiitNttifiittttttttN 3790 LOCATE 6, 10 3800 PRINT 80ite length is determined by ground speed, rotor speed 8 3010 LOCATE 7, 10 3820 PRINT 8and rather of bledes" 3030 LOCATE 0, 10 3040 PRINT 801te length changes tillage quality and required power.8 3850 LmATE 9, 10 3860 PRINT 8Smsll bite length provides fine tilth, but requires high power.8 3870 LOCATE 11, 10 . 3000 PRINT 83 8< bite length (cm) <8 12, and <8 10 for speed <8 2kah . 8 3090 LOCATE 16, 10 3900 INPUT 8BITE LENGTH (cm) 8", BITE 3910 IF BITE < 3 (ll BITE > 12 0010 3970 ELSE 3920 3920 IF BITE > 10 All) SPEED < 2 THEN GOTO 3930 ELSE 4030 3930 LOCATE 13, 10 3940 PRINT 8THE FIGURE IS 100 LEARGE FCN! THE GRGMID SPEED!“ 3950 DEEP 3960 0010 3090 3970 LOCATE 16, 10 3900 PRINT 8 8 3990 LOCATE 13, 10 4000 PRINT 81HE FIGURE IS OUT OF RANGE! 8 4010 0EEP 4020 0010 3090 4030 RETURN 4040 CLS 4050 LOCATE 2, 10 ‘060 PRI'T Ifitflfittififitt*****ttfi*fiiit...titttitfltttfiit0.0.0.9.!titN 4070 LOCATE 3, 10 4080 PRINT 8 SOIL CODITICNI" 4090 LOCATE 4, 10 ‘100 PR‘IT I.......fittififififiitiiii*fiflfitfiitiiiiiifltiiiitiiifittfitttI 212 4110 LOCATE 6, 10 4120 PRINT 8Soil type is assumed to be BLACK COTTON (HEAVY CLAY).8 4130 LOCATE 7, 10 4140 PRINT 8Soil condition is required to estimate required8 4150 LOCATE 0, 10 4160 PRINT 8power of tractor(s).8 4170 LOCATE 9, 10 4100 PRINT 8Soft soil condition increases required power due to motion 8 4190 LOCATE 10, 10 4200 PRINT 8resistance while dry soil requires high power for tillage.8 4210 LOCATE 11, 10 4220 PRINT 81illage is perforaed after flooding. Over softened soil 8 4230 LOCATE 12, 10 4240 PRINT 8msy cause tractor sinkage problems.8 4250 LOCATE 14, 10 4260 PRINT 81. FIRM (10 cm of sinkage)8 4270 LOCATE 15, 10 4200 PRINT 82. SOFT (20 cm of sinkage)8 4290 LOCATE 16, 10 4300 PRINT 83. VERY SOFT (30 on of sinkage)" 4310 LOCATE 17, 10 4320 PRINT 84. EXTREMELY SOFT (40 cm of sinkage)8 4330 LOCATE 20, 10 4340 INPUT 8YOUR SELECTION IS 88, MOIST 4350 IF MOIST < 1 OR MOIST >8 5 THEN GOTO 4360 ELSE 4300 4360 DEEP 4370 0010 4330 4300 MOIST 8 INT(MOIST) 4390 IF MOIST 8 1 THEN GOTO 4430 4400 IF MOIST 8 2 THEN 0010 4490 4410 IF MOIST 8 3 THEN 0010 4550 4420 IF MOIST 8 4 THEN 0010 4610 4430 SOILS 8 8FIRM8 4440 MOTION 8 1.2 4450 CRRF2 8 .252 4460 CRRF4 8 .224 4470 CRRR 8 .194 4400 RETURN 4490 SOILS 8 8SOFT8 4500 MOTION 8 1.3 4510 CRRF2 8 .356 4520 CRRF4 8 .310 4530 CRRR 8 .275 4540 RETURN 4550 SOILS 8 8VERY SOF18 4560 MOTION 8 1.4 4570 CRRF2 8 .436 4500 CRRF4 8 .39 4590 CRRR 8 .337 4600 RETURN 4610 SOILS 8 8EXTREMELY SOF18 4620 MOTION 8 1.5 4630 CRRF2 8 .5030001 22113 4640 CRRF4 8 .45 4650 CRRR 8 .309 4660 RETURN 4670 CLS 4600 LOCATE 2, 10 m "I“? WW"Q“OOM*§O”"CC*flittttitittttIt 4700 LOCATE 3, 10 4710 PRINT 8 ALTITUDE FACTOR" 4720 LOCATE 4, 10 ‘m P“ ‘ "1' MQWWOOMCWN”itittfliittttttttttII 4740 LOCATE 6, 10 4750 PRINT 8It is known that engine power output decreases at high altitude8 4760 LOCATE 7, 10 4770 PRINT 8due to thinner air concentration.8 4700 LOCATE 9, 10 4790 INPUT 8UOULD YOU LIKE TO CONSIDER ALTITUDE EFFECT ON POUER OUTPUT? (Y/N)“; ALTS 4800 IF ALTS 8 8Y8 OR ALTS 8 8y8 THEN GOTO 4860 4810 IF ALTS 8 8N8 OR ALTS 8 8n“ THEN GOTO 4970 4820 DEEP 4030 LOCATE 9, 10 4840 PRINT 8 8 4850 0010 4780 4060 LOCATE 11, 10 4070 PRINT 8The altitude at MIS office is 1159 meters. The maxim- is 2000 m.8 4880 LOCATE 14, 10 4090 INPUT 8ALTITUDE (m) 88, ALT 4900 IF ALT >8 0 AND ALT <8 2000 THEN GOTO 4950 4910 BEEP 4920 LOCATE 14, 10 4930 PRINT 8 8 4940 0010 4060 4950 C.ALT 8 1.035 ‘ (ALT / 303) 4960 RETURN 4970 ALT 8 0: C.ALT 8 1 4900 RETURN ‘m immenmmeeeememmn CALwurmmflitttttittttttttfltttt 5000 COLOR 7, 0 5010 CLS 5020 LOCATE 2, 10 5030 "I"? mneeemmeeeemmeeeeeeeneeeueeeeesee11 5040 LOCATE 3, 10 5050 PRINT 8 PONER CALCULATIONS8 5060 LOCATE 4, 10 5070 "I"? neeeeeenmeeeeeeeemeeeemeeeeeeemeeeese11 5000 LOCATE 6, 10 5090 PRINT 8Tractor type 88; 00S S100 LOCATE 7, 10 5110 PRINT 8Rotary tiller width (cu) 88; ROTARY.NIDTH 5120 LOCATE 0, 10 5130 PRINT 8Tillsge depth (cm) 88; DEPTH 5140 LOCATE 9, 10 5150 PRINT 80round speed (kl/h) 88; GROUND 5160 LOCATE 10, 10 22144 5170 PRINT “Bite length (cm) 8“; BITE 5100 LOCATE 11, 10 5190 PRINT 8Soil condition 88; SOILS 5200 PONER.TIL 8 (5.341 + 8.11 8 SPEED - .6384 * BITE) 8 ROTARY.NIDTH 8 DEPTH / 1000 5210 PONER.PROV 8 MOTION 8 PONER.TIL 8 C.AL1 8 1.3737 5220 ’PRINT PONER.PROV 5230 1RACTOR.N 8 0 5240 IF NOS 8 82008 THEN GOTO 5250 ELSE 5280 5250 TRACTOR.0 8 POUER.PROV 8 565.2 + 3112 5260 PONER.MOT 8 TRACTOR.H 8 (CRRF2 8 ALF + CRRR 8 ALR) 8 GROUND / 3600 5270 0010 5310 5280 TRACTOR.N 8 PONER.PROV 8 563 + 4621 5290 PONER.MOT 8 TRACTOR.H 8 (CRRF4 8 ALF + CRRR 8 ALR) 8 GROUND / 3600 5300 PONER 8 PONER.TIL + PONER.MOT 5310 PONER.CAL 8 (PONER.TIL + POHER.MOT) 8 C.ALT 8 1.3737 5320 'PRINT 8NEIGH188, TRACTOR.U 5330 'LOCATE 16, 10 5340 'PRINT 8PROVISIONAL88, POUER.PROV, 8CALCULATED88, POUER.CAL 5350 IF CINT(PONER.CAL 8 10) > CINT(PONER.PROV 8 10) OR CINT(PONER.CAL 8 10) < CINTIPOUER.PROV 8 10) 536° '°"E""°V ' '°“ER'C‘L THEN 0010 5360 ELSE 5390 5370 SEEP 5300 0010 5240 5390 PRINT 5400 PRINT 8PONER REQUIREMENT FOR TILLAGE (kN) 88; POUER.TIL 5410 'LOCATE 14, 10 5420 PRINT 8PONER REQUIREMENT FOR MOTION RESISTANCE (RH) 8"; PONER.MOT 5430 'LOCATE 15, 10 5440 TRACT.SIZE 8 CINT(PONER.CAL 8 10) / 10 5450 PRINT 81RACTOR SIZE (RU) 88; TRACT.SIZE 5460 PRINT 5470 PRINT 8 HIT ANY KEY TO PROCEEDI8 5480 AS 8 INKEYS: IF AS 8 88 THEN 5480 5490 RETURN 5500 COLOR 7, 1, 1 5510 CLS 5520 LOCATE 2, 10 5530 PR l '1 MWWNWitt"MittttfltttttttttIt 5540 LOCATE 3, 10 5550 PRINT 8 HEATHER CONDITION8 5560 LOCATE 4, 10 5570 PR 1 “I ameeeeemnnmeemeeeeee It 5500 LOCATE 6, 10 5590 PRINT 8Ueether condition is required to estimate possible8 5600 LOCATE 7, 10 5610 PRINT 8working time during the work period.8 5620 LOCATE 9, 10 5630 PRINT 8Irrigation water availability limits the tillage operation.8 5640 LOCATE 10, 10 5650 PRINT 8The constraint was given as percentage of available time.8 5660 LOCATE 12, 10 5670 PRINT 81. DROUGHT CONDITION (limited to 50% in Feburuary and March)8 5600 LOCATE 14, 10 5690 PRINT 82. DRY CONDITION (limited to 50% in Feburuary and 75% in March)8 215 5700 LOCATE 16, 10 5710 PRINT 83. MINIMAL CONDITIOI (limited to 75% in February)" 5720 LOCATE 18, 10 5730 PRINT 84. 151 CONDITION (No water constraint)“ 5740 LOCATE 20, 10 5750 INPUT 8YGJR SELECTIGI IS 88, HEATHER 5760 IF HEATHER >8 1 AND lEATNER < 5 THEN GOTO 5790 5770 BEEP 5780 0010 5280 5790 HEATHER 8 INT(l£ATHER) 5800 DIM MK.DAY(214) 5810 IF HEATHER . 1 THEN 0010 5850 5820 IF HEATHER = 2 THEN 0010 5950 5830 IF HEATHER . 3 THEN 0010 6090 5840 IF HEATHER . 4 THEN 0010 6230 5850 HEATHERS s 8DROUGHT8 5860 FOR I 8 1 TO 59 5870 HORK.DAY(I) 8 .5 5880 PRINT 1, H0RX.0A1(I) 5090 NEXT I 5900 FOR I 8 59 TO 213 5910 H0RX.0A1(I) 8 1 5920 PRINT I, HORK.DAY(I) 5930 NEXT I 5940 0010 6280 5950 HEATHERS 8 8DRY8 5960 FOR I . 1 10 28 5970 HORK.DAY(I) 8 .5 5980 PRINT 1, HORK.DAY(I) 5990 NEXT I 6000 FOR I 8 29 10 50 6010 ”LDAYU) 8 .75 6020 PRINT I, HORK.DAY(I) 6030 NEXT I 6040Fm185910213 6050 HORK.DAY(I) 8 1 6060 PRINT I, HORK.DAY(I) 6070 NEXT I 6080 0010 6280 6090 HEATHERS . 8NORMAL8 6100 FOR I 8 1 10 28 6110 HORK.DAY(I) 8 .75 6120 PRINT 1, HORK.DAY(I) 6130 NEXT I 6140FCNII8291059 6150 MLDAYU) 8 1 6160 PRINT I, ”LDAYU) 6170 NEXT I 6180 FOR I . 59 10 213 6190 HORK.DAY(I) 8 1 6200 PRINT I, HORK.DAY(I) 6210 NEXT I 6220 0010 6280 121.6 6230 KATHER8 8 “HET” 6240 FOR I 8 1 To 213 6250 “LOAN” = 1 6260 PRINT I, HORK.DAY(I) 6270 NEXT I 6280 RETLRN 6290 CLS 6300 LOCATE 2, 5 6310 PR ‘ NT uneemeeeeeemueeeeeeeeeeeeeeeeeeeteetueeneeeeeenneeee II 6320 LOCATE 3, 5 6330 PRINT ' STARTING DATE AND DURATION" 6340 LOCATE 4, 5 6350 PR I "1 ammneeeeeeeeeeeeeeeeeeeeeeeeeemeeeeeeeeeeeeeeeeeeeeeau 6360 LOCATE 5, 5 6370 PRINT ”February 1 is the FIRST day of the tillage season" 6380 LOCATE 6, 5 6390 PRINT “Tillage should be started by Narch 31. (Day 59)“ 6400 LOCATE 7, 5 6410 PRINT “(e.g. Input 29 if you start tillage on Narch 1S" 6420 LOCATE 9, 10 6430 INPUT "INITIAL DAY 8', INIT.DATE 6440 IF INIT.DATE >8 1 AND INIT.DATE < 60 8010 6470 6450 BEEP 6460 GOTO 6420 6470 INIT.DATE I INT(INI-T.DATE) 6480 LOCATE 12, 5 6490 PRINT 'W*PERICD OF LAND PREPARATION*”**“******"' 6500 LOCATE 13, 5 6510 PRINT “Tillage should be completed by Septelber 30.“ 6520 LOCATE 14, 5 6530 PRINT “Day of tillage coapletion 8< 243rd day ' 6540 LOCATE 15, 5 6550 PRINT “(e.g. INPUT 150 if you end land preparation in 150 days)“ 6560 LOCATE 17, 10 6570 INPUT “PERIOD OF LAND PREPARATION (day)=', PERIOD 6580 IF PERIm + INIT.DATE - 1 < 1 m PERIm + INIT.DATE - 1 > 243 GOTO 6480 6590 PERICD I INTIPERIW) 6600 RETURN 6610 CLS 6620 LOCATE 2, 10 “30 P“ l “t Wtflttveeaeeemeeteeeemeemeaeaeemmeeeeau 6640 LOCATE 3, 10 6650 PRINT " MK HMS AND “K PATTERN“ 6660 LOCATE 4, 10 “70 pa 1 “I Mmeneeemeeeemeeeeeeenueeemeeeeemeetees II 6680 LOCATE 5, 10 6690 PRINT “The average gross work hours per day in NHea is about 10 hours and" 6700 LOCATE 6, 10 6710 PRINT “the average net Hork hour is 8.5 hours due to transportation,“ 6720 LOCATE 7, 10 6730 PRINT Ilunch break and schedling. (4 8< Hork hour <8 12)" 6740 LOCATE 9, 10 6750 INPUT “NET NORK HOURS PER DAY (h)= ', HOUR :21‘7 6760 IF HOUR >= 4 AND HOUR <= 12 THEN GOTO 6790 6770 BEEP 6780 GOTO 6740 6790 LOCATE 11, 10 6800 PRINT "The machine availability for tillage in HHea is about 80%“ 6810 LOCATE 12, 10 6820 PRINT “due to other operations, repairs and maintenance work.“ 6830 LOCATE 13, 10 6840 PRINT “(50 8< machine availability (X) <8 100“ 6850 LOCATE 15, 10 6860 INPUT “MACHINE AVAILABILITY (1)3“, AVAIL 6870 IF AVAIL >8 50 AND AVAIL <8 100 THEN GOTO 6920 6880 BEEP 6890 LOCATE 15, 10 6900 PRINT 8 8 6910 GOTO 6850 6920 AVAIL 8 AVAIL * .01 6930 LOCATE 17, 10 _ “‘0 "I"? Imm”"m*“SELEc1 MK pAn'ERuteeeeemm'aeeeeu 6950 PRINT ' 1. HORK EVERYDAY” 6960 PRINT " 2. HORK EVERYDAY EXCEPT SUNDAYS AND HOLIDAYS” 6970 PRINT ' 3. HORK HEEKDAYS AND HALF A DAY ON SATURDAYS" 6980 PRINT ' 4. HORK HEEKDAYS EXCEPT HOLIDAYS" 6990 LOCATE 23, 10 7000 INPUT “YOUR SELECTION IS 8', PAT 7010 IF PAT > 4 0R PAT < 1 GOTO 7020 ELSE GOTO 7060 7020 BEEP 7030 LOCATE 23, 10 7040 PRINT 8 8 7050 GOTO 6990 7060 PAT 8 INTIPAT) 7070 IF PAT 8 1 THEN H0RX.PAT 8 11 ELSE 7080 7080 IF PAT 8 2 THEN HORK.PAT 8 .833 ELSE 7090 7090 8 .767 ELSE 7100 8 .7 ELSE 7110 IF PAT 8 3 THEN HORK.PAT 7100 IF PAT 8 4 THEN HORK.PAT 7110 HORK.HOUR 8 0 7120 FOR I 8 INIT.DATE To INIT.DATE + PERIOD - 1 7130 T 8 TIHER + I 7140 RAN 8 RND(-T) 8 2 8 (1 - AVAIL) + (2 8 AVAIL - 1) 7150 'PRINT 'ramdomP, RAN 7160 HoRX.H00R 8 H0RX.H00R + HOUR 8 HORK.DAY(I) 8 RAN 7170 'PRINT I, H0RX.H00R, HOUR 8 HORK.DAY(I) 8 RAN, HORK.DAY(I) 7180 vs: 8 INXETS: IF as 8 88 THEN 6945 7190 NEXT I 7200 TOTAL.HOUR 8 INT(HORK.HOUR 8 HORK.PAT) 7210 'PRINT HORK.HOUR, TOTAL.HOUR 7220 PRINT 7230 PRINT 'HIT ANY KEY To PROCEED!“ 7240 cs 8 INKEYS: IP cs 8 88 THEN 7240 7250 RETURN 7260 CLS 7270 LOCATE 2, 10 Elli! 7280 PR1“? litiitiifiiiiittititfifiiiiiiiitfi****tt******fit*itt*t**t***u 7290 LOCATE 3, 10 7300 PRINT " FIELD EFFICIENCY INFORMATICNI" 7310 LOCATE 4, 10 7320 PRI'T It...tittifittitfitfiiflttttitttttttitfitttittitfitttttfitfifitfitu 7330 LOCATE 5, 10 7340 PRINT ”Plot size , field length, and operator skill level are required" 7350 LOCATE 6, 10 7360 PRINT ”for field capacity calculations. The default values are:' 7370 LOCATE 7, 10 7380 PRINT “PLOT SIZE (ha) 8 0.405“: PLOT.SI2E 8 .405 7390 LOCATE 8, 10 7400 PRINT “FIELD LENGTH (m) 8 123 ': FIELD.LENGTH 8 123 7410 LOCATE 9, 10 7420 INPUT '00 YO) HANT TO CHANGE VALUES? (Y/N)“, YNS 7430 IF YNS 8 “y“ OR YNS 8 “Y“ THEN GOTO 7490 7440 IF VHS 8 “n” OR VHS 8 “N" THEN GOTO 7650 7450 DEEP 7460 LOCATE 9, 10 7470 PRINT 8 8 7480 ERTO 7410 7490 LOCATE 11, 10 7500 PRINT “Input neH plot size and field length. plot size 8< 2 ha 8 7510 LOCATE 12, 10 7520 PRINT "Field length should be longer than filed Hidth. 8 7530 LOCATE 14, 10 7540 INPUT 'PLOT SIZE (ha) 8', PLOT.SIZE 7550 IF PLOT.SIZE <8 2 AID PLOT.SIZ£ > 0 THEN GOTO 7600 7560 BEEP 7570 LOCATE 15, 10 7580 PRINT 8 8 7590 ERTO 7530 7600 LOCATE 15, 10 7610 INPUT “FIELD LENGTH (m) 8", FIELD.LENGTH 7620 IF FIELD.LENGTH >8 SORIPLOT.SIZE * 10000) THEN GOTO 7650 7630 DEEP 7640 GOTO 7600 7650 FIELD.NIDTN 8 PLOT.SIZE * 10000 / FIELD.LENGTH 7660 LOCATE 13, 10 7670 PRINT "FIELD NIDTH (m) 8', FIELD.NIDTH 7680 LOCATE 15, 10 7690 PRINT “SELECT OPERATOR LEVEL' 7700 LocATE 17, 10 7710 PRINT '1. HOST EXPERIENCED 8 7720 LOCATE 18, 10 7730 PRINT '2. EXPERIENCED ' 7740 LOCATE 19, 10 7750 PRINT '3. LESS EXPERIENCED 8 7760 LOCATE 21, 10 7770 INPUT “OPERATOR EXPERIENCE IS 8', EXPER 7780 IF EXPER >8 1 AND EXPER < 4 THEN GOTO 7830 7790 BEEP 219 7800 LOCATE 21, 10 7810 PRINT ' “ 7820 GOTO 7760 7830 EXPER 8 INTCEXPER) 7840 IF EXPER 8 1 THEN GOTD 7870 7850 IF EXPER 8 2 THEN GOTO 7900 7860 IF EXPER 8 3 THEN GOTO 7930 7870 HORK.HIDTH 8 (ROTARY.HIDTH - 30) l 100 7880 OPES 8 “HOST EXPERIENCED“ 7890 GOTO 7950 7900 HORK.HIDTH 8 (ROTARY.HIDTH - 40) / 100 7910 OPES 8 "EXPERIENCED" 7920 GOTO 7950 7930 HORK.HIDTH 8 (ROTARY.HIDTH - 50) l 100 7940 OPES 8 “LESS EXPERIENCED“ 7950 CLS 7960 LOCATE 1, 10 "To "1" IWWOQFlELD ”K Ilmtittntmtfittitittu 7980 LOCATE 3, 10 7990 PRINT “PLOT SIZE (he) 8', PLOT.SIZE, 8000 LOCATE 4, 10 8010 PRINT “FIELD LENGTH (m) 8', FIELD.LENGTH, 8020 LOCATE 5, 10 8030 PRINT “FIELD HIDTH (m) 8“, FIELD.HIDTH, 8040 LOCATE 6, 10 8050 PRINT “OPERATOR EXPERIENCE 8', OPES, 8060 LOCATE 8, 10 8070 PRINT “Tillage machine sinkage is a major trouble in NHea.“ 8080 LOCATE 9, 10 8090 PRINT “The average lost time due to sinkage is about 15 minutes per sinkage." 8100 LOCATE 10, 10 8110 PRINT '(0 8< sinkage frequency (times/ha) <8 10)" 8120 LOCATE 12, 10 8130 INPUT "FREQUENCY OF SINKAGE (tim88/ha)8', SINK 8140 IF SINK >8 0 AND SINK <8 10 THEN GOTO 8190 8150 BEEP 8160 LOCATE 12, 10 8170 PRINT “ “ 8180 GOTO 8120 8190 EACH.SINK 8 21.85 - 1.25 * SINK 8200 LOCATE 14, 10 8210 PRINT “LOST TINE FOR EACH SINKAGE (min)8“. CINT(EACH.SINK) 8220 LOCATE 18, 10 8230 PRINT I'Loat time due to adjustments, refueling, minor repairs and 8 8240 LOCATE 19, 10 8250 PRINT “changing plot (excluding sinkage) should be provided. 8 8260 LOCATE 20, 10 8270 PRINT "The maxim- lost time is 120 (min/ha)." 8280 LOCATE 22, 10 8290 INPUT “LOST TIHE (min/ha) 8', LOST.TINE 8300 IF LOST.TINE >8 0 AND LOST.TINE <8 120 THEN GOTO 8350 8310 BEEP 8320 LOCATE 20, 10 8330 PRINT ' N 220 8340 (SOTO 8280 8350 TIEJIL 8 10 I “LHIDTH I GRCXJND 8360 IF EXPER 8 1 THEN GOTO 8390 8370 IF EXPER 8 2 THEN GOTO 8420 8380 IF EXPER 8 3 THEN GOTO 8450 8390 DIST.TLRN 8 3.14 " ROTARY.HIDTH * (2 8' FIELD.HIDTH / MKJIDTH) I 100 8400 TIEJIRN 8 (4 ‘ FIELD.HIDTH + DIST.TURN) I GRwND I 1000 I PLOT.SIZE 8410 GOTO 8470 8420 DIST.TlRN 8 3.14 * ROTARY.HIDTH * (4 + FIELD.HIDTH I MKJIDTH) I 100 8430 TIIEJLRN 8 (7 * FIELD.HIDTH + DIST.TlRN) I GRwND I 1000 / PLOT.SIZE 8440 GOTO 8470 8450 DIST.TIRN 8 3.14 * ROTARY.HIDTH "’ (6 8 FIELD.HIDTH I MKJIDTH) I 100 8460 TIFEJLRN 8 (8 * FIELD.HIDTH 1* DIST.TURN) I GRCXJND I 1000 I PLOT.SIZE 8470 TIIE.SINK 8 EACH.SINK ' SINK I 60 8480 TIIE.ACT 8 LOST.TINE I 60 8490 TIEJOST 8 TIEJLIIN + TIK.SINK 8 TINE.ACT 8500 FIELD.EFF 8 TIIEJIL I (TIEJIL + TIFE.LOST) 8510 TFC 8 1 I TIIEJIL 8520 EFC 8 TFC ' FIELD.EFF 8530 RETLRN 8540 COL“ 7, 0 8550 CLS 8560 LNATE 2, 10 3570 pm." memmmtamtttwmeewmaeu 8580 LmATE 3, 10 8590 PRINT " EFFECTIVE FIELD CAPACITY AND MER OF MACHINES“ 8600 LOCATE 4, 10 8610 PRINT aWeemeueetmmuemmu 8620 LmATE 5, 10 8630 PRINT 'THENETICAL TIE Fm TILLAGE (II/I18) 8"; TIKJIL 8640 LmATE 6, 10 8650 PRINT ”TIE Fm TIRNING (IT/III) 8"; TIKJIRN 8660 LOCATE 7, 10 8670 PRINT 'TIFE Fm SINKAGE TRCXBLES (hlha) 8"; TIIE.SINK 8680 LCXZATE 8, 10 8690 PRINT “TIE LOST DIE TO OTHER ACTIVITIES (hlha) 8"; TIEJCT 8700 LmATE 9, 10 8710 PRINT “TOTAL NGl-PRwUCTIVE TIIE (IT/III) 8"; TINE.LOST 8720 LmATE 10, 10 8730 PRINT “FIELD EFFICIENCY8 "; FIELD.EFF 8740 LmATE 11, 10 8750 PRINT “EFFECTIVE FIELD CAPACITY (III/h) 8"; EFC 8760 LxATE 14, 10 8770 INPUT “TOTAL TILLAGE AREA (118) 8"; TOTAL.AREA 8780 IF TOTAL.AREA < 100 THEN 8790 ELSE 8810 8790 BEEP 8800 GOTO 8760 8810 NULTRAC 8 TOTAL.AREA I EFC I TOTALJHMR 8820 NULNACH 8 CINT(M.TRAC ' AVAIL) 8830 MILSP 8 CINT(NIM.TRAC * (1 . AVAIL” 8840 ”.TOTAL 8 ”.NACH '0 “.SP 8850 LxATE 16, 10 221 8860 PRINT nausea or Raoumso TILLAGE mcmue sers =u; uuumcu 8870 LOCATE 20, 10 8880 PRINT “HIT ANY KEY TO PROCEED ' 8890 BS 8 INKEYS: IF BS 8 '"' THEN 8890 8900 RETURN 8910 '**************reliability dimension************* 8920 DIN A(NUN.NACH * 5): A(1) 8 1: L 8 1 8930 N 8 NUH.NACH + 1 8940 COLOR 2, 0, 0 8950 CLS 8960 LOCATE 2, 10 8970 PR I '1’ Immttittttm.“ttmflittttiflfltttt*tttttt N 8980 PRINT ' RELIABILITY“ 8990 LOCATE 4, 10 mo "‘"1 WWW"”M*WO§*QO 9010 LOCATE 6, 10 9020 PRINT “NUNBER OF SPARE NACHINES 8', NUN.SP 9030 LOCATE 7, 10 9040 PRINT ”MER OF TOTAL NACHINES 8 ", Nlli.TOTAL 9050 USE 8 MJRAC * TOTAL.HM I Nll1.TOTAL 9060 LOCATE 8, 10 9070 'PRINT "MER OF TOTAL HACHINES 8", NULTOTAL 9080 LOCATE 9, 10 9090 FT 8 HOUR * PERIOD * NORX.PAT 9100 PRINT "FIELD TINE (h) 8', FT 9110 LOCATE 10, 10 9120 PRINT “SELECT RELIABILITY OF EACH NACHINE SET (TRACTOR+TILLER)“ 9130 LOCATE 12, 10 9140 PRINT '1. FIXED RELIABILITY“ 9150 PRINT ' A reliability is assigned to all machine sets“ 9160 LOCATE 14, 10 9170 PRINT '2. FIXED RELIABILITY“ 9180 PRINT 8 Reliability of each machine is indiviaually assigned“ 9190 LOCATE 16, 10 9200 PRINT '3. FIXED RELIABILITY BASED ON HOURS OF USE" 9210 PRINT 8 Reliability of each machine is determined by field time“ 9220 LOCATE 18, 10 9230 PRINT '4. RANDOHLY GENERATED RELIABILITY“ 9240 PRINT ' Reliability of each machine is randomly generated.“ 9250 LOCATE 20, 10 9260 PRINT '5. RANDOHLY GENERATED RELIABILITY BASED ON HOUR USE“ 9270 PRINT 8 Reliability of each machine varies depend on field time, F 9280 PRINT ' and random factor was included.“ 9290 LOCATE 23, 10 9300 INPUT “YOUR SELECTION IS 8', AGE 9310 IF AGE >8 1 AND ACE < 6 THEN GOTO 9340 9320 BEEP 9330 OOTO 9290 9340 AGE 8 INT(AGE) 9350 CLS 9360 IF AGE 8 1 THEN GOTO 9410 9370 IF AGE 8 2 THEN OOTO 9580 9380 IF AGE 8 3 THEN GOTO 9720 9390 IF AGE 8 4 THEN GOTO 9820 12212 9400 IF AGE 8 5 THEN GOTO 10010 9410 LOCATE 5, 10 9420 PRINT “You selected FIXED RELIABILITY. In put a mit reliability.“ 9430 LOCATE 7, 10 9440 INPUT “RELIABILITY OF EVERY NACHINE SET IS (0 < r < 1)8", P 9450 IFP>0AIOP<1THENOOTO9490 9460 BEEP 9470 GOTO 9440 9480 CLS 9490 FCN! .1 81 TO NULTOTAL: H 8 J + 1: 9500 PRINT "N"; J; ")8“; P 9510 IF DAILSP < .1 THEN L 8 H - NULSP 9520 IF IRRIJIACH < .1 THEN A01) 8 A01) * A(NllI.NACH) * P: H 8 NlliJIACH 9530 FmI8HT0LSTEP-1 9540 AU) 8 AU) 1’ (A(I - 1) - A(I)) * P 9550 NEXT I 9560 NEXT J 9570 GOTO 10130 9580 LmATE 5, 10 9590 PRINT "INPUT RELIABILITY OF EACH NAHCINE SET (0 < 1' < 1)" 9600FNJ81T0Nlfl.TOTAL:H8J+1 9610 PRINT “N": J; “)8“: INPUT P 9620 IFP>0AIDP<1THENGOTO9650 9630 BEEP 9640 .TO 9610 9650 IF “JP < .1 THEN L 8 H - NULSP 9660 IF "MACH < J THEN MN) 8 A01) 1’ A(NllI.NACH) * P: H 8 NULHACH 9670 FNI8HTOLSTEP '1 9680 AU) 8 AU) 1’ (A(I - 1) ' A(I)) * P 9690 NEXT I 9700 NEXT J 9710 COTO 10130 9720 PRINT " RELIABILITY OF EACH MACHINE” 9730 FCN! .1 8 1 TO NllI.TOTAL: H 8 J 9 1: P 8 2.7182818! " -(.0004559 * FT) 9740 PRINT “N"; J; “)8“; P 9750 IF IDLSP < J THEN L 8 H - NULSP 9760 IF NIHJIACH < .1 THEN A01) 8 A01) + A(Nlli.NACH) * P: H 8 NULNACH 9770 FIRI-HTOLSTEP -1 9780 AU) 8 A(I) * (A(I - 1) - A(I)) * P 9790 NEXT I 9800 NEXT .1 9810 0010 10130 9820 LOCATE s, 10 9830 INPUT “THE AVERAGE RELIABILITY or NACHINE SETS a", P 9840 IF P < 1 AND 9 > 0 THEN 0010 9870 9850 BEEP: CLS 9860 0010 9820 9870 CLS 9880 PRINT “RELIABILITY or EACH NACHINE =- 9890 FOR J - 1 10 NUH.TOTAL: u . J + 1 9900 T - TINER + I 9910 IF P > .5 THEN I - RNDI-T) * 2 * (1 - P) t 2 * P - 1 9920 IF P <- .5 rues a . RND(-T) * 2 . p 9930 PRINT 'rI'; J; -)--; n .2123 9940 IF NUH.SP < J THEN L = H - NUN.SP 9950 IF NUN.NACH < J THEN A(H) 8 A(H) * A(NUH.HACH) * R: H = NUN.HACH 9960 FOR I 8 H T0 L STEP -1 9970 A(I) 8 A(I) 8 (A(I - 1) ' A(I)) * R 9980 NEXT I 9990 NEXT J 10000 0010 10130 10010 PRINT “RELIABILITY OF EACH HACHIHE -" 10020 FOR J . 1 10 NUH.TOTAL: H = J + 1: P = 2.7182818# * -(.0004559 . FT) 10030 1 . TINER . I 10040 IF P > .5 THEN R - RND('T) * 2 * (1 - P) t 2 . P - 1 10050 IF P <8 .5 THEN a . RND(-T) * 2 * P 10060 PRINT -r<-; J; 81-8; a 10070 IF DRILSP < .1 THEN L =H - 11011.» 10080 IF HUH.HAcH < J THEN A(H) 8 A(H) + A(NUH.NACH) . R: H . NUH.NACH 10090 FOR I . H 10 L STEP -1 10100 A(I) 8 A(I) + (AII - 1) - AIIII * R 10110 NEXT I 10120 NEXT J 10130 CLS 10140 SYS.RE . CINT(A(N) * 1000) / 1000 10150 LOCATE 10, 10 10160 PRINT usvsren RELIABILITY Is-v, SYS.RE 10170 LOCATE 22, 10 10180 PRINT “HIT ANY KEY 10 PROCEED!“ 10190 cs . INKEYS: IF cs . .. THEN 10190 10200 COLOR 7, 0, 0 10210 CLS 10220 INPUT “UOULD 100 LIKE 10 12v OTHER VALUES r02 HACHINE RELIABILITY? (Y/N)"; HRS 10230 IF HRS . “Y“ 02 HRS s “y“ THEN GOTO 8950 10240 IF HRS s “N" on HRS = “n“ THEN 8010 10270 10250 BEEP 10260 0010 10220 10270 RETURN 10280 COLOR 7, 1, 1 10290 CLS 103m PR 1'1 "W“MW‘tflflfiitittt......"ittfififlil’fififlfltifitfii*t********* II 10310 PRINT ' COST ANALYSIS" 10320 PRINT ntitttflttttittttttttitttttttttittttttttttttttttttttittttttitittttu 10330 LOCATE 4, 10 10340 PRINT "TILLAGE MACHINERY INFORMATION“ 10350 LOCATE 5, 10 10360 PRINT ITRACTOR TYPE 8'; HDS, "TRACTOR SIZE (RN) 8“; TRACT.SIZE 10370 LOCATE 6, 10 10380 PRINT “ROTARY TILLER UIDTH (Cl) 8", ROTARY.HIDTH 10390 LOCATE 7, 10 10400 PRINT “ANNUAL USE OF TILLAGE MACHINES (h) 8“, TOTAL.HOUR 10410 IF H03 8 “2ND“ THEN LPT 8 1000 * (‘4.8 8 12.2 * TRACT.SIZE) 10420 IF U03 8 '4UD' THEN LPT 8 1000 * (-36.3 + 151 * TRACT.SIZE) 10430 LPR 8 (162.3 * (“205 + 4.57 * ROTARY.HIDTH)’ 10440 LPR 8 INT(LPR) 10450 PRINT ”TRACTOR PRICE8'; LPT, "TILLER PRICE8'; LPR 10460 PRINT 10470 PRINT “YEAR 8', “TRACTOR: AVERAGE ANNUAL COST - ANNUAL COST 8' 224 10480 FOR N 8 1 TO 8 10490 TRACT.REN(0) 8 LPT 10500 TRACT.REH(N) 8 LPT * .6 8 .935 ‘ (N * USE I 1000) 10510 TRACT.DEP(N) 8 TRACT.REH(N - 1) - TRACT.REH(N) 10520 TRACT.AVG.DEP(N) 8 (LPT - TRACT.REN(N)) / N 10530 TRACT.REP(O) 8 0 10540 TRACT.AVG.REP(N) 8 (LPT * .045 F (N * USE I 1000) ‘ 2) / N 10550 TRACT.REP(N) 8 (LPT * .045 * (N * USE I 1000) ‘ 2) 10560 TRACT.YEAR.REP(N) 8 TRACT.REP(N) - TRACT.REP(N - 1) 10570 TRACT.AVG.COST(N) 8 TRACT.AVG.DEP(N) + TRACT.AVG.REP(N) 10580 TRACT.YEAR.COST(N) 8 TRACT.DEP(N) + TRACT.YEAR.REP(N) 10590 BRAKE.TRACT(N) 8 TRACT.AVG.COST(N) - TRACT.YEAR.COST(N) 10600 PRINT N, BRAKE.TRACT(N) 10610 NEXT N 10620 PRINT “YEAR 8', “TILLER: AVERAGE ANNUAL COST - ANNUAL COST 8" 10630 FOR N 8 1 TO 4 10640 ROTARY.REH(0) 8 LPR 10650 ROTARY.REH(H) 8 LPR * .56 8 .66 ‘ (H 8 USE I 1184) 10660 ROTARY.DEP(H) 8 ROTARY.REN(N - 1) - ROTARY.REN(N) 10670 ROTARY.AVC.REP(N) 8 (LPR * .281 8 (N 8 USE I 1000) ‘ 2) / N 10680 ROTARY.AVO.DEP(H) 8 (LPR - ROTARY.REH(H)) I N 10690 ROTARY.REP(0) 8 0 10700 ROTARY.REP(N) 8 (LPR * .281 8 (N 8 USE I 1000) ‘ 2) 10710 ROTARY.YEAR.REP(H) 8 ROTARY.REP(H) - ROTARY.REP(N - 1) 10720 ROTARY.AVC.COST(H) 8 ROTARY.AVC.DEP(N) + ROTARY.AVG.REP(H) 10730 ROTARY.YEAR.COST(N) 8 ROTARY.DEP(H) + ROTARY.YEAR.REP(N) 10740 BRAKE.ROTARY(H) 8 ROTARY.AVG.COST(N) - ROTARY.YEAR.COST(H) 10750 PRINT N, BRAKE.ROTARY(H) 10760 NEXT N 10770 PRINT 10780 PRINT 8Trade when annual cost overcomes the average cost." 10790 PRINT 8Nuea trade plan is 5 years for tractors and 2 years for tillers.“ 10800 PRINT 10810 INPUT “INPUT TINE TO REPLACE TRACTOR(Year) 8", USEN 10820 IF USEN >8 1 THEN GOTO 10850 10830 BEEP 10840 GOTO 10810 10850 PRINT 10860 INPUT ”INPUT TIHE TO REPLACE ROTARY TILLER(Year) 8', USEN 10870 IF USEN >8 1 THEN GOTO 10900 10880 BEEP 10890 GOTO 10860 10900 CLS 10910 LOCATE 11, 10 10920 PRINT “TRACTOR LIST PRICE (8h) 8', LPT 10930 LOCATE 12, 10 10940 PRINT “ROTARY TILLER LIST PRICE (8h) 8', LPR 10950 LOCATE 14, 10 10960 USEN 8 CINT(USEN) 10970 "*'*******'RENAINING VALUE'************'* 10980 TRACT.REN 8 LPT 8 .6 8 .935 ‘ (USEN 8 USE I 1000) 10990 'TRACT.REN 8 CINTITRACT.REH) 225 11000 ROTARY.REN 8 LPR * .56 * .66 A (USEN 8 USE I 1184) 11010 'ROTARY.REN 8 CINT(ROTARY.REN) 11020 LOCATE 18, 10 11030 PRINT “REMAINING VALUE OF TRACTOR 8“, TRACT.REN 11040 LOCATE 19, 10 11050 PRINT IREMAINING VALUE OF ROTARY TILLER 8', ROTARY.REN 11060 LOCATE 22, 10 11070 PRINT “NIT ANY KEY TO PROCEED!" 11080 E8 8 INKEYO: IF ES 8 8” THEN 11080 11090 RETURN 11100 COLOR 7, 1, 1 11110 CLS 11120 LOCATE 2, 1 11130 Palut I.*tt*.**tt*fi.**fit**ttififititfittifi*fififittiiifltifiiiitiiifltififl 1111.0 001111 8 0m: 111000110110118 11150 PR1“? liftiiiiittiiitfl*********fififitfi*iiCititfiiiiit*flfifitfii*****tfl 11150 1.0c01s 5, 10 11170 001111 80000011 110100 01 mass-- 11100 1.00015 0, 10 11190 001111 81051 (sh/L) . 128: 0001 . 12 11200 100010 9, 10 11210 001111 8011. (sh/L) - 1.58: OIL . 1.5 11220 10cm 10, 10 11230 001111 80001310011011 1011) . 7008: ms . 700 1121.0 Laura 11, 10 11250 001111 81115000110: (sh/year) . 10008: 111s . 1000 11260 100012 12, 10 11270 001111 80000 110131155 (sh/year) - 5008: 0000 . 500 11200 LOCATE 13, 10 11290 001111 800200100 01110101 501001 (obs/year) - 355208: 10000 - 355201 11300 10001: 11., 10 11310 001111 800200100 OVERTINE 0051 (0110/11) . 208: M0 - 20 11320 10cm 15, 10 11330 001111 800001 0105 00000c£0 001015 (sh/kg) . 5.58: 0102 . 5.5 1131.0 10cm 16, 10 11350 001111 85550 (011/110) - 27118: seen - 276 11360 100015 17, 10 11370 00101 80001111250 (sh/ha) . 23108: 1:01 . 2310 113110 1.0001: 13, 10 11390 00101 8000010015 (ah/ha) . 0128: cues = 142 111.00 10001: 19, 10 11110 001111 811011011110 01100025 (sh/bag) =208: 110110 . 20 111.20 1x01: 23, 10 111.30 111001 800 you 110111 10 0110110: 001cm 111/1118, 1115 111.10 11 1115 - 8y" 00 1115 . 818 111511 11010 11460 111.50 01210011 111.50 CLS 111.70 100010 2. 1 11‘80 PRINT Itfititfifitfifififififiitfitififi*fltfiflfitfiittfiififiitfitfitiifiittfitiifltfltu 11490 PRINT ' PRICE INFORMATION“ 11500 PRINT Itttfitttttttttttttttttiititiifitttttttttttitattitttfittfitttu 11510 LOCATE 5, 10 11520 PRINT "INPUT NEH VALUE OF PRICES“ 226 11530 LOCATE 8, 10 11540 FUEL 8 0: INPUT “FUEL (sh/L) 8“; FUEL 11550 LOCATE 9, 10 11560 OIL 8 0: INPUT 8OIL (sh/L) 88; OIL 11570 LOCATE 10, 10 11580 REGS 8 0: INPUT 8REGISTRATION (sh) 88; REGS 11590 LOCATE 11, 10 11600 INS 8 0: INPUT 8INSURANCE (sh/year) 8“; INS 11610 LOCATE 12, 10 11620 ROAD 8 0: INPUT 8ROAD LICENSE (sh/year) 88; ROAD 11630 LOCATE 13, 10 11640 LABOR 8 0: INPUT 8OPERATOR ANNUAL(shs/Year) 8“; LABOR 11650 LOCATE 14, 10 11660 LABOR 8 0: INPUT 8OPERATOR OVERTINE(shs/h) 8'; OVER 11670 LOCATE 15, 10 11680 RICE 8 0: INPUT 8PADDY RICE PRODUCER PRICE (sh/k9) 8'; RICE 11690 LOCATE 16, 10 11700 SEED 8 0: INPUT 8SEED (sh/ha) 8'; SEED 11710 LOCATE 17, 10 11720 FERT 8 0: INPUT 8FERTILIZER (sh/ha) 8“; FERT 11730 LOCATE 18, 10 11740 CHEN 8 0: INPUT 8CHEMICAL (sh/ha) 8“; CHEN 11750 LOCATE 19, 10 11760 HAND 8 0: INPUT 8HANDLING CHARGES (sh/bag) 8'; HAND 11770 RETURN 11780 CLS 11790 LOCATE 2, 1 ttttttttitt 11000 p01~1 a 088189.88.ittittttttttfittttttttttttttitttttitttI 11810 PRINT 8 COST ESTIIIATE'I 11020 p01'1 n000000000.0000000000000000.000000000000000000000000000000:u 11830 TRACT.DEP 8 (LPT - TRACT.REN) I USEN 11840 ROTARY.OEP 8 (LPR . ROTARY.REH) I USE! 11850 PRINT ”TRACTOR DEPRECIATION (sh/year) 8 8; TRACT.OEP 11860 PRINT 8TILLER OEPRECIATION (Sh/Vllr) 8 8; ROTARY.DEP 11870 AV0.0EP 8 TRACT.DEP + ROTARY.OEP 11880 AVO.FIX 8 CINTCREGS I USEN 8 INS + ROID) 11890 PRINT 8AVG. FIXED COST (Sh/year) 8 8; AVO.FIX 11900 IttttttttttttttttthEL + OIL COST8888888888888888888888 11910 X 8 POHER I TRACT.SIZE 11920 FUEL.CON 8 2.64 8 X 8 3.91 - .203 8 SQR(738 8 X + 173) 11930 C.FUEL 8 FUEL.CON 8 POUER 8 USE 8 FUEL 11940 PRINT 8FUEL COST (Oh/Inchine) 8 8; INT(C.FUEL) 11950 OIL.CON 8 .00059 8 TRACT.SIZE + .02169 11960 C.OIL 8 OIL.CON 8 USE 8 OIL 11970 PRINT 8OIL COST (Sh/IIChine) 8 8; INT(C.OIL) 11900 litttttttttttittttREpAla 005100000000000000.0000... 11990 TRACT.REPAIR 8 LPT 8 .045 8 (USEN 8 USE I 1000) 8 2 12000 ROTARY.REPAIR 8 LPR 8 .281 8 (USEN 8 USE I 1000) 8 2 12010 TRACT.AVG.REP 8 TRACT.REPAIR I USEN 12020 PRINT 8AVG. TRACTOR REPAIR COST (sh/year) 8', TRACT.AVG.REP 12030 ROTARY.AVO.REP 8 ROTARY.REPAIR I USEN 12040 PRINT 8AVG. TILLER REPAIR COST (Sh/YCOP) 8', ROTARY.AVG.REP 12050 AVG.REP 8 TRACT.AVO.REP + ROTARY.AVG.REP 227 M 1m I m COSTm***.*iflmfim*“imti 12070.0VER.C 8 CINT(OVER 8 (HOUR + 1.5 ' 8) 8 PERIOD 8 UORK.PAT) 12080 LABOR.C 8 OVER.C 8 LABOR 12090 '88888888888TOTAL ANNUAL MACHINERY COST88888888888888 12100 TOTAL.COST 8 INT((AVG.DEP + C.FUEL 8 C.OIL + AVG.REP + AVG.FIX + LABOR.C) 8 NUM.TOTAL) 12110 PRINT 8TOTAL ANNUAL COST (sh) 8 8; TOTAL.COST 12120 MACH.COST 8 INT(TOTAL.COST / TOTAL.AREA) 12130 PRINT ”ANNUAL MACHINERY COST (sh/he) 8 8; MACH.COST 121‘0 litiittttttttttt‘xpECTED YIELD8888888888888888888 12150 YIELD 8 5686 - 5.28 8 PERIOD 12160 SALE 8 INTCYIELD 8 RICE) 12170 PRINT 8SALE OF RICE (Bh/hB) 8 8; SALE 12100 10.0900009090099003" 1~p01 00510000000000.0000... 12190 HAND.C 8 INT(YIELD I 75 8 HAND) 12200 FARM.COST 8 INT(SEED + FERT 8 CHEM + HAND.C) 12210 PRINT “FARM INPUT COST (sh/ha) 8 8; FARM.COST 12220 NET.RETURN 8 SALE - MACH.COST - FARM.COST 12230 PRINT 8NET RETURN (Sh/ha) 8 8; NET.RETURN 12240 LOCATE 20, 10 12250 PRINT "HIT ANY KEY TO PROCEED!“ 12260 AS 8 INKEYS: IF AS 8 8' THEN 12260 12270 RETURN 12200 Ittfitttttttittt80......tittttttttttttfitfittt*ttttttitttttttttttitttttfittttt 12290 COLOR 7, 0, 0 12300 CLS 12310 LOCATE 1, 1 12320 PRINT nttattatattntettttttttRESULT SUMMARY888888888888888888888888” 12330 PRINT 8ROTARY TILLER UIDTH (CI) 8 8; ROTARY.HIDTH 12340 PRINT ”TILLAGE DEPTH (CI) 8 8; DEPTH 12350 PRINT “GROUND SPEED (leh)8 8: GROUND 12360 PRINT ”BITE LENGTH (CII8 8; BITE 12370 PRINT "SOIL CONDITION 8 8; SOILS 12380 PRINT ITRACTOR TYPE 8 8; NDS 12390 PRINT 12400 PRINT 12410 PRINT 12420 PRINT 12430 PRINT 12440 PRINT 8011111015 101) 8 8; 01.1 8001150 100 TILLAGE (1111) . 8; 011111001150.111 8 100) I 100 8001150 000 11011011 05513101105 11:11) 8 8; c1011001150.1101 8 100) / 100 81000100 3125 11:11) . 8; 01111110001.5125 8 100) I 100 8HIT ANY KEY TO PROCEED!“ 12450 AS 8 INKEYS: IF AS 8 '8 THEN 12450 12460 CLS 12470 PRINT 12480 PRINT 12490 PRINT 12500 PRINT 12510 PRINT 12520 PRINT 12530 PRINT 12540 PRINT 12550 PRINT 12560 PRINT 12570 PRINT 12580 PRINT 12590 PRINT “HEATHER CONDITION8 8; UEATNERS 8DAY FOR STARTING TILLAGE8 8; INIT.DATE 8TILLAGE DURATION (d878)8 8; PERIOD "NET NORK HOURS (h/day) 8 8; HOUR "MACHINE AVAILABILITY (decilal) 8 8; AVAIL 'HORK PATTERN COEFFICIENT 8 8; NORK.PAT “TOTAL AVAILABLE TIME (h) 8 8; TOTAL.HOUR “PLOT SIZE (HI) 8 8; PLOT.SIZE 8FIELD LENGTH (I) 8 8; FIELD.LENGTH 'FIELD UIDTH (I) 8 8; CINT(FIELD.UIDTH 8 100) I 100 “OPERATOR SKILL 8 8; OPES 8SINKAGE FREQUENCY (tilts/h.) 8 8; SINK 8LOST TIME (lin/HB) 8 8: LOST.TIME 228 12600 PRINT “FIELD EFFICIENCY (OGCImll)8 “; CINT(FIELD.EFF 8 1000) I 1000 12610 PRINT “THEORETICAL FIELD CAPACITY (ha/hr)= 8; CINT(TFC 8 1000) / 1000 12620 PRINT “EFFECTIVE FIELD CAPACITY (ha/hr)8 “; CINT(EFC 8 1000) / 1000 12630 PRINT 12640 PRINT “HIT ANY KEY TO PROCEED!“ 12650 AS 8 INKEYS: IF AS 8 “8 THEN 12650 12660 CLS 12670 PRINT “TOTAL FIELD AREA (h8)8 8; TOTAL.AREA 12680 PRINT “NUMBER OF REQUIRED MACHINE8 “; NUM.MACH 12690 PRINT “NUMBER OF SPARE MACHINE8 “; NUM.SP 12700 PRINT “ANNUAL TRACTOR USE (h) 8 8; USE 12710 PRINT “SYSTEM RELIABILITY 8 “; SYS.RE 12720 PRINT “TRACT“ PRICE (8h) 8 “; LPT 12730 PRINT 12740 PRINT 12750 PRINT 12760 PRINT 12770 PRINT 12780 PRINT 12790 PRINT 12800 PRINT 12810 PRINT 12820 PRINT 12830 PRINT 12840 PRINT 12850 PRINT 12860 PRINT 12870 PRINT 12880 PRINT 12890 PRINT 12900 PRINT 12910 PRINT 12920 PRINT 12930 PRINT 12940 PRINT “ROTARY TILLER PRICE (Ch) 8 8; LPR 8TIHE TD TRADE TRACTOR (year) 8 8; USEN 8TIME TD TRADE TILLER (year) 8 8; USEN 8TRACTOR DEPRECIATION (sh/year) 8 8; TRACT.DEP 8TILLER DEPRECIATION (sh/year) 8 8; ROTARY.DEP I'TRACTOR INSURANCE I FEES (sh/year) 8 8; AVG.FIX 8FUEL COST (sh/nachine) 8 8; C.FI£L 8OIL COST (ah/Inchine) 8 8; C.OIL “AVG. TRACTOR REPAIR COST (sh/year)8 “; TRACT.AVG.REP “AVG. TILLER REPAIR COST (sh/year) 8 “; ROTARY.AVG.REP 8ANNUAL LABOR COST (sh/year) 8 8; LABOR.C 8NACHINERY COST (eh/Ha) 8 8; NACH.OOST 8SEEO COST (sh/he) 8 8; SEED 8PESTICIDE COST (sh/ha) 8 8; CHEN 8FERTILIZER OOST (sh/ha) 8 8; FERT 8HANDLING COST (sh/ha) 8 8; HAND.C 8FARN INPUT COST (sh/ha) 8 8; FARN.COST 8ANNUAL OOST (sh/ha) 8 8; NACH.OOST + FARH.OOST 8PADDY YIELD (kg/ha) 8 8; YIELD 8CROSS RETURN (ah/ha) 8 8; SALE 8NET RETURN (sh/ha) 8 8; NET.RETURN 12950 PRINT “HIT ANY KEY TO PROCEED!“ 12960 AS 8 INKEYS: IF AS 8 ““ THEN 12960 12970 RETURN 12980 0 Wm1u1 I “G RewLIS‘Itflflfltfiflittfi 12990 CLS 13000 LOCATE 10, 10 13010 PRINT “88888888888PRINTING MACHINERY SELECTION RESULTS8888888888" 13020 LPRINT 13030 LPRINT 13040 LPRINT 13050 LPRINT 13060 LPRINT 13070 LPRINT 13080 LPRINT 13090 LPRINT 13100 LPRINT 13110 LPRINT 13120 LPRINT “ROTARY NIDTH (CI)8 “; ROTARY.HIDTH “TILLING DEPTH (CI)8 8; DEPTH “GRCIND SPEED (Wh)8 8: GRCINI) “BITE LENGTH (CI)8 8; BITE “SOIL CONDITION 8 “; SOILS “TRACTOR TYPE 8 “; HDS “ALTITUDE (I) 8 “; ALT “PONER FOR TILLAGE (RN) 8 “; CINT(POHER.TIL 8 100) I 100 “PONER FOR MOTION (KH) 8 8: CINT(POUER.MOT 8 100) I 100 “TRACTOR SIZE (RN) 8 “; CINT(TRACT.SI2E 8 100) I 100 “HEATHER CONDITION8 “; UEATHERS 13130 LPRINT 13140 LPRINT 13150 LPRINT 13160 LPRINT 13170 LPRINT 13180 LPRINT 13190 LPRINT 13200 LPRINT 13210 LPRINT 13220 LPRINT 13230 LPRINT 13240 LPRINT 13250 LPRINT 13260 LPRINT 13270 LPRINT 13280 LPRINT 13290 LPRINT 13300 CLS 13310 LOCATE 13320 13330 13340 13350 13360 13370 13380 13390 13400 13410 13420 13430 13440 13450 13460 13470 13480 13490 LPRINT LPRINT LPRINT LPRINT RETURN CLS LOCATE LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT 13500 LPRINT 13510 LPRINT 13520 LPRINT 13530 LPRINT 13540 LPRINT 13550 LPRINT 13560 LPRINT 13570 LPRINT 13580 LPRINT 13590 LPRINT 13600 LPRINT 13610 LPRINT 13620 RETURN 13630 CLS 13640 LOCATE 12:29 8001 100 51001100 1110105- 8; 1011.0015 81111005 00001100 (days)8 8; 050100 8051 0000 00005 (h/day) = 8; 0000 80000105 0v0110011111 (aces-61) . 8; 0v011 80000 0011500 00511101501 . 8; 0000.001 810101 0v0110015 1105 (h) = 8; 10101.0000 80101 5125 (00) . 8; 0101.5125 811510 150010 (01 . 8; 11510.150010 811510 01010 (m) 8 8; 0101(11510.01010 8 100) / 100 800500100 50111 - 8; 0055 85100005 105005001 (tines/ha) - 8; 5100 81051 1105 (010/00) . 8; 1051.1105 811510 5111015001: 8; 0101111510.511 8 1000) / 1000 810500511001 11510 00000111 (ha/hr): 8; 01011110 8 1000) I 1000 8511501105 11510 00000111 (ha/hr)8 8; 01011510 8 1000) / 1000 810101 11510 0050 1001s 8; 10101.0050 8000050 01 05001050 0000105: 8; 000.0000 10, 10 PRINT “88888888888PRINTING SYSTEM RELIABILITY RESULTS8888888888“ “NUMBER OF REOUIRED MACHINE8 “; NUM.MACH “NUMBER OF SPARE MACHINE8 “; NUM.SP “ANNUAL TRACTOR USE (h) 8 “; USE “SYSTEM RELIABILITY 8 “; SYS.RE 10, 10 00101 8*888888888800101100 0051 00011515 055011588888880888 “TRACTOR PRICE (8h) 8 “; LPT “ROTARY TILLER PRICE (8h) 8 “; LPR PRINT “TIME TO TRADE TRACTOR (year) 8 8; USEN PRINT “TIME TO TRADE TILLER (year) 8 “; USEM “TRACTOR DEPRECIATION (sh/year) 8 8; TRACT.DEP “TILLER DEPRECIATION (Sh/year) 8 “; ROTARY.DEP “TRACTOR INSURANCE & FEE (sh/Year) 8 “; AVG.FIX “FUEL COST (sh/machine) 8 “; C.FUEL “OIL COST (eh/nechine) 8 8; C.OIL “AVG. TRACTOR REPAIR COST (sh/year): “; TRACT.AVG.REP “AVG. ROTARY REPAIR COST (sh/year) 8 “; ROTARY.AVO.REP “ANNUAL LABOR COST (sh/year) 8 “; LABOR.C “MACHINERY COST (OH/h.) 8 “; NACH.COST “SEED COST (sh/ha) 8 “; SEED “PESTICIDE OOST (sh/ha) 8 8; CHEN “FERTILIZER COST (sh/ha) 8 “; FERT “HANDLING COST (sh/ha) 8 “; HAND.C “FARN INPUT COST (sh/he) 8 8: FARN.COST “ANNUAL COST (Oh/Ha) 8 “; HACH.COST + FARN.COST “GROSS RETURN (sh/ha) 8 “; SALE “NET RETURN (Sh/HI) 8 8; NET.RETURN 10, 10 13650 00101 88888888888800101100 011 0550115888118888888 13660 LPRINT 13670 LPRINT 13680 LPRINT 13690 LPRINT 13700 LPRINT 13710 LPRINT 13720 LPRINT 13730 LPRINT 13740 LPRINT 13750 LPRINT 13760 LPRINT 13770 LPRINT 13780 LPRINT 13790 LPRINT 13800 LPRINT 13810 LPRINT 13820 LPRINT 13830 LPRINT 13840 LPRINT 13850 LPRINT 13860 LPRINT 13870 LPRINT 13880 LPRINT 13890 LPRINT 13900 LPRINT 13910 LPRINT 13920 LPRINT 13930 LPRINT 13940 LPRINT 13950 LPRINT 13960 LPRINT 13970 LPRINT 13980 LPRINT 13990 LPRINT II3I) 8001001 01010 (00)- 8; 001001.01010 81111100 05010 (an)8 8; 05010 8000000 50550 (001018 8; 000000 80115 150010 10018 8; 0115 85011 000011100 . 8; 50115 81000100 1105 . 8; 005 801111005 (0) . 8; 011 800050 100 1111005 (00) a 8; 0101100050.111 8 100) / 100 800050 100 001100 (00) s 8; 0101100050.001 8 100) / 100 81000100 5125 (00) 8 8; 0101(10001.5125 8 100) I 100 80501050 000011100: 8; 05010505 8001 100 51001100 1111005 - 8; 1011.0015 81111005 000111100 (dam)- 8; 050110) 8051 0000 00005 (h/day) = 8; 0000 80000105 0v0110011111 (0001001) 8 8; 0v011 80000 0011500 00511101501 . 8; 0000.001 810101 0v0110015 1105 (h) 8 8; 10101.0000 80101 5125 (00) . 8; 0101.5125 811510 150010 (0) 8 8; 11510.150010 811510 01010 (0) . 8; 0101(11510.01010 8 100) I 100 800500100 50111 - 8; 0055 85100005 105005001 (100) . 8; 5100 81051 1105 (min/ha) 8 8; 1051.1105 811510 5111015001: 8; 0101(11510.511 8 1000) I 1000 810500511001 11510 00000111 (ha/hr)8 8; 01011110 8 1000) I 1000 8511501105 11510 00000111 (ha/hr)8 8; 01011510 8 1000) / 1000 810101 11510 0050 (h0)8 8; 10101.0050 8000050 01 05001050 0000105: 8; 000.0000 8000050 01 50005 0000105: 8; 000.50 811510 1105 (h) 8 8; 11 8515150 05110011111 - 8; 515.05 8000001 1000100 055 (h) 8 8; 055 81000100 00105 (0h) 8 8; 101 8001001 111150 00105 (0h) 8 8; 100 14000 PRINT “TIME TO TRADE TRACTOR (year) 8 “; USEN 14010 PRINT “TIME TO TRADE TILLER (year) 8 “; USEM 14020 PRINT “TRACTOR DEPRECIATION (sh/year) 8 “; TRACT.DEP 14030 PRINT “TILLER DEPRECIATION (sh/year) 8 “; ROTARY.DEP 14040 100101 14050 100101 14060 100101 14070 100101 14000 100101 14090 100101 14100 100101 14110 100101 14120 100101 14130 100101 14140 100101 14150 100101 14160 100101 14170 100101 14100 100101 14190 100101 80050005 050050101100 (sh/machine) . 8; 0v0.050 81000100 105000005 5 155 (sh/year) 8 8; 0v0.110 81051 0051 (sh/uechine) . 8; 0.1051 8011 0051 (sh/machine) . 8; 010110.011) 80v0. 1000100 050010 0051 (sh/year)8 8; 10001.000.050 8000. 111150 050010 0051 (sh/year) s 8; 001001.0v0.050 8000001 10000 0051 (sh/year) . 8; 10000.0 8000010501 0051 (sh/ha) a 8; 0000.0051 85550 0051 (sh/he) 8 8; 5550 8055110105 0051 (sh/he) . 8; 0050 81501111250 0051 (sh/ha) s 8; 1501 800001100 0051 (sh/ha) . 8; 0000.0 81000 10001 0051 (sh/ha) . 8; 1000.0051 8000001 0051 (sh/he) . 8; 0000.0051 + 1000.0051 800001 11510 (kg/00) - 8; 0101111510) 800055 051000 (sh/ha) = 8; 5015 231 14200 LPRINT “NET RETURN (sh/ha) = “; NET.RETURN 14210 RETURN 14220 CLS 14230 LOCATE 10, 10 14240 PRINT “CREATING A FILE TO SAVE THE RESULTS“ 14250 LOCATE 12, 10 14260 PRINT ”Default file nan: is '8:\RESULTS.8AS'8 14270 FILENANES I '8:\RESULTS.RAS' 14280 LOCATE 14, 10 14290 INPUT 'NOULD YOU LIKE TO CHANGE THE FILE NAME? (YIN)". CFNS 14300 IF CFNS I “Y“ OR CFNS I “y“ THEN GOTO 14340 14310 IF CFNS 8 “N8 OR CFNS 8 "11'I THEN GOTO 14370 14320 ' BEEP 14330 GOTO 14280 14340 CLS 14350 LOCATE 10, 10 14360 INPUT “INPUT NEH FILE NAME “; FILENAMES 14370 CLS 14380 LOCATE 10, 10 14390 PRINT “**********‘**SAVING RESULTS IN A FILE**************“ 14400 LOCATE 13, 10 14410 PRINT “FILE NAME 8“; FILENANES 14420 OPEN FILENAMES FOR OUTPUT AS #1 14430 PRINT '1, ROTARY.HIDTH 14440 PRINT #1, DEPTN 14450 PRINT #1, GROUND 14460 PRINT #1, SITE 14470 PRINT #1, SOILS 14480 PRINT #1, 008 14490 PRINT '1, ALT 14500 PRINT '1, CINT(PONER.TIL * 100) l 100 14510 PRINT '1, CINT(PONER.NOT * 100) I 100 14520 PRINT '1, CINT(TRACT.SIZE * 100) l 100 14530 PRINT '1, TRACTOR.U 14540 PRINT #1, UEATNERS 14550 PRINT #1, INIT.DATE 14560 PRINT #1, PERIOD 14570 PRINT #1, HOUR 14580 PRINT #1, AVAIL 14590 PRINT #1, NORK.PAT 14600 PRINT #1, TOTAL.NOUR 14610 PRINT #1, PLOT.SIZE 14620 PRINT #1, FIELD.LENGTH 14630 PRINT #1, CINT(FIELD.NIDTN * 100) I 100 14640 PRINT #1, OPES 14650 PRINT #1, SINK 14660 PRINT #1, LOST.TIME 14670 PRINT '1, CINT(FIELO.EFF * 1000) I 1000 14680 PRINT #1, CINT(TFC * 1000) l 1000 14690 PRINT ’1, CINT(EFC * 1000) I 1000 14700 PRINT ‘1, TOTAL.AREA 14710 PRINT #1, NUM.MACN 14720 PRINT #1, NUM.SP 16730 PRINT 16760 PRINT 16750 PRINT 16760 PRINT 16770 PRINT 16780 PRINT 16790 PRINT 16800 PRINT 16810 PRINT 16820 PRINT 16830 PRINT 16860 PRINT 16850 PRINT 16860 PRINT 16870 PRINT 16880 PRINT 16890 PRINT 16900 PRINT 16910 PRINT 16920 PRINT 16930 PRINT 16960 PRINT 16950 PRINT 16960 PRINT 16970 PRINT 16980 PRINT 16990 PRINT 15000 CLOSE 15010 END #1, a1, #1, :1, #1, :1, #1, #1, #1, I1, #1, :1, In, :1, :1, :1, t1, t1, :1, :1, :1, a1, a1, :1, c1, #1, :1, 232 FT SYS.RE USE LPT LPR USEN USEN TRACT.DEP ROTARY.DEP AVG.0EP AVG.FIX C.FUEL C.OIL TRACT.RVG.REP ROTARY.AVG.REP LABOR.C NACN.COST SEED CHEN FERT HAND.C FARN.COST NACN.COST + FARN.COST CINT(YIELD) RICE SALE NET.RETURN 233 D.2. Program List of Checker Program 1000 CLS 1010 LOCATE 2, 10 1020 p0101 Itittiitittit.tttttttttttit...tittiittttttttfitfittttfltit...u 1030 PRINT ' RELIABILITY“ 1060 LOCATE 6, 10 1050 p0101 litttttittttttfitifittttit.tittttttttttttfitfiifittttfitittittttu 1060 OIM R(55), C(55) 1070 LOCATE 6, 10 1080 PRINT 'THIS PROGRAM COMPUTES RELIABILITY OF A SYSTEM UITH“ 1090 PRINT ' M0 SPARE MACHINE SETS“ 1100 PRINT ' ONE SPARE MACHINE SET“ 1110 PRINT ' THO SPARE MACHINE SETS“ 1120 PRINT ' THREE SPARE MACHINE SETS” f 1130 PRINT ' FOUR SPARE MACHINE SETS“ ' 1100 100112 13, 10 1150 INPUT “INPUT REYUIRED NUMBER OF MACHINE SETS (2 '< N <8 50)”; N 1m0nu>=2mouasomm1w0 1170 0259 1100 0010 1140 1190 u . 1u11u) 1200 LOCATE 1s, 10 ; 1210 10001 ~10901 A SINGLE nsannanrv 100 ALL NACHINE sers (0 < a < 1)-; a 1220 11 n > 0 AND a < 1 rneu 1250 1230 BEEP 1260 GOTO 1200 1250 O 8 1 - R 1 260 I WWWWCWQMOMOQRR* it t. 1270 'RELIABILITY OF MEN MACHINE SET 1200 atttttattttcttaattuo ExtRAttttttittttttttittttttttitt 1290 SYS.REO 3 R 9 M 1300 PRINT 1310 PRINT ' SYSTEM RELIABILITY UITH HO EXTRA MACHINE SETS“; SYS.REO 1320 nttttttattctcatattous 5x1RAtctctatttacttttatttttactttt 1330 REO I R S (N * 1) 1360 RE1 ' (N 0 1) ' (R C N) * 0 1350 SYS.RE1 ' REO + RE1 1360 PRINT ' SYSTEM RELIABILITY UITH ONE EXTRA MACHINE SET“; SYS.RE1 1370 littitfitttittttttituo EXT“Atttittitttitttttttttfittfittfi 1380 REO 8 0 1390 RE1 8 0 1600 REO 8 R ‘ (N + 2) 1610 RE1 3 (N + 2) ' (R ‘ (N + 1)) * 0 1620 REZ 3 (N * 2) * (N * 1) / 2 8 (R A N) ' 0 A 2 1630 SYS.REZ 8 REO # RE1 0 REZ 1660 PRINT ' SYSTEM RELIABILITY NITH THO EXTRA MACHINE SETS”; SYS.REZ 234 1‘50 I ”umtittiflnT "REE EX‘I’RAififlflitttiii'ktifitt*tfltittt 1660 REO 8 0 1670 RE1 8 0 1680 REZ 3 0 1690 REO = R A (N + 3) 1500 RE1 3 (N + 3) * (R 9 (N + 2)) ' 0 1510 REZ 8 (N + 3) * (N + 2) I 2 * (R A (N + 1)) * 0 C 2 1520 RE3 8 (N + 3) * (H + 2) * (N + 1) / 6 * (R 9 N) * 0 A 3 1530 SYS.RE3 8 REO + RE1 + REZ + RE3 1560 PRINT ' SYSTEM RELIABILITY UITH THREE EXTRA MACHINE SETS”; SYS.RE3 1550 litttittttttttttiifoun ext“At.tit.ttttttttttttttttttttt 1560 RED 8 0 1570 RE1 = 0 1580 REZ - 0 1590 RE3 s 0 1600 REO . R ‘ (u + 6) 1610 RE1 (l + 6) * (R ‘ (I + 3)) * a 1620 R62 (N + 6) * (I + 3) I 2 * (R ‘ (N + 2)) ' 0 A 2 1630 RE3 (u + 6) * (u + 3) * (u + 2) / 6 * (R ‘ (I + 1)) * o ‘ 3 1660 R66 = (N + 6) * (l + 3) * (I + 2) * (l + 1) I 26 * (R ‘ (l)) * 0 * 6 1650 SYS.RE6 = REO + REI + REZ + RE3 + RE6 12:: PRINT ' SYSTEM RELIABILITY NITH FOUR EXTRA MACHINE SETS“; SYS.RE6 END LIST OF REFERENCES LIST OF REFERENCES Al-Soboh, G., A.K. Srivastava, T.H. Burkhardt and J.D. Kelly. 1981 . A Mixed-Integer Linear Programming (MILP) Machinery Selection Model for Navybean Production Systems. TRANSACTIONS of the ASAE 29(1):8l-84,89. ASAE D497 1991. Agricultural Machinery Management Data. ASAE Standards. pp295-301. St. Joseph, MI 49085. ASAE EP456 1991. Test and Reliability Guidelines. ASAE Standards. pp289-294. St. Joseph, MI 49085. ASAE EP496 1991. Agricultural Machinery Management. ASAE Standards. pp289-294. St. Joseph, MI 49085. ASAE 8220.4 1991. Tire Selection Tables for Agricultural Machines of Future Design. ASAE Standards. pp78-79. St. Joseph, MI 49085. ASAE 8313.2 1991. Soil Cone Penetrometer. ASAE Standards. pp591. St. Joseph, MI 49085. Bekker, M. G. 1969. Introduction to Terrain-Vehicle Systems. The University of Michigan Press. Ann Arbor, Michigan. Bowers, W. 1987. Fundamentals of Machine Operations: Machinery Management. Deere and Company. Molinme, IL 61265. Burrows, W.C. and J .C. Siemens. 1974. Determination of Optimum Machinery for Corn-Soybean Farms. TRANSACTIONS of the ASAE 17(6):1130-1135. Chen, L.H. and R.W. McClendon. 1985. Soybean and.Wheat Double Cropping Simulation Model. TRANSACTIONS of the ASAE 28(1):65-69. Colvin, T.S., K.L. McConnel and B.J. Catus. 1989. ”TERMS": A Computer Model for Field Simulation. TRANSACTIONS of the ASAE 32(2):39l-396. Dent, J.B., M.J. Nlackie and S.R. Harrison. 1979. System Simulation in Agriculture. Applied science Publisher Ltd. London. 235 236 Doster D.H., C.L. Dobbinns, P.V. Preckel, Y. Han and S.D. Parsons. 1990. Can You Find A Better Machinery Size. ASAE paper No.90-1554. ASAE, St. Joseph, MI 49085. Downs, H.W., R.K. Taylor and A. Al-Janobi. 1990. A Decision Aid for Optimizing Tractor-Implement Systems. ASAE paper No.90-1569. ASAE, St. Joseph, MI 49085. Gibb, J. A. C., Theo. J. Willcosks and David E. Vose. 1986. Non-engineering Factors Affecting Agricultural Mechanization Schemes, ASAE Paper No. 86-5004. Hargreaves, G.H. 1991. Improving The Use of Weather Data. ASAE paper No.91-4070. ASAE, St. Joseph, MI 49085. Hughes, H.A. and J.B. Holtman. 1976. Machinery Complement Selection Based On Time Constraints. TRANSACTIONS of the ASAE 19(5):812-814. Hunt, D. 1983. Farm Power and Machinery Management. 8th ed. Iowa State University Press. Ames, Iowa 50010. Implement & Tractor. 1985. Implement & Tractor Red Book. Intertec Publishing Corp. Overland Park, KS 66212. Implement 8 Tractor. 1986. Implement 6 Tractor Red Book.’ Intertec Publishing Corp. Overland Park, KS 66212. Implement 6 Tractor. 1987. Implement 6 Tractor Red Book. Intertec Publishing Corp. Overland Park, KS 66212. Iseki & Co. Ltd. 1989. T6020 Operator's Manual. Iseki & Co., Ltd. Tokyo 102 Japan. Ismail, W.I.W. 1991. Simulation Model for Field Crop Production. Machinery System. Ph.D. Dissertation. Department of Agricultural Engineering, Michigan State University. East Lansing, MI 48824. Jones, J.W., P. Jones and.P.A, Everett. 1987. Combining Expert Systems and Agricultural Models: A Case Study. TRANSACTIONS of the ASAE 30(5):1308-1314. Kawamura, N. et al. 1992. Agricultural Machinery and Implements. 2nd ed. (Japanese) Bun-ei-do Publishing Co. Ltd. Tokyo, Japan. Kepner, R. A., R. Bainer and E.L. Barger. 1978. Principles of Farm Machinery. 3rd ed. AVI Publishing Co. Inc. Westport, Connecticut. 237 Kimaru, M. K. 1992. Personal communications. Workshop superintendent, Mwea Irrigation Settlement. Wanguru, Kenya. Kinsey, B. H. 1976. Economic Research and Farm Machinery Design.in.Eastern.Africa, Development Studies Discussion Paper No. 10. Kliest, Ted. 1985. Regional and Seasonal Food Problems in Kenya, African Studies Center Report No. 10. Kotzabassis C., B.A.Stout. and. H.T. ‘Wiedemann. 1990 Farm Machinery Selection and Management Expert System. ASAE. paper No.90-7018. ASAE St. Joseph, MI 49085. Mayfield, W., G.S. Hines and L. Roberts. 1981. A New Method for Estimating Farm Machinery Costs. TRANSACTIONS of the ASAE 24(6):1446-1448. Mohdahar, 1991. Handout. NIB Mwea Irrigation Settlement. Wanguru, Kenya Muhtar, H.A. 1982. Machinery Selection Model. Unpublished Ph.D. Dissertation. Department of Agricultural Engineering, Michigan State University. East Lansing, MI 48824. ' Myers, R. H., K. L. Wong and H. M. Gordy. 1964. Reliability Engineering for Electronic Systems. John Wiley & Sons, Inc., New York National Irrigation Board. 1985. Annual Report and Accounts 1984-1985. National Irrigation Board. 1979. Technical Report No.17. Ahero Irrigation Research Station - Results of long rains and short rains seasons 1978 (April 1978 - March 1979). Matsuyama PlOW' Mfg. Co., Ltd. 1989. Niplo Agricultural Machinery. Matsuyama Plow Mfg. Co., Ltd. Maruko, Nagano 386—04 Japan. Nogyo Kikai Chosakai. 1985. Price list of agricultural machinery. (Japanese) Oskoui, K.E., O.M. Morgan and W.B. Voorhees. 1990. Manages-An Expert System for Machinery Management. ASAE paper No.90- 1643. ASAE St. Joseph, MI 49085. Pingali, Prabju, Yves Bigot and Hans P. Binswanger. 1987. Agricultural Mechanization and the Evolution of Farming Systems in Sub-Saharan Africa. 238 Republic of Kenya. 1981. Sessional Paper No. 4 of 1981 on National Food Policy. Republic of Kenya. 1984. Development Plan 1984-1988. Republic of Kenya. 1989. Development Plan 1989-1993. Republic of Kenya. 1991. Statistical Abstract 1991, Central Bureau of Statistics. Republic of Kenya. 1991. Economic Survey 1991, Central Bureau of Statistics. Robb,J.G., D.E. Ellis and J.A. Smith. 1990. Whole Farm Field Machine Cost Program: WFMACH$. ASAE paper No.90-1560. ASAE, St. Joseph, MI 49085. Rosenberg, S.E., A-A. Rotz, J.R. Black and H.Am Muhtar. 1982. Prediction of Suitable Days for Field Work. ASAE paper No.90-1562. ASAE, St. Joseph, MI 49085. Rotz, C.A., H.A. Muhtar and J.R. Black. 1983. A.Multiple Crop Machinery Selection Algorithm. TRANSACTIONS of the ASAE 26(6):1644-1649. Singh, D. 1978. Field Machinery System Modelling Requirements of Selected michigan Cash Crop production Systems. Ph.D. Dissertation, Department of Agricultural Engineering, Michigan State University. East Lansing, MI 48824. Singh, D., T.H. Burkhardt, J.B. Holtman, L.J. Connor and L.S. Robertson. 1979a. Field Machinery Requirements As Influenced by Crop Rotations and Tillage Practices. TRANSACTIONS of the ASAE 22(4):?02-709. Singh, D. and J.B. Holtman. 1979b. An Heuristic Agricultural Field.Machinery Selection.Algorithm.for Multicrop Farms. TRANSACTIONS of the ASAE 22(4):?64-770. Sunohara. 1992. Personal communications. International division, Matsuyama Plow Co. Ltd. Maruko, Nagano 386-04 Japan. Tamura, M. 1992. Personal communications. Mwea Irrigation Agricultural Development, Japan International Cooperation Agency. Nairobi, Kenya. Wakui, M. 1963. On the Adequate Division of a Working Unit for Tillage Job from the Viewpoint of Efficient Turning Operation in the Rice Field. Journal of Japanese Society of Agricultural Machinery 25(1):17-21. 239 Willcocks, Theo. J. 1986. A Diagnostic Approach for Mechanization in Developing Countries, ASAE Paper No. 86-5010. Wolak, F.J. 1981. Development of a Field Machinery Selection Model. Ph.D. Dissertation. Department of Agricultural Engineering, Michigan State University. East Lansing, MI 48824. World Bank. 1989. Price Prospects for Major Primary Commodities, 1988-2000. v.2. Young, L. H. 1980. Effects of Temperature and Preflooding on Rice Yields in Kenya. Expl. Agriculture 16:425-429.