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' -' ", | r .ithv' .. .-I ‘.\'!v I: 4mm Willi/7W Ill“! all Mr 310580 3716“”l Michian Static ‘llnuvcxsuar This is to certify that the thesis entitled RICE HARVESTING LOSSES AND LABOR REQUIREMENT: A FIELD STUDY AND COMPUTER SIMULATION MODEL, YOGYAKARTA, INDONESIA presented by David w. Gaiser, Jr. has been accepted towards fulfillment of the requirements for Ph.D degreein AET W1 Major professor Date [OI/Z 71/49 0 0-7 639 OVERDUE FINES: 25¢ per day per item RETUHIME LIBRARY MATERIALS: Place in book return to remove charge from circulation records l: Pr" mow 5w. ;. . . ‘0-3 \TT'L'I u'U“ “in ‘ ‘ ‘ " zK-'."‘ 0'; A A . ‘ . Witt-I" 3" ‘ ‘ Mic-Mean Sale se‘flr, 't‘v ~ ~6— “3.42 in partial fulfinmrt s‘ "o :esz m for a: earn an? poem: as mum RICE HARVESTING LOSSES AND LABOR REQUIREMENT: A FIELD STUDY AND COMPUTER SIMULATION MODEL. YOGYAKARTA, INDONESIA By David H. Gaiser, Jr. , ..—- ”x A DISSERTATION Submitted to v ‘ Michigan State University v??e ’TNOY" in partial fulfillment of the requirements ' "' ' for the degree of DOCTOR OF PHILOSOPHY .Department of Agricultural Engineering I980 -s_,.‘- ér/los‘h’? ABSTRACT RICE HARVESTING LOSSES AND LABOR REQUIREMENT: A FIELD STUDY AND COMPUTER SIMULATION MODEL, YOGYAKARTA, INDONESIA By David N. Gaiser, Jr. This investigation differed from other loss studies in two ways: 1) losses were identified under farm conditions, and 2) identification of losses from a random sample of fields formed the basis of the investiga- tion rather than identifying losses by fixed and predetermined experiments. The objectives of the project were: 1) to measure field losses of exist- ing cutting and threshing methods, 2) to evaluate these methods as regards labor requirements and profitability, and 3) to analyze the effect of alternative technologies and their impact upon the reduction of rice losses, labor requirement, and profitability. A pilot study pretested the small-plot technique used for data collection and also developed information for determining the sample size, i.e., the number of fields and plots per field. For the randomized study, the statistical population was defined as irrigated paddy fields produc- ing high-yielding varieties and harvested from March to June in 1979. Two harvesting systems were identified within the Province of Yogyakarta: the traditional ani-ani system and the newly introduced sickle system. And each system was associated with different marketing arrangements: the ani-ani knife and foot-treading were used if the farmer harvested the paddy, but the sickle and beating were used if the farmer sold the crop to a buyer prior to harvest. Cutting losses were of two types: l) shattered and dropped losses and 2) uncut losses; threshing losses were the unthreshed grains on the stalk after threshing. Ani-ani system losses were 8.26%, and uncut losses dominated at 4.48%. Sickle system losses were 8.83% and threshing losses dominated at 5.63%. But area losses were only 5.33% due to assumed re- threshing and gleaning recovery practices. Using the randomized data as the initial conditions, a simulation model was designed for evaluating a 40-day peak harvest period. The uniqueness of the model was the identification of a delayed loss component, i.e., the length of time that paddy must wait before being harvested. This delay was a function of three factors: crop maturity rate, crew harvesting rate, and the availability of a harvest crew. In order to reduce ani-ani uncut losses, a 4-man sickle crew was modeled, but this resulted in a threshing bottleneck. So a power thresher was substituted for beating and alleviated this problem. . The Hayami-Ruttan Induced Development Model formed the economic framework for the study, and each system was compared through a partial budgetary analysis. The sickle system, if used by the farmer, increased the farmer's gross return after harvest costs by 7%. But if the farmer sold the paddy to a buyer, he gained only a slight cost advantage (3.4%). When the power thresher was substituted for manual threshing, system returns slightly favored the power thresher, given the assumptions of the analysis. The conclusions were partly as follows: 1. The sickle system has the greatest potential for controlling losses--but only with an improved thresh- ing method such as a pedal thresher or power thresher. The sickle system required 257 man-hours per hectare while the ani-ani required ll63 man-hours per hectare therefore reducing harvest labor by 78%. An obvious concern would be employment opportunities, but this need not be a problem if sickle technology responds to labor shortages, as seemed to be the case in this province. Farmers have not adopted the sickle for cutting paddy because it did not appear that the additional return was sufficient enough to warrant a con- trary social position on the part of the farmer. With the simulation model, it was possible to identify a paddy waiting time delay before the ripened paddy was cut; the delay can be used to identify additional field losses before and during cutting. Approved 776M 1 M Major Professor Approved Z Department Chairman 3" ‘ i . :.:‘JFV'S V“ S. m- . . and instiit :.. _ ,.. M'St‘cil" ' . I,‘ H‘ Cl ' l'f '1"",-~..irgn.', guidan.v. _.; . . .. : q;rr.g,, State Un‘ .... ‘ Dr. 7.83. J.'~ :- Dedica‘t’ed to my "if-e- . , " q'00! why IISO serch n' '\ 'a:=: GILLIAN ‘ '- . ~.q§estioos and ideas ”I 159'va patience. encouragement... ._ WWW,“ Md understand-ing! (ti 9r “.ym‘ penetrating 3' '7 tr: I" . "r r :un! .. : ‘ 7'- andrafliz iraaidflrv. SIQI’HHLGLL _I ' in." r‘ and Dr. Stunt's v:irr ‘ 1‘ . ; - u,n4,¢,19g. ’— l :1" . Dean Herr/ii? 7. .¢,-' ‘ ‘ .‘l: . i. i. 9-.” -- ,‘q.(ag':,n_ h” ;_‘ i; 7“? and. UHiVEIS II. nh‘l' 'l'”? ‘I. -'_r‘~" " WIOMflC Uf 101‘. Fri/3 ,-. a 1"“.ni '“logistics and cinii 'n. n.»' A. . the unfailingly c gaziard ape Hedi University who par-wins "‘2, .+_,;.~.. ACKNOWLEDGEMENTS The author gratefully acknowledges the support of the individuals and institutions who contributed to this study and made it possible. In particular the author wishes to express his appreciation to: Dr. Merle L. Esmay, my major professor, for his encouragement, guidance, and unfailing assistance both in the field and at Michigan State University. Dr. Ram Narasimhan, Dr. Robert D. Stevens and Dr. B. A. Stout, who also served on my guidance committee, for their helpful suggestions and ideas in developing this dissertation. Dr. Narasimhan contributed significantly to the development of the simulation model. Dr. Stevens' penetrating questions led to the establishment of the economic framework, and Dr. Stout's editing clarified many passages in this manuscript. Dean Hendro P. Sayid of the Facultas Teknologi Pertanian, Gadjah Mada University, who committed the resources of the University to the development of this project. Soemangat, my counterpart, who organized the logistics and gained the local cooperation that was so necessary. Suhargo who unfailingly organized and supervised the day to day data collection activities that insured the project's success. Dr. Maria Astuti of Gadjah Mada University who provided the needed statistical guidance. The faculty members of the Facultas Teknologi Pertanian for their interest and assistance throughout the study. iii United States Agency for International Development (both in a i iihshington and Jakarta) who felt the study was worthy of financial support, and especially Dr. H. Smith Greig who spearheaded the project's acceptance and supervised its implementation. LIPI for their acceptance and sponsorship of this study on behalf of the Indonesian government. iv CHAPTER 1. 1.1 1.2 1 3 1.4 1.5 1.6 CHAPTER 2. 2.1 2.2 2.3 2.4 2.5 TABLE OF CONTENTS INTRODUCTION Scope and Objective Terms of Reference Economic and Technical Framework 1.3.1 The Relevance of the Induced Development Model 1.3.2 Harvest Systems 1.3.3 Economic Framework Cultural Institutions Background 1.5.1 Java and the National Scene 1.5.2 The Province of Yogyakarta Problem Statement DATA COLLECTION METHODOLOGY The Studied System Statistical Population 2.2.1 Production Method: Irrigated Paddy 2.2.2 Varieties 2.2.3 Seasonal Limitation Pilot Investigation 2.3.1 Plot Design and Placement 2.3.2 Field and Laboratory Procedure 2.3.3 Results and Evaluation Randomized Study 2.4.1 Randomization and Field Selection 2.4.2 Field Data Collection Supplemental Data 27 27 29 29 30 32 33 36 40 41 45 47 CHAPTER 3. 3.1 3.2 3.3 3.4 3.5 3.6 3.7 CHAPTER 4. 4.1 4.2 4.3 CHAPTER 5. 5.1 5.2 DATA EVALUATION Data to be Evaluated Ani-ani Losses 3.2.1 Average Ani-ani Losses 3.2.2 Losses by Variety 3.2.3 Losses as a Function of Moisture Content 3.2.4 Loss Variability and Sample Size Sickle and Beating Losses 3.3.1 Varietal Differences and Moisture Content 3.3.2 Loss Variability and Sample Size Ani-ani and Foot-treading Labor 3.4.1 Labor Variability and Sample Size Sickle and Beating Labor 3.5.1 Labor Variability and Sample Size Data Summary Small Plot Technique Evaluation 3 7 1 Shattering Losses 3 7 2 Uncut Losses 3.7.3 Foot-treading Losses 3 7 4 Ani-ani and Sickle Cutting Labor 3 7 5 Foot-treading Labor AREA LOSS MODEL Farmer Interviews Area Losses Re-evaluation of Ani-ani Sickle Losses SYSTEM IDENTIFICATION System Identification System Alternatives vi 77 77 81 CHAPTER 6. 6.1 6.2 6.3 6.4 6.5 CHAPTER 7. 7.1 7.2 7.3 THE SIMULATION MODEL Simulation: An Introduction 6.1.1 Simulation Defined 6.1.2 The Appropriateness of Simulation Model Design Considerations 6.2.1 Model Classification 6.2.2 Time Advance Method 6.2.3 Choice of a Simulation Language: GPSS 6.2.4 Review of GPSS Ani-ani Loss and Labor Simulation Model: An Overview 6.3.1 Modeling Objectives 6.3.2 Generalized Concept 6.3.3 Statement of the Modeling Problem 6.3.4 Model Discussion Ani-ani Labor Model 6.4.1 Modeling Approach 6.4.2 Model Statistics 6.4.3 Model Verification 6.4.4 Alternative Modeling Configurations Ani-ani Loss Model SIMULATION RESULTS AND DISCUSSION Traditional Ani-ani System 7.1.1 Losses 7.1.2 Labor 7.1.3 Summary Sickle System 7.2.1 Sickle and Ani-ani Losses 7.2.2 Sickle and Ani-ani Labor 7.2.3 Summary Power Thresher 7.3.1 Losses 7.3.2 Labor Requirement Page 112 117 120 120 122 122 124 124 124 128 128 128 129 Page 7.4 Financial Analysis 130 7.4.1 Hypothesis One 130 7.4.2 Hypothesis Two 132 7.4.3 Hypothesis Three 133 7.4.4 Summary 134 CHAPTER 8. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS FOR RESEARCH 138 8.1 Summary 138 8.2 Conclusions 148 8.2.1 Conclusions Based Upon the Field Data 148 8.2.2 Conclusions of the Simulation Model 151 8.2.3 Conclusions Based Upon the Economic Analysis 152 8.3 Recommendations for Additional Research 153 APPENDIX A. LOSS AND LABOR DATA SUMMARY 155 APPENDIX B. ANALYSES OF VARIANCE FOR LOSS AND LABOR DATA 179 APPENDIX C. STANDARD DEVIATIONS ASSOCIATED WITH VARIOUS FIELD AND PLOT COMBINATIONS FOR LOSS AND LABOR DATA 185 APPENDIX D. BASIC GPSS CONCEPTS 195 viii 1.2 1.3 1.4 1.5 1.7 1.8 2.1 2.2 3.6 3.7 4.1 4.2 6.1 6.2 6.3 6.4 LIST OF TABLES Control of paddy under different marketing arrangements Indonesian rice imports (1000 metric tons) Rough rice comparison for southeast Asian countries-- 1974-1976 Indonesian production trends, 1950-1976 Summary of Indonesian rice production by region for the years 1968 and 1977 Indonesian population and sectoral distribution of employment, 1961, 1971, and 1976. Comparative population densities Demographic and sawah characteristics for the province of Yogyakarta An estimate of sample size--fie1ds and plots for percent shattering, uncut, and total ani-ani losses and for ani-ani labor Random site locations and sampling stages Ani-ani and sickle losses with confidence intervals at the 95% level Labor requirement for ani-ani and sickle systems with confidence intervals at the 95% level Area losses assuming gleaning and rethreshing recoveries Ani-ani and sickle losses with rethreshing Interarrival time between days of crop maturity GPSS program for basic ani-ani labor model Block count and savevalues after firSt season's simulation GPSS program for ani-ani loss model Page 12 17 20 21 22 39 44 66 68 75 76 97 101 111 114 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.1 N 7.13 8.1 Ani-ani simulation and field data losses Ani-ani system: area cut, area uncut, harvest crew delay, and paddy waiting time for 10 simulated seasons Ani-ani system: labor rates and crew time (crew of 4) for 10 simulated seasons Sickle and ani-ani losses over a ten season simulation Sickle system: area cut, area uncut, harvest crew delay, and paddy waiting time for 10 simulated seasons Sickle system: labor rates and crew time (crew of 4) for 10 simulated seasons Sickle system: area cut, area uncut, and paddy waiting time without harvest crew delays for 10 simulation runs Ani-ani and sickle losses with the power thresher Cost comparison between farmer (ani-ani) and buyer (sickle) systems for the harvest of one hectare Hourly and area labor costs for ani-ani and sickle systems Return to the farmer from use of the sickle system with beating and rethreshing for one hectare Effect of power thresher on ani—ani and sickle system returns after harvest Estimate of hourly power thresher costs Loss and labor values for the ani-ani and sickle systems Page 121 23 123 125 126 126 127 129 131 132 133 136 137 143 2.1 2.2 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.1 6.1 7.1 8.1 LIST OF FIGURES Percent harvested area by month - Yogyakarta, DIY - 1975, 76, 77. Source: Luas Panen Bulanan, Dinas Pertanian & Perikanan DIY. Sample selection stages Frequency histograms: ani-ani losses Frequency histogram: foot-treading losses Yield and moisture content for ani-ani study Frequency histograms: random sickle cutting losses Frequency histograms: beating losses Ani-ani labor Foot-treading labor Frequency histograms: sickle cutting labor Frequency histogram: beating labor Man-h/ton Area loss model A generalized flow chart Steady state for paddy waiting time with the 12-man ani-ani and 4-man sickle systems Flowchart of 40-day peak season simulation xi Page 31 42 52 53 55 58 59 61 62 64 65 73 96 119 146 CHAPTER 1 INTRODUCTION One quarter of Indonesia's record 1978/79 rice crop disappeared and was irretrievably lost. The rice was produced but did not reach the needy because of losses that occurred during the postproduction period.1 Recognition of this national problem was stated by Logistics Board Chairman Salimun: An estimated 4.38 million metric tons were lost after harvesting last year's (1978/79) record crop of 17.8 million tons...(that year) about 8% of the losses occurred in the paddy fields, 5% during transportation, 2% in drying, 5% in milling, and 5% in storehouses (Asian Wall Street Journal, 1979). A loss of this magnitude is significant for any nation but especially so for the world's fifth most populous country striving to achieve food self-sufficiency but chronically faced with yearly rice deficits. 1.1 Scope and Objective The solution to this problem obviously depends on an increased sup- ply of rice, which can be accomplished in two ways: 1) expanding the agricultural land base and emphasizing greater land productivity, and 2) reducing losses once the crop has been produced. This paper reports the results of a research project conducted in 1979 which focused on rice losses during the first stage of the postproduction period--the harvesting process. The project had three objectives: 1The postproduction period is defined as the period after biological maturity beginning with harvesting. 1. To measure field losses of existing rice cutting and threshing methods within the Province of Yogyakarta. 2. To evaluate these methods not only as to losses but also as they influenced labor usage and profitability. 3. To analyze the effect of alternative technologies in reducing rice losses and their impact upon labor requirements, and profitability. The basic question was: what was happening in the farmer's fields? Past research concentrated mainly upon experimental methods to identify loss characteristics of different technologies, for example: Chung and Lee (1978) compared five harvesting systems in experimental blocks on a farmer's field; Samson and Duff (1973) indicated in experimental trials significant differences in loss levels when comparing varieties, wet and dry seasons, and moisture content at time of harvest; Duff and LoZada (1978) compared four different harvest systems in the Bicol on farmer's fields randomly selected; and in Indonesia, Djojomartono (1979) compared different harvesting methods on a government seed farm. This experimental methodology has been most important in identifying loss characteristics of different technologies and the factors influencing these losses. But these experimental methods have not quantified losses occurring in farmer's fields for a given region. Instead the experimental results through extrapolation have implied area losses, if different harvesting methods were used. 3 This research departed from these past experimental studies in that a random survey of farmers' fields was conducted to identify field losses and labor requirements associated with prevailing harvest prac- tices within the research area, the Province of Yogyakarta in central Java. The research approach first identified the prevailing systems and the losses and labor requirement associated with each (Chapters II, III, and IV). The results of these field data then defined the initial con- ditions for a simulation model, which analyzed component changes to mini- mize losses and to evaluate changes in the labor requirement (Chapters V, VI, and VII). Using the Hayami and Ruttan Induced Development Model(l97l) as a theoretical basis, the economic approach (Chapter VII) employed a partial budgetary analysis to give a financial comparison for four systems: 1) ani-ani knife and foot-treading; 2) sickle and beating; 3) ani-ani knife and a power thresher; and 4) sickle and power thresher. The objec- tive was to identify significant high-payoff areas of input use through increased profitability. 1.2 Terms of Reference 1. The food grain pipeline can be described as the overall movement of food from production, distribution, to utilization, which can be conveniently divided into the production and postproduction phases. a. The production phase encompasses those inputs and activities from the initiation of production to the point of crop maturity, while b. The postproduction phase commences with harvest and extends to human consumption. 2. Rice postproduction can be divided into two parts: the harvest and postharvest periods. a. The harvest period involves: cutting of the rice panicles from the growing rice plant, moving the crop from its growing site, and separating the rice grains from the panicles. b. Postharvest refers to those activities after harvest until the time of consumption. Rice losses were defined in this study as the weight loss of rice, corrected to 14% moisture content, that would have been available for consumption had it remained in the food pipe line. Losses were not considered in the broader context of any change in the avail- ability, edibility, wholesomeness, or quality of the food that results from it not being available for consumption by people (Harris and Lindblad, 1978). Three types of harvest losses were considered in this investigation: a. Uncut losses--rice grain remaining in the field on the rice plants after the completion of cutting. b. Shattering losses--rice grain that shattered to the ground and rice panicles dropped as the crop was cut. c. Threshing losses--rice grain remaining on the straw after threshing. Handling losses were excluded as these losses appeared quite small for handling methods used. There has been considerable variation in nomenclature regarding the various stages of rice from its growing state in the field to final consumption. For the purposes of this report, rice termin- ology is standardized as follows: a. Egggy7-rice plant growing in the field. b. Rice grain--(synonomous terms: rough rice and gabah)--grain separated from the stalk and panicle: the ripened ovary and its associated structures including the lemma, palea, rachilla. and sterile lemmas, (Rice Production Manual, 1970). c. Panicle--a group of spikelets borne on the uppermost node of the culm (Esmay, gt_gl, 1979). The spikelets contain the rice grain. d. Stalk paddy--rice grain still attached to the panicles on the stalk (Esmay, gt_gl. 1979). e. Brown rice--(synonomous term: beras, husked or hulled rice)-- rice grain with the husk or hull (lemma and palea) removed. f. Padi sawah--terraced fields surrounded by low banks (bunds) where the rice plants are grown under flooded conditions. 1.3 Economic and Technical Framework The establishment of a framework for this study requires a melding of agricultural development theory, the technological characteristics of the phenomena under study, and the financial worthiness of these tech- nological inputs as the generator of change. Agricultural development theory gives a unifying action in the pursuit of a pattern for develop- ment. The technological inputs represent the physical means for more efficient resource use during the production process, and the rate of diffusion of technological inputs can be viewed as a function of their profitability. 1.3.1 'The Relevance of the Induced Development Model Hayami and Ruttan's Induced Development Model (1971) developed as an extension of T. H. Schultz's earlier work, Transforming Traditional Agriculture (1964). To paraphrase Schultz's concepts: In traditional agriculture, farmers are efficient allocators of the resources at their disposal, which is the result of years of trial and error. So over time an equilibrium has been reached where: 1) the state of the arts is unchanged, 2) the state of preference and motives for holding and acquiring sources of income are constant, and 3) both of these states have remained constant long enough so that the marginal preferences and motives for acquiring these resources, as a source of income, are at an equilibrium with the marginal productivity of these resources. In other words, a continued investment in traditional inputs will produce very little in terms of an additional income stream. Thus the transformation process from traditional agriculture is an investment problem dependent upon a flow of new higher return inputs, i.e., the inputs of scientific agriculture, including human knowledge as part of the material inputs. These are reproducible inputs, which are the principal sources of high productivity in modern agriculture, and economic growth from the agricultural sector depends mainly upon the availability and price of these nontraditional agricultural factors. Agricultural technology is highly location-specific, and what is useful in developed countries is not directly transferable to developing areas. But what is transferable is a body of useful knowledge that can develop new factors appropriate to the biological and other conditions that are specific to the agricul- ture of poor communities. Finally, the rate of acceptance of new factors of production is a function of their profitability, with due allowance for risk and uncertainty. As Hayami and Ruttan (1971) state: This implies three types of relatively high productive investments for agricultural development: a) in the capacity of agricultural experiment stations to pro- duce new technical knowledge; b) in the capacity of the industrial sector to develop, produce, and market new technical inputs; and c) in the capacity of farmers to use modern agricultural factors effectively. But they contend that the Schultz model remains incomplete as a theory of agricultural development. It does not fully specify the mechanism by which resources are allocated between the public and private sector. It does not explain how economic conditions induce the development and adoptation of a particular set of technologies. Nor does it explain the process by which factor and product prices induce investment in research in a particular direction. The Induced Development Model attempts to extend the concepts of Schultz in these directions. The theory is based upon four elements: 1) induced innovation in the private sector; 2) induced innovation in the public sector; 3) interaction between technical change and institu- tional development; and 4) dynamic sequences of technical change and economic growth. Induced innovation in the private sector rests upon relative factor costs. Competitive firms will allocate funds for new technological development that facilitates the substitution of less costly factors of production for the more costly factors of production. Thus the entreprenurial effort is directed towards saving the more costly factor. Similarly, in a country in which a factor is more expensive relative to another factor, innovative efforts will be directed towards saving the less expensive factor. The inducement mechanism in the public sector is based upon the response of research scientists and public administrators to resource endowments and economic change. Farmers are induced by factor prices to save the resources in scarce supply, which have high costs. They press the public research institutions to develop new technology and also demand that agricultural firms supply modern technical inputs that substitute for scarce factors of production. Thus changes in relative product and factor prices signal the priorities that society places on the goals of research. Since technical change is dependent upon the progress of general science, the research linkage is vital to provide new sources of growth from scientific progress. Institutions that affect technological change can also be induced to change so that both individuals and society are able to take advantage of new technological opportunities under favorable market conditions. Institutional innovations occur because it appears profitable for indi- viduals or groups in society to undertake the costs of making the insti- tutional changes. But profitable opportunities do not always lead to an immediate institutional change because the benefits are usually not equally distributed. So vested interests, which may lose from such changes, resist institutional change resulting in time lags and possibly social and political stresses. As Hayami and Ruttan note, "economic growth ultimately depends on the flexibility and efficiency of society in trans- forming itself in response to technical and economic opportunities." The fourth part of the Induced Development Theory, dynamic sequences, is a critical element in inducing technical change and economic growth because this represents imbalance or disequilibrium. Disequilibrium creates bottlenecks. This in turn focuses attention, by the private and public sectors, on the solution of these problems for attaining a more efficient resource allocation. As an example, the introduction of the reaper in the United States was a response to a limited labor supply in order to improve timeliness of operation so as to reduce crop spoilage and losses. This was at a time when land supply was expanding faster than labor supply, and inventive efforts were focused upon solving a farm problem resulting from a relative labor scarcity within the economy. A cumulative effect of technical change in agricultural production can also influence bottlenecks, and hence changes, in other sectors of the economy. As an example, the introduction of high-yielding rice varieties has resulted in a greatly increased product causing marketing problems, a forward linkage effect. And a backward linkage effect has resulted in the increased production of fertilizer from the industrial sector as a new input for agricultural sector. The Induced Development Model and this research study interact in several ways: 1. Whether or not cutting and threshing are areas of high—payoff. And are cost reductions signi- ficant enough to induce technological change? 2. The study appears to support the Hayami and Ruttan hypothesis of institutional change. 3. Through the modeling effort, bottlenecks can be identified, changes instituted, and different bottleneck areas examined as a result of these changes. 50 the model offers an opportunity to observe the dynamic sequences and disequilibrium that may result from solving a particular technological problem. 10 1.3.2 Harvest Systems The technical framework is based upon the prevailing harvest systems. Two types of systems existed: l) the traditional ani-ani system, and 2) the newly introduced sickle system. Each system involves a different marketing arrangement. If the farmer conducts the harvest himself, the ani-ani system is used. Under this system the paddy is marketed at some point after harvest. If the farmer, however, sells the crop to a buyer before harvest, then the buyer conducts the harvests with his own crew, who usually cut with the sickle. The ani-ani involved separately cutting each panicle with the tra- ditional ani-ani knife, a hand-palmed knife which cuts the plant as the fingers pull the stalk across the knife's cutting edge. Threshing was traditionally done by foot-treading, which was fairly efficient from a loss standpoint and labor intensive. Ani-ani cutting was highly selective and very labor intensive. The sickle system involved the use of a sickle for cutting and beating for threshing. Both techniques were considerably more labor efficient than the ani-ani and foot-treading. The sickle is a nonselec- tive method since an entire plant is cut with one stroke rather than cutting only the individual panicles. Because the sickle cuts the crop closer to the ground than the ani-ani, longer straw results so threshing is done by beating rather than by foot-treading, which is not as effective as beating with a longer straw length. The evolving change from ani-ani to sickle closely parallels the change from traditional varieties to HYV's. Because traditional varieties matured unevenly, a selective technique that would harvest only ripened panicles was needed. In the past, the ani-ani accomplished this. A 11 harvester using the ani-ani knife could proceed through the rice paddy cutting only the ripened panicles, wait a few days for more panicles to ripen, and repeat the process--certainly an efficient method for maxi- mizing production under these conditions. l.3.3 Economic Framework As indicated in the previous section, the change in harvesting tech- niques from ani-ani to sickle is more than simply a change in methods but a change in marketing arrangements. So an economic examination needs to consider the two marketing strategies and at which point a comparison will be made. Table 1.1 illustrates the several points in the two systems where the added value and costs can be compared. When the farmer sells to a buyer, he loses control of the crop to the buyer just prior to harvest, when the contractual arrangements are agreed upon. Under the traditional ani-ani system, the farmer maintains control of the crop through harvest and can dispose of it either after harvest, and before milling, or after milling. Usually the buyer will maintain control until after milling but does have the option of disposing of the crop earlier. The overall cost for ani-ani could be evaluated either after harvest, after drying and before milling, or after milling; the overall cost, when selling to a buyer, would usually be evaluated after milling. The scope of this study extends only through harvest and before drying prices and costs need to be identified at this point in the system. The financial approach to be taken will be a partial budgetary analysis. Thus the analysis will be based upon the value-added rather than simply a physical comparison of production quantities. Four systems will be compared: 1) the traditional ani-ani and foot-treading, 2) sickle and heating, 3) ani—ani and power thresher, and 4) sickle and power 12 Table 1.1 Control of paddy under different marketing arrangements. System Stages Ani-ani Sickle 1. Before harvest Farmer Buyer 2. Harvest Farmer Buyer 3. After harvest and before Farmer Buyer (or miller) drying 4. After drying and before Farmer Buyer (or miller) milling 5. After milling Farmer or miller Buyer (or miller) 6. Wholesale Grain dealer Grain dealer thresher. Field data were not collected for the power thresher, because it was not used in the research area, so secondary data provided the basic information. The crucial element will be labor costs and the extent that family labor is used. But the study did not delve deeply into a labor analysis and neither identified the degree of family labor involved nor the opportunity cost for such labor within the makeup of a harvest crew. 0f the sampled fields, however, only 2% was totally cut with family labor. Harvest crews usually received in-kind payment rather than an hourly or daily wage. For ani-ani crews this varied from a one-sixth to a one- twelth crop share with 89% of the sampled area being equally split be- tween a one-eighth and a one-tenth share. For sickle harvesting crop shares varied between one-twelth and one-seventeenth with the modal class being one-fifteenth. Although the sickle harvesters received less, their increased labor efficiency held the potential for earning a greater return per day than the ani-ani harvesters. In some instances, however, l3 sickle crews were paid a daily wage rate, but this was not a normal practice. Several complicating factors were observed and not covered by this research effort: 1. Often the farmer did not receive payment for the crop if sold prior to harvest, until after the buyer had sold it to a grain dealer. If the crop brought less than anticipated, a renegotiation of the contract followed. 50 the transfer of risk from the farmer to the buyer did not take place. Consequently, the question arises: was the agreement merely a relief to the farmer for a labor constraint or a means of circumventing the more costly traditional ani-ani system? In sugarcane areas, it was observed that there was a competition for harvesting labor between rice and sugarcane, since these crops overlapped at the end of the rice harvest season and at the beginning of the sugarcane harvest. This resulted in fewer paddy harvest crews, and larger crop shares had to be offered. The study does not reflect the costs related to this situation. 1.4 Cultural Institutions Although it is not the purpose of this study to evaluate cultural or sociological practices, it is important to at least recognize atti- tudes that have tended to institutionalize certain harvesting practices. l4 specifically village attitudes towards the use of the ani-ani and the practice of gleaning. When harvesting the local, traditional varieties the ani-ani has been the accepted method. The ”rice goddess" looks with favor upon the ani-ani as a desirable harvesting method, but she looks with disfavor upon the use of the sickle with traditional varieties. As suggested the ani-ani is a highly labor intensive technique, but the use of the sickle is much less so. The composition of the harvest crews has been mainly from the landless within the village so this has been an impor- tant source of food for this group. The Indonesian's place a great deal of importance in sharing the bounties of the advantaged with the disad- vantaged, which has perpetuated the use of the ani-ani as a village employment scheme, rather than the use of the sickle. The sickle is not a new tool--0nly new to rice harvesting. With the introduction of the HYVs, attitudes have begun to change with some acceptance for the more efficient sickle. Since these varieties are not native to Indonesia, the "rice goddess" is neutral regarding the use of the sickle. It appears, however, that village criticism does result if the farmer himself uses the sickle, but it is acceptable if he no longer owns the crop. Then an outsider to the village can employ whatever harvesting technique he wishes. This seems to be what is happening when the farmer sells his crop in the field to a buyer, usually an outsider to the village. To what extent the farmer participates in the reduced harvesting cost is unclear, but society is internalizing a more productive technique in this circumvention process. Acceptance of the buyer's practices seems to support the Hayami and Ruttan (1971) hypothesis that "institutional innovations occur because it appears profitable for 15 individuals or groups in society to undertake the costs." As they further state, "a major source of institutional change has been an effort by society to internalize the benefits of innovative activity to provide economic incentives for productivity increases.” Sickle harvesting would reflect increased human productivity, and reduced harvest costs would be the economic incentive. The practice of gleaning permits villagers, who are not harvesters, to enter the field following the harvesting crew to retrieve for their own use what the harvesters have missed. The gleaners are freely allowed onto the field, and what they recover need not be shared with the farmer. At times the practice has been abused by harvesters and gleaners working in consort. This has resulted in banning of the practice in some areas, but not in the sampled area where 75% of the farmers permitted gleaning. Thus gleaning has been an institution to help the village poor (Collier, 1973). Gleaning is identified with ani-ani harvesting rather than with sickle harvesting because ani-ani cutting leaves more uncut panicles in the field. This is because the sickle harvester cuts the rice plant closer to the ground capturing the lower panicles that the ani-ani har- vester might otherwise miss. As a result, a controversy has developed questioning all cutting methods which interfere with this institutional practice. But the question needs to be asked: how pervasive is gleaning and what is the best means for moving the rice into the food pipeline-- through normal channels or through a system of sharing such as gleaning. The farm survey indicated that gleaning was practiced on only 49% of the sampled area, which was definitely less than one might have 16 expected from the literature. This lessening of gleaning interest probably has reduced village resistance towards sickle cutting. 1.5 Background As a prelude to the problem statement, this section reviews the changes in rice production and population from 1961 through 1976. These observations and implications have been derived from the decennial cen- suses of 1961 and 1971 and the Intercensal Population Survey conducted in 1976. Similar problems exist between the island of Java and the re- search area, the Province of Yogyakarta: the pressure of an expanding population upon limited land resources, the need for rice self-sufficiency, and concern for the employment opportunities within the rural sector. But the problems of the Outer Islands are opposite to those of Java so what is needed to solve Java's problems may not be appropriate elsewhere. 1.5.1 Java and the National Scene In 1977/78 Indonesia imported 2.3 million metric tons of rice and about 1.0 million tons of wheat; this far exceeded the rice importation for any of the previous years (Table 1.2). Although the improved 1978/79 wet season will probably sharply reduce import needs, still it is esti- mated by the World Bank (1978) that an additional 1.3 million metric tons will be needed in 1979/80. Yet Indonesian yields rank among the highest of the Southeast Asian countries (Table 1.3), although far below Japan, Taiwan and Korea. Production trends since 1950 are summarized in Table 1.4. Overall production increased 125%; land devoted to rice has risen 46%; and yields increased 154%. The introduction of high yielding varieties (HYV) since the mid-sixties has had a strong influence upon these increased yields, 17 Table 1.2 Indonesian rice imports (1000 metric tons). 1951 408.9 1972 748.0 1955 126.8 1973 1663.8 1960 962.0 1974 1070.4 1961 1063.8 1975 675.4 1965 203.2 1976 1296.1 1970 955.6 1977 1964.1 1971 502.9 1978 1838.3 Source: 1951-1975: Palacpac, Adelita C. World Rice Statistics (1978). 1975-1978: USDA, Foreign Commodity programs. Table 1.3 Rough rice comparison for southeast Asian countries--l974-l976 Production % World Yield (1000 MT) Production (MT/HA) Burma 9,038 2.3 1.8 Cambodia 1,367 0.4 1.3 Indonesia 22,862 6.6 2.7 Laos 919 0.3 1.3 Malaysia 1,982 0.6 2.6 Philippines 6,092 1.8 1.7 Thailand 15,153 4.4 1.8 Vietnam 11,430 3.3 2.7 68 ,843 20.0 _2_l_ Source: Palacpac, Adelita C. World Rice Statistics (1978). 18 Table 1.4 Indonesian production trends, 1950-1976 Production Area ha Yield (lOOOmt ) (1000/ha) (kg/ha) 1950 10,202 5,700 1,790 1955 12,726 6,570 1,937 1960 14,866 7,285 2,041 1965 15.053 7,327 2,054 1970 19,323 8,135 2,375 1971 20,182 8,324 2,425 1972 19,386 7,898 2,455 1973 21,481 8,404 2,556 1974 22,464 8,509 2.640 1975 22,330 8,495 2,629 1976 23,103 8,364 2,726 Source: Palacpac, Adelita C. World Rice Statistics (1978). as well as total production. The importance of irrigated and wetland rice production is illustrated in Table 1.5: about 94% of Indonesia's estimated 1977 production can be attributed to irrigated or rainfed paddy--an obvious reason for more intensive irrigated paddy research. Population must be considered as an integral part of Java's prob- lems because it influences both the demand for rice and the choice of harvesting technology. In a country with an abundant labor supply, a harvest system evolves around labor intensive techniques. An overview of the population and employment characteristics is presented in Table 1.6. The most recent 1976 Intercensal Population Survey has placed the total population at a 130 million people, with 83 million living on the island 19 .Aommmpv xcmm upgoz ”mogzom .mumg zuzogm Fmaccm mmmgm>mw .w wow; Fence .m m.~ om._ mo.~ ~.F- «mm.p Nom.F o.¢- “NF._ nmm.P ewmmcoccH .o F.N m~._ mo.F ¢.—- owF._ me.F m.m- Nmm omN.F muccpm_ Lapse .n o.m N¢.F o_._ m.N- wen Fme m.m- mew woe m>aa .m wow; ucmpxga .N m.~ eo.m F¢.~ m.m mom.FN pom.mp m.F P_~.N emm.w mwmmcoucm .o m.~ mn.m mp.~ “.3 mmm.m mo¢.m o.N eoo.m mom.~ mucepm? amuse .n m.~ NN.m mm.m m.m emm.mu mmm.m N.F nep.c umw.m m>uq .m acupuoz\eapameaH ._ ovum Rump wmmfi mama uan wom_ mpam NNmP wmmp .gszLu .euzOLu .gpzotw a a a cowmmm Ae;\uev Ape .ooopv Am; .oocpv ”u—mw> cowpuzwoga ”uwumm>cm: mug< .Nump use wwmp memo» mg» so» copmos xn :oruosuoeq mow; cowmocoucm mo xgmsasm m.p anah 20 Table 1.6 Indonesian population and sectoral distribution of employment, 1961, 1971, and 1976. 1961 1971 1976 Population (1000) 97,019 119,233 130,241 Employment: (in percent) Agriculture, forestry, fishing 73.4 65.9 61.8 Manufacturing 5.8 7.8 8.4 Mining and quarrying 0.3 0.2 0.2 Construction 1.8 2.0 1.7 Electricity, gas, water 0.2 0.1 0.1 Trade, finance, insurance 6.8 11.2 14.5 Transport, storage, communication 2.1 2.4 2.7 Services 9.6 10.4 10.6 Total: 100.0 100.0 100.0 Source: World Bank (1979b). of Java. This population is primarily rural--on1y 18% living in the urban areas. As might be expected with a rural population, the agricultural sector provides most of the job opportunities, although there has been a movement from agriculture into the trade and manufacturing sectors since 1961. But the real impact of population pressures is dramatically shown by the population densities in Table 1.7. Indonesia with 731 persons per square kilometer of arable land closely rivals Bangladesh (the Province of Yoghyakarta exceeds Bangladesh), which has long been noted for its serious overpopulation problems. 21 Table 1.7 Comparative population densities (a) Population densities of selected countries and regions. Countries/Regions Perigpg/km‘ Indonesia 73] Bangladesh 775 Philippines 563 Japan 198 USA 102 Southeast Asia 437 South Asia 392 East Asia 181 1Based upon arable land. Source: Palacpac, Adelita C. World Rice Statistics (1978). 1.5.2 The Provippe of Yogyakarta The province of Yogyakarta is situated in central Java with its southern boundary on the Indian Ocean, approximately 400 kilometers to the east and south of Jakarta. Territorially, it is only 3169 square kilometers, which accounts for just 2.4% of the Java-Madura area and 0.17% of the entire area of Indonesia. But Yogyakarta exhibits many of the basic problems of Java previously mentioned. Administratively, the province is divided into four districts: Bantul, Sleman, Gunung Kidul, and Kulon Progo in addition to the city of Yogyakarta. Table 1.8 summarizes several key attributes of the area. 22 Table 1.8 Demographic and sawah characteristics for the province of Yogyakarta. City of Kabupaten Kulon Gunung Characteristics Yogyakarta Bantul Sleman Progo Kidul Population, 1971 342,267 568,636 588,304 370,646 620,145 census % population 13.7 22.8 23.6 14.9 24.9 Population growth rate (%) 1930-1961 2.7 0.8 0.8 0.5 1.7 1961-1971 1.1 1.3 1.3 1.0 0.8 Population density, 97 1971, KM2 10,557 1,122 1,023 657 418 % sawah of total land (1968) 2.2 38.5 47.9 20.4 4.2 Sawah intensity1 1.51 1.31 1.42 0.97 0.63 % work force engaged in agriculture, 1971 2.3 44.9 49.8 55.3 89.8 1Area of rice land harvested/total area of rice land (1964-1968 average). Source: McDonald and Sontosudarmo (1976). Population of the province remained fairly constant: nearly 2.5 million in the 1971 census and an estimated 2.6 million in 1976. This corresponded to an annual growth rate of 1.2%. On the basis of overall land area, the current population density was 832 persons per square kilometer. This would definitely be higher if only arable land were con- sidered. Excluding the city of Yogyakarta, the density was highest in the padi sawah areas of Bantul and Sleman exceeding 1,000 in both in- stances. This illustrates the competition between people and arable land 23 for agricultural production. A result of this competition is a large group of landless laborers (McDonald, 1976). The distribution of the population between rural and urban follows the pattern of Java with 13.7% in the province's major urban center, the city of Yogyakarta. Although much of the rice land follows the Yogyakarta-Solo plain, traditionally a fertile rice-producing area, Yogyakarta has not been producing sufficient rice to meet the needs of its population. Most of the rice was raised as wetland rice rather than as dryland rice. 0f the total production in 1977, wetland rice accounted for 86% while dryland rice composed the balance. Compared to Java-Madura as a whole, this amounted to 2.5% of the overall production. In yield per hectare, however, the province ranked near the top in 1977: 3481 kg/ha compared to an aver- age 3220 kg/ha for Java, even though the province had a higher proportion of dryland rice, 14% as opposed to 6% for all Java (Central Bureau of Statistics, 1978). Also, the sawah intensities in Table 1.8 indicate a higher proportion of irrigation for the major production areas of Bantul and Sleman. 1.6 Problem Statement A recent World Bank report concluded: Even assuming an expanded Government investment in the food crop subsector, improved technology, efficient supporting services and marketing arrange- ments, and a growth rate of food production at an average rate of 3.5% p.a. (per annum) it is likely that Indonesia will continue to face a basic food deficit up to 1985 a.d 1990...It is projected that the real deficit could be between 2 and 3 million tons in 1990 (World Bank, 1978a). This however represents only part of the problem when considering different harvesting technologies; the other half of the problem in- volves income distribution through employment opportunities. In a country 24 faced with a burgeoning population, which is primarily rural, changes that reduce rural employment opportunities may be as devastating as an overall food deficit. Before proceeding with the problem statement, the people who will be affected by a change in harvest technology, and their needs, should be identified because these participants are likely to have conflicting as well as mutual concerns. Consequently, a final evaluation of any suggested change will require trade-offs between these various interests. The participants are: 1) the farmers, 2) the rural landless laborers, 3) the urban population, and 4) the government as representative of a societal perspective. The participants and their needs are: l. Landed farmers: a. Increased physical production U' Profitable returns c. Reduced costs d. An adequate consumptive supply for family use e. A reasonable cash flow from a marketable supply where marketable supply is defined as net production, after harvest less consumptive supply 2. Tenant farmers: a. The above points b. A reasonable expectation of land tenure security--the avoidance of land consolida- tion through a technological change 3. Rural landless laborers: a. Employment opportunities 25 b. An adequate return for invested labor, either a monetary wage or crop share c. Participation in the development process Urban dwellers: a. Sufficient food supply b. Available throughout the year c. Low food costs Government: a. Improved income distribution and employ ment opportunities throughout different levels of society, especially amongst the landless who generally compose the larger part of the population b. Increased GNP c. Maintenance of power and minimal political unrest d. Technological change which will enhance the above and further the development process Recognizing the above, the problem is to select a harvesting system which will-- 1. increase rice supplies through the reduction of harvest losses; not disrupt existing labor patterns and relationships beyond the capability of the agricultural and non- agricultural sectors to absorb such displaced labor; not disproportionately concentrate income amongst the already financially advantaged; oh 26 4. be initially operable given the existing skills of the user and the existing infrastructure; 5. require only a modest cash investment on the part of the farmer; and 6. be compatible with both government development policies and cultural values. Although this statement expresses a solution in terms of the ideal, an eventual solution will require trade-offs between competing goals and objectives giving due consideration to the breadth of the problem, as defined by the problem statement. The development of this paper will proceed as follows: 1) Chapters II and III describe the data collection procedures and the analysis of these data; 2) Chapter IV reviews the farmer survey and projects the system losses in Chapter III to an overall area loss; 3) Chapters V through VII develop and discuss the simulation model; and 4) Chapter VIII summarizes and presents the conclusion of the study. The literature review is presented throughout the dissertation, e.g., data discussion and model development, rather than within the content of a traditional literature review chapter. This approach was motivated because of the lack of literature dealing specifically with ani-ani and sickle harvesting in Indonesia. The ani-ani is a cutting technique localized to Indonesia, and sickle harvesting, as practiced in Indonesia, differs from other Asian countries, i.e., the paddy is neither cut at or near ground level nor is it laid on the ground at cutting to be gathered 1ater--both practices that influence loss and labor values. Consequently, these factors plus different varieties and environ- mental conditions negate the value of comparing these loss and labor results with those from other countries. CHAPTER 2 DATA COLLECTION METHODOLOGY A random survey of fields was the basic technique used to shed light on the question, what was happening in farmer's fields during har- vest? The data collection phase of this study can be divided into three parts: 1) a pilot investigation, 2) a study of randomly selected fields, and 3) the collection of nonrandom supplemental data. The pilot study pre- tested the loss and labor collection procedure prior to the initiation of the random survey. The random survey consisted of: loss and labor data derived from five random plots within each field, and interviews with farmers operating those fields. The supplemental data followed the random survey for the purpose of filling in the voids left by that study, i.e., threshing by beating and additional sickle-cutting data to augment the random sickle data. Since each stage of the research investigation was influenced by the preceeding events, this section will chronologically discuss the methodology as follows: 1) the studied system, 2) a definition of the statistical population, 3) the pilot investigation, 4) the randomized study, and 5) the supplemental data. 2.1 The Studied System Before continuing with the data collection procedure, a review of the ani-ani and sickle systems, as practiced in the field, will place the methodology in perspective. Of interest is identification of the steps involved in cutting and threshing so that points of potential loss 27 28 can be identified. The sequence of events relating to these systems were as follows: 1) rice panicles were individually cut and transferred from the cutting hand to the storage hand; 2) when the storage hand was full, the flag leaf was usually stripped and the paddy transferred to a bamboo basket carried by the harvester; 3) after the basket was filled, the paddy was transferred to a plastic sack at the edge of the field; 4) after cutting was finished, the filled sack was secured and transported to the threshing site; and finally 5) the stalk paddy was spread on bamboo mats or a concrete surface and threshed. The points where identified losses occurred were: 1) during cutting, 2) transferring paddy from the cutting hand to the storage hand, 3) dur- ing removal of the flag leaf, and 4) while threshing the paddy. Poten- tial losses could also occur when the paddy was transferred, from the storage hand to the basket, from the basket to the sack, during transpor- tation to the threshing site, and spillage during threshing. The risk of loss at these points was small, and dropped panicles were retrieved. Both the baskets and the sacks were in good condition so that leakage was practically nil, and repeated inspections during the season verified this. Also, the threshing mats were found to be in good condition: tightly woven and sufficiently large to avoid spillage. Consequently, data were not collected at these particular points. Labor data were collected only during the cutting and threshing and did not include transportation time. The fields were close to the thresh- ing sites so that transportation time was minimal, usually about twenty minutes. Thus, combined cutting and threshing time captured most of the labor associated with the ani-ani system., The sickle system paralleled the ani-ani system except that: 1) losses from the transference of panicles from the cutting hand to the 29 storage hand were eliminated; and 2) total labor for the sickle system was not reflected by combined cutting and threshing labor time because the harvested fields might be several kilometers from the threshing site so that transportation time could be of greater significance than with the ani-ani. Also, additional labor was involved in moving the stalk paddy sacks from the field onto the transportation wagons. 2.2 Statistical Population The loss objectives of this study were previously stated only in general terms, i.e., harvest losses at the farm level within the Province of Yogyakarta. This implies losses under a wide variety of conditions: dryland, irrigated, traditional varieties, and high-yielding varieties-- all within an unspecified time frame. 50 it was necessary to limit the coverage of this survey by defining the statistical population. Three parameters were considered: 1) production method, 2) varieties, and 3) harvest time. 2.2.1 Production Method: Irrigated Paddy The importance of wetland (rainfed and irrigated) padi sawah pro- duction on Java was seen from Table 1.5: 97% of the total rice produc- tion in 1977 was from wetland cultivation. And Soemartono (1974) reported that in 1973 74% of Java's production was irrigated. In the province of Yogyakarta during 1977, wetland production accounted for 86% of the areas polished dry rice production (Central Bureau of Statistics, 1978). Although irrigation was not separated from rainfed conditions in these data, sawah intensities (Table 1.8) for Bantul and Sleman, which together accounted for 86% of this province's padi sawah area in 1968, indicated a high percentage of irrigation. Thus, the limitation of this study to 30 irrigated padi sawah kept the research within the mainstream of the pre- vailing production method. 2.2.2 Varieties Because harvesting techniques and losses, especially shattering and threshing losses, are related to varietal characteristics, the statisti- cal population needed to be defined according to varietal type. But rather than specify particular varieties, the distinction was drawn only between traditional varieties and HYVs with this study concerned only with the latter. Although explicit data were lacking, HYVs were closely associated with irrigation and did predominate in the research area. The cropping intensity data in Table 1.8 tended to verify this. Except for one field of IR30, IR36 and IR38 were the only two varieties encoun- tered during the random study. A brown plant hopper infestation and associated viruses at the time of this study was the reason, since IR36 and IR38 were considered resistant to brown plant hopper problems. 2.2.3 Seasonal Limitation Rice was harvested throughout the year, but the principle harvest season occurred between February and July (Figure 2.1). The random study was, therefore, concentrated during this period, specifically from March to June. The statistical population was defined as irrigated padi sawah fields: 1) capable of producing more than one rice crop a year, given normal water availability, 2) planted to high-yielding varieties within the Province of Yogyakarta, and 3) limited to those fields harvested from March to June in 1979. 31 Percent Harvested Hectares Figure 2.1 Percent harvested area by month - Yogyakarta, DIY - 1975, 76, 77. Source: Luas Panen Bulanan, Dinas Pertanian & Perikanan DIY. 32 2.3 Pilot Investigation The purpose of the pilot investigation was to acquire information that would aid in the execution of the randomized survey. This study was designed to: 1. Evaluate the suitability of a small-plot technique to estimate grain losses and the labor required in the cutting stage. 2. Estimate grain loss and labor variability, which would assist in establishing sample sizes. 3. Indicate whether the sampling emphasis should be placed on fields or on plots within fields. 4. Determine the influence of time and monetary limitations upon ultimate sample size. The pilot study should ideally model the main study, but a lack of time made this impossible. Instead, the pilot study concentrated on what was judged to be the most variable Operation: cutting with the ani-ani knife. Twelve fields were purposely selected at convenient locations and five plots (an arbitrary decision) were randomly described within each field, which provided a total of 60 loss observations. Originally, it was intended that the labor rate would be determined from timing the actual field crew. The difficulty was the lack of resources to separately moni- tor field crews throughout the harvest season. Also, reliance on the local crew to furnish accurate information was questionable because crew size often varied during the day, and they did not always have watches. For these reasons, labor timing was tried on a plot basis. 33 2.3.1 Plot Design and Placement Two types of losses occurred during cutting: l) shattered and dropped losses, and 2) the matured grain left in the field on uncut panicles. Previous studies, for example Maranan and Duff (1978), relied on selective grain counts before and after cutting to determine shattering losses. There were, however, several problems with the grain count method and reliance upon gathering uncut losses after a field had been harvested: 1. Grain counts were not possible if field con- ditions were unsuitable, e.g., flooded or muddy fields. Since the main study was to be conducted during a part of the rainy season, and flooded or muddy conditions were anticipated, a more direct technique seemed advisable. As it de- veloped, 43% of the fields in the random study were flooded or muddy. It was feared that the experimental error associated with grain counting might mask the actual shattering losses. Uncut losses were difficult to recover after a field had been cut because of trampling. Also, if gleaners were present, an evaluation of uncut losses was impossible because the gleaners usually followed directly behind the harvesters recovering what had been missed. These potential problems led to the trial of a small-plot technique that would directly measure both shattering and uncut losses. The procedure was as follows: 34 Plot locations within a field were randomly selected. The selection process did not include a one meter perimeter surrounding the field to avoid possible border effects. A one-square meter plot was established by removing adjacent two or three rows surrounding the plot. Plastic strips were stapled to bamboo, forming adjustable panels that were rolled, inserted between the rice plant rows, and unrolled to form a V—shaped trough. The plastic strips, 35 cm wide and 160 cm long, permitted ample flexi- bility since row widths varied from 20 to 25 cm. The 160 cm length also allowed a 30 cm extension at each end of the plot; this served to catch shattering losses from the overhanging plants at the ends. Besides placement of the strips between the plant rows, a plastic strip was placed on the outer edges of the plot to catch those of the shattering loss. The unfurled plastic strips were clipped together at the ends and in the middle; the outside corners were staked into position to draw the plot taught. This resulted in a firm collecting surface, giving complete coverage of the plot area, but still leaving the natural orientation of the crop undisturbed. 35 With reasonable care, premature shattering was not a problem. Weeds could cause a problem if they pushed the plastic upwards causing some loss of shattered grains. This was overcome by a centered bamboo strip on the un- derside of the plastic panel. Larger plots were tried but were found un- satisfactory because the harvesters had difficulty reaching the center plants. 2.3.2 Field and Laboratory Procedure One harvester cut a plot: the yield sample was first harvested, next the uncut panicles were gathered, and lastly the shattered grains were removed from the plastic strips. Cutting labor was determined by timing the harvester as the yield sample was taken. The shattered and uncut losses were collected and put into plastic bags, and the field sample was threshed by foot-treading. The pilot study did not include threshing so these data were only kept for the randomized study. Field moisture readings were taken at the time the plots were harvested, usually mid- to late morning. At the laboratory the samples were weighed, dried to 14% moisture, and reweighed. Three 100 gram lots were weighed from each yield sample and the percentage of full grains and hulls determined. Air separation was used to remove the hulls and light grains. The shattered and uncut samples were cleaned and weighed in a similar fashion. The following data were obtained: 1. Field moisture content Gross yield (gm/m ) and percent cleaned grain Shattering loss (gm/m ), cleaned grain hum . Uncut loss (gm/m ), cleaned grain 36 The gross yield samples were weighed at both field moisture content and after sun drying at 14% moisture; all other samples were only weighed at 14% moisture after sun drying. 2.3.3 Results and Evaluation Since the purpose of the pilot investigation was to develOp informa- tion that would aid in the development of the randomized study, this section will not discuss the relevancy of the loss and labor values per se (Appendix A, Table A1) but rather their implications in determining procedures for the random field survey. Sample size was one of the first concerns: the greater the variability, the larger the sample needed. And the problem of sample size in this study was two-fold: should the sampling emphasis be placed on fields or plots, and 2) how many units of each should be sample? In order to estimate field and plot variabilities, four analyses of variance (ANOVA) were performed (Appendix B, Table 81). To gain an insight to these questions, an analysis of variance (ANOVA) was per- formed (Appendix B, Table Bl). The choice of the pr0per analysis of variance model is between a fixed effect model (model I) or a random effects model (model II). A fixed effects model is appropriate where the researcher determines and fixes the treatments and the interest is be- tween the results of the treatment and the differences between them (Sokal and Rohlf, 1969). The previously mentioned investigations were fixed effect models because the researchers determined the treatments in order to observe specific treatment differences. On the other hand, the random effects model is appropriate where the interest centers around determining an attribute of a larger popula- tion. The attribute, or effect, is then considered random rather than 37 fixed. Since the objective of this study was to identify loss and labor characteristics of existing harvest methods in a large population of paddy fields, the random effects model was used, which is as follows: Yij "' 1*" Bi ”'1' “ fim 2-' where: Yij is the value of the dependent loss (or labor) variable in the it“ plot from the ith field. p, is the common mean of all observations. s(,)j is the it“ plot effect within the ith field. eij is the random error term. a. is the ith field effect. The expected mean squares for the random effects model are: E (MSTR) 02 + to; 2.2 02 2.3 E (MSE) where: MSTR is mean square treatment. MSE is mean square error 0% is the field variance, and r is the number of replications. Since the ANOVA derived the mean square for fields (MSTR) and the mean square error (MSE), the respective variances could be estimated. But the MSE consisted of two components that could not be separated, a plot and error variance: 2 = 2 . . + 2.. 2.4 ° 08 (1)3 0813 38 Thus an overstatement of the population variance was an inherent result, unlesso§(i)j==0. Nevertheless these estimated variances do support a means for estimating sample size. Snedecor and Cochran (1967) suggested a method based upon the assump- tion of normality that utilized a confidence interval approach for deter- mining sample size. First, the researcher must decide how accurate the results should be in terms of the scale being measured and set these limits (L), plus or minus L. A probability statement can then be made that the mean lies between these extremes. For example, at the 95% probability level there is a 95% chance that the mean lies between the limits 1. - 1.96 9- and - 1.96 9- . Calculations yield the equation: T1 11 L = 1.96 9 2.5 T1 and expressed in the terms of the sample size, n: n = [(1.96)2 x a: )/L2 2.6 Table 2.1 estimates different sample sizes for different limits about the mean. The field and plot variances were calculated from the ANOVs in Table 81. The pilot investigation indicated that, to determine loss and labor values within an overall variation of 25%, 50-60 fields should be sampled. Since this result was based upon five plots per field, these number of plots were continued. This appeared feasible with the available resources so this field range was set as an objective. The plot harvesting procedure functioned well under several adverse conditions: flooded and muddy fields and lodged paddy. And under such conditions a random sample would not have been possible without a direct measurement technique. The harvester's cutting behavior appeared to 39 Table 2.1 An estimate of sample size--fields and plots for percent shattering, uncut, and total ani-ani losses and for ani-ani labor. (a) Shattering loss. Number Plots Fields Total Per Field Limits: + L % Mean Ufiits Field Variance Plot Variance 5 0.098 0.8495 2.5863 340 1,035 3 10 0.196 85 259 3 12.5 0.245 54 166 3 15 0.294 38 115 3 R' = 1.96 (b) Uncut loss. 5 0.342 3.9286 8.8851 129 292 2.3 6 0.4104 90 203 2.3 7.5 0.5130 57 130 2.3 10 0.6840 32 73 2.3 i’ = 6.84 (c) Total ani-ani loss. 5 0.440 2.522 13.2568 50 263 5.3 6 0.528 35 183 5.2 7.5 0.660 22 117 5.3 10 0.6840 13 66 5.1 R' = 8.80 (d) Ani-ani labor--Man-hr/ha 5 43.75 1.729 - 105 2.627 - 10“ 347 53 1 10 87.50 87 13 1 12.5. 109.375 56 84 l 15 131.25 39 6 l x = 875 40 remain normal: they did not overcut, as suggested by the large uncut loss, nor did the harvesters appear to out faster than one might expect. An important decision at this point regarded the use of the small plots to record cutting labor. Resources were limited so that a separate labor crew could not be hired, but an important criteria would be whether or not the cutting time responded to different yield levels, i.e., the larger the yield the greater the cutting time. So man-hours per hectare were regressed on yield, which yielded an r2 = .58. Although not a large r2, it did give encouragement to the method. 2.4 Randomized Study The randomized study consisted of two parts, 1) a random selection of fields, and 2) interviews with the farmers operating those fields. This section will review: the randomization procedure and field selec- tion, the farmer interviews, the collection of field data, and the laboratory procedures. A primary problem was how to develop a sampling frame of fields. It was first necessary to identify the general area to be included in the study. Yogyakarta province is divided into four districts, Sleman, Bantul, Gunung Kidul, and Kulon Progo, excluding the city of Yogyakarta. So the beginning point was to identify those dis- tricts (Kabupatens), which generally conformed to the described statistical population. It was necessary to separate irrigated areas from rainfed and dry- land rice production. Two criteria were used: 1) wetland areas identi- fied from satellite imagery taken during a dry month, and 2) cropping intensities, since cropping intensities greater than 1.0 were usually related to irrigation practices. The satellite pictures had been taken in September 1972-~normally the driest month of the year and 1972 was abnormally dry. 41 Both criteria identified the districts of Sleman and Bantul as mostly irrigated, and Gunung Kidul was rejected as was most of Kulon Progo. But a problem developed because the Kali Progo irrigation pro- ject irrigated a southern part of the Kulon Progo district. This area was several hours away from the base of the operations in Yogyakarta and the-time involved would have definitely reduced our ability to reach our sampling objectives. And since harvesting practices were the same as for Bantul and Sleman, it was excluded. Thus, the final district selection consisted only of the kabupatens Bantul and Sleman. 2.4.1 Randomization and Field Selection Randomization is frequently a compromise between statistical theory and the practicality of its implementation. This survey was no excep- tion. Where deviation occurred, the bias was slanted towards concentrat- ing the sample in areas of greatest padi sawah importance. Logistically, this provided access to more fields from a single location and reduced the traveling and field monitoring time, which had proven to be a problem in the pilot study. A four-stage sampling procedure was employed that paralled the administrative heirarchy within each district (Fig. 2.2). The subdis- trict level (kecamatan) was the first sampling stage. All the kecama- tans in each district were listed and then weighted according to their contribution to the total harvested padi sawah area. Of the thirty-four kecamatans that were listed, nineteen were randomly selected. The choice of nineteen was arbitrary as it was believed that an excess of 50% of the kecamatans would adequately represent Bantul and Sleman. In the second sampling stage, 10 kalUrahans (villages) were selected from the 19 kecamatans (subdistricts). All of the kalurahans were listed 42 SAMPLING STAGE ADMINISTRATIVE LEVEL KABUPATEN (DISTRICT) KECAMATAN (SUBDISTRICT) 1 KAHLURAHAN (VILLAGE) 1 DUKUH (SUBVILLAGE) 4 FIELDS-FARMERS Figure 2.2 Sample selection stages. 43 and 10 randomly selected. The kalurahans were not weighted because data of harvested padi sawah area, were not available. It was not attempted to limit the selection of only one kalurahan for each kecamatan be- cause different harvesting practices, which might influence losses and labor usage, were not identifiable at either the kabupaten or kecamatan level. As a result, two of the selected kecamatans, Jetis and Bantul, had dual representation. In the third sampling stage, 10 dukuhs (subvillages which were the smallest governmental administrative unit) were selected from the 10 kahurahans. The village head was asked to select the most important dukuh in terms of padi sawah production for the current season. Thus the dukuh selection was not random, unless several dukuhs were of equal importance. The reason for this deviation was two-fold: 1. It can be correctly argued that this difficulty might have been overcome if the padi sawah- producing characteristics of the dukuhs had been identified in advance and followed by a random selection. The problem was, however, that this would have required identifying 150-200 dukuhs when the harvest season was nearly at hand. The decision was, therefore, made to concentrate upon the most important padi sawah dukuhs. 2. Sugarcane was a competing crop for padi sawah land. What might have been a padi sawah area last season could be a sugarcane area this season. So an outright random selection could have re- sulted in sampling an area with a large propor- tion of sugarcane. With this a likely possibility, 44 the number of available padi sawah fields could have been severely limited, resulting in logis- tical problems due to a small number of available fields at harvest time. There were no discernible differences between the harvest practices employed as a result of this decision. Thus, it was believed that the bias was not detrimental. Table 2.2 summarizes the selected sites through the three sampling stages. Table 2.2 Random site locations and sampling stages. Stage 1: Stage 2: Stage 3: Site . Karbupaten Kecamatan Kahlurahan Dukuh l Sleman Berbah Sendangtirto Gundu 2 Sleman Gamping Balecatur Sumbur 3 Sleman Godean Sidoluhur Mertosutan 4 Sleman Kalasan Tirtomartani Brintikan 5 Sleman Sleman Pandawoharjo Plangan 6 Sleman Sleman Trimulyo Ngemplak 7 Bantul Bantul Trienggo Priyan 8 Bantul Jetis Patalan Bakulan-Netan 9 Bantul Jetis Trimulyo Blawong 10 Bantul Piyungan Srimartani Kuwasen In the fourth and final sampling stage, 120 fields were selected from 10 dukuhs. Two records within the dukuhs were available: 1) a listing of all fields, and 2) a listing of all farmers. The field list- ing was used as the sampling frame in all but two dukuhs where only a 45 farmer listing was available. But since the field-farmer relationship was so close, it was assumed that one could act as a proxy for the other. The pilot investigation indicated 50 to 60 fields should be sampled. But if only six fields per dukuh were selected, there was a potential problem: some of the selected fields might not be ready to harvest or they might already have been harvested at the time the sample- gathering crew was in that area. To adjust for this problem, twelve rather than six fields were chosen from each dukuh. A systematic sampling procedure was followed from a random entry point in the field or farmer list, every twelfth unit was sampled. Con- sequently, the total sample consisted of 120 fields from which 60 were used. 2.4.2 Field Data Collection The first need required an estimate of harvest dates for the fields. The person farming each field was identified and interviewed. Since these interviews were conducted close to, or during, the early harvest season, the estimates were reasonably accurate. The sample- gathering crew was next scheduled to sample an area during its harvest peak, as indicated from these interviews. This was reasonably success- ful, although some areas were sampled just prior or just after the peak because of logistical considerations. Before the arrival of the sample-gathering crew, the headman of the dukuh arranged for sampling six of the twelve fields. If six fields were not available, then a field, or several fields, in the immediate area were randomly selected as a substitute. This procedure was used at three of the 10 sites involving 10 of the 60 sampled fields. 46 It should be noted that the random selection of fields resulted in 48 fields using the ani-ani knife and 12 fields using the sickle. In both cases the same sample collection procedure was used as described for the pilot study. Since the random study included threshing losses, the yield samples were foot-treaded for these data. After threshing, all of the samples were tightly secured in plastic bags. In summary, the following field samples were collected from both the ani-ani and sickle plots: 1) gross yield sample, 2) shattering losses, 3) uncut losses, and 4) a threshed straw sample. Moisture content was determined from the gross yield sample upon return to the laboratory. Readings were not taken in the field because of battery problems. When this problem was resolved and field readings were possible, the research was nearing its completion so the initial procedure was continued in order to standardize the readings. The same laboratory procedure described for the pilot investigation was used for the random study except that threshing losses were also determined. The threshing straw was sundried and then handstripped with the loss reported on a full-grain basis. The following information was obtained: 1. Plot moiSture content 2. Gross yield (gm/square meter) on a wet and dry basis, i.e., 14% moisture content 3. The percentage, by weight, of hulls and full grains 4. Shattering losses (gm/square meter) for both full grains and hulls at 14% moisture 47 5. Uncut losses (gm/square meter) for both full grains and hulls at 14% moisture 6. Threshing losses (gm/square meter) on a full grain basis at 14% moisture 7. Threshed straw weight (gm/square meter) 2.5 Supplemental Data The sickle study was extended into June to include an additional 12 fields and 60 observations. These data were collected from fields purposely selected from the same area of the last six random fields. The field collection of the data was handled in the same manner as for the random study, and field selection was based upon the availability of fields-ready-to-harvest, which at this time was becoming a problem. Whether or not these data should be used to augment the random sickle data is considered in the next chapter. Threshing by beating was another nonrandom study conducted during June. Beating was the usual practice for threshing paddy cut with the sickle. But because beating required a large sample (20 to 40 kilograms), it was not possible to collect these data from the small plot samples. These data represented 55 sacks gathered from one field. The sack con- taining the cut stalk was weighed, and the thresher timed as he was beating. The threshed grain was then weighed and a moisture reading made. One straw sample per sack was taken, dried to 14% moisture, and hand- stripped to determine the loss. These beating data were necessary so that the sickle and ani-ani systems could be compared. It would not be appropriate to consider foot-treading data in lieu of beating with a sickle-length cut since this was not a normal practice. Samples that 48 had been foot-treaded with the longer straw showed losses of 5.74% for the 12 random fields and 7.88% for the 12 supplemental fields (Table 3A, Appendix A). CHAPTER 3 DATA EVALUATION The data presented in this section will be considered within the framework of the two harvesting methods, ani-ani and sickle. The los- ses associated with each will be discussed first then followed by the labor data. A summation of the loss and labor values, as a measure of technical efficiency of each system, and plot evaluation, will conclude this section. The analyses of variance, bivariate correlations, and t-tests were all performed with the SPSS statistical package. 3.1 Data to be Evaluated This study consisted of three distinct sets of data: the pilot investigation, the randomized study, and the collection of supplemental data, i.e., beating data and additional sickle data. The concern was whether or not the pilot and supplemental data should be included with the random data during the data analysis since the fields in these studies were not randomly selected. Probably of more importance is whether or not the typical situation being investigated is represented by the field selection. If the degree of randomness can be expressed on a continuum between random and nonrandom extremes, the pilot study and supplemental beating data would approach the nonrandom end of the continuum whereas the supplemental sickle data would gravitate toward the random end. The pilot data were rejected as not being typical of the random study since they were collected in December, 1978 under different seasonal conditions. 49 50 The beating data were retained because they were necessary to complete the sickle harvest system. The problem was whether or not the 12 supplemental sickle fields should be included. Six of these fields were from the same dukuh (sub- village) as the last random sickle sample but the last six fields represented a different dukuh within this village. A case can be made for either rejecting or including the supplemental sickle data. Since this study emphasized the random sickle data, the nonrandom data (Table A3, Appendix A) was not used. 3.2 Ani-ani Losses Grain losses can be discussed in absolute terms (weight loss) or in relative terms to the total yield (percentage loss). When comparing grain losses at different yield levels, a weight loss comparison, with- out reference to yield, gives a restrictive interpretation. The report- ing of grain losses as a percentage of yield, however, is open to a broader comparison, if percent loss is the same at any yield-level. To test the hypothesis that percent losses were not closely correlated to yield, the observed losses (shattered, uncut, and threshing) in percent were regressed on cleaned yield. The small r2 values (.05, .08, .002, respectively) indicated little correlation. Losses were, therefore, reported in this study on a percentage basis. These percentage losses were calculated as follows: _ Loss m mz' % LOSS - W X 100 3.1 As a researcher, losses calculated on the basis of potential yield method might be preferred, but from the standpoint of a decision-maker losses are more apt to be interpreted in terms of harvest yield. And 51 because this paper is directed to decision-makers, the harvested yield method was used. However, it should be recognized that this can accen- tuate certain observations. With a low yield and high weight loss, a high percentage loss would result. But because potential yield also includes the loss value in the denominator, a smaller percent loss would result. For example, one plot observation was: cleaned padi 324 gm/mz, uncut loss 73.7 gm/mz, shattered loss 9.2 gm/mz, and threshing loss 3.5 gm/mz. On a potential yield basis, uncut loss for this observation would have been approximately 18%, but on the basis of harvested yield the loss increased to nearly 23%. 3.2.1 Average Ani-ani Losses The average ani-ani cutting losses were 5.88% at an overall mois- ture content of 20.3% and an average yield of 4.46 tons per hectare (Appendix A, Table A2). Shattered losses, including dropped panicles during removal of the flag leaf and transfer of the cut panicles from the cutting hand to the storage hand, were 1.40%. Uncut losses were 4.48%. Thus the importance of uncut losses is evident since they con- stituted 76% of the overall cutting loss; in the pilot study the uncut loss was nearly 78%. The 5.88% ani-ani loss compared with ani-ani losses ranging from 5 to 10% as reported by Esmay, et a1 (1979). Foot-treading losses were 2.38% so that ani-ani system losses were, on average, 8.26%. The distributional characteristics of these ungrouped data are summarized in Appendix A. All of the observed losses resulted in high coefficients of variation and marked skewedness to the right. This is more readily seen in the frequency histograms for ani-ani losses (Figure 3.1) and foot-treading losses (Figure 3.2). Nearly 80% of shattering losses were equal to or below a 2% loss; almost 80% of the uncut losses Frequency (f) Frequency (f) Frequency (f) 52 60 .55 a. Ani-ani shattering losses 48 46 46 [—4 — 36 ii. 24 24 r——~ 12 .11. 0 0 0.5 1.0 1.5 20 2.6 3.0 3.5 4.0 4.5 5.0 5.0 9.5 10010.5 % Loss 50 49 — . b. Ani-ani uncut losses 3O 20 10 2 o 3 o 1.0 2.0 3.0 4.0 50 6.0 7.0 8.0 9.0 10.011.012.01 .0 24.0 % Loss 45 43 1——1 41 35 c. Total ani-ani losses 36 ‘F—_i' 35 ‘ 27 36 13 ~-——————1 16 ‘ 23 9 $3 3 0 21:12. 1.22 2.25 3.37 4.60 5.62 we 7.37 9.0010.1211.25 16.87 29.0 % Loss Figure 3.1 Frequency histograms: ani-ani losses. Frequency 53 45 42 37 36 50 27 ______w 42 19 18 ‘ 32 9 14 4 0 J— 121:1: 8.00 12.00 0 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 % Loss Figure 3.2 Frequency histogram: foot-treading losses. 54 were equal to, or below 6%; and nearly 80% of the foot-treading losses were equal to or below 3.50%. 3.2.2 Losses by Variety Except for one field of IR30, IR36 and IR38 were the only varieties encountered. The mean ani-ani cutting loss for IR36 was 5.99% and for IR38 was 5.64% of loss. This compared to ani-ani cutting losses of 3.16 and 1.93 for IR36 and IR38 as reported by Djojomartono (1979). But losses were for the dry season and wet season respectively and were not observed concurrently. A t-test was performed to determine if there was a significant difference between IR36 and IR38; only shattering losses were significantly different at the 5% level with IR38 being 1.65% and IR36 being 1.31%. Except for shattering losses, it was concluded that varieties would not be a satisfactory explanation of these losses. 3.2.3 Losses as a Function of Moisture Content As a partial explanation, shattering and threshing losses were regressed on moisture content. An r2 value of .03 indicated little correlation between this losses and moisture content. Crop moisture con- tent and average yield are given in Figure 3.3. 3.2.4 Loss Variability and Sample Size The variability of these data is better described through an analysis of variance (Appendix B, Table 82) rather than the variance for the ungrouped data in Table A6. The expected mean square of the fields and plots can be stated: 36 24 Frequency 12 50 40 30 20 Frequency b 16 51 43 15 55 a. 19 Yield 4 1 1.50 2.25 3.00 3.75 4.50 5.25 6.00 6.25 7.5 6.25 Tons/Hectare 16 41 F"""1 30 b. Moisture content 6.8 17.65 16.90 19.95 21.0 22.05 22.75 23.6 24.65 Moisture Content Figure 3.3 Yield and moisture content for ani-ani study. 56 Source .25 Mean Square Expected Mean Square ° 2 Fields 47 MS 0 p + 50 f Plots 192 MS 02p 239 And for each loss the expected variance for fields and plots within fields would be: 2 = 3.2 6 p MS 2 _ MS - MS The variation of the mean for the ani-ani method can now be stated for each loss: 02 02 p + _ f 3.4 n plots k fieldE’ a. 6 x Sample sizes can now be investigated, and this was done in Appendix C. Since sample size is a compromise between resources and the number of fields and plots, these tables relate different combinations of plots and fields and the expected standard deviation for each combination. 3.3 Sickle and Beating Losses Table A3 summarizes the random and the supplemental sickle losses. For the random study, shattering losses were 1.28%, uncut losses 1.92%, and overall sickle losses 3.20%. The supplemental study resulted in shattering losses of 1.42%, uncut losses of 2.34%, and sickle cutting losses of 3.76%. This compared to total sickle cutting losses, as re- ported by Djojomartono (1979), of 2.69% and 1.70% for dry season and wet season harvesting respectively. Beating losses averaged 5.63%. The uncut losses seem higher than would be expected, especially when comparing the random data of Godean (0.76%) and Berbah (3.08%). The 57 explanation lies in the length of cut; the harvesters at Godean were cutting the paddy closer to the ground so that the lower panicles were harvested. Thus, the improper use of the sickle can result in higher than expected uncut losses. The distributional characteristics of the ungrouped data are shown in Appendix A, and additional insight is provided by the frequency his- tograms (Figures 3.4 and 3.5). The sickle data was skewed to the right. Nearly 87% of the shattered loss were equal to or below 2.16%; 87% of the uncut losses were equal to or below 4.00%; and 85% of the total sickle cutting losses equaled or were below 6.00%. Although beating losses (Figure 3.5) varied considerably from 2.98% to 12.41%, 76% of these los- ses were equal to or below 6.36% loss. 3.3.1 Varietal Differences and Moisture Content Varietal differences and effect of moisture content were not analyzed because of the limited number of random observations. 3.3.2 Loss Variability and Sample Size Analyses of variance (Appendix B, Table B3) were used to project sample size charts (Tables CS, C6 and C7 in Appendix C) for sickle shat- tering, uncut, and cutting losses. Beating losses were not considered because those data were not random. 3.4 Ani-ani and Foot-treading Labor Ani-ani cutting and foot-treading labor are summarized in Appendix A, Table A4. These data are presented as both man-hours per hectare, and man-hours per ton, as derived from the plot data. Ani-ani labor averaged 195 man-hours per ton and 837 man-hours per hectare. Djojomartono (1979) reported 455 man-hours per hectare and Frequency (f) Frequency (f) Frequency (f) 25 20 F-_1 0.0 .54 25 23 21 1.08 58 a. Shatter losses - sickle 3 1: 1.62 2.16 2.70 3.24 6.48 % Loss b. Uncut losses - sickle 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 12.0 25 0.0 1.5 24 % Loss c. Total 3.0 4.5 6.0 7.5 9.0 10.5120 % Loss Figure 3.4 Frequency histograms: random sickle cutting losses. Frequency (f) 20 15 10 16 59 21 8 5 2 2 L h 2.77 3.96 5.16 6.36 7.56 8.76 9.96 12.36 % Loss Figure 3.5 Frequency histogram: beating losses. 60 588 man-hours per hectare for IR38 and IR36 during the 1978 dry and wet seasons respectively. Converted to man-hours per ton, the labor require- ment reported by Djojomartono was 90 and 198 man-hours per ton respec- tively. The ungrouped data are summarized in Appendix A. The grouped data are summarized in Figure 3.6 as frequency histograms. On a hectare basis, 71% of the observations fell between 330 and 960 man-hours per hec- tare, and on a tonnage basis 71% of the observations fell between 110 and 230 man-hours per ton. Foot-treading averaged approximately 78 man-hours per ton. This compared to foot-treading time of 35 man-hours per ton developed from Djojomartono's work. Foot-treading time based upon plot observations appeared too high, so field observations of a foot-treading crew were made in Godean. Twenty-eight observations were taken yielding an average rate of 33 man-hours per ton; this was at 21.7% MC and without the empty hulls removed. Correcting for empty hulls at 90.3% and moisture at 14% this approximated 26 kilograms per man-hour, or 40 man-hours per ton. The ungrouped foot-treading data is summarized in Appendix A; Figure 3.7 summarizes the grouped data. The frequency histograms are skewed to the right with nearly 70% equal to or below 89 man-hours per ton while forty-five percent of the observations fell between 35 and 71 man-hours per ton, which contains the results reported by the Godean study. 3.4.1 Labor Variability and Sample Size As was the case for determining loss variability, labor variability is better described through an analysis of variance (Appendix B, Table 84). The same procedure for estimating sample size was followed for Tables C8, 09, and C10 in Appendix C, as previously discussed. Frequency Frequency 32 16 50 40 30 20 10 27 76 40 61 a. Ani-ani cutting: man-hour per hectare 120 330 540 750 960 11701380 15901800 Man-h/ha‘ 48 . . . 45 --1 b. An1-an1 cutting: ~--w man-hour per ton 38 41 #1 27 12 25 l 4 50601101401702002602t01903 36 38 7o Man-h/ton Figure 3.6 Ani-ani labor. Frequency Frequency 36 24 12 36 24 12 62 5455 r-!' l a. Man-hours per ton 36 26 2o .—__1 18 ‘ 17 5 1———‘ 4 16 35 53 71 69 107 125 143 179 233 Man-h/ton 67 .___ b. Man-hours per hectare 43 '--w 41 22 16 100160220 280340400460520580640700 820 Man-h/ha Figure 3.7 Foot-treading labor. 63 3.5 Sickle and Beating Labor The sickle labor data is summarized in Table A5 at Appendix A. Labor averaged nearly 42 man-hours per ton for the random data, and 60 man-hours per ton for the non-random fields and 51 man-hours per ton overall. On an area basis, the labor requirement was about 165 man-hours per hectare for the random data, 168 man-hours per hectare for the non- random data, and 166 man-hours overall. Djojomartono (1979) reported 53 and 122 man-hours per hectare for sickle cutting. The more normal thresh- ing by beating resulted in 23.43 man-hours per ton for the 55 observations at Kalitirto in June. The ungrouped cutting data are summarized in Appendix A. The grouped sickle data are presented as frequency histograms in Figure 3.8; the beating labor data are summarized in Figure 3.9. 3.5.1 Labor Variability and Sample Size An analysis of variance for the random sickle cutting data is found in Appendix B, Table 85; an estimate of standard deviations for various sample sizes is given in Appendix C, Table 011 and C12. 3.6 Data Summary Ani-ani and sickle losses, with their respective confidence limits, are summarized in Table 3.6. Cutting losses were reduced 2.68%, or 45%, through the use of the sickle, but a 136% increase in beating losses negated these gains so that the sickle system losses were slightly higher than those for the ani-ani system. However, a note of caution needs to be interjected: foot-treading the small plot samples was probably more efficient than with the larger samples usually used resulting in a lower loss, and the beating losses represented the efforts of only one crew. Frequency (f) Frequency (f) 64 25 a. Man-hours per hectare 20 20 F__1 1 16 1 16 10 10 [—— 7 5 2 2 2 W o 1 44 94 145 196 247 298 349 400 451 Man-h/ha 25 b. Man-hours per ton 20 2° 18 _1 15 1o 5 o 1 15 30 46 a) 75 in 105 Man-h/ton Figure 3.8 Frequency histogram: sickle cutting labor. 65 26 2. _—l 21 20 15 10 5 . 5 "—1 1 Figure 3.9 Frequency histogram: beating labor Man-h/ton. 66 Table 3.6. Ani-ani and sickle losses with confidence intervals at the 95% level.1 Ani-ani Sickle 1. Cutting losses Shatter 1.40 ns2 1.28 ns2 95% C.I. 1.17 - 1.63 1.05 - 1.51 Uncut 4.48**2 l.92**2 95% C.I. 3.97 - 4.99 1.09 - 2.75 Total 5.88** 3.20** 95% C.I. 5.27 - 6.49 2.08 - 4.32 2. Threshing losses3 Foot-treading 2.38 -- 95% C.I. 1.97 - 2.79 Beating -- 5.63 3. System losses“ 8.26 8.83 1Confidence intervals were developed from the standard deviations in Appendix C. 2An SPSS t-test routine was used to compare ani-ani and sickle systems: (a) Shattered and dropped losses were not significant: t = 0.75; df = 298; tabular too 05 = 1.96. (b) Uncut losses were highly significant: t = 6.52; df = 298; tabular t“) 0] = 2.5758. (c) Cut losses were highly significant: t = 5.83; df = 5.83. 3Threshing losses were not statistically compared because beating losses were nonrandom and included only one threshing site. l'System losses were not statistically compared because threshing losses could not be included. 67 Nevertheless, this does indicate that a more efficient threshing method is probably needed, if losses are to be minimized with the sickle. Furthermore, this demonstrates the need to analyze harvesting losses as a system since a change in a part of the system may adversely influence another part of that system. But the impact of change need not be adverse. For example, a pedal thresher might be considered as an improved technique. This would require the need for a longer-cut stalk and would probably eliminate most of the uncut losses. The labor requirement between these two systems is compared in Table 3.7. The differences in labor usage was quite dramatic: the sickle reduced cutting labor 78%, beating reduced threshing labor 70%, and the overall labor requirement was reduced 76% with the sickle system. The drastic change in labor and the unchanged loss indicates the economics of the problem. The farmer's interest lies with reducing his labor costs while the interests of society may be better served through maximizing employment opportunities. But before a labor saving practice is condemned out of hand the question of why that practice was adopted must be considered. In the case of both areas using the sickle in this study, conversations indicated that the sickle was used because local labor was not available for harvesting. So rather than responding strictly to a cost reduction incentive, the farmer was responding to an existing labor problem. Not that the farmer would not opt for reduced harvest costs but that social and cultural pressures for peer approval seemed to have held this movement in check. 3.7 Small Plot Technigue Evaluation The data presentation would be incomplete without some consideration given to the small plot sampling technique that generated these data: 68 Table 3.7. Labor requirement for ani-ani and sickle systems with confidence intervals at the 95% level.1 (a) Man-hours per ton. Ani-ani Sickle 1. Cutting labor 195“""'2 42**2 95% C.I. 180 - 210 30 - 54 2. Threshing labor Foot-treading 78 -- 95% C.I. 69 - 87 Beating -- 23 3. Total 273 65 (b) Man-hours per hectare. Ani-ani Sickle 1. Cutting labor 837**3 165:”:3 95% C.I. 774 - 900 128 - 202 2. Threshing labor Foot-treading 326 -- 95% C.I. 292 - 360 Beating -- 103 3. Total 1163 268 1See Appendix C for confidence intervals and standard deviations. 2Highly significant: t 18.48; df = 298; too' 0] = 2.5758. 3Highly significant: t 16.04; df = 298. " 69 3.7.1 Shattering Losses This method directly determined the shattered and dropped losses which resulted from crop contact by the harvester, during the stripping of the flag leaf, and during the transfer of the panicles from the cut- ting hand to the storage hand. The grain count method could not have been used on 43% of the fields because of muddy and flooded field condi- tions. With reasonable care in laying out the plot, shattering losses before cutting were negligible. The plots functioned equally well for both ani-ani and sickle cutting and were satisfactory in lodged conditions. 3.7.2 Uncut Losses A first reaction was that uncut losses would be biased on the low- side on the assumption that the harvester would be more careful when cutting a small plot. But this did not appear to be the case, although in individual instances this did happen. Visual inspection of adjacent fields seemed to negate this concern as a serious problem, and the range of these data tend to confirm this viewpoint. Ideally, it would have been preferable to check the field for uncut losses after the har- vest had been completed, but because the fields were left trampled, and with the presence of gleaners, this was not practical. 3.7.3 Foot-treading Losses Among the losses, foot-treading was considered the weakest area with the small plot technique. These small samples were more thoroughly threshed than would have been the case with larger samples, and it probably would have been preferable to use actual field observations for collecting these data. 70 3.7.4 Ani-ani and Sickle Cutting Labor The small plot technique captured the wide variation inherent in most hand labor operations. Again a downward bias might be expected but this did not occur. The plots did create a physical impediment to the worker when the water and mud reached calf depth. Then manuverability of the harvester was impaired, it being more difficult to work around the ends and sides of the plot. 3.7.5 Foot-treading Labor Because the treaded samples were small, foot-treading was difficult to evaluate. A judgement error in timing could lead to a large sampling error. Field observations on a sack basis would be preferable. In summary, the technique was satisfactory for the collection of cutting losses, but foot-treading losses probably tended towards a down- ward bias. Cutting labor appeared satisfactory, but foot-treading labor did not. Both labor methods need to be compared to field observations. CHAPTER 4 AREA LOSS MODEL Chapter three summarized the ani-ani and sickle data as they per- tained to the technical efficiency for each system. From the viewpoint of the individual farmer, this is appropriate. But from the societal per- spective, the area losses are of a greater concern because area harvest- ing practices are not homogeneous as is the case of the individual farm. The interviews with the farmer-operators and the technical efficiencies developed in the last chapter will serve as a basis for modeling the area loss. 4.1' Farmer Interviews The purpose of these interviews were several fold: 1. To ascertain the postproduction system in use within the area 2. To develop a pattern of rice maturities for the research area that would serve for scheduling the research crew 3. To determine the extent that gleaning was practiced 4. To identify harvest costs 5. To ascertain the farmer's perception of harvest delays 0f the 120 farmers associated with the random field sample, 112 were interviewed representing 21.28 hectares of paddy production. Thus, 71 72 the average farmer was growing 1900 square meters, or 0.19 hectares, of paddy. Figure 4.1 summarizes the results of these interviews as pertains to the area losses. The harvesting method was clearly associated with how the crop was sold. If the farmer sold or consumed his own crop, then the crop was harvested using the traditional ani-ani system. On the other hand, if the crop was sold in the field to a buyer, then the buyer generally used the sickle system. The interviews indicated that the farmers harvested 86% of the paddy area and buyers harvested the remaining 14%. Gleaning constituted a loss recovery practice from a societal stand- point. This practice was commonly associated with ani-ani cutting rather than with sickle cutting because of the greater uncut loss. Gleaning was permitted by 75% of the farmers representing 78% of the sampled area, but gleaning was actually practiced on only 49% of the field area. Al- though gleaning was not practiced following the sickle in the model. there were exceptions, but this was not typical. Foot-treading was practiced by farmers representing 96% of the area under the ani-ani system while beating was practiced over 4% of the area. The threshing practice associated with sickle cutting was beating. The farmers did practice rethreshing usually a week to ten days after the field had been harvested. The interviews also indicated that 81% of the crop was rethreshed under the ani-ani system. Although this same relationship could not be verified for the sickle system from the interviews, rethreshing by the farmer was often practiced after sickle cutting so the assumption was made that the same relationship held for the sickle system. Farmer 86% Ani-Ani 49% 931% Yes No _ J77 F"Jk"‘l Foot- - Treading Beat1ng 81% 19% Yes No Figure 4.1 73 Matured Paddy Harvester :<::¢ Buyer ) Cutting — Sickle Gleaning “" Threshing 117 Beating 81% 19% No Area loss model. 74 4.2 Area Losses Table 4.1 summarizes the average area losses. Data were not available regarding the efficiency of gleaning and rethreshing so the assumption was made that 50% of the uncut losses were recovered by gleaning and 50% of the threshing losses were recovered by rethreshing. The average area loss was 5.33% given the mixture of harvesting tech- niques indicated from the interviews. 4.3 Re-evaluation of Ani-ani Sickle Losses From the viewpoint of the farmer, the losses due to the technical efficiencies presented in the last chapter need to be revised in the light of rethreshing practices. This was done in Table 4.2. With im- proved threshing results the sickle system would indicate an improved recovery of nearly 15%. Although the farmer had sold his crop at harvest, it was not clear whether he or the buyer retained the beaten straw for rethreshing or other use. Whoever does have the rethreshing rights will stand to benefit from the grain recovery on the assumption that the opportunity cost for labor would warrant the effort. 75 Table 4.1 Area losses assuming gleaning and rethreshing recoveries. Ani-Ani Sickle 1. Cutting: a. Shatter 1.40 l 28 b. Uncut 4.48 1.92 c. Total 5.88 3.20 2. Gleaning recoveryl (2.24) - 3. Net cutting loss 3.64 3.20 4. Threshing: a. Foot-treading 2.38 @ .96 2.28 - b. Beating 5.63 0 .04 0.23 5.63 c. Total 2.51 5.63 5. Rethreshing recovery:2 a. .81 x 2.51 x .5 = 1.02 (1.02) (2.28) b. .81 x 5.63 x .5 = 2.28 6. Net threshing loss 1.49 3.34 7. Area system loss 5.13 6.54 8. Contribution to area loss 4.41 0.92 a. 5.13 x .86 = 4.41 b. 6.54 x .14 = 0.92 9. Area loss 5.33 1Assuming a 50% recovery of uncut losses. 2Assuming a 50% recovery of threshing losses. 76 Table 4.2 Ani-ani and sickle losses with rethreshing. Ani-Ani Sickle 1. Cutting a. Shatter 1.40 1.28 ' b. Uncut 4.48 1.92 c. Total '5788 3.20 2. Threshing a. Foot-treading 2.38 - b. Beating - 5 63 c. Rethreshing1 (1.19) (2.81) d. Total 1 l9 2 82 3. Total losses 7.07 6.02 1Assuming a 50% threshing loss recovery. CHAPTER 5 SYSTEM IDENTIFICATION Systems analysis can be viewed as a sequence of five interactive steps: 1) feasibility evaluation, 2) abstract modeling, 3) implementa- tion design, 4) implementation, and 5) system operation (Manetsch and Park, 1977). This chapter will concentrate upon the feasibility evalua- tion and abstract modeling phases. 5.1 System Identification The feasibility stage begins with an expressed set of needs that the eventual system will address itself. The identification of the system takes these needs as inputs and yields a problem formulation as an output. System alternatives are generated and filtered for physical, social, and economic realizability with the final outcome being a set of viable alternatives. The needs are stated in Chapter I so will only be summarized here. The needs of farmers are to increase their profitability through in- creasing their output and/or decreasing their costs. This involves the use of new inputs, at acceptable risk levels, to generate an additional income stream on the assumption that they presently are efficiently allocating the known means of production at their disposal. The largest population segment on Java are the rural landless laborers, and their needs are closely identified with employment opportunities during the harvest period, which provides a significant portion of their yearly rice requirement. The urban sector requires an adequate supply of rice 77 78 at affordable prices. The needs of the government, as adjudicator of the national interest, are closely identified with food self-sufficiency as an important factor in development policy. The system variables can be identified from the standpoint of six variable categories (Manetsch and Park, 1977): desired outputs, undesir- able outputs, environmental variables, system parameters, noncontrollable overt variables, and controllable overt variables. 1. Desired outputs: a. Increased overall rice production through reduced harvesting losses b. Improved profitability and return on investments to the farmers at acceptable risk levels c. Maintenance of employment opportunities amongst the rural landless d. Reduced manual drudgery during harvest 2. Undesired outputs: a. Skewed income distribution b. A negative or zero contribution to profitability c. A negative or zero contribution to production growth implying an inefficient allocation of capital and unnecessary reduction of employment opportunities 3. Design parameters--decision variables that are fixed during the operation of the system. They are attributes of the system's structure and 79 would include those variables that establish the boundaries of the system. The design parameters which fix the system are: a. Geographical location--the province of Yogakarta Harvest phase of the rice postproduction process during the March to June harvest period Irrigated paddy and high-yielding varieties as opposed to traditional varieties and dryland production Environmental variables-~noncontrollable variables outside of the system that affect the system. They themselves are un- affected, or only weakly influenced, by the system: a. Climatic and weather variables influence the rate of maturity, panicle formation, shattering losses, lodging, etc. Wage rate, interest rate, import prices, and rice prices Farm size--inf1uences income level and, hence, capital intensity and possibly timeliness of harvest concerns Government policies, e.g., currency valuation that may distort capital/labor relationships through over or under evaluation 80 e. The system does not differentiate between specific high-yielding varieties-- these are, therefore, considered external to the system 5. Noncontrollable overt variables--necessary inputs for the system to function but not important in altering the operating system a. Production cultural practices b. A minimal level of labor and capital c. A minimal skill level d. Supportive infrastructure 6. Measures of system performances--criteria to be used in evaluating alternative system designs. Since this is a comparative study the measure- ment criteria for alternative solutions will use the existing ani-ani system as a basis for comparison as follows: 6. Percent losses b. Labor requirement--man-hours per season,man-hours per hectare, and man-hours per ton c. Waiting time (days) that a required area must wait before being harvested d. Additional costs and returns In summary, the systems to be studied are: harvest losses andlabor usage associated with harvest practices as are occurring on farmer's fields in the province of Yogyakarta under irrigated conditions. The system is 81 further limited to high-yielding rice varieties and the March-June harvest period. 5.2 System Alternatives The traditional ani-ani system will be the benchmark to judge al- ternatives. In general, this implies changes in cutting, changes in threshing, or changes in both cutting and threshing. Specifically, the alternatives to be considered will be: the sickle and power thresher. These changes are within the existing skill-level of the local popula- tion, compatible with the infrastructure, and locally reproducible. The combine has been omitted because of its strong labor-saving bias and limited application. In addition, the wet field conditions encoun- tered in this study, the lack of supportive assistance, particularly service and parts, and the large foreign exchange requirement would suggest that the other alternatives would be of greater benefit in the near to intermediate term. CHAPTER 6 THE SIMULATION MODEL This chapter develops the simulation model used to analyze alterna- tive harvesting techniques. The model can be viewed as two components or submodels: a loss submodel and a labor submodel, which can be run to- gether or independently of each other. The major sections of this chapter will consider: 1) the meaning of simulation, its appropriateness, and characteristics of simulation models; 2) model design considerations in- cluding model classification, time advance method, the choice of GPSS as the simulation language; and 3) description of the basic ani-ani model. 6.1 Simulation: An Introduction 6.1.1 Simulation Defined Simulation has been defined in varying degrees of generality or specificity. Churchman (1963) has attempted to avoid the ambiguities and inconsistencies in present-day usage by explicitly defining simula- tion as follows: X' simulates y' is true, if and only if: a) x and y are formal systems; b) y is taken to be the real system; c) x is taken to be an approximation to the real system; and d) the rules of validity in x are nonserror-free. Shubick (1960) suggests a broader concept in line with the more popular definitions: A simulation of a model or a system or an organism is the operation of a model or simulator which is the representation of the system or organism. The model is amenable to manipulation which would be 82 83 impossible, too expensive, or impractical to perform on the entity it portrays. The operation of the model can be studied and from it, properties con- cerning the behavior of the actual system or subsystem can be inferred. While Manetsch and Park (1977) suggest a simulation model to be: A model which computes the time path of model variables for a specific set of system inputs and a specific set of values for model parameters. Essentially simulation is a technique of constructing and operating a model of a system in order to study the behavior of that system. The type of model to be developed in this paper will be a procedural model, i.e., a model that expresses the dynamic relationships that are hypo- thesized to exist in a real situation by means of a series of elementary operations on the appropriate variables. Simulation, as viewed from this perspective, follows the definition suggested by Emshoff and Sisson (1970): A simulation is a model of some situation in which the elements of the situation are represented by arithmetic and logical processes that can be executed on a computer to predict the dynamic properties of the situation. It is in this context that simulation is used in this paper. 6.1.2 The Appropriateness of Simulation The question arises whether or not simulation is the appropriate technique for analyzing the problem at hand. Naylor (1968) suggests three major considerations--applicability, cost, and simplicity—-which must be answered in the affirmative. Is there a reasonable certainty that simulation will yield an approximate result, if not an exact solu- tion, at a reasonable cost level? Does the simulation technique under consideration lend itself to relatively easy interpretation by those who will use the results? 84 Simulation is applicable where nonlinearity, randomness, or sheer complexity is an important characteristic of the system being modeled. The existence of these conditions would tend to make mathematical models intractable, which implies that the interrelationships among the compo- nents of the model are known and mathematically definable. 0n the other hand, when it is not possible to express these relationships in a suit- able mathematical form, a viable alternative is to establish operating rules and study the system's behavior through a simulation model. However, this need not preclude a mathematical description and solution for parts of the overall problem. Without attempting to present an exhaustive documentation of the reasons for using a simulation methodology, a few points as related to the expectations of the ensuing model will be suggested: 1. Simulation can serve as a filter for testing and evaluating new policies and decision rules for operating a system before running the risk of experimenting on the real system. 2. The simulation model can yield an insight as to which variables are most important in the system, and how these variables interact. 3. When new design components are introduced into a system, simulation can be used to help force bottlenecks and other problems that may arise in the real system. 4. The effect of different environmental factors upon the system can be projected through appropriate alternatives in the model. As with any analytical procedure, there are certain disadvantages, and simulation is no exception. These shortcomings should be recognized 85 from the outset: 1. Simulation may not give an Optimum in the sense of a unique solution. But from various alternatives, 6 "best solution" should be identifiable. This emphasizes the need to initially brainstorm all possible alternatives. Furthermore, if "optimum" is equated to a "best solution" given available resources and time rather than a "best solution" in the absolute sense. The accuracy of simulation results may be somewhat unpredictable because random variables are manipulated and the experiment is conducted with limited samples. The data itself may not be accurate and/or the logic of the model may be incorrect when com- pared to the actual situation. This may make it difficult, if not impossible, to validate the model. Simplifying assumptions are necessary in order to cut through the complexities of the problem. Consequently, extrapolation of results to a domain outside of the cases studied may not be appropriate without due conSideration given to these simplifying assumptions. 86 5. The results of simulation experiments are almost always autocorrelated. Therefore, design of simulation experiments and inter- pretation of simulation results must consider this effect. With these misgivings in mind, a simulation approach seems justified as a means of coping with the complexities involved. 6.2 Model Desigp‘Considerations Three model design considerations are introduced because they have a bearing upon an understanding of the model's structure and logic: model classification, time advance mechanism, and simulation language. The first two influence the simulation language to be used. The language itself must be able to capture the essentials of the real system and translate these into a reasonable computer representation of that system. Obviously, an understanding of the language is a prerequisite to the understanding of the model. 6.2.1 Model Classification Naylor (1968) observed that classification of simulation models is "completely arbitrary." For convenience, he has identified four cate- gories that seem appropriate to follow: deterministic, stochastic, static, and dynamic. Since this classification scheme is not mutually exclusive, it might be more instructive to view them as factors which describe a simulation model rather than as a classification set per se. The first contrast to be considered is whether the model is essentially determin- istic or stochastic. In a deterministic model, neither the exogenous or endogenous variables are allowed to be random variables and, the operating 87 characteristics are assumed to be exact relationships rather than probability density functions. A stochastic model, on the other hand, has at least one of its operating characteristics defined by a probability density function. A primary objective for developing a simulation model is to pro- vide a realistic representation of the behavior of the real system. If historical data or direct observation suggests that "unexplainable" variations exist in the value of some phenomenon, then the phenomenon should be represented in the simulator as a stochastic process (Emshoff and Sisson, 1970). Emshoff and Sisson further note that "unexplainable" means that the analyst is unable or unwilling (e.g., for economic reasons) to seek a deterministic cause for the phenomenon. The basic complexities of the problem, e.g., degree of plant variability, human variability in the performance of different tasks, moisture content, management practices, etc., strongly suggest a need to stochastically represent these data. Thus, the interaction of these variables (besides the economic constraints of the study) leads to a stochastic model rather than the seeking of a deterministic solution. The model can also be classified as dynamic since the state of the model changes through time as a result of changes in inputs and the interaction among model elements. This becomes quite evident as the logic of GPSS unfolds. Another consideration is whether the model should be classified as continuous or discrete. Variables can change in four ways (Emshoff and Sisson, 1970): l) in a continuous fashion at any point in time, 2) in a discrete fashion at any point in time, 3)in a discrete fashion but only at certain points in time, and 4) in a continu00s fashion but also at 88 only certain points in time. This model is characterized by a discrete time dimension while the measured variables are continuous. Changes in rice losses and labor requirement occur only as certain events take place, e.g., cutting and threshing. Consequently, the loss and labor variables change in a continuous fashion but only as these events occur. In summary, this model can be classified as stochastic, dynamic, since values change through time, and discrete in the time dimension but continuous with respect to the studied loss and labor variables. 6.2.2 Time Advance Method There are two choices for advancing the computer model through time: 1) a unit time advance (also referred to as a time slice, and 2) an event time advance. A unit time advance can be used for either dis- crete or continuous models. In this case, the model is moved through time at fixed time increments and is scanned by the computer to determine if an event has occurred at that computer clocktime. If so, the state of the system is updated. The primary concern is to be able to determine a time interval small enough so that the probability of multiple events is negligible. Also, there is some loss of information since events are not specifically defined but approximated within a fixed time interval. The event, or variable time, advance is based upon the occurrence of an event rather than upon a fixed time interval to evaluate the state of the system. Naturally, the event advance system would be inappropriate if one of the variables changed continuously and could not be approxi- mated by discrete events. But such is not the case in the harvesting and threshing model developed in this paper. Harvesting and threshing are two distinct events that make necessary the evaluation of the system, and it is at these times only that the state variables change. Consequently, 89 the event time advance is used to move the model through simulated time, as this best captures the dynamics of the phenomenon being modeled. It should be noted that either method treats the time variable as being continuous. 6.2.3 Choice of a Simulation Language: GPSS Closely allied to the time advance method is the choice of the simulation language. The language to be used can be approached from two standpoints: 1) either a general purpose language, such as FORTRAN, or 2) a specialized simulation language, such as GPSS. The language in effect restates the problem in the form of a problem model, which the computer can translate into a simulation program. Almost all simulations require some common function which a language must perform: 1. Create random numbers 2. Create random varieties 3. Advance time either by one unit or by the next event 4. Record data for output 5. Perform statistical analysis on recorded data 6. Arrange outputs in specified formats 7. Detect logical inconsistencies and other error conditions The user wants a language that facilitates model formation, is easy to program, provides good error diagnostics, and most important, is applicable to the problem at hand. 90 A multi-purpose language, e.g., FORTRAN, has the advantage that the user has greater flexibility in structuring the simulation to correspond with the specific problem. It has the disadvantage that con- siderably more programming effort, and, hence, a higher-skill level is required on the part of the programmer. As Manetsch and Park (1977) note: FORTRAN as it stands is really not a viable simulation option given the availability of FORTRAN-based languages such as FORDYN and GASP. FORTRAN is best viewed as a foundation language to which many kinds of useful software can be added (i.e., GASP, FORDYN, optimization routines, etc.) to yield an extremely versatile simulation package. The principal advantage of using a special-purpose simulation lan- guage is that it requires less programming time. Since these languages are designed for problems of a specific type, their flexibility may be limited. However, their error checking techniques are usually far superior to those provided by FORTRAN, ALGOL, etc. (Naylor, 1968). The choice of language is really concerned with the overall efficiency for the given set of resources, especially skill-level, financial, and time constraints. The analyst should be able to concentrate upon problem formulation and solution rather than upon programming techniques. GPSS was selected as the simulation since it meets the aforemen- tioned considerations. But, more importantly, GPSS offered a queuing con- cept to the problem at hand without undue programming involvement, and the labor input to a particular postproduction system is strongly de- pendent upon how long labor must wait between events. And the losses incurred in the postproduction period are strongly dependent upon how long the mature crop must wait before, and during, the harvesting process. Delay at any point in the sequence of harvesting operations increases losses regardless of the techniques used. 91 As will be seen in the next section, GPSS is an integer-valued system. Although this presents some print-out problems, the short-comings of GPSS are outweighed by its capacity to cope with the loss and labor problem to which this model addresses itself. 6.2.4 Review of GPSS This section will only highlight the main features of GPSS that are applicable to this model. The reader is encouraged to review Appen- dix D for a more complete understanding of the language. GPSS is a com- plete language oriented towards problems in which items flow through a series of processing and/or storage functions. The items of flow are transactions, and they have an implicit meaning: in this case, an area of paddy. The instructions of GPSS are called blocks because they are asso- ciated with the blocks of the flow chart of the model. They can be thought of as subroutines in a FORTRAN program, and model conceptualiza- tion usually takes place at the block diagram level. During the course of the simulation, transactions move from block to block as the execution of the GPSS model takes place. Thus, the transactions become the dynamic entities in a GPSS model. Time passes as various events occur in real systems, and such events also occur in the simulation model against a back-drop of time. This is accomplished through a simulated clock, automatically maintained by the GPSS processor. The time unit is implicitly determined by the analyst, and transactions are moved according to the occurrence of an event in simulated time. The processor regards each transaction as being on one of several "chains." When the transaction is active in the 92 model, it is on the Current Events Chain. When it is inactive, the transaction is on the Future Events Chain. At the start of a simulation, there are no transactions in the model, but as the simulation proceeds transactions are brought into the model through the GENERATE block at specific times, according to the logical requirements of the model. This model also utilizes a SPLIT block to introduce additional transactions into the model. Transactions are removed from the model by means of the TERMINATE block. Other trans- action-oriented blocks are: l) the ADVANCE block, which causes a time delay in the transaction's movement, and 2) the ASSIGN and PRIORITY blocks, which influence transaction attributes. Each transaction has a set of parameters (0 to 100) which represent numeric properties asso- ciated with that transaction. For example, a loss or labor value associated with an area of paddy, and represented by a transaction, would be carried with that transaction if the respective values were assigned to particular transaction parameters for that transaction. This is accomplished by means of the ASSIGN block, which can operate in replace- ment, decrement, or increment modes. Transaction flow is modified within this model by means of the TRANSFER block. Normally, the flow is sequential but a nonsequential movement can be specified with the TRANSFER block. Two other blocks used to modify transaction flow in this model are the GATE and TEST blocks. Flow is based upon logical conditions to be tested at these blocks. In the refusal mode transactions wait until the logical condition is satis- fied. The test associated with the GATE block is the condition of the LOGIC switch, whether it is on or off. 93 Facilities are permanent entities representing a single server, i.e., a service. In this model there are two facilities: a cutting facility and a threshing facility. Representation of a facility requires the use of two blocks--a SEIZE block and a RELEASE block. The SEIZE block tests the facilitie's status: it refuses entry if the facility is occupied and permits entry if the facility is free. The RELEASE block releases the facility, making it available to another transaction. A queue is another permanent entity used to gather statistics where involuntary waiting occurs within a model. Two blocks are used to accomplish this--the QUEUE and DEPART blocks. The QUEUE block initiates the joining of a queue by a transaction, and the DEPART block identifies when the transaction leaves that queue. Storage of numeric values is handled by means of the SAVEVALUE block. These blocks are used to retain values of variables for later use in the simulation program or for information to be retained for printing. With respect to statistical outputs, the facility and queue entities automatically do this. But a third block is also used--the TABULATE block. This block causes an entry to be made into a table. The nature of the tabulation depends upon the TABLE definition statement associated with the TABULATE block. But the result is a frequency distri- bution in tabular form of the specified attribute. Three types of nonblock entities are used in this model: arithmetic VARIABLES, FUNCTIONS, and random number generators. A variable may be arithmetic or boolean but only the arithmetic is used. FUNCTIONS can be discrete or continuous, both of which are employed. Again, the reader is referred to Appendix D for a more detailed discussion of GPSS. 94 6.3 Ani-ani Loss and Labor Simulation Model: An Overview 6.3.1 Modeling Objectives The purpose of this model is to provide an evaluative base for analyzing rice losses and labor requirement, given the current state of the arts, and the effect of technological changes in cutting and/or threshing upon these losses and upon the labor requirement. The current state of the art, as employed in the Yogyakarta area, was cutting with the ani-ani, a hand-palmed knife, and threshing by foot-treading. A change was occurring in that the sickle was replacing foot-treading. Consequently, the first objective of the model was to simulate the ani-ani and foot-treading system. This established a set of initial con- ditions against which alternative techniques could be evaluated. Next, the model evaluated the sickle-beating system, since this was a new harvesting practice for this area. Thus, the stage was set to introduce component changes, e.g., different threshers, into these two systems and evaluate their effectiveness and potential problems that might develop. This study is concerned with only the first stages of the post- production period so another modeling objective was the model should be expandable to include drying and storage at the farm level. An eventual result would then be a comprehensive model for the postproduction period until the paddy leaves the farm gate and enters the marketing channels. 6.3.2 Generalized Concept The flow chart in Figure 6.1 generalizes the modeling concept. An area of paddy ripens and is ready for harvest and enters the first processing stage, cutting, at which time the labor required and the losses are determined. If this is not the last processing activity to occur, the paddy proceeds to the next stage, threshing in this model. 95 Another model might add a transportation segment before threshing. And so the process is repeated for each activity being modeled until all processing is completed. 6.3.3 Statement of the ModelingpProblem An area of paddy matures during the peak harvesting period and is ready to be cut at the beginning of each day. If a harvesting crew is available, cutting commences at seven or eight in the morning and con- tinues for eight hours, or until the ripened area has been harvested. The crop is then transported to the threshing area, but transportation time is ignored because the fields are close to the threshing site. The threshing operation follows and is not constrained by time, but from a practical standpoint it would not exceed five hours. The farmer survey indicated that the harvest season extended from the last of February to the beginning of June, and the time of greatest harvest demand was a 40-day period from the end of March to the first of June. During this period, nearly 70% of the total area ripened. It is, therefore, this time span that constitutes the peak season to be modeled. The farmer survey further indicated that 15% of the paddy area experienced a delay in the arrival of the harvest crew. This delay varied from 2 to 10 days from the time that the farmer wanted to start harvesting. The model simulates this environment and determines the effect of different technological combinations upon rice losses and labor require- ments during this peak harvesting period. 96 ENTER A PROCESSING STAGE COMPUTE LABOR INPUT COMPUTE RICE LOSS LAST N0 . STAGE? . YES TRANSACTION LEAVES MODEL Figure 6.1. A generalized flow chart. 97 in a theoretical capacity of 103,323 m2 for a crew of 12. 3. But 70% of the sampled area matured during the 40-day peak period of the harvest season. Thus, 72,261 m2 was concentrated during this time. 4. Maturity, however, was reached on only 23 of the 40 days. Consequently, an average 3,142 m2 ripened for any given day that paddy reached maturity. 5. So to simulate this situation four transactions, each implicitly representing 800 m2, were brought into the model each day maturity was reached according to the following table: Table 6.1. Interarrival time between days of crop maturity. Days Between Maturity Area Matured (%) 1 65.1 7.1 6.5 11.4 2.1 0101-wa 7.8 6.3.4 Model Discussion This section examines the model's logic by following the trans- action flow. The accompanying computer program form of the model 98 6.4 Ani-ani Labor Model The model discussion is divided into two parts: 1) the ani-ani labor model, and 2) the ani-ani rice loss model. To repeat, the model can also be viewed as a completely integrated unit. But discussion is simplified under a partioned approach, and there may be times when the model would be used to analyze labor or loss data separately. 6.4.1 Modeling Approach This model is designed for a 40-day simulation to represent the period of peak harvest demand. But the length of the simulation can be varied depending upon the season being modeled. The model consists of a major segment, and six supporting segments. In the major segment (model segment 1), matured paddy enters the model and moves through the cutting and threshing stages. The first supporting segment (model segment 2) controls the inter- arrival time, or maturity rate, of paddy. The Logic Switch, area, com- municates to the main program that paddy has matured, and this brings matured paddy into the cutting queue. The arrival of the cutting crew and the beginning of cutting is controlled in model segment 3. Closely allied with this is the availability of a cutting crew. This condition is simu- lated in model segment 4 with the use of a SAVEVALUE block, which sig- nals the availability or nonavailability of a harvest crew to model segment 3. If this condition is fulfilled, model segment 3 then communi- cates the beginning of cutting to the major segment through two Logic Switches, CUTl and CUT2. Model segment 5 stops cutting and initiates threshing. Again communication is maintained with the major segment through Logic Switches, in this case Logic Switch THR. The remaining model segments perform 99 housekeeping functions: 1) model segment 6 updates SAVEVALUE locations at the end of each daily run, and 2) model segment 7 empties the queue at the end of the simulation and stops the run. This is necessary, especially if a large queue should develop, so that queue statistics are calculated without a downward bias. In GPSS average waiting time in a queue is calculated by dividing the total simulated time by the num- ber of entries in the queue, including those that have not departed. Consequently, the holding time is considered for those entries still in the queue resulting in a downward bias of this statistic. The final emptying of the queue avoids this problem. An important feature of the model is its ability to interface the simulated maturity rate with the observed crop maturity data. Since maturity rate is based upon the number of transactions introduced, each representing an implicit area as defined by the programmer, it is necessary to describe this procedure: 1. The area introduced was based upon the average cutting rate for an ani-ani crew of 12, since this was the average crew size observed during the 90-day sampling period. 2. The average cutting rate was 837 man-hours per hectare; this equates to a crew rate of 1147 m2 per eight hour day (equation 6.5.1). 1 ha m2 men crew-h _ m2 '837'man-fi x 10’000 FE'X 12 crew x 8 day - 1147 aay' (6.5.1) Assuming a 90 day harvest season, this resulted 100 (Table 6.2) is the basis for discussion. The implicit time unit used to capture the dynamics of the phenomenon is one minute. The discussion will revolve around the basic model (blocks 1-49), which will be viewed in four sections: 1. The interarrival sequence of matured paddy ready for harvesting (blocks 1 through 7 and model segment 2); 2. Paddy waiting to be cut (blocks 8 through 17); 3. Movement of paddy through the cutting process (blocks 18 through 42, and model segments 3 and 4); and 4. Movement of paddy through the threshing process (blocks 43 through 49 and model segment 5). The value of this model is strongly dependent upon how effectively the observed rate of maturity is interfaced with the model components. A transaction, the area transaction, is introduced into the model at the first GENERATE block (block 1). It opens the Logic Switch, area, and proceeds through the gate (block 4), which this Logic Switch con- trolls. The area transaction is then split into four identical ones (block 5), these proceed to the waiting queue (block 8) for the cutting crew. The area transaction closes the gate (block 6) by setting the controlling logic switch to a closed position and then loops back to the area gate (block 4), waiting for the gate to open. The opening of this gate is controlled in model segment 2 when the interarrival timer transaction opens the area Logic Switch, based upon a frequency distri- bution of the observed data. Again, the close association between the Logic Switches and Gates forms the communication linkage between various parts of the model that controls transaction movement. 101 Table 6.2 GPSS program for basic ani-ani labor model. GPSS/360 LIVE. CAPDI 3.000 40000 50000 6.000 7.000 30000 °0000 IOOOCO 110000 12.300 13.000 14.000 156000 160000 176000 IPoJOO 196030 206000 216000 220000 23.000 29.000 25.005 260000 27603: 255000 29.000 30.000 31.000 320000 33.000 39.000 356000 566000 370000 336000 336000 400000 416000 420000 43.000 44.000 456000 .60000 470000 48.000 49.000 50.000 5I6030 520000 550000 540000 55.000 56.000. 57.000 58.000 PODIFICAIION LEVEL 4 HTS IOOEL IANIOQI 21:29:10 09‘22°80 ILOC OPEIATION AeBoCeDoEeFeG COHHENTS SI'ULATE o 2990 . . oeoooeeooeeoooeeooooooeoooeottooooeooooooetooecoone.oeoeeeeoooeeoeaeeeoe I I I I I BASIC ANI‘ANI LABOR NODEL I I I . 1111: 01111: 1 11111011: . I I IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII I I I I .ooooocoocoooooccono “6LE CF OffiuT‘ous occoonoocooooocuucnloc I 12C I GPSS ENTITY INTERPRETATION I20 I--.-0--0¢--¢--ooooo-Oc-GOCOOObb-.-noun-OOO-coo-nncoone-noonuacout---125 I ISO I TRANSACTIONS 190 I NDDEL SEGPENT I AN AREA OF PADDY 153 I PI: CUTTING LABOR VALUES 160 I P2: THRESHING LABOR VALUES ITO I P3: PA)DY AREA ISO I NOBEL SEGHENT 2 TINER FOR NATURITY RATE I90 I NODEL SEG'ENT 3 CUTTING TIHER 200 I NODEL SEGMENT 4 HARVEST CREU DELAY TINEP 210 I HODEL SEGMENT 5 THRESHING TIHER 220 I NOBEL SEGPEAT G UPDATES SAVEVALUES 230 I NODEL SEGHENT 7 SIPULATION TIMER 240 I 250 I LOGIC SiITCNES 260 I AREA CONTROLS RIPENED PADDY 255 I CUTI CONTROLS TRANSACTION FLON 270 I INTO CUTTING FACILITY AT END 263 I OF EACH DAY. 290 I CUT2 CDTROLS TRANSACTION FLOb 300 I THROUGH QUEUE dAITo 310 I THR CONTROLS TRANSACTION FLOG 320 I INTO THRESHING GUEUE. 330 I FACILITIES 340 I CUT CUTTING CREH 350 I THR THRESHING CREU 350 ‘ . 370 ------------~------ nooct Loerc -----o~--------------------356 - 390 I NATURED PADDY ENTERS NODEL THRU SPLIT BLOCK (BLOCK 5). 400 I CUTTING CREN ARRIVES EACH HORNING AT 7:00AM. IF ACRE! QIG I IS AVAILABLE. CUTTING ENDS P HOURS LATER UHEN 420 I THIESHING STARTS-IF THE LAST TRANSACTION TO EE CUT CANNOTASO I BE COHPLETED HHEN CUTTING STOPS. IT IS DIVIVED SO THAT 400 I THE LNCUT AREA IS HARVESTED THE NEXT DAY. 450 e 460 I THIS HODEL SIHULATES A 40-0AY HARVESTING PERIOD. UHICH 470 I REPRESENTS THE PERIOD OF PEAK HARVEST BEHANDo 48C 0 490 102 Table 6.2 (Cont1nued) GPSS/360 VERSION I NODIFICATION LEVEL 4 NTS NODEL (ANIO4I 2I3293I0 09-22-00 LINER CARD. BLOCK. ILOC OPERATION AOBICIDOEIFOG CONHENTS 59.000 59 . .u............... "R 1‘.LE “F‘."1o~s “coco-acoocuosooo GOOOOO 60 I 3005 GIoOOO GI AREAU VARIABLE RSRIPE'RSAREAC SOIO I20000 ‘2 CUT FVARIABLE ‘NSAREA-RGUNCUTI’NSAREAIPI NEU CUTTING TIHE 3020 63.000 ‘3 CUTAR VARIABLE RSAREA'ASUNCUT PARTIALLY CUT AREA 3030 04.000 G4 CUTHA VARIABLE (NSDUAITIXSAREAI'ISAREAU AREA CUT PER SEASON 3035 050000 ‘5 CUTLR FVARIABLE PIIGOIIOOOOIRSAREA/XSCRENI CREH CUTING TINE 3040 660000 66 I IN CREN'HIN 5050 670000 G7 I DINENSIONS 3 SO60 $0.000 GB I NAN‘H/HAININ-HAINN-SONISONICREN’NAN 8 CREN’NIN 3070 690000 69 I 3060 700000 70 CUTNH VARIABLE ASAREAC’XSCREUI AREA CUT PER NAN-H 3090 7I0000 TI CUTDF FVARIABLE RSUNCUT’RSAREAIPI OFFSPRING CUTTING TINE 4000 72.000 72 DIFF VARIABLE RSEXIT'XSENTEP CUTTING TINE LEFT AFTER CUTTING 40IO 73.000 73 I STOPS. 4020 74.000 74 EXIT VARIABLE CIIPI TIRE CUTTING CREU FINISHES 4030 756000 75 I 406I 760000 76 RIPE VARIAELE RSXACTSIXSAREA HARVEST DENANDED EACH IAT 4070 77.000 77 SYSTH VARIAELE ASCUTTHIXSTHRTN AGSYSTN= DAILY SYSTEN TINE 4000 706000 7B THR FVARIABLE (RSAREA-RSUNCUTI’RSAREAIP2 NEN THRESHING TINE 4090 79.000 79 THRLR FVARIABLE P2IGOII00OOIRSAREA’AGCREdz THRESHING CREU TIHE 4095 60.000 60 I NHEN CUTTING STOPS 4096 010000 OI I DIPENSIONS: CREU'NIN TSEE CUTLR) 4097 02.000 02 THRDF FVARIABLE RGUNCUTIISAREAIPz OFFSPRING THRESHING TINE 4I00 83.000 BS UNCUT F".I‘B.E RSDIFF/PIIRSAREA UNCJT AREA IN FACILITY CUT 4IIO 04.000 .4 I 4I20 85.000 BS I"“"-'°""""' SAVEVALUE DEFINITIONS "°"°"°""‘°"'4I30 060000 .6 I 4I40 81.000 B7 I XSAREA 3 INPLICIT VALUE OF AACTS ENTERED AT FIRST SPLIT BLDCK4ISC 866000 00 I XSAREAC= CJT AREA (DAILY) I SUNNATIDN OF P3 4160 09.000 B9 I ISAREAU= UNCUT AREA (DAILY) 4170 90.000 ’0 I ASCOUNTz CURRENT BLOCK COUNT AT BLOCK LOCATION COUNT 4IBO 91.000 OI I NSCREII= HARVEST CREU SIZE 4190 92.000 ’2 I RSCREN2= THRESHING CREU 4I95 900000 ’3 I RSCJT 3 ADJUSTED THRESHING TIHE 4200 ’40000 ’4 I NSCUTNH: AREA CUT PER NAN'H EACH DAY 4205 950000 ’5 I NSCUTTN= TOTAL CREH CUTTING TIHE (DAILY) 4210 960000 ,6 I NSDAILY: 24 HRS (I440 NINI 4220 97.000 97 I ASDELAY= INDICATES IF THRESHING CREU IS AVAILABLE 4230 90.000 ’0 I RSDIFF S CUTTING TINE LEFT AFTER CUTTING STOPS 4240 990000 99 I RSENTER: TINE THRESHING XACT STOPS CUTTING AND STARTS 4250 1000000 100 I THRESHING: B HR AFTER CUTTING STARTS 4260 IOIoOOO IOI I RSERIT 3 TINF LST IACT IN CUTTIG FACILITY LEAVES 4270 IOZOOOO I02 I ISIAT 3 INTERARRIVAL TINE FDR CURRENT DAY 4200 103.000 I03 I ISRIPE = PADDY READY TO BE HARVESTED (HARVEST DENAND) 4290 I046000 I04 I XSSTOP = END OF RUN (TIHE) UHEN OUAIT IS EHPTIED 4295 105.000 I05 I XSSYSTN: TOTAL SYSTEN TIHE (DAILY BASISI 4300 IOGOBOO I06 I ASTHRLRz CREU THRESHING TIRE FOR ONE DAY 4310 1070000 I07 I XSTHRTN= CREE THRESHING TINE (DAILY, 4320 IOBoBOO 106 I RSTIREI= cUTTING TINE FOR SEASON 4330 IDS-000 I09 I XSTINE2= THRESHING TIRE FOR SEASON 4340 IIBoOOO IIO I RSTIHE3= CUTTING I THRESHING TINF FOR SEASON 4355 III-000 III I ISUNCUT= UNCUT AREA IN FACILITY CUT EACH DAY CUTTING STOPS 4355 112.800 I12 I ISUAIT 3 LENGTH OF CREU DELAY 4360 110.000 II: I NGRACTS: NUNBER 0F HARVEST AACTS TO ENTER HARVEST SYSTEN 4370 II4oBOO I14 I 4380 Table 6.2 (Continued) 6'55’360 LINE. CARD. II50000 II5 AI60000 II6 I170000 II7 AIOODGG I38 II90000 II9 I20¢000 I20 32I0000 I2I 3220600 I22 3230000 I23 I240000 124 I250000 I25 I260000 I26 3270000 I27 1280000 I28 1290000 I29 I300000 I30 131.000 I31 I32o000 I32 I330000 133 1340000 134 I350000 135 1360000 136 1370000 137 1380000 I38 I39o000 I39 I400000 I40 A4Io000 I4I 3420000 142 143.000 143 1440000 144 145.000 I45 146.000 146 I470000 I47 1480000 I48 1490000 I49 I500000 I50 I5I0000 I51 I520000 I52 1530000 I53 I540000 I54 I550000 155 1560000 I56 I579000 I57 I580000 I58 1590000 159 I600000 I60 I610000 I61 1620000 162 1630000 I63 1640000 164 1650000 I65 I660000 I66 I670000 I67 I680000 I68 I69o000 I69 170.000 170 BLOCK. 103 VERSION I NOOIFICAYION LEVEL 4 R78 IOOEL CANI04) 21:29:10 09-22-80 ILOC OPEIAYION AoOoC-DOEOFOG CONIENTS I-------.°"-'--- SAVEVALUE INIIIALIZAYIONS '.---'-°--.-‘-43’0 o 4400 INITIAL NCENIER44O3’R5RIPE432OOINSOAILYII440’N3NAC7504 4410 INIIIAL ISAREAQOOOIltIAYQI44OII$SIOP057600 4420 INIIIAL IICRCUIOI2IXSCRE02912 i 4450 O 'HU‘UtDUINO‘ . Q.--..--------..--..-- . FUNCYION DEFINIYIONS ----------.--4460 I 4470 ANILR FUNCIIOV RN2OCII ANI CUYIING LABOR 0 NAN-NINA 4480 .9610’00594660’014907IO’0299'760’04094610’0569006U’O77909IO 4490 09190960109690!010I0979010601101115 4600 a 45I0 DELAY FUNCIIOV RN2906 CUYYING CREU DELAYED 4520 oI2802880/o352057601.51507200’054798640/070901008011OI4400 4530 I 4540 FYYLR FUNCYION RN2OC9 FOOI’YREAOING LABOR I HAN’H/HA 4550 09220/002992501oII99280/026903101.6390340I08399370’o9399400 4560 09890430/1046I 4570 I 4580 IAY FUNCYION RN2ODG CROP RAIURIIY: INTERARRIVAL YINE 4590 065014401072102880/078604320,090.5760’092107200’I08640 4600 I 4610 .u-..-.-.---------- "BLE DEF3~313°~S 0------oooooocououocoqszo I 4630 ANI YAGLE PIO6I0050II2 NISIDGRAN: ANI LABOR 4640 FOOI YABLE P20220030010 HISIOGRAR: FOOY'IREADING LABOR 4660 IAI YAOLE RSIAIOI44OOI44098 HISIOGRAN: INIERARRIVAL IINE 4670 YHRYN YAOLE RSIHR7H0300309I4 HISIOGRAH: DAILY IHRESHING 7IHE 4675 CUYIH IAGLf NSCUIYH060Q6OOI4 HISTOGRAN: DAILY CUTYING IIHE 4676 NAIIZ OTAOLE UAIIO720OI440026 HISIOGRAH: OUAIT RESIDENCE IINE 4680 - qsao Ono-oooouococooonno "ODEL SE‘HENT 1: “"VESI SYSIER -0-....-.70° o 4710_ GENEIAIE OOIOIOIO5 AREA AACICIRANSACYIONI AT IIRE I 4720 LOGIC S AREA OPEN AREA GAIE 4730 UAII ASSIGN 3OASAREA SEI P3: AREA YD 8E HARVESIED 4740 GAIE LS AREA IS AREA GATE OPEN 4750 SPLIT RSRACYSOOUAII SPLIY RACIS GO TO CUTING QUEUE 4760 LOGIC R AREA CLOSE AREA GAIE 477C YRANSFER OUAII AREA RACI REPOSIIIONED 4780 I CIRCULAIES DURING SIHULAIION 4790 OUAIY OUEUE UAII IHIS OUEUE HOLDS NACYS VAIIING 4800 I FDR FACILIIY CUT. 4810 ASSIGN IOFNSANILR SEI ’13 ANI LABOR CHAN'H/HAI 4820 YABULAYE ANI DISIRIOUIION: ANI LABOR 4830 ASSIGN IOVGCUTLR SCI '13 CREE CUIYING IINE ‘HINI 4840 ASSIGN 20'NSEIYLR SE7 P2: FODI'IREADING LABOR 4850 YAOULAYE FOOY DISYRIOUIION: FOOT’IREAD LABOR 4860 COUNI ASSIGN 2OVSIHRLR SET P2: CREI YHRESHING VINE (RINI4870 GATE LS CUI? GATE PREVENIS IACIS FROH LEAVING 4880 I QUEUE UNIIL SEIZE BLOCK FREE. 4890 LOGIC R CUT2 CLOSE GAYE 4900 DEPART UAIY LEAVE OUEUE 4910 OCUY SEIIE CUT SEIZE CUTTING CREU 4930 GAIL LS CUII OPENS UHEN CUIYING STARTS 4940 SAVEVALUE ERIYOVSERII YIHE CUTYING CREU FINISHES 4960 Table 6.2 5.55/350 LINE. CARD. 1710000 171 1720300 172 173.000 173 1700.00 170 175.000 175 1760000 176 177.000 177 ITO-000 175 179.000 179 1800000 100 101.000 101 1020000 132 103.000 183 185.000 180 185.000 185 1860000 106 107.000 107 108.000 180 109.000 109 190.000 190 1910000 191 192.000 192 193.000 193 195.000 199 195.000 195, 196.000 196 1570000 197 198.000 198 1990000 199 200.000 200 201.000 201 2020000 202 203.000 203 200.000 200 205.000 205 2050000 205 207.000 207 208.000 208 209.000 209 210.000 210 2110000 211 212.000 212 213.000 213 2150000 21. 215.000 215 2150000 215 2170000 217 218.000 210 219.000 219 220.000 220 221.000 221 2220000 222 2230000 223 2290000 220 225.000 225 2250000 225 104 (Cont1nued) VERSION 1 NODIFICATION LEVEL 5 NTS NOOEL IANIOQI 21:29:10 09‘22-80 BLOCK. OLDC OPERATION AQBOCCDOEOFOS CORRENTS 21 TEST 0 ISEIITQISEITEROSTDI IS CUTTING IN PROGRESS UHEN A970 0 CUTTING STOPS 0900 22 SAVEVALJE DIFFQVSDIFF RERAININC CUTTING TIRE UHEN A990 ' CUTTING STOPS. 5000 23 SAVEVALUE UNCUTOVSUNCUT AAUNCUT=UNCUT AREA VHEN CUTTING 5010 . STOPS. 5020 2A LOGIC R CUT1 LAST RACT CLOSES CUT1 DATE 5030 25 PRIORITY 2 PRIORITY FDR SEIlE BLOCK 5000 25 SPLIT IOOEF OFFSPRING REPRESENTS UNCUT AREA 5050 0 TO BE CUT NEXT DAY. 5060 27 ASSIGN IOVSCUT NEU CUTTINC TIRE 5070 2D ASSISN 29VSTHR NEU THRESHINC TIRE 5050 29 ASSISN 39VSCUTAR P3: AREA TO 3E CUT N00 5090 30 TRANSFER QSTOI PARENT IACT DOES T0 FACILITY CUT 5100 31 OFF ASSIEN 30XSUNCUT P3: AREA TO BE CUT TDHORRDU 5110 32 ASSIGN IOVSCUTDF OFFSPRINC CUTTING TIRE 5120 33 ASSIGN 20VSTHRDE OFFSPRING THRESHING TINE 5130 3A LOGIC R CUT2 CLOSE CATE CJT2 51.0 35 TRANSFE‘ oOCUT 60 TO BLOCK OCUT 5150 36 STDI SAVEVALJE CUTTNOQPI ISCUTTN= TOTAL CUTTING TIRE TO 5170 0 ENTER CUTTING FACILITY. 5180 37 SAVEVALJE AREAC99P3 AREA CUT DAILY 5190 30 LOGIC S CUT2 OPEN DATE CUT2 5200 39 ADVANCE P1 CREI CUTS PADDY 5210 AD RELEASE CUT CUTTINS FINISNED 5220 AI OTHR PRICiITV 2 NEU PRIORITY LEVEL CIVES 5230 0 PREFERENCE TO THRESNINC 52Q0 02 DATE LS THR GATE OPENS UdEN THRESHINC STARTS 5250 03 GUEUE THRO SACRED STALK PADDY IAITS 5260 AA SEIlE THR SEIlE THRESHING CREU 5270 05 DEPART TNRO LEAVE THRESHINC QUEUE 528W AS ADVANCE P2 PADDY THRESHED 5290 .7 RELEASE THR THRESHING FIVISHED 5300 QB SAVEVALUE THRTNOOP2 CREI TNRESHINO TIRE CDAILYI 5310 N9 TERVINATE PADDY LEAVES SYSTER 532C 0 5335 .---.----...-. NOOEL SESNENT 2: INTER ARRIVAL TINE AIATI TIMER 5300 0 5350 50 OENERATE 90291 5360 51 BACK TABULATE IAT INTERARRIVAL TINE 5370 52 ADVALCE XSIAT HOLD FOR NEXT IAT 5380 53 SAVEVALJE IATOFNSIAT XSIAT= NEAT INTRARRIVAL TINE 5390 5. SAVEVALJE SIPEOQVSRIPE NARVEST DENANDED EACH IAT 5000 55 LOGIC S AREA OPEN AREA GATE (SEGMENT 1) 5A10 55 TRANSFER QBACK IAT TINER CIRCULATES DURING 51' 5520 0 5&30 0------------ NODEL SECRENT 3: CUTTINS TINER ----------'---5AAO - 5450 57 GENERATE 90391 5A60 5D CTO5 TEST E X3DELAY000HOLD IF K‘DELAY=09CREU IS AVAILABLE 5070 59 LOGIC S CUTI OPEN LS CUTI 5080 50 LOSIC S CUT2 OPEN CUT2 GATE 5§90 ‘1 ”OLD ADVANCE RSDAILY IACT NELD 29 HOURS 5500 ‘2 SAVEVALUE ENTER’QRSDAILY UPDATE TD NEXT DAYS ENTRY 5510 ‘3 SAVEVALJE THRTN90 SET TO zERD . 5520 A. SAVEVALUE CUTTNOD SET TO IERD 5530 291.000 Table 6.2 09551360 LINE. CARD. 227.000 227 228.000 228 229.000 229 230.000 230 231.000 231 232.000 232 233.000 233 239.000 239 -235.000 235 236.000 236 237.000 237 230.000 230 239.000 239 290.000 290 291.000 291 292.000 292 293.000 293 299.000 299 295.000 295 296.000 296 297.000 297 298.000 298 299.000 299 250.000 250 251.000 251 252.000 252 253.000 253 259.000 259 255.000 255 256.000 256 257.000 257 258.000 258 259.000 259 260.000 260 261.000 261 262.000 262 263.000 263 269.000 269 265.000 265 266.000 266 267.000 267 268.000 258 269.000 269 270.000 270 271.000 271 272.000 272 273.000 273 279.000 279 275.000 275 276.000 276 277.000 277 270.000 275 279.000 279 200.000 250 251.000 281 252.000 252 283.000 253 209.000 209 285.000 255 256.000 256 287.000 257 288.000 250 209.000 229 290.000 290 291 (Cont1nued) VERSION 1 RODIFICATION LEVEL 9 OLOCRI .LOC 65 AG TRANSFER I . --------------- I 67 . GENERATE I 68 GTOO ADVANCE 69 TRANSFER 70 SAVEVALJE 71 SAVEVALUE 72 AOVMCI 73 SAVEVALUE 79 TRANSFER I Q ------------ ---- I 75 GENERATE 76 GTO9 LOCI: R I 77 LOGIC S 78 PRIORITY 79 BUFFER I 00 LOGIC R 01 PRIO'ITV 82 ADVANCE 03 TRANSFER I .------------ I 89 GENERATE 85 GTO9 ADVANCE 86 SAVEVALJE 07 SAVEVALJF I 88 SAVEVALJE O9 SAVEVALUE 90 TEST NE 91 TABULATE 92 GT010 TEST NE 93 TABULATE 99 GT011 SAVEVALJE 95 SAVEVALJE 96 SAVEVALJE 97 SAVEVALJE 98 TRANSFER I Q --------- ---- I I 99 GENERATE 100 ADVANCE 101 G707 LOGIC S 102 TEST E 103 LOGIC R 109 SAVEVALJE 105 TER'INATE 106 GTOG BUFFER 107 TRANSFER I Q ------------- ---- I START END 105 RTS OPERATION AoBoCoOoEoFoG SAVEVALUE RNEACOD .0705 0.9101 ISOAILY .BSQQGTOB DELAYOI UAITQFNSDELAV IGUAIT OELAV00 oGTOO 0.90301 CUTI THR 2 THR 0 RSOAILV OGTOQ RODEL SEGRENT 6: 0.01 ISOAILV AREAUQVSAREAU RIPE018AREAU SVSTPQVASYSTR CUTRHQVSCUTRH ISCUTTNQDQGTDIO CUTTR XSTNRTNQDQGTOII THRTP TIREIOOASCUTTR TIHE2OQISTHRTH TINE3O.!SSVSTH COUNTOUSCOUNT .GTO9 NODEL SEGRENT 7: 0020101 RASTOP CUT2 USCOUNTOOOGTOG CUT2 CUTNAQVSCUTHA 1 .6707 1 ROOEL (AN1091 RODEL SEGRENT 9: RODEL SEGIENT 5: CONTROL CARDS 21:29:10 09-22.80 CONNENTS SET TO lERD 5590 LOOP FOR DURATION OF SINULATION 5550 5551 NARVEST CREU DELAY "0'5552 5553 PRIORITY 1: NEU RGDELAY BEFORE 5559 TEST BY CUTTING TINER 5555 CREU DELAY CHECKED EACN DAY 5556 TRANSFER IN STATISTICAL NODE 5557 RSDELAY=1 3 CREU NOT AVAILABLE 5558 5559 LENGTH OF CREU DELAY 5560 RSDELAY=0 3 CREG AVAILABLE 5561 5562 5565 THRESHING TIRER --’°."5570 5580 AFTER 8 HRS THR TIMER ENTERS 5590 LS CUTI CLOSES GATE IF ALL RACTS 5600 ARE CUT BEFORE THR TINER ARRIVES 5610 LS TNR OPENS GATE 5620 RE'START SCAN OF CEC 563D NOVE RACTS TD THRESHING CREU 569C RACTS NOVEO THRU TNR GATE 5650 LS TNR CLOSES THR GATE 5650 ORIGINAL PRIORITY RESTORED 5670 TNRESHING TIRER HELD 29 HOURS 568D LOOP DURING SINULATION 569C 5700 UPDATE SAVEVALUES -"---°...5710 5720 5730 UPDATE DAILY 579D IGAREAU=UNCUT AREA AT DAYS END 575C RENAINING HARVEST DENAND AT 5760 END OF DAY. 5770 RSSYSTN= SYSTEN TIRE ADAILYI 5780 RGCUTNH= AREA CUT PER NAN-H 5790 IF RSCUTTN=00 SKIP TABULATE BLK 5792 DAILY CUTTING TIRE 5793 IF RSTHRTN30. SKIP TABULATE BLOCK5795 DAILY THRESHING TINE 5796 TOTAL CUTTING LAOOR (DEA D59 5800 TOTAL THRESHING TINE (SEASON) 5810 TOTAL SYSTEN LABOR (SEASON) 5820 5830 5850 5860 ENPTY DUEUE NAIT "'°'--’.'5870 AND END SINULATION. 5871 5880 5890 STOP AFTER 90 DAYS 5895 OPEN GATE VITH LSSCUT2 5900 IF BLOCK COURT NOT 3 OQBUFFER 5910 CLOSE GATE VITN LSSCUT2 592D AREA CUT PER SEASON 5925 SINULATION ENDS 5930 CLEAR OUAIT 5990 5950 6080 ------------- -------- -- - o‘ D 9 o 6100 START FOR SEASON 1 106 The use of the SPLIT block to introduce transactions into the model differs from the more direct method of using the GENERATE block to enter transactions. This is because a multiple number of simultaneous transactions cannot be randomly introduced through the GENERATE block. Another approach would be to let one transaction represent the entire 3200 m2. But this was rejected because GPSS queuing statistics would be meaningless without a uniform transaction size. Each time a trans- action is divided it cannot re-enter the cutting queue and represent an area different from its implied area without distorting the meaning of queue waiting time. The second model section controls the flow of transactions through the cutting queue (blocks 8 through l7) prior to the arrival of the harvest crew. This portion is straight forward: the queue is entered; labor values are assigned to transaction parameters; and transactions then depart the queue one at a time. A distinguishing feature is the use of another gate (block 15) controlled by Logic Switch CUT2. The purpose of this gate is to let only one transaction at a time out of the queue until the SEIZE block (block 18) is available so that all the waiting time is statistically recorded. The cutting timer in model segment 3 opens this gate (block 60). The next series of blocks (18 through 42) perform the cutting process. These blocks handle transactions involving two different situa- tions: l) transactions completely cut in one day, and 2) transactions only partially cut in one day. Closely allied with cutting are model segments 3 and 4. Model segment 3 starts the cutting process and model segment 4 creates a cutting crew delay. When the cutting timer arrives in model segment 3 (block 57) and a crew is available, both cutting 107 gates are opened by Logic Switches CUTl and CUTZ (blocks 59 and 60). As will be seen later, the purpose of the CUTl gate (block l9) is to keep a transaction in the preceeding SEIZE block at the end of the day's simulation. But before these gates can be opened, a test (block 58) is made to determine whether or not a cutting crew is available. Model segment 4 sets the conditions for the test based upon the value in SAVEVALUE DELAY. If SAVEVALUE DELAY is zero, then a crew is available; if SAVEVALUE DELAY is one, a crew is not available. A TRANSFER block (block 69) in the statistical mode is used to determine the frequency of the delay: 85 percent of the time a delay is not in effect, and the transaction circulates between blocks 68 and 69 while the other 15 per- cent of the time a delay is in effect. The delaying transaction is held in the ADVANCE block (block 72) for the time that the delay is in effect, which is controlled by FUNCTION DELAY (block 7l). Once a cutting crew is available, the cutting timer (segment 3) opens the two cutting gates (block l5 and block l9) in the major model and is then held for 24 hours (block 6l) before re-entering the model. If a delay is in effect, the cutting timer circulates this loop (blocks 55 through 66) but by-passes the two logic switches (blocks 59 and 60). A paddy transaction in the main model now moves out of the waiting queue (block 14) to a TEST block (block 2l) where two different con- ditions are distinguished: l) those transactions that will be cut during the current day, and 2) those transactions that will only be partially cut at the time cutting stops. To make this distinction in simulated time, the time when cutting ends (determined at block 20 by V3 EXIT) is compared to the simulated time when threshing begins (determined at block 62 by X3 ENTER). If exit time is less than or equal to the start 108 of threshing, the paddy transaction is routed through the harvesting process (blocks 36 to 42). However, if the transaction will not be completely cut during the current harvesting day, but must be continued the next day, then this paddy transaction must be separated into two parts: l) that area to be currently processed; and 2) that area to be processed the next day. This division is made at the second SPLIT block (block 26). The parent transaction represents the area to be currently processed while the offspring represents the area to be cut the next day. The parent trans- action is routed through blocks 26 through 30, where new cutting and threshing values are determined, and then proceeds normally through blocks 36 to 42, the actual cutting process. The offspring in turn also acquires new values (blocks 31 through 35) and then proceeds back to the SEIZE block (block l8) to await the next day's processing. At this point, the offspring transaction will be held when the gate (block 19) is closed by the threshing timer trans- action (block 77). Otherwise, the transaction would immediately proceed to the cutting facility (block 39), but after cutting has ceased for the day. As it now stands, this transaction will be processed as soon as cutting begins again. Threshing follows cutting and is the next event to occur, con- trolled by the threshing timer in model segment 5. Cutting lasts for eight hours at which time the threshing timer transaction enters the model. After cutting, the paddy transactions waited at the threshing gate (block 42) to enter the threshing queue at the threshing gate (block 42). This is analogous to the cut stalk paddy, sacked in the field, waiting to be transported to the threshing site. If cutting was 109 completed before the threshing timer arrived, then this timer closed the CUTl gate (block 19) at block 76. Next, the threshing timer moves all waiting paddy transactions into the threshing queue by means of the BUFFER block (block 80) and is then held for 24 hours before re- entering the model. Threshing is then completed in routine fashion: the transactions moving out of the threshing queue and through the thresh- ing facility are then terminated (block 49). The remaining model segments, 6 and 7, are devoted to housekeeping functions. Model segment 6 updates the SAVEVALUE locations devoted to the retention of certain values: uncut area, daily system time, thresh- ing time, and seasonal cutting, labor, and total labor requirements. Model segment 7 empties the cutting queue so that the holding time for these transactions are included in the queue, QNAIT, statistics. Finally, the simulation is terminated in this segment (block 102). 6.4.2 Model Statistics Two queues are designated: the cutting queue and the threshing queue. Of these two, the cutting queue is most significant, since this identifies the length of time that the matured paddy waits before being cut. It, therefore, forms the basis for comparing the effectiveness of different cutting techniques and estimating losses before cutting. A greater insight regarding this waiting time is obtained from the QTABLE, NAITZ, which results in a frequency distribution of waiting time for all the transactions entering this queue. In addition to the QTABLE, five other tables are produced: 1) cut- ting labor (man-h/ha), 2) threshing labor (man-h/ha), 3) a distribution indicating the number and length of harvesting crew delays, and 4) two 110 distributions of daily cutting and threshing time. Other tables can be added, or tables deleted, without affecting the operation of the model. Cutting and threshing facility statistics are automatically pro— duced with GPSS indicating the average time each transaction has spent in each facility. But these statistics are not meaningful as a statement of average residence time because not all of the entering transactions were of the same paddy area. So the absolute times were recorded direct- ly with the use of SAVEVALUE blocks, CUTTM (block 36) and THRTM (block 48). Other variables of interest that are printed are: l) uncut area, 2) seasonal cutting labor, 3) seasonal threshing labor, and 5) seasonal labor for the system. 6.4.3 Model Verification The model can only be internally verified. This can be accomplished by interpreting block counts at different points in the model and com- paring these values with the observed data and with internally calcu- lated values. In this regard, a good verification is whether or not the uncut area represented by the block count, after the queue NAIT has been emptied, is the same as the calculated uncut area. Referring to the block counts in Table 6.3, the current content of block 17 is 58 representing 800 m2 each for a total area of 46,400 m2. Adding to this the partially uncut area in block 18 of 564 m2 (from XS UNCUT), the total uncut area should be 46,964 m2. And this is the value in SAVEVALUE AREAU calculated from VARIABLE AREAU. One hundred transactions entered the cutting queue representing 80,000 m2; this compared with the calculated value of 72,258 mzdiscussed in section 6.4. The crew delay was understated for the 40-day simulation, having occurred only twice (block 70) during this run. But the statistical 111 4¢hoh as 4dpoh ommnu oNnc ac. OOOoc u=4t> bauxxzu zu04m oa— O. no so am no .0 no a. no xUO4o on O0 0. no a. no .0 no N. no uxxau :u04m nut—b btn tbhau D¢ux¢ .12 4u ooh- ocoa >4~¢o hnN Luna Na 0 apocx nooon «whzu onon Gen bauz: coo (uxc Noon ua4t> out u:4‘) out u34¢> NcooOh an nnNonuo .cz .n2¢¢b .0! out-uh t¢¢bsu1uh oznbctuucn clan-um amt-4" hzuzczu zu04o 4.h0b plateau 3004c 4450» 0O o. a on N On a. 9 ON ON no a. o as an No o. o hp a on o. a as on on u o a» On .0 N o .h On no N a up On No N - Nb on 40 N o as a. uu¢=u zuc4a 4cho» haw-1:0 x9949 4¢h0h co ON a 9» ch On ON a ON as on ON a 0N «h h» ON a 5N can on mm 0 ON can an ON a nN can on ON a .N can nu ON a nN ecu Nn ON a NN can an on o “N can uccau 80°40 4ch0» bxuccau 10°40 4¢»Oh Nach- wrung nut—h bud) Naucu pnuu U¢u¢¢ .82 acruuuzoz- uua4<>u’¢m 01034435 to munch!“ «natal on O0 00 so do mo .0 no No no eN On an nu on ma on nu Nu «a “1130 x094- 30640 anon each NnN Nu OOOo u34¢ reuhqun4~ba u .zmuxau goo... xuouu uba4omod Neck- 0 . b OOG. OOO. ¢<¢u>¢ on on a a u On On 4m>mm we. pcsou xuopm Nat-b noun [bxxh uaucu usu- b ZOO-906000906 20999006 .- DOC-99009060 uxcau uncau .cz u34ctu>¢n Oprahzou >h~4~u¢u has don no” oo— nan New «on 20040 on On an hm on on cm no Nm 4n 10°4m d F‘NnCflOFUOO pzuxcau 80°40 mbzaou x904m 30°40 u6~h<4u¢ m.o mpnmh 112 transfer mode operates on a random basis and, consequently, requires a large sample size to be accurate. The two delays totaled l0 days; this implied 30 cutting days.So there should have been 29 transactions only partially cut; this is verified by the total block counts (blocks 22-30) using this routing. This assumed that there was always some uncut area waiting to be cut, and examination of the individual runs verified this. Finally, 129 transactions were introduced into the major segment, and they should be accountable: seventy transactions moved through the sys- tem and were terminated (block 49), and another 59 were waiting to be cut when the simulation ended (blocks 17 and 18). 6.4.4 Alternative Modeling Configurations The viability of any model rests to a large extent upon its adapta- bility for evaluating alternative problem solutions as well as accept- ing additional model enrichment. This was a strong motivation for con- structing a GPSS model. Consider, for example, the changes needed to evaluate a sickle-cutting and beating-threshing system with the same sequence of events. The only changes required are: 1. New frequency distributions to replace the ani-ani and foot-treading distributions with sickle and beating data. 2. Different crew sizes for harvesting and threshing. 3. Different table definitions for cutting and threshing labor values and different table definitions for cutting and threshing time. Beyond these slight adjustments, no further changes are necessary. ll3 Given these two basic systems-~the ani-ani and sickle systems-- the effect of different threshing techniques are easily observed by simply changing the threshing crew size and the threshing frequency distribution to correspond to the threshing technique being used. If however the threshing technique changes the sequence of events, then the model can be adjusted to meet those different conditions. For example, the use of a pedal thresher changes the threshing sequence: the paddy is first cut, laid on the ground in bundles, picked up by the threshing crew, and then taken to the pedal thresher in the field. To model this change, a gathering stage can be added between cutting and entering the threshing queue. Furthermore, the model can be enriched by adding a weather model segment to cause a harvesting and/or threshing delay similar to the harvest crew delay. Also, transportation segments can be added, if this is an important factor. The model is expandable to include drying and storage activities by sequencing these events after threshing. The model would then simu- late the entire postproduction sequence until the paddy leaves the farm gate. These latter changes are beyond the scope of the present study, but it is important to recognize the model's potential in this regard. 6.5 Ani-ani Loss Model The modeling approach is quite simple: a transaction representing an area of paddy is generated and moves sequentially through each event when losses are determined and calculated before termination. The model (Table 6.4) consists of only one model segment, but this need not preclude the addition of additional segments to represent different systems. The ani-ani loss model consists of the following 114 Table 6.4 GPSS program for ani-ani loss model. GPSS/360 LINE. CARD. 1.000 2.000 3.000 Q.DOO 5.000 5.000 1.000 8.003 9.000 10.005 10 11.000 II 12.009 12 13.000 13 1A.DDO 1Q A5000O 15 16.000 lb 17.000 17 13.000 18 19.000 19 20.000 20 21.005 21 22.000 22 23.090 23 2Q.ODO 2Q 25.000 25 26.000 26 27.303 27 28.000 28 29.000 29 30.000 30 31.003 OI 320000 32 33.000 33 30.D°° 3‘ 35.000 35 36.000 36 37.000 37 38.000 38 39.050 39 90.300 ‘0 .loooo OI ‘2.0°D Q2 43.003 Q3 Q90000 QQ Q5.DD° .5 Q§.ODO .6 .7.000 .7 QOODDO Q8 O9oooo .9 50.000 50 51.900 51 52.000 52 53.000 53 5‘0000 5. 55.000 55 56.930 56 ODN’UOUMO‘ VERSION 1 BLOCKS HODIFICATION LEVEL Q ITS MODEL CANIOOI 2II2D:06 09-22-80 'LDC OPERATION A.O.C.D.E.F.5 COIRENTS SIRULATE . -- ---.--- .--- ---- --------”.----.--. -.--.--.----- --- ---------- .- -- --- . . 0 LOSSES: ARI'ANI STSTER 0 a . ..---- --- . --.. ----“-------------””----.----.------ ---------------- I .I-..------.--.---- "OOELING ‘PP'O‘C'. , 002 0 PADDY ARRIVES EACH TIRE UNIT AND PROCEEDS THRU A SERIES OF 003 0 ASSIST AND TABULATE BLOCKS VITH LOSS VALJES CARRIED BY O09 0 TRANSACTION FARARETERS AND RECORDED IN THE APPROPRIATE TABLE. 005 0 DELAYEC LCSS IS CDIPUTED BY TVD VARIABLES. RILO AND DELAY. 006 I BASED ON DATA OF DJOJORARTNOII9T9I. DDT . 521 0 IO ------------------ FUNCTION DEFINITIONS -----.-----'-----------------20 I SO 0 DATA TRANSFORMATIONIDATA MULTIPLILD DY 100 Q5 ' TO INTERGIZE DATA. SO I 60 ASHTR FUNCTION RNI.CII ANI-ANI SHATTERED LOSSES 5: D.Ol.19?.5CI.O§1.IDD/.633.ISD/.779.ZDO/.679.2501.525.BODI.954.350I 60 .97.QDDI.96€.Q50II.0.1050 TD . BO AUNCT FUNCTION RN1.CIO ANI-ANI UNCUT LOSSES 9O 0.3/.0‘201Ctlol7.200/.32.30DI.525.4CDI.662.SOD/.792.6DOI.97.IDDDI IOD .992.IZOOII.ZAOO IIO . 120 FTTRD FUNCTION RN1.C13 FOOT- TREADING LOSSES 130 D.Ol.079.501.25¢.10CloQ08.150lo¢9292001.617.2501.683.300[.792.350/ IND .85.!00/o9.¢501.925.500/.9BB.800/1.D.1200 150 c ISO NDAY FUNCTIOV RNI.DT hARVEST DELAY USING ANI-ANI 275 o12.2l.24.6/.50.IOI.67.IQI.75.IBI.9I.22/I.26 276 a 278 -------------------- VARIABLE DEFINITIONS --.---------------------°-283 t 230 CUT VARIABLE PIOF? ANI CUTTING LOSS 3C3 RECVR VARIAELE 0.12 50 PERCENT TNRESHING RECOVERY 310 NET VARIAELE PA-PS NET THRESHINS LOSS 320 RILO FVARIAELE I(SSOIIOIOCTQSIIDOD-XSNDAYIIOI00 KGIHA LOSS 322 DELAY FVAFIAELE ASKILOIIOO/XSYIELD'IOODD PERCENT LOSS: TO CHANGE 32. 0 DIVIDE BY IOU. 325 SYSTN VARIABLE PSOPSOXSDELAY SYSTEH LOSSES 330 0 3RD t------------------ SAVEVALUE DEFINITIONS ------------------3.0 0 39C 0 ASYIELD: AVERAGE YIELD FOR THIS STUDY 453 0 XSIILO = RGIHA LOST DUE TO PADDY IAITING (DELAYED LOSS) 9.0 0 ISNDAY : NuIBER OF DAYS DELAYED .50 . XSDELAY : HARVEST DELAY LOSS A60 0 XSSYSTR : TOTAL SYSTEH LOSS 470 . .83 .------------------ INITIALIZE SAVEVALUES -----------------&90 . 530 Table 6.4 GPSS/360 LINER CARD. 57.000 58.000 59.003 60.000 61.006 62.006 63.000 69.000 65.000 66.0CO 67.000 68.000 69.000 70.000 TI.DOC 72.000 73.300 79.300 75.000 76.00C 77.000 78.000 79.050 80.900 01.000 62.000 83.0C0 89.000 65.000 86.000 87.000 88.000 69.000 q0.000 51.0(0 92.000 93.000 90.000 95.000 96.0(6 97.000 98.000 99.000 100.000 101.093 102.0C0 57 58 59 6O 61 62 65 6. 65 66 67 68 69 TO 71 72 73 7. 75 76 77 7B 79 80 BI 82 83 B. 85 B6 87 BB 89 90 91 92 93 9Q 95 96 97 98 99 100 101 102 115 (Continued) VERSION 1 BLOCK. A-O‘OOQ'U'OUNH O p u h-w.--w.—h-u 0¢I~JO‘U!OIJTV N O 21 22 RODIFICATION LEVEL A PLOC .o-~------o--.----- "BLE ozrluxtxons . ASHTR AUNCT ACUT FTTRO FTRLC FTVET DELAY ASYS . . .D--...--..--. 0...- "O DEL SE6" [”1 Q -- ---------------- "ODSL SEGFENT O OPERATION A.B.C.D.E.F.O INITIAL ISYIELD.Q.IQ TABLE PI.SD.SO.B TABLE P2.IDO.IDO.29 TABLE P3.IDO.IDO.29 TABLE PQ.50.50.8 TABLE P5.50.50.8 TAOLE P6.SD.50.8 TABLE ISDELAY.50.25.0 TABLE 97.I30.IDD.29 GENERATE I ASSISN I.FNSASHTR TABULATE ASHTR ASSIEN 2.FNSAUNCT TABULATE AUNCT ASSIGN S.VSCUT TABULATE ACUT assxsu .cFNSFTTRD TABULATE FTTRD asszen s.vsaccvn TABULATE FTRLC ASSIGN 6.VSNET TABULATE FTNET SAVEVALJE NOAY.FNSNDAY SAVEVALJE KILO.VSKILO SAVEVALJE DELAY.VSDELAT TABULATE DELAY , ASSIGN 7.VSSYSTF TAEULATE asvs TERMINATE GENERATE IOI TERHINATE I START 1 END ITS NODEL AANIDQ) CONTROL CARDS 21:20:06 09-22-80 CONRENTS AVERAGE YIELD PER RECTARE 510 350 ' :70 ANI SHATTERING LOSSES 390 ANI UNCUT LOSSES S90 ANI CJTTING LOSSES .CD FDDT-TREAOINS LOSSES .10 FDOT-TREADING RECOVERY .20 NET FOOD-TRLAOING LOSSES .30 DELAY LOSSES .55 ANI-ANI SYSTEM LOSSES ..D .50 550 1: ANI-ANI SYSTEP----------------5.D 55: PADDY ARRIVES SET PI : SNATTERING LOSS 550 ANI SNATTERING LOSS TABLE 510 SET P2 : UVCUT LOSS 580 ANI UNCUT LOSS TABLE 590 SET P3 = CUTTING LOSS .00 ANI CUTTING LOSS TABLE 610 SET P. : POOT-TREADINC LOSS 620 FOOT-TREADINS LOSS TABLE 630 SET P5 = FOOT-TREAOING RECOVERY 6.? FOOT-TREADINC RECOVERY TABLE 650 SET F6 = NET FOOT-TREACING LOSS 650 FOOT-TREADINC NET LOSS TABLE 670 DAYS DELAYED 692 KB I RA DELAYED LOSS 65. DELAYED LOSSoPERCENT 656 HARVEST DELAY LOSS 697 SET P7 = ANI SYSTEM LOSS 69% FREQUENCY DISTRIBUTIONIANI SYSTER 700 TRANSACTION EAITS ”COEL 37: 550 3: s IIUL"10~ '1.‘.--.--------.—.-890 906 TIHER ENDS SIIULATIDN 915 SHUT-0FF RUN 920 930 693 START FOR SEASON I 116 sequence of events: l) shattering losses; 2) uncut losses; 3) total cut- ting losses; 4) gross threshing losses; 5) threshing recovery; 6) net threshing losses; 7) harvest delay losses; and 8) system losses. Shattering, uncut, and gross threshing losses were determined from the frequency distributions of the data and are not generalized. The harvest delay loss resulted from the harvest delay in the labor model in conjunction with the ani-ani shattering delay reported by Djojomartono (1979). This loss is based upon the number of days that the paddy is delayed from the time the farmer believes harvest should begin. Cutting losses are a summation of shattering and uncut losses, and system losses are a summation of cutting losses, net threshing losses, and harvest delay losses. Net threshing losses reflect the practice of rethreshing several days after harvest. It was assumed that a 50% recovery was made, but data were not collected to support this assumption. The statistic of interest was the average loss at each event, and this was collected through the use of TABULATE blocks. Thus, a frequency table was derived for each loss, which also resulted in the mean and standard deviation of the sampled data. The only validation was the in- ternal consistency of the tabulated values with the actual data, and the results were consistent with these data. Changing the model for different systems simply involves changing the frequency distributions for the appropriate losses. CHAPTER 7 SIMULATION RESULTS AND DISCUSSION The simulation examined ten 40-day peak season periods to evaluate the changes in losses and labor input, and considered the following system configurations: l) the traditional ani-ani system, 2) the more newly introduced sickle system, and 3) changes in both systems through the addition of a power thresher. The analytical approach first examined the ani-ani system to define the status quo. 0f first concern was the size of ani-ani crew to be used, because this influenced both the time ripened paddy must wait before being cut and the total labor requirement. Since the average ani- ani crew was 12 at the sampled locations, this was used; for the sickle the average observed crew size was four. The sickle system was next examined for the purpose of reducing both uncut losses and the time that ripened paddy had to wait. An obvious solution would be to increase the ani-ani crew size. But this raises a critical point: can the crew size be indiscriminantly increased? And if it can be increased, to what limits? The assumption used in this model is that the average ani-ani crew cannot be increased beyond the average observed crew size, but the sickle crew can be increased to the same size as the average ani-ani crew. The rationale is as follows: l. The model is concerned only with the peak period of labor demand and during this time labor is assumed to be fully employed. 117 118 2. Since an average ani-ani crew of 12 was observed in the sampled area, an increase in the average crew size would not be possible without a labor reserve. But since labor is fully employed and there is not a labor surplus, this places an upper limit of l2 for the average ani-ani crew. 3. Also, because labor is limited, the sickle crew cannot be more than 12. Individual crews can increase in size but only at the sacrifice of labor being drawn from other areas causing a harvest delay at these points. Finally, a power thresher was introduced as a new threshing technique for the ani-ani and sickle cutting methods. These data were derived from Djojomartono's work (1979) in Indonesia using the same rice varieties, IR36 and IR38, but in West Java. A modeling concern revolves around the number of samples needed to reach a steady state condition. Since this model will not be used to statistically test the different system alternatives because the beating data was not randomly gathered ,the sample size is not so critical. But to check when a steady state point for the variable of interest, paddy waiting time, has been reached, Emshoff and Sisson (1970) suggest two approaches: 1) when a moving average of the output has stabilized, the steady state point has been reached; and 2) when a sequence of observations in which the output equally divides above and below the mean, the steady state point has probably been reached. The moving average method was used to identify a steady state condition for both the ani-ani and sickle systems (Figure 7.1). This point was entered at the seventh run for Days Delay a. Days Delay Figure 7.l b. 119 Simulation Run Sickle system Simulation Run Ani-ani system steady state Steady state for paddy waiting time with the lZ-man ani-ani and 4-man sickle systems. 120 the ani-ani and at the eighth run for the sickle. So the simulation was not extended beyond ten runs. The remainder of this section discusses the modeling results first from the technical viewpoint and then from a financial perspective. 7.l Traditional Ani-ani System 7.l.l Losses The losses resulting from the simulation are shown in Table 7.l. Ten seasonal runs were made with lOO observations per run. Because the frequency distributions of the losses were specific to the actual field data, and not generalized, these losses closely paralleled the field losses reported in Chapter III. The new component, which is of greatest interest to the modeling procedure, is the delay losses resulting from two factors: the inability of the crew to adequately handle the rate of maturity, and the delay caused by the unavailability of a harvest crew. The inclusion of this delay requires a stochastic approach since the delay is dependent upon three random variates: l) maturity rate, 2) harvest crew delay, and 3) harvest crew rate. Although the absolute value of the loss delay is questionable, the need to evaluate this effect is not. The magnitude of the delay rests upon two sets of data: l) the time that paddy must wait before harvesting, and 2) the shattering loss identi- fied with these time delays. Both sets of data used in this model should be regarded as "guesstimates." The time harvest crew delay was based on farmers recollections during the interview and not upon good quantitative evaluations. Shattering losses after maturity were based upon the work of Djojomartono (l979) in West Java using IR36. 121 Table 7.1 Ani-ani simulation and field data losses. Simulation Field Data Shatter loss 1.42 1.40 Uncut loss 4_._§_9_ gig Cut loss 6.01 5.86 Gross threshing loss 2.44 2.38 Threshing recovery1 l_._2_2 l_._l_9 Net threshing loss 1.22 1.19 Harvest delay loss2 l;§§ _:;__ System loss3 8.61 7.04 1An assumed 50% recovery rate. 2Average delay was 12.8 days. 3System losses were not statistically compared because the beating losses were not random and represented only one site. Djojomartono identified shattering losses associated with both ani- ani harvesting and sickle cutting. Neither loss varied greatly. Ani-ani losses ranged from 1.95% at the time the farmer decided the crop was ready for harvest (time 0) to 2.00% after nine days. Sickle losses ranged from 1.76% to 2.18% during the same time sequence. For the purposes of this model, an estimate was made between Moeljarno's two equations: PHL = 57.7 + 0.16* ND 7.l SHL = 52.14 + 1.37* ND 7.2 where: PHL = ani-ani shattering losses, kg/ha SHL = sickle shattering losses, kg/ha ND = number of days 122 The estimated equation was: LOSS = 55.0 + 0.743* ND 7.3 The model extends equation 7.3 beyond the nine day limit of Djojomartono's data: 15 days for the sickle and 26 days for the ani-ani. Consequently, this assumes a continued linear relationship, which may be incorrect. Furthermore, it might be questioned whether or not it is appropriate to base equation 7.3 on a kilogram loss value related to that of Djojomartono's at time 0. Another approach would have been to relate the pr0portion of loss to yield in Djojomartono's study to the average yield in this study and determine a different value at time 0. But the relationship of loss as a function of yield in this study was very low (r2 = 0.05) so this approach was not pursued. 7.1.2 Labor An average 7.55 hectares (Table 7.2) were introduced into the model for each 40-day simulation. Of this, a 12-man ani-ani crew was able to cut only 2.55 hectares, or 34%. The average paddy transaction had to wait, on average, 12.8 days, given a total harvest crew delay of 17.6 days. The ani-ani cutting rate averaged 830 man-h per hectare (Table 7.3). Table 7.3 also provides the average crew time: 8.0 hours were spent cutting, and 3.2 hours were involved with threshing for a total daily crew time of 11.2 hours. 7.1.3 Summary The conclusion focuses upon the uncut losses associated with the ani—ani method. This loss is excessive and needs to be reduced. So the introduction of the sickle was proposed. With the sickle all the stalks 123 Table 7.2 Ani-ani system: area cut, area uncut, harvest crew delay, and paddy waiting time for 10 simulated seasons. - . Area Area Area Harvest Paddy 5123;:t10n Matared Cut Unc t Crew Delay Waiting Time (m ) (m2) (m ) (days) (dayS) l 80,000 33,036 46,964 10 12.8 2 70,400 25,874 44,526 16 11.4 3 89,600 32,591 57,009 11 12.4 4 83,200 19,976 63,224 22 15.5 5 67,200 34,207 32,993 11 8.2 6 80,000 20,776 59,224 23 14.8 7 54,400 22,271 32,129 22 13.4 8 73,600 17,116 56,484 25 12.9 9 73,600 26,380 47,220 16 13.7 10 83,200 22,597 60,603 20 12.9 Mean 75,520 25,482 50,037 17 6 12.8 (7.55 ha) (2.55 ha) (5.0 ha) Table 7.3 Ani-ani system: labor rates and crew time (crew of 4) for 10 simulated seasons. - - Cutting Foot-treading Cutting Foot-treading 51:3;2t10n T‘Rate Rate Time Time (man-h/ha) (man-h/ha) (hrs) (hrs) 1 838 332 8.0 3.2 2 824 325 8.0 3.0 3 839 335 8.0 3.3 4 820 329 8.0 3.3 5 830 322 8.0 3.2 6 829 330 8.0 3.3 7 829 322 8.0 3.3 8 811 331 8.0 3.3 9 843 326 8.0 3.0 10 837 323 8.0 3.2 Mean 830 327 8.0 3.2 124 are cut, and they are cut closer to the ground thereby leaving less un- cut panicles. Uncut losses can be eliminated entirely if the cut is made at ground level, but this was not the common practice. 7.2 Sickle System 7.2.1 Sickle and Ani-ani Losses The losses resulting from the simulation, and compared to the field data, are shown in Table 7.4a. As expected, the results are similar and not significantly different. Of primary interest is the comparison of these losses with the ani-ani system (Table 7.4b). The sickle was effective in reducing the uncut losses by 56%, but the beating method used with the sickle increased the threshing losses dramatically (130%). The more labor-efficient sickle did reduce the delay losses, but the magnitude did not change greatly so the net effect slightly favored the use of the sickle. To repeat, the importance of the delay loss in an absolute sense is lost due to the lack of specificity of the data. But its significance should not be overlooked. 7.2.2 Sickle and Ani-ani Labor The sickle harvesting crew of four were able to cut 4.25 hectares (Table 7.5) or 56% of the 7.55 hectares that had matured. This compared to 34% of the matured paddy that was cut by the 12-man ani-ani crew with the same maturity rate and harvest crew delay. The average paddy transaction had to wait, on average, 8.2 days. Thus the 4-man sickle crew reduced the overall delay 36%. The sickle crew averaged 7.9 hours for cutting and 4.9 hours for threshing resulting in a labor input of nearly 13 hours a day. The greater threshing time was not unexpected since the volume of material greatly increased. This resulted in a total daily input of 125 Table 7.4 Sickle and ani-ani losses over a ten season simulation. (a) Simulated sickle and field data losses. Sickle Field Data Shattering losses 1.27 1.28 Uncut losses Z415; lgjfig Cutting losses 3.29 3.20 Gross foot-treading losses 5-50 5.74 Threshing recovery1 L811 2&1 Net foot-treading losses 2.80 2.87 Delay losses2 1421§ _;;__ System losses 7.34- 6.07 (b) Comparison of simulated ani-ani and sickle losses. Ani-ani Sickle Shattering losses 1.42 1.27 Uncut losses 54312 24112 Cutting losses 6.01 3.29 Gross beating losses 2 - 44 5 . 60 Threshing recovery1 3.22.2 _2_-_8_0 Net beating losses 1-22 2.80 Delay losses3 l;§i§_ 1:315 System losses 8.61 ‘7.34 1Assumed 50% recovery rate. 2Average delay was 12.8 days. 3Average delay was 8.2 days. 126 Table 7.5 Sickle system: area cut, area uncut, harvest crew delay, and paddy waiting time for 10 simulated seasons. . . Area Area Area Harvest Paddy 5123;:t10n Matured Cut Uncgt Crew Delay Waiting Time (m2) (m2) (m ) (days) (dayS) 1 80,000 57,507 22,493 10 7.1 2 70,400 41,616 28,784 16 6.3 3 89,600 55,562 34,038 11 7.9 4 83,200 34,836 48,364 22 11.5 5 67,200 48,685 18,515 11 3.4 6 80,000 35,763 44,237 23 10.9 7 54,400 34,864 19,536 22 8.8 8 73,600 30,429 43,171 25 9.2 9 73,600 45,864 27,736 16 8.3 10 83,200 39,765 43,435 20 8.6 Mean 75,520 42,489 33,031 17.6 8.2 (7.55 ha) (4.25 ha) (3.3 ha) Table 7.6 Sickle system: labor rates and crew time (crew of 4) for 10 simulated seasons. - - Cutting Beatin Cutting Beatin 51333121210" Rate Rate 9 Time Time 9 (man-h/ha) (man-h/ha) (hrs) (hrs) 1 165 104 8.0 5.1 2 160 100 7.7 4.4 3 166 105 8.0 5.2 4 158 100 8.0 5.1 5 162 97 7.8 4.7 6 163 102 8.0 5.3 7 161 95 7.7 4.8 8 154 102 7.9 5.4 9 167 99 8.0 4.8 10 165 98 8.0 5.0 Mean 162 100 7.9 4.9 127 nearly 52 man-hours for the four-man sickle crew and 134 man-hours for the 12-man ani-ani crew: an average reduction of 61% with the 4-man sickle crew (Table 7.6). It can be argued that if the sickle were completely substituted for the ani-ani then a harvest crew delay would be eliminated. This situation was modeled eliminating the harvest crew delay segment of the model with the results presented in Table 7.7. The average paddy waiting time was reduced to less than two days delay, but the area of matured paddy was approximately 10% less than when the crew delay was operative. This resulted from a different sequence of random numbers due to the removal of FNS DELAY. Yet a steady state condition regarding paddy waiting time did start with the sixth simulation run based upon a moving average about the mean, as previously mentioned. Nevertheless, it would appear from these runs that paddy waiting time for each transaction would on average revolve closely around a two-day delay. From a practical standpoint, paddy waiting time was eliminated. Table 7.7 Sickle system: area cut, area uncut, and paddy waiting time without harvest crew delays for 10 simulation runs. Simulation Area Area Area Paddy Runs Material Cut Uncut Waiting Time 1 70,400 68,962 1,438 2.2 2 67,200 67,099 101 0.8 3 89,600 76,137 13,463 3.8 4 70,400 70,400 - 1.0 5 60,800 60,800 - 1.3 6 48,000 48,000 - 0.8 7 51,200 51,200 - 0.5 8 76,800 68,766 8,034 1.8 9 67,200 67,200 - 1.0 10 83,200 66,988 16,212 1.9 68,480 64,555 3,925 T75— (6.85 ha) (6.46 ha) (0.39 ha) 128 7.2.3 Summary 0n the surface this would imply an employment problem. But during the peak season, the released labor would provide an additional labor force to be employed elsewhere in the harvest thus offering the opportunity for larger ani-ani crews elsewhere to reduce paddy waiting time. This assumes that adoption occurs at a moderate rate, and this appeared reasonable since only 14% of the sampled area was cut with a sickle while the HYVs have been grown in the area for approximately 10 years. The introduction of the sickle with the harvest crew delay demon- strated the effect of Hayami and Ruttan's dynamic sequences of events: the relief of the cutting problem has created another bottleneck area-- threshing. Consequently, two problems resulted: 1) threshing losses in- creased because of a less technically efficient method, and 2) the total daily threshing time was nearing an impractical point, i.e., it is un- likely that five hours on average will be spent threshing. To remedy the threshing problem, the power thresher was introduced. 7.3 Power Thresher The power thresher was used in both the ani-ani and sickle systems with crew delays. In the sickle system it served to relieve a threshing bottleneck, and in the ani-ani system it served to reduce threshing time so that cutting time could be increased within a 12-hour working day constraint. 7.3.1 Losses One type of power thresher loss reported by Djojomartono (1979), was low being 0.6%. Natakusuma (1976) reported loss in Indonesia ranging from 1.5% to 5.0%. So for the purposes of this model, a 2.50% loss was assumed. 129 On this basis there would not be a significant difference in substituting the power thresher for foot-treading in the ani-ani system (Table 7.8), but there is a definite advantage to the sickle system in reducing the beating losses by nearly half. The specific power thresher that Djojomartono used was difficult to determine. So the assumption is that it was a through-type powered drum thresher of the same type that are locally manu- factured. In addition, the assumption is made that this power thresher operates equally as well with the ani-ani-cut straw as with the locally sickle—cut straw. Straw cut with a sickle was somewhat longer than the ani-ani-cut straw but definitely not as long as straw cut at ground level in other Asian countries. Table 7.8 Ani-ani and sickle losses with the power thresher. Ani-ani Sickle Shattering losses 1.42 1.27 Uncut ggggg 2:92. Cut 6.01 3.29 Gross threshing losses 2.50 2.50 Threshing recovery ];2§_ 1.25 Net threshing losses 1.25 1.25 Delay losses 1438 14g§_ System losses 8.64 5.79 7.3.2 Labor Requirement The labor rate identified by Djotomartono (1979) for the power thresher was 463 kilograms per machine hour using a 4-man crew or approxi- mately 116 kg per man. But the labor model requires an initial input 130 expressed as man-hours per hectare rather than crew hours. Assuming an average yield of 4414 kg per hectare, this equated to 38 machine hours per hectare or a total labor time of 152 man-hours per hectare. The model evaluated this power thresher with both a 12-man ani-ani cutting crew and a 4-man sickle crew. The only change was the threshing time, which averaged 1.87 hours when substituted in the sickle system and 1.1 hours when substituted in the ani-ani system. 7.4 Financial Analysis The financial analysis was approached through three hypotheses: 1. There is a potential that the farmer will realize a saving in harvest costs through a more beneficial contractural price if he sells his crop to a buyer, who uses the more efficient and cheaper sickle, than if he harvested the crop himself.) 2. The sickle is a cost reducing method, if employed by the farmer. 3. The power thresher reduces harvesting costs for both the ani-ani system and the sickle system. 7.4.1 Hypothesis One The hypothesis that the farmer shares reduced harvesting costs with the buyer through a more favorable contractural price than if he harvested the crop himself was examined in Table 7.9. The average harvested yield in this study was adjusted for loss recoveries before the harvest crew shares were deducted. The gross return indicated that there was some 131 Table 7.9 Cost comparison between farmer (ani-ani) and buyer (sickle) systems for the harvest of one hectare. Farmer Buyer (ani-ani) (sickle) Yield (Kg/ha)1 4,414 4,414 Cutting loss (Kg/ha)2 -- 120 Threshing loss (Kg/ha)2 -7O Adjusted yield (Kg/ha) 4,414 4,464 Harvesting share3 490 298 Production, less harvest share (Kg/ha) 3,924 4,166 Rough rice price (Rp/kg)“ 85 85 Gross return (Rp/ha) 333,540 354,110 Farmer's opportunity cost and buyer's 3,471 12,804 labor cost (Rp/ha)5 System return (Rp/ha) 330,069 341,306 1Average study yield before saved losses are added. 2The loss differential between the two systems is reflected by an increase or decrease in the sickle system: 1) cutting losses: + 2.72% and thresh- ing losses: - 1.58% (Table 7.4). 3Harvest crew shares were 1/9 for the ani-ani and 1/15 for the sickle. l*This was the survey price for rough dried rice after sun drying. Since sun drying is a constant factor for both systems, the price was not adjusted to reflect this cost. 5The buyer's labor was considered to be twice that of the hourly crew labor (Table 7.9) for the cost of harvesting one hectare. Farmer's labor cost was an assumed opportunity cost equal to an ani-ani harvester. . , 1157 man-h/ha _ crew-h R _ R Farmer 5 cost. 12-man crew - 96 _—EE—_ x 36 crew- - 3,471 RE- Buyer's cost: 223mgzngtéca = 66 £Efi§zfl.x 194 R crew- : 32. 12,804 ha 132 saving after assuming the buyer's labor was twice the hourly sickle crew cost (Table 7.10). When this was considered, there may be a possibility of a small potential gain from lower harvest cost, if the farmer contracted with a buyer. Table 7.10 Hourly and area labor costs for ani-ani and sickle systems. Farmer Buyer (Ani-ani System) (Sickle System) 1. Adjusted yie1d (kg/ha)1 4,414 4,464 2. Crew's crop share (%)2 11.11 6.67 3. Crew's share (kg/ha) 490 298 Rough rice value (Rp/kg) 85 85 5. Cost (Rp/ha) 41,650 25,330 6. Man-h/ha)3 1,157 262 7. Estimated hourly rate earned 36 97 (Rp/man-h) 1See Table 7.9. 2Ani-ani crew's harvest share: 1/9 3See Tables 7.3 and 7.6. 7.4.2 Hypothesis Two A comparison of costs of the ani-ani and sickle system, if used by the farmer, can be seed by comparing the gross return in Tables 7.11 and 7.9. The cost savings from using the sickle amounted to Rp 23,589 addi- tional return, or a 7% increase in gross revenues after harvest costs. Although this supports hypothesis two, it is not a high enough return to warrant a contrary social position on the part of the farmer. The sickle's 133 role therefore probably more closely related to a lack of harvest labor, than to its immediate financial impact. Table 7.11 Return to the farmer from use of the sickle system with beating and rethreshing for one hectare. Adjusted Yield (kg/ha)1 4,464 Less harvest share2 298 Production after harvest share 4,166 Rethreshing recovery (kg/ha)3 70 Net production (kg/ha) 4,236 Rough rice price (Rp/kg)“ 85 Gross return (Rp/ha) 360,060 Farmer's opportunity cost (Rp/ha)5 6,402 Farmer's return (Rp/ha) 353,658 1See Table 7.9. 2Sickle crew's crop share: 1/15 32.80% beating recovery- 1.22% ani-ani recovery = 1.58% differential (Table 7.4b). “Price includes sun drying so returns are slightly overstated. SFarmer's opportunity cost assumed equal to hourly harvester's rate for sickle (Table 7.10). 262 man-h/ha 4-man crew Farmer's cost = = 66 crew-h/ha x 97 Rp/crew-h==6,402 Rp/ha 7.4.3 Hypothesis Three The hypothesis that the power thresher reduces harvest costs for the ani-ani and sickle systems is examined in Table 7.12. The following analysis assumes: l) the thresher is operated 80% of the time during the 134 40-day peak season and 30% of the time during the balance of the harvesting season, 2) there are two harvest seasons a year, since this typified the research area, and 3) the farmer hires a power thresher crew with the ani- ani system. An hourly cost estimate of Rp 399 for the one power thresher being modeled is presented in Table 7.13. These costs were based upon the only available estimates by Djojomartono (1979). The hypothesis is examined by a partial budgetary analysis in Table 7.12. Yield was adjusted for loss recoveries, and labor costs were based upon the previous cutting and threshing rates. The advantage in favor of the sickle-power thresher system (line 7 of Table 7.11) was due to the efficiency and, hence, the lower cutting costs with the sickle. The power threshing costs were nearly equal but a little higher for the sickle system because of the increased volume of material. Thus the gross return after threshing reflected the use of the sickle in cutting rather than the result of the power thresher under the prevailing cost ratios. But the added-values (line 13) from use of the power thresher with these sys- tems would suggest that there is a small potential increase in system re- turns associated with the power thresher as compared to the ani-ani and sickle systems with manual threshing. j[,4.4 Summary The farmer apparently has little potential for increasing income by liiring a buyer to harvest with a sickle, under the prevailing wage and <:ost conditions. So his motivation for using the sickle harvester is larobably due to: the pressure of time, he does not want to bother, risk avoidance, etc. The sickle system was cost effective and did result in an improved return of seven percent, under the prevailing assumptions and 135 cost ratios. But the improved earnings were probably not sufficient to go against accepted village values. When one type of power thresher was modeled the gross after harvest returns improved only slightly for both the ani-ani and sickle systems. Yet the ani-ani and sickle systems using the power thresher were slightly superior to these two systems using manual threshing methods. This analysis was definitely handicapped because of a lack of primary data and the fact that data was available for only one type of machine. 136 Table 7.12 Effect of power thresher on ani-ani and sickle system returns after harvest. Ani-ani Sickle Difference 1. Yield (kg/ha)1 4,414 4,414 2. Cutting loss re- -- 120 covery (kg/ha2 3. Threshing loss re- covery3 -- -- 4. Adjusted yield 4,414 4,534 (kg/ha) 5. Gross return (Rp/kg)“ 375,190 385,390 10,200 6. Cutting labor costs 29,880 15,714 -14,166 7. Gross return after 345,310 369,676 24,366 cutting (Rp/kg) 8. Power threshing costs (Rp/t and Rp/ha) (a) Labor costs (Rp/t)5 1,350 (b) Machine costs (Rp/t) 862 (c) Total costs (Rp/t) 2,212 (d) Costs (Rp/ha) 9,764 10,029 265 9. Gross return after threshing 335,546 359,647 24,101 10. Farmer's opportunity costs and buyer's labor costs6 3,471 12,804 11. Gross return after threshing and before drying (Rp/ha) 332,075 346,843 14,768 12. Gross return with manual threshing (Rp/ha)6 330,069 341,306 11,237 13. Added value from use of the power thresher in these harvesting systems 2,006 5,537 3,531 1Average study yield before loss recovery. 2See Table 7.9. 3Assumed equal losses l'85 Rp/kg was survey price for rough rice, but this reflects a sun drying cost. So returns are slightly overstated. 5Power thresher costs (Rp/t) (a) Labor: 1,000 §§-/ 463 §§-= 2.1598 Q-x 3 men x 125 R man- = 810 Operator's labor: 2.1598 x 1 x 250 540 Total labor (Rp/t) I350 137 Table 7.12 (Footnotes continued) 5(continued) . 399 R h (b) Machine costs: 63 g/h = 0.8618 Rp/kg x 1000 kg/t = 862 Rp/t See Table 7.13 for estimated hourly machine costs. 6See Table 7.9. Table 7.13 Estimate of hourly power thresher costs. Yearly Estimated Hourly Item Cost Hourly Cost (Rp) Use/Yr (Rp) 1. Depreciation 45,0001 4702 96 2. Interest 20,6253 470 44 3. Repair & maintenance 30,000“ 470 64 4. Fuel & lubrication -- -- 1355 5. Miscellaneous6 75,000 470 160 6. Total hourly cost (items 1-4) 339 7. Total hourly cost (items 1-5 499 1purchase cost-salvage = Rp 250,000 - Rp 25,000 yrs of life 5 2Estimate yearly use: 1. Peak season--40 days x .80 use = 32 days x 5 hr/day = 160 hrs. 2. Remainder of season--50 days x .30 use = 15 days x 5 hr/day = 75 hrs. 3. Use per season--235 hrs/season x 2 seasons = 470 hrs/year. 3purchase cost + salvage = Rp 250,000 + 25,000 2 2 = Rp 45,000 per year = Rp 137,500 average yearly investment. “Djojomartono (1979) estimated repair costs at 12% of purchase cost for a power thresher. sDjojomartono (1979), for this power thresher, estimated: 1) fuel consump- tion at ll/hr * Rp 100/2 = Rp 100/h; 2) lubrication at .11/h * Rp 350/1 = Rp 35/hr. 6An unspecified cost category used by Djojomartono (1979): 30% of purchase cost. CHAPTER 8 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS FOR RESEARCH 8.1 Summary In the quest for food self-sufficiency throughout Southeast Asia, the magnitude of rice postproduction losses are of great concern. Many statements have been made regarding the extent of these losses but little effort has been made to quantify them, except under experimentally con- trolled conditions at research sites rather than in production areas. The question that needs to be answered is: what are these losses in farmers fields? It is around this question that this research study revolved. The focus was on rice losses during the first stage of the postproduction period--harvesting. The objectives of the project were: 1) to measure field losses of existing cutting and threshing methods, 2) to evaluate these methods as regards labor requirements and profitability, and 3) to analyze the effect of alternative technologies and their impact upon the reduction of rice losses, labor requirement, profitability, and capital needs. The study can be viewed in two parts: 1) the data collection and analysis phase, and 2) the simulation model for analyzing alternative technologies. Development theory provides an important framework for this study. From the Schultz (1964) viewpoint, traditional agriculture has, over many years of trial and error, reached a point that traditional inputs are being most efficiently used and further investment in them adds very little to the income stream. This implies a state of equilibrium between 138 139 the costs of traditional inputs and the income derived from them. To break this equilibrium trap, a new flow of high-payoff inputs are needed. Hayami and Ruttan (1971) extended the Schultz theory with their Induced Development Model and integrated the dynamics of the public and private sectors and institutions of society through a series of dynamic sequence of events. The driving force towards change being product and factor prices equated to profitability. As one bottleneck was solved through a more productive technology, another would develop leading to a dynamic sequence of events. This research interacts with these theories in several ways: 1) determining whether or not cutting and threshing are high-payoff areas to induce technological change, 2) identifying bottle- necks through the simulation model, and 3) supporting the Hayami and Ruttan position that institutions adjust to internalize "the benefits of innovative activity to provide economic incentives for productivity increase." This study identified two harvesting systems in the research area: the traditional ani-ani system and the newly introduced sickle system. But these systems were not merely changes in technology but were closely identified with different marketing arrangements. When the farmer har- vests his own crop, the ani-ani is used. But if the farmer sells his crop just prior to harvest to a buyer, the sickle is used. And the use of the sickle has closely paralleled the varietal change from traditional varieties to high yielding varieties. Compounding the problem of changing harvest technologies are two cultural considerations. First, the ani-ani system is highly labor inten- sive and provides an important means of employment amongst the villagers. Second, a common practice, gleaning, permits the landless and disadvantaged 140 of the village to follow behind the harvest crew retrieving what has been missed or dropped. The uncut loss is high with the ani-ani but low with the sickle, as this study verified. And a controversy has developed ques- tioning all cutting methods that interfere with this institutional prac- tice of gleaning. But the question needs to be asked: how pervasive is gleaning and what is the best method for moving rice into the food pipe- 1ine--through normal marketing channels or through a system of sharing such as gleaning. A related problem is one of increasing harvest productivity without causing serious unemployment problems. Thus, the eventual solution cannot be considered as optimum in the traditional sense but rather as a trade- off between the needs of the farmer, the rural landless, the urban p0pu- 1ation, and the needs of society. This study deviated from other loss studies in two ways: 1) losses were identified under farm conditions, and 2) identification of losses from a random sample of fields formed the basis of the investigation rather than by fixed and predetermined experiments. The data collection phase consisted of three parts. The first part was the pilot investiga- tion that pretested the data collection methodology and developed infor- mation for determining the sample size of the random study. The second part was the random field study; this also included a farmer survey just prior to harvest. The third part of the investigation was the acquisition of additional data to supplement the sickle and to provide beating data-- the threshing technique associated with sickle harvesting. Both systems followed the same sequence of events: hand cutting, sacking in the field, transporting to the threshing site, and threshing. Sickle cutting did not involve laying the crop in the field and then gathering for threshing, a procedure used in most other Asian countries 141 where sickle cutting is practiced. The potential loss points were: 1) shattered, dropped, and uncut losses during cutting, 2) dropped panicles during transfer to a bamboo basket carried by the harvester, 3) dropped panicles during transfer from the basket to a plastic sack in the field, and 4) threshing losses. Data were collected at all loss points except for losses during transportation because the sacks were in good condition. Nor was data collected when the baskets were emptied into the sack at the edge of the field because dropped panicles were very few, and those that dropped were retrieved. The data collection procedure employed a small plot technique: several rows were removed from around the one-square meter plot of paddy, and plastic strips were placed and secured between the rows and outer edges of the plot. A harvester cut the plot, and the plastic strips re- tained the shattered and dropped losses. The harvested sample was foot- treaded and the unthreshed grain identified. After cutting the yield sample, the uncut panicles were harvested to determine the uncut loss, and labor data were identified at the plot level from timing the cutting and threshing operations. This procedure differed from the usual method of grain counts for two reasons: 1) standing water or muddy fields prevailed 43% of the time during harvest, which would have negated the grain count method and, hence, a random selection of fields; and 2) it was feared that the experi- mental error from the grain counting procedure would mask the shattered and dropped loss effect. The pilot investigation tested this procedure. Cutting losses appeared reasonable and the plots were not overcut as the magnitude of these losses indicated. The labor data were the real unknown. It was 142 decided that if the labor data responded to variations in plot yield the practice would be continued. So cutting labor was regressed on yield which resulted in an r2 of .585 and a highly significant positive slope. Although not a large r2, it gave encouragement to the practice and was used in the random study. These pilot data also indicated that 50-60 fields with five plots per field would identify the overall ani-ani cutting losses within an overall 25% variation from the mean. Consequently, this became the sample target for the random study. Unfortunately, sickle harvesting was not included in the pilot study for lack of time and available sickle crews. The statistical population for the random study was defined as irrigated paddy fields producing high-yielding varieties and harvested during the last of the 1979 wet season from the first of March to the first of June. The study was confined to irrigated fields because it had been estimated that 74% of the rice production on Java was under irrigated conditions (Soemartono, 1974). High yielding varieties pre- dominated in the area, and the March to June period was the main harvest season. Losses were reported in percent rather than on a weight basis because losses in this study were not closely correlated to yield exhibiting r2 values of .08 or less, when losses were regressed on yield. Also, losses were based upon the cleaned yield sample rather than the potential yield. This was because losses are more apt to be equated to harvested yield by a decision-maker rather than to potential yield. A four-stage random sampling procedure was employed: 1) sampling of governmental subdistricts, 2) sampling of villages within subdistricts, 3) sampling of subvillage units, and 4) sampling of fields and farmers 143 Table 8.1 Loss and labor values for the ani-ani and sickle systems. (a) Ani-ani and sickle losses (percent). Ani-ani Sickle Shattered and dropped losses 1.40ns l.28ns Uncut losses 4.48** 1.92** Total cutting losses 5.88** 3.20** Threshing losses1 2L3§ 5463 System losses1 8.26 8.83 Threshing recovery2 ' 1419 gygg Net system losses1 7.07 6.01 (b) Ani-ani and sickle labor (man-h/ha). Ani-ani Sickle Cutting labor 837** 165** Threshing labor1 _§2§_ _gg? System labor1 1163 257 ns = not significant ** = highly significant (see Table 3.7 for t-values) 1Statistical test not performed because beating data were not random and were obtained at only one site. 2Assumed 50% recovery rate from rethreshing. 3Based upon an average yield of 4414 kg/ha and a beating rate of 48 kg/man-h. 144 within the subvillage units. At the subvillage level a complete listing of fields and the farmers existed. So this formed the sampling frame and a systematic sampling procedure was used. The results of the random study are summarized in Table 8.1. The ani-ani system involved cutting with the ani-ani, a hand-palmed knife, and foot-treading as the threshing method. Cutting losses were reduced 46% with the sickle due to the reduction in uncut losses. However, beat- ing was less efficient than foot-treading and the overall result showed no significant differences between the two systems before rethreshing. From the farmer survey, it was indicated that rethreshing was practiced. On this basis, the advantage lay with the sickle, reducing the overall loss by 15%, as compared to the ani-ani. On the other hand, if the sickle-cut straw was not rethreshed, then the sickle system lost 25% more than the ani-ani system with rethreshing. The significant difference between the two systems rested with the labor input: the sickle system reduced the labor requirement 78%. Area losses were also evaluated. The random survey indicated that 86% of the sampled area was harvested with the ani-ani and 14% harvested with the sickle. Also, 49% of the area was actually gleaned, and 51% was not. Assuming a 50% gleaning and rethreshing recovery, the area loss from a societal perspective was 5.33%. This compared to the commonly quoted BULOG of‘loss estimate in Indonesian paddy fields of 8% (Gunarto, 1978). The ani—ani data established the basis for defining the initial con- ditions of the computer simulation model to evaluate alternative systems. The model was designed for simulating a 40-day peak harvest period, as determined by the maturity rate of the sampled fields. The uniqueness of the model is not its replication of sampled data, but the evaluation of a 145 delayed loss component, i.e., the length of time that ripened paddy must wait before being harvested. This delay is a function of three factors: crop maturity rate, crew harvesting rate, and the availability of a harvest crew. The model integrates these factors into a delayed loss component, which cannot be accomplished on a stochastic basis except through simula- tion. Because this states the problem in queuing terms, the model was developed around the GPSS language. Figure 8.1 outlines the procedure of the model. An area of paddy enters the model as paddy matures. If a harvest crew is not available, the paddy must wait until the next day, when the condition is again checked. The cutting time is simulated for eight hours so, if all the paddy can- not be cut that day, the uncut portion joins the paddy waiting queue while the area that will be cut proceeds to the cutting facility. After cutting, the paddy is sacked and waits in the field until threshing begins. There is no constraint on threshing time. When threshing is completed that day's simulation ends, and routine housekeeping functions are performed before the next day begins. A 12-man ani-ani crew was simulated since this was the average size crew in the sampled area. This size crew was able to cut only 34% of the ripened paddy, and on average paddy had to wait 12.8 days. Each day that the crew worked it cut for eight hours and threshed for 3.2 hours for a working day of 11.2 hours. In an effort to reduce uncut losses and paddy waiting time, the sickle was introduced. But why not just increase the ani-ani crew size to reduce the paddy waiting time? To increase the ani-ani crew size, on an area basis, beyond 12 members would have required a reserve labor supply, but the assumption was that labor was fully employed during the 146 cm? 1 Crew Season Continues? Update Seasonal Values fl\ A Available? Matured Paddy Waits :: :\ Area Completely Cut Today? Area Cut Tomorrow Area Cut Today Paddy Area Cut: Loss 8 Labor Values Paddy Waits for Threshing Threshing Started? Paddy Threshed: Loss & Labor Values 4' Figure 8.1 Flowchart of 40-day peak season simulation. 147 peak season so a reserve labor force did not exist. A 4-man sickle crew was next simulated. This crew was able to harvest 56% of the same matured paddy area, and the paddy waiting time was reduced 44% to 8.2 days. The sickle crew cut for 7.9 hours and threshed for 4.9 hours for a 13-hour working day. Without the crew delay, paddy waiting time was reduced to 1.5 days. The delay loss with the crew delay was reduced from 1.38% with ani-ani to 1.25% with the 4-man sickle crew. But too much significance must not be placed in these specific values because of the questionable data base. The power thresher was then introduced to both the sickle and ani- ani system. The objective with the sickle system was to relieve a thresh- ing bottleneck. The objective with the ani-ani system was to reduce the threshing time so that the cutting time could be extended, therefore, reducing paddy waiting time. In both cases threshing time was significantly reduced to: 1.87 hours for the sickle system and 1.1 hours for the ani- ani system. The ani-ani system, when the farmer conducts the harvest, and the sickle system, when harvesting is done by the buyer, can be evaluated at several points in the postproduction system: before harvest, after har— vest and before drying, after drying and before milling, and after milling. This study evaluated each system after harvest but before drying. Three hypotheses were tested: 1. There is a potential that the farmer will realize a saving in harvest costs through a more beneficial contractural price if he sells his crop to a buyer, who uses the more efficient and cheaper sickle, than if he harvested the crop himself. 148 2. The sickle system is a cost-reducing method, if undertaken by the farmer. 3. The power thresher reduces harvesting costs for both the ani-ani and sickle system. A partial budgetary analysis was used to evaluate these hypotheses. Examining hypothesis one, the yield was adjusted for losses and the har- vest crew's labor costs deducted. The gross return favored the sickle system by 6%. Next the buyer's labor was deducted at an assumed rate of twice the hourly cost of the harvest crew. This reduced the cost advantage with the buyer to only 3.4%. Thus, hypothesis one was rejected, although there was a small potential for gain. In hypothesis two, the farmer did benefit, if he used the sickle himself, and increased his return 7%, if threshing recovery was considered--but not a high-payoff return. It is probably not a high enough return to warrant a contrary social posi- tion on the part of the farmer. Turning to hypothesis three, the power thresher was evaluated with each system: harvesting costs did not materially change between systems, but overall system costs were slightly reduced when compared to these two systems using manual threshing methods. But this must be couched in terms of the assumptions that were made regarding the power thresher and given the assumed factor and product prices, especially wage rates and cost ratios. 8.2 Conclusions 8.2.1 Conclusions Based Upon the Field Data 1. Field losses in the Province of Yogyakarta were identified as 8.26% for the ani-ani system and 8.83% for the sickle system during the main March to June harvest season in 1979. But when these losses were considered on an area basis, and adjusted for assumed rethreshing and 149 gleaning recovery rates, area losses were reduced to 5.33%. On this basis, losses were definitely less than the often quoted BULOG paddy loss estimates of 8% (Gunarto, 1978). For the ani-ani system, uncut losses were identified as the principal type of loss, this being 54% of the overall loss. And for the sickle system, threshing by beating was identified as the principal loss, which was 64% of the total loss. Reduced losses with the sickle system therefore depend upon a more efficient threshing technique. The uncut losses with the ani-ani are not controllable so an 8% loss is probably as efficient as the system will become before rethreshing. However, from the standpoint of Djojomartono's work in 1979 this view- point is arguable since he reported ani-ani cutting losses of 3.16% and 1.93%. But his observations were restricted to just one situation: ani-ani cutting on a government seed farm. Although the ani-ani system before rethreshing was slightly superior to the sickle system in reducing losses, the sickle system has the greatest potential for controlling harvesting losses--but only with a more efficient threshing method other than beating. Although foot- treading controlled ani-ani threshing losses, foot-treading the longer cut sickle straw was not effective since these losses averaged 6.81% (Appendix A, Table A3). Consequently, a threshing solution is dependent upon a technological change such as a power thresher or a pedal thresher rather than the existing manual methods. Also, the sickle has the potential for eliminating uncut losses so a total reduction of sickle losses to less than 5% would seem a reasonable expectation. This would imply a reduction in harvesting losses of nearly 4%, which would equate to a one-time increase in production exceeding the 3.5% annual rice production growth rate on Java between 1968 and 1967 (Table 1.5). 3. 150 The sickle system was highly labor efficient when compared to the ani- ani system reducing the labor requirement from 1163 to 257 man-hours per hectare--a reduction of 78%. A natural concern is one of rural employment with such a drastic change in manpower. But this need not be the case if technological adoption follows rather than preceeds labor problems. And this appeared to be the case in Yogyakarta: labor shortages were developing to which the sickle system was responding. The fact that nearly 10 years after the HYVs were introduced sickle harvesting was still not widely practiced could tend to support this follower role in using the sickle for rice harvesting. There is a much more active role for an improved sickle system in the transmigration areas where labor is in short supply. This fact plus the potential for reduced harvesting losses would warrant a strongly coordinated effort on the part of industry and the government in developing an improved harvesting system for these particular areas. The small plot technique was essential for implementing a randomized field sampling procedure in collecting cutting losses because wet field conditions would have excluded 43% of the sampled fields because the alternative grain counting procedure could not have been practiced under standing water and muddy field conditions. However, the small plot technique was unable to identify beating losses because the samples were too small for an effective evaluation. In addition, foot-treading losses were probably understated because with the small samples a more thorough threshing resulted. Samples for these losses should probably be taken from randomly selected field crews. Also, the collection of labor data with these one-square meter plots is subject to argument and needs further investigations with both 6. 151 larger plots and field-wide comparisons. The latter would probably be preferred since a field-wide comparison would also reflect rest time. Gleaning was not as widely practiced as might have been expected from the farmer survey: 75% of the farmers permitted gleaning, but gleaning was only practiced on 49% of the sampled fields. Under the assumption that gleaners effectively recovered only 50% of the uncut and dropped losses, this would imply an effective recovery of only one-fourth of the uncut losses on an overall area basis. Certainly not a strong argument for excluding the introduction of more productive harvesting practices. Besides not all of the field losses go unrecovered: domestic ducks often graze the paddys after harvest. 8.2.2 Conclusions of the Simulation Model 1. 2. 3. With the simulation model it was possible to identify a paddy waiting time delay before the ripened paddy was cut. This paddy delay was the function of three variables: 1) paddy maturity rate, 2) harvest crew availability, and 3) crew harvesting rate. The delay was used to identify additional field losses before and during cutting, and it also pro- vides a basis for evaluating the technical performance of different system configurations. The results of the model indicated that the power thresher had the potential for eliminating a threshing time constraint with the 4-man sickle crew. Furthermore, the power thresher reduced threshing time significantly with the ani-ani system so that cutting time could be extended and threshing accomplished within the same 12-hour working day. The model can be used to evaluate changes within the system that result from changes elsewhere in the system. For example, a 4-man sickle crew was introduced to reduce ani-ani uncut losses and paddy waiting time, 8.2. 152 and this resulted in a threshing time labor constraint. So the power thresher was introduced to relieve this new bottleneck. Thus the model can be used to evaluate Hayami and Ruttan's dynamic sequence of events before changes are introduced in the real-world. The model can be expanded to include drying and storage so that the entire on-farm postproduction sequence can be examined. In addition, the model can be further enriched to include other variables, e.g., grain quality and weather delays. GPSS has been a useful language for this problem and has wide applica— tion to agricultural engineering technology, especially where these problems can be stated in queuing terms. 3 Conclusions Based Upon the Economic Analysis The farmer apparently has little additional income to gain from the use of the sickle under present conditions. Although the sickle, if used by the farmer, was cost effective and did result in an improved return of 7%, it would not appear to be high enough to warrant a contrary social position on the part of the farmer. Substituting one type of power thresher in both the ani-ani and sickle systems neither increased nor decreased threshing costs, when compar- ing these two improved systems Under the assumptions of the analysis. But compared to manual threshing, both the ani-ani and sickle system with this power thresher were slightly superior to these systems using manual threshing methods. Consequently, the economic viability of power threshers will depend upon their unit threshing costs and upon changing relative prices of labor, energy, and capital. Although the farmer gained a very slight cost advantage from selling his paddy to a buyer before harvest, it did not appear to be great 153 enough to encourage this practice for this reason. But the buyer using the sickle did receive an added financial return. Thus the indi- cation that sickle harvesting was advantageous to the buyer, and in- stitutions were accepting its limited use, supports the Hayami and Ruttan (1971) contention that institutions adjust to internalize "the benefits of innovative activity to provide economic incentives for productivity increases." 8.3 Recommendations for Additional Research An improved thresher for use with the longer cut sickle straw needs to be identified. But research should first center around existing pedal and power threshers before considering design and development of a new thresher. The simulation model indicated that research is needed to identify paddy losses for time delays before cutting of longer than nine days. Drying and on-farm storage research needs to be added to the harvest sequence. Also, changes in grain quality, i.e., head yield and broken, need to be added to the physical losses of this study. Data within this model need strengthening in the following areas: a. Crop maturity rate b. Harvest crew delays c. Financial data for alternative technologies d. Rethreshing recovery rates e. Gleaning recovery rates f. Sickle and beating data Generalizing the loss and labor probability density functions used in this model would aid in the development of other loss and labor simulation models. 154 6. An area labor model needs to be conceptualized to compare different adoption rates of alternative techniques and their potential impact on the employment environment and cr0p loss delays. 155 APPENDIX A LOSS AND LABOR DATA SUMMARY 156 Table A1. Pilot investigation summary. Clean Shatter Uncut Ani-Ani Cutting Field Variety Yield Loss1 Lossl Loss1 Labor (T/HA) (%) (Z) (7.) (Man-H/HA) 1 Chitarum 5.98 2.04 5.78 7.82 2 Chitarum 7.66 0.91 6.12 7.03 3 Chitarum 8.18 0.98 11.45 12.43 4 Chitarum 6.94 0.78 5.25 6.04 5 C - 4 1.89 2.00 7.34 9.35 456 6 IR - 26 2.39 1.83 6.17 8.00 588 7 Bogor-BP 3.53 1.73 8.53 10.26 8 Bogor-BP 2.43 1.55 9.95 11.51 611 9 IR - 36 3.64 2.34 6.08 8.42 680 10 IR - 38 4.85 1.45 3.26 4.71 1503 11 IR - 30 4.79 2.76 8.22 10.98 1430 12 IR - 26 4.13 5.17 3.87 9.04 860 Mean 4.70 1.96 6.84 8.80 875 1Loss Calculation: loss(g/m2) Percent loss = x 100 Yield (cleaned) (g/mz) 157 mm.~ H~.m Na.e Ha.o m.- om Na.m om.H m~.e oo.H hm.a m.H~ cm mH m a so.oa e~.~ om.~ ~m.o mm.a c.- om mH m mm oa.w Nq.~ mm.m mH.H H~.m H.HN on mH m NN camaum Ha.~ m~.a oa.m am.o aw.e «.mu om mH m Hm owwaahue No.m mm.m mo.~ oe.o Hq.m «.mm cm «H m cm Hauaam mm.o an.m ao.m aa.o aw.q a.- cm «H n ma mm.oa aq.~ Hm.~ om.H mH.a o.o~ cm MH.NH Hm.e no.“ 8H.m “a.~ om.m m.o~ om mH m wH m~.oH om.~ mm.~ a~.m ao.~ NH.8 ~.o~ on mH m AH ma.o mo.H oa.m me.a mq.H ao.m m.mH on «H m 8H maosmam 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mmmH m ma m.omH m.bom m.moa v.mma om.H v.5H mmmH m Ha o.mh m.wbm m.mv ~.hhH mn.m m.bH mmmH m 0H ¢.moa m.mmm h.mo o.mHN om.m H.Hm mmmH m m ouuwumcmccmm m.¢m o.mmm v.mn m.mHN mm.m m.Hm mmmH m m smnuwm m.mw m.mHm m.¢m m.mmm mo.¢ H.mm mmmH m h m.vn m.om~ b.0v m.hma vm.m v.mm om o.m® o.Hom 0.0m m.~m ma.m m.mm mmmH m w m.hHH m.hma H.Nm m.mm~ No.a «.mm mmmH m m H.om ~.mmm H.m~ m.moa mm.m m.mm mmmH m v accsw m.vm w.va h.¢m m.NmH mv.v m.mm mmmH m m ouufiuwcmccwm b.mh ¢.¢vm H.0m o.NmH vv.m o.Hm mmmH m m nmnuwm H.mo o.HmH o.mv N.Nma mm.m o.HN mmmH m H B\mlcmz ¢m\mucmz 9\m|:mz ¢m\mlcmz A¢m\av 02 oz wva um onHm xumflum> mmo cflmwm mmua uonmq mcflnmmuuuuoom hogan meUHm Gamay . “omscflucoov m¢ mflnme 165 Table A6. Ani-ani losses (percent): distributional characteristics of the ungrouped data. Shatter Uncut Beating Mean 1.400 4.464 2.368 Variance 1.335 8.225 3.319 Std. Dev. 1.156 2.868 1.822 Range 0.11-10.16 0.30-22.75 0.15-11.180 C.V. Pct 82.543 64.257 76.93 Std. Error 0.075 0.185 0.118 Kurtosis .14.414 7.991 4.050 Skewness 2.729 1.986 1.619 166 Table A7. Random sickle and beating losses (percent): distributional characteristics of the ungrouped data. Shatter Uncut Beating Mean 1.276 1.920 5.628 Variance 1.174 3.537 2.541 Std. Dev. 1.083 1.881 1.594 Range 0.16—6.290 0.07-10.23 2.98-12.14O C.V. Pct 84.897 97.95 28.322 Std. Error 0.140 0.243 0.215 Kurtosis 7.259 5.667 4.219 Skewness 2.320 2.052 1.521 Table A8. Ani-ani labor: ungrouped data. 167 distributional characteristics of the Man-H/Ton Man-H/Ha Mean 195.0 837.2 Variance 539.0 77, 261 Std. Dev. 73.415 277.958 Range 65.8-727.9 177-1686 C.V. Pct 37.286 33.260 Std. Error 4.739 17.942 Kurtosis 11.336 0.223 Skewness 2.234 0.599 168 Table A9. Foot-treading: distributional characteristics of the ungrouped data. Man-H/Ton Man-H/Ha Mean 77.727 325. Variance 1452.822 20130.959 Std. Dev. 38.116 141.884 Range 22.8-227.9 111.0-806.0 C.V. Pct 49.038 43.686 Std. Error 2.460 9.159 Kurtosis 1.196 0.697 Skewness 1.093 1.054 169 Table A10. Sickle and beating labor: distributional characteristics of ungrouped data. (a) Sickle labor. Man-H/Ton Man-H/Ha Mean 41.9 164.783 Variance 543.207 6494.952 Std. Dev. 23.307 80.591 Range 13.3-99.6 54-439 C.V. Pct 54.483 48.907 Std. Error 3.009 10.404 Kurtosis -0.46 2.495 Skewness 1.021 1.476 (b) Beating labor. Man-H/Ton Mean 23.433 Variance 166.579 Std. Dev. 12.907 Range 12.38-86.81 C.V. Pct 55.078 Std. Error 1.740 Kurtosis 14.465 Skewness 3.590 170 Table A11. Frequency distribution of ani-ani shattered and dropped losses. Class Limits Cumulative Z Loss Midpoint Frequency Frequency 0.1 - 0.50 .25 46 .192 .51 - 1.00 .75 60 .441 1.01 - 1.50 1.25 46 .633 1.51 - 2.00 1.75 35 .779 2.01 - 2.50 2.25 24 .879 2.51 - 3.00 2.75 11 .925 3.01 - 3.50 3.25 7 .954 3.51 - 4.00 3.75 4 .970 4.01 - 4.50 4.50 4 .988 4.51 - 10.50 7.50 3 1.000 Total observations 240 Distribution mean 1.40 Table A12. Frequency distribution of ani-ani uncut losses. Class Limits Cumulative Z Loss Midpoint Frequency Frequency 0.1 - 1.00 .50 10 .042 1.01 — 2.00 1.50 31 .170 2.01 - 3.00 2.50 36 .320 3.01 - 4.00 3.50 49 .525 4.01 - 5.00 4.50 33 .662 5.01 - 6.00 5.50 31 .792 6.01 - 10.00 8.00 43 .970 10.01 - 12.00 11.00 5 .992 12.01 - 24.00 18.00 9 1.000 Total observations 240 Distribution mean 4.45 17] Table A13. Frequency distribution of ani-ani cutting losses. Class Limits Cumulative Z Loss Midpoint Frequency Frequency 1.226 - 2.250 1.6875 15 .062 2.251 - 3.375 2.8125 36 .212 3.376 - 4.500 3.9375 43 .392 4.501 - 5.625 5.0625 41 .562 5.626 - 6.750 6.1875 35 .708 6.751 - 9.000 7.875 36 .858 9.001 - 11.250 10.125 23 .954 11.251 - 16.875 14.0625 8 .987 16.876 - 27.000 21.9375 3 1.000 Total observations 240 Distribution mean 5.89 Table A14. Frequency distribution of foot-treading losses. Class Limits Cumulative Z Loss Midpoint Frequency Frequency 0.01 - .50 .25 19 .079 0.51 - 1.00 .75 42 .254 1.01 - 1.50 1.25 37 .408 1.51 - 2.50 2.00 50 .617 2.51 - 3.50 3.00 42 .792 3.51 - 5.00 4.25 32 .925 5.01 - 8.00 6.50 14 .983 8.01 - 12.00 10.00 4 1.000 Total observations 240 Distribution mean 2.40 172 Table A15. Frequency distribution of yields for ani-ani study. Class Limits Cumulative tons/ha Midpoint Frequency Frequency 1.501 - 2.250 1.875 9 .037 2.251 - 3.000 2.625 15 .010 3.001 - 3.750 3.375 45 .279 3.751 - 4.500 4.125 60 .529 4.501 — 5.250 4.875 51 .742 5.251 - 6.000 5.625 38 .900 6.001 - 6.750 6.375 19 .979 6.751 - 7.500 7.125 4 .995 7.501 - 8.250 7.825 1 1.000 Total observations 240 Distribution mean 4.45 Table A16. Frequency distribution of moisture content for ani-ani study. Class Limits Z Moisture Cumulative Content Midpoint Frequency Frequency 16.80 - 17.85 17.5 16 .071 17.86 - 18.90 18.2 38 .24 18.91 - 19.95 19.6 41 .422 19.96 - 21.00 20.65 45 .622 21.01 - 72.05 21.75 45 .822 22.06 - 23.10 22.75 30 .956 23.11 - 24.15 23.80 9 .996 24.16 — 25.20 24.85 1 1.000 Total observations 225 Distribution mean 20.5 173 Table A17. Frequency distribution of sickle shattered and dropped losses, random data. Class Limits Cumulative Z Losses Midpoint Frequency Frequency 0.01 - 0.54 .27 14 .233 0.55 - 1.08 .81 21 .583 1.09 - 1.62 1.35 10 .750 1.63 - 2.16 1.89 7 .867 2.17 - 3.24 2.70 5 .950 3.25 - 6.48 4.86 3 1.000 Total observations 60 Distribution mean 1.26 Table A18. Frequency distribution of sickle uncut losses, random data. Class Limits Cumulative Z Losses Midpoint Frequency Frequency 0.01 - 1.00 .50 23 .383 1.01 - 2.00 1.50 17 .667 2.01 - 3.00 2.50 8 .800 3.01 - 4.00 3.50 4 .867 4.01 - 5.00 4.50 4 .933 5.01 - 7.00 6.00 3 .987 7.01 - 12.00 9.50 1 1.000 Total observations 60 Distribution mean 1.94 174 Table A19. Frequency distribution of total sickle cutting losses, random data. Class Limits Cumulative Z Losses Midpoint Frequency Frequency .01 - 1.50 .75 15 .250 1.51 — 3.00 2.25 24 .650 3.01 - 4.50 3.75 5 .733 4.51 - 6.00 5.25 7 .850 6.01 - 7.50 6.75 4 .917 7.51 - 9.00 8.25 4 .983 9.01 - 12.00 10.50 1 1.000 Total observations 60 Distribution mean 3.19 Class Limits Cumulative Z Losses Midpoint Frequency Frequency 2.77 - 3.96 3.365 5 .091 3.97 - 5.16 4.565 16 .382 5.17 - 6.36 5.675 21 .764 6.37 - 7.56 6.965 8 .909 8.77 - 9.96 9.365 2 .982 9.97 - 12.36 11.165 1 1.000 Total observations 55 Distribution mean 5.65 175 Table A21. Frequency distribution of ani—ani cutting labor, man—hours per hectare. Class Limits Cumulative Man-h/ha Midpoint Frequency Frequency 121 — 330 222.5 2 .08 331 - 540 435.5 27 .121 541 - 750 645.5 68 .404 751 - 960 855.5 76 .721 961 - 1170 1065.5 40 .888 1171 - 1380 1275.5 15 .95 1381 - 1590 1485.5 10 .992 1591 - 1800 1695.5 2 1.000 Total observations 240 Distribution mean 838 Table A22. Frequency distribution of ani—ani cutting labor, man-hours per ton. Class Limits Cumulative Man-h/ton Midpoint Frequency Frequency 51 — 110 80 12 .05 111 - 140 125 38 .208 141 - 170 155 45 .396 171 — 200 185 48 .596 201 - 230 215 41 .767 231 - 260 245 27 .879 261 - 380 320 25 .983 381 - 770 575 4 1.000 Total observations 240 Distribution mean 198 176 Table A23. Frequency distribution of foot-treading labor, man-hours per ton. Class Limits Cumulative Man-h/ton Midpoint Frequency Frequency 18 - 35 26 20 .083 36 - 53 44 54 .308 '54 - 71 62 55 .538 72 - 89 80 36 .688 90 - 107 98 26 .796 108 - 125 116 18 .871 126 — 143 134 17 .942 144 - 179 161 10 .983 180 - 233 206 4 1.000 Total observations 240 Distribution mean 78 Table A24. Frequency distribution of foot-treading labor, man-hours per hectare. Class Limits Cumulative Man-h/ton Midpoint Frequency Frequency 101 - 160 130 16 .067 161 - 220 190 43 .246 221 - 280 250 57 .483 281 - 340 310 41 .654 341 - 400 370 22 .746 401 - 460 430 16 .812 461 - 580 520 29 .933 581 - 700 640 11 .979 701 - 820 760 5 1.000 Total observations 240 Distribution mean 326 177 Table A24. Frequency distribution of sickle cutting labor, man-hours per hectare. Class Limits Cumulative Man-h/ha Midpoint Frequency Frequency 44 - 94 69 10 .167 95 - 145 120 16 .433 146 — 196 171 20 .767 197 - 247 222 7 .803 248 - 298 273 2 .917 299 - 349 324 2 .950 350 - 400 375 2 .983 401 - 451 426 1 1.000 Total observations 60 Distribution mean 166 Table A25. Frequency distribution of sickle cutting labor, man-hours per ton Class Limits Cumulative Man-h/ton Midpoint Frequency Frequency 1 — 15 8 4 .067 16 - 30 23 20 .400 31 - 45 38 18 .700 46 — 60 53 5 .783 61 - 75 68 4 .850 76 - 90 83 4 .917 91 - 105 98 5 1.000 Total observations 60 Distribution mean 42 178 Table A25. Frequency distribution of beating labor, man—hours per ton. Class Limits Cumulative Man-h/ton Midpoint Frequency Frequency 8.1 - 18.5 13.3 21 .382 18.6 — 29.0 23.8 26 .8545 29.1 - 39.5 34.3 5 .946 39.6 - 50.0 44.1 1 .964 50.1 - 92.0 71.1 2 1.000 Total observations 55 Distribution mean 23 179 APPENDIX B ANALYSES OF VARIANCE FOR LOSS AND LABOR DATA 180 Table B1. Analysis of Variance: Pilot Investigation Sum of Mean F Source DF Squares Squares Ratio1 a. Percent Shatter ** Fields 11 75.1724 6.8339 2.6423 Plots 48 124.1428 2.5863 Total 59 199.3153 b. Percent Uncut * Fields 11 313.8093 28.5282 3.2108 Plots 48 426.4867 8.8851 Total 59 740.2964 c. Percent Ani-ani Fields 11 284.5345 25.8668 1.9512ns Plots 48 636.3266 13.2568 Total 59 920.8610 d. Cutting Labor (Man-H/Ha) ** Fields 6 5.345—106 8.909-105 33.91 Plots 28 7.356-10S 2.627-10“ Total 34 6.081-106 1 **Highly significant: F(11’48)’01 = 2.64; F(6’28).01 = 3.53 ns/not significant: F(11 48) 05 = 1.99 , 9° 181 Table 82. Analysis of Variances: Ani-ani Losses. Source DF Sum of Mean F Squares Squares Ratio1 a. Shattering Losses Fields 47 158.9850 3.3827 4.056** Plots 192 160.1446 .8341 Total 239 319.1296 b. Uncut Losses Fields 47 758.9514 16.1479 2.568** Plots 192 1207.4159 6.2886 Total 239 1966.3673 c. Ani-ani Losses Fields 47 1083.1027 23.0447 2.9075** Plots 192 1521.7928 7.9260 Total 239 2604.8955 d. Threshing Losses Fields 47 493.6813 10.5039 6.7322** Plots 192 299.5212 1.5600 Total 239 793.2025 1 **Highly significant: F(47,00), .01 = 1.54 182 Table B3. Analysis of Variance: Random Sickle Losses. Source DF Sum of Mean F Squares Squares Ratio1 a. Shattering Losses Fields 11 18.3749 1.6704 1.5764ns Plots 48 50.8626 1.0596 Total 59 b. Uncut Losses ** Fields 11 114.2872 10.3897 5.2818 Plots 48 94.4199 1.9671 Total 59 208.7071 c. Sickle Losses ** Fields 11 205.1627 18.6512 5.6730 Plots 48 157.8111 3.2877 Total 59 362.9738 1 **Highly significant: = 2.64 F(11,48), .01 ns/not significant: = 1.99 F(11,48), .01 183 Table B4. Analysis of Variance: Ani-ani and Foot-treading Labor. Source DF Sum of Mean F Squares Squares Ratio a. Ani-ani Labor (Man-Hours Per Ton) *7: Fields 47 660,805 14060 4.303 Plots 192 627,358 3267 Total 239 1,288,163 b. Ani-ani Labor (Man-Hours Per Hectare) %* Fields 47 2,410.33 51.2837 7.05 Plots 192 1,396.40 7.2729 Total 239 3,806.73 0. Foot-treading Labor (Man-Hours Per Ton) ** Fields 47 238,410 5073 8.95 Plots 192 347,225 567 Total 239 585,635 d. Foot-treading Labor (Man-Hours Per Hectare) ** Fields 47 3462538 73761 10.49 Plots 192 1348761 7025 Total 239 4811299 1 **Highly significant: F = 1.54 (47,00), .01 184 Table B5. Analysis of Variance: Random Sickle Labor. Source DF Sume of Mean F Squares Squares Ratio a. Man—Hours Per Hectare ** Fields 11 220,767 20070 5.93 Plots 48 162,435 3384 Total 59 383,202 b. Man-Hours Per Ton ** Fields 11 21,457 1951 8.84 Plots 48 10,592 221 Total 59 32,049 1 *iflighly significant: F(11,48), .01 = 2.64 185 APPENDIX C STANDARD DEVIATIONS ASSOCIATED WITH VARIOUS FIELD AND PLOT COMBINATIONS FOR LOSS AND LABOR DATA 186 APPENDIX C STANDARD DEVIATIONS ASSOCIATED WITH VARIOUS FIELD AND PLOT COMBINATIONS FOR LOSS AND LABOR DATA The purpose of this appendix is to provide researchers with calcu- lated standard deviations for various plot and field combinations based upon the variabilities of this study. This should assist future studies in estimating an initial sample size of plots and fields. The standard deviations were determined as follows: n fiR' S: where 02 = MSTR - MSE F r 2- GP — MSE and MSTR = mean square treatment error MSE = mean square error r = plot replications per field n = number of fields k = number of plots The tables could be used to give guidance to the following questions: 1. Given a cost constraint, what combination of fields and plots would give an acceptable standard deviation? 187 2. What combination of fields and plots should be considered to produce a standard deviation within specified limits? For example, if cost or time constraints indicated that only 30 fields could be sampled, what plot combination might be used to give an accept- able standard deviation and interval range? Using Table C2, three plots per field yields a standard deviation of .3682 resulting in an interval of 3.76 §_4.48 §_ 5.20 at the 95% confidence level. If the number of plots are increased to five per field the interval is tightened to 3.84 §_4.48 §_5.12. The conclusion might be that three plots per field would be sufficient to give an acceptable result. The same question can also be explored by estimating the cost of reducing the standard deviation: in this case reducing the standard deviation from .3437 to .328l or 4.5%. The question of a mean range of 1% loss might be explored: what standard deviation could be associated within this limitation? At the 95% confidence level, this would require a standard deviation of .2551. Again, referring to Table C2, several combinations are possible: 50 fields with 5 plots per field, 60 fields with 3 or 4 plots per field, or 80 fields with 2 plots per field are possible selections. Thus, these tables do offer the researcher a beginning point, as his first estimate. 188 Table Cl. Standard deviations: ani-ani shattering losses for different field and plot combinations. Number of Fields 10 20 30 40 48 50 60 70 80 100 l .3666 .2592 .2116 .1833 .1673 .1639 .1497 .1386 .1296 .1159 2 .3044 .2153 .1758 .1522 .1390 .1361 .1243 .1151 .1076 .0963 +3 3 .2807 .1985 .1620 .1403 .1281 .1255 .1146 .1061 .0992 .0888 534 .2680 .1895 .1547 .1340 .1223 .1199 .1094 .1013 .0948 .0847 25 .2601 .1839 .1502 .1301 .1187* .1163 .1062 .0983 .0920 .0823 E7 .2508 .1773 .1448 .1254 .1145 .1121 .1024 .0948 .0887 .0793 10 .2435 .1722 .1406 .1218 .1112 .1089 .0994 .0921 .0861 .0770 *Standard deviation for this study. Interval: 1.17 5_1.40 5_1.63; MSTR = 3.3827; MSE = 0.8341; df = 239; t 05 = 1.96 Table C2. Standard deviations: ani-ani uncut losses for different field and plot combinations. Number of Fields 10 20 30 40 48 50 60 70 80 100 l .9089 .6427 .5247 .4544 .4148 .4065 .3710 .3435 .3213 .2874 2 .7153 .5058 .4130 .3576 .3265 .3199 .2920 .2703 .2529 .2262 +3 3 .6378 .4510 .3682 .3189 .2911 .2852 .2604 .2411 .2255 .2017 534 .5953 .4210 .3437 .2977 .2717 .2662 .2430 .2250 .2105 .1883 g 5 .5683 .4018 .3281 .2841 .2594* .2541 .2320 .2148 .2008 .1797 E 7 .5357 .3788 .3093 .2679 .2445 .2396 .2187 .2025 .1894 .1694 10 .5100 .3603 .2944 .2550 .2328 .2281 .2082 .1928 .1803 .1613 *Standard deviation for this study. Interval: 3.97 §_4.48 5_4.99; MSTR = 16.1479; MSE = 6.2886; df = 239; t 05 = 1.96 189 Table C3. Standard deviations: total ani-ani losses for different field and plot combinations. Number of Fields 10 20 30 4O 48 50 60 70 80 100 1 1.047 .7399 .6041 .5232 .4776 .4680 .4272 .3955 .3700 .3309 2 .8359 .5910 .4826 .4179 .3815 .3738 .3412 .3159 .2955 .2643 -§ 3 .7527 .5322 .4346 .3764 .3436 .3366 .3073 .2845 .2661 .2380 ES 4 .7075 .5003 .4085 .3537 .3229 .3164 .2888 .2674 .2501 .2237 g 5 .6789 .4800 .3920 .3394 .3099* .3036 .2772 .2566 .2400 .2147 E 7 .6447 .4559 .3722 .3223 .2943 .2883 .2632 .2437 .2279 .2039 10 .6178 .4368 .3567 .3089 .2820 .2763 .2522 .2335 .2184 .1954 *Standard deviation for this study. Interval: 5.27 5_5.88 5_6.49; MSTR = 23.0447; MSE = 7.9260; df = 239; t.05 = 1.96 Table C4. Standard deviations: foot-treading losses for different field and plot combinations. Number of Fields 10 20 3O 40 48 50 60 70 80 100 1 .5787 .4092 .3341 .2893 .2641 .2588 .2362 .2187 .2046 .1830 2 .5068 .3584 .2926 .2534 .2313 .2267 .2069 .1916 .1792 .1603 -§ 3 .4805 .3398 .2774 .2402 .2193 .2149 .1962 .1816 .1699 .1519 ES 4 .4668 .3301 .2695 .2334 .2131 .2087 .1906 .1764 .1650 .1476 g 5 .4583 .3241 .2646 .2292 .2092* .2050 .1871 .1732 .1620 .1449 E 7 .4485 .3171 .2589 .2243 .2047 .2006 .1831 .1695 .1586 .1418 10 .4410 .3118 .2546 .2205 .2013 .1972 .1800 .1667 .1559 .1395 *Standard deviation for this study. MSTR = 10.5039; MSE = 1.560; df = 239; t.05 = 1.96 Interval: 1.97 §_2.38 f_2.79; 190 Table C5. Standard deviations: sickle shattering losses for different field and plot combinations. Number of Fields 10 12 20 30 40 50 60 7O 80 100 1 .3438 .3138 .2431 .1985 .1719 .1537 .1403 .1299 .1215 .1087 2 .2553 .2331 .1805 .1474 .1277 .1142 .1042 .0965 .0903 .0807 .33 .2180 .1990 .1542 .1259 .1090 .0975 .0890 .0824 .0771 .0689 534 .1967 .1796 .1391 .1136 .0984 .0880 .0803 .0744 .0696 .0622 25 .1828 .1669* .1292 .1055 .0914 .0817 .0747 .0691 .0646 .0578 E7 .1654 .1510 .1769 .0955 .0827 .0740 .0675 .0625 .0585 .0523 10 .1510 .1379 .1068 .0872 .0755 .0675 .0617 .0571 .0534 .0478 * Standard deviation for this study. Interval: 1.05 5_1.28 5_l.5l; MSTR = 1.6704; MSE = 1.0596; df = 59; t 05 = 2.0 Table C6. Standard deviations: sickle uncut losses for different field and plot combinations. Number of Fields 10 12 20 30 4O 50 60 70 80 100 1 .6043 .5516 .4273 .3489 .3021 .2702 .2467 .2284 .2136 .1911 2 .5165 .4715 .3652 .2982 .2583 .2310 .2109 .1952 .1826 .1633 .3 3 .4838 .4416 .3421 .2793 .2419 .2163 .1975 .1828 .1710 .1530 534 .4665 .4259 .3299 .2693 .2333 .2086 .1905 .1763 .1649 .1475 g 5 .4558 .4161* .3223 .2632 .2279 .2039 .1861 .1723 .1612 .1442 E7 .4433 .4047 .3135 .2560 .2217 .1983 .1810 .1676 .1567 .1402 10 .4337 .3959 .3067 .2504 .2169 .1940 .1771 .1639 .1533 .1372 *Standard deviation for this study. Interval: MSTR = 10.3897; MSE = 1.9671; df = 59; t 05 1.09 5_l.92 5_2.75; .0 191 Table C7. Standard deviations: total sickle losses for several field and plot combinations. Number of Fields 10 12 20 30 40 50 60 70 80 100 1 .7975 .7280 .5639 .4604 .3988 .3567 .3256 .3014 .2820 .2522 2 .6868 .6269 .4856 .3965 .3434 .3071 .2804 .2596 .2428 .2172 +3 3 .6456 .5894 .4565 .3728 .3228 .2887 .2636 .2440 .2283 .2042 534 .6241 .5697 .4413 .3603 .3120 .2791 .2548 .2359 .2206 .1973 25 .6100 .5575* .4319 .3526 .3054 .2731 .2493 .2308 .2159 .1931 E7 .5952 .5433 .4209 .3436 .2976 .2662 .2430 .2250 .2104 .1882 10 .5832 .5324 .4124 .3367 .2916 .2608 .2381 .2204 .2062 .1844 *Standard deviation for this study. Interval: 2.08 §_3.20 5_4.32; MSTR = 18.6512; MSE = 3.2877; df = 59; t.05 = 2.0 Table C8. Standard deviations: ani-ani labor for different field and plot combinations (man-h/ha). Number of Fields 10 20 30 40 48 50 60 70 80 100 1 88.30 62.44 50.98 44.15 40.31 39.49 36.05 33.38 31.22 27.92 2 77.72 54.95 44.87 38.86 35.47 34.76 31.73 29.37 27.48 24.58 -E 3 73.85 52.22 42.64 36.93 33.71 33.03 30.15 27.91 26.11 23.35 534 71.84 50.80 41.48 35.92 32.79 32.13 29.33 27.15 25.40 22.71 2 5 70.61 49.93 40.76 35.30 32.22* 31.58 28.82 26.69 24.96 22.33 Eg'7 69.17 48.91 39.93 34.58 31.57 30.93 28.24 26.14 24.45 21.87 10 68.07 48.13 39.30 34.04 31.07 30.44 27.79 25.73 24.07 21.53 *Standard deviation for this study. Interval: 774 < 837 5_900; MSTR = 249,262; MSE = 35,156; df = 239; t.05 = 1.96 192 Table C9. Standard deviations: ani-ani labor for different field and plot combinations (man-h/ton). Number of Fields 10 20 30 4O 48 50 60 70 80 100 1 23.29 16.47 13.45 11.65 10.63 10.42 9.510 8.803 8.236 7.366 2 19.47 13.77 11.24 9.737 8.883 8.709 7.950 7.360 6.884 6.159 -E 3 18.02 12.74 10.40 9.010 8.255 8.060 7.358 6.811 6.371 5.699 E 4 17.25 12.20 9.959 8.624 7.873 7.714 7.042 6.520 6.099 5.454 g 5 16.77 11.86 9.681 8.384 7.654* 7.500 6.846 6.339 5.929 5.302 :3 7 16.20 11.46 9.354 8.101 7.396 7.247 6.614 6.124 5.729 5.123 10 15.76 11.15 9.101 7.882 7.196 7.050 6.437 5.959 5.573 4.986 *Standard deviation for this study. Interval: 180 < 195 §_210; MSTR = 14060.67; MSE = 3267.49; df = 239; t 05 = 1796 Table C10. Standard deviations: foot-treading labor for different field and plot combinations (man-h/ton). Number of Fields 10 20 30 40 48 50 60 70 80 100 1 12.12 8.569 6.996 6.059 5.530 5.419 4.947 4.580 4.284 3.831 2 10.88 7.697 6.284 5.442 4.969 4.868 4.443 4.113 3.849 3.441 -§ 3 10.44 7.383 6.029 5.220 4.766 4.670 4.262 3.947 3.691 3.301 E 4 10.21 7.221 5.897 5.107 4.661 4.568 4.170 3.860 3.610 3.230 g 5 10.07 7.122 5.816 5.037 4.598* 4.504 4.112 3.808 3.561 3.186 E 7 9.910 7.008 5.721 4.956 4.523 4.432 4.047 3.746 3.503 3.134 10 9.788 6.920 5.650 4.893 4.468 4.378 3.996 3.700 3.460 3.095 *Standard deviation for this study. Interval: 68.8 5 77.8 5_86.8; MSTR = 5,073; MSE = 567; df = 239; t.05 = 1.96 Table C11. N—J 01-900 \1 Number of Plots ._4 O 10 193 Standard deviations: foot-treading labor for different field and plot combinations (man-h/ha). 20 30 40 48 Number of Fields 50 7O 80 100 45.12 31.90 26.05 22. 56 20.59 20. 18 17.05 15.95 14.27 41.04 29.02 23.69 20. 18.73 18.35 15.51 14.51 12.98 39.59 27.99 22.86 19. 18.07 17.70 14.96 14.00 12.52 38.84 27.46 22.42 19. 17.73 17.37 14.68 13.73 12.28 38.39 27.14 22.16 19. 17.52* 17. 17 14.51 13.57 12.14 37.86 26.77 21.86 18. 17.28 16.93 15.46 14.31 13.39 11.97 37.46 26.49 21.63 18. 17.10 16.75 15.29 14.16 13.24 11.85 *Standard deviation for MSTR = 73,671; MSE = 7,025; df = 239; t this study. Interval: = 1.96-— 292 < 326 3 360; Table C12. Standard deviations: random sickle labor for different field and plot combinations (man-h/ha). Number of Fields 10 12 20 3O 40 50 60 70 80 100 1 25.93 23.67 18.33 14.97 12.96 11.59 10.58 9.799 9.167 8.199 2 22.43 20.47 15.86 12.95 11.21 10.03 9.156 8.477 7.929 7.091 -E 3 21.13 19.29 14.94 12.20 10.57 9.450 8.627 7.987 7.470 6.682 534 20.45 18.67 14.46 11.81 10.23 9.147 8.350 7.730 7.231 6.468 g 5 20.03 18.29* 14.17 11.57 10.02 8.960 8.180 7.572 7.083 6.336 37 19.55 17.84 13.82 11.29 9.773 8.741 7.930 7.388 6.910 6.181 10 19.17 17.50 13.56 11.07 9.586 8.573 7.827 7.247 6.779 6.062 *Standard deviation for this study. Interval: 128 §_165 5_202; MSTR = 20,070; MSE = 3,384; df = 59; t 05 = 2.0 194 Table C13. Standard deviations: random sickle labor for different field and plot combinations (man-h/ton). Number of Fields 10 12 20 30 40 50 60 70 80 100 1 7.530 6.873 5.324 4.348 3.765 3.368 3.074 2.847 2.662 2.381 2 6.757 6.168 4.778 3.900 3.379 3.021 2.759 2.553 2.389 2.137 _g 316'479 5.913 4.580 3.740 3.240 2.898 2.644 2.449 2.290 2.049 834.6.334 5.782 4.480 3.658 3.168 2.832 2.587 2.394 2.240 2.003 ‘E 5:61247 5.702* 4.418 3.607 3.123 2.793 2.550 2.361 2.209 1.976 'E 7 6.144 5.610 4.344 3.548 3.072 2.749 2.509 2.322 2.172 1.943 :zlo 6.068 5.539 4.290 3.502 3.033 2.713 2.477 2.293 2.146 1.919 *Standard deviation for this study. Interval: 30 5_42 < 54, MSTR = 1951; MSE = 221; df = 59; t 05 = 2.0 195 APPENDIX D BASIC GPSS CONCEPTS 196 TABLE OF CONTENTS D.1 Transactions: The Dynamic Aspects of GPSS 0.2 The Simulation Clock D.3 GPSS Blocks D.3.1 0.3.2 0.3.3 0.3.4 D.3.5 D.3.6 Punchcard Format Transaction-oriented Blocks GENERATE SPLIT TERMINATE START Card ADVANCE ASSIGN PRIORITY Equipment and Process-oriented Blocks: GPSS Entities Facility SEIZE and RELEASE QUEUE and DEPART LOGIC Transaction Flow Modification TRANSFER GATE TEST BUFFER Storage and Retrieval of Information SAVEVALUE Accumulation of Statistical Information TABULATE Permanent Page 198 199 201 202 203 203 205 205 206 207 208 208 209 209 210 212 213 213 213 214 215 216 216 216 217 217 197 0.4 Attributes 0.4.1 Standard Numerical Attributes 0.4.2 Functions 0.4.3 Transaction Parameters 0.4.4 Arithmetic Variables 0.5 Statistical Output Page 218 218 219 221 222 223 198 APPENDIX D BASIC GPSS CONCEPTS GPSS (General Purpose Simulation System) is both a language and a computer program.1 As a language it has a specific vocabulary and syntax with which models can be unambiguously described. As a computer program, it interprets a model described in GPSS language through a GPSS proces- sor, thus making it possible to conduct experiments with the model on the computer. The GPSS model can be expressed either as a block diagram or as a punchcard equivalent of the block diagram. Instructions in GPSS are called blocks because they are associated with the blocks of the flow- chart of a model, an exception being the control card instructions. Each block can be equated to a called subroutine in a FORTRAN program. There is a set of more than forty blocks; this model, in its basic form, will contain sixteen. The logical requirements of the system being modeled dictate which blocks are used. When the model is implemented, it is the relationship of these blocks which is analogous to the interaction of the elements of the real system being modeled. ‘ 0.1' Transactions: The Dynamic Aspects of GPSS Transactions are the dynamic, or moving entities, of GPSS. They can be thought of as units of traffic moving through the model whose meaning is determined by the model builder, e.g., in this model a transaction 1The material for this section has been primarily abstracted from Schriber (1974). 199 will represent 800 square meters of paddy. When a transaction enters a block, a subroutine is activated that changes the state of the model. Thus, the blocks can be perceived as the static part of the model and the transactions the dynamic portion. Although there may be many transactions in the model, only one transaction at a time is moved by the GPSS processor until it comes to rest for one of three reasons: 1) the transaction moves into a block whose purpose is to hold it for a prescribed period of time, 2) a trans- action enters a block whose purpose is to remove it from the model, or 3) the transaction attempts to move into the next block in its path, but that block refuses to let it enter. When the transaction comes to rest, the forward movement of another transaction is initiated, etc. 0.2 The Simulation Clock, Current Event, and Future Event Chains Time passes as events occur in the real world and, in like manner, it is necessary for simulation events to occur against a background of time. As a consequence, the GPSS processor automatically maintains a simulated clock. To further elaborate, when a simulation begins, the processor generates transaction arrivals into the system at specific points in simulated time. The simulated clock is then set to the earliest time that a transaction is to enter the model; this first transaction is brought into the model1 and moved through as many blocks as possible. Eventually, there is nothing further to occur during this time period. The simulated clock is updated to the time that the next event is to take place, and the appropriate transactions, which correspond to this time period, are individually moved. And so the process is repeated. In this manner, the 1Provision for simultaneous arrivals is handled by GPSS but will not be dealt with here. See Schriber, p 70. 200 passage of time is simulated. An advantage of GPSS is that the simulated clock is automatically updated according to the logic described by the model.1 Several features of GPSS and the GPSS clock are worthy of emphasis: 1. GPSS clock is an integer clock, i.e., it does not register fractional time units. An implicit unit of time is determined by the analyst, e.g., an hour, minute, etc. The analyst must decide upon the smallest time unit that realistically reflects the real-system events in the model. GPSS is a "next event" simulator: after the model has been updated, the clock is advanced to the nearest time at which one or more events are scheduled to occur. Potential clock readings are skipped when no events are scheduled to occur. Thus, for practical purposes, execution time requirements are independent of the implicit time unit chosen by the analyst. The question arises: how does the GPSS Processor distinguish between transactions that are active within the model (not necessarily moving because a logic condition may have blocked their ability to move) and transactions that are delayed to move at some future time. The Proces- sor regards each transaction as being on one of several chains: current events chain, future events chains, user chains, interrupt chains, and matching chains. Only the current and future chains have application here. 1 See Schriber, p 59-71, for a more detailed explanation concerning the internal logic of the GPSS processor. 201 The current events chain is composed of all transactions which are scheduled to be moved through one or more blocks at the current instant of simulated time, or as soon asgpossible (ASAP). Included on the current events chain are those transactions experiencing a blocked condition in the model. The future events chain consists of those transactions not scheduled to move through one or more blocks until some future time. This condition can result in two ways: 1. A transaction is in ADVANCE block and is not scheduled to attempt to move to the next sequen- tial block until some future time. 2. A transaction has been scheduled to enter a model at some future time via a GENERATE block. The meaning of the ADVANCE and GENERATE blocks will be evident in the following section. The transactions are ordered on the future events chain according to their future time of scheduled movement. Thus the question, where is a transaction in the model, depends upon the point of view: 1. From the block diagram point of view, the trans- action is in a particular block. 2. From a point of view of chains, the transaction is on a particular chain. A transaction is, therefore, simultaneously "in a block" and "on a chain," for example, in an ADVANCE block and on the future events chain. D.3 GPSS Blocks Each block carries information falling into three categories: Location, operation, and operands. Block locations are designated 202 numerically by the processor in order of their occurrence. The model builder, however, has the Option of providing symbolic location names. A designated location named by the builder is only necessary if there is a needed reference within the program to that block. Symbolic names are composed of three to five alphanumeric charac- ters with the restriction that the first three be alphabetic. The operation of a block is designated by a verb suggestive task. Each block has its own predefined verb, e.g., GENERATE, QUEUE, TERMINATE. When the punchcard version of the block diagram is prepared, these pre- defined verbs appear in columns 8-18 of the punchcard. Columns 2-6 contain the Optional block locations (optional only from the standpoint of the model builder since the processor always assigns a numeric location to each block), and columns 19-71 contain the operands. D.3.1 Punchcard Format This model is presented in punchcard form so a further explanation of the punchcard construction may be helpful. The punchcard must be con- secutively punched without an intervening blank in the operand field (columns 19-71). The first intervening blank in the operand field causes the scan to be stopped. Explanatory comments can be added on that card after this intervening blank. Placement of an asterisk in column 1 makes the entire card a comment card. Commas are used in the operand field to separate the operands. The various blocks have operands associated with them. The operands provide the specific information on which the block's action is based, and the operands can be associated with arguments used to call subrou- tines. The number of operands is related to the type of block. No block has more than seven operands. In general, these are designated A, B, C, 203 0, E, F, and G. If only some Of the Operands are used, e.g., A- and 0-, the intervening Operands, B- and C-, are designated on the punchcard by commas. TheEL-and C-Operands will then take their default values and the scan will continue on to operand D. For some blocks Operands are required while other Operands may be Optional. All Operands must be non-negative integers when Operands are supplied as constants. One exception is with the TRANSFER block; it can assume a decimal value in the statistical mode. 0.3.2 Transaction-oriented Blocks Transactions in this model are created in two ways: through a GENERATE block and through a SPLIT block. The GENERATE block introduces transactions at interarrival times specified by the A- and B-Operands and/or the C-Operand. The block diagram configuration is: GENERATE A. B, C,D,E,F,G Figure 01. GENERATE block. A-Operand: Specifies the average interarrival time Of a transaction, i.e., the average time between consecutive transactions at the GENERATE block. Its default value is zero. B-Operand: Specifies the half-width Of the range over which the inter- arrival time is understood tO be uniformly distributed. Its default value is zero. A FUNCTION designation (to be explained later) in the A- I A 1 Figure 02. SPLIT block. The offspring are identical to the parent in most respects: same priority level, same number of transaction parameters (to be considered later), and same entrance time into the model. The parent moves to the next sequential block; the offspring is rerouted via the block location of the B-Operand. Transactions are removed from a model whenever they encounter a TERMINATE block. TERMINATE blocks always accept transactions, and there may be any number Of such blocks within a model. The configuration and operands are as follows: A-operand: This is an optional operand which decreases the termination counter by the value of the A-Operand. Its default value is zero which implies that transaction terminations at this point does not decrement the termination counter. Figure 03. Terminate block. 206 The START card controls the duration of the simulation. It does not have a block designation but is considered at this point because it relates to the terminatiOn counter and control of the simulation run. The GPSS processor starts the simulation when it encounters the START card. This card has four operands, A-, B-, C-, and D-, but only the A- Operand is of importance at this point. The A-operand initializes the termination counter. An example might best be considered to demonstrate how the duration of a simulation can be controlled. Consider a two block segment which causes the simulation to stop at time 100: GENERATE 100 START Card: Start . . . . 1 Figure 04. START card - Control of simulation duration. A transaction is generated for time 100. At time 100 on the simulated clock, the GPSS processor will move this transaction into the TERMINATE block. Since the A-Operand at this block is 1, the termination counter is also decremented by l. The START card had initialized this counter to 1 at the beginning of the simulation. When the termination counter is decremented to zero, the simulation ends. Consequently, when the trans- action at time 100 enters the TERMINATE block, the termination counter is decremented by 1. Since the counter is now zero, the simulation ends. 207 This example would actually represent a timer segment of a more complete model where the model has been in operation since time 1. The ADVANCE block provides the means to delay transactions. When a transaction enters the ADVANCE block, the time that it will remain in the block is computed using the A- and B-Operands. As with the GENERATE block, the A- and B-operands specify the mean time and the spread (if a function, the function modifier), respectively. If a FUNCTION is speci- fied as the A-operand, the holding time is the value after the evaluation of the function. Holding times may also be specified as constants rather than as functions. ADVANCE blocks can be used anywhere in the model for delaying purposes and may also be used as a dummy block with zero hold- ing time, if A- and B-Operands are defaulted. Its configuration is: _1_ ADVANCE A, B 1' Figure 05. ADVANCE block. Access to or handling of transaction attributes in this model are identified with the ASSIGN and PRIORITY blocks. Each transaction carries with it a set of transaction parameters, the number of which are speci- fied by the F-Operand of the GENERATE block. The ASSIGN block is used to enter numerical values into, or modify the contents of, a transaction parameter. The block configuration is: 208 A, B, C (:_ ASSIGN ‘:) Figure D6. ASSIGN block. Only the A- and B-operands are considered here. The A-operand is the transaction parameter number, and there may be O-lOO different parameters. The B-Operand is the value to be assigned to that parameter. If the A-operand is followed by + or -, the value to be assigned is added or subtracted to the A-operand. If a sign is not indicated after the A-operand, then the specified value replaces the current contents of that transaction parameter. The ASSIGN block, therefore, can be used in replacement, increment, and decrement modes. Indirect parameter specifi- cation is possible with the ASSIGN, but this feature is not used in this model. The PRIORITY block is used to set the priority level of a transac- tion to a specified priority level from O to 127. Transactions are moved according to their priority level, with the higher priority taking precedence. Figure 07 indicates its configuration: A [ PRIORITY ;] Figure 07. PRIORITY block. 209 The A-operand is the new priority level assigned to the entering transaction. The E-operand of the GENERATE block assigns the original priority level, and its default value is zero. 0.3.3 Equipment and Process-Oriented Blocks: Permanent GPSS Entities A facility is a permanent entity which can accommodate one transaction at a time. It can be thought Of as performing a service which can repre- sent people, e.g., a cutting crew or equipment such as threshers. Thus a facility is synonomous with a server. A facility is characterized by two properties in GPSS: 1. Each facility (server) can respond to 93g demand for service at a time. If a new service demand arises when a facility is already provid- ing service, the new demand must: a) either wait its turn to be served, or b) go elsewhere (there is an interrupt capability beyond the needs of this model). 2. When a facility has been engaged, time elapses while the service demanded is being performed. This is defined as service time. Names are given to facilities so that they can be distinguished from one another. These are supplied by the model builder rather than being predefined. The names can be symbolic or numeric. Numeric names must be positive integers. The la-gest valid number of facilities equals the largest number of allowable facilities in a model, which is a function of computer memory capacity, e.g., a capacity of 64K can accommodate a maximum 35 facilities]. 1For additional information see Schriber, p 507. 210 When a facility is to be engaged, a sequence of steps are involved: 1. Wait for the facility, if necessary, over an interval of time. 2. Engage the facility when it is available at a point in time. 3. Hold the facility while the service is performed over an interval of time. 4. Release the facility after the service has been performed at a point in time. The SEIZE and RELEASE blocks relate to steps 2 and 4, respectively. Again, the entities in GPSS which place demands upon facilities are transactions. If a transaction is to engage a facility, it must first seize it by entering the SEIZE block. This actuates a subroutine which changes the facility's status from "not in use" to "in use." If the facility is already engaged, the transaction must wait until the facility is "not in use." A reversal of a facility status from engaged to free is accomplished after a transaction has been served. It then leaves the facility and enters the RELEASE block when the facility status is changed from "in use” to "not in use,” thus permitting another trans- action to move into the SEIZE block and engage that facility. Consequently, the SEIZE and RELEASE blocks are a complementary pair. The block config- urations are designated in Figure 07. A SEIZE RELEASE Figure 07. SEIZE and RELEASE blocks. 211 The A-Operand in both cases is the facility being engaged and released. It is not necessary to designate facilities before referring to them at the SEIZE block because the processor recognizes their exis- tence at this point. The RELEASE block never refuses entry. The QUEUE and DEPART blocks are another complementary pair of blocks related to the engagement of a facility. As was noted earlier, if a facility was engaged, a transaction could not gain entry to that facil- ity through the SEIZE block until the facility was no longer in use. Thus, the transaction (or transactions) had to wait and a queue was automatically formed by the GPSS processor. Then, why is a QUEUE block specified if the queue automatically forms? The QUEUE and DEPART blocks are statistical gathering blocks when waiting occurs. They accumulate information concerning the waiting period and answer such questions as: 1. How many entries were there to the waiting line? 2. How many of these entries actually had to wait? 3. What was the maximum number of waiting entries at any one time? 4. What was the average number waiting? 5. Of those who had to wait, how long did they wait? The QUEUE block makes a transaction a member of a queue (specified by the A-Operand) and adds to the queue's contents the number of units specified by the B-operand (default value of B- is 1). The DEPART block is used to remove a transaction from a queue and decrement the queue content by the value specified as the B-operand. The configurations are: 212 QUEUE DEPART Figure 08. QUEUE and DEPART blocks. The QUEUE entity can be placed at any point where waiting occurs in the model, and statistics are desired. The naming conventions follow those for facilities, either numeric or symbolic names. Usually, but not necessarily, the QUEUE block is placed before a block that can refuse entry. Neither the QUEUE nor DEPART block themselves can refuse entry. A transaction can be a member of more than one queue at a time, up to a maximum of five. The LOGIC block is used to modify the status of a Logic Switch. The status of the switch can be tested elsewhere in the model by GATE blocks to influence the flow of transactions. This block sets the Logic Switch named by the A-operand. The X- mnemonic in Figure 09 specifies the condition of the switch: 5, sets the logic switch (analogous to "on" or "open"); R, resets the switch (analogous to "Off" or "closed"); I, inverts the logic switch (a flip-flop operation that changes set to reset and reset to set). At the start of a simulation all Logic Switches are in the reset position, unless otherwise designated by the analyst. 213 e—i—s LOGIC Figure 09. LOGIC switch. 0.3.4 Transaction Flow Modification These blocks allow the analyst to alter transaction flow other than to the next sequential block. The first of these blocks, the TRANSFER block, will be considered in only two of its four modes: the uncondition- al and the statistical modes. The TRANSFER block configuration is: (B) (a) Unconditional mode. (C) TRANSFER A (B) (b) Statistical mode. Figure 010. TRANSFER block. In the unconditional mode, the A-Operand is omitted and only the B-operand is used indicating the nonsequential block location to which the transaction proceeds. The statistical mode utilizes the A-, B- and C-operands: the A-operand indicates the fraction of time that the 214 entering transaction will transfer to the block location designated by the C-operand; the rest of the time the transaction will proceed to the block location specified by the B-operand. If the B-operand is omitted, then the transaction proceeds to the next sequential block. The TRANSFER block's statistical mode is the only block whose operand is not an integer value. Blocks GATE and TEST are used to control the flow of transactions based on logical conditions to be tested in the model. Both blocks can operate in two modes: 1. Refusal mode - in this case the transaction is accepted in the block only if the condition is met. 2. Alternate exit mode - in this case the trans- action is always accepted in the block and immediately sent to the next sequential block if the condition is met; otherwise, it is sent to an alternate address. Figure 011 represents the configuration for the GATE block. The A- operand is the name of a logic switch. The X- logic mnemonic indicates the setting that is required for the test to be true: LS, tests for set condition (on), LR, tests for reset condition (off). The option B-operand provides the block location when the alternate exit mode is used. (B) Figure 011. GATE block. 215 The TEST block (Figure 012) tests the relation between two Standard Numerical Attributes (values to be discussed shortly). Figure 012. TEST block. The A-Operand is the name of the first Standard Numerical Attribute. The B-operand is the name of the second Standard Numerical Attribute. The X-mnemonic is the relational operator to be used in the test: Relational Question implied in the _Operator TEST block context G Is A greater than B? GE Is A greater than or equal to B? E Is A equal to B? NE Is A not equal to B? LE é: A less than or equal to L Is A less than B? Theoptional C-operand supplies the block location when the alternate exit mode is used. A fourth transaction flow modifier used in this model is the BUFFER block. As previously explained, GPSS moves a transaction as far as possible until it is delayed by an ADVANCE block or a logical condition. At times the analyst may want to modify the Operation of GPSS by stopping a transaction at a given block (for a zero time) and allow the processing 216 Of other transactions elsewhere in the model. The BUFFER block (Figure 013) permits this. BUFFER 1 Figure 013. BUFFER block. This block, which has no operands, stops processing of the current trans- action, and restarts the scanning of the current events chain at the front Of the chain, commencing with the first transaction of the highest priority. The BUFFER block involves no time delay so that the trans- action is always processed by the scan at the same clock time when the buffer Operation was initiated. 0.3.5 Storage and Retrieval of Information The SAVEVALUE (Figure 014) block stores the values of the variable in save locations to be used later by the analyst or preserved as output information at the end or during the simulation. SAVEVALUE [A’MJ 4 Figure 014. SAVEVALUE block. This block gives access to the save location specified by the A-operand to store, add, or subtract information defined by the B- and C-operands. 217 As with the ASSIGN block, the + or - sign must follow the A-operand to add or subtract information. The B-Operand is the value to be stored, added, or subtracted. The C-operand, which specifies the size of the location, is optional and not used in this model. 0.3.6 Accumulation Of Statistical Information Suppose it is of interest to analyze the values assumed by a random variable during the course of a simulation in order to estimate the properties of a population, of which the random values are a sample. The TABULATE block (Figure 015), in conjunction with the TABLE definition card, will produce a frequency distribution for the desired random variable computing the mean and standard deviation, as well as frequency __1_1 TABULATE ‘ A Figure 015. TABULATE block. classes. The A-operand names the table into which the value is to be entered. This has been initially specified by a TABLE card, which names the table and specifies: the random variable, the first frequency class, the width of each interval, and the number of frequency classes. This model incorporates a special table provided by GPSS called the QTABLE. The QTABLE provides a distribution of queue residence time rather than just the average residence time in the queue. This is not a block image 218 but a table definition card that is defined by the analyst at the start of the simulation. This concludes the block images used in this model. A summary of the attributes associated with the model follows. 0.4 Attributes 0.4.1 Standard Numerical Attributes The permanent and temporary entities described have numerical attributes called Standard Numerical Attributes; they are standard to the system because GPSS automatically calculates them. Some examples would be: current and total block count at all blocks during the course of the simulation, relative simulation clock time, the current content of queue, transaction parameters, random number generators, functions, and numerical constants. The analyst can control the course of the simu- lation dynamically by having the model make direct use of these proper- ties. For example, the rate at which a bank teller works may be directly related to the length of the teller's queue. This model uses the follow- ing SNAs with the symbolic reference code in parentheses: 1. Relative clock time (C1). 2. Current value of random number generator (RNJ)- 3. Current block count (WS-block location name). 4. Transaction parameter value (Pj). 5. Value of a savevalue location (XS-save location name). 6. Current value of an arithmetic variable (VS-variable name). 219 7. Current value of a function (FNz-function name). 0.4.2 Functions FUNCTIONS are computational entities that are evaluated by the processor when referenced. They are used to describe nonuniform dis- tributions especially as applied to interarrival and service times (but not limited only to this application). Whenever a random variate from a nonuniform distribution is required, a FUNCTION definition is used. To describe a nonuniform distribution, actually two or more cards are needed: the first card is called the function header card, and the succeeding cards are called the function follower cards. The function header card contains the word FUNCTION in the Operational field (columns 8-10), with A- and B-operands. The name of the referenced function goes in the block location field (columns 2-6). The A-operand designates the GPSS random generator to be used1 and is punched as RNj, where j = 1, 2 . . 8. The B-operand specifies the number of data points used as inputs. It is punched as 0n (where n = number of data points) for a dis- crete distribution or as Cn for a continuous distribution. The input data are punched on follower cards in pairs. The basic information is (Xi, Yi) where Xi is the cumulative probability value and Yi is the corresponding value of the random variable. These basic units (Xi and Yi) are ordered so that the Xi's form an ascending sequence on the follower card. 1There are eight possible random generators in GPSS. 220 Graphically, this can be depicted for a continuous function: Function Value O :0 d --- i an Figure 016. Continuous function interpolation. Linear interpolation is performed, for example, between the points 30 and 40 with the resulting value returned as the function's value (Xi). Functions are referenced in the program by the following notation: 1. FN$sn, where sn is the symbolic name, or 2. FNj, where j is the numeric name. Two examples of function referencing follow: 1 GENERATE FN$ IAT ADVANCE FN 10 a. GENERATE block. b. ADVANCE block Figure 017. Function referencing at GENERATE and ADVANCE blocks. Interarrival times of transactions at the GENERATE block (Figure 017a) to the distribution of the function named IAT (a constant interarrival rate). Delay time, i.e., service time, at the ADVANCE block (Figure 017b) would be determined according to the distribution of the function named 10. 221 Functions substituted for the B-operand in a GENERATE or ADVANCE block have special meaning in defining a Poisson or exponential distribu- tion. Neither of these are involved in the current model. 0.4.3 Transaction Parameters Transactions have numeric properties and each transaction possesses a set of parameters (from O to 100 different parameters in GPSS). As a transaction moves through a model, its parameter value can be assigned and modified according to the logic of the model. Some of the pertinent features of transaction parameters are: 1. The number of parameters that a transaction can have is specified by the F-operand of the GENERATE block. The default condition is 12 parameters. The name of a parameter consists of two parts: A family name and a specific family member. P is the family name. Integers 1 through 100 indicate the specific family member. For example, P10 indicates parameter 10 of a transaction. Parameters gggggt_be named symbolically. Values of parameters are signed integers. When a transaction enters a model, its initial value is zero. The meaning of a parameter is determined by the analyst. A numeric encoding scheme may be used to identify attributes which the parameter is to carry through the model. Or, the numeric value itself may be of direct significance. 222 6. Parameters may be used whenever the use of an SNA is appropriate: for example, as block operands, function arguments, or in variables. When parameter values are used as data, the data values are taken only from the parameter set of the active transaction, i.e., the transaction being processed. 7. Since a specific parameter value is associated with a specific transaction, and that trans- action is removed from the model through a TERMINATE block, so are the specific parameter values associated with that transaction, unless they are saved in a savevalue location. 0.4.4 Arithmetic Variables An arithmetic variable is a user-defined computational entity. It is referenced by Vj or V$sn where "j" is the number of the variable, if the variable has been named numerically, and "sn" is the name of the variable, if it has been named symbolically. The value of an arithmetic variable is the value of the user-defined arithmetic expression. An arithmetic expression, in turn, is a collection of data specifications connected by arithmetic gperators. The possible arithmetic operators are addition, subtraction, multiplication, integer division, and modular division. An arithmetic variable is defined by a variable definition card which is divided into three fields: location, operation, and operands (the same as with the earlier definition cards). The name, VARIABLE, occupies the operational field and the arithmetic expression occupies 223 the operand field, not to extend beyond column 71. If an arithmetic expression extends beyond column 71, it must be broken down into two or more variables. 0.5 GPSS Statistical Output It was earlier stated that a desirable feature of GPSS was its statistical capability. The SEIZE and RELEASE blocks have been described as complementary blocks which control a facility, i.e., a service. Some questions of interest regarding a service might be: 1. What fraction of time was the facility busy? 2. How many different times was the facility captured? 3. What was the average service time per capture of the facility? Answers to these questions are automatically provided at the end of the simulation period. The printed output can be summarized as follows: 1. Names of various facilities used in the model. 2. The fraction of time that each facility was in use (state of capture) during the simulation. 3. The number of times the facility was seized. 4. The average holding time per capture. 5. The number of the transaction, which currently has the facility captured. 6. The number of transactions, which have preempted the facility. Since a facility in this model represents a component in a harvest- ing system (e.g., a thresher or a cutting method), of particular interest 224 is the average threshing or cutting time and the utilization fraction of each component.‘ In like manner, GPSS automatically provides a set of queue statistics which would be directed to such concerns as: 1. How many entries were there to the waiting line? How many of these entries were forced to wait? What was the maximum queue length at its longest? What was the average number of entries waiting? Of these entries which had to wait, how much time did they spend in the queue on the average? The output for the queues can be summarized thusly: 1. 2. Name of the queue(s). The maximum content of the queue during the simulation run. Average value of the queue content. Total entries to the queue. The number of entries which did not wait (called zero entries). The percent of non-waiting (zero) entries. The average time each queue entry spent waiting (including zero entries). Same as for Number 7 gxggpt_for non-zero entries only. The name of the GPSS table in which the 225 distribution of queue residence time is being tabulated. 10. Current values Of the queue content at the time the simulation was terminated. Of special interest to this study is the average waiting time before the ripened paddy is cut. Item 9 deserves brief mention because it relates to an optional output which the analyst can designate. These are three: a. The mean, standard deviation, and frequency dis- tribution of the residence time in a queue (QTABLE mode) may be of as great importance as the average waiting time. The QTABLE card causes this information to be printed in table form. b. The distribution of interarrival time (IA-mode) at points within the system may be of importance which can be produced in table form by use of a TABULATE block. c. The analyst may want to estimate the distribu- tion of the arrival rate at which a transaction arrives at a point within the system (RT-mode). Again, the TABULATE block can be used. These three table modes are special uses of the table entity as regards queuing statistics. The normal table statistics are automatically produced at the end of the simulation. 226 In addition, the statistical output includes all nonzero SAVEVALUE locations. These will represent the status of the system other than what has taken place in the time dimension. This concludes the basic GPSS concepts that are utilized in the crop loss and labor models, which is necessary to the understanding of the punchcard representation of this model. 227 BIBLIOGRAPHY BIBLIOGRAPHY "Post Crop Losses Said To Cost Indonesia 25% of Its Rice." Asian Wall Street Journal. 16 June 1979, p. 6. Bobillier, P. A.; Kahan, B. C.; and Probst, A. R. Simulation with GPSS and GPSS V. New Jersey: Prentice Hall, Inc., 1976. Central Bureau of Statistics (CBS). Statistical Yearbook of Indonesia, 1977. Jakarta: Central Bureau of Statistics, 1978. Chung, C. J. and Lee, C. H. Final Report of Post-Harvest Rice Systems (Korea). Ottawa, Canada: International Development Research Centre, 1978. Churchman, C. West. "An Analysis of the Concept of Simulation." Symposium on Simulation Models. Edited by Austin C. Hoggat and Fredrick E. Balderson. Cincinnatti; South-Western Publishing Co., 1963. Quoted in Naylor et a1. Computer Simulation Techniques. 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