LIMEARY “ll!liliillllzllljjiflllilLllliifllflflflilljlfllfllflfl * Miciib W niversity {Hm This is to certify that the thesis entitled MICRO-ECONOMIC EFFECTS OF TECHNOLOGICAL CHANGE ON SMALLHOLDER AGRICULTURE IN NORTHERN NIGERIA: A LINEAR PROGRAMMING ANALYSIS presented by Enefiok George Etuk has been accepted towards fulfillment of the requirements for PhoDo degree in AgY‘ICUItUY‘aI Economics “(Cd/zany“ W Major professor Date Apr‘fl I7, 1979 0-7639 OVERDUE FINES ARE 25¢ PER DAY PER ITEM Return to book drop to remove this checkout from your record. *wggr 2004 MICRO-ECONOMIC EFFECTS OF TECHNOLOGICAL CHANGE ON SMALLHOLDER AGRICULTURE IN NORTHERN NIGERIA: A LINEAR PROGRAMMING ANALYSIS BY Enefiok George Etuk A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1979 KJI/ (/32! 12/ slag- ABSTRACT MICRO-ECONOMIC EFFECTS OF TECHNOLOGICAL CHANGE ON SMALLHOLDER AGRICULTURE IN NORTHERN NIGERIA: A LINEAR PROGRAMMING ANALYSIS BY Enefiok George Etuk The primary purpose of this study was to analyze empirically the changes in farm income, enterprise combina— tion, resource use and productivity, and in the elasticities of supply for selected crops that may be associated with the adoption of a new technology (embodied in the use of modern inputs) on a small farm in Northern Nigeria. Using farm survey data obtained from the Zaria area of Kaduna state, optimum farm plans were generated for a representative small farm with traditional and new tech— nologies. A comparative analysis of these optimum farm plans was used to obtain indications of the direct change in farm income, farm resource use and productivity and in the cropping pattern that could result from the inter- polation of the elements of the new technology into the existing farming system. A static linear programming model, formulated to maximize total gross margins subject to Enefiok George Etuk meeting the minimum grain consumption requirements of the farming household, was used as the computational tool in the farm planning exercises. The results of the analysis showed that the introduction of the new technology would induce significant increases in farm income, resource use and productivity as well as substantial reallocations of the land resource among crop enterprises. Most of the crop enterprises included in the model were in a better competitive position when produced with modern inputs in the rates assumed in the study. The amount of labor available for work on the farm in peak months was found to be a critically limiting factor in agricultural production with the new technology. The introduction of credit opportunities to permit the availa- bility of operating capital for the hiring of additional labor during the peak months, and/or an increase in the number of manhours devoted to farming by household members, substantially improved the potential for achieving increases in farm income, output, resource use and productivity with the new technology. Since the health and nutrition of small farmers are important factors in determining the amount of work that they can undertake on the farm, it was suggested that programs designed to improve the health and nutrition of these farmers should be made an integral part of agricultural development efforts. Given high and increasing wage rates, the absence of a landless class of laborers, and the problems that have frequently frustrated Enefiok George Etuk the administration of credit programs in developing countries (problems such as low repayment rates and the diversion of credit funds for non-farming purposes), it was also suggested that the provision of credit should be considered a short— term solution. The introduction of measures (such as the selective mechanization of farm operations) that significantly improve the efficiency of labor utilization during peak periods was suggested as a long-term solution to the labor bottleneck problem. With the assumptions made in the study, the optimum level of fertilizer use was found to be relatively insens- itive to changes in the prices of chemical fertilizers. The removal of the subsidy on chemical fertilizers did not affect the optimum amounts of fertilizer used. Parametric programming was used to generate normative supply functions for groundnut and tomatoes (the two most important cash crops in the study area) under traditional and new technologies. These step supply functions were transformed into smooth, continuous functions by means of regression analysis and price elasticities of supply were calculated for the two crops. The elasticity coefficients (which indicate percentage changes in output) were converted into absolute changes in the output of the crops in response to a one percent change in their prices. The results indicated that the introduction of the new technology would increase the supply of groundnut and tomatoes but would reduce their price elasticities of supply within the price Enefiok George Etuk range used in the analysis. However, absolute changes in. the output of tomatoes in response to a one percent change in its price increased with the introduction of the new technology. Thus the adoption of the new technology would enhance the effectiveness of price increases in inducing absolute increases in the output of tomatoes. This was also found to be true for groundnut, but only when credit was made available with the new technology. Dedicated to my parents Nette and George Etukudo for all the sacrifices they have made for my education in Nigeria and the United States ii ACKNOWLEDGMENTS I wish to express my deepest gratitude to Professor Warren Vincent, my major professor and thesis supervisor, for his guidance and encouragement during the preparation of this dissertation. I also want to thank other members of my committee, Drs. Lester V. Manderscheid, Peter Matlon, Gerry Schwab, and Carl Liedholm, for their contributions and assistance. I benefited a great deal from Peter Matlon's research experience in Nigeria. I am indebted to Professor Carl Eicher for his intel— lectual stimulation, constructive criticism and personal interest in my progress. Professor Eicher served as my major professor until he went away on sabbatical leave. Dr. Derek Byerlee was also very helpful during my stay at Michigan State University. Drs. G. O. I. Abalu and R. Palmer-Jones of the Depart- ment of Agricultural Economics, Ahmadu Bello University, read and reacted to drafts of the first chapter. Their comments and suggestions were very helpful. Members of the Guided Change Project team, especially Mr. Theo DeWit, provided invaluable assistance during the collection of data. Their assistance is sincerely appreciated. iii I am grateful to the African-American Institute (AFGRAD) for financing my graduate studies at Michigan State Univer- sity and to the Institute for Agricultural Research at Ahmadu Bello University for financing the research project on which this dissertation is based. A sincere debt of gratitude is owed to my wife Alice and my daughter Uduak for their patience, understanding and encouragement during the preparation of the dissertation. Finally, I am grateful to God for keeping me alive. iv Chapter I II III TABLE OF CONTENTS INTRODUCTION, PJPJH COMP—l The Problem and Its Setting Objectives . . The Research Approach. . 1.3.1 The Analytical Framework _l.3.2 Sources of Data 1.3.3 Some Limitations of the Research Design. 1.3.4 Construction of the Representative Farm. Organization of the Study. CHARACTERISTICS OF AGRICULTURAL PRODUCTION NNNNN 0301+th THE WWW WNH IN THE STUDY AREA Physical Characteristics of the Study Area. . . . . . Land Use . Kinds of Crops and Cropping Pattern Farm Labor Force . . . . Farm Capital Technology of Agricultural Production: STRUCTURE OF THE LINEAR PROGRAMMING MODELS FOR THE STUDY AREA. Introduction . . The Objective Function Activities in the Model. . a) Crop production activities b) Labor hiring activities. . . c) Capital borrowing activities d) Fertilizer buying activities e) Grain consumption activities f) Crop selling activities. g) Transfer activities, Restrictions in the Model. a) Land restriction . b) Labor restrictions 33 33 36 37 44 Chapter IV VI 0) Operating capital restrictions d) Grain consumption constraints. e) Non-negative restriction . 3.5 Some Limitations of the Model. ANALYSIS OF RESULTS FROM APPLICATIONS OF THE LINEAR PROGRAMMING MODELS. 4.1 Optimum Organization of the Represent— ative Farm with Traditional Technology and Existing Resource Levels. . . 4.2 Derived Effects of the New Technology with Existing Resource Levels 4.3 Effects of Varying Family Labor and Operating Capital on the Optimum Organization of the Representative Farm under New Technology 4.4 Comparison of Optimal and Actual Organizations of the Representa- tive Farm under New Technology and Existing Resource Levels. 4.5 Summary. NORMATIVE SUPPLY FUNCTIONS FOR SELECTED CROPS UNDER TRADITIONAL AND NEW TECHNOLOGIES Introduction Normative Supply Functions for Groundnut and Tomatoes. . 5.2.1 Groundnut supply functions. 5. 2.2 Tomato supply functions 5.3 The Effect of New Technology on Price Elasticities of Supply for Groundnut and Tomatoes. 0101 NH SUMMARY, POLICY IMPLICATIONS, LIMITATIONS OF THE STUDY AND SUGGESTIONS FOR FURTHER RESEARCH . Summary. Policy Implications. Limitations and Suggestions for. Further Studies (DOUG) 00101-4 vi Page 74 75 75 75 77 78 87 93 100 102 105 105 113 114 119 122 138 138 145 148 APPENDICES Page A AVERAGE HECTARES DEVOTED TO DIFFERENT CROP ENTERPRISES UNDER TRADITIONAL TECHNOLOGY. . . . . . . . . . . . . . . . . . . 151 B NUMBER OF SAMPLE FARMERS GROWING DIFFERENT CROP ENTERPRISES. . . . . . . . . . . 152 C EXPLANATION OF ABBREVIATIONS USED IN THE LINEAR PROGRAMMING MATRIX . . . . . . . . . 154 D EXPLANATION OF ABBREVIATIONS USED IN FIGURES 5.2 AND 5.3 . . . . . . . . . . . . . . 158 BIBLIOGRAPHY. . . . . . . . . . . . . . . . . . . . . . 159 vii LIST OF TABLES Page Rainfall Distribution by Month in MM . . . . . . 35 Kinds and Amounts of Crop Enterprises on the Representative Farm . . . . . . . . . . . 38 Composition of the Representative Farm Family in the Study Area. . . . . . . . . . 39 Monthly Wage Rates and Labor Inputs on the Representative Farm . . . . . . . . 41 Cash Expenses of the Representative Farm by Month . . . . . . . . . . . . . . . 45 Crop Production Activities . . . . . . . . . . . 55 A Comparison of Average Total Labor Requirement per Hectare for Selected Crop Enterprises under Traditional and New Technologies . . . . . . . . . . . . . . 60 A Comparison of Average Monthly Labor Input per Hectare for the Whole—Farm Under Traditional and New Technologies . . . . . 62 A Comparison of Average Yields of Selected Crop Enterprises under Traditional and New Technologies . . . . . . . . . . . . . . . . 63 A Comparison of Actual Rates of Fertilizer Use with Recommended Levels for Selected Crop Enterprises . . . . . . . . . . . . . . . . 65 Labor Hiring Activities. . . . . . . . . . . . . 66 Capital Borrowing Activities . . . . . . . . . . 68 Fertilizer Buying, Grain Consumption and Crop Selling Activities. . . . . . . . . . . 70 Transfer Activities. . . . . . . . . . . . . . . 72 viii Table 4.1 Optimum Organization of the Representative Farm under Traditional Technology and Existing Resource Levels . . . . Marginal Value Products (MVP' s) under Traditional Technology and Existing Resource Levels. . . . A Comparison of the Optimum Farm Organizations of the Representative Farm under Traditional and New Tech— nologies with Existing Resource Levels A Comparison of Marginal Value Products (MVPs) under Traditional and New Technologies with Existing Resource Levels . . . . . . . . . . Optimum Organization of the Representative Farm with New Technology and Variable Resources. . . Marginal Value Products (MVPs) under New Technology and Variable Resources. A Comparison of Optimal and Actual Farm Organizations under New Technology and Existing Resource Levels . . . . . A Comparison of Normative Supply Functions for Groundnut under Traditional and New Technologies with Existing Resource Levels Normative Supply Functions for Groundnut with New Technology and Variable Resources A Comparison of Normative Supply Functions for Tomatoes under Traditional and New Technologies with Existing Resource Levels Normative Supply Functions for Tomatoes with New Technology and Variable Resources Estimated Supply Equations for Groundnut Estimated Supply Equations for Tomatoes. Price Elasticities of Supply for Groundnut Price Elasticities of Supply for Tomatoes. ix Page 79 82 88 91 96 97 .101 .115 .118 .120 .121 .126 .127 .129 .131 Table 5.9 Absolute Changes in Output in Response to a 1 Percent Its Price . . . . . . Absolute Changes in Output in Response to a 1 Percent Its Price . . . . . Page of Groundnut Change in . . . . .133 of Tomatoes Change in . . . . .135 Figure 5.1 Farm—Firm Step Supply Function 5.2 Normative Supply Functions for Groundnut 5.3 Normative Supply Functions for Tomatoes. LIST OF FIGURES xi Page 112 117 123 CHAPTER I INTRODUCTION 1.1 The Problem and Its Setting Before the mid—1960's the agricultural sector was dom— inant in the Nigerian economy. It provided employment for over seventy percent of the labor force, contributed more than half of the gross Domestic Product, accounted for over eighty percent of all foreign exchange earnings and was a major source of public revenues as well as capital for in— vestment in other sectors (IBRD, 1974 77). In addition, agriculture supplied an overwhelming proportion of the country's food requirements. In 1963, for example, food imports constituted less than 3.8 percent of the domestic agricultural produce and consisted mostly of luxury consump- tion goods (Wells, 1974). The performance of the agricultural sector has been deteriorating during the last decade. Between 1966 and 1972, the annual rate of growth of the agricultural sector was 1.5 percent which was lower than the population growth rate of 2.5 percent per annum, and less than those of other sectors such as manufacturing, construction and minerals (Falusi, 197312). Food production has not responded ade— quately to increases in the demand for food. As a result, there have been sharp and frequent rises in food prices. Robinson (1974:6) has reported that ”. . . between 1968 and 1973 wholesale prices of the major food crops (except rice) more than doubled." This situation has necessitated the diversion of scarce foreign exchange to the importation of basic staples which had previously been produced locally and in sufficient quantities to satisfy domestic needs. Nigeria's expenditure on food imports increased from fifty million Naira in 1970 to four hundred million Naira in 1976 (Abdullahi, 1977zl). There are indications that the magni— tude of food deficits may increase if the present trend remains unchecked (F.M.A.N.R., 1974). The production of raw materials has also not kept pace with increases in the needs of expanding local industries. For example, Nigeria was a cotton exporter for many years but now she has to import cotton to meet the needs of domestic textile indus- tries because domestic cotton output is sometimes inade- quate (Hunter, et al., 1976233). A significant constraint on the development of agri- culture is the low level of farm investment (Ogunfowora, 1972:74). The result is that land and labor continue to be the main inputs in agricultural production in Nigeria. The use of purchased inputs such as fertilizers, pesticides and herbicides is extremely low. In 1970—71, for example, only 13,000 tons of plant nutrients were used on all crops in all states in the country (Robinson, 197421). The average national level of fertilizer consumption is 1.28 Kg per cropped hectare. This is well below the recommended average of 18.18 Kg per hectare (Federal Republic of Nigeria, 1975:63). The tools used in farming are mostly hoes and cutlasses. Conditions related to climate such as the tse tse fly and the shortage of fodder restrict the use of ox-drawn imple— ments. Power equipment and large agricultural machinery such as farm tractors are virtually absent (IBRD, 1974:78). This low level of agricultural production technology has been cited as one of the causes of the poor performance of the agricultural sector (Falusi, l973:2). Other factors that may have contributed to the unsatisfactory situation in the agricultural sector include the decrease in the avail— able labor force in the rural areas resulting from rural to urban migration, the shortage of trained agricultural personnel, pest outbreak, poor storage and unorganized marketing systems, and unfavorable weather conditions (AERLS, 1978zl). The future role of the agricultural sector in Nigeria‘s economic development is clearly specified in the Third National Development Plan, 1975-80. Not only is most of the population to continue to derive their living from agriculture, but most of the increases in the labor force may have to be absorbed in the agricultural sector since the elasticity of employment with respect to output in the industrial sector is relatively low. In addition, the sector is expected to meet the demand for better staple food which is growing at the rate of 5 percent per annum. It is also expected to supply sufficient raw materials for expanding local industries, contribute to foreign exchange earnings through increases in exports as well as provide a major market for industrial sector products. The capacity of the agricultural sector to perform these functions may be seriously handicapped by the low level of agricultural production technology. This raises the need for the intro— duction of new technologies designed to increase the output and income of smallholders through improvements in produc— tivity. Hopper (1975:10) has identified four types of oppor- tunities for the development of traditional agriculture.1 These are: a) an extension of the area of land under cultivation, b) a reorganization of traditional inputs in an effort to improve efficiency in production, c) the utilization of more inputs under indigenous technological conditions, e.g., ”a larger appli— cation of labor and capital to make land improve- ments such as an extension of irrigation, land terracing and shaping for better water control", d) ”. . . the use of new inputs singly or in combin— ation with traditional production factors in a new technical relationship", i.e., the intro— duction of a new technology. 1Agricultural development could involve some combina- tion of two or more of these approaches. (Norman, 1974:24). Increases in the area of land under cultivation has been the major means to higher output and income for Nigerian farmers (Helleiner, 1966; Buntjer, 1973). However, it is likely that increases in acreage may not be an impor— tant source of output growth in the future. In some parts of Nigeria, e.g. Kano state, high population densities have brought about scarcity of land. In other areas, increased use of land, under conditions of increasing population pressure, could ”upset the arable/fallow balance” and ”accelerate the fertility loss in [a] traditional system [that is] characteristically dependent on an arable/fallow sequence to maintain yields per acre” (Collinson, 1972:63). A number of studies have shown that allocative effi— ciency is high in traditional agriculture (Tax, 1953; Hopper, 1965; Norman, 1970; Helleiner, 1973; etc.). As a result, Schultz (1964:39) concluded that no appreciable increase in agricultural production could be achieved by reallocating the factors at the disposal of subsistence farmers that are bound by traditional agriculture.2 Limited potential also exists for the development of agriculture through the utilization of more inputs under traditional technology. As Hopper (1974:11) has indicated, "the marginal increment to production of additions to either land area, or to traditional forms of capital and labor on 2Some economists argue to the contrary. See Lipton (1968, pp. 327—351). old lands, will be small. This is partly because of the. low productivity per worker and per unit soil area inherent in older technologies, and partly because of diminishing returns to the inputs added to those already used in present production process." A number of agricultural economists believe that the greatest opportunity for the development of traditional agriculture seems to be in the introduction of improved technology.3 Johnston and Mellor (19691362) think that ". . . the most practical and economical approach to achiev— ing sizeable increases in agricultural productivity and output lies in enhancing the efficiency of the existing agricultural economy through the introduction of modern technology.” Hopper (l975:6) has noted that ”the develop— ment problem for most of the world's nations is how best to accomplish a transformation from a stage where a national economic product is derived primarily from the practice of traditional agrarianism to the stage where output is gener- ated from the use of modern science—based technologies.” Norman (1974120) observed that future increases in agri— cultural production in Northern Nigeria, particularly in the densely populated areas, could only be achieved through substantial increases in land productivity which would 3Technological change is usually defined in relation to changes in the production function. Such changes may come about either through the use of new factors of production or through the adoption of new ways to use previously known factors (Johnson, 1964). Nicholson (1972) refers to the use of new factors as embodied technical change. require new technology. Considerable support for technolr ogical change in traditional agriculture has also been provided by the experiences of Japan, Taiwan and countries in North America and Western Europe that have been success— ful in increasing agricultural output and productivity (Johnston, 1960). According to Mellor (1973:4) it is the interaction of the rising demand for food and diminishing returns in traditional agriculture that gives new technol- ogy such a prominent role in the development of traditional agriculture. There is a choice of strategy in the improvement of technology in traditional agriculture. The strategy decision is whether to encourage technological improvements that raise yields within the existing small farm structure or to encourage the development of large mechanized farms (F.A.O., 1969:65). Collinson (1972:75) has characterized the former strategy as ”improvement” while the latter approach is described as "transformation”. The transforma- tion approach involves structural change whereas improvement involves "intensification" which is usually associated with better seed, improved cultural practices and the use of purchased inputs, particularly fertilizers and insecti— cides. For most of the colonial period in Nigeria, the emphasis was on improvement. During the 1960's there was a switch to transformation. This took the form of large settlement schemes. These schemes failed.4 The govern— ments of Nigeria now believe that the introduction of simple technologies in the form of improved seeds, seed dressing, fertilizers and improved cultural practices is one of the quickest ways of improving agricultural production tech- nology and raising the productivity of agricultural resources (Etuk, 1977). The rationale for this belief is probably based on the experiences of the green revolution countries in Asia.5 Besides it has been indicated that this type of technology could lead to the growth of employment oppor— tunities in agriculture (Johnston and Cownie, 1969). There are some problems associated with the large scale introduction of the green revolution technology among peasant farmers in developing countries. Some of the conditions required for the successful application of the technology, such as the availability and correct use of physical inputs, well prepared fields, adequate protection from pests and weeds and easy access to technical advice are still beyond the scope of the peasant farmer (Pearse, 1977:135). These "expensively—created” conditions are mostly found on research stations (where the new technology 4See Kreinin (1963), Chambers (1969); Basu, Adegboye and Olatunbosun (1969) for some of the reasons for the failure of the schemes. 5This kind of technology formed the basis of the green revolution in India and Latin America. In September 1971, the Federal Military Government sent a team of leading Nigerian agriculturalists to visit seven ”green revolution countries to study how these countries have solved their food production problems.” (AERLS, 197822). originated) and are probably responsible for the spec- tacular results obtained from the use of the technology on these stations. The use of the technology on peasant farms is often characterized by the application of inade— quate amounts of fertilizer and untimely or delayed use of both fertilizers and pesticides because of poorly organized and overloaded delivery systems or local scarcity resulting in the black marketing of supplies. It has also been noted that change from the traditional to the new technology involves a movement away from an agriculture ”whose know—how is passed down by older cultivators and whose inputs are products of local farms and villages” towards one in which the know—how emanates from ”scientific centers” and the inputs are obtained from industry (Pearse, 1977:137). As Ishikawa (1970) had observed, such a shift may not be easily achieved. The rapid spread of the green revolution technology in Asian countries was the result of organized programs designed to facilitate and promote the correct use of the elements of the technology. The main components of these programs included (Pearse, l977:130)2 a) a technological package6 designed to fit the eco- logical conditions of the regions in which it is to be applied, 6A technological package is defined here as a set of complementary inputs whose proportions have been pre— determined scientifically. 10 b) arrangements for the communication of the know— ledge of the technology to farmers, c) measures to ensure the availability of physical inputs like improved seeds, fertilizers and pesticides, d) measures to ”favor the prospect of profitable sale sufficiently attractive to compensate for the greatly increased production costs and risks involved", e) a credit system to facilitate the payment for physical inputs and the financing of additional cultivation expenses such as the hiring of labor. In Nigeria, a variety of measures aimed at promoting improvement in the technology of agricultural production through the introduction and expansion in the use of ferti— lizers and other modern inputs have been initiated. A subsidy scheme has been established to encourage the use of chemical fertilizers which is the main improved tech— nological input (Norman, 1974229). The rate of fertilizer subsidy in Nigeria in the period 1968—69 to 1971—72 was about 50 percent of the state store price (F.A.O., 1974). This was increased to about 75 percent in 1976. In the Third National Development Plan 1975—80, the Federal Government is to supply over 1% million metric tons of fertilizer to the states at a capital cost of over seventy million Naira. An agricultural bank has also been established to provide farmers with the capital needed for the purchase ll of the new farm inputs. The Bank has been provided with, 150 million Naira in the present plan period to supply credit to farmers. A National Accelerated Food Production Programme (NAFPP) and an ”Operation Feed the Nation” (OFN) program have been launched to boost food production through increased use of modern inputs on food crops. The use of these inputs could significantly alter the relative resource requirements of crop enterprises as well as their relative net revenues. Such changes in the tech— nical and economic circumstances within which peasant farmers make their decisions about resource allocation could sub— stantially affect the pattern of allocation of farm resources. This could have pronounced effects on cropping patterns, farm income, employment and on the productivities of farm resources (Dalrymple, 1969243). Gotsch and Falcon (1975) have reported that about 20 percent increase in cropped acreage, about 70 percent increase in farm income and substantial reallocation of land resources among crops were associated with the introduction of the green revolution technology on a representative farm in the Pakistan Punjab. In Northern Nigeria, Ogunfowora (1972) has predicted that sole crops are in a better "competitive position” than crop mixtures under improved technological conditions. The results of the same study has also indicated that the intro- duction of improved technology could result in a higher return per unit of capital. Other studies (Falusi, 1973; Norman, 1976a, b, 0; Spencer and Byerlee, 1976) have shown 12 that changes in labor requirements as well as in returns. to labor are associated with technological change in small— holder agriculture. Data from the ex post experience of the green revolution countries of Asia have also indicated that the technology has important farm level implications. More attempts to predict the likely effects of new technology are needed to provide an adequate basis for the assessment of the technology. Zalla et al. (1977224) have drawn attention to the discrepancies that exist among the results of previous studies. Additional studies could shed more light on such discrepancies and enhance our understand— ing of the farm level economic effects of technological change. The effects of new technology tend to be location— specific in nature due to differences in the cropping options open to farmers (Gotsch, 197129; Collier, 19772351). The implication is that generalizations of the results of a few studies over wide geographical areas may not be valid. This means that analysis of the consequences of changes in the technology of agricultural production are required in more areas in order to broaden the scope of knowledge concerning the role of new technology in traditional agri— culture. The need for micro studies of the economic implications of new agricultural production technology is particularly acute in Northern Nigeria. Although the use of modern inputs in the area has increased significantly in the last few years, relatively few attempts have been made to collect 13 input-output data on the new technology and to determine the probable effects of the adoption of this technology on the production activities of the small farm. The result is that there is a dearth of basic input-output data on the use of modern inputs under farmers' conditions in Northern Nigeria. There is also paucity of relevant quantitative information on the changes in key farm variables such as income, crop mix and resource productivity that are likely to be associated with the use of these inputs on the small farm. The lack of such micro information could widen the gap between the production unit, particularly smallholders, and policy makers and planners. Upton (19732268) has observed that the rate of agricultural development depends on the extent to which the changes in the pattern of produc- tion on the individual farm units that make up the agri- cultural sector contribute to the desired development objectives. Since the ultimate objective of government is to raise farm income and resource productivity, policy makers and planners can only anticipate and evaluate fully the effects of current agricultural development policies and strategies if they understand the improvements in resource productivity and the income of the small farm that are likely to be generated by the use of modern inputs. The analysis of the potential effects of the new technology on the optimum pattern of resource allocation could also provide extension workers with "advisory content” as well as with "an understanding of the reorganizational difficulties 14 the farmer is likely to meet." These difficulties could. then be discussed with the farmer in order to "alleviate much of the uncertainty felt by the farmer about both the demand the change will make on him and on the know-how of the extension worker" (Collinson, 1972:93). The decline in the production of major crops such as groundnuts has often been attributed to the comparatively low prices offered to producers of such crops by the mar- keting agencies. Given that farmers are rational and tend to respond positively to increases in commodity prices, policy makers have sometimes been called upon to raise producer prices in order to increase output. Price increases ranging from 10 to 150 percent have recently been set for major commodities (New Nigerian, April 1, 1975). Decisions on the appropriate increases in prices require a good know- ledge of the price elasticity of supply. It is likely that the adoption of a new technology could have a significant effect on the responsiveness of farmers to price incentives. For example, Gotsch and Falcon (1975:35) found that new technology "exerted a profound influence on both the optimal level of output at current prices (shifts in the supply curve) and the elasticity of farmers price responses." This implies that supply elasticities calculated prior to the introduction of the new technology could be misleading and that an understanding of the effect of the new technology on the price elasticity of supply would be a prerequisite for informed price policy making. 15 1.2 Objectives The primary aim of this study is to analyze empir- ically the likely farm-level effects of the use of modern inputs7 by small farmers in Northern Nigeria. The specific objectives are: 1. To obtain basic input~output data for selected crop enterprises from small farmers who have adopted the new technology. 2. To ascertain the changes in the cropping pattern, farm labor use, farm income and in the productivities of farm resources that are likely to be associated with the use of modern inputs on a small farm in Northern Nigeria. 3. To examine the potential effect of the new technology on the price elasticity of supply for selected crops. 4. To derive from the results of the study some impli- cations for agricultural development policies and strategies. 1.3 The Research Approach Previous attempts to predict the farm-level economic effects of technological change on smallholder agriculture in Northern Nigeria have been limited to partial budgeting of single crop enterprises or to whole-farm planning 7The new technology examined in this study is embodied in the use of these inputs. The level of application of the new technology is that found in the study area in the survey year. 16 exercises in which the data on the new technology have been obtained from research station experiments or demonstra- tion plot trials (Norman, 1976a, b, c; Ogunfowora, 1972). As a result, the information obtained from these efforts have been inadequate as a basis for the evaluation of the new technology. The subsistence objectives of the small- holder necessitate diversification, so that farmers are interested not just in a single crop enterprise but in the farm business as a whole (Olayide et a1., 1972; Blagburn, 1961). Upton (19732197) has noted the dangers of not study- ing the whole farm in situations where more than one crop is of major economic importance. Not only are important supplementary, complementary and competitive relationships among crop enterprises ignored, but it is also difficult to assess the opportunity cost of resources used. The use of research station and demonstration plot data overlooks the marked differences between the conditions at these stations and those within which farmers operate as well as the ability of farmers to adapt the new technology to their own circumstances. Research stations are usually located in the best agricultural areas of the ecological zone in which they are situated, and there is often "consi- derable control of farmer compliance and limitation on his freedom of adaptation" in demonstration trials on farmers' fields (Palmer-Jones, 1978). The objectives of this study are achieved by means of a whole-farm planning approach based on farm survey data 17 obtained from Giwa District in the Zaria area of Northern. Nigeria. In addition to having a relatively large body of base data on agriculture, significant amounts of modern inputs have recently been distributed in the area. 1.3.1 The Analytical Framework 'A linear programming model of the "representative farm” in the study area is used to obtain the optimum farm plan under traditional technology and resource constraints. Activities, constraints, production coefficients and net prices reflecting the use of modern inputs under farmers' conditions are introduced into the model which is then solved to give the optimum farm plan under the new tech- nology. A comparative analysis of these farm plans is used to obtain indications of the direct change in farm income, farm resource productivity and cropping pattern that could result from the interpolation of the elements of the new technology into the existing farming system. Static, normative supply curves for the production of major crops under both technologies are derived by means of parametric programming. Estimates of the price elasticities of these supply curves are derived from statistical analysis and are compared to obtain indications of the likely effect of new technology on the responsiveness of crop production to changes in product prices. The use of linearprogramming as the computational tool in the farm planning exercises is based on the premise that "peasant farmers tend to behave in ways which optimize 18 their objectives given the constraints within which they . operate". Low (1974264) has cited a number of studies of African farmers which support this premise. The technique was first used in the analysis of smallholder production decisions in African agriculture in the late 1950's. Since then, the number of applications of the linear programming approach to African agriculture has increased tremendously.8 Mudahar (1974:2) has indicated that the main advantage of the programming approach is that it "allows for several farm commodities as farm activities, seasonal labor and land constraints, more than one production technique, land-labor-capital substitution, and a choice among several farm activities which are subject to different economic, resource and behavioral constraints.” Thus linear program- ming can be used to provide a more adequate analytical description of whole-farm situations than other commonly used calculation techniques of farm planning. Another important advantage of the linear programming method is that it allows the determination of certain important economic measures of the optimal plan (Hardaker, 1971264). For example, it is possible to say how stable the optimal plan is, measured in terms of the change in the net revenue of each enterprise needed to bring about a change in the levels of the activities in the optimal 8See Mwangi (1978:58-60); and Ruigu (1978:117-118) for a brief but good review of some of the applications of the linear programming approach to African agriculture. 19 solution. Similarly, the productivity of the farm resources can be assessed and the importance of the various planning constraints evaluated. While the linear programming technique provides a versatile tool for planning, it has several limitations. Many of its assumptions are unrealistic. For example, it is often assumed that farmers have no enterprise prefer- ences, they have perfect knowledge of their alternatives and risk and uncertainty do not enter the choice criteria. Upton (1974) has provided an excellent discussion of some of the methodological problems that constitute the most important limitations to the application of linear program— ming to peasant agriculture. However, the advantages of the technique outweigh these methodological limitations. 1.3.2 Sources of Data In order to develop farm plans by linear programming, data are typically needed on the production alternatives on the farm, the technical coefficients of production, prices of inputs and outputs, and the resources that are available or can be made available on the farm. These data are obtained from either primary or secondary sources. In deciding on the sources of data to use, it is important to consider both the relevance and the reliability of the information obtained. The use of secondary sources has the advantage of cheapness and the relative speed with which the data can be assembled; but the data so obtained may not provide reliable estimates of the corresponding 20 parameters of the population in question. One of the problems faced by social science researchers in most developing countries is the lack of reliable data from secondary sources. Official sources may not have the data that are needed or the available data may be very inaccurate. This has made the collection of primary data from the field a common need in the execution of social science research in these countries. The data that are used for the empirical analysis in this study were obtained from both secondary and primary sources. Given the resources available for the study, it was not possible to collect primary data from farmers who had not adopted the new technology. It was hoped that the baseline study conducted at the start of the Guided Change Project (GCP) in 1974 would provide adequate input-output data on the traditional technology. However, data from the baseline study were later found to be incomplete and could therefore not be used. The best available input- output data on traditional technology was obtained from the report of a farm management study conducted in the area in 1966-67 (Norman, 1972). In that study, 124 randomly selected farm families from three villages were interviewed twice weekly throughout the survey year. Thirty-eight of the farmers in the sample were drawn from Hanwa, a village which is only about one kilometer from the one that was surveyed for data on the new technology. The average farm family in Hanwa consisted of seven persons, 21 of which two were male adults. The average size of holding was 2.94 hectares. The average cultivated area was 2.87 hectares fragmented into about seven fields. Table A-1 in Appendix A shows the average hectares that were devoted to different crop enterprises. About 2,381 manhours of labor, consisting of 2,069 man- hours of family labor and 312 manhours of hired labor, were used on the average farm. Since there have not been any major changes in the structure of agricultural production, it is assumed that the data from that study adequately describe the characteristics of traditional technology as presently used on small farms in the area. A survey of two local markets conducted by Theo DeWit provided data on output prices. The major source of data for determining the production alternatives open to the farmers, the resources available, prices of inputs and input—output coefficients of production with the new tech— nology was a farm survey conducted by the author under the auspices of the Guided Change Project (GCP) of the Depart- ment of Agricultural-Economics and Rural Sociology, Ahmadu Bello University. This survey covered the period from March 1977 to February 1978. The purpose of the survey was to obtain input-output data that reflect the use of modern inputs under farmers' conditions in the study area. The heterogeneity of Giwa district in terms of natural conditions, kinds of cash crops grown, urban influence, market opportunities and methods of production led to the 22 use of a two~stage sampling procedure for the selection of respondents. The first stage consisted of a classification of vil- lages in the district into "homogeneous" farming areas on the basis of the major cash crops grown, distance from Zaria and the levels of modern input use. Three types of farming areas were identified for the district. One of these areas (Area I) lies behind the Kufena Rock to the south of Wusasa. It is about two to four kilometers from Zaria. Groundnuts and vegetables (tomatoes, peppers and okra) are the important cash crops in this area, which has also been reported to have the highest rates of application of modern inputs (DeWit, 1978). Another farming area (Area II) is situated near Shika, about eight to ten kilometers from Zaria. Root crops (mostly yams and potatoes) are the main cash crops. The third farming area (Area III) consists of villages located about twenty to thirty kilometers southwest of Zaria. The most commonly grown cash crop in this area is rice which is cultivated on "marshland". Cotton is also widely grown in the area. The lowest levels of modern input use have been recorded on fields in this area. Access to this area from Samaru is extremely difficult during the wet season. Cochrane (1963238) and Collinson (1972296) have indicated that the division of a heterogeneous population into internally "homogeneous" subpopulations could facili- tate data collection procedures by removing natural 23 conditions, urban influence, market opportunities and methods of production as sources of [interfarm] variations. Given the resources available for the study, only Area I was purposively selected for further investigation. Among the villages in this area, Pan Hauya was chosen for the survey for logistical reasons. This village was assumed to be typical of other villages in the selected area in important attributes influencing the pattern of production. In the second stage, 80 households were randomly selected from a list of 300 households in the village. From this sample, a subsample of 50 households, including only those that were certain to obtain modern inputs during the 1977—78 cropping season, was drawn for the survey. The cost route or multiple visit method was used to collect information from the farmers. The researcher and two experienced enumerators from the Guided Change Project interviewed the respondents once a week throughout the cropping season. A number of factors were responsible for the choice of the cost route method. Firstly, most of the farmers interviewed are illiterate, keep no records and had to rely on their memory for the required information. Secondly, the length of memory recall is limited. Thirdly, the information required included ”continuous”, ”nonregis— tered” data such as daily family and hired labor inputs. Data were collected on inputs, outputs, production 24 practices, and expenses for each field farmed by each house— hold in the subsample. Information on ”gandu" fields was supplied by the gandu head, while "gayauna" operators answered questions concerning gayauna fields.9 The problems encountered during the survey are similar to those dicussed by Spencer (1972), Norman (1973), Collin— son (1974), Kearl (1976), Ejiga (1977), and Palmer-Jones (1977). 1.3.3 Some Limitations of the Research Design The design of a study is concerned with the blueprint or scheme for the collection, measurement and analysis of data. It is an important aspect of a study because it affects the validity of the inferences that Can be drawn from the results of the study. Ideally, the appropriate research design is determined by the objectives of the study. In practice, however, the design of a study fre— quently is a compromise dictated by the limitations of the resources available for the study and the availability of data. The ideal research design for achieving the objectives of the present study would have been the controlled experimental design. Such a design (also known as the "Pretest-Posttest Control Group Design") would have involved 9"Gandu" fields are those farmed by the entire house- hold under the supervision of the head of the household. Fields controlled by household members other than the family head are known as "gayauna" fields. 25 the collection of data from two equivalent groups of farmers before and after the introduction of the new technology to one of the two groups of farmers (Stouffer, 1950; Campbell and Stanley, 1966213). Given the natural social setting in which this study was conducted and the resources avail- able for the study, the use of the experimental design was not feasible. Therefore a ”quasi-experimental” design involving the use of data from separate samples of farmers in different years was adopted. In this design [described by Campbell and Stanley (1966253) as the "Separate-Sample Pretest-Posttest Design”] data were collected from one sample before the introduction of the new technology and from another sample after the introduction of the new technology. Both Stouffer (1950) and Campbell and Stanley (1966234) have encouraged the use of ”quasi-experimental designs" in situations where the controlled experimental design is not feasible. They have also stressed that in such situations it is important to be aware of the limitations or imperfections in the adopted design so as to avoid "overinterpretations" of the results of the study. The purpose of this section is to point out the main threat to the validity of the inferences drawn in this study. The use of data from different groups of farmers in different years may not provide a firm basis for making inferences about the effect of the new technology, since such a research design does not provide any way of 26 eliminating or discovering the effects of other factors such as differences in management, weather, labor produc- tivity and the physical characteristics of the area. There is always the disturbing possibility that the pop- ulations of the two samples were initially different and that the observed effects are the result of these other factors rather than technological adoption. Thus the major limitation of the "quasi-experimental" design adopted in this study is that some of the uncontrolled factors may constitute plausible rival explanations of the observed differences between the two sets of farmers. Seltiz et al. (1959293) have pointed out that in the social sciences, where there is little knowledge of what factors need to be controlled, and where many of the relevant factors are difficult or impossible to control, there is no way to be completely certain of the validity of inferences that may be drawn. This possibility of invalid inference makes it necessary to evaluate research findings in the context of other knowledge. They argue that "the establish- ment of confidence in the imputation of any causal relation- ship between events requires repetition of research and the relating of the findings to other research". In this study, inferences are made on the assumption that the cir— cumstances of the two groups of farmers are comparable and that the effects of factors other than technological change are relatively minor. 27 1.3.4 Construction of the Representative Farm The prohibitively high cost of programming every farm unit has led to the use of the representative farm [or more accurately, the representative resource situation (Plaxico and Tweeten, 1963:1458)] as the unit of linear programming analysis. Barnard and Nix (19762363) have indicated that ”in areas where there is reasonable homogeneity in at least some of the major resources —- particularly with respect to natural factors, such as soil type, topography and climate -- linear programming can be used to obtain solutions to 'model"or 'representative' farm situations in order to guide planning on individual farms".10 The usefulness of the representative farm approach is limited by the manner in which the representative farm is constructed. Collinson (19722125) has discussed three alternative tech- niques for deriving representative farms. These are: a) the identification of a particular farm as the typical farm, b) the use of an "average farm" (derived from the means of resources, input-output and net price coefficients of a sample of farms) as the repre- sentative farm, 10Even in these areas, individual farms are likely to display considerable variation around a particular repre- sentative situation (when account is taken of both quanti— tative and qualitative aspects of farm resources). Thus solutions covering a whole range of situations may be required if differences in factors such as farm size and the number of workers are to be accOmmodated. 28 c) the synthesis of a "hypothetical” or composite farm from different components of the population. The identification of a typical farm unit requires consideration of a wide range of relevant criteria. Not only is the selection of these criteria difficult, but also data on them may be unavailable or difficult to collect. Besides, even when the data are available, it is not easy to find a single farm that could validly be considered typical in all respects. An important limitation to the use of the "average farm” is that it brings with it the problem of aggregation bias (Collinson,19722134). This has been demonstrated by Frick and Andrews (1965), Day (1963), Hartley (1962), and Buckwell and Hazel (1972). While the synthesis of a composite farm reduces the aggregation bias, it involves the stratification of the population on the basis of characteristics of farms and farmers which strongly influence the particular decision under study. Farm economics research has shown that nonphysical variables such as institutional restrictions, motivations, prefer- ences, managerial ability, etc., have a profound impact on farm organization, production efficiency and earnings, and deserve being built into stratification schemes (Plaxico and Tweeten, 196221463). One practical weakness of the synthesis procedure is that it is difficult to quantify several of these institutional and human factors and even more difficult to determine their distribution within a 29 population. As Carter (196321454) has pointed out, their. quantification is necessary to provide a basis for strati- fication in sampling. The choice of the method of representative farm construction depends on the purpose for which the results of the study are to be used. If the study is designed to estimate regional or national supply response, which would require that the results for the representative farm be "raised” to give an aggregate estimate, then methods of benchmark farm construction which minimize aggregation bias are needed. However, when the objective, as in this study, is to identify the direction of farm adjustment and estimate the degree of farmer's response to changing prices in a given area, the problems of aggregation bias and their control are not relevant, and the use of the average farm can be justified. Even in the estimation of aggregate supply functions, the benefits of a reduction in aggre- gation bias, achieved through a rigorous construction of benchmark farms must be balanced against the costs (both in terms of time and money) of reducing aggregation bias (Ogunfowora, 1972225). In the present study, the representative farm is based on data obtained from the survey conducted by the author in 1977-78. The farms in the sample were considered to be sufficiently similar with respect to the key variables that affect farm adjustment. The average farm was used as the unit of analysis. Only ten percent of the farms 30 in the sample were more than six hectares in size. These. farms were excluded from the derivation of the representa- tive farm in order to reduce the upward bias of the average farm size. Thus, the levels of the initial resources of the representative farm are based on the means of the resources of sample farms that were less than six hectares in size. Only one representative farm was used for the analysis so as to provide an opportunity for detailed examination of numerous problem situations using parametric techniques. Sharples (1969) has advocated a reduction in the number of representative farms used in supply analysis. He contends that the major economic relationships that research- ers have sought to isolate do not differ greatly among representative farms and can be adequately accommodated by parametric programming on fewer representative farms. He also argued that a reduction in the number of represent- ative farms was necessary for timeliness of the results of the study which is vital particularly for short run analysis. The representative farm apprOach is used in this study to indicate "average results" for a homogeneous group of farms.11 No attempt is made to estimate aggregate results. Sharples (19692359) has stressed the importance of this 11Since every farm is unique, it is not possible to eliminate within—group variation entirely (unless each individual farm is treated as a separate group). There- fore, a group is only homogeneous in relation to the whole population (Upton, 19742120). 1.. 31 type of micro, farm-firm analysis for providing valuable insights that could aid the understanding of short run supply response at the aggregate level. He argues that information on the "Potential economic impact of a change in an instrument variable on a farmer's income and organiz— ation must not be ignored just because it cannot be plugged into a neat mathematical aggregation formula." 1.4 Organization of the Study This chapter was devoted to the definition of the prob— lem and its setting, the statement of the specific objec- tives of the study and the methodological approach adopted in achieving these objectives. In Chapter II there is a description of the characteristics of farming in the study area as revealed in the analysis of the data obtained in the survey conducted by the author in 1977-78. Chapter III presents the structure of the linear programming models used to represent the planning environment of the representative farm in the study area. Model activities, restrictions, technical coefficients and prices are discussed. In Chapter IV the results of the various applications of the models are reported. The derived effects of the new tech— nology on farm income, cropping patterns and resource use are discussed. The response of the production of selected crops to changes in their prices is examined in Chapter V. Normative supply curves for the production of the selected crops under traditional and new technologies are presented and 32 the effect of the new technology on estimates of the price elasticity of supply for the crops are discussed. Chapter VI contains the summary, the policy implications of the results of the study, its limitations and some suggestions for future research. CHAPTER II CHARACTERISTICS OF AGRICULTURAL PRODUCTION IN THE STUDY AREA Proper representation of an agricultural situation in a linear programming framework requires a good knowledge of the structure of farming in the area under investiga- tion. This chapter describes some attributes of the farm- ing system in the study area as revealed in the analysis of the data generated in the survey conducted by the author in 1977-78. The description is presented in terms of the characteristics of the representative farm. 2.1 Physical Characteristics of the Study Area The study area is situated in the Zaria area of Kaduna state which is located in the Northern Guinea Savanna ecological zone. The natural vegetation is savanna woodland. The land is a gently undulating plain at an altitude of 610 to 914 meters. The soils are typically leached ferru- ginous tropical soils. There are two distinct seasons -- the dry season and the wet season. The wet season, which usually begins in March or April,can last for about 145 to 185 days. In 1977, the year this study was conducted, the wet season extended from May to October. The annual rainfall was 745.5 mm. Details of the average rainfall distribution 33 34 in the study area are given in Table 2.1. While the annual rainfall in the study year was higher than that of the drought year of 1973, it was only 67.3 percent of the long term average and considerably lower than those of the three preceding years (1974, 1975, and 1976). The length of the growing season was even shorter than that of the drought year of 1973. Thus 1977 could be considered a "bad" crop year. The implication is that the results of this study would be more typical of a "bad" crop year than either an ”average" or ”good" crop year, 2.2 Land Use Farms vary in size from just over one hectare to almost ten hectares. About ninety percent of the farms in the sample were less than six hectares each in size. The size of the representative farm was 2.83 hectares. The farms tended to be fragmented consisting of an average of about six fields. The average size of field was about 0.55 hectares. There are two types of fields -- gandu fields and gayauna fields. Gandu fields are those farmed by the entire household under the supervision of the gandu head. Gayauna fields are controlled by household members other than the family head. 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NE. . no 0 v.2 m... «.2 ..< o... m... .... .5325... 9.3.8.30 we. uH>Huo< :oH nozuoum no.5 Aura—Juno“; H .m Smfih 57 as the enterprises that most adequately depict the important production opportunities available to the smallholder in the study area. They were significant in terms of their contribution to family food requirements and farm income. Their significance was reflected in the relative proportion of total cultivated acreage that was devoted to these crops. Two activities are specified for each crop enterprise. One of the activities represents production of the crop enterprise using modern inputs while the other represents production without modern inputs. The most common ferti— lization level for the crop enterprise was specified in the production with modern inputs. Data limitations did not allow the inclusion of more than one fertilization level for each crop enterprise. When two or more crops were interplanted in a mixture, the production activity was defined in terms of the mixture, rather than the individual crops in the mixture. Input— output relationships were calculated for the field or enterprise as a whole. Crawford (1978) has discussed some of the problems associated with using crop mixtures as production activities in a linear programming model. The activity unit (i.e., the amount of crop production that each unit of activity represents) is one hectare. The objective function coefficients (Cj) for the crop production activities represent the costs of seed and seed dressing for each unit of activity and are assigned negative signs. The input—output coefficients for traditional and new 58 technologies are presented in Table 3.1. These coefficients are the amounts of input required per unit of activity. They specify how the magnitude of a constraint or restric- tion would be influenced by an increase of one unit of each activity in the model. The coefficients that signify a decrease in the magnitude of a restriction carry positive signs while coefficients indicating an increase in the magni— tude of a restriction have negative signs associated with them. In this study, the technical coefficients of production with traditional technology were obtained from the report of a farm management study conducted by D. W. Norman in the study area in 1966-67. Some of the characteristics of the farmers interviewed in that study were presented in Chapter I. That study was the most relevant and reliable secondary source available for obtaining such coefficients. The input-output coefficients of production with the new technology were obtained from a farm survey conducted by the author in 1977-78. The elements of the survey design were described in Chapter I. Average input—output coeffi- cients for the activities in the model were determined from fields considered to be similar in rate of fertilizer application, monthly labor use and yields per hectare. It was assumed that such fields were also similar in seeding rate, plant population and level of management. Each coefficient is the mean of a small sample of observations from relatively similar fields. The differences between the traditional technology 59 coefficients and the new technology coefficients must be interpreted cautiously given that the two sets of data were generated from separate samples of farmers in different years. If the populations of the two samples were initially different, then not all of the observed differences between the coefficients can be attributed to the introduction of the new technology. It is assumed in this study that the circumstances of the two groups of farmers were equivalent and that most (if not all) of the observed differences in the coefficients are the result of technological adoption. Table 3.2 compares the average annual labor requirement under traditional and new technologies for selected crop enterprises. In spite of some of the labor—saving adaptations of farmers indicated in Chapter II, the adoption of the new technology resulted in increased labor requirements in all cases. The increases in labor requirements ranged from 13.1 percent for sole crop pepper to 41.6 percent for sole crop tomatoes. These increases result mostly from addi— tional labor requirements for fertilizer application, better weeding and the harvesting of heavier yields under the new technology. Tomatoes has the highest increase in labor requirement. This is probably due to additional labor requirements for better seedbed preparation and nursery practices. The amount of time allocated to a particular crop enterprise and how well the farming operations are performed may depend upon the relative importance of the 60 TABLE 3.2 A COMPARISON OF AVERAGE TOTAL LABOR REQUIREMENT PER HECTARE FOR SELECTED CROP ENTERPRISES UNDER TRADITIONAL AND NEW TECHNOLOGIES Labor Requirement Crop Traditional New ' Change Enterprise Technology Technology Percent ML/GC 610.69 722.95 . 18.4 ML/GC/CP 729.91 837.27 14.7 GC 310.47 407.40 31.2 MZ/GC ——- 492.00 -- MZ --- 500.16 -- GN 598.8 694.36 16.0 TM 183.07 259.25 41.6 PPP 487.56 551.35 13.1 SOURCE: Computed. 61 crop enterprise in the farming system. Thus the relative labor requirements of different crop enterprises is specific to the farming system under study and may be different for the same enterpriseixrother farming systems. Table 3.3 compares the average monthly labor input per hectare for the whole farm under traditional and new technologies. It shows that the adoption of the new technology resulted in increases in the labor input per hectare in all months except February, April and May. The highest increases in the labor input per hectare occurred in June, October, November and December. The increase in June is due to additional labor input for fertilizer application and better weeding. The increases in October, November and December are due to additional labor inputs for harvesting the heavier yields resulting from the introduction of the new technology. The increase in the labor input in the month of December is very high. Since the rains came late in the year that data were collected on the new technology, and planting was delayed, it is likely that part of the increase in the labor input in December is due to the effect of a late harvest. The average annual labor input per hectare increased by 16.6 percent with the introduction of the new technology. Table 3.4 shows that the adoption of the new technology gave rise to increases in the yields of crops. The highest increases in yields occurred in millet/guinea corn, sole crop groundnut and sole crop tomatoes enterprises. These are the most 62 TABLE 3.3 A COMPARISON OF AVERAGE MONTHLY LABOR INPUT PER HECTARE FOR THE WHOLE-FARM UNDER TRADITIONAL AND NEW TECHNOLOGIES Average Labor Input per Hectare TFEditional New Change Month Technology Technology Percent March 14.35 17.67 . 23.1 April 51.31 49.47 -3.6 May 89.03 87.63 —l.6 June 96.17 119.79 24.6 July 91.35 101.77 11.4 August 81.37 92.93 14.2 September 62.82 . 71.73 14.2 October 61.02 88.34 44.8 November 65.06 89.40 37.4 December 41.70 70.67 69.5 January 31.54 36.75 16.5 February 22.95 -—- -- TOTAL 708.67 826.15 16.6 SOURCE: Computed. 63 TABLE 3.4 A COMPARISON OF AVERAGE YIELDS OF SELECTED CROP ENTERPRISES UNDER TRADITIONAL AND NEW TECHNOLOGIES Yields per Hectare (KG/HA) Crop Traditional New ‘ Change Enterprise Technology Technology Percent ML 307 - 412 . 34.2 ML/GC { GC 680 827 21.6 ML 261 295 13.0 ML/GC/CP {G0 588 636 8.2 CP 128 126 1.6 GC 600 708 18.0 GN 876 1229 40.3 TM 253 365 44.3 PPP 339 351 3.5 SOURCE: Computed. aThe relative proportions of individual crops in a crop mixture are assumed to be the same under both traditional and new technologies. 64 important food and cash crop enterprises in the study area. The relatively high increases in yields are probably due to greater use of improved seed varieties, better manage- ment practices and the use of seed dressing. The level of fertilizer use on the millet/guinea corn and groundnut enterprises was relatively high as shown in Table 3.5 which compares actual and recommended levels of fertilizer use for selected crop enterprises. The actual levels are much lower than the recommended levels for all crop enter- prises except sole crop groundnut. The most common level of fertilizer use on sole crop groundnut fields in the study area was two bags or 100 kg of superphosphate per hectare, which is about the same as the recommended level. This is probably due to increased extension activities on groundnut fields. b) Labor hiring activities Farmers in the study area use hired labor to augment the stock of family labor available for work on the family farm. Labor hiring activities are represented in Columns A15 to A26 in Table 3.6. Hired labor is obtained under a variety of arrangements, including exchange and contract systems as well as simply hiring on a daily or per hour basis. Work paid for by the hour was the most common and for simplicity all non—family labor in the model is assumed to be hired on a per hour basis. The activity unit is one manhour. The prices used are the wage rates per manhour prevailing 65 TABLE 3.5 A COMPARISON OF ACTUAL RATES OF FERTILIZER USE WITH RECOMMENDED LEVELS FOR SELECTED CROP ENTERPRISES Levels of Fertilizer Use (KG/HA) Crop Actualb Recommendeda Enterprise Supa‘ Sulfa .Supa Sulfa ML/GC 116 105 —-- ——- ML/GC/CP 106 95 --- ——- GC 86 41 125 125 MZ/GC 22 31 --- --- MZ 48 40 220 157 GN 100 0 94 0 TM 44 43 ——— --- PPP 58 32 250-500 250 aThe recommended levels were obtained from Extension guides issued by AERLS. There are no recommendations for crop mixtures. bThe actual levels are the most common fertilization levels found in the survey conducted by the author in 1977-78. 66 .U chcoQo< .H.U mHan cow .m:o..:.>cpnnc 0o :oHuaanLZt acme .00031800 Hubmpom 00.0 m sm.o a sea co mm mm.oH W hN.0 z :00 00 0N 0v.mH w vw.o 3 con 00 mm Hm.vH w mm.o x >02 00 mm hm.mH W NN.0 x 000 00 .m on..m w mm.o x gem 00 cm 3.0m w and x as... .00 m. 0v.mH w sm.0 z .25 00 m. H0.0m w mN.o z :36 00 h. m..v. w sm.c x as: co 0. no.0. v 0m.o z na< 00 m. 00.0 M 0N.0 z ca: 00 v. H0 W HI m: Him HL m. H0 w H: m: new Ha m. mo. w H- m: can He .. HmH w Hu 0: >32 He 0. .n. w .1 m: .00 HA 3 so. w .u m: now He w me w H: :: ms< Ha s wvH w H: m: .:w He 0 on. w H: m: :36 Ha m va w H: m: an: Hm v so w .- m: .g2 Ha m on v H: m: as: HR m >m.0| um.0| vm.on mm.0u mm.0a mm.on om.0| sm.ou ww.on um.ou 0N.0| 0N.oa mzm :aHm Hm: H0 Hm: H0 Hm: H0 Am: H0 Hm: H0 Am: .0 Ham H0 Amm H0 Ham H0 Hm: H0 Am: .0 Am: .0 o.:: moonsomo: .cz pom Hm new Hm own Hm >oz Hm 500 H: com H: ws< Hm Haw Hm :28 Hz as: H9 .02 H: as: Hz a 30m 0N< mm< vm< mm< mm< Hc< 0m< mH< wH< sH< 0H< mH< co.momma w>HuomeO onuH>Hpo< wchHm nonaH «mmHBH>HHU< DszHm momopHnw 0o coHuaCHchm coma .09.:0200 “000.00 00.0 m .- z sea 00 mm 00.0. w .n x saw 00 em «v.0. w .u z 000 00 mm .m.0. w .- z >02 00 mm sm.0. w .u z 000 00 .m 00..m w .u x can 00 0m em on w .- z ms< 00 0. 00.0. w .1 z .00 00 w. .0.0m w .- z :00 00 A. m..0. w .- x as: 00 0. 50.0. v .- z .02 00 0. 00.0 n .- z he: 00 0. 0..0- n..0- 0..0- 0..0- m..0- m..0- m..0- 0..0- m..0- m..0- 0..0- m..0| mmm ca.m H.z0 ..20 ..z0 ..20 ..z0 ..z0 ..30 ..z0 ..x0 ..a0 ..z0 ..z0 0.00 m00.:ommm .02 com cm can om 000 0m >oz 0m .00 um new 0m 0:2 cm .50 0m :20 00 4a: 0m .02 um ta: om a see . 0 00¢ s02 002 002 402 002 «02 .02 002 0m< 002 sm< acmpows. o>HpoomnO moHuH>Hpo< mcHsonnom HmpHnmu mmmHHH>HBU< ozmaommom HoHnnw Ho :oHpmcwHon .Hom2 .00500500 “momsom sum u H cm 000 on mmH n H 0x 0H: mm 0 u H ox mam 2m 0 u H 0. was mm 0 u H on 020 mm 0 u H om was .0 0 u H 02 mac 00 0 u H H 02 moo mm 0 m H H 02 0H: 0m 00. w on- 02 2mHam em 00. w om- 02 2aam 0m H0.0m v 00.H 00.. z :50 co sH m2.0 mm.0 02.0 mm.0 Hm.0 0H.0 0H.0 0H.0 0H.0 00.Hu 00.Hn was 20.0 .02 H0 .02 H0 .02 H0 .02 H0 .02 H0 .0. .0 H02 .0 Hum H0 .00 H0 H02mH0 H02mH0 5.00 moonsomom .oz cam 290 200 sz com com Hzm 000 H20 2HH0mm 2a0mm «IIMdell 302 00 02 A2 02 02 22 me «2 H2 02 mm .o 2 2 2 2 2 2 2 2 2 2 2 20.5020. 0>Hpomnno moHuH>Huo< wcHHHow dono can :oHuQESmco0 :Hauo .mastm HoNHHHuHmm ammHBH>HBO< DZHHHmm QOmU Qz< ZOHBQEDWZOU zHwunna we cofiuacmaaxm pom“ .00000500 ”000000 00.0 m H H- 000 00 0a 00.0H w H H- :00 0o 20 «2.0H w H H- own 00 00 H0.2H w H H- >oz 00 «a 00.0H w H H- 000 00 H0 00.Hm w H H- 000 00 on 00.00 w H H- 0:2 0o 0H 02.0H w H H- H00 00 0H H0.00 w H H- 0:0 00 pH «H.2H w H H- ma: 00 0H 00.0H v H H- 002 0o 0H 00.0 n ., H as: 00 2H H00 0 0 0 0 0 0 0 0 0 0 0 0 022 cuHm 000020 0000 0000 020a 2000 0000 0200 200a 000a 020a 2200 2:09 mmousommm .oz .IIIJI 30m H02 002 002 002 s02 002 002 202 002 002 H02 002 H.00 QOH uocfih 0>Haoonno moHuH>Huo< uwmmcmue amMHBH>HBU< mmmmz Qz< wwoqozmume 3mz mBHB BDZQZDomw mom mZOHBDZDm wqmmbm m>HB¢Emoz N.m mam<8 119 relax the labor constraint at the peak month which is still restricting under the assumptions made about family labor. The largest increases in the supply of groundnut are obtained with a combined increase in the availability of family labor and credit opportunities. The implication is that the relax- ation of both family labor and credit restrictions has the greatest potential for achieving increases in the supply of groundnut under the new technology. 5.2.2 Tomato supply functions The normative supply functions for tomatoes were also obtained by parametrically varying the price of tomatoes over the range 0.38 Naira per kilogram to 0.76 Naira per kilogram, and obtaining the corresponding optimum solutions.5 The quantity of tomatoes produced at each price level under traditional and new technologies with existing resources are compared in Table 5.3. As in the case of groundnut, the results indicate that the adoption of the new technol- ogy increases the quantity of tomatoes produced at each price. This increase in the supply of tomatoes results from both an expansion in the acreage under tomatoes and an increase in yield. Table 5.4 shows the normative supply functions for tomatoes with new technology and relaxed family labor and 5The prevailing price in the study area during the survey period was 0.38 Naira per kilogram. The price range used in the supply analysis represents the expected range of price increase. 120 TABLE 5.3 A COMPARISON OF NORMATIVE SUPPLY FUNCTIONS FOR TOMATOES UNDER TRADITIONAL AND NEW TECHNOLOGIES WITH EXISTING RESOURCE LEVELS Traditional Technology New Technology Price Range Quantity Price Range Quantity (N) _ '(KGl * (N)‘ ' (KG) 0.3747-0.433l 148.57 0.3390-0.4044 356.23 0.4331-0.4574 192.27 0.4044-0.4707 526.94 0.4574-0.4798 294.59 0.4707-0.4992 551.18 0.4798-0.5927 301.56 0.4992-0.5878 589.50 0.5927-0.6714 352.78 0.5878-l.9300 623.23 0 .6714-1312.8334 380.75 SOURCE: Computed. 121 .0005QEOD ”HomDOm ww.mv® 0m0®.OImbhm.o 00.wH® Hmmb.OIN0mw.o ww.m¢® Hb.oooooom Ibaw®.o oo.®om muum.OImmmv.o om.mH® N0m®.OIbmwm.o N®.wH® ham®.01m0mm.o wv.oom mmm¢.OIthv.o wm.mmm bmwm.OIHmmm.o mN.mN® comm.lehwm.o w®.m®m m0mm.onmmwm.o mm.mmm wabv.onvmm0.o mm.®bw Hamm.OIH©wv.o om.mwm wbwm.OINmm0.o mo.b®¢ NN0&.OINwm0.O 0®.mflm 0mm0.OI0000.o mN.®©0 H®w0.0Ibwov.o wH.Hmm Nmmv.OIFON0.O wb.0mm Nwmv.OIvaw.o mm.®mm 0000.0Iwmmm.o mv.HmN bwov.OI0mow.o 0®.®Nm hOb0.OI0000.o m0.owm mm0¢.OImm©m.o mm.mmm wmmm.OIHNmm.o N0.mm 0m00.OI®m®m.o mN.®mm 0000.0Iommm.o 0003 cc A003 33 A00: 300 H000 900 330000 00000 09.20 H085 045005 00.00005 000 0.0000. $028080. 302 530000 00:00 8H0... 30000 000 0&5qu0009 302 530000 00:00 0020 H005 5005 00000.55 0:0 005000009 302 mmomDOmmm m5m¢Hm<> 92¢ wcoqozmomfi smz mBHB mmOBHB0 90905 959809 90 09909908 90 909899 099 .99908 90905 9009 099 99 00 9099 00 99908 9000 0909 00 9903 09 0995593 09 05903 0909808 959805 9099 0089000 003 959 090 0 .0099QEOD “mombom .908 0903 9090009009 905 09099989000 099 99 9900909 9 90 99009599w9m* .908 0903 9090009009 905 09099989000 099 59 9900909 5 90 9900999909m** 999H990H90>2 990090 000 50.50 9 90905 959809 000009095 OO.H O mom.wvwmm 5%.mwoml 990995 699 thHonnomB sz H0.H0 909H900HH0>2 0H0090 00.0 on. 09.0005 N0.wmmm 005-980m 090 9005099009 302 090905 959809 90 5m.5v 9995990590>< 000009095 vm.mHm mm. **®m.0®m bN.®¢bH MOHIHEOW Una hWOHocfiomB 302 90.50 000990009 09990995 Hb.om mm. *Hm.o 00.0mwm mOH 0HQSOQ 6:9 thHOGQOOB 302 59.50 000990009 09990999 090 mw.vm Ow. **Hw.NbHH mv.HMNN wOHIHEOm thHOCSOGB HNQOprvNHB >00 5 o 8905 50>05 00990009 9 mm 0 0 5090990995 090 9005099009 BDZQZDOmU mom mZOHB< 990090 00.00 09. **m5.990 00.0005 00519800 090 9005099009 302 090909 959809 90 90.5v 999599059099 000009095 00.00 00. *000.000 09.0505 005-9500 000 9005000009 302 90.5V 000950009 09990999 00.95 00. 90.000 00.500 005-9500 000 9005000009 302 90.5v 000950009 09990999 090 00.05 09. *00.000 00.090 005:9800 9005099009 50909990099 9.0 5m 0m 8909 50>09 00950009 9 mm 5090990999 090 9005099009 m909¢209 909 920599909 999999 9999259m9 0.0 9qm<9 128 had been met. ,Elasticities of supply with respect to own price were calculated for groundnut and tomatoes using the equation _§9_..13 e'sp S Q Price elasticities of supply for groundnut under traditional and new technologies were calculated at different price- quantity points within the price range 0.20 Naira per kilo- gram to 0.40 Naira per kilogram. These price elasticities of supply are shown in Table 5.7. The elasticity coeffi— cients are positive and indicate the percentage increase in the quantity of groundnut produced in response to a 1 percent increase in price. With traditional technology, the price elasticity of supply for groundnut varies from 3.41 at the price of 0.20 Naira per kilogram to 1.17 at the price of 0.35 Naira per kilogram. Since the elasticity coefficients are greater than one, the supply of groundnut over the stated price range is elastic. The price elasti- city of supply with the new technology and existing resources is constant at 0.81 within the price range used in the analysis. The elasticity coefficients are less than one so that the supply of groundnut over the stated price range is slightly inelastic under the new technology. Thus the introduction of new technology resulted in a decrease in the price elasticity of supply for groundnut at all prices within the stated price range. This means that the per— centage change in the quantity of groundnut produced in response to a 1 percent change in its price, over the price .Umwsmfioo HMDmDOm 129 In m>.o mm.o Hw.o pH.H mm.o nu ow.o hm.o Hw.o Hm.H mm.o II hm.o mm.o Hw.o om.H mN.o II mm.o mm.o Hw.o ow.H mm.o II mo.H om.o Hw.o Hm.m mm.o mv.v mN.H Hm.o Hw.o av.m om.o gonad hafiasm hpflafinmaws>< gonad mafladm moonsomom woohsomom Aom\zv oomwonosH pflcopo cam oomwoaqu can wcwpmflxm odd wcwpmfixm moahm can pfloonu hmoHoonooB 3oz >woaocnoo9 3oz zonoqsooB 3oz new sonoqnooB mmoHosnooE 3oz Homospflomne mmHBHUHBm¢ Honda mawemm moohzomom moohsommm A0M\mv ommwopoaH uHooHU was oommoaocH was wsflpwflxm can wsfipmwxm oofipm was pfioono monosaooB 3oz zonocsooB Roz honoasooB 3oz can mwoaosnooe mwofiosnooe 3oz . 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