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A”; 1*__ :-~ 74.1;- ', 3’ :;_‘--‘ “"4' J U] THE-LC" T— f! 7 .. .....'. 8 Wchigcn S 1.; 3:3 I-r——- -' w-vw‘i This is to certify that the thesis entitled ECONOMIC ANALYSIS OF RESOURCE ALLOCATION IN TRADITIONAL AGRICULTURE: CASE OF THE BOROMO FARM IN BURKINA FASO presented by Adama Bonkian has been accepted towards fulfillment of the requirements for __Mastex;'_s_degree in Agricullunal Economi cs O\QJ\€’~\A \D, 2 (Lawn 5 Major professor Date Juiy 31. 1985 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution —____._._ ._. ._ _ .,_ _._ __ _._ __ .. . . ..fi. ' MSU LIBRARIES .—:—». RETURNING MATERIALS: Place in book drop to remove this checkout from your record. FINES wiI] be charged if book is returned after the date stamped below. ECONOMIC ANALYSIS OF RESOURCE ALLOCATION IN TRADITIONAL AGRICULTURE: CASE OF THE BOROMO FARM IN BURKINA FASO By Adama Bonkian A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Agricultural Economics 1985 ABSTRACT ECONOMIC ANALYSIS OF RESOURCE ALLOCATION IN TRADITIONAL AGRICULTURE: CASE OF THE BOROMO FARM IN BURKINA FASO by Adama Bonkian The scarcity of resources faced by traditional farmers is a constraint to the development of improved technologies. After describing the Boromo region farming system in the western central Burkina Faso, this thesis develops a linear programming model to analyze the competition between a cash crOp (cotton) and food crops (red and white sorghum; maize; cowpeas) in terms of resource allocation. Two farming technologies - animal traction and hand tools - and two cropping patterns - sole cropping and mixed cropping - are evaluated. The results suggest that (l) farmers tend to allocate more fertilizer to the cash crop than to the food crops; (2) in the prevailing conditions, hand tool technology and mixed cropping tend to be economically superior to animal traction technology and sole cropping. Policy to increase producer price and crop yields would enhance profitability of animal traction and encourage farmers to practice sole cropping. ACKNOWLEDGEMENTS I am indebted to many individuals and Institutions for their assistance and cooperation throughout my graduate program at Michigan State University. I wish to express my appreciation to my major Professor Dr. Gerald D. Schwab for his guidance and helpful criticism in constructing the model. Special thanks are due to Dr. Richard H. Bernsten for his comments and critical review of the draft of this thesis. Appreciation is also extended to Dr. Carl Liedhom for his interest and comments. His inputs have made a valuable improvement in this work. I am thankful to Chris Wolf for his assistance in the computer work. A particular expression of gratitude is due to Dr. Warrent Vincent for his moral and intellectual support at the beginning of my study program. I am alone responsible for all errors and omissions. I am indebted to the United States Agency for International Development (USAID) for its financial support throughout my graduate work. I am also grateful to the Department of Agricultural Economics at Michigan State University for the training facilities they offered me. My thanks go also to the Economic Program team of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) in Burkina Faso for the data used in this thesis. Dr. Peter J. Matlon will always be remembered for his stimulation, encouragement and help beyond measurement. Finally, thanks to all my friends and colleagues at Michigan State University for the many ways in which they have facilitated the completion of my graduate program. it TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES Chapter I INTRODUCTION 1. The Problem 2. Objectives and Organization 11 OVERVIEW OF THE FARMING SYSTEM 1. The Farming System 1.1 The climate and Infrastructure 1.2 The Cropping Pattern 2. Resource Allocation Between Cotton and Cereals 2.1 Labor 2.2 Land 2.3 Capital 3. The Marketing of Cotton and Cereals 3.1 Marketing of Cereals 3.2 Marketing of Cotton 4. Concluding Remarks III DATA COLLECTION AND METHODOLOGY 1. The Approach 2. Data Collection 2.1 The Survey Methodology 2.2 The Data Collected 3. The Data Limitations 3.1 Wage Rate Estimation 3.2 Animal Maintenance Costs 3.3 The Prices of Cereals and Cotton 4. Concluding Remarks IV REVIEW OF LITERATURE 1. Some Aspects of ICRISAT Economic Program Research 1.1 Pilot Study of Farming System 1.2 Methodology and Profile of Farm Units 1.3 Factor Use and Productivity for Major Crops 1.4 Effect of animal traction iii 5. Test of New Technology: The Sorghum Variety E 35-1 2.1 The Methodology 2.2 The New Variety's Performance Other Studies on Resource Allocation Controversy in Resource Allocation Studies 4.1 Resource Allocation Efficiency and Analytical Tools 4.2 Price Response Concluding Remarks V A FARM MODEL IN THE BOROMO REGION 1. 2. 5. Theoretical Aspects of the Model The Activities and the Objective Function Values 2.1 The Activities 2.2 The Objective Function Values Resources Available and Constraints 3.1 Land Labor Available Animal Power Available Consumption Requirements Operating Capital Non-negative Constraint ivation of the Input-Output oefficients .1 Land 4.2 Labor 4 . 3 Production Coefficients 4.4 Operating Capital Concluding Remarks bib-DUNN O O\\n+‘\»N 4’09 '1 VI RESULTS FROM THE BASIC MODEL AND SENSITIVITY ANALYSIS 1. 2. 3. The Optimal solution 1.1 The Base Run 1.2 Consistency of the Optimal Solution with Reality Sensitivity Analysis 2.1 Economic Parameters 2.2 Technical Parameters Concluding Remarks VII CONCLUSION AND POLICY RECOMMENDATIONS l. 2. 3. Summary Policy Implications Areas for Further Research 3.1 Agricultural Products Market 3.2 Agricultural Factors Market 3.3 Credit Institutions 3.4 Irrigation and Water Resource Development iv 33 34 34 35 39 39 41 43 44 44 47 59 59 6O 61 61 62 62 62 62 63 63 64 65 65 65 67 72 75 78 80 8O 81 82 83 84 85 85 4. Limitation of the Model and Critical Assumptions 86 4.1 Profit Maximization in Traditional Agriculture 86 4.2 Price Assumption 88 4.3 Exchange Labor Costs 88 APPENDIX A List of Abbreviations used in L.P. Matrix 90 APPENDIX B Base Run solution 93 APPENDIX C List of Crop Enterprise Budget Tables 101 BIBLIOGRAPHY 112-116 Table 2 . I 2.2 3.1 3.2 3.3 3.4 3.5 3.6 3.7 5.1 5.2 6.1 6.2 6.3 6.4 LIST OF TABLES Cumulative Frequency Distribution of Land Preparation Time for Animal Traction and Manual Farmers in Koho OFNACER Cereal Operations 1971/72 - 1978/79 Size and Composition of Households Stratified by Ownership of Animal Traction Equipment ICRISAT Study Villages, 1981 Labor Hiring Activities in Burkina Faso, 1982 Average Cash Expenses for Maintaining Traction Animals by Quarters (1978-79) White Sorghum Prices in Burkina Faso (CFA francs/kilo) Maize Prices in Burkina Faso (CFA francs/kilo) Millet Prices in Burkina Faso (CFA francs/kilo) Cotton Prices in Burkina Faso (CFA francs/kilo) Percent Area Sown to Principle Cereal Based Mixtures, Boromo Zone, 1981 Linear Programming Tableau Results from the Basic Model: Enterprises in the Optimal Solution Results from the Basic Model: Slack and Shadow Prices in the Optimal Solution Results from the Basic Model: Cost of Forcing in Nonoptimal Enterprises Effect of Producer Price of Sorghum Increase on the Optimal Solution vi Page 11 16 23 26 27 28 28 29 29 46 48-56 66 69 7O 73 6.5 6.6 6.7 6.8 6.9 Effect of Cotton Price Increase on the Optimal Solution Effect of Capital Constraint Release on the Optimal Solution Effect of Fertilizer Subsidy Removal on the Optimal Solution Effect of Oxen Plowed Cotton (sole crop) Yields Increase on the Optimal Solution Area of Cotton and Quantity of Fertilizer in Solution When Cotton Oxen Plowed Yields are Increased Effect of Oxen Plowed Maize Yields Increase on the Optimal Solution vii 73 74 75 76 77 78 Figure 2.1 2.2 2.3 2.4 2.5 2.6 2.7 LIST OF FIGURES Location of Boromo in Africa Burkina Faso Annual Average Rainfall, 1982 Population Density, I975 (pers./sq km) Cumulative Frequency of Land Preparation Time for Animal Traction and Hand Tool Farmers Example of Crops Rotation Cycle (Boromo, 1982) Marketing Channels for cereals in Burkina Faso viii Page 12 l4 l5 CHAPTER I INTRODUCTION Generation of farm income through efficient agricultural production provides the real productivity base from which all other objectives can be discussed. Without efficient income generation the entire rural sector will be acting as a drug on both macroeconomic performance and the ability of policy makers to deal with hunger and malnutrition. (Timmer, 1983) Agriculture is the most important sector of Burkina Faso's economy. It employs 80-90% of the 7 million population, comprises one third of the gross domestic product (G.D.P.) and supplies almost all of the export. In the past two decades Burkina Faso has experienced slow agricultural growth. The principal objective of the government today is to regain food self-sufficiency and expand national income from agriculture.1 Economic growth in the country is necessarily linked to the progress of the agricultural sector. This study investigates the economic relationships which characterize peasant agriculture in the Boromo region, in the western central part of Burkina Faso. Knowledge of these relationships should assist in identifying opportunities to increase production. The first section of this chapter raises some issues about the agricultural sector and leads to three important questions related to input allocation, the competition between mixed and sole cropping system and farming technology. l The most recent to date on Burkina Faso government objectives in terms of food policy is presented by Steve Haggblade in a report prepared for U.S.A.I.D./Upper Volta on July 16, 1984. "An Overview of food Security in Upper Volta." Burkina Faso was formerly Upper Volta. All of the literature in the text referred to the country under its old name. I -2- 1. The Problem The two major objectives set by the government for the agricultural sector are to replace food imports with increased cereals production and to diversify into export crops. Achieving food self-sufficiency in the context of the traditional agriculture of Burkina Faso depends very much on the performance of the smallholders. The efforts to increase food availability have focussed on production. The issue about production is whether to increase food production directly or to increase output of non-food items which can then be traded for food. The government has opted for the direct production of food rather than non-food items. The government has used credit, input subsidies, extension support and research infrastructure as policy tools to promote particular commodities. For example, the fertilizer subsidies policy shows that over half of the fertilizer is used in cotton production while the remainder is divided among all the other crops. Thus, roughly half of the fertilizer subsidy of 1.4 billion CFA francs in 1982 went to cotton producers. 2 Animal traction is the major technological innovation proposed by the government. It has been used thoughout the country but is more concentrated in the cotton production areas such as the Boromo region in western central Burkina Faso. Higher yield, an increase in land area under production, and removal of labor bottlenecks are generally believed to be among the advantages of using animal traction. The costs and returns of animal traction are difficult to determine because of ambiguous costs like labor for herding, feed supplements, damage to crops and highly variable rates of utilization. The main issue in the use 2 The exchange rate between the U.S. dollar and the CFA franc is about $1 = 475 CFA francs in 1985. -3- of animal traction is whether the value of initial increases in agricultural production pay for the additional costs of animal traction. The argument against this short term benefit cost viewpoint is that adoption of the technique may be necessary before other measures to raise productivity and conserve land are likely to be profitable for farmers, such as improved varieties. In the context of traditional farming in Burkina Faso, resource allocation between cotton and food crops under hand tool and animal traction technologies is still an unresolved policy issue. This question is even more important in the Boromo region where cotton production is integrated in almost all farm plans. Whether cotton production should be intensified through higher fertilizer application and animal traction cultivation hinges not only upon the projected benefits, but also upon the possible opportunity cost in terms of foregone food grain production. 2. Objectives and Organization Today, the development of cotton as a cash crop in Burkina Faso is controversial among both farmers and decision makers because of the nationwide food shortage and the government's objective to achieve food self-sufficiency from domestic food production. Cotton growing diverts significant resources from traditional food crops, and directly reduces the domestic food supply. This study examines the reluctance of farmers in central western Burkina Faso to stop growing cotton despite the increase in food availability that could be achieved from increased food crop production. The objectives of the study are to: I. test whether a mixed cropping system (cotton and cereals included) can effectively compete with a sole cropping system within the existing farm organization, resource base and skill of farmers. -4- 2. evaluate the competitive position of sole cropping and mixed cropping system cultivated under hand tool and animal traction technologies. 3. examine the competition for land, labor, and purchased input in cotton versus cereals production. To test these interrelated questions the study will be organized in seven chapters. Chapter II explores the farming system in the Boromo region and the allocation of resources between cotton and cereals. Chapter 111 describes the data collection methodology and the field activities. It also explains the approach used to study the problem of resource allocation at the farm level in Boromo and the limitation of the data collected during the survey. Chapter IV reviews the literature on farming system in Burkina Faso and elsewhere in Africa. Chapter V shows how the farm model in the Boromo region was constructed. Chapter VI gives the results of the maximization procedure used in running the model. A sensitivity analysis is performed in this chapter, showing the effect of change in fertilizer price, crop yield, and capital availability under different assumptions. The final chapter presents conclusions, makes policy recommendations and identifies the limitations of the analysis. CHAPTER II OVERVIEW OF THE FARMING SYSTEM This chapter deals with the characteristics of the farming system in the Boromo region of Burkina Faso. The first section examines the climate, infrastructure and cropping pattern. Next the allocation of land, labor and capital between cotton and cereals is discussed. The last section provides an introduction to the marketing system in Burkina Faso with particular emphasis on the aspects which might have an effect on the Boromo region farmers' decision making process. The institutional framework of the marketing system is comprised of the Societe Voltaique des fibres Textiles (SOFITEX) which is the marketing agency for cotton, the Office National des Cereales (OFNACER) which is the cereals marketing board, and the licenced private traders. Also,the Organismes Regionaux de Development (ORDs) intervene in the marketing system by collecting cereals for the OFNACER. I. The Farming System 1.1 The Climate and Infractructure The study site chosen is in the Subprefecture of Boromo, an area of about 4,000 square kilometers in the central western part of the country (see figures 2.1 and 2.2). It is a Guinea savana or south Soudanian zone, with a long term average annual rainfall of 980 mm distributed unequally from May to October. Compared to other regions in the country, Boromo has relatively good agricultural potential with high rainfall and a low effective population density of 30 inhabitants per square km (see figures 2.3 and 2.4). Cotton is the most important cash crop. The major food crops are white and red sorghum, maize and to a lesser extent millet. There is no major irrigation infrastructure such as dams, -5- (Q . \__‘, ‘° "' mm a mo 090 099' Burkina Faso «mtvomw t ‘0 30"; Source: Adapted from C.L. Delgado (1979) p.19 Figure 2.1 Location of Boromo in Africa Am .a .mmm~ .o~uou .p.q EocL emuaou< "muczomv ommm mcwxczm N.N mcamwm 0.8. 8 o 0—3.9. mmL< £0.— mwmwm \\\\ a}... . .333 xi. s mfific .dSZ -8- tanks or runoff control in any village in the study region which covers approximatly 250 square km. Groundwater is not used for irrigation. Farmers invest little in water control and almost none in irrigation. They practice shifting cultivation by moving from one field to another whenever they want. The ready availability of bush land lessens the need for water and erosion control investments by permitting fallowing. Cheap land also permits regeneration of grass and makes other types of soil and water conservation less necessary. No individual ownership rights are established on certain resources such as pastures and trees. There is no strong mechanism, either market or political, to control the exploitation of communal properties in the villages. There is a general lack of public services such as health, water, electricity, education, transport and road maintenance in the Boromo region. However, there is an agricultural extension service which links the research stations and the farmers in the introduction of new technologies. Private enterprise consist only of diesel powered grain mills, hand textile manufactures, construction and sporadic transport firms. 1.2 1.1 J / a. . /// I/ 0.1 1 / K // o '/ . A //// YMO N130 YAKO ' Figure 2.3 Figure 2.4 Annual Average Rainfall in three Population Density in three Locations of Burkina Faso, 1982 Locations of Bu kina Faso, 1975 (pers/kmk ). Source: ICRISAT Annual Report 1981 p. H. 54 1.2 The Cropping Pattern Intercropping is a common and widespread practice throughout the Boromo region. It serves to spread risks and labor demand, thereby increasing income stability. Mixed cropping is a systematic planting pattern which permits a specific spatial arrangement of many crOps on the same field (D.W.Norman et a1. 1982). Two or more different crops are planted on the same hill, a half meter apart from each other. Depending on the soil type and its moisture storage capacity one can identify two major cropping systems: 1) a combination of two crops of different maturity cycles, e.g. a 90 day crop with a full season 120 day crop. Example: early millet with late millet; early maize with late sorghum. 2) a combination of crops with rather similar maturity cycles; e.g. various cereals or legumes systems. Example: full season sorghum or millet with cowpeas. Soil quality is the decisive factor in determining which intercropping system is the most appropriate. Intercropping of cereals and cowpeas is the most common combination across the study region. Under the traditional farming system cowpeas are added to the cereals at very low density and used as a grain and forage crop. A study conducted by the International Crop Reseach Institute for the Semi Arid Tropics (ICRISAT) has shown that cotton is the dominant cash crop in the Boromo area. It competes with food grains, occupying 50% of the equivalent of millet and sorghum areas combined. Maize is the most frequently intercropped cereal, primarily with cotton between hills, and on the same rows, and with sorghum where the later serves as a border crop. -10- 2. Resource Allocation Between Cotton and Cereals Despite the high input level of labor and fertilizer required for cotton growing and the obvious lack of resources, remarkably few farmers choose to grow cereals. Almost all the farmers grow both cereals and cotton and therefore have to solve the problem of optimum allocation of land, labor and capital between crops on farms. The most commonly available fertilizer in Burkina Faso is the cotton complex, 14:23:15 NPK which is imported (ICRISAT Annual Report 1982, p. G40). Although this fertilizer formula was primarily developed for use on cotton, it is also the most common mix used by farmers on food grains because of its availability within the country. Despite a 60% increase in fertilizer imports between 1977 and 1981, total national use remains low. The average application rate was less than 5 kg per hectare on cereals in 1981. In the Boromo region the application rate is about 80 kg per hectare for maize and above 100 kg per hectare for cotton. Current development plans in the country call for a subtantial increase in fertilizer imports with the possible gradual elimination of the existing subsidies. In 1982, the FAO Burkina Faso fertilizer project estimated the real farm gate price of cotton complex fertilizer at 127 CFA francs per kg compare with 65 CFA francs per kg charged by the Organismes Regionaux de Development (ORD's) in 1982, and 55 CFA francs charged by SOFITEX. This represents subsidies borne by the government of 49% and 57%, respectively. 2.1 Egg; The supply and allocation of labor remain two important problems at the farm level. Most of the farming activities are done by family members although occasionally the farmer may get assistance from people outside the family. The heaviest period of family labor usage occurs in the days following the first heavy -1]- rains of the year. This usually corresponds to late May or early June. The entire family is mobilized to plant the crops as quickly as possible. Other peak periods of labor usage are the first and second weeding sometime in July and August and the harvesting from September to December, depending on the crops and rainfall pattern. During these periods work days are eight to ten hours long for the entire family. Farmers recognize the relationship between yields and timeliness of some farm activities. Some farmers avoid the planting season labor bottlenecks by planting directly into the untilled soil. The area plowed for both animal traction and manual farmers (Table 2.1) is relatively large for the first group during the first two weeks of the rainy season. A few manual farmers completed the land preparation task early (figure 2.5), while most of them completed it between the third and seventh week. Table 2.1 Cumulative Frequency Distribution of Land Preparation Time for Animal Traction and Manual Farmers in Koho Area Plowed in Hectares at each Date Animal Traction Manuals! Area Area Weeks in ha % Cumul in ha % Cumul 1 4.5 10 .5 6 2 16.5 40 50 .5 6 12 3 4.0 8 58 l. 5 17 29 4 1.5 3 61 1.5 17 46 5 4.0 8 69 1.0 12 58 6 3.0 7 76 1.0 12 70 7 8.0 18 94 1.0 12 82 8 2.0 4 98 .5 6 88 9 .5 l 99 0 0 88 10 .5 1 100 .5 6 94 11 0 0 - . 5 6 100 Total 44.5 100 100 8.5 100 100 Source: Unpublished Survey Data *Includes only farmers who did land preparation. More than 50% of the area was not plowed. -12- .mcmsgoa Pooh use: new .5568..— FEE< Low 2.: 558.823. 25.. Lo 3533.: 9,322.3 m.~ 6.5m: 1252(3— + 20.5.0.4”: 4<2_Z< D mxmwg FF or m m h w m to m. N. _. O _ _ .r _ _ _ _ _ _ a O 1.. .. [or .t ..om u n I O (D L‘ r ON. 5‘ L‘ U GDP [1 VEBV _-lO .LNEOHBd -13- Farmers can alleviate labor bottlenecks and increase yield through more timely farming operations by using animal traction equipment. This should allow an expansion of cultivated areas by reducing the labor requirement of weeding (Merrit W. Sargent et al., 1981). However, requirements such as planting in rows and adequately trained animals must be met before the farmers are able to adopt animal drawn weeding. 2.2 _L_afl_ Following ICRISAT work in Burkina Faso, the distinction "next to house", "in village" and "in bush" can be made about land resources in the Boromo region. "Next to house" land is defined as the area within a radius of 30 to 40 meters of the house (Delgado, 1978). This land is fertile because it receives all the households' organic wastes and manure from small ruminants and poultry. Some crops such as maize, tobacco, vegetables and sorghum require fertile land, and will be grown on land "next to house". Since this land is close to the dwelling, it is farmed every year. However, it remains fertile because household wastes are spread on it all year long. No purchased inputs are used on crops grown near the house. "In village" land is defined as the fields that are less fertile than those on the "next to house" land. Millet, sorghum cowpeas and groundnuts are usually grown on this land during the wet season. It is adjacent to the "next to house" land and extends over 300 meters from the village. "In bush" land is far from the zone of human habitation. "In bush" fields are usually over two km from the village. Depending on fertility of the natural soil, any crop can be planted on "in bush" land. Cotton, cereals and vegetables are grown on "in bush" land in a rotation system. Cotton is the main crop which receives purchased inputs, primarily fertilizer. Maize and sorghum are sometimes fertilized, but never millet. -14- The rotation system in the cropping pattern allows other crops to benefit from residual cotton fertilizers. In the cycle of rotation, maize and sorghum - either single or intercropped- have to come before millet. The rotation cycle can be illustrated as follows: years 1 2 3 4 5 6 --C----C +MZ----RS+M Z----RS+M Z----M L+CP----ML+CP---- 10 year 10 year fallow fallow C=cotton; MZ=maize; RS=red sorghum; CP=cowpeas; ML=millet Figure 2.6 Example of Crops Rotation Cycle (Boromo 1982) Because millet is more resistant to drought and poor fertility, it is most often grown on old fields or grown in the last years before the field is fallowed. 2.3 Capital In the Boromo region, capital is required for investment in land development and equipment, and for seasonal financing of labor and intermediate inputs. In general, finances are supplied by the farm family itself, without much recourse to external funds. Boromo farmers finance their farming with cash earned from small sale of locally made cloth and manufactured products sold in the village. The sale of ruminants, poultry and some agricultural commodities is also an important income source for farmers. However, external funds are sometimes used to purchase modern inputs and equipment. SOFITEX is the cotton development and marketing agency in the country. It provides fertilizer to farmers on a short term loan basis for cotton growing. At harvest, SOFITEX buys all the cotton produced from the farmers. Farmers who get loans for inputs purchase have to repay SOFITEX at the end of the cropping season when the cotton crop is sold. -15- Animal traction farmers need to hold cash all year long because they have to buy crop residues, salt and medicine for their oxen. A study conducted in eastern Burkina Faso has found that 3,804 CFA francs (about US $9; 1985) is needed to maintain two oxen for a year. 3. The Marketing of cotton and Cereals The objectives a society can reasonably hold for its marketing sector are analogous to the four basic objectives for the food system as a whole: efficient economic growth, a more equal distribution of incomes, nutritional well being, and food security. Because it links the production and consumption sectors, marketing can contribute to all four objectives through the efficiency with which it communicates signals of scarcity and abundance to decisionmakers. (Timmer et. al., 1983, p.151) 3.1 Marketingof Cereals Donor agencies (The World Bank,l982) increasingly observe that the lack of an efficient marketing system in Burkina Faso constitutes a constraint to an increase in cereals production. The intervention of the government in the marketing system began in 1970 with the creation of the Office National des Cereales (OFNACER). In 1974 OFNACER was mandated to purchase stocks from the ORD. The ORDs and the licenced traders had a monopoly on cereal purchases, whereas OFNACER would monopolize cereals sales to consumers. OFNACE ' Consumers ’ Traders Figure 2.7 Marketing Channels for. Cereals in Burkina Faso. Because of the lack of sufficient transport, stocking capacity and good management, the ORD's were unable to play their assigned marketing role. As shown in Table 2.2, the role of OFNACER remained dominated by the distribution of imported grains. -15- Hoxu .oam. .css .¢uu9 >...a.os uu__aa=» eo_uolso~e. u:- .msm_ .aomaoeouoao .uucoaos neon-son Joann cwuvaum >sueaou ou.o) coaa: ._~ ..o> ..oeem ea» c. c.05o room so omeLOum see >u._o¢ 00.»; .ue.uoxcut ..oeom :9 aa_uxmm4~u scam 0.4ap "nausea; .voecaou \u .>.¢o m~\.n\n sauces. -.ou «o.~m \ .eo.uaa_aun—v no.» glow mc.v:.uc~ \I .unosuosa v.0 VOOG Love: «on! nucosI— \ .e.aau .oe_m_uo loss von.>u¢ \M .Axauaoaomv :o.u~n_—e.useuaou no o:c_ou_o> ego-oom ecu mo >u...A-n¢0¢n0~ ecu one: 00.; mo no.0u ten non-£0535 .mhxahm. ..u:: \M 3:08 .. .~.a_ mo.an ea. oe.. :_.~. .5.6. s~.n no.6 ......c~sa .. mm.~ao.. ~m.mo.._ mm.own.. -- mo.mo¢ .. oo.mw4 ._.mnm uoa.o> .. ~m.mn a¢.:~ mn.u~ n6..~ .a.a~ ~o.nn mm.a~ m~.on no....:a:a LuaniiumqflliiiimeIIMm \an ex It _ :0 an. .ouc .. cm.m:~.n ~..~:_.n ~n.oao.. -- ms.¢o~.. .. so.oa: an.~om no:.~> .. 8.3 m~.am 3.8 3.2 2.3 3.2“ 2 .o~ 3.3 32:26 - .32 .. «n.5mm.~ «o.woo.~ om.oua._ -- mo.mno .. mm.m~:. «n.4mm .oa.a> .. a~.mn «c.6m no.u_ ~o.m m~.a~ em.a~ .a.- a~.mn no.._.co:o \Nnucmman .ousuu «m.muo o...m~ an.mo~ -- c..m¢m .. ~..o. m~.~n .o:.a> no.6. m~.m_ o~.m o~.m _o.o_ 6:.m. -.~ «5.6 am.. «0....coaa manages _ouo. co «sausagaa .00509 i mam a am ham as am. i_~ mm im~\:~m_ auxnsm. nax~um_ -\_~m_ \oo¢\ H. \wm \o _ m~\ _ \@ \ww \m _ \. mu\mump . N~\anp mcowumamao meamu amu/ R where R is the net income Cl is the net return per unit of the Jth activity expressed in CFA francs. Xi is the Jth activity. A = m x n matrix of technical coefficients for the activities. x = n x 1 vector of activities level. b = m x 1 vector of resource restriction. The objective function is maximized subject to a set of 41 linear constraints. These are: - three land constraints - twelve monthly labor constraints - one animal power constraint - six constraints on the average yield permitted - one fertilizer supply constraint - three animal feed constraints - one minimum subsistence requirement constraint - fourteen capital constraints and/or transfer rows -45- Table 5.1 Percent Area Sown to Principle Cereal Based Mixtures, Boromo Zone, 1981 Sayero: Mixture Manual Traction White sorghum Sole 41 86 l. Maize 1 - 2. Cowpea 52 14 3. Cowpea-Maize l - 4. Sesame 5 - Red sorghum Sole 77 82 l. Maize 10 7 2. Maize-Sweet potatoe - 8 3. Cowpea 2 l 4. Cowpea-Sesame l - 5. Sesame 4 - 6. Groundnut-Earthpeas 6 Millet Sole 84 53 l. Cowpea 8 - 2. Cowpea-Maize 7 - 3. Cowpea-Groundnut 1 30 4. White sorghum - 17 Maize Sole 13 16 1. White sorghum 3 4 2. Red sorghum 39 39 3. Cotton 19 ll 4. Rice 11 - 5. Sweet potatoe - 12 6. Taro-Cotton - 10 7. Others 15 8 Cotton Sole 47 30 1. Maize 19 46 2. Groundnut 12 l 3. Cowpea 15 22 4. Earthpeas-Groundnut 4 - 5. COWpea-Earthpeas 3 - 6. Earthpeas - l Cereal Sole 42 45 Mixed 26 14 Total Sole 55 55 Mixed 45 45 Source: ICRISAT Annual Report 1982 p. G 19. -47- 2. The Activities and the Objective Function Values 2.1 The activities This section discusses the possibilities permitted by the model for the activities and the derivation of the objective function. 2.1.1 The Activities in the Model Table 5.2 lists the 74 activities along with abbreviation used in the tableau (Appendix A). The choice of possible cropping activities is determined by the technology, soil type, and crop mixtures. A distinction is made between cash crop (cotton) and food crop production activities including white and red sorghum, millet, and maize. It is assumed that cotton mixed with any other crop is a cash crop production enterprise since cotton is usually the most important in intercropping with other crops. Eight groups of activities are defined: food crop production activities and cotton production activities, both with hand tool and animal traction technologies; labor hiring activities, labor exchange activities, equipment hiring, capital transfers, consumption and borrowing activities. 2.1.2 Crop Production Activities Cropping activities are defined in terms of sole or crop mixtures by combining the individual crop codes. For instance WS + MZ + CP will stand for a white sorghum, maize and cowpeas mixture. Only the most common mixtures found in the study area are presented in Appendix A. ' 2.1.3 Labor Hiring Activities Labor hiring activities occur from May to December. Labor is hired any time during this period-especially from June to August for weeding and -48- 0.50 0.50 on. am. «1... One- 000.: ha .0 «w an hm an an an“ up. can an. an a. 00 ha «a Oh Q- 55 «On 500 0'. own New an Ow. v. noun noun Our-o can own «0' «no v6. 0.. 0 pm on «a 0.. 00 on. «On 0 was- 0.1: 'n ha 0. an 05 an n. on on an Ow. 00. «on «an .0 50 OF «O. o o u u . O O XQOup XAOU adxmc UIXQO u5xao (fixaa moxac 62850 09856 me5D 34850 >3850 $3250 >8850 ‘ .v 01 an on he on an cm on «n .6 On an ON 4(h—acu 02—b¢aw50 w5c5m wmo5m w-55m huw5m 203m em on an v« on >4553m whaazn 505 ~05 N85 485 mun mla an pm On a. O— h. .CFmZOU ZO—bUDOOBQ 252‘ 5145 0845 U545 (545 uo45 0245 0045 um45 3145 >545 wfi45 >145 O. a. v. n- N- .4 O. O O b D a ' COQ¢4 >4—8<5 n4 «4 .4 n a . me—Clhmzou 92(4 4.. ZO—h0235 w>~h0wfin° Oven Oven 00. 00 u 'a u 00.- ..n. 00h- one- on an en ON an @— awn no a.“ nun ON 00. «a 0-. 00 won av. Gun .01 00. av. «an «O — — u p v 0 O 850 N! 850 N! we hu 0 O 850 IN! Ihu IN! .48 Inc Im) 0 as on p u p u . p O O O 48 mm h 0 n v numb—>nbu¢ smoanwh uCHEEmpwoum Hausa; N.m oHan -49- v.. #0 .0 no“ can“ anon .«u 00“. 0'0- mu 0? an «a fin On. 0.. '0 #0. 0h Ch 0 9.. v0 .0 man 0"" ovvn v0: buo- 005. .0 0v 0' ha Ohm 000 0b. 00. «v 0.. «h 0 1v. '0 .0 now 000' 000v 5h. hhfl.c 5. FM on 0m 00d 000 hh hh Own 00 an vv. '0 .m Dow Deva new“ ..0.. .0 006 On an. 0.1 no" '0 v.. v0 .0 non mon- hv av 00 0.. on. 00 .0. #0 ha 9.. '0 .0 new 055 v. 005. Oh on an an ha 00 on .0 no '6. , 0 v.. '0 .0 man 4.. 0 850mb 850u 54850 08x50 N5850 47850 u0x50 02850 00850 um850 3(850 >0850 u3¥50 >8850 4(h—5(0 02—h43 w505m um05m m105m h¢u5m 000m >4550m wk: 505 >05 N85 485 mun m35 .v Ow on on ha 00 mm vn no an .0 On an on mac pm On mu QN an 52— «N .N CH 0. o. h. .uhm200 20~h000005 IhZC 5(45 0845 M545 (745 w045 0245 0045 mm45 0445 >345 u345 >845 0. a. Q. n. a. .. O. 0 0 h 0 a v «0044 >4—815 04 N4 .4 o N . mh2~<0hm200 02(4 20~h0205 u>uhuw300 b¢50 N8 ma 0. h<50 N8 #0 n. bdbu v. h~h04 -50- on an- mm on- On On: ill Ali 850wh .v X50w Ow 5(850 00 08850 on w5¥50 en (0850 00 00850 no 02850 ca 00850 an 00 um¥50 an no 0(850 .n on >0850 On 00 w0¥50 ON 00 >8850 ON 44550m mh052~ 505 Nd >05 .n N85 ON 485 0. m55 0. 035 h. .Chm200 20—h000055 2h24 0. 5<45 a. 0845 v. 0545 n. (045 N. 0045 .. 0245 O. 0045 .o 5045 .1 0(45 .1 >045 .. 0045 .1 >845 50044 >4~8(5 n4 0 «4 N .4 . mh2~<5hm200 02(4 COQFOC 00- an- on- 00: 00: 20~h0205 w>uh00000 5024 0224 0024 an «a um24 0(24 >024 N024 >824 mw—h—>.>0< .N 06 0. 0. h. -51- L‘l 8505b .w 8505 Ow 54850 00 08850 00 55850 >0 40850 00 50850 no .- 02850 wn . .- 00850 00 . .- 50850 N0 . .u 04850 .0 . .1 >0850 On . .o 50850 0N . 00 >8850 0N 44.5540 02~b45550 55050 hN 50050 0N 50050 0N .5550 wN 5000 0N >45500 0.052. 505 NN #05 .N N85 ON 485 0. 055 0. 035 b. .5h0200 20—h000055 2>24 0. 5445 0. 0845 w. 5545 0. 4045 N. 5045 .. 0245 O. . 0045 0 . 5045 0 . 0445 h . >045 0 5045 0 .1 >845 50044 >4~845 04 0 N4 N .4 . 0h2u45h0200 0244 Q 2- o o o o o c on- o o o 0 23523 32338 00.8 50.8 04.8 >0h8 50>8 >8b8 205h4 0045 5045 0445 >045 05~h~>~h04 00 wa 00 N0 .0 On 0N 0N 5N 0N 0N -52- . 8505. . .1 8505 . .1 54850 . .u 08850 . .u 55850 . .. 40850 . .. 50850 . 02850 00850 50850 04850 >0850 .50850 >8850 .w Ow 00 00 .0 00 00 wn 00 N0 .0 On 0N 0N 44.5540 025.45550 55050 50050 50050 .5550 5000 .N 0N 0N wN 0N >45500 0.052. . 505 . .05 . N85 . 485 . . 055 . 005 NN .N ON 0. 0. .. .5.0200 20~.000055 2.24 5445 0845 5545 4045 5045 0245 0045 5045 0445 >045 5045 >845 0. 0. w. n. . 50044 >4~845 04 N4 .4 n N . 0.2.45.0200 0244 ..V 00 N0 00 00 00 00 O O O O O O O 205.0205 5>—.05000 .1000 .00 N80 480 050 000 .8 54.8 08.8 55.8 40.8 50.8 02.8 05..~>—.04 0w .w 0w 0w ww 0w Nw .w Ow 00 00 .0 00 -53- 0w- ‘ 8505. .w 8505 Ow 54850 00 08850 00 55850 .0 40850 00 50850 00 02850 w0 00850 00 50850 N0 04850 .0 >0850 O0 50850 0N >8850 0N 44.~540 02—.4555O 55050 hN .1 50050 0N .1 50050 0N .i .5550 wN 5000 0N >45500 0.052. .- 505 NN .05 .N .n N85 ON .n 485 0. .i 055 0. .n 035 5. .5.0200 20~.000055 2.24 0. 5445 0. 0845 w. 5545 0. 4045 N. 5045 .. 0245 O. 0045 5045 0445 >045 5045 >845 50044 >4—845 04 0 N4 N .4 . 0.2.45.0200 0244 QDOFOO 8.. 0w: 0w- 00- 00.- 00- 00. 00- 00- 20~.0205 5>—.05000 .0 050200 000200 55050 50050 50050 .5550 500 N80 480 050 000 05...)..04 00 00 w0 00 N0 .0 O0 0w -54- 8505. .w 8505 Ow 54850 00 08850 00 55850 .0 40850 00 50850 00 02850 w0 00850 00 50850 N0 .- 04850 .0 .. >0850 00 .- 50850 0N .u >885O 0N 44.~540 02~.45550 55050 .N 50050 0N 50050 0N .5550 wN . . . 5000 0N >45500 0.052— . 505 NN .05 .N . N85 ON . 485 0. 055 0. 005 .. .5.0200 20~.000055 2.24 0. 5445 0. 0845 w. 5545 0. 4045 N. 5045 .. 0245 O. 0045 0 5045 0 0445 0 >045 0 5045 0 >845 w 50044 >4.845 04 0 N4 N .4 . 0.2.45.0200 0244 8.. h....n O0...i Nw...n 00...- O O O 20~.0205 5>~.05000 04350 >0050 50050 >8050 500200 N80200 480200 05...)..04 00 00 wo 00 N0 .0 00 -55- 8505. 8505 .- 54850 .i 08850 .a 55850 .u 40850 .- 50850 .u 02850 .a 00850 .- 50850 04850 >0850 50850 >8850 ON ON 44.—540 02—.45550 55050 50050 50050 .5550 5000 .N 0N 0N wN 0N >45500 0.052— 505 .05 N85 485 055 035 NN .N ON 0. 0. R. .5.0200 20..000055 2.24 5445 0845 5545 4045 5045 0245 0045 5045 0445 >045 5045 >845 0. 0. w. 0. N. .. O. 0 0 h 0 0 w 50044 >4—845 04 N4 .4 0.2.45.0200 0244 a: 00...: 0N0..u NwO..- wmo..- .00..- 00..- NCO..- 00...- 205.0205 5>.>05000 54050 08050 55050 40050 50050 02050 00050 50050 05...)..04 w. 0. N. .. O. 00 00 .0 -55- Hmacm no powwouu Hmsvm no mmoq u m H 3308 203 225-33.. 3- .0.040 554.052 555 4 20 554 05..—>—.04 444 ..v u05.02 .w 0000N 0 8505. .w Ow O . 8505 Ow 00 O . 54850 00 00 O . 08850 00 .0 0 . 55850 .0 00 O . 40850 00 00 O . 50850 00 w0 O . 02850 w0 00 O . 00850 00 N0 0 . 50850 N0 .0 0 . 04850 .0 O0 O . >0850 O0 0N 0 . 50850 ON ON 0000N . >8850 0N 44.n540 02—.45550 .N O . 55050 .N 0N O . 50050 ON ON O . 50050 0N wN O . ..5550 wN 0N Ow0N 0 5000 0N >45500 0.052— NN 0 . 505 NN .N O . .05 .N ON 0 . N85 ON 0. 0 . 485 0. 0. O . 055 0. h. 0 . 005 .. .5.0200 20..000055 0. 00 . 2.24 0. 0. 00 . 5445 0. w. 00 . 0845 w. 0. .h . 5545 0. N. O. . 4045 N. .. O.. . 5045 .. O. 00. . 0245 O. 0 00N . 0045 0 0 won . 5045 0 . 0N0 . 0445 h 0 000 . >045 0 0 00N . 5045 0 w 00. . >845 w 50044 >4u845 0 00.N . 04 0 N Ow.. . N4 N . 00. . .4 . A . 0.2—45.0200 0244 . 20—.0205 5>—.05000 .005 025 020.0 .N. 05...>—.04 -57- November to December for harvesting. The wage rates discussed earlier in Chapter II are 350 CF A francs/day (food included) for the peak period of weeding and harvesting and 300 CFA francs/day for non-peak periods. 2.1.l+ Exchange Labor Labor is exchanged during the peak labor demand periods of land preparation, weeding and tillage. Farmers participating in labor associations work for each other without any payment in monetary terms. Exchange labor is cost free in the model. 2.1.5 Equipment Hiring Equipment is hired primarily during land preparation. Farmers may hire an oxen traction team for a work day. This activity may include a piece of equipment such as a plow. 2.1.6 Cotton Selling All cotton produced is sold in February at the farm gate price to the SOFITEX marketing service. This price was 62 CFA francs/kg in 1982. It is assumed that all cotton will be sold at this price without any discount for quality. 2.1.7 Input Buying Activities to buy inputs include fertilizers purchased at the subsidized price of 55 CFA francs/kg and animal feed on the quarterly basis discussed earlier in Chapter II. Appendix C displays budget tables for all cropping enterprises in the model. Appendix C is important in the farm income analysis because the data help to explain the linear program tableau and the internal structure of the farm. Each table displays three main categories: the value of the output, the variable costs and the performance measures including the gross income and the gross margin. -58- The output section comprises the average physical output of all enterprises in a particular combination of crops as well as the value of production. Prices used to value production are the same as those used in the model. The value of the output represents the average gross revenue realized by a farmer who grows one hectare of the crop enterprise in question. In the case of mixed cropping, the output values of each crop are summed to provide the total value of the farm output. Depending on the technology used to perform work on the farm, the variable costs section may include the hiring of farm equipment for animal traction. Fertilizer cost appears in the budgets only for cotton and maize in sole or mixed cropping. Non-wage payment such as drink and food provided by the household head to non-family workers are not in the budget because they are not usually paid in cash and these costs are repaid in kind when the farmer does exchange labor. Each enterprise performance was evaluated in gross margin format by substracting the variable costs from the total value of output. 2.1.8 Capital Transfers Monetary capital is transfered all year long from May to April, especially for animal traction farmers. From May to August the amount of capital used in farming increases because farmers pay for fertilizer and labor hiring activities which normally continue until December. Operating capital requirements are also modeled for animal maintenance costs, the wage rate and the average amount of fertilizer used. Capital was entered in the model on a monthly basis with a savings reserve starting in May. 2.1.9 Borrowing It is assumed in the model that farmers could‘borrow from money lenders and from friends to supplement their personal cash to finance farming. Also, it is -59- widely known in the survey area that SOFITEX provides short term loan to farmers using inputs in cotton. The official interest rate charged is 15.5% (World Bank, 1982 p. 172). 2.2 The Objective Function Values Hiring activities for hand labor and the animal traction were entered in the objective function in each month as a cost (CFA francs/hour) estimated from the free market. The objective function value for income producing crops are based on CFA francs per kilogram of product sold. The selling activity for each crop was entered at the official farm gate price and the buying activity at the official market price. The same principle applies to the feed buying activities. Borrowing is entered with an interest rate of 15.5% per year. This rate decreases by .0125% per month from May to March, assuming that the farmer can borrow at any time. 3. Resources Available and constraints 3.1 fl The model includes 5.0 hectares for a mean household of 13 members for both hand tool and animal traction farmers. The land farmed by the mean household in the model is composed with .9 ha of "near the house" land, 1.4 ha of "in village" land and 2.5 ha of "in bush" land. Even though land seems to be "unlimited" and "free" its use in farming faces some constraints such as the trip from the village to the field, the level of fertility and the farming technology. The number of fields per household in each class of land is quite variable. Usually farmers have fewer fields on land "next to house" than on any other land class. For instance, they may plow 2 fields "next to house"; 3 on "in village" land and 6 on "in bush" land. -50- 3.2 Labor Available 3.2.1 Structure of the constraint Michael Collinson (1983) suggested that the season be divided into periods in model building. The periods may be based on regular time intervals--weeks, fortnights, or months-—or they may vary with the necessary timeliness of particular farm operations. Agricultural labor demand is related to rainfall, cropping pattern and the technology used. For our model, monthly intervals are used, justified by the labor intensive operations and the practice of staggered planting. The monthly interval is a matter of convenience and the interpretation of the results should allow some flexibility. 3.2.2 Estimation of Labor Available The estimated number of hours of work available in each month is divided by the number of households in the sample. All labor categories were converted to a man-hour equivalent. Man and woman hours were considered to have the same values, whereas for children under 10 years old, hours were multiplied by .5 and converted to man hours (D.W. Norman,197l). Hired labor was not counted in household labor available. All labor in the model was used in farming; no off-farm employment of labor was considered. The quantification of labor availability and use over the season is a major objective of investigation. Michael Collinson (1983) treats both availability and use of labor as flows which are meaningful only at points in time. The estimation of labor use is straightforward. On the other hand, its availability is more difficult to evaluate. Among the techniques used for estimating labor availability, the constraint of observed usage at peak periods is accepted as a limit throughout the seasons (Michael Collinson, 1983 p. 197). The family is the main source of labor supply in peasant farming. Three factors that affect labor availability are: age, number of -5]- hours on a working day, and time spent on off-farm work. These factors give different bases for the amount of labor available and a wide variation in total farming hours. Another aspect which should be considered is the specialization by sex in the family. Woman labor and man labor are not perfectly substitutable. The effect of climate or weather can be a limitation on work performed on the farm. Some farm activities such as plowing cannot be done for more than 6 hours a day; likewise, planting requires a certain minimum amount of rain. In the Boromo region we are going to estimate labor availability with the assumption that men specialize in land preparation, weeding and thinning, whereas women sow and harvest. 3.3 Animal Power Available All animal team hours were recorded. The constraint is found by adding up the animal time and by dividing the total by the number of equipped households. This gives a constraint of 50 hours per household. Animal traction is used mostly from June to August for preparation and weeding of fields, but it is aggregated as one constraint in the model. 3.4 Consumption Requirements The FAO estimated that the average person in the country must consume 180 kg of cereals per year to meet the minimun requirement of 2,370 calories per person per day. For the average household of 13 persons the annual minimum cereal consumption should be 2340 kg. 3.5 Operating Capital This includes all production expenses on fertilizers, hired labor and costs for animal maintenance. The model assumes that hand tool farmers have access to the same amount of capital as animal traction farmers. -62- 3.6 Non-negative constraint None of the activities discussed can be operated at negative levels. '5. Derivation of the Input-output Coefficients The input-output coefficients (aij's) express the amount of input i needed for one unit of activity j. For instance in the case of labor input, a coefficient for the activity white sorghum (W5) with hand tool technology will tell us the hours of labor required per hectare for the production of one unit of white sorghum during the defined time period. The following section discusses some problems and the derivation of the technical coefficients in the model. 14.1 Land The average farm land per household was used as the total land constraint. The constraint on each type of land was found from the area used for specific crops usually grown on the type of land in question. For instance, maize is usually grown on land "next to house". Therefore, all maize fields were cumulated and divided by the total number of households to estimate land "next to house". One hectare of each land type is allocated to the corresponding enterprise to estimate the net return from one hectare of each enterprise. The land coefficient is one for all corresponding enterprises. #.2 Labor The derivation of labor coefficients in an L.P. model can be done either with average coefficients, "synthetic coefficients" based on " subjective evaluation of the data"(Crawford,l980) or by using multiple regression technique as suggested by Balcet and Chandler (1981). The approach followed in this study is the average coefficients per hectare. The ratio obtained by dividing the total number of hours spent on an enterprise within a particular time period by the total area of land in hectares allocated to the enterprise in question, is the input-output coefficient. -53- The calculation for the labor usage coefficients can be illustrated as: n n aij for labor is: jgl T / jgl F where aij ij ij (j = 1...n) and (1 = l...12) input-output coefficient for labor during month i for 1 hectare of activity j. Tij = total number of hours spent in all fields of activity j during month i. Fij = total area in hectares allocated to crop production activity j during month i. i = monthly time period j = cropping activities This calculation is done for all crop production activities under each production technology. Implicitly, it is assumed that all crops in the crop mixtures are produced with uniform technology, either hand tool or animal drawn technology. 4.3 Production Coefficients These were found by dividing the total output in kilograms by the total area in hectares planted with the specific crop or crop combination. 4.4 Ojerating Cflital Fertilizer used per hectare of each crop enterprise times the subsidized price of 55 CFA francs/kg was entered in the model for each crop and combination of crop on which fertilizer was spread. The cost of hired labor is entered in May, June, September and October for non-peak periods at a wage rate of 30 CFA francs/hour; and in July, August, November and December for the peak periods of weeding and harvesting at a wage rate of 35 CFA francs/hour. Animal maintenance costs are entered quarterly for all crops under animal traction technology. The expenses during each quarter were divided by the number of activities performed with animal traction. This ratio was entered in the first month of each quarter for all animal plowed enterprises. It is assumed that the technology is evenly used on all enterprises. -54- Concluding Remarks Three aspects of the farming in Boromo appear in this farm model tableau: 1) the technology used in farming; 2) the cropping pattern, either sole or mixed cropping; and 3) the utilization of land, labor and capital in both technological group of farmers. The main purpose is to determine the optimal farm plan. It is expected that this farm plan will indicate which technology is the most profitable for farmers and the competitve position of sole and mixed cropping. In addition, since there is a fixed supply of resources, the linear program will estimate the income generating potential of additional resources. CHAPTER VI RESULTS FROM THE BASIC MODEL AND SENSITIVITY ANALYSIS Economists of underdeveloped countries are beginning to realize that the farmer is no fool. A non-fool, in a static environment, learns to live 'efficiently': to optimize, given his value and constraints, and to teach his children to do the same. (Lipton, 1968) The preceding chapters have provided the framework for analyzing the economics of resource use on farms in the Boromo region of Burkina faso. The specific farm problems being addressed were: 1) the relative profitability of single cropping versus mixed cropping systems, 2) the profitability of cropping systems under animal traction and hand cultivation, 3) the allocation of land, labor and purchased inputs between cotton and cereals. This chapter will focus on the analysis of the model's optimal solution and sensitivity analysis of critical variables. The results must be interpreted with care, keeping in mind the context of the Boromo region farm environment and the main assumption of profit maximization. Sensitivity analysis will be performed on critical parameters to determine their impact on the optimal solution. The capital constraint will be increased to determine if credit availability affects decisions by farmers in the Boromo region. The fertilizer price will be increased in the objective function to observe the effect of subsidy suppression on farmers' decisions. Among the objective function values, the producer price will be increased to observe its effect on the optimal solution. The impact of an increase in the actual crop yields will also be analyzed. 1. The Optimal Solution 1.1 The Base Run The objective function is maximized by three hand tool technology enterprises and one oxen traction enterprise: Single-cropped red sorghum; red sorghum and maize intercropped; and two enterprises of cotton-maize-cowpeas -55- -55_ intercropped. Only one of the intercropped cotton-maize-cowpeas is under oxen plow technology. Table 6.1 displays the enterprises in the solution with the amount of land used. The objective function is maximized at 27,540 CFA francs. All available land "next to house" and "in village" is utilized, whereas there still remains 1.1 hectare of unused "bush" land. The marginal value product of the first type of land is 93,707 CFA francs per hectare and 36,431 for the second type (see Table 6.2). The high MVP of "in village" land can be explained by the fact that it is very fertile and does not require any purchased input to grow crops. Table 6.1 and 6.2 show that only "in bush" land is left over and has a zero shadow price which indicates an excess supply of this land class at existing fixed resource levels. All the estimated initial capital was exhausted. The farmer has to borrow 3,506 CFA francs. Table 6.1 Results From the Basic Model: Enterprises in the Optimal Solution Optimal Upper limit Enterprises level imposed by Activities label in ha constraints(ha) Red sorghum * "in village" (manual) RSm l. 12 1. 4 Red sorghun and Maize "next to house"(rranual) RS-nMZin .90 .9 Cotton,Maize and Cowpea* "in village" (manual) CT+MZ+CPm .057 1. 4 Cotton, Maize and Cowpeas "in bush" (traction) CT+MZ+CPat .22 2. 5 Note: 1) Maximized objective function value is 27,540 CFA francs. 2) For details see Appendix B. (*) Crops which could also be grown on "in bush" land. In this case, the constraint limit would be 2.5 ha. -57- Table 6.2 displays the shadow price of the resources in the optimal solution. A labor shadow price of zero at all time periods except in January, May6 and November reflects the fact that there is generally a labor surplus. The usual labor bottlenecks during planting (June) and weeding (July, August) have been removed with the flow of exchange labor. However, the January, May and November labor bottlenecks are due to simultaneous cotton harvesting, crop transport and land preparation. The results indicate that hand tool technology is economically superior to animal traction technology, given the assumptions and structure as described previously. Likewise, intercropping seems to be more profitable than single cropping since mostly mixed crops appear in the optimal plan. However, Table 6.3 shows that cotton under oxen traction is the second most competitive activity, followed by hand plowed cotton, oxen plowed millet and oxen plowed maize, as suggested by their penalty costs on a per hectare basis. 1.2 Consistency of the Optimal Solution with Reality The maximized objective function of 27,540 CFA francs is a realistic figure for the study region. It could however be somewhat over or underestimated depending on whether the market price of the commodities is above or below the official price used in the model. In any case, the net profit of 27,540 CFA francs per hectare remains in the range of likely figures for the peasant farmer in Boromo. The amount of money borrowed to meet the capital requirement (3,506 CFA francs) is more likely to be underestimated because of the difficulties in finding an accurate figure for initial money available to an average farmer. Internal financing is common in traditional agriculture because at low technology levels, capital requirements are minimal. At the current stage of agricultural development in the region, the financial needs of animal traction farmers are I .‘a I”? - -53- Table 6.2 Results From the Basic Model: Slack and Shadow Prices in the Optimal Solution Shadow Prices Row# Resource (CFA francs) 1 L1 93, 707 2 L2 36,431 3 L3 0 4 FLMY 60 5 FLJE 0 6 FLJY 0 7 FIAJ 0 8 FLSE 0 9 FLCI‘. 0 10 mm 25 11 FIJI 0 12 FLJA 132 13 FLFE 0 14 FIJVC 0 l5 FLAP 0 l6 PNI'H 0 28 GKMY 1.029 29 GKJE 1.029 30 (IKJY 1.029 31 dlKAU 1.029 32 GKSE 1.029 33 on