Gender-Differentiated Constraints in Malian Semi-Subsistence Production:
Implications for Integrated Pest Management and Food Security
by
Adam D. Russ
Thesis submitted to the Faculty of the
Virginia Polytechnic Institute and State University
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
IN
AGRICULTURAL AND APPLIED ECONOMICS
APPROVED:
Dnt & Zh Ks utah. Jib -
Daniel B. Taylor, Co-Qhair Revathi Balakrishnan, Co-Chair, Bene Wm KLE4.
George W. Norton Michael K. Bertelsen
October 9, 1996
Blacksburg, Virginia
Key Words: Mali, Gender, Agriculture, Integrated Pest Management, Food Security Ck
CSS V355 (ANS RR7G Coos GENDER-DIFFERENTIATED CONSTRAINTS IN MALIAN SEMI- SUBSISTENCE PRODUCTION: IMPLICATIONS FOR INTEGRATED PEST MANAGEMENT AND FOOD SECURITY
by
Adam D. Russ
Daniel B. Taylor and Revathi Balakrishnan, Co-Chairs
Agricultural and Applied Economics
(ABSTRACT)
While a more concentrated effort has been made in the last decade to understand the complex household behavior patterns and structures of Mali’s crop production systems and incorporate them into the prevailing research paradigms, information is still lagging in terms of knowledge related to the impacts of gender in agricultural production.
This study examines the effects of resource allocation and production decisions on the attainment of food security and net revenue maximization for farmers located in the
Koulikoro region of rural Mali. A linear programming model is used to determine how gender-differentiated constraints, size of the household, and potential integrated pest management (IPM) technologies could influence specific nutrient deficiencies and the ability to achieve household food security. The results suggest that IPM has the greatest potential to enhance the ability of farmers to attain higher food self-sufficiency levels by targeting women’s crop production systems. Increasing the probability of successful adoption and sustainability of natural resource management practices through IPM should positively influence food security through improved resource allocation, higher crop yields, and prevented pesticide dependency. A more thorough understanding of intra- household and community gender relations in Mali is needed so that gender-differentiated constraints can be recognized as obstacles to overcome rather than barriers to IPM adoption. Acknowledgments
I wish to express my sincere thanks to Dr. Revathi Balakrishnan and Dr. Daniel B.
Taylor, the co-chairs of my committee, for their continuous support, counsel, and encouragement during the course of my research. Also, special thanks to my other committee members, Dr. George W. Norton and Dr. Michael K. Bertelsen, for their beneficial recommendations and guidance.
I] would also like to thank Mr. Makan Fofana and Madame Sissoko for their assistance and warm hospitality. Sincere gratitude also goes out to the many farmers in
Mali who patiently shared their knowledge and experiences, without which this study would not have been possible.
The generous and professional support of the entire staff at the Office of
International Research and Development is also greatly appreciated. In addition, I would like to thank my fellow graduate students in the department of agricultural and applied economics for their advice and friendship during my graduate studies.
Special thanks goes to my family and friends for their constant devotion and understanding. Finally, I wish to especially thank my study partner, data management consultant, and wife for her unwavering love and inspiration.
iV Table of Contents
List of Figures vil List of Tables Vill
Introduction 1.1. | Problem Statement 1.1.1. Importance of Agricultural Sector 1.1.2. Pesticide Usage, Pest Damage, and Role of Integrated Pest Management 1.1.3. Gender Relations in Mali’s Subsistence Production System 1.2. Purpose and Objectives 1.3. | Hypotheses 1.4. Brief Overview of Methodology 1.5. Thesis Organization
Review of Literature 2.1. Malian Agricultural Situation 2.2. General Overview of Household Organization in Mali 2.2.1. Semi-Subsistence Household Structure 2.2.2. Gender-Differentiated Relations and Responsibilities 14 2.3. | Food Security in the Malian Context 2.4. Gender-Differentiated Production Constraints 2.4.1. Access to and Security of Land Tenure 2.4.2. Labor Availability/Time 2.4.3. Capital and Credit Access 2.4.4. Technology/Information Dissemination/Extension 2.5. Gender-Differentiated Pest Management Knowledge 2.6. Integrated Pest Management and Its Food Security and Gender Implications
Go Go Methodology 29 3.1. Site Description 29 3.2. Data Source 31 3.2.1 Research Design/Questionnaire Development 31 3.2.2. Sample Design 32 3.3. Multi-Divisional Linear Programming Model 33 3.3.1. Brief Overview of Linear Programming 33 3.3.2. Modeling Agricultural Household Behavior 35 3.3.3. Model Specification 36 3.3.4. Model Validation 44 3.4. Model Scenarios 44 3.4.1. Ideal Environment 46 3.4.2. Consumption Preference 46 3.4.3. Production Preference 47 3.4.4. Lack of Wage Employment Opportunities 47 3.4.5. Pest Damage Elimination 48
4. Results and Discussion 49 4.1. Summary Statistics 49 4.2. Model Validation 71 4.3. | Model Scenario Results and Discussion 75 4.4. Model Sensitivity Analysis 101 4.4.1. Elimination of Dah as a Consumption Crop 101 4.4.2. Variations in Length of Growing Season 107 4.4.3. Reduction in Millet Yield 113
5. Summary and Conclusions 122 5.1. Significance of Results 122 5.2. Gender-Differentiated Production in Mali and Relevance for Integrated Pest Management Adoption 128 5.3. Limitations of the Study 135 5.4. Recommendations for Future Research 137
REFERENCES 140
APPENDICES 148 Appendix A Questionnaire for the Head of the Household 148 Appendix B Questionnaire for Male and Female Farmers Active in Agricultural Production 155 Appendix C Representation of One of the Programmed Model Scenarios in GAMS 173 Appendix D_ Nutritional Composition of Foods 203 Appendix E Average Annual Meat Consumption in Mali 204
VITA 205
Vi List of Figures
2.1. Land Allocation in a Malian Semi-Subsistence Agricultural Household 20 3.1. | Mali and Its Surrounding Neighbors 30 3.2. | Multi-Divisional Representation of the Model 38
Vit List of Tables
4.1, Mourdiah and Sirakorola Village Demographics 50 4.2. Ethnic Group Breakdown by Village 5] 4.3. Level of Education in Mourdiah and Sirakorola 53 4.4. Area of Field Types in Hectares by Household Size in Mourdiah and Sirakorola 54 4.5. Average Number of Individual Plots Allocated to Men and Women 56 4.6. Crop Yields by Field Type, Crop Prices, and Person Days Required in Sirakorola 37 4.7. Crop Yields by Field Type, Crop Prices, and Person Days Required in Mourdiah 58 4.8. Variable Inputs Per Hectare Used by Field Type in Mourdiah and Sirakorola 60 4.9. Pest Damage Impact on Food Security Indicated by Men and Women 62 4.10. Number of Respondents Indicating Prime Pest Constraint to Production by Crop 63 4.11. Proportion of Crop Loss Due to Prime Pest Constraint in Mourdiah 64 4.12. Proportion of Crop Loss Due to Prime Pest Constraint in Sirakorola 65 4.13. Nutritional Requirements for the Average Size Household in Sirakorola 66 4.14. Nutritional Requirements for the Small Size Household in Sirakorola 67 4.15. Nutritional Requirements for the Large Size Household in Sirakorola 68 4.16. Household Breakdown by Age and Sex, and Labor Availability by Household Size in Sirakorola 70 4.17. Crops Grown and Area by Field Type in Sirakorola 72 4.18. Indicated Number of Months Deficit for Food Crops in Sirakorola 74 4.19. Subsistence Production: Crops Produced and Consumed and Land Devoted to Cultivation by Household Size 76 4.20a. Overall Household - Market Access With Wage Employment: Crops Produced. Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size 79 4.20b. Common Land - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size 80 4.20c. Men’s Individual Land - Market Access With Wage Employment: Crops Produced, Consumed. Purchased, and Sold and Land Devoted to Cultivation by Household Size 81
Vill 4.20d. Women’s Individual Land - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size 82 4.21a. Overall Household - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size 84 4.21b. Common Land - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size 85 . Men’s Individual Land - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size 86 4.21d. Women’s Individual Land - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size 87 4.22. Subsistence Production: Average Household Land and Labor Usage 89 4.23. Market Access: Average Household Land and Labor Usage 90 4.24. Subsistence Production: Total Caloric and Nutrient Deficit Values by Household Size 93 4.25. Market Access Without Employment: Total Caloric and Nutrient Deficit Values by Household Size 95 4.26. Market Access: Household Size Performance Comparison by Revenue Generation 96 4.27. Land Holdings Per Capita by Household Size 98 4.28. Subsistence Production: Average Size Household Binding Nutrient Deficiencies 99 Market Access: Average Size Household Binding Nutrient Deficiencies 100 Overall Household Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 103 Common Land Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 104 4.32. Men’s Individual Land Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 105 Women’s Individual Land Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 106
1X 4.34. Subsistence Production: Land and Labor Usage in Two Month Growing Season 108 4.35. Market Access Without Dah Consumption: Land and Labor Usage in a Two Month Growing Season 109 4.36. Subsistence Production in a Two Month Growing Season: Food Self-Sufficiency Level Achieved 11] 4.37. Market Access in a Two Month Growing Season: Food Self-Sufficiency Level Achieved 112 4.38. Subsistence Production with 50% Reduced Millet Yield: Crops Produced and Consumed and Land Devoted to Cultivation 114 4.39. Overall Household with 50% Reduced Millet Yield and without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 115 4.40. Common Land with 50% Reduced Millet Yield and without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 116 4.41. Men’s Individual Land with 50% Reduced Millet Yield and without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 117 4.42. Women’s Individual Land with 50% Reduced Millet Yield and without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation 118 Subsistence Production with 50% Reduced Millet Yield: Food Self-Sufficiency Level Achieved 120 4.44. Market Access with 50% Reduced Millet Yield: Food Self-Sufficiency Level Achieved 121 5.1. Level of Self-Sufficiency in Crop Production for the Overall Household in Sirakorola by Household Size 131 5.2. Level of Self-Sufficiency in Crop Production for Common Fields in Sirakorola by Household Size 132 Level of Self-Sufficiency in Crop Production for Men’s Individual Fields in Sirakorola by Household Size 133 Level of Self-Sufficiency in Crop Production for Women’s Individual Fields in Sirakorola by Household Size 134 Gender-Differentiated Constraints in Malian Semi-Subsistence Production: Implications for Integrated Pest Management and Food Security
Chapter 1: Introduction
1.1. Problem Statement
1.1.1. Importance of Agricultural Sector
Although Mali is blessed with the rich inland delta of the Niger River, the semi- arid woodlands and near-desert climate of the rest of the country coupled with a single, short, and often unpredictable rainy season shape the conditions under which the 10.8 million inhabitants and their agricultural industry must operate (UN, 1995).
Technological inputs vary by region and crop, however, Malian agriculture is largely dominated by subsistence farming with very low levels of access to technological development and inputs (EIU, 1994). Even with such climatic challenges and limited access to capital, agriculture is by far the dominant industry in Mali. It accounts for over
85% of the workforce and represents roughly 45% of GDP, with an average annual growth rate of 2.5% from 1980 to 1991 (UNDP, 1994).
Despite this growth, however, food production per capita has had a negative average annual growth rate for the past twenty-five years in Mali. A population growth rate of 3.1%, one of the highest in the world, has simply outstripped production (World
Bank, 1994). The continual decline in food availability has important nutritional ramifications for Mali’s people and, in particular, its children. This lack of sufficient food security largely explains Mali’s low life expectancy of 48 years (World Bank, 1994) and the high rates of malnutrition and mortality of children under five (CIHI, 1995).
1.1.2. Pesticide Usage, Pest Damage, and Role of Integrated Pest Management
There is a concerted effort on the part of Mali’s large agricultural industry and its government to make effective strides toward reversing these trends and attaining the common goal of food security. A number of intervening factors must be combated in order to realize this goal, of which pest damage to crops is an important one. Pest damage to food crops can create a serious food deficit situation in subsistence farming households. Insect pests, diseases, weeds, birds, and rodents are significant constraints that can, when conditions are ripe, outweigh all other production constraints, including rainfall. Striga, for example, a weed that competes with millet and sorghum for nutrients, causes an average 40% yield loss per year in Mali and across West Africa (Diawara,
1993).
Structural adjustment programs that induce reductions of imports such as pesticides following devaluation of the currency, as well as pressure from the international community to adopt “environmentally friendly” production measures leave the Malian government and scientists with a difficult task. They must decrease pest damage without pesticide dependency to ensure food security needs of the farm households. In the hopes of accomplishing this task, the government of Mali has turned to
Integrated Pest Management (IPM) to control pests in cotton, cereal crops, and vegetables
(PANNA, 1993). IPM, through the selection, integration and use of control methods
based on information concerning the crop, associated pests and beneficial organisms,
strives to achieve goals such as agricultural sustainability and food security that have
economic, ecological and sociological components (IPM CRSP Workshop, 1994). Since
Mali’s cropping system is characterized by low levels of inputs and pesticide usage
(cotton production as the exception), the focus of IPM in Mali emphasizes: (1)
developing IPM technologies and adapting them to the social and economic setting to
improve yields, (2) researching existing indigenous pest management strategies, (3)
incorporating indigenous information in the development of new technologies, and (4)
preventing pesticide dependency before it begins (Balakrishnan, 1994).
1.1.3. Gender Relations in Mali’s Subsistence Production System
To effectively contribute toward achieving the goal of food security, IPM efforts
in Mali require a detailed gender analysis because intra-household dynamics affect
farming practices. Since development efforts occur within a social context, gender
analysis can be utilized to better specify research that will increase production efficiency
and contribute to equity among groups in society (Feldstein et al, 1988). One of the primary purposes of gender analysis for IPM is to identify gender-based divisions of labor and access to and control over resources so that the often invisible nature of
women’s contribution to agriculture and the household (Moser, 1993) will not be ignored
in the evaluation of economic and social impacts of IPM alternatives.
There are strong Islamic influences on the productive and reproductive roles of
the farm households within Mali's semi-subsistence production system. The
predominance of polygamy within the social organization of Malian village households is
a distinctive gender concern specific to the subsistence production system. Household
composition and size largely influences the impact that an additional wife will have on the needs of the household. Often times, a woman with a co-wife has more time to
devote toward her individual fields and has a greater opportunity to travel to other
villages due to the sharing of domestic work and the care of her husband (Toulmin,
1992).
Women’s responsibilities in Malian agriculture are often determined by household
organization, power of the lineage group, age, marital status, number and age of children, number of co-wives, religious practice, ethnic group, and wealth (Creevey, 1986). The amount of land that a women receives from her husband as her individual field is directly related to her household responsibilities and order of marriage. This established household structure characterized by hierarchy, in which the first female spouse is a prime decision maker and the younger female spouse(s) is (are) responsible for performing the agricultural tasks, has important intra-household dynamic implications. These intra-household gender dynamics can have_a direct impact on the level of IPM technology adoption.
In Mali, the organization of household production is complex. Household organization may differ depending on the ethnic group that predominates in a particular village. In general, however, overall land is allocated based on extended family lineage by the village chief (usually a male). Becker records that:
‘Under the level of chief, a complex hierarchy of social relations gives certain persons rights to land as well as labor obligations to lands under another person’s tenureship. These households and sub-household levels of the social structure are where the bulk of agricultural production occurs” (1990, p. 315).
Production land in the Malian subsistence system is organized into two categories of fields: common fields and individual fields, or foroba (literally meaning ‘big field’) and suroforo (‘night field’), respectively in Bambara. Each active member of the household, under the management of the household head, is responsible for helping out on the common fields. The production on the common fields is generally directed toward subsistence crops and the output is intended for the consumption of the entire household managed by the household head. Output from individual plots can be reserved for personal use, whether for sale in the market to generate income or for consumption to make-up the food difference that common field production does not provide. A scientific understanding of the socio-economic and cultural conditions in which resource decisions and allocation patterns are determined at the household level is fundamental to successful
IPM development efforts. Earlier studies indicate that Malian women are vital to the subsistence production process (Creevey, 1986; Toulmin, 1992; Caldwell, 1993; Yeboah and Gutherie, 1994), yet their limited access to resources such as new technologies, inputs, and credit indicates that social, economic, and cultural factors are closely linked with gender-based intra- household conflicts and inequities. Though there are wide disparities in women’s responsibilities in Malian agriculture, their production activities generally entail planting, weeding, harvesting, and storing crops produced on the common fields, in the men’s individual fields as hired labor (paid in cash or in grain), and in their own fields as well
(Caldwell, 1990). In many households, the men apportion grain for home consumption; women produce grain for household consumption and also are responsible for producing the crops to make condiment sauces which provide important nutrients and flavor to common dishes such as millet porridge (USAID/SARSA). While both men and women often grow the primary grains of sorghum and millet, peanuts and vegetable crops are typically the sole responsibility of women. Women are also responsible for raising small ruminants to supplement the family diet with milk and meat.
IPM strategies must seek out the indigenous knowledge and active participation of all those that will be impacted by interventions in order to increase the probability of adoption and sustainability of IPM practices and, hence, to improve the economic and nutritional status of all members of the farm household. 1.2. Purpose and Objectives
The purpose of this study is to explore the social and economic aspects of gender that can potentially affect the adoption of IPM technologies in four rural villages located in south-central Mali. Gender relations and access to resources in subsistence production are examined to help ensure that, in developing IPM strategies, gender constraints to [IPM adoption are reduced. This study will focus on the production of four key crops: millet, sorghum, cowpea, and peanuts, although other crops are also incorporated into the analysis.
It is anticipated that the results of this study will influence policy recommendations by presenting concise sociological and economic evidence to support the need to reduce gender-differentiated constraints to IPM adoption. Increasing the probability of successful adoption and sustainability of natural resource management practices through IPM should positively influence food security through improved resource allocation, higher crop yields, and prevented pesticide dependency.
The objectives of this study are: l. to estimate the effects of gender-differentiated constraints on the ability to meet
household nutritional needs and maximize net revenue through changes in
resource allocation and production decisions in two regions located in rural Mali; 2. to estimate the potential effects of pest damage elimination through best case
integrated pest management technology adoption on men and women's crop
production systems and, hence, food security;
3. to determine if gender-differentiated household labor allocation patterns and
access to resources are influenced by household size and the degree to which
household size impacts the ability to meet household food security needs; and
4, to estimate the extent to which dietary energy. stress and specific nutrient
deficiencies impact villagers located in rural Mali, now and in various scenarios.
1.3. Hypotheses
It is hypothesized that: l. gender-differentiated constraints are a major obstacle to revenue maximization
and the attainment of adequate nutritional consumption for the household;
2. pest damage reduction technologies like integrated pest management, equally
dispersed, will have a greater positive impact for women farmers because pest
damage to women’s crop production systems is more severe in terms of yield loss
as compared to men’s production systems; and 3. men and women in larger households will have more equal access to resources
than men and women in smaller households and larger households will be better
able to meet the food security needs of the household members.
1.4. Brief Overview of Methodology
Data is drawn upon from a baseline socio-economic survey conducted in 1994 through 1995 in collaboration with U.S. and Malian scientists in two selected regions of rural Mali. Information obtained from recent participatory assessments are also drawn upon for background purposes. The results of the socio-economic survey are used to examine the aforementioned objectives and hypotheses.
The research method incorporates a sample design researching gender- differentiated constraints in semi-subsistence, polygamous households. A multi- divisional linear programming model is utilized to investigate the gender equity ramifications of Mali's current subsistence production system on food security and net revenue maximization.
1.5. Thesis Organization
The aim of this thesis is to determine if the reduction of gender-differentiated constraints will positively impact the ability of rural Malian farmers to meet the food security needs of the household. In Chapter 2, recent articles detailing characteristics of semi-subsistence crop production systems in rural Mali and the Sahel are examined. An emphasis is placed on household organization, gender-differentiated production, pest management, and food security in the Malian context. Chapter 3 describes the methodology incorporated for the thesis, including a brief description of mathematical programming and how other researchers have analyzed similar studies. Chapter 4 provides a summary and discussion of the study's results. Finally, Chapter 5 gives an account of the significance of the results and what policy implications that they may have for IPM in Mali.
10 Chapter 2: Review of Literature
2.1. Malian Agricultural Situation
The Republic of Mali is a large country, approximately twice the size of Texas
(1,240,000 km7?), located in the West African Sahel. After nearly seventy years of French colonial rule, during which the country was primarily known as French Sudan, Mali became an independent nation in 1960. It is a country rich in both culture and ethnic diversity with the Bambara, Malinké, Peulh, Sarakolé, and Songhay comprising the largest representative ethnic groups. The primary economic activity that occupies all of these groups is agricultural production, often combined with pastoralism, as over 85% of
Mali's population works in agriculture (UNDP, 1994). The main agricultural exports are cotton and livestock, while the major food crops are sorghum, millet, peanuts, maize, rice. cowpeas, and assorted vegetables.
Though Mali's 10.8 million inhabitants (UN, 1995) face one of the world's lowest population densities at 8 people per square kilometer, only 1.7% of the land is suitable for crop production (World Bank, 1994). Of this land, only 10% 1s irrigated (UNDP, 1994), meaning that producers on 90% of the arable land are dependent upon a short (June to
October) and often sporadic rainy season to meet the food security needs of Mali's rapidly growing population. This unpredictable rainfall, coupled with low soil fertility, periodic high incidence of pest damage, and a temperature range between 70° F and 110° F during the growing season, means that Malian women and men must farm under some of the
1] most severe environmental conditions in the world. The Malian government and its people clearly face a monumental task in their goal to become self-sufficient in food production.
2.2. General Overview of Household Organization in Mali
In a nation where over 75% of the population is located in rural areas (UN, 1995), agricultural growth becomes a precondition for economic growth. Understanding how rural institutions and households operate in a socio-economic milieu, therefore, is an essential component to the development process. Knowledge of decision-making dynamics and resource allocation patterns at the household level is vital for effective and sustainable development practices. as the household is the key production unit in Mali’s semi-subsistence agriculture.
2.2.1. Semi-Subsistence Household Structure
The basic structure of households in Mali varies depending on the region and ethnic affiliation of the respective village within which it resides. Specific distinctions between operational patterns within predominantly Bambara, Malinké, and Sarakolé households, for instance, have been noted in various studies in Mali (Lewis, 1978;
Toulmin, 1992; Yeboah and Gutherie, 1994).
The importance of individual versus common fields in terms of labor and capital
12 devoted to each will often be governed by the ethnic group that is most prevalent in a particular village. Bambara households generally stress common field production, called foroba in Bambara, and the members of the household eat from a common granary.
Individual fields in this crop production system are called suroforo, meaning ‘night field’ in Bambara, signifying that labor is applied to individual fields only after the work on the common field is accomplished (Toulmin, 1992). The Malinké, in contrast, fall under what Becker terms a “loose federation of independent personal production units” (1990, p. 317). Common fields are also farmed by the Malinké, but individual fields take precedence, are much larger, and provide the majority of agricultural output for the household’s consumption and cash needs.
The household structure and size have important ramifications for the household’s ability to attain food security, particularly in times of drought and high pest damage
(Frankenberger and Lynham, 1993). The organization and ethnicity of the household and village also have considerable effects on the intra-household dynamics which determine the productive and reproductive roles of the household members and their degree of decision-making authority and community participation.
What is common among Malian households, however, is that they are the central units of production and consumption in semi-subsistence agriculture. Agricultural land and labor is divided between common fields and men and women’s individual fields.
Each active member of the household is responsible for contributing a portion of the joint
13 labor necessary for agricultural tasks in the common fields and the production is generally devoted to subsistence crops intended for the consumption of the entire household. Output from men and women’s individual fields can go toward sale in the market to generate cash for items such as clothing for the children, medical supplies, and school uniforms or it may be used to supplement the food supplied by the common fields.
2.2.2. Gender-Differentiated Relations and Responsibilities
“The husband is customarily supposed to supply only a house and grain to his wife in Bambara society; the rest is the responsibility of the wife . . . Society expects women to take care of their own fields and accomplish certain tasks in the men’s fields, trad(e) in the marketplace, gather firewood, carry water, cook, clean, nurse and care for the children” (Thiam, 1986, p. 77).
Household and production responsibilities for men and women in Malian semi- subsistence agriculture are, in many ways, similar to those of other Sahelian countries, but very distinctive in other respects. Mali is fairly unique among Sub-Saharan African countries in the fact that only 14% of households are headed by women (UN, 1995). This is largely ascribed to the common cultural practice of a woman marrying one of her husband’s brothers in the event of her husband’s death (Luery, 1989). As approximately
65% of the population in Mali is Muslim (Imperato, 1989), the religious practices of
Islam can have a considerable influence on the productive and reproductive roles of men and women at both the village and household level. The traditional custom of polygamy has significant implications for women’s roles and responsibilities which are determined by a woman’s age and order of marriage. The addition of a new wife to the household
14 often allows for domestic chores such as cooking and caring for the children to be dispersed and provides women with more time to devote toward agricultural production activities on their own “elds (Toulmin, 1992).
Both men and women are very involved in agricultural production tasks with the breakdown in responsibilities often depending on household organization; power of the lineage group; age; marital status; number, age, and gender of children; number of co- wives: religious practice: ethnic group; and wealth (Creevey. 1986). In general, men are responsible for plowing the agricultural fields, building the storage huts, and growing sorghum and millet. As previously mentioned, women’s agricultural responsibilities typically involve sowing, weeding, harvesting, and storing crops produced on the common fields, working in the men’s fields where their labor is paid in cash or in grain, and fulfilling the required cultivation tasks in their own fields (Caldwell, 1990). Malian women farmers also grow sorghum and millet, however, they are customarily the sole producers of groundnuts and vegetables. The feeding, herding, and caring of small ruminants such as goats and sheep falls under the general agricultural duties of women, as well.
Performing the necessary agricultural tasks in the common fields, in men’s individual fields, and in their own fields are only a portion of Malian women’s daily responsibilities. Collecting water and fuelwood, caring for the children, traveling to the market to sell goods and purchase necessary supplies, cleaning the household, preparing
15 condiment sauces, and cooking the grain and vegetables for the household members all put a tremendous strain on women’s time, health, and general welfare (Gladwin and
McMillan, 1989). Gender-differentiated relations and responsibilities in Malian semi- subsistence households, the differing resources that men and women farmers have available, and the specific constraints that they face must be clearly understood for successful and sustainable development programs that strive to achieve food security.
2.3. Food Security in the Malian Context
Food insecurity is the lack of consistent access to enough food for an active and healthy life (World Bank, 1988). The precarious nature of food insecurity has continually plagued the people of Mali and the Sahel, in general, for the past few decades. In recent history, Mali has experienced two severe food crisis situations caused by droughts, one in the early 1970s and another in the mid 1980s. These food shortage emergencies led to the deaths of hundreds of thousands of people due to starvation and hunger-related illnesses. Even more insidious than the sudden occurrences of famine, however, are the continual effects of malnutrition, particularly for children. One in five children in Mali will die before they reach the age of five (UN, 1995). With the number of mouths to feed in Mali projected to double by the year 2013 (UNDP, 1994), finding solutions to combat insufficient food availability takes on tremendous importance.
In order to address the food insecurity problem and take proactive steps toward its
16 alleviation, development efforts, to date, have primarily focused on: 1) increasing crop yields through the use of new varieties and biotechnology, and 2) controlling population growth through contraception use and family planning strategies in order to lessen the burden on already thinly spread resources. While such approaches have had some positive impacts in selective regions, they also have experienced a number of failures and questionable results in others. Sweeping changes in the agricultural environment such as those brought on by the Green Revolution have had no significant impact on Mali or any of the other countries in the West African Sahel (Morgenthau, 1986; National Academy of Sciences, 1996). Population control measures are a highly controversial issue, particularly in an Islamic country such as Mali where religious ideologies wield a large influence over family relationships and cultural values.
More recently, a growing number of studies show that reducing gender- differentiated constraints so that women have greater access to needed resources for agricultural production can be an extremely effective method of decreasing the threat of food shortfall, malnutrition, and attaining the goal of food security (Singh, 1988;
Tibaijuka, 1994; Alderman et al., 1995; Udry, 1996). Challenging existing cultural norms of behavior and institutional constructs by incorporating gender research in agricultural development, however, can be controversial and met with opposition. Such conflict can be minimized or avoided when the socio-economic environment is clearly understood and cultural values are respected.
17 When looking at other case studies involving women's roles in agriculture, there appears to be some potential for increased production by beginning to address these gender-differentiated constraints. Alderman, Hoddinott, Haddad, and Udry (1995), in an economic analysis of farm households in Burkina Faso, which borders Mali to the southeast, find that total crop production can be increased by 10 to 20 percent simply by reallocating variable factors of production such as fertilizer and labor more equally across farm plots. In Tanzania, it has been shown that altering gender relations on smallholder banana-coffee farms can increase cash income as well as labor and capital productivity anywhere from 10 to 44 percent (Tibaijuka, 1994). If even the smallest figure of a 10 percent increase were to be achieved in Mali, this increase could very well be the difference of one month or more of food available for an entire household in the leanest times of the year. A study involving rural agricultural production in Burkina Faso reveals that an hour of work done by a female farmer is "nearly six times as productive as an hour of male labor" (Singh, 1988, p. 147). Clearly, reducing gender-differentiated constraints in subsistence agriculture 1s a promising endeavor that needs to be fully incorporated into the development paradigm if success toward attaining food security is to be realized.
18 2.4. Gender-Differentiated Production Constraints
2.4.1. Access to and Security of Land Tenure
Land tenure in Mali is almost exclusively patrilineal, with the village chief "at least nominally" allocating land to the heads of each household (Becker, 1990). The head of the household (primarily the oldest male member) has the authority of land and sometimes labor distribution over all the members of the household. Figure 2.1 demonstrates a typical semi-subsistence agriculture land distribution scheme in Mall.
Whether or not a woman has land to farm as her own and the size of the plot she receives is entirely dependent upon her husband (or father in some cases). Land rights are usually determined by a woman's order of marriage, with the first wife having greater land access and less obligation in terms of labor toward the common and men's individual fields.
Luery reports that a woman in the region of l'Opération Haute Vallée (OHV) in Mali is
"not eligible to receive her own land to farm until after her third year of marriage" (1989, p. 17).
Women are often granted only uncertain tenure or usufruct rights to the land that they farm, so there is a lack of incentive for them to invest in long-term, sustainable agricultural farming practices. Such disincentives coupled with the fact that the land women do farm as their own tends to be smaller and of poorer quality (Quisumbing et al.,
1995) -- frequently being fields set aside to lie fallow -- means that the yields that women farmers achieve are typically lower than men's. The lower soil fertility and lack of time Head
(oie) Wife (wie) yy Ca) (ws) CU O
Figure 2.1: Land Allocation in a Malian Semi-Subsistence Agricultural Household and resources that are available to devote to women's individual fields leave them more susceptible to insect, weed, and disease damage.
2.4.2. Labor Availability/Time
The burden of time and labor that is devoted to productive and reproductive activities disproportionally falls on women (McSweeny and Freedman, 1982; Agarwal,
1983; Burfisher and Horenstein, 1985; Warner, Al-Hassan, and Kydd, 1995). In a time allocation survey conducted in Burkina Faso, for example, it was found that women spend two hours and forty-five minutes more daily on food production, supply, and distribution than do men (McSweeney, 1979).
The hierarchical structure of the household is a key determinant in the amount of time that members must apply to the needs of the household. Older women, in general, have much more time to devote to their individual plots because their daughter(s)-in-law are responsible for the cooking and child care. Older women are often free of any obligation to work on the common fields and they have access to their sons' labor to help out with the crops on their individual fields.
Recent trends in the push to improve cash cropping in various development projects has often had a detrimental impact on women’s welfare because social and economic roles are not adequately understood or given proper attention to in project planning and implementation. Burfisher and Horenstein (1985) discovered that an
21 agricultural project in Nigeria that targeted cash crops increased men's workload by 6% and women's by 17%. Since men typically control the income generated from cash cropping, such projects often force women to decrease their time spent on subsistence crop production in their own fields having a direct impact on food security for the household.
Seasonal migration for work by men may leave women with an even greater role in the production and household activities to take on by themselves or with the help of their children. To what extent that seasonal migration impacts Malian households largely depends on the region, with some studies (particularly in the South) reporting that 76% of all households are affected by migration (IER/DRSPR, 1994) and other studies reporting that the overall effects of migration for Mali are minimal as the proportion of the population in urban areas is actually predicted to decline by the year 2000 (UNDP, 1994).
The high incidence of malnutrition in Mali has a direct impact on both the productivity of male and female workers while in the fields and on the number of days that they are unavailable for production activities due to illness. With one of the world's highest fertility rates at 7.1', short time-spans between pregnancies exacerbate such health problems for Malian women due to nutritional depletion. In addition to maintaining their own personal health, women are also the prime caretakers of other household members who fall ill. This disease burden drains the productive capacity of both the sick
' This figure indicates the average number of children that a woman in Mali gives birth to in her lifetime.
22 individual and the woman who must take care of them (Kirjavainen, 1996).
2.4.3. Capital and Credit Access
A lack of available credit is a problem for both men and women in Mali's semi-
subsistence agriculture, but the inadequacies are felt most heavily by women. Distinctive
obstacles to obtaining credit for women such as higher incidence of illiteracy, lack of
sufficient capital, and travel restrictions (Luery, 1989) mean that women have a much
more difficult time in gaining access to outside finances leading to further restrictions in
their potential to improve land and crop yields on women's fields. Mali’s patriarchal
customs of land tenure and ownership leave women with inadequate collateral to use for
formal credit avenues, leaving moneylenders viewing loaning money to women as too
risky (Lycette, 1984). The social relationships between husband and wife in Malian
culture also influence a woman's access to credit because a man is considered "shamed" if
his wife or wives incur debts without his expressed consent. If, for any reason, the wife is
unable to repay her debts on time, it shows that the "husband has limited authority over
his wife and that he is therefore not superior to her" (Grosz-Ngaté, 1989, p. 171).
Village associations in Mali are designed with the idea that credit and information will disseminate through the men to the women. This "trickle-down" effect is not often
observed or sufficient to meet women's credit or informational needs. In a study on women's economic activities in Mali, Luery (1989) found that women ranked their
23 husbands as the last source they go to for financial needs, while friends were cited as the number one source of borrowing for women.
Informal credit institutions such as Mali’s Rotating Savings and Credit
Associations (RoSCAs), called ‘pari'in Bambara and Malinké, have provided women with access to varying levels of credit and sometimes capital through their respective village-wide ‘tons’ - informal organizational structures in which women of a similar age group and status join together (Luery, 1989). Each woman in the pari contributes a certain amount of money or good at fixed time periods into a joint "pot" and the collection is then given to a selected member, rotating over time (Grigsby, 1990). Small and informal credit institutions, while offering some opportunities through self-initiated channels, nonetheless, fall severely short of providing the widespread credit access that men and women farmers need throughout Mali.
2.4.4. Technology/Information Dissemination/Extension
Agricultural extension is an important channel for disbursing pertinent information related to agricultural technologies, new seed varieties, fertilizers, pesticides, and pricing information. A frequent and active dialogue between extension agents and farmers is important for timely and effective information dissemination. Unfortunately, however, extension has often failed in providing technical assistance and information exchange with one of the most important groups in all of agriculture -- women.
24 Quisumbing, Brown, Feldstein, Haddad, and Pefia (1995) assert that there are four fundamental constraints to women's access to extension services: (1) cultural norms often prevent male extension officers meeting with women farmers, (2) household responsibilities and travel restrictions limit women's mobility to attend informational meetings away from the home, (3) extension services are often not offered in the local language and women are less likely than men to speak the national language, and (4) the number of female extension agents is too small.
All four of these constraints are evident in varying degrees throughout Mali. In order to communicate and share knowledge with women farmers in Mali, it is vital to be aware of these constraints and find solutions to get around them. Information is not effectively passed on from husband to wife as Luery (1989) records that women farmers in Mali report having learned nothing from their husbands and the bulk of their knowledge comes from their mothers before marriage and from other women farmers.
2.5. Gender-Differentiated Pest Management Knowledge
Due to gender-blindness in many development projects and in historically male- biased extension, the amount of information that is available on women's indigenous pest management knowledge is limited. Recent studies in Mali and elsewhere, however, are beginning to document that rural men and women farmers have an extensive knowledge of insects, weeds, and diseases that impact their crops (Caldwell et al., 1994;
25 Balakrishnan, 1995). Detailed seasonal calendars show characteristics such as insect life- cycles and periods of highest occurrence of weeds and diseases related to various crops during pre- and post-harvest activities. Researchers in all disciplines are finding women's knowledge in the agricultural sector to be a very valuable. albeit previously untapped, information source. Dirar records, for example, that, "With respect to food technology, the African woman has vast knowledge which must form the basis for development in this area" (1992, p. 454). Given women farmers' key role in agriculture, this should come as no surprise. Incorporating the concerns and specific constraints of all the end-users in the planning stages and development efforts throughout the life of the project is vital for its success.
2.6. Integrated Pest Management (IPM) and Its Food Security and Gender
Implications
IPM seeks to improve the economic and social livelihoods of farmers through the control of harmful pests in a sustainable agricultural framework. Norton and Mullen report that IPM:
““... emphasizes the integration of pest suppression technologies such as biological control, e.g., using beneficial organisms against pest organisms; cultural control, e.g., using rotations and cultivations to reduce pest problems; . . . and chemical control. e.g., judiciously using pesticides and other chemicals in a responsible manner” (1994, p. i).
Due to its relatively low access to agricultural inputs and minimal dependence on pesticides, the focus for Mali’s semi-subsistence agricultural sector is primarily involved
26 with IPM as a management strategy.
Josué Dioné (1991), of the Institut du Sahel in Bamako, Mali, argues that there are five rudimentary causes of food insecurity in the Sahel:
1.) poverty - the “prime cause”;
2.) lack of appropriate agricultural technologies;
3.) the undermining of financing opportunities by agricultural surplus extraction
Strategies such as taxes of crops and livestock and head taxes;
4.) severe imperfection in labor markets; and
5.) rapid urbanization.
Dioné criticizes structural adjustment policies that primarily focus on output market liberalization and price reforms because, he maintains, they merely address one of the five fundamental causes of food insecurity. Structural adjustment policies, initiated in over 30 countries in the early 1980s, created an environment to “correct past discrimination against the agricultural sector by improving the exchange rate, enhancing the structure of overall incentives, and introducing fiscal discipline” (Lele, 1990, p. 47).
What these policies are perhaps most castigated for, however, is having too narrow of a focus and ignoring the complex linkages between macroeconomic policies and poverty, income, employment, nutrition, social services, and growth.
The IPM Collaborative Research Support Program (IPM CRSP) in Mali is in an excellent position to directly confront a broader range of these food insecurity issues.
27 Effective IPM development efforts can directly address the prime food security constraint of poverty through increased production by identifying women’s important role as producers of agricultural commodities. As a useful technology alternative, IPM can play a key role in finding appropriate agricultural technologies for Mali’s agricultural sector.
The push for cotton production by some development groups in Mali, for instance, “has had some positive income effects for the farmers, but on the national level the vulnerability for famine has risen. Modernization of Malian agriculture has aggravated the external dependence of the small-scale farmers by the rise of the prices for imported agricultural inputs” (Krings, 1991, p.126). IPM adoption has the potential to dramatically reduce the reliance on insecticides, herbicides, fungicides, and rodenticides and, thereby, alleviate some of the external debt burden faced by the Malian government and the debt impact on men and women farmers.
28 Chapter 3: Methodology 3.1. Site Description
The study site is comprised of two regions in rural Mali located in different agro- ecological zones (Map 3.1). Dontieribougou and Koroma are the two villages chosen to represent the Sirakorola region which lies in what is known as the Guinean Zone and is approximately 100 km northeast of the capital Bamako. Koira and Douabougou are the villages chosen in the Mourdiah region which is located in the Sahelian Zone and lies approximately 240 km north of Bamako.
The temperature range throughout the year in Mourdiah and Sirakorola varies between 16 to 40 degrees centigrade (61 to 104 degrees Fahrenheit), with the hottest period occurring March through June. The amount of rainfall is often unpredictable with wide disparities from one year to the next. In Sirakorola, for example, the total annual rainfall in 1990 was 583 mm, whereas two years later in 1992, annual rainfall amounted to 983 mm, a difference of 69 percent (Caldwell et al., 1994).
The villages in Sirakorola are within close distance to the Opération Haute Vallée du Niger (OHVN) extension office and are easily accessible for field visits by monitors.
Common field production is the primary focus of farmers in the villages of Sirakorola and the Bambara are the predominant ethnic group with the ethnic groups of the Peulh and Malinké accounting for approximately 10% of the region. Millet, sorghum, peanuts, and okra are the major crops produced in this region. The rainy season in Sirakorola Mali
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Figure 3.1: Mali and Its Surrounding Neighbors
30
typically begins in early June and lasts through October. Rains come later in Mourdiah which has important implications for planting and weeding times. Farmers in Mourdiah face a considerably drier climate and poorer soil quality than do farmers in the Sirakorola region. Millet is the prime crop grown in this region. It has a higher tolerance to drought than does sorghum, but it is generally more susceptible to pest damage. Individual field production dominates the crop production system in the villages of Mourdiah and the two major ethnic groups are the Sarakolé and Bambara. Visits by extension agents are less frequent in Mourdiah due to its great distance from the main rural development organizations located in Bamako.
The sites chosen for the study are representative villages for Mali in agricultural production practices, ethnicity, and social organization. This factor potentially enables the results of this study to be informative and applicable to other regions of rural Mali and the Sahel with similar characteristics. The differences between the two regions in this study allows for a cross-gender analysis of contrasting agro-ecological zones, primary crops grown, ethnicity, and household organization.
3.2. Data Source
G2 2.1 Research Design/Questionnaire Development
The main data utilized in this study comes from the Integrated Pest Management
Collaborative Research Support Program (IPM CRSP) baseline socio-economic survey
31 conducted from 1994 through 1995. IPM CRSP is a United States Agency for
International Development (USAID) funded project whose purpose is to “develop and implement a replicable approach to IPM that will help reduce: (1) agricultural losses due to pests, (2) damage to national ecosystems, and (3) pollution and contamination in food and water supplies” (IPM CRSP, 1994, p. 12). The IPM CRSP in Mal: takes a participatory and interdisciplinary approach to IPM for semi-arid transitional production systems.
The questionnaires used in the survey were developed in collaboration with U.S. and Malian scientists. The data was collected by one male and one female field technician in each of the two regions. Respondents were chosen in such a way as to provide, as much as possible, an accurate representation of village members based on ethnicity, class, and gender.
3.2.2. Sample Design
Two hundred and eighty-five respondents in two regions of rural Mali comprise the sample set for the socio-economic survey. The data is generated from responses of the head of the production unit in one questionnaire and male and female farmers that are active in daily agricultural production in a more detailed questionnaire (see Appendix 1 and 2). The sample design is created in order to focus the data collection so that respondents share information in their specialized area of knowledge. The head of the
32 household is asked questions concerning the overall economic status and social characteristics of the household. The emphasis is on household demographics, land allocation, and general agricultural production decisions and activities. Discussions with men and women farmers based on a more extensive questionnaire are aimed at finding out more comprehensive crop input and output information related to practices such as pest management techniques, equipment usage, labor allocation decisions, and adaptations to food insecurity. The following chapter presents and discusses summary
Statistics related to household demographics and agricultural production activities that are revealed in the two questionnaires.
3.3. Multi-Divisional Linear Programming Model
3.3.1. Brief Overview of Linear Programming
Linear Programming (LP) is a mathematical tool of analysis that enables the minimization or maximization of a desired objective, such as cost or revenue, by finding an optimal combination of activities among all possible alternatives subject to specified constraints. As a modeling format, LP is ideally suited for modeling the economics of agricultural production due to the nature of farmers’ decisions based on responses to inputs, upper and lower bounds on resources, variations in controllable activities such as new crop varieties, and changes in uncontrollable activities such as the weather (Hazell and Norton, 1986). A key advantage of a linear programming model that has been tested
33 and deemed to be a reasonably accurate depiction of the real world setting 1s that it allows farmers and agricultural researchers to determine which parameters of the model are most sensitive in determining the global optimal solution. A series of “what if" questions can then be incorporated into the model to find out what might happen if, for instance, crop prices were to drop by 15 percent or rainfall was to be twice as plentiful in the following year.
An LP model, being an abstract idealization of the actual agricultural environment being studied, is based on the following assumptions:
1.) proportionality - all decision making variables in the model must have
an exponent of 1, so the objective function value and resource requirements per
unit of activity are proportional to the level of activity used;
2.) additivity - the total contribution of activities either to the objective
function or the resource requirements are the sum of their individual
contributions;
3.) divisibility - decision making variables can take on any value, whether it be
integer or non-integer values; and
4.) certainty - parameter values are assumed to be known constants (Hillier and
Lieberman, 1995, pp. 38-44).
These assumptions serve merely as general guidelines and are implicit in the LP model.
Alternative methods of analysis should be sought if one or more of these assumptions are
34 severely violated.
3.3.2. Modeling Agricultural Household Behavior
Over the past decade, evidence has been increasingly shifting the neoclassical
economic paradigm surrounding the theory of agricultural household behavioral models, particularly in regards to Sub-Saharan Africa. While some economic researchers continue to view the household as a single-minded utility maximizing unit, many are beginning to seek out a new approach to modeling the complex intra-household dynamics that exist in household resource allocation and decision-making structures (Sen, 1983;
Fapohunda, 1988; Katz, 1991; Koopman, 1991; Toulmin, 1992; Moser, 1993; Tibaijuka,
1994; Alderman et al., 1995; Udry, 1996).
Sen (1983) asserts that household relations exist in a state of "co-operative conflict" in which some interactions are facilitated by common goals and interests while others are in direct opposition. For the vast majority of households involved in agricultural production, income is not pooled and preferences for consumption and leisure are not uniformly shared. It is unrealistic to assume that every member in a given household has identical wants and needs or that the head decision maker is fully aware of and altruistically endeavoring to satisfy every individual's best interest. 3.3.3. Model Specification
The linear programming model in this study is designed to determine if available resources for agricultural production are allocated in the most effective manner for the attainment of household food security and net revenue maximization. The model incorporates a multi-divisional structure which looks at the household, first as an overall production unit and then as three separate and distinct subsectors: common fields, men’s individual fields, and women’s individual fields. As mentioned in Chapter 2, economic and socio-cultural characteristics play a large role in determining how resources are divided among these three subsectors. Figure 3.1 shows the multi-divisional organization where resource constraints that are common to the entire household are listed across the top and subsector specific resource constraints are grouped accordingly below.
Equations (1) through (15) gives a basic overview of the equations that the model uses to solve the net revenue maximizing objective function given the resource, marketing, and nutritional constraints. Two primary model formats are used in this study.
The first looks at the case of pure subsistence production in which agricultural production is strictly for the purpose of household consumption. Farmers can neither buy nor sell food crops and there are no opportunities for wage employment. The second format allows for full market access and opportunities for off-farm wage employment exist.
Depending on the location of a particular village will largely determine which of these
36 two formats more closely parallels economic reality, as some villages are located close to major roads and market places, while others are significantly far enough away to prevent income generating activities through sale or wage earnings. Various scenarios conducted in this study bring elements of both of these extremes together in order to focus directly on the more common real-life situation of semi-subsistence production.
37 Common Men’s Women’s Fields Individual Individual Fields Fields
Resource
Constraints For All
Resource Constraints For Common
Resource
Constraints For Men
Resource Constraints For Women
Figure 3.2: Multi-Divisional Representation of the Model
38
A simplified representation of the model is as follows:
(1) = Maximize X PR(YD,.AC, - CC; - BC) + x LW,. WR, - 2 CL,- AL;
7 » CP; * AP,, - yu CS; * AS; = » CM; * AM; = a CF; * AF;
- 0 CX,. AX,- 2 CEP,. AEP, - 2: CEH,. AEH;
- 2 CEC; AEC; - 2: CED, AED,
subject to:
(2) & AC, (3) YLR.AC;+ ULW, <2 ULB, +2 AL, i k k 1} i (4) YPR(YD,.AC, -CC,- BC.) + 2 LW,. WR, - 2 CL,» AL, 1 k I - 2 CEP,. AEP;,- 2: CEH, AEH, - & CEC, AEC, - 4 CED,;. AED,> 0 (5) 2LR.AC,.WR,+ 2 LW,. WR,< 4 LB,» WRy 1 k k (6) &CA,.EP(CC;+BC,)> 2 2 CA, 1 k | (7) 2 CA,.EP(CC,+BC,) < Max-Cal 39 (8) » PT,.EP(CC, + BC,) > » x PT,, (9) 2 FA,.EP,(CC, + BC,) > 2 x FA, (10) 2% CH,. EP,(CC, + BC;) > x » CH,, (11) > IR;« EP(CC, + BC,) > » » IR,, (12) 2 VA,.EP,(CC, + BC,) > x x VAy (13) x FO, .EP,(CC; + BC,) > x x FO, (14) x VC,.EP,(CC,, + BC,) > x x VC, (15) All activity levels > 0 where: 1 = crop type (bambara nuts, cotton, cowpea, dah, fonio, garden peas, maize, millet, okra, peanuts, rice, sorghum) j = field type (common, men’s individual, women’s individual) k = sex of individual (male. female) = age grouping of individual (0 - 2, 3 - 6, 7 - 14, 15+) PR, = price of ith crop YD, = yield per hectare of ith crop on jth field type AC; = area cultivated of ith crop by jth field type CC, = produced crop consumption of ith crop BC, = bought crop consumption of ith crop LW, = amount of wage labor by kth sex WR, = wage rate received by kth sex 40 CL;, = cost of hired labor by ith crop on jth field type AL, = amount of hired labor used by ith crop on jth field type CP; = cost of pesticides by ith crop on jth field type AP, = amount of pesticides used by ith crop on jth field type CS,, = cost of seed by ith crop on jth field type AS, = amount of seed used by ith crop on jth field type CM, = cost of manure by ith crop on jth field type AM,, = amount of manure used by ith crop on jth field type CF; = cost of chemical fertilizer by ith crop on jth field type AF; = amount of chemical fertilizer used by ith crop on jth field type CX; = cost of x by ith crop on jth field type AX; = amount of x used by ith crop on jth field type CEP;, = cost of plow by ith crop on jth field type AEP; = amount of plow usage by ith crop on jth field type CEH; = cost of harrow by ith crop on jth field type AEH,; = amount of harrow usage by ith crop on jth field type CEC;; = cost of cart by ith crop on jth field type AEC; = amount of cart usage by ith crop on jth field type CED; = cost of hoe by ith crop on jth field type AED,; = amount of hoe usage by ith crop on jth field type LA, = landholdings of household by jth field type LR, = labor required for the production of ith crop LB,, = labor available by kth sex in Ith age group CA, = kilocalories per kilogram of ith crop CA,, = kilocalories required by kth sex in Ith age group EP, = edible portion of ith crop PT, = grams of protein contained in one kilogram of ith crop PT, = grams of protein required by kth sex in Ith age group CH; = grams of carbohydrates contained in one kilogram of ith crop CH,, = grams of carbohydrates required by kth sex in Ith age group FA; = grams of fat contained in one kilogram of ith crop FA,, = grams of fat required by kth sex in Ith age group IR, = milligrams of iron contained in one kilogram of ith crop IR,, = milligrams of iron required by kth sex in Ith age group VA, = milligrams of vitamin A contained in one kilogram of ith crop VA, = milligrams of vitamin A required by kth sex in Ith age group FO, = micrograms of folate contained in one kilogram of ith crop FO,, = micrograms of folate required by kth sex in Ith age group VC, = milligrams of vitamin C contained in one kilogram of ith crop VC,, = milligrams of vitamin C required by kth sex in Ith age group 4] The objective function (1) sums the total amount of revenue that can be generated from sold crop production, employment, the money spent to purchase other food crops, and the cost of various inputs and equipment utilized in the production process. Equations (2) through (15) represent the constraints that must be satisfied for the objective function value to be feasible. The land constraint is represented in equation (2) simply stating that the total land area cultivated by the household on all three field types must not exceed the total land available for the household. Equation (3) mandates that the labor required to produce all the crops grown on the three field types plus the amount of household labor that is devoted to wage employment is less than or equal to the total available labor of the household and the amount of external labor that is hired for on-farm work. The capital constraint in equation (4) essentially says that the amount of revenue that can be generated from wage employment off the farm and on-farm crop production, less the harvest for household consumption, must at least equal the total costs of purchased food crops, variable factor inputs, and equipment. Equation (5) stipulates that labor required for all agricultural activities, multiplied by the wage rate and subtracting from this the amount of income drawn from the household’s off-farm wage employment, must be less than or equal to total available labor of the household times the potential wage rate for men and women. Similar to equation (3), this equation ensures that total household labor cannot be marketed beyond its availability. 42 Equation (6) through (14) express the household consumption requirements for calories and key nutrients. They are, respectively: (6) minimum caloric intake, (7) maximum caloric intake, (8) protein intake, (9) fat intake, (10) carbohydrate intake, (11) iron intake, (12) vitamin A intake, (13) folate intake, and (14) vitamin C intake. As an example, equation (8) indicates that the amount of protein in the edible portion of on- farm crops produced and purchased crops consumed must at /east meet minimum protein intake requirements summed over all individuals in the household by sex and age. Equation (7) states that household caloric consumption cannot exceed a given amount. For example, if crop choices are limited and the major crop consumed in a particular region is potatoes, which is low in iron, individuals would not consume three times the amount of their caloric needs in order to meet their iron intake requirement. At some level, individuals would reach a caloric consumption satiation point. Lastly, the non-negativity condition is represented in equation (15). This merely assures that there are no negative activity levels in the model. 3.3.4. Model Validation In order to determine if the results generated from the model are reliable and reasonably valid, a validation test is conducted. In the baseline socio-economic survey in which the data for the model comes from, the head of the household and men and women farmers active in agricultural production are asked to indicate whether or not the 43 household was able to meet the food security needs of the household members in 1994. If the answer is “No”, the respondents are then asked to indicate the number of months deficit by crop that they fell short of meeting such needs. From this information, the degree to which households were nutritionally deficit for the year can be calculated. If the results of the model are reasonably valid, then substituting the exact level of activities that farmers indicate doing in 1994 into the model (i.e. same amount of land planted to various crops and same amount of input and equipment usage) will give a result that closely parallels the amount of nutritional deficit indicated by farmers in the survey. Other means of determining if the model’s output is relatively accurate is performed by comparing the results with other agricultural production and nutritional studies conducted in the same region. 3.4. Model Scenarios Separate runs of the model are made for only one of the two regions in the study area. Agricultural production activity data generated from the socio-economic survey in Mourdiah are unable to be incorporated into the linear programming model due to a number of omitted details and inconsistencies. Only four men in Douabougou and eight men in Koira were surveyed about their production activities in the more detailed questionnaire while nearly three times as many women responded. This lack of balance among men and women respondents prevents a reliable cross-gender comparison of 44 responses to crop production, pest damage, pest management knowledge, and food security perceptions within the same unit of production. Additionally, an unclear questionnaire format, miscommunication between designers of the survey and field technicians, and/or confusion in French to Bambara language translations led to incomplete data collection in Mourdiah. Men and women survey participants only indicated their labor activities, pest management techniques, and crop inputs in relation to their individual fields. Heads of the household detailed land allotted to common fields, though the matching information for common fields from the questionnaire for men and women farmers is absent. Summary statistics for Mourdiah, however, are presented in Chapter 4 along with Sirakorola in order to compare general household and agricultural characteristics between two regions in different agro-ecological zones. For the villages in Sirakorola, five different scenarios compare the economic and sociological characteristics on common fields, women’s individual fields, and men’s individual fields. The first scenario starts with an ideal world environment and, increasingly, reality constraints are added in later scenarios to get a more realistic view of the real-life situation. Parameter values from three different household sizes are also utilized among the differing scenarios to compare differences in labor and resource allocation and food security performance. 45 3.4.1. Ideal Environment This scenario is adapted to both the subsistence and market access/wage employment model formats. It represents the ideal case in which: 1) any crop can be grown on any plot of land that the household has at its disposal, for example, crop hectarage is indeterminant of soil condition and everyone has equal knowledge and familiarity with all twelve crop types produced in the region; 2) household members have no preparatory or consumption preferences and will only consume what is most nutritionally complete and/or most economical to purchase or produce; and 3) for the market access format only, any person who wants a job at the average daily wage can have it at any time of year. 3.4.2. Consumption Preference The unrealistic assumption that people have no taste and preferences for food consumption is eliminated in this scenario. Based on consumption patterns of major crops indicated by farmers in the study regions, this scenario mandates that at least 47 percent of all calories consumed must come from sorghum and millet, strictly from production for the subsistence format or from either production or crop purchases in the market access format. 46 3.4.3. Production Preference Incorporating production preferences into the model makes strides toward the consideration of soil fertility, climate adaptability, traditional farming practices, and farmer knowledge and familiarity. Sorghum and millet are by far the most widely grown crops in all of Mali due to their performance in short season, high temperature, and low rainfall cropping environments. Established from survey responses, the model in this scenario requires that at least 75 percent of all land in production be devoted to sorghum and/or millet and at least 85 percent of the crop area on common land involve sorghum and millet cultivation. 3.4.4. Lack of Wage Employment Opportunities Applying only to the market access model format, this scenario does not allow income generation from off-farm employment. This scenario more closely mimics the actual situation in Mali as compared to anyone being able to get a wage earning job at any time of the year. The vast majority of income from off-farm employment to the household comes from remittances generated by migration to urban centers during the dry season. The majority of people who migrate to find work are men, though unmarried voung girls and women will sometimes travel to work in the cities during the off-season as well. 47 3.4.5. Pest Damage Elimination Pest damage to crop production in rural Mali is often a major deterrent that significantly impacts crop yields. This scenario incorporates a hypothetical pest management technology that completely reduces damage caused by pests in order to see how the overall household food security situation is impacted and also how such technology might affect crop production systems of men and women farmers. Direct comparisons of the impact that pest damage reduction has on crop yields are possible in men and women’s field plots that are cultivating the same type of crop or crop mixes. 48 Chapter 4. Results and Discussion As described in Chapter 3, this study uses a linear programming approach to analyze the economic and nutritional ramifications of the gendered organization of semi- subsistence agricultural production in rural Mali. A brief look and discussion of some of the key summary statistics in the two study regions is followed by a more extensive review and examination of the results of the modeled scenarios. 4.1. Summary Statistics Population and household demographics are depicted in Table 4.1. Breakdowns by age and sex give a “snap-shot” view of the make-up of the household. Of particular interest is the fact that males and females under the age of 15 comprise roughly 45 percent of the population in the two regions. This is consistent with high national figures in birth and population growth rates meaning that current food security pressures will likely be felt even more strongly in the near future. There is a dramatic difference in the size of the average household in the two regions as the size in Mourdiah is nearly twice as large as the average household in Sirakorola. This size contrast is largely attributed to the differences in ethnic groups represented in the two regions (Table 4.2). Sirakorola is predominantly Bambara with only 5 percent of all villagers indicating an ethnic group other than Bambara. Mourdiah, on the other hand, has a large number of both Bambara and Sarakolé in the two chosen 49 Table 4.1: Mourdiah and Sirakorola Village Demographics Douabougou | Koira | Mourdiah | Koroma | Dontieri- | Sirakorola Total bougou Total 0-2 years 14 20 34 13 20 33 Male 3-6 years 29 52 81 28 32 60 7-14 years 5] 86 137 36 39 75 15+ years 147 197 344 84 126 210 Total Males 241 355 596 161 217 378 Q-2 years 9 14 23 9 16 25 Female 3-6 years 28 47 75 23 44 67 7-14 years 47 85 132 27 48 75 15+ years 147 215 362 84 114 198 Total Females 231 361 592 143 222 365 No. of | Households 23 37 60 32 35 67 Average Household 20.5 19.5 19.9 9.5 12.6 11.1 Size 50 Table 4.2: Ethnic Group Breakdown by Village Ethnic Group Douabougou Koira Koroma Dontieribougou Total Bambara | No. 472 149 280 423 1324 % 85.7 19.4 92.1 96.8 64.6 Bobo No. 2 ] 0 0 3 % 0.4 0.1 0 0 0.2 Dogon No. 0 2 0 0 2 % 0 0.3 0 0 0.1 Maure No. 0 10 0 0 10 % 0 1.3 0 0 0.5 Malinké | No. 0 0 0 10 10 % 0 0 0 2.3 0.5 Minianka | No. l 0 0 0 1 % 0.2 0 0 0 0.1 Peulh No. 41 27 23 4 95 % 7.4 3.5 7.6 0.9 4.6 Sarakolé | No. 26 578 ] 0 605 % 4.7 75.4 0.3 0 29.5 Songhoi | No. 0 0 0 0 0 % 0 0 0 0 0 Sénoufo | No. I 0 0 0 1 % 0.2 0 0 0 0.1 51 villages. As discussed in Chapter 2, cultural practices have a tremendous influence on household organization and the structure of agricultural production. The fact that the two regions are located in different agro-ecological zones may also be impacting the variations in household size. Education levels of survey respondents in the two regions appear in Table 4.3. The difference in education levels between Mourdiah and Sirakorola is dramatic with all but one of the respondents in Mourdiah reporting no level of education as compared to 50 percent (58 out of 116) 1n Sirakorola. The education levels reported by men and women in Sirakorola is closely related, though women appear to have a slightly higher overall level of education attained than do men. As formal schooling is generally instructed in French or Arabic, the knowledge of these languages generally indicates the highest level of education attained. Table 4.4 outlines the average field sizes of common and individual fields by size groupings of the household. Due to data limitations, only a selected amount of information for households in Mourdiah is shown here. The villages in Mourdiah do farm on common fields, however, their individual fields take precedence and constitute the largest amount of their time and resources. In Sirakorola, the reverse is true with individual fields playing a much smaller role than the larger common fields. In both regions and for all three household size groupings, men’s individual fields are considerably bigger than women’s individual fields, anywhere from 90 to 500 percent 52 Table 4.3: Level of Education in Mourdiah and Sirakorola Douabougou Koira Dontieribougou Koroma None 4 (100%) 8 (100%) 24 (75%) 8 (31%) Men Local 0 0 5 (16%) 16 (62%) Arabic 0 0 2 (6%) 1 (4%) French 0 0 1 (3%) 1 (4%) None 13 (93%) 19 (100%) 21 (66%) 5 (19%) Women Local 0 0 9 (28%) 18 (69%) Arabic 0 0 1 3%) 0 French 1 (7%) 0 1 3%) 3 (12%) 53 Table 4.4: Area of Field Types in Hectares by Household Size in Mourdiah and Sirakorola Size of Household Field T Koira Dontieribougou Koroma Small Common * 5.90 3.30 Households < 10 Men's 1.26 0.56 6.9 Women’s 0.54 0.24 Average Common 5.65 4.76 Mourdiah: 19.9 Men's . 1.15 1.68 Sirakorola: 11.1 Women’s . ; 0.56 Large Common . 7.20 Households > 10 Men's . 1.54 14.3 Women’s . 0.66 * This data is not available for questionnaire I] ** Data not calculated 54 larger. The average number of plots, allocated by the household head, that make up men and women’s individual fields are shown in Table 4.5. In both Mourdiah and Sirakorola, men have approximately twice as many plots as women to cultivate. This is significant when coupled with more land for production as it provides men with greater opportunities for a more diverse crop mix and rotation scheme. The twelve major crops grown in the study area and their yields, prices, and person days per hectare are shown in Tables 4.6 and 4.7. The major cereals produced are millet, sorghum, rice, fonio (a grain common throughout West Africa), and maize. The main vegetable crops harvested in the two regions are okra, garden peas, cowpeas (usually intercropped with sorghum or millet), peanuts, bambara nuts, and dah. Bambara nuts are legumes high in protein that are fairly similar to peanuts. Dah 1s a fibrous, local hibiscus crop often used as a sauce for consumption purposes and is also used to make cord. Person days per hectare is the sum of time required for sowing, weeding, harvesting, and other farm operations, where applicable, to the specific crop in production. It is important to note that no delineation between men and women’s labor is incorporated into these figures. Such information could not be determined with the data from this study. Other economic studies in the region have reported men’s and women’s productivity differentials ranging from a woman’s hour of labor equaling 0.6 hours of a man’s labor (Toulmin, 1992) to an hour of female work being nearly six times as 55 Table 4.5: Average Number of Individual Plots Allocated to Men and Women Men’s Fields Women’s Fields 56 Table 4.6: Crop Yields by Field Type, Crop Prices, and Person Days Required in Sirakorola Yield on Yield on Yield on Person Common Field Men’s Field Women’s Field Prices Days Crops (Kg/ha) (Kg/ha) (Kg/ha) (FCFA/kg) | (days/ha)# Bambara Nut 42) 42) 42) 7\ 120.3" Cotton 463 463 463 825 139.5 Cowpea 140 140 61 70 101.2" Dah 125 125 88.5 25 14.2 Fonio 316 316 316 75 102.4" Garden Pea 207 207 207 900* 101.2" Maize 200 200 200 76* 220.2 Millet 1485 1811 1244 39 102.4 Okra 1280* 1280* 568* 299 101.2 Peanut 697 697 406 71 120.3 Rice 979* 979* 979* 225** 95.4 |L__Sorghum 516 571 444 4] 108.4 # Yeboah, Anthony K. and Richard L. Gutherie. “Farming Systems Research and Extension in Mali - 1986-1994.” Table 8, p. 16. ‘ Estimated from above study * IER Annual Report. Annuaire Statistique Du Mali: 1994. ** From Sirakorola Market Prices in Caldwell et al. USAID - IPM CRSP Trip Report. June 27 - July 23, 1994 Bamako, Mali. 57 Table 4.7: Crop Yields by Field Type, Crop Prices, and Person Days Required in Mourdiah Yield on Person Common Field Prices Days Crops (Kg/ha) (FCFA/kg) (days/ha)# Bambara Nut 425 ** 120.3" Cotton 463 139.5 Cowpea 389 101.2" Dah 452 14.2 Fonio ** 102.4" Garden Pea ** 101.2" Maize *% 220.2 Millet 399 102.4 Okra 1280* 101.2 Peanut 505 58 120.3 Rice 979* ** 95.4 Sorghum 330 50 108.4 # Yeboah, Anthony K. and Richard L. Gutherie. “Farming Systems Research and Extension in Mali - 1986-1994.” Table 8, p. 16. “ Estimated from above study * TER Annual Report. Annuaire Statistique Du Mali: 1994. ** Not available 58 productive as an hour of male work (Singh, 1988). Crop yields for all three field types are shown for Sirakorola, but due to data unavailability, only crop yields for common fields in Mourdiah are shown. Variations due to the two regions lying in different agro-ecological zones are apparent as there is a noticeable difference in reported yields in the two regions. For the major crops such as sorghum, millet, and peanuts, the yields in Sirakorola are much higher, whereas the reverse is true for cowpea and dah. Indicated crop prices among the two regions also vary, however, with no obvious market relationship to type of crop. This is perhaps due to time of year differences in when the crops were bought or sold. In Sirakorola, yields on major crops such as millet, sorghum, cowpea, and okra are 31, 22, 55, and 56 percent lower, respectively, on women’s individual fields than yields on men’s individual fields. Yields on women’s fields for sorghum and millet are also 14 and 16 percent lower as compared to common fields. Reasons to explain such differences in crop yields between field types become evident with the depiction of variable factor inputs to production in Table 4.8. In Sirakorola, more hired labor is used on women’s individual fields than the other two field types, however, unlike either common or men’s individual fields, no manure or chemical fertilizers are utilized for women’s crop production. In Mourdiah, men’s individual fields also use significantly more inputs than women’s fields in terms of manure and pesticide usage. Only 10.5 percent of households in Sirakorola report using pesticides on common fields and 59 Table 4.8: Variable Inputs Per Hectare Used by Field Type in Mourdiah and Sirakorola Common Men’s Women’s Common Men’s Women’s Fields Fields Fields Fields Fields Fields Manure (kg) ** 74.6 15.0 1505.7 1300.0 0.0 (carts)* (carts)* (kg) (kg) (kg) Hired Labor (da) * 1.0 0.0 10.5 0.0 15.9 (days) (days) (days) (days) (days) Chemical Fertilizer (kg) + 0.0 0.0 28.0 5.0 0.0 (kg) (kg) (kg) (kg) (kg) Pesticides ** 20.5 1.0 10.5% of 0.0% 0.0% (carts)* (carts)* households reporting some usage * It is unknown what the equivalent amount in kilograms that 1 cart equals ** Data not available 60 pesticides are not used on either women or men’s individual fields. Table 4.9 shows the perceived pest damage impact on food security indicated by men and women respondents in Mourdiah and Sirakorola. There is a significant difference between the two regions as all but one of the respondents in Mourdiah report that pest damage does influence the ability of the household to achieve food security while approximately 27 percent of men and women in Sirakorola feel that pests affect food security. In Sirakorola, nearly 86 percent of women responded that pest damage is not a serious deterrent to household food security as compared to roughly 60 percent of male respondents. This lower indicated pest impact on food security for women seems inconsistent with the higher incidence of pest damage to crop yields reported by women reflected in Table 4.6. Reasonings for this inconsistency may be attributable to differences in agricultural production activities, pest management knowledge, food processing and preparation responsibilities, information concerning levels of crop stores, and/or interviewer and question bias in the data collection process. The prime pest constraints to crop production and their subsequent impact on crop losses in Mourdiah and Sirakorola are shown in Tables 4.10, 4.11, and 4.12. Millet and sorghum, for example, appear to be damaged by birds, weeds, and insects, while okra, on the other hand, is most notably destroyed by insects. Crop damage from animals and diseases do contribute to crop loss in both regions, however, they seem to be less of a problem than birds, weeds, and insects. For sorghum and millet, the percentage of crop 61 Table 4.9: Pest Damage Impact on Food Security Indicated by Men and Women Douabougou Dontieribougou Koroma Yes No Yes No Yes No Yes No Women No. 13 1 17 2 5 27 3 23 % 93 7 89 11 16 84 12 88 Men No. 4 0 8 0 12 20 li 15 % 100 0 100 0 38 62 42 58 62 Table 4.10: Number of Respondents Indicating Prime Pest Constraint to Production by Crop 2 0 Insects 3 0 Birds 16 8 Weeds 8 1] Insects 32 6 Disease 3 0 Birds 21 15 Weeds 16 14 Insects 34 17 Disease 3 l Birds 7 1 Weeds 2 0 Insects 7 8 Weeds 4 3 Insects ] 7 Disease 0 1 Animals 0 ] Weeds 0 Insects 0 63 Animals 0 ] 63 Table 4.11: Proportion of Crop Loss Due to Prime Pest Constraint in Mourdiah 80.0% 0.0% 0.0% 0.0% 0.0% 20.0% 64 Table 4.12: | Proportion of Crop Loss Due to Prime Pest Constraint in Sirakorola 65 loss in Mourdiah is much more severe as compared to Sirakorola. Over 20 percent of respondents in Mourdiah report a 75 percent or greater crop loss due to the prime pest constraint on sorghum and millet, whereas less than 5 percent in Sirakorola indicate such a high level of loss. Tables 4.13, 4.14, and 4.15 present the caloric and major nutrient and micronutrient requirements of the three household size groupings in Sirakorola. Requirements are weighted based on age and sex composition of the household. An adjusted annual total is calculated by subtracting the amount of calories and nutrients obtained from cow, goat, sheep, and poultry meat consumption (see Appendix 4 and 5). Meat consumption data is taken from a study in the Koulikoro region of Mali that encompasses both Mourdiah and Sirakorola (IER, 1995). Labor availability and age and sex composition by household size appear in Table 4.16. Since labor data from the survey is unavailable, the amount of labor available for production activities used in the model is estimated. The model assumes that men and women |5 years and older work 6 days a week throughout the year for a total of 312 days. For male and female children in the 7 to 14 year age group, it is assumed that half the amount of adult labor is available equaling 156 annual work days. This lower figure for children old enough to help out on the farm incorporates both time away from the fields due to schooling and a lower marginal productivity of labor. 66 Table 4.13: | Nutritional Requirements for the Average Size Household in Sirakorola Energy Protein Fat Carbohy | Iron | Vitamin | Folate | Vitamin (keal)* (g)* (g)* drates (g)* A (ug)* C (g)* (Re)* (mg)*_| a SV L—_—— — ee 7.8% 0-2 779.2 19.9 30.3 297.0 8.7 314.3 25.1 17.3 17.1% | 3-6 | 3,056.0 53.1 85.4 1,163.5 17.1 759.2 112.0 38.0 10.1% M 2,395.8 61.8 71.4 912.6 15.7 560.6 125.6 25.8 7-14 10.1% F 2,183.9 61.8 60.5 831.9 15.7 560.6 132.3 25.8 7-14 28.3% M 9,248.0 179.1 260.7 3,524.5 34.6 | 1,884.8 | 628.3 94.2 IS+ 26.6% F 6,318.6 141.7 174.2 2,406.4 70.9 | 1,476.3 | 501.9 88.6 IS + Daily Total 23,955 508 677 9,126 162 5,550 1,524 289 Annual 8,743,575 185.420 247,105 3,330,952 59,130 | 2,025,750 | 556,260 105,485 * King, Felicity Savage and Ann Burgess. Nutrition for Developing Countries. 1993. 67 Table 4.14: | Nutritional Requirements for the Small Size Household in Sirakorola Protein Carbohy Vitamin | Folate {| Vitamin (kcal)* (g)* (g)* drates A (ug)* Cc (g)* (Re)* (mg)* | 5.7% 354.0 9.0 13.8 134.8 3.9 142.7 11.4 7.9 13.0% 1,444.0 25.0 40.0 550.0 8.1 359.0 53.0 18.0 7.3% 1,077.0 26.0 30.0 410.0 7.1 252.0 56.0 11.6 8.8% 1,182.0 31.0 33.0 450.0 8.5 304.0 71.2 14.0 32.6% 6,624.0 128.0 187.0 2,525.0 25.0 1,350.0 | 450.0 67.5 32.6% 4,815.0 108.0 133.0 1,834.0 54.0 1,125.0 | 383.0 67.5 Daily 15,496 327 437 5.904 107 3,533 1,025 187 Annual 5,647,128 117,740 159,239 2,154,887 38,602 | 1,288,508 | 372,929 68,073 5,656,040 119,370 159.432 2,154,887 38,909 | 1,289.436 | 373,979 68,073 * King, Felicity Savage and Ann Burgess. Nutrition for Developing Countries. 1993. M = male, F = female, HH = household kcal = kilocalories, g = grams, Re = , ug = micrograms, mg = milligrams 68 Table 4.15: | Nutritional Requirements for the Large Size Household in Sirakorola Protein Carbohy Vitamin Vitamin (g)* drates A C (g)* (Re)* (mg)* 8.5% 0-2 1,098.0 28.0 42.7 418.0 12.2 443.0 35.4 24.4 18.5% | 3-6 4,267.0 74.2 119.0 1,624.0 | 23.9 | 1,060.0 156.4 53.0 11.1% M 3,389.0 81.0 93.6 1,291.0 | 22.2 793.0 177.6 36.5 7-14 10.5% F 2,938.0 77.0 81.4 1,119.0 | 21.1 | 754.0 | 178.0 | 34.7 7-14 26.7% M 11,252.0 218.0 317.0 4,288.0 | 42.0 | 2,293.0 | 764.4 114.7 15+ 24.5% F 7,511.0 169.0 207.0 2,861.0 | 84.0 | 1,755.0 | 597.0 105.3 se eee ntataenne Reece Reena sraareeataatate Daily Total 30,455 647 861 11.601 205 7,098 1,909 Annual Total 11,107,163 234,598 313,963 4,234,365 74,66 | 2,589,842 | 695,662 134,539 4 * King, Felicity Savage and Ann Burgess. Nutrition for Developing Countries. 1993. 69 Table 4.16: Household Breakdown by Age and Sex, and Labor Availability by Household Size in Sirakorola Small Average Large Number Available Number Available | Number | Available in House- Labor in House- Labor in Labor hold (da) hold (da) House- (da) hold 0-2 years 0.39 0.0 0.87 0.0 1.22 0.0 3-6 years 0.90 0.0 1.90 0.0 2.65 0.0 Males 7-14 years 0.50 78.6 1.12 174.7 1.59 247.4 Females 7-14 years 0.61 94.7 1.12 174.7 1.51 235.6 Males 15+ years 2.25 702.0 3.14 979.7 3.82 1192.5 Females 15+ years 2.25 702.0 2.95 920.4 3.5] 1095.1 70 4.2. Model Validation Due to some key missing information and the unreliable nature of the data collected in the Mourdiah region, as previously discussed, the linear programming model and economic analysis of this study strictly focuses on the data from the two villages in Sirakorola. For this reason, the model validation is only tested for the activity levels indicated by respondents in Sirakorola. As detailed in Chapter 3, a model validation is used to test the representativeness of the results generated from the study’s linear programming model. Table 4.17 summarizes some of the production activities utilized that are indicated by the study respondents. Other activity levels include such things as the amount of fertilizers and pesticides used, availability of labor, and cost of equipment used in production. These exact levels of resource usage and crop mixes are programmed into the model and then run to determine the level of nutritional deficiency faced by the average household. The initial run of the model yields an infeasible result due to the strict caloric and nutrient levels required to be achieved. These levels are then uniformly relaxed across all calorie, nutrient, and micronutrient constraints until a feasible solution is obtained. The point at which the model becomes feasible is when all nutritional constraints are lowered by 57 percent. This model result indicates that the average household in Sirakorola, utilizing the same resource allocation and crop mix as indicated in the socio-economic survey, faced an annual 57 percent nutritional consumption deficit in 1994. More 71 Table 4.17: Crops Grown and Area by Field Type in Sirakorola Co Peanut Maize Bambara Nut 1.9 Okra 0 Watermelon ; 2.0 Cotton . 1.0 Fonio . 1.5 Rice 0 Sesame . 0.25 * Only one woman responded to questions concerning common fields ** One man responded to questions concerning women's individual fields as well *** Area cultivated is unable to be determined: Cowpea production in Mali is generally intercropped with millet or sorghum. The data, however, does not indicate the degree to which millet and sorghum is grown separately or in association with cowpea. 72 precisely, this means that, given the annual amount of consumption for the average size household in Sirakorola, household members were 57 percent below the ‘safe level of intake’ of energy and nutrients for healthy ‘functioning’ and adequate body stores throughout the year (James and Schofield, 1990). As can be seen in Table 4.18, respondents in the socio-economic survey of which this study 1s based upon indicate that they are, on average, deficit in food crops for 6.57 months out of the year. This roughly translates to an annual nutritional deficit of 54.8 percent. These numbers are staggering in their high levels and the food insecurity and malnutrition problems that they represent. They are, however, consistent with previous studies in the same zone that report the average level of food self-sufficiency achieved by households in 1989 and 1990 as 46 and 79 percent, respectively (Yeboah and Gutherie, 1994). 73 Table 4.18: | Indicated Number of Months Deficit for Food Crops in Sirakorola Crops Data Sirakorola Ave. of Months Deficit 3.33 Dah Min. of Months Deficit 0.00 Max. of Months Deficit 9 Ave. of Months Deficit 5.43 Sorghum Min. of Months Deficit 0.00 Max. of Months Deficit 1] Ave. of Months Deficit 5.7] Millet Min. of Months Deficit 0.00 Max. of Months Deficit 10 Ave. of Months Deficit 8.00 Cowpea Min. of Months Deficit 6 Max. of Months Deficit 10 Ave. of Months Deficit 5.51 Peanut Min. of Months Deficit 0.00 Max. of Months Deficit 12 Ave. of Months Deficit 0.00 Maize Min. of Months Deficit 0.00 Max. of Months Deficit 0.00 Ave. of Months Deficit 10.00 Bambara Nut Min. of Months Deficit 10 Max. of Months Deficit 10 Ave. of Months Deficit 7.69 Okra Min. of Months Deficit 0.00 ‘Max. of Months Deficit a 74 4.3. Model Scenario Results and Discussion The LP model designed for this study uses the mathematical programming software called the General Algebraic Modeling System (GAMS) (Brooke, Kendrick, and Meeraus, 1988) in order to solve for the objective function of net revenue maximization subject to a variety of resource, marketing, and nutritional constraints. The following tables and discussion summarize the output resulting from the various production scenarios in the two primary model formats of pure subsistence production and market access with wage employment opportunities. The overall model incorporates land, labor, and capital availability, production activities, variable inputs, and the nutritional requirements of the entire household. Model specifications for the three field types are based on the proportional amount of the overall land that each is allocated. Labor, marketing, and nutritional constraints are then weighted for each field type based on the proportion of overall land that the field type represents. Table 4.19 gives a breakdown of the amount of land devoted to chosen crops for the three pure subsistence model scenarios. Millet, okra, and peanuts are grown by the overall household and on the three field types. A small portion of women’s individual land is also used for cotton production. Each of the household size groupings devotes land among its chosen crops in relatively the same proportion with okra occupying the majority of the land in the ideal environment scenario followed by millet and peanuts, 75 Table 4.19: | Subsistence Production: Crops Produced and Consumed and Land Devoted to Cultivation by Household Size Small HH Ave. HH Large HH Ideal | SorMil | No Pest } Ideal | SorMil | No Pest } Ideal | SorMil | No Pest Envir. | Prod. Dam- Envir. | Prod. Dam- Envir. | Prod. Dam- age age age (ha) Millet 1.28 4.20 4.20 1.92 6.23 6.23 2.33 7.50 7.50 Okra 3.81 1.40 1.40 5.62 2.08 2.08 6.74 2.50 2.50 Peanut 0.5] 0.0 0.0 0.77 0.0 0.0 0.93 0.0 0.0 Common __ (ha) Millet 1.13 3.62 3.62 1.60 5.36 5.36 1.91 6.46 6.46 Okra 2.63 0.78 0.78 3.78 0.95 0.95 4.48 1.04 1.04 Peanut 0.65 0.0 0.0 0.93 0.0 0.0 1.11 0.0 0.0 Men: (ha) Millet 0.16 0.16 0.16 0.30 0.30 0.30 0.38 0.38 0.38 Okra 0.61 0.61 0.61 1.00 1.00 1.00 1.22 1.22 1.22 Peanut 0.06 0.06 0.06 0.12 0.12 0.12 0.15 0.15 0.15 Women: (ha) Cotton 0.04 0.04 0.0 0.04 0.04 0.0 0.05 0.05 0.0 Millet 0.06 0.06 0.08 0.10 0.10 0.13 0.12 0.12 0.17 Okra 0.23 0.23 0.25 0.40 0.40 0.40 0.52 0.52 0.51 Peanut 0.03 0.03 0.03 0.05 0.05 0.05 0.06 0.06 0.07 76 respectively. For the sorghum and millet production scenario, the overall household and the common fields allocate 75 percent and 85 percent of land, respectively, to millet production and the rest of the available land goes toward the cultivation of okra. Pest damage elimination in the third pure subsistence production scenario is achieved by increasing crop yields to the level that could be obtained if indicated crop loss due to pests were to be taken out. This change in yields only affects crops grown on common and women’s individual fields as pest damage is not reported to be a major hindrance for realized yields in millet, okra, or peanut production on men’s individual fields. Pest damage does influence sorghum production on men’s fields, however, the change in sorghum yields after pest damage elimination is not high enough to induce men’s fields from altering production activities. As pest damage is eliminated, land distribution by crop does not change for any of the subsectors except for women’s individual fields. Women in small, average, and large size households all shift their individual land in cotton production to increase their land area devoted to millet production. Common field production remains in the same land proportions because millet, the crop reported to be most heavily impacted by pests on common land, is already being grown on 85 percent of the land. Land allocation by crop for the market access model with wage employment is depicted in Tables 4.20a-d. In the ideal environment scenario, the overall household and common and men’s individual land is entirely devoted to okra production while women’s 77 individual land is strictly reserved for cotton production. The vast majority of output from okra cultivation and all of the cotton harvest is sold in the market. Money generated from such sales and from employment earnings is then used to purchase cowpea and dah for household consumption. The high levels of dah consumption is unrealistic for two reasons: (1) dah is usually only a small part of the diet in Mali and is often grown for export, and (2) levels of 30 to 60 thousand kilograms is only evident as a model result because the maximum caloric constraint could not be satisfied along with the other nutrient requirements. The second scenario with wage employment mandates that 47 percent of required calories come from either sorghum and/or millet consumption. This figure is directly based on the amount of calories from sorghum and millet calculated from the socio- economic survey responses on consumption. With this stipulation, each of the three household size groupings and all of the field types devote just enough land to millet production in order to meet this consumption requirement then put the rest of the land in okra production or cotton production in the case of women’s fields. This fact signifies that the opportunity cost of labor is higher for own farm work than for wage employment because the model stipulates that it is more cost effective to produce millet for household consumption than simply to leave all of the land in okra or cotton production and use the income from sales to purchase the necessary millet from the market. Additionally, all available labor is devoted to agricultural production until the land constraint becomes 78 Table 4.20a: Overall Household - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH Large HH Ideal | SorMil | SorMil | Ideal SorMil | SorMil | Ideal | SorMil | SorMil Envir. | Cons. Prod. | Envir. Cons. Prod. { Envir. | Cons. Prod. Produced (ha) Millet 0.0 0.46 4.20 0.0 0.70 6.23 0.0 0.90 7.50 Okra 5.60 5.14 1.40 8.30 7.59 2.08 10.00 9.09 2.50 Consumed _ (kg) Cowpea 77.8 77.8 77.8 115.0 115.0 115.0 144.0 144.0 144.0 Dah 31,045 | 30,215 | 30,215 | 48,778 | 47,495 | 47,495 | 62,420 | 60,788 | 60,788 Millet 0.0 830.7 | 830.7 0.0 1,283 1,283 0.0 1,633 1,633 Okra 174.7 174.7 174.7 | 271.0 271.0 271.0 | 345.8 | 345.8 | 345.8 Purchased (kg) Cowpea 778 77.8 77.8 115.0 115.0 115.0 144.0 144.0 144.0 Dah 31,045 | 30.215 | 30,215 | 48,778 | 47,495 | 47,495 | 62,420 | 60,788 | 60,788 Sold (kg) Millet 0.0 0.0 6,776 0.0 0.0 9,99] 0.0 0.0 11,950 Okra 6,993 6,406 1.617 | 10,353 9,447 2,385 | 12,454 | 11,292 | 2,854 79 Table 4.20b: Common Land - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH Large HH Ideal | SorMil | SorMil | Ideal SorMil | SorMil | Ideal | SorMil | SorMil Envir. | Cons. Prod. Envir. Cons. Prod. Envir. | Cons. Prod. Produced (ha) Millet 0.0 0.44 3.62 0.0 0.66 5.36 0.0 0.83 6.46 Okra 4.40 3.96 0.78 6.31 5.65 0.95 7.50 6.68 1.04 Consumed (kg) Cowpea 61.1 61.1 61.1 87.4 87.4 87.4 108.0 108.0 108.0 Dah 24,402 | 23,749 | 23,749 | 37,071 36,096 | 36,096 | 46,815 | 45,591 | 45,591 Millet 0.0 653.0 | 653.0 0.0 975.0 975.0 0.0 1,225 1,225 Okra 137.3 137.3 137.3 | 206.0 206.0 206.0 | 259.4 | 259.4 | 259.4 Purchased (kg) Cowpea 61.1 61.1 61.1 87.4 87.4 87.4 108.0 108.0 108.0 Dah 24,402 | 23,749 | 23,749 | 37,071 36,096 | 36,096 | 46.815 | 45,591 |} 45,591 Sold (kg) Millet 0.0 0.0 4,720 0.0 0.0 6,988 0.0 0.0 8,369 Okra 5.497 | 4,934 866 7,868 7,027 1,005 | 9,340 | 8,285 1,072 80 Table 4.20c: Men’s Individual Land - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH . Large HH Idea] SorMil | SorMil Ideal SorMil SorMil Ideal SorMil | SorMil Envir. Cons. Prod. Envir. Cons. Prod. Envir. Cons. Prod. Produced (ha) Millet 0.0 0.07 0.07 0.0 0.12 0.12 0.0 0.16 0.16 Okra 0.84 0.77 0.77 1.4] 1.29 1.29 1.75 1.59 1.59 Consumed (kg) Cowpea 11.7 11.7 11.7 19.5 19.5 19.5 25.2 25.2 25.2 Dah 4.657 | 4,532 | 4,532 8,292 8,074 8,074 | 10,924 | 10,638 | 10,638 Millet 0.0 124.6 124.6 0.0 218.1 218.1 0.0 285.7 285.7 Okra 26.2 26.2 26.2 46.1 46.1 46.1] 60.5 60.5 60.5 Purchased (kg) Cowpea 11.7 11.7 11.7 19.5 19.5 19.5 25.2 25.2 25.2 Dah 4.657 | 4,532 | 4,532 | 8,292 8,074 | 8,074 | 10,924 | 10,638 | 10,638 Sold (kg) Okra 1,049 | 961 961 1,760 1,606 1,606 | 2,180 | 1,977 | 1,977 81 Table 4.20d: Women’s Individual Land - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH Large HH Ideal SorMil | SorMil Ideal SorMil SorMil Ideal SorMil | SorMil Envir. Cons. Prod. Envir. Cons. Prod. Envir. Cons. Prod. Produced (ha) Cotton 0.36 0.32 0.32 0.58 0.51 0.51 0.75 0.67 0.67 Millet 0.0 0.04 0.04 0.0 0.07 0.07 0.0 0.08 0.08 Consumed (kg) Cowpea 5.0 5.0 5.0 8.0 8.0 8.0 10.8 10.8 10.8 Dah 1,987 1934 1934 3,415 3,325 3,325 | 4,682 | 4,577 | 4,577 Millet 0.0 53.2 53.2 0.0 89.8 89.8 0.0 104.5 104.5 Okra 11.2 11.2 11.2 19.0 19.0 19.0 25.9 25.9 25.9 Purchased (kg) Cowpea 5.0 5.0 5.0 8.0 8.0 8.0 10.8 10.8 10.8 Dah 1,987 | 1934 | 1934 | 3,415 3,325 3,325 | 4,682 | 4,577 | 4,577 Okra 11.2 11.2 11.2 19.0 19.0 19.0 25.9 25.9 25.9 Sold (kg) Cotton 166 146 146 269 236 236 347 308 308 binding. Only then is any surplus labor apportioned to wage employment. The sorghum and millet consumption and production scenario further shifts the amount of land allocated to millet production to 75 percent of land available for the overall household and 85 percent for the common fields. This additional millet production is sold in the market as, again, only 47 percent of caloric requirements must come from sorghum and/or millet consumption. Crop land allocation for the market access scenarios without wage employment are shown in Tables 4.21a-d. Crops produced and the proportion of land devoted to each is fairly similar to the sorghum and millet production and consumption scenario with wage earnings except for crop land allocated on women’s individual fields. As the case with the pure subsistence model format, women’s land use changes dramatically once pest damage to crops is eliminated. For the market access scenario without the opportunity for wage employment, all the land that is previously devoted to cotton production transfers to the cultivation of okra. Differences in the results between the market access scenarios with and without wage earnings are evident in the amount of cowpea, dah, and okra purchased and consumed. In the absence of wage earnings, purchases for cowpea and dah drop by approximately 21 percent and okra production falls by a level of 22 to 24 percent. Households that do not have extra income at their disposal due to wage employment have considerably less buying power as they can only use revenue generated from produced crop sales to purchase other food crops for 83 Table 4.21a: Overall Household - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH Large HH No No Pest No No Pest No No Pest Employment | Damage | Employment Damage Employment Damage All: Produced (ha) Millet 4.20 4.20 6.23 6.23 7.50 7.50 Okra 1.20 1.20 2.08 2.08 2.50 2.50 Consumed (kg) Cowpea 61.4 61.4 93.1 93.1 112.3 112.3 Dah 23,695 23,695 38,227 38,228 47,055 47,055 Millet 830.7 830.7 1,283 1,283 1,633 1,633 Okra 138.0 138.0 219.5 219.5 269.7 269.7 Purchased (kg) Cowpea 61.4 61.4 93.1] 93.1 112.3 112.3 Dah 23,695 23,695 38,227 38,228 47,055 47,055 Sold (kg) Millet 6,776 7,606 9,991 11,282 11,950 13,583 Okra 1,654 1,654 2,437 2,437 2,930 2,930 84 Table 4.21b: Common Land - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH Large HH No No Pest No No Pest No No Pest ce Employment | Damage | Employment Damage Employment Damage Produced (ha) Millet 3.62 3.62 5.36 5.36 6.46 6.46 Okra 0.78 0.78 0.95 0.95 1.04 1.04 Consumed = (kg) Cowpea 39.7 44.0 51.6 57.7 58.3 65.9 Dah 15,208 16,916 20,897 23,492 24,056 27,333 Millet 653.0 653.0 975.0 975.0 1,225 1,225 Okra 89.3 98.9 121.5 136.0 140.1 158.2 Purchased (kg) Cowpea 39.7 44.0 51.6 57.7 58.3 65.9 Dah 15,208 16.916 20,897 23,492 24,056 27,333 Sold (kg) Millet 4,720 6,556 6,988 9,707 8,369 11,699 Okra 914.2 904.6 1,089 1,075 1,191 1,173 85 Table 4.21c: Men’s Individual Land - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH Large HH No No Pest No No Pest No No Pest a Employment | Damage | Employment | Damage Employment Damage Produced (ha) Millet 0.07 0.07 0.12 0.12 0.16 0.16 Okra 0.77 0.77 1.29 1.29 1.59 1.59 Consumed (kg) Cowpea 11.7 11.7 19.5 19.5 25.2 25.2 Dah 4,532 4,532 8,074 8,074 10,638 10,638 Millet 124.6 124.6 218.1 218.1 285.7 285.7 Okra 26.2 26.2 46.1 46.1 60.5 60.5 Purchased (kg) Cowpea 11.7 11.7 19.5 19.5 25.2 25.2 Dah 4,532 4,532 8,074 8,074 10,638 10,638 Sold (kg) Okra 960.7 960.7 1,606 1,606 1,977 1,977 86 Table 4.21d: Women’s Individual Land - Market Access With No Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation by Household Size Small HH Average HH Large HH No No Pest No No Pest No No Pest oy Employment | Damage | Employment Damage Employment Damage Produced (ha) Cotton 0.32 0.0 0.51 0.0 0.65 0.0 Millet 0.04 0.03 0.07 0.05 0.10 0.07 Okra 0.0 0.33 0.0 0.53 0.0 0.68 Consumed (kg) Cowpea 5.0 5.0 8.0 8.0 10.8 10.8 Dah 1,934 1,934 3,325 3,325 4,559 4,559 Millet 53.2 53.2 89.9 89.9 122.5 122.5 Okra 11.2 11.2 19.0 19.0 25.9 25.9 Purchased (kg) Cowpea 5.0 5.0 8.0 8.0 10.8 10.8 Dah 1,934 1,934 3,325 3,325 4,559 4,559 Okra 11.2 0.0 19.0 0.0 25.9 0.0 Sold (kg) Cotton 146.3 0.0 235.7 0.0 301.9 0.0 Millet 0.0 0.0 0.0 0.0 0.0 4.27 Okra 0.0 422.4 0.0 678.4 0.0 870.4 87 consumption. Table 4.22 shows the degree to which available land and labor is utilized for the three pure subsistence scenarios. In each scenario and for all field types, available land is fully utilized and there is an abundance of available labor. Such high levels of surplus labor is not surprising for the pure subsistence models because the main agricultural production season in Mali is typically no longer than four months out of the year. The pure subsistence model only allows labor to be channeled toward crop production on the available land, so without off-farm wage earning opportunities, the labor constraint is not binding in the optimal solution. This is inconsistent with reality, however, as labor is generally binding in Mali and the Sahel, particularly during the agricultural growing season. The positive shadow prices presented in Table 4.22 indicate the marginal value of land in Sirakorola. This value lets farmers know how much they could expect to receive in terms of revenue by adding one more hectare of land to production. Since, in the survey, the majority of household heads in Sirakorola responded that the total land area devoted to crop cultivation in 1994 was less than the total land area available for the household, the possibility of adding an extra hectare of land to production is not all that unrealistic if labor were truly to be in surplus. Information from Table 4.23, however, shows that when wage employment opportunities do exist, labor no longer is in excess. For the ideal environment and the 88 Table 4.22: | Subsistence Production: Average Household Land and Labor Usage Ideal ~— Envir. Scen- Sor/ Prod. Cons. Pest Dam- Elim. ario Mil age - | Activ | Upper | Shadow | Activ | Upper | Shadow | Activ | Upper | Shadow ey i Level | Bound Price* | Level | Bound Price* | Level | Bound Price* All (FCFA) (FCFA) (FCFA) Land (ha) | 8.30 8.30 382,720 | 8.30 8.30 382,720 | 8.30 8.30 382,720 Labor(da) | 797.6 | 2,251.0 - 788.1 | 2,251.0 - 788.1 | 2,251.0 - Common (FCFA) (FCFA) (FCFA) Land (ha) | 6.31 6.31 382,720 | 6.31 6.31 382,720 | 6.31 6.31 382,720 Labor(da) | 630.9 | 1,710.8 - 617.8 | 1,710.0 - 617.8 | 1,710.8 - -| Men (FCFA) (FCFA) (FCFA) Land (ha) | 1.41 1.41 382,720 | 1.41 1.4] 382,720 | 1.41 1.41 382,720 Labor(da) | 145.4 382.7 - 145.4 382.7 - 145.4 382.7 - Women (FCFA) (FCFA) (FCFA) Land (ha) | 0.58 0.58 381,980 | 0.58 0.58 381,980 | 0.58 0.58 382,720 Labor(da) | 29.1 157.6 - 29.1 157.6 - 27.7 157.6 - * Exchange rate is $1 U.S. = 500 FCFA Activ Level = Activity level at which the land or labor resource is being used Upper Bound = The maximum amount of land or labor resources that are available for use 89 Table 4.23: | Market Access: Average Household Land and Labor Usage Ideal Envir. Scenario SorMil Prod. Cons. No Empl. Scenario Activ Upper Shadow Activ Upper Shadow | Activ Upper Shadow Level Bound Price* Level Bound Price* Level Bound Price* All (FCFA) (FCFA) (FCFA) Land (ha) 8.30 8.30 337,180 8.30 8.30 382,720 8.30 8.30 382,720 Labor 2.251.0 } 2,251.0 450.0 2,251.0 | 2,251.0 450.0 788.1 | 2,251.0 - (da) M wage 980.0 980.0 500.0 980.0 980.0 500.0 N/A N/A N/A (FCFA) W wage 490.3 921.0 - 482.9 921.0 - N/A N/A N/A (FCFA) Common (FCFA) (FCFA) (FCFA) Land (ha) 6.31 6.31 337,180 6.31 6.31 382,720 6.31 6.31 382,720 Labor 1,710.8 1,710.8 450.0 1,710.8 } 1,710.8 450.0 617.8 1,710.8 - (da) M wage 744.8 744.8 500.0 744.8 744.8 500.0 N/A N/A N/A (FCFA) W wage 354.6 700.0 - 348.2 700.0 - N/A N/A N/A (FCFA) Men (FCFA) (FCFA) (FCFA) Land (ha) 1.41 1.41 382,720 1.41 1.41 382,720 1.41 1.41 382,720 Labor 309.4 382.7 - 309.5 382.7 - 142.9 382.7 - (da) M wage 166.6 166.6 500.0 166.6 166.6 500.0 N/A N/A N/A Women (FCFA) (FCFA) (FCFA) Land (ha) 0.58 0.58 381,970 0.58 0.58 381,980 0.58 0.58 382,720 Labor 113.2 157.6 - 110.5 157.6 - 46.1 157.6 - (da) W wage 64.5 64.5 450.0 64.5 64.5 450.0 N/A N/A N/A * Exchange rate is $1 U.S. = 500 FCFA Activ Level = Activity level at which the land or labor resource is being used Upper Bound = The maximum amount of land or labor resources that are available for use 90 sorghum and millet production and consumption scenarios, the model says that all adult males and some of the females are working off-farm for wage employment. The on-farm work is being done by the rest of the women and children old enough to help out on the farm. While this is certainly an extreme result compared to what is happening in reality, the general direction of the results is consistent with numerous studies in Mali and the Sahel reporting that a high percentage of men migrate to find off-farm employment and women are responsible for producing approximately 75 percent of all food crops consumed by the household (Toulmin, 1992; Leisinger and Schmitt, 1995; Davies, 1996). The marginal value of labor for the overall household and the common land subsector says that an additional day of labor is equal to 450 FCFA which is equivalent to the daily labor wage potential indicated in the survey for women. Approaches to nutrition in developing countries are beginning to shift focus from solely considering protein-energy malnutrition (PEM) to a more complete method also emphasizing inadequacies in micronutrients (Behrman, 1993). Complex interactions exist between PEM and the bodily absorption levels of important micronutrients such as vitamin A and vitamin C. “Clearly, immunocompetence is affected by both PEM and by specific nutrient deficiencies. Overall immune status reflects the sum of several different nutritional inputs” (Bates, 1990, p. 128). Merely addressing caloric and/or protein deficiencies is an over-simplification of the causes of malnutrition (Pelletier et al., 1995). Indeed, in a recent study on agricultural productivity effects of calories, Deolaliker (1988) 91 found that caloric intake has no significant positive effect on farm output. For these reasons and due to the lack of necessary data for a more complete nutritional analysis, calories and specific nutrients are grouped together for this study. Nutritional deficiencies for the pure subsistence model scenarios are given in Table 4.24. Unlike the land allocation decisions that were found to be generally proportional in crop selection among household size, there are noticeable variations in food security performance by household size. The smaller household is the most self- sufficient in food production with only a 5 percent nutritional deficit value for the overall household. The average and large household sizes have an 8 and 12 percent caloric and nutrient deficit for the overall household, respectively. The differences may seem slight, however, a household facing a 12 percent versus a household with a 5 percent nutritional deficit will run out of food stocks, assuming similar rates of consumption, approximately one month sooner in the leanest times of the year. Men’s individual fields in the average size household perform the best in meeting their contributions to the household food supply with large and small size households falling closely behind. Women’s individual fields among the three household size groupings are comparable. Requiring that 75 percent of land for the overall household and 85 percent of common land be invested in sorghum and/or millet production has a tremendous impact on the household’s ability to meet total caloric and nutrient needs of the household members. As adding this sorghum and millet constraint more closely parallels what 92 Table 4.24: | Subsistence Production: Total Caloric and Nutrient Deficit Values by Household Size Small HH Ave. HH Large HH Ideal SorMil No Pest Ideal | SorMil | NoPest Ideal SorMil | No Pest Envir Prod. Dam- Envir. Prod. Dam- Envir. Prod. Dam- age age age All 5% 51% 51% 8% 51% 51% 12% 53% 53% Common 13% 60% 55% 17% 63% 59% 21% 66% 62% Men 20% N/A 20% 16% N/A 16% 18% N/A 18% Women 55% N/A 9% 55% N/A 10% 57% N/A 13% N/A - Not applicable because the sorghum and millet constraint was only imposed on the overall and common model. 93 farmers report they are growing in terms of cereal production, the nutritional deficiencies, like the model validation test, are very close to what respondents indicate. Looking at the scenario when pest damage to crop yields is no longer a problem, the common land subsector increases food self-sufficiency by 4 to 5 percent. For women’s individual fields, on the other hand, the increased performance is striking. For all household sizes, food self-sufficiency on women’s fields is doubled from around 45 percent with pest damage to 90 percent once the negative effects of pests on crop yields is eliminated. For the market access scenarios where full employment opportunities exist, all household size groupings and field types can meet the caloric and nutrient needs of their household members. Table 4.25 shows the annual levels of deficiency faced when no wage earnings are possible, but farmers can still buy and sell products in the market. While maintaining the relatively same proportions among household sizes, the deficiency levels are approximately 30 percent lower than the similar scenarios under the pure subsistence model. Due to the absence of the sorghum and millet production constraints on men and women’s individual fields, they can meet the proportional nutritional requirements even without employment earnings. Since the market access with employment scenarios completely satisfies all nutritional requirements, household sizes and their subsectors are compared based on how large their income generation is over and above their costs and consumption (Table 4.26). 94 Table 4.25: Market Access Without Employment: Total Caloric and Nutrient Deficit Values by Household Size Small HH Average HH Large HH No No Pest No No Pest No No Pest Employment Damage | Employment | Damage | Employment Damage All 21% 21% 19% 19% 22% 22% Common 35% 28% 41% 34% 46% 39% Men 0.0 0.0 0.0 0.0 0.0 0.0 Women 0.0 0.0 0.0 0.0 0.0 0.0 95 Table 4.26: Market Access: Household Size Performance Comparison by Revenue Generation Small HH Average HH Large HH Ideal SorMil Ideal SorMil Ideal SorMil Environ. Production Environ. Production Environ. Production All Obj Value 1,670,912 346,231 2,423,780 459,692 2,876,133 508,543 (FCFA) per capita* 4843 100.4 436.7 82.8 402.3 71.1 ($U.S.) Common Obj Value 1,355,897 169,791 1,881,299 123,219 2,195,044 76,178 (FCFA) per capita* 393.0 49.2 339.0 22.2 307.0 10.7 ($U.S.) Men Obj Value 179,062 155,843 330,866 290,229 411,111 357,873 (FCFA) per capita* 51.9 45.2 59.6 52.3 57.5 50.1 ($U.S.) Women Obj Value 89,218 74,224 144,806 119,480 183,353 153,885 (FCFA) per capita* 25.9 21.5 26.1 21.5 25.6 21.5 ($U.S.) * Per capita calculations based on a $1 U.S. = 500 FCFA exchange rate 96 For the overall household, as was the case with food self-sufficiency performance, small households are doing the best with the highest per capita income, while average and large households are second and third, respectively. As can be seen in Table 4.27, this variation in performance among household sizes is directly attributable to land in production per household member. Table 4.28 and 4.29 isolate the specific nutrient constraints that are binding to the optimal solution in both the pure subsistence and market access model formats. The shadow prices in both tables indicate how much the objective function value will be lowered if the consumption requirement for the household of the particular nutrient is increased by one unit. While which particular nutrient constraints that are binding varies by scenario and model format, vitamin A’s presence as a scare good is evident in every case. This fact lends further validity to the model’s results as numerous nutritional studies in Mali and the Sahel report a high prevalence of vitamin A deficiency, particularly among children (Le Francois et al., 1980; Semega and Toureau, 1980; Lefevre, 1986; UNICEF, 1989; Bonnet et al., 1992; Rankins et al., 1993). 97 Table 4.27: | Land Holdings Per Capita by Household Size Total Land in Land Members per Production per capita Household (ha) (ha) Small HH 6.9 5.6 0.81 Average HH 11.1 8.3 0.75 Large HH 14.3 10.0 0.70 98 Table 4.28: | Subsistence Production: Average Size Household Binding Nutrient Deficiencies Ideal Shadow SorMil Shadow Pest Shadow Environment Price Production Price Damage Price and Consumption Elimination Vitamin A -1.101 All Fat -1.571 Vitamin A -1.154 Vitamin A -1.154 Carbohydrates -0.194 Vitamin A -1.085 Common Fat -2.883 Vitamin A -1.154 Vitamin A -1.154 Carbohydrates -0.232 Vitamin A -1.101 Men Fat -1.57] * * Vitamin A -1.101 Carbohydrates -0.194 Vitamin A -2.527 Women Fat -2.701 * * Vitamin A -1.101 Carbohydrates -0.224 * Not applicable 99 Table 4.29: Market Access: Average Size Household Binding Nutrient Deficiencies Ideal Shadow SorMil Shadow No Shadow Environment Price Production Price Employment Price and Consumption All Vitamin A -0.625 Vitamin A -0.625 Vitamin A -0.625 Vitamin C -0.355 Vitamin C -0.355 Vitamin C -0.355 Common Vitamin A -0.625 Vitamin A -0.625 Vitamin A -0.625 Vitamin C -0.355 Vitamin C -0.355 Vitamin C -0.355 Men Vitamin A -0.625 Vitamin A -0.625 Vitamin A -0.625 Vitamin C -0.355 Vitamin C -0.355 Vitamin C -0.355 Women Vitamin A -0.625 Vitamin A -0.625 Vitamin A -0.625 Vitamin C -0.355 Vitamin C -0.355 Vitamin C -0.355 100 4.4. Model Sensitivity Analysis In mathematical programming, it is often necessary to conduct a sensitivity analysis due to uncertainty concerning the true value of a given parameter or in order to determine how sensitive the solution is to the parameters in the model This process allows for the discovery of weaknesses in the model and serves to evaluate the reliability of the model’s results. 44.1. Elimination of Dah as a Consumption Crop The results from the market access scenarios shown in Tables 20a-21d indicate that farmers should use the majority of their income earnings from sold crops in the market in order to purchase dah for household consumption. A possible reason for this result could be due to incorrect nutritional parameter values for dah used in the model. While dah is grown as a household consumption crop to be used as a sauce to flavor common Malian dishes, it is only done so on a limited scale. Very little land is devoted to dah production and much of its output is exported to neighboring countries to be used in a popular variety of tea. For these reasons, it is unrealistic to assume that Malian farmers will consume large quantities of dah to meet household nutritional requirements. The model is adjusted to allow for dah production, but only to be sold in the market place and not for household consumption. Market access with wage employment results without dah consumption appear in 101 Tables 30-33. As dah only arose as a purchased crop in the original model, land devoted to crop production does not change for the overall household or the three field types. For the ideal environment scenario, the only change is that millet is now purchased for household consumption instead of dah. Millet also replaces dah purchases in the sorghum/millet production and consumption scenario, however, cowpea and okra consumption also slightly declines. Millet output is no longer sold as all of it goes directly to household consumption. 102 Table 4.30: | Overall Household Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Sorghum/Millet Change from Environment Scenario with Production and Scenario with Dah Consumption Dah Consumption Consumption Produced (ha) Millet 0.0 0.0 6.23 0.0 Okra 8.30 0.0 2.08 0.0 Consumed (kg) Cowpea 115.0 0.0 102.3 - 12.7 Millet 48,778 + 48,778 43,412 +.42,129 Okra 271.0 0.0 241.2 - 29.8 Purchased (kg) Cowpea 115.0 0.0 102.3 - 12.7 Millet 48,778 + 48,778 32,139 + 32,139 Sold (kg) Millet 0.0 0.0 0.0 - 9,99] Okra 10,353 0.0 2,415 + 29.8 103 Table 4.31: Common Land Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Sorghum/Millet Change from Environment Scenario with Production and Scenario with Dah Consumption Dah Consumption Consumption Produced (ha) Millet 0.0 0.0 5.36 0.0 Okra | 6.31 0.0 0.95 0.0 Consumed (kg) Cowpea 87.4 0.0 63.8 - 23.6 Millet 37,071 + 37,071 27,062 + 26,087 Okra 206.0 0.0 150.4 - 55.6 Purchased (kg) Cowpea 87.4 0.0 63.8 - 23.6 Millet 37,071 + 37,071 26,087 + 26,087 Sold (kg) Millet 0.0 0.0 0.0 - 6,988 Okra 7,868 0.0 1,061 + 55.6 104 Table 4.32: | Men’s Individual Land Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Environment Scenario with Dah Consumption Produced (ha) Millet 0.0 0.0 Okra 1.41 0.0 Consumed (kg) Cowpea 19.5 0.0 Millet 8,292 + 8,292 Okra 46.1 0.0 Purchased (kg) Cowpea 19.5 0.0 Millet 8,292 + 8,292 Sold (kg) Millet 0.0 0.0 Okra 1,760 0.0 105 Table 4.33: | Women’s Individual Land Without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Environment Scenario with Dah Consumption Produced (ha) Cotton 0.58 0.0 Okra 0.0 0.0 Consumed (kg) Cowpea 8.0 0.0 Millet 3,414 + 3,414 Okra 19.0 0.0 Purchased (kg) Cowpea 8.0 0.0 Millet 3,414 + 3,414 Okra 19.0 0.0 Sold (kg) Cotton 269.0 0.0 106 4.4.2. Variations in Length of Growing Season Due to the unpredictable nature in the timing and amount of rainfall in Mali, large variations in the length of the agricultural growing season are experienced from year to year. While the model in this study is a static representation in that it only incorporates resource usage, production decisions, and agricultural output from a single year’s worth of data, potential ramifications of a shortened growing season should be evaluated. Based on variability in the Malian growing season, labor availability to agricultural production is reduced to four and two months, respectively, in the model. No change is evident between the results from the four month labor availability constraint and the original model. This is not surprising because a single season production system is implicit in the crop labor requirement parameters used in the model. Tables 4.34 and 4.35 show the land and labor usage for the subsistence and market access scenarios when a two month growing season is induced. Land continues to be binding for the overall household and for the three field types with minor changes in land shadow prices from the original model. Labor, however, is now binding in the optimal solution as well, with the shadow prices reflecting daily wage earning potential for men and women farmers. 107 Table 4.34: | Subsistence Production: Land and Labor Usage in Two Month Growing Season Idea] Environ. Scenario { Sorghum/Millet Production& Consum. Activity Upper Shadow Activity Upper Shadow Level Bound Price* Level Bound Price* All (FCFA) (FCFA) Land (ha) 8.30 8.30 337,180 8.30 8.30 337,180 Labor(da) 459.9 459.9 450.0 459.9 459.9 450.0 Common (FCFA) (FCFA) Land (ha) 6.31 6.3] 332,120 6.31 6.31 332,120 Labor(da) 349.5 349.5 500.0 349.5 349.5 500.0 Men (FCFA) Land (ha) 1.4] 1.4] 337,180 * * 7 > Labor(da) 78.2 78.2 500.0 * * ** Women (FCFA) Land (ha) 0.58 0.58 381,980 * * * Labor(da) 29.1 32.2 - * * * ** * Exchange rate is $1 U.S. = 500 FCFA ** Not applicable Activity Level = Activity level at which the land or labor resource is being used Upper Bound = The maximum amount of land or labor resources that are available for use 108 Table 4.35: | Market Access Without Dah Consumption: Land and Labor Usage in a Two Month Growing Season Ideal Environ. Scenario Sorghum/Millet Production & Consum. Activity Upper Shadow Activity Upper Shadow Level Bound Price* Level Bound Price* All (FCFA) (FCFA) Land (ha) 8.30 8.30 337,180 8.30 8.30 337,180 Labor (da) 459.9 459.9 450.0 459.9 459.9 450.0 Common (FCFA) (FCFA) Land (ha) 6.31 6.31 332,120 6.31 6.31 332,120 Labor (da) 349.5 349.5 500.0 349.5 349.5 500.0 Men (FCFA) Land (ha) 1.41 1.41 337,180 * * * * * Labor (da) 78.2 78.2 500.0 ** * * Women (FCFA) Land (ha) 0.58 0.58 319,200 * * + * Labor (da) 32.2 32.2 450.0 * * * * Exchange rate is $1 U.S. = 500 FCFA ** Not applicable Activity Level = Activity level at which the land or labor resource is being used Upper Bound = The maximum amount of land or labor resources that are available for use 109 Differences in food self-sufficiency levels attained between the original model and the two month labor availability adjustment appear in Tables 4.36 and 4.37. As expected, a shortened agricultural growing season lowers the level of food self- sufficiency that can be achieved. Smaller changes are evident in the pure subsistence scenarios as compared to the market access scenarios reflecting the potential that year- round wage employment opportunities can have on household food security. 110 Table 4.36: | Subsistence Production in a Two Month Growing Season: Percentage Food Self-Sufficiency Level Achieved Ideal Change from Sorghum/Millet Change from Environment original Production & original model Consumption model All 87 -5 42 -7 Common 78 -5 28 -9 Men 79 a) * * Women 45 0 * * * Not applicable 111 Table 4.37: | Market Access in a Two Month Growing Season: Percentage Food Self-Sufficiency Level Achieved Ideal Change from Sorghum/Millet Change from boas Environment original Production & original ae model Consumption model All 238 - 42 83 -47 Common 234 - 46 67 - 43 Men 196 - 44 * * Women 198 - 52 * * * Not applicable 4.43. Reduction in Millet Yield As the major crop produced and consumed in the Sirakorola region of Mali, fluctuations in millet yield can significantly influence a household’s ability to meet its nutritional requirements from year to year. The millet yields used in the original model (1485 kg/ha on common land, 1811 kg/ha on men’s individual fields, and 1244 kg/ha on women’s individual fields), while nor unrealistic” given the high levels of reported manure and fertilizer usage, are, nonetheless, fairly high when compared to other studies in the region of the past five years. Similar to the sensitivity analysis that looks at a two month agricultural growing season, a reduction in millet yield by 50 percent gives an insight into the potential ramifications of a season with short rainfall or a high incidence of pest damage on a major crop grown in the region. Table 4.38-42 indicate the changes in crop production decisions that result from a 50 percent decline in millet yield (without dah consumption) for the pure subsistence and market access scenarios. For the ideal environment scenario in pure subsistence, land previously devoted to millet production is replaced with rice production. A slight decrease in land devoted to okra and peanut production also results. When a 75 and 85 percent sorghum/millet constraint is added to the overall household and common field, - Millet yields of 1,500 kg/ha and over are common and yields of well over 2,000 kg/ha are possible as reported in many studies covering Mali and other Sahelian countries (see Bationo, A. and A. U. Mokwunye, 1991; Christianson, C. B. and P. L. G. Vlek, 1991; Bationo, A., C. B. Christianson, and M. C. Klaij, 1993; Klaij, M. C., C. Renard, and K. C. Reddy, 1994) 113 Table 4.38: Subsistence Production with 50% Reduced Millet Yield: Crops Produced and Consumed and Land Devoted to Cultivation Ideal Change from Sorghum/Millet Change from Environment Scenario with Production & Scenario with higher millet Consumption higher millet yield yield All: Millet (ha) 0.0 - 1.92 6.23 0.0 Okra (ha) 4.97 - 0.65 2.03 - 0.05 Peanut (ha) 0.70 - 0.07 0.04 + 0.04 Rice (ha) 2.63 + 2.63 0.0 0.0 Common Millet (ha) 0.0 - 1.60 5.36 0.0 Okra (ha) 3.49 - 0.29 0.95 0.0 Peanut (ha) 0.90 - 0.03 0.0 0.0 Rice (ha) 1.92 + 1,92 0.0 0.0 Men: Millet (ha) 0.0 - 0.03 * * Okra (ha) 0.89 - 0.11 * * Peanut (ha) 0.11 - 0.01 * * Rice (ha) 0.41 + 0.4] * * Women: Cotton (ha) 0.04 0.0 * * Millet (ha) 0.0 - 0.10 * * Okra (ha) 0.39 - 0.01 * * Peanut (ha) 0.05 0.0 * * Rice (ha) 0.10 + 0.10 * * * Not applicable 114 Table 4.39: Overall Household with 50% Reduced Millet Yield and without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Sorghum/Millet Change from Environment Scenario with Production and Scenario with Dah Consumption Consumption Dah Consumption Produced (ha) Millet 0.0 0.0 6.23 0.0 Okra 8.30 0.0 2.08 0.0 Consumed (kg) Cowpea 115.0 0.0 89.7 - 25.3 Millet 48,778 + 48,778 38,047 + 36,764 Okra 271.0 0.0 211.4 - 59.6 Purchased (kg) Cowpea 115.0 0.0 89.7 - 25.3 Millet 48,778 + 48,778 32,410 + 32,410 Sold (kg) Millet 0.0 0.0 0.0 - 9,99] Okra 10,353 0.0 2.445 + 59.6 115 Table 4.40: Common Land with 50% Reduced Millet Yield and without Dah Consumption- Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Sorghum/Millet Change from Environment Scenario with Production and Scenario with Dah Consumption Dah Consumption Consumption Produced (ha) Millet 0.0 0.0 5.36 0.0 Okra 6.31 0.0 0.95 0.0 Consumed (kg) Cowpea 87.4 0.0 55.0 - 32.4 Millet 37,071 + 37,071 23,355 + 22,380 Okra 206.0 0.0 130.0 - 76.0 Purchased (kg) Cowpea 87.4 0.0 55.0 - 32.4 Millet 37,071 + 37,071 975.0 + 975.0 Sold (kg) Millet 0.0 0.0 0.0 - 6,988 Okra 7,868 0.0 1,081 + 76.0 116 Table 4.41: | Men’s Individual Land with 50% Reduced Millet Yield and without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Environment Scenario with Dah Consumption Produced (ha) Millet 0.0 0.0 Okra 1.41 0.0 Consumed (kg) Cowpea 19.5 0.0 Millet 8,292 + 8,292 Okra 46.1 0.0 Purchased (kg) Cowpea 19.5 0.0 Millet 8,292 + 8,292 Sold (kg) Millet 0.0 0.0 Okra 1,760 0.0 117 Table 4.42: | Women’s Individual Land with 50% Reduced Millet Yield and without Dah Consumption - Market Access With Wage Employment: Crops Produced, Consumed, Purchased, and Sold and Land Devoted to Cultivation Ideal Change from Environment Scenario with Dah , Consumption Produced (ha) Cotton 0.58 0.0 Okra 0.0 0.0 Consumed (kg) Cowpea 8.0 0.0 Millet 3,414 + 3,414 Okra 19.0 0.0 Purchased (kg) Cowpea 8.0 0.0 Millet 3,414 + 3,414 Okra 19.0 0.0 Sold (kg) Cotton 269.0 0.0 118 respectively, not much change is evident. It is interesting to note that millet, even with its 50 percent lower yield, is still chosen over sorghum. For the scenarios with market access, land devoted to production does not change. Slight variations in amounts of crops consumed, purchased, and sold arise, however, these changes are mostly attributable to the fact that dah is no longer available for consumption. Diminished food self-sufficiency levels that result from the lower millet yield for the subsistence and market access scenarios are shown in Table 4.37 and 4.38. These lower levels are quite substantial, particularly for the pure subsistence scenarios, when considering the high levels of malnutrition characterized in the results with even the high millet yields. 119 Table 4.43: | Subsistence Production with 50% Reduced Millet Yield: Food Self- Sufficiency Level Achieved Ideal Change from Sorghum/Millet Change from Environment Scenario with Production & Scenario with higher millet yield Consumption higher millet yield All 74 - 18 37 - 12 Common 71 - 12 25 - 12 Men 68 - 16 * * Women 41 -4 * ® * Not applicable 120 Table 4.44: Market Access with 50% Reduced Millet Yield: Food Self-Sufficiency Level Achieved Ideal Change from Sorghum/Millet Change from Environment Scenario with Production & Scenario with higher millet yield Consumption higher millet yield All 223 - 57 78 - 52 Common 22] - 359 63 -47 Men 197 - 43 x * Women 204 - 46 * * * Not applicable 12] Chapter 5. Summary and Conclusions This study has examined the effects of resource allocation and production decisions on the attainment of food security and net revenue maximization for farmers in two villages located in the Guinean Zone of rural Mali. A linear programming model was used to determine how gender-differentiated constraints, size of the household, and potential IPM technologies could influence specific nutrient deficiencies and the ability to achieve household food security. 5.1. Significance of Results As initially hypothesized, model results and information about the socio-economic environment detailed in this study affirm that gender-differentiated constraints negatively influence household performance in terms of revenue maximization and the ability to meet household nutritional needs. This conclusion is not immediately apparent, however, because, just as Schultz (1964) effectively argued in his ground-breaking study on traditional agriculture, the model in this study suggests that the representative Malian farmers are rational decision makers who positively respond to price incentives. Responses from the socio-economic survey, the LP model in this study, and other studies in the region all indicate that a large proportion of men should and do migrate in order to find wage employment, the majority of on-farm work is done by women with the help of their children, and this agricultural production by women and children is performed on 122 common and men’s individual fields. Male migration makes perfect sense because employment opportunities are scarce in rural areas, men receive higher wages than women, and religious and cultural practices often limit women’s ability to travel and seek employment. By the same token, women and children performing the largest share of agricultural work on common and men’s individual fields is also a very logical result given the fact that women generally do not migrate and women’s individual fields are usually located on the poorest quality agricultural land. Household decision makers are generally assigning labor resources to their most productive activities for the household only when social norms of behavior and cultural restrictions to gender equity are considered given. Gender-differentiated constraints to production, however, still exist, preventing the household unit from realizing its full potential. As mentioned earlier, women receive lower wages and fewer income generating opportunities than do men due to cultural restrictions. Often times, the only income that a woman receives is from crop sales of output produced on her individual fields. This lack of income and purchasing power is significant becuase numerous studies in the Sahel and other parts of Africa show that women’s incomes are more strongly associated with improvements 1n children’s health and household nutritional status than are men’s incomes (Fapohunda, 1988; Kennedy and Peters, 1992; Hopkins, Levin, and Haddad, 1994; Quisumbing et al., 1995). In addition, women are allocated much smaller land to farm as their own compared to men. Their individual fields generally receive less inputs, are of poorer land quality, and have greater levels of 123 pest damage thereby leading to lower crop yields. This, coupled with the fact that women generally do not hold title to the land that they farm, further aggrevates their realized productivity in that credit is lacking for input purchases and equipment rental on their plots. With respect to variable input allocation and chosen crop mix for production and consumption, however, the argument of farmer rationality is much less conclusive as it is for labor allocation decisions. The average amount of variable factor inputs used by field type in Sirakorola was shown in Table 4.8. Manure and chemical fertilizer usage is reported to be entirely concentrated on common and men’s individual fields. While this study does not determine if fertilizer inputs exhibit continuously diminishing returns or the level of usage at which total product begins to decline, other studies have consistently shown that reallocating factor inputs so that women have greater access to production resources such as fertilizer will benefit the entire household by raising output (Singh, 1988; Toulmin, 1992; Udry, 1996). The extreme inequitable levels of variable input allocation to field types in Sirakorola, nevertheless, strongly suggest that a potential increase in overall household performance exists with a simple reallocation of resources such as manure and chemical fertilizer. Model results for the subsistence scenarios indicate that farmers should primarily grow millet, peanuts, and okra in order to best meet nutritional needs and maximize net revenue. As seen in Table 4.17, this is not too far off from what farmers in Sirakorola are doing as roughly 77 percent of all crop land is devoted to these three crops. When buying 124 and selling food crops in the market are possible activities and any crop can be grown on any plot of land without sacrificing yields, the results of the model stipulate that the majority of land should be devoted to okra production. Most of its output should then be sold to generate revenue in order to purchase cowpea and dah for household consumption. Land variation, however, does have a significant influence in determining crop performance as different crops are better or more ill-suited than others for a given soil type and texture, topography, and vegetation located near-by. General conclusions that can be drawn from this study in terms of trying to find the optimal crop production and consumption mixes for the region are that okra, cowpea, peanuts, and dah should be looked at more closely due to their respective production, price, and nutritional benefits to farmers in Mali. Since farmers in Sirakorola indicate that peanuts and dah are primarily grown by women and okra is exclusively a woman’s crop (Table 4.17), a greater understanding of the relationship of women to daily farm operations and gender-specific constraints that exist will lead to more effective technology and policy implementation. Differences among field types in reported impacts of pests on crop yield has important significance for the adoption of new IPM technologies. In a cropping system characterized by a high risk of food insecurity, low income-generating opportunities, and minimal levels of purchased inputs, deciding on what type of production intervention to utilize and where to most effectively apply it is crucial. This study shows, as was originally hypothesized, that the greatest potential for pest damage reduction technologies 125 such as IPM to improve household nutrition lies in adapting methods to mesh with the characteristics of women’s crop production systems. As shown in Table 4.24, nutritional deficiency levels decrease approximately 7 percent for common field production, 0 percent on men’s individual fields, and as much as 46 percent on women’s individual fields when pest damage is eliminated. Since women are often the key producers on common and men’s individual fields as well, incorporating the unique production concerns and needs of women farmers in IPM development will vastly improve adoption and effectiveness. Particularly in periods of drought where low rainfall is unable to sustain much of the alternative biomass that supports pests such as birds, insects, and rodents, pest damage to food crops becomes a serious threat to household food security levels (Frankenberger and Lynham, 1993). Pest damage in Sirakorola, as in much of the rest of Mali, is a significant obstacle to the attainment of household food and nutritional security in times of low and/or untimely rainfall. It is unfortunate that data quality and availability problems prohibited the linear programming analysis for the villages in Mourdiah as nearly all of the respondents in Mourdiah (shown in Table 4.9) indicated that they perceive pest damage to have a significant impact on household food security. The clear distinction between the two regions in reported pest damage impact means that IPM technologies may be more favorably received and have a higher adoption rate in Mourdiah as opposed to Sirakorola. 126 Contrary to the original hypothesis with respect to household size, this study found that smaller households are performing slightly better than larger ones in terms of meeting household nutritional requirements. It is generally argued that larger households benefit from economies of scale in agricultural production and are better able to diversify income sources, thereby spreading risk. However, larger households sometimes suffer from heightened labor-incentive problems, more complex distributional issues, and a loss of family cohesiveness as compared to smaller households (Toulmin, 1992). The labor- incentive problem can arise when output from production is shared with a large group, making the labor contribution of one individual less noticeable, thereby causing an individual to work less hard. As was shown in Chapter 4, the superior performance of the smaller household grouping in this study is explained by the higher availability of land per capita. Results from the model show that vitamin A is the most difficult nutrient for households in Sirakorola to satisfy. This is not surprising given the absence of many of the foods that are rich in vitamin A such as carrots, eggs, red palm oil, and animal and fish livers. Vitamin A deficiency particularly affects young children and can cause blindness, intestinal and respiratory infections, and death. A common result of vitamin A deficiency is called “night blindness” in which people (e.g. women) cannot see in the dim light of the evening. This can easily be cured in one to two days by consuming the proper amounts of vitamin A. A short-term solution to vitamin A deficiency is to give supplements to those at risk in order to help them build up their liver stores. A longer and 127 more sustainable solution to vitamin A deficiency disorders and prevention is to make available, educate, and encourage families to eat vitamin A rich foods (King and Burgess, 1993). 5.2. Gender-Differentiated Production in Mali and Relevance for Integrated Pest Management Adoption Variations among ethnic groups, villages, regions, within countries, and among countries are often significant and there is a danger in projecting specific results across a wide area because it can lead to very misleading conclusions in, for instance, technology choices or policy recommendations. It is important to recognize that men and women farmers in Mali, while sharing many similarities, often face very different constraints and play different roles in rural household structures and crop production systems than do men and women in many other parts of Africa. Since a key criterion in the IPM CRSP site determination process in Mali was a village’s demographic and agricultural representativeness in the region, however, results from this study on farmers in Sirakorola can be carefully generalized across a much broader range. The Sahel, lying on the southern edge of the Sahara desert, spans seven countries, is comprised of more than 48 million people, and is about 5,000 kilometers long and 300 kilometers wide (Leisinger and Schmitt, 1995; United Nations, 1995). Similar ethnic groups, religious and cultural practices, crops grown, and agro-ecological conditions in Sirakorola are encountered in 128 much of the Guinean Zone of Mali and even, to a lesser degree, in neighboring countries across the Sahel like Burkina Faso, Senegal, and Niger. Tables 5.1 to 5.4 summarizes some of the key results from the study with respect to levels of self-sufficiency in crop production for the various model scenarios. In order to make comparisons across scenarios easier, revenue values, where applicable, are converted to food self-sufficiency values (right-hand sides of the nutritional constraints were progressively increased in order to determine self-sufficiency levels). Average household size results are compared with the current agricultural practices indicated in the socio-economic survey represented in the model validation. Tables 5.1 to 5.4 also present the differences in the three household size performances and among field types for the model scenarios. As shown, the subsistence scenarios more closely parallel the results from the current practices signifying that full employment opportunities 1n the market access scenarios may not be that realistic for Malian household members. Differences in food self-sufficiency levels among the model scenarios for the overall household in Sirakorola appear in Table 5.1. A dramatic difference exists between the ideal environment scenarios under the two model formats as wage employment and market access opportunities yield a food self-sufficiency level more than five times as high as under pure subsistence. Tables 5.1 and 5.2 show that smaller size households are generally outperforming the larger size households for the overall household and on common fields. Results from men’s and women’s individual fields 129 appear in Tables 5.3 and 5.4. The large increase in food self-sufficiency when pest damage is eliminated is evident in both the pure subsistence and market access scenarios for women’s individual fields. Seeking to more accurately model agricultural household behavior by taking gender-specific production and consumption constraints into account calls for a more complicated modeling strategy that relies on further disaggregated and detailed data sources. This implies taking a new approach in the development planning and implementation stages. Greater knowledge of the intra-household decision making process and bargaining positions held by men and women farmers must be obtained to have an increased chance of success for targeted policy goals and technology adoption. Tables 4.10, 4.11, and 4.12 showed the prime pest constraints to production indicated by men and women farmers in Mourdiah and Sirakorola and the percentage crop loss due to these constraints. This information, coupled with results from the model for Sirakorola with respect to pest damage, can be used to determine which form of pest damage should be targeted for specific crops and the potential gains in food security and/or net revenue levels that can be anticipated. 130 Table 5.1: Level of Self-Sufficiency in Crop Production for the Overall Household in Sirakorola by Household Size Average Small Large Self- Change Self- Self- Scenario Sufficiency from Sufficiency | Sufficiency Level (%) Current Level (%) Level (%) Current Practices: 43 - N/A N/A Activity Levels from Socio-economic survey Subsistence: 92 +49 95 88 Ideal Environment Subsistence: 49 +6 49 47 Sorghum/Millet Consumption and Production Subsistence: 49 +6 49 47 Sorghum/Millet Consumption and Production No Pest Damage Market Access/Wage Employment: 280 + 237 305 275 Ideal Environment Market Access/Wage Employment: 260 +217 285 255 Sorghum/Millet Consumption Market Access/Wage Employment: 130 + 87 145 135 Sorghum/Millet Consumption and Production Market Access/No Wage Employment: 81 + 38 79 78 Sorghum/Millet Consumption and Production Market Access/No Wage Employment: 81 + 38 79 78 Sorghum/Millet Consumption and Production No Pest Damage N/A - not applicable 131 Table 5.2: Level of Self-Sufficiency in Crop Production for Common Fields in Sirakorola by Household Size Average Small Large Self- Change Self- Self- Scenario Sufficiency from Sufficiency | Sufficiency Level (%) Current Level (%) | Level (%) Current Practices: 45 - N/A N/A Activity Levels from Socio-economic survey Subsistence: 83 + 38 87 79 Idea] Environment Subsistence: 37 -8 40 34 Sorghum/Millet Consumption and Production Subsistence: 4] -4 45 38 Sorghum/Millet Consumption and Production No Pest Damage Market Access/Wage Employment: 280 + 235 305 275 Ideal Environment Market Access/Wage Employment: 260 +215 285 255 Sorghum/Millet Consumption Market Access/Wage Employment: 110 + 65 125 105 Sorghum/Millet Consumption and Production Market Access/No Wage Employment: 59 + 14 65 54 Sorghum/Millet Consumption and Production Market Access/No Wage Employment: 66 +2) 72 61 Sorghum/Millet Consumption and Production No Pest Damage N/A - not applicable 132 Table 5.3: Level of Self-Sufficiency in Crop Production for Men’s Individual Fields in Sirakorola by Household Size Average Small Large Self- Change Self- Self- Scenario Sufficiency from Sufficiency | Sufficiency Level (%) Current Level (%) Level (%) Current Practices: 10 - N/A N/A Activity Levels from Socio-economic survey Subsistence: 84 + 74 80 82 Ideal Environment Subsistence: 84 +74 80 82 No Pest Damage Market Access/Wage Employment: 240 + 230 244 245 Ideal Environment Market Access/Wage Employment: 230 + 220 227 224 Sorghum/Millet Consumption Market Access/No Wage Employment: 193 +183 179 188 Sorghum/Millet Consumption Market Access/No Wage Employment: 193 + 183 179 188 Sorghum/Millet Consumption No Pest Damage N/A - not applicable 133 Table 5.4: Level of Self-Sufficiency in Crop Production for Women’s Individual Fields in Sirakorola by Household Size Average Small Large Self- Change Self- Self- Scenario Sufficiency from Sufficiency | Sufficiency Level (%) Current Level (%) | Level (%) Current Practices: 43 - N/A N/A Activity Levels from Socio-economic survey Subsistence: 45 +2 45 43 Ideal Environment Subsistence: 90 +47 91 87 No Pest Damage Market Access/Wage Employment: 250 + 207 263 246 Ideal Environment Market Access/Wage Employment: 230 + 187 236 226 Sorghum/Millet Consumption Market Access/NoWage Employment: 195 + 152 200 190 Sorghum/Millet Consumption Market Access/No Wage Employment: 207 + 164 213 199 Sorghum/Millet Consumption No Pest Damage N/A - not applicable 134 5.3. Limitations of the Study Perhaps the major limitation to this study is the fact that crop yields were only given by the heads of the household and variations in yields among field types were strictly delineated by indicated loss due to pest damage. No direct information for this study in relation to field location or soil quality is available. Field size and crop yields indicated by the survey respondents were not checked or verified by field technicians. As four different field technicians were responsible for collecting the data in the two regions, the existence of interviewer bias is a possibility. Further, a male field technician in each region was responsible for surveying the male respondents in that region, whereas a female field technician strictly surveyed the female respondents in each region. This could influence cross-gender comparisons of the data results if, for instance, the male field technician consistently asked questions related to food security differently than the female field technician in the same region. There is also a chance that some details in the information may have been lost due to language translations from the questionnaire in written French to the verbal exchange in Bambara, back to the response written in French. In order to minimize the appearance of the above mentioned problems, all the field technicians were present during a detailed discussion of the administration of the survey. The LP model used in this study is primarily based on data from a single year’s production and consumption activities. The nature of this model is a fairly static 135 representation of a real world dynamic setting. The important concept of risk in agricultural production is not incorporated into this model other than in very broad notions of how self-sufficient or nutrient deficit households may be in various scenarios. Extreme variations in annual rainfall, as previously explained, can significantly alter realized crop yields from year to year. As agricultural research and development efforts continue in these regions, however, this economic analysis limitation will be rectified by having a multi-year database to draw upon. There is a lack of detailed information about employment opportunities, market access, and the amount of household income coming from remittances from workers who migrate. The two extremes of no employment opportunities and full employment opportunities for all who seek it in this study are certainly oversimplifications of the real world setting. This study does not take into consideration the fact that labor requirements vary depending on the time period of the growing season as specific labor availability and allocation per time period is unknown. Specific information related to intra-household dynamics such as level of decision authority and bargaining positions that different members in the household possess is also absent from this model. Follow-up field visits with these same respondents in surveys by other researchers might increase our understanding of how and why production decisions and resource allocations are made the way they are and how new cropping techniques might best be fused with existing practices. 136 The pest damage elimination scenario utilized in this study is overly optimistic as no pest damage reduction technology, at least in the foreseeable future, is going to completely eradicate the threat of pests to crop production. In addition, one would probably never use a technology that completely eliminated all pests as it would likely lead to resistance rather than a sustainable production system. Based on the lack of data from the socio-economic survey, however, results from this scenario do give a fairly good indication as to potential target levels of increased food self-sufficiency that could be achieved and which sectors of the crop production system might benefit most from IPM technologies. Only the crops that are actively grown by farmers in the region were included in the crops available for consumption in the model. Seasonal fruits such as mangoes and bananas were not included and this could have some important implications for the specific nutrient deficiencies suggested in the model results. Due to the structure of the survey, it was impossible to tie household head respondents and men or women respondents active in day-to-day agricultural production to the same household. This prevented important cross-linkages in the analysis such as variations in food security. pest damage, or crop yield responses by members of the same household. 137 5.4. Recommendations for Future Research Success for IPM in Mali relies on its interdisciplinary and participatory framework. Biological and social scientists must work closely together and with farmers in the field maintaining an open-mind, a willingness to share ideas, and respect for one another’s background, experience, and needs. Such an approach includes a thorough understanding of intra-household and community gender relations. The official recognition of women as key producers in agricultural production in Mali and the West African Sahel is a fairly recent occurrence in the international development literature. While a more concentrated effort has been made in the last decade to understand the complex household behavior patterns and structures of Mali’s crop production systems and to incorporate them into the prevailing research paradigms, information is still lagging in terms of knowledge related to the impacts of gender in agricultural production. More detailed information in regards to the nutritional composition of dah is necessary to determine if it should be looked at more closely in crop production technologies. Further scenarios of the model should be re-run taking out the nutritional contributions of dah to more accurately model the crop production system for men and women farmers in Sirakorola. As shown in the model results from Chapter 4, IPM has the greatest potential to enhance the ability of farmers to achieve higher food self-sufficiency levels by targeting 138 women farmers in Sirakorola, particularly in okra production, and men and women farmers growing sorghum and millet in Mourdiah. Knowledge of currently practiced indigenous pest management techniques needs to be explored in depth in order to effectively mesh standard IPM technology with low cost, available, and familiar indigenous methods. Food processing and preparation, nutritional information, and taste and preferences of Malian men and women farmers need to be considered in the adaptation of new crop varieties, for example, that are more resistant to pest damage. Further research for IPM in Mali is needed on the impact of socio-cultural constraints to production that are influenced by variations in ethnic group. 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United States Agency for International Development and the Southeastern Consortium for International Development, 1994. 147 Appendix A: Questionnaire for Head of the Household IPM CRSP-IER/Mali - Enquéte socio-économique 1995 - Questionnaire | Questionnaire pour le chef de l'Unité de Production (UP) Nom de I'enquéteur: Village: Zone: No. d'UP: Nom du chef d'UP: Ethnique de |'UP : Fiche N° 1 Démographie de I'UP N° Nom et Lien avec le Age Sexe Ethinie Education Catégories Prenom chef 1 | 2 3 4 5 6 7 8 Voir codes 1: Voir 1: rien 0=enfant homm codes 2: e alpha.lang. actif(ve) 2: locale 2=agé et femme 3: arabe dispensé 4: francais des travaux 148 IPM CRSP-IER/Mali - Enquéte socio-économique 1995 - Questionnaire I Questionnaire pour le chef de Unité de Production (UP) Nom de I'enquéteur: Village: Zone: No. d'UP: Nom du chef d'UP: Fiche N° 2 Informations sur le Foncier Superficie de terre totale disponible a l"UP: (en Ha) Superficie de terre en culture en 1994:(en Ha) Superficie totale utilisée en parcelle commune par I'UP en 1994: (en Ha) Superficie totale attribuée en parcelles individuelles 4 des membres de l'UP en 1994 (en Ha): Fiche N° 3 Attribution et utilisation de la terre par genre Membre Nombre de Superficie Membre Nombre de Superficie masculin parcelles cultivée en feminin Nom parcelles cultivée en Nom et attribuées a Ha et Prenom attribuées a Ha Prenom chaque chaque homme. femme IPM CRSP-IER/Mali - Enquéte socio-économique 1995 - Questionnaire I Questionnaire pour le chef de l'Unité de Production (UP) Nom de |'enquéteur: Village: Zone: No. d'UP: Nom du chef d'UP: Fiche N° 5: Information sur les parcelles le cultures Durée (nombre d'années) pour laquelle les parcelles pour la culture sont attribuées aux hommes de |'UP: Durée (nombre d'années) pour laquelle les parcelles pour la culture sont attribuées aux femmes de I'UP: Production sur les parcelles communes cultivées en 1994 Cultures Rendement (estimé en Quantité vendue Quantité disponible Kg/Ha) (estimé en Kg) pour consommation (estimé en Kg) Voir code de cultures 150 IPM CRSP-IER/Mali - Enquéte socio-économique 1995 - Questionnaire I Questionnaire pour le chef de l'Unité de Production (UP) Nom de !'enquéteur: Village: Zone: No. d'UP: Nom du chef d'UP: Fiche N° 6: Utilisation de la production 1. En moyenne, est-ce que la production des parcelles communes est suffisante pour les besoins de |'UP vente? Oui Non (si oui, allez a la question 2; sinon, allez a la question 3) 2. Si oui, 4 qui, 4 quel endroit vendez-vous les produits de la récolte? Cultures Quantité Type d'unité Taille de Prix par | Vendua Vendu Satisfaction vendues vendue utilisé l'unité unité qui ou avec le prix obtenu Voir code l=sac FCFA l= O=pas satisfait de cultures 2=Kg coopérative }=un peu 2=nésocian satisfait 8 2=satisfait t local 3=trés satisfait 3=négocian t de l'extérieur 4=marché local 3, La récolte de 1994 couvrent elle les besoins alimentaires de ]'UP? Out Non 151 IPM CRSP-IER/Mali - Enquéte socio-économique 1995 - Questionnaire I Questionnaire pour le chef de I'Unité de Production (UP) Nom de l'enquéteur: Village: Zone: No. d'UP: Nom du chef d'UP: 4. Donnees de stockages de la récolte pour les besoins alimentaires de I'UP: Culture Nombre de mois Culture Nombre de mois Culture Nombre de mois 5. Si non, nombre de mois de deficit: Culture Nombre de mois Culture Nombre de mois Culture Nombre de mois 6. Les strategies utilisées pour ateindre les prochines recoltes 7. SVP indiquez le quantité totale vedue (parcelles communes en 1994), Culture quantité Kg ou # Sacs Culture quantité Kg ou # Sacs Culture quantité Kg ou # Sac 152 IPM CRSP-IER/Mali - Enquéte socio-économique 1995 - Questionnaire I Questionnaire pour le chef de I'Unité de Production (UP) Nom de I'enquéteur: Village: Zone: No. d'UP: Nom du chef d'UP: 8. Autres sources de revenu pour I'UP: (Ex. bétail, produits animaux, produits de la forét, revenus de location, etc.) Fiche N° 7. Information sur l'equipoment (possession et utilisation) Type Nombre loué | Nombre possédé Nombre (nom) emprunté 153 IPM CRSP-IER/Mali - Enquéte socio-économique 1995 - Questionnaire I Questionnaire pour le chef de I'Unité de Production (UP) Nom de I'enquéteur: Village: Zone: No. d'UP: Nom du chef d'UP: Fiche N° 8 Information Complementaires sur les produit chemiques Avez-vous obtenu du crédit pour I'achat de produits chimiques pour le contréle des nuisibles en 1994? i. parcelle commune li. parcelle individuelles Oul Non Ou Non Pour quelle cultures? Pour quelle Cultures? Si oul, quelle a été la source de ce crédit? i. pour les parcelles communes ii. pour les parcelle individuelles Si vous utilisez des moyens de lutte chimiques, SVP indiquez les sources de I'information ou de la documentation. Culture Produits Source d'information Fréquence de l'information recue Culture: 1=Insecticide 1=Recherche 1=Sorgho 2=Herbicide 2=Vulgarisation 2=Millet 3=Fongicide 3=ONG 5=Niébé 4= Rien 4=Fouanisseur Prive 10= Arachide O=autre (a precisor) 154 Appendix B: Questionnaire for Male and Female Farmers Active in Agricultural Production IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire IT Questionnaire pour l'exploitant son épouse Questionnaire No: Code de I'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Sexe du (de la )répondant(e): M F Age du (de la) répondant(e): Niveau d'éducation Lien avec le chefde l'UP (Ex. Fils ainé et sa premiere épouse) 155 IPM CRSP Maii/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondani(e): Fiche N° 1. Caractéristiques bio-physiques des parcelles Type de Cultures Superficie Emplacement de la Topograp Type Text Type de parcelle en cultivée en parcelle hie du lieu de ure végétation cultivee 1994 1994 sol avoisinante l= Voir en Ha l= l= = = Voir commune code Case Plateau Leger agrile code de 2= de 2= 2= = ux végétation individuel cultures Brouse Plaine Moye = le 3= n argil (homme) Bas fond = o- 3= Lourd limo individuel = le Sable (femme) ux Sablo- limo 156 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N® 2. Types d'intrants utilisés sur les différentes parcelles (1994) Cultures sur Type de Superficie Type Type Quantité cout Source(s) les parcelles parcelle d'intrant d'unité (nombre par d'obtention communes utilisé d'unités) unité de ]'intrant 157 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire JI Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N°3. Meécanisation en 1994 Culture Type de Equipement Dans quel but? Mode Forme de Colt parcelle utilisé (operation) d'obtention paiment Cultures: I= Liste l=Nettoyage l= l= F.CFA 1=Millet Commune 2=Labour UP agent 2=Sorgho 2= 3=Semis 2= 2= S=Niébé individuelle 4=Epandage Emprunt Exchange 10= (homme) 5=Sarclage 3= de service Arachide 3= 6=Traitment Location 3=contre 0O=autre individuelle phytosani 4=nature (a (femme) 7=Arrosage 5=autre precisor) 8=Revolte (a precisor) 158 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N° 4. Perception des pertes en récoltes en 1994. Cultures Type de Facteur ayant Proportion Méthode de lutte parcelle causé les pertes les plus significatives Cultures: 1=Commune 1=oiseaux 0O=pertes quasi 1=Manual 1=Millet 2=mauvaises nulles 2=Macanique 2=Sorgho 2= herbes 1= 25% 3=Chemique S=Niébé individuelle 3=insectes 2= 25%-50% 4=Biologique 10= Arachide (homme) 4=maladise 3= 50% -75% O=autre 3= 5=Autres 4= >75% (a precisor) individuelle (a precisor) 5= 100% (femme) 159 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N°S. Méthodes de lutte contre les nuisibles selon la culture et le genre avant la récolte. Cultures Type de Nuisible Moment de Partie Methode de Comment? A M Tache parcelle premiére apparition de la lutte quel é effectu du nuisible plante mom t ée par atlaquee ent? h oO d e s Cultures l= Liste l=levee j= l= 1=sarclage i= 1=Millet Commune 2= Racine manual 2=retrait Homm 2=Sorgho 2= Vegeratif 2= 2= manuel e S=Niébe individuel 3= tige mecanique des 2= 10= le montaison 3= 3= nuisibles Femme Arachide (homme) 4= Feuille Chemique 3=lutte = U=Autres 3= epiaison 4= 4= chimique Gargon (a precisor) individuel S= fleure Biologique 4=gardien (<16 le recolte 5= nage ans) (femme) epis contre les 4=Fille fruits Olseaux (<16an 6= 5=meéthod s) graine e (traditionn elle) 6=autre (spécifier) 160 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire I Questionnaire pour Il'exploitant son épouse Questionnaire No: Code de I'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N° 6. Methode de lutte contre Jes nuisibles selon la culture et le genre aprés la récolte. Cultures Type de parcelle Méthodes Tache effectuée par Culture: 1=Commune Taches: 1=Homme 1=Sorgho 2=individuelle (homme) l=sarclage 2=Femme 2=Millet 3=individuelle (femme) 2=retrait manuel des nuisibles 3=Gar¢on (<16 ans) 5=Niébé 3=lutte chimique 4=Fille (<16 ans) 10= Arachide 4=gardiennage contre les 0=Autres oiseaux (a precisor) 5=méthode traditionnelle 6=autre (a precisor) 161 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondani(e): Fiche N° 7. Méthodes traditionnelles lutte contre les nuisibles dans les parcelles communes Culture Méthode Produit Prépara-tion Efficacité par Colts en Coiits Disponibi- tradition- local du produit rapport aux temps par moneétaires lité par nelle de utilisé par produits rapport aux par rapport rapport aux controle chimiques produits aux produits chimiques produits chimiques chimiques Culture: Ouvert Ouvert = 1= plus l= prend 1= plus 1= plus 1=Sorgho Homme efficace plus de élevé disponible 2=Millet = temps S=Niébé Femme 2= aussi 2= 2=aussi 10= efficace que 2= prend le identique disponible Arachide meme 0=autre 3= moins temps 3= moindre 3=moins (a precisor) efficace que disponible 3= prend 99= 99= moins de incertain 99= incertain temps incertain O=pas de 99= cout incertain monétaire 162 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire IT Questionnaire pour |'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N° 8.Méthodes traditionnelles de lutte contre les nuisibles dans les parcelles individuelles Culture Méthode Produit Prépara- Efficacité par Coits en Coits Disponibi- tradition- local tion du rapport aux temps par monétaires lité par nelle de utilisé produit par produits rapport aux par rapport rapport aux contréle chimiques produits aux produits chimiques produits chimiques chimiques Culture: Ouvert Ouvert = 1= plus 1= prend 1= plus l= plus 1=Sorgho Homme efficace plus de élevé disponible 2=Millet = temps 5=Niébé Femme 2= aussi 2= 2=aussi 10= efficace que 2= prend le identique disponible Arachide meme temps O=autre (a 3= moins 3= moindre 3=moins precasor) efficace que 3= prend disponible moins de 99= 99= temps incertain 99= incertain incertain 99= O=pas de incertain cout moneétaire 163 IPM CRSP Mali/TER - Enquéte Socio-économique 1995 - Questionnaire IT Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l’enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Utilisez-vous des feuilles de neem la lutte contre les nuisibles des grains en stockage ? a. Cultures recoltées de la parcelle commune __ b.Cultures recoltées des parcelles individuelles Oui Non Oui Non Quelle cultures? Quelles cultures? Fiche N° 9. Raison d'utilisation de methode traditionelles au lien méthode chemiques: Méthode Culture Raison Culture: 1= familiarité 1=Sorgho 2= experience avec la méthode 2=Millet 3= n'est pas au courant d'autres méthodes 5=Niébé 3= plus facile a exécuter 10= Arachide 4=moins domageable pour la santé O= autre (a precisor) 5=moins domageable pour l'environnement 6=produits chimiques ne sont pas disponibles 7=autre (a precier) 164 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour l'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de Ja) répondant(e): Fiche N°10. Avez-vous utilisé des produits chimiques pour le contréle des nuisibles (vertébrés, insectes, mauvaises herbes,maladies) sur vos cultures en 1994? Type de Culture Produit Raison parcelle l= Culture: 1= familiarité Commune 1=Sorgho 2= expérience avec la méthode 2= 2=Millet 3= n'est pas au courant d'autres méthodes individueile S=Niébé 3= plus facile a exécuter (homme) 10= Arachide 4=moins domageable pour la santé 3= 0= 5=moins domageable pour l'environnement individuelle autre (a precisor) 6=produits chimiques ne sont pas disponibles (femme) 7=autre (a precisor) 165 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire IT Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Avez-vous uttilisé des produits chimiques pour le contréle des nuisibles (vertébrés, insectes, mauvaises herbes, maladies) sur vos cultures dans le passé? Oui Non Type de Culture Produit Avant ou apreés la récolte? parcelle |= Culture: 1= avant Commune 1=Sorgho 2 = aprés 2= 2=Millet individuelle S5=Niébé (Homme) 10= Arachide 3= O=autre individuelle (a precisor) (femme) 166 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l’enquéteur : Zone: Village: . Nom et pronom (de la) répondant(e): Fiche N° 11 Information Complementaires sur les produit chemiques Avez-vous obtenu du crédit pour l'achat de produits chimiques pour le contréle des nuisibles en 1994? 1. parcelle commune ii. parcelle individuelles Oui Non Oui Non Pour quelle cultures? Pour quelle cultures? Si oul, quelle a été la source de ce crédit? i. pour les parcelles communes ii, pour les parcelle individuelles Si vous utilisez des moyens de lutte chimiques, SVP indiquez les sources de l'information ou de la documentation. Culture Produits Source d'information Fréquence de l'information recue Culture: 1=Insecticide 1=Recherche 1=Sorgho 2=Herbicide 2=Vulgarisation 2=Millet 3=Fongicide 3=ONG S=Niébé 4= Rien 4=Fouanisseur Prive 10= Arachide O=auue (a precisor) 167 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N° 12 Formation a l'utilization des méthodes chemiques Avez-vous assisté 4 une ou plusieurs sessions de formation sur l'utillisation des insecticides/ herbicides/ fungicides ? Oul Non (si oui, répondez aux questions |??, 2?? et 3?? sinon allez directement a Ja question 17) 1. Qui a organisé la session de formation? 2. Quelle a été la durée (nombre de jours) de cette ou ces session(s)? wo wo Ou s'est deroulée cette ou ces session(s)? 168 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l’enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N°13. Sources d'information sur des méthodes non-chimiques de contréle des nuisibles: Culture Méthode de lutte Source de Dans quelle mesure | Dans quelle non-chimique l'information pensez-vous que mesure aimeriez- l'utilisation des vous en savoir méthodes non- davantage sur les chimiques est méthodes de lutte necessaire? non-chimiques disponibles? Culture: 1=Radio 0O=pas nécessaire 0O=pas nécessaire 1=Sorgho 2=agent de l=un peu _ nécessaire l=un peu nécessaire 2=Millet vulgarisation 2= nécessaire 2= nécessaire S=Niébé 3=autre fermier 3= trés nécessaire 3= trés nécessaire 10= Arachide 4=autre source 99=ne sais pas 99=ne sais pas O=autre (a precisor} (specifiez) O=ne sais pas 169 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Fiche N° 14. Information sur la production el son utilisation En moyenne, la production des parcelles individuelles suffit-elle pour les besoins alimentaires du manage et procure parfots des suplus pouvant étre vendus ou échangés? Oul Non (si oul, répondez a la question 1) 1. Si vous vendez des produits de la récolte, a qui, 4 quel endroit les vendez-vous? Cultures Quantité Type Taille Prix par | Vendua | Ov Satisfaction vendues vendue d'unité de unité qui? avec le prix l'unité obtenu Culture: 0=0 l= O=pas satisfait 1=Sorgho l= coopérativ l=un peu satisfiait 2=Millet 25% e, 2=satisfait 5=Niébé 2= 2=négocia 3=tres satisfait 10= Arachide 25 to 50% nt local O=autre (a 3=50 to 3=négocia precisor) 75% nt de 4= lextérieur _>75% 5= 4= marché 100% local 170 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire II Questionnaire pour I'exploitant son épouse Questionnaire No: Code de l'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Si la récolte issue des parcelles individuelles suffit pour les besoins alimentaires du menge, précisez la donée du stockage pour la consommation. (s'il n'y a pas eu de récolte, écrivez zéro pour le nombre de mois a stocker) Culture Nombre de mois Culture Nombre de mois Culture Nombre de mois Si non, nombre de mois de deficit: Culture Nombre de mois Culture Nombre de mois Culture Nombre de mois Les strategies utilisées pour attendre les prochines recolte 171 IPM CRSP Mali/IER - Enquéte Socio-économique 1995 - Questionnaire IT Questionnaire pour l'exploitant son épouse Questionnaire No: Code de !'enquéteur : Zone: Village: Nom et pronom (de la) répondant(e): Est-ce que I'étendue des pertes causées par les nuisibles affecte les besoins alimentaires de I'UP? Oui Non Si Oui, répondez aux questions 1, 2 et 3, sinon allez directement a la question 4) 1. Si oui, comment rencontrez-vous les besoins alimentaires de !'UP quand les pertes causées par les nuisibles sont importantes? 2. Vendez-vous du bétail pour l'achat de grains pour ]'UP quand les stocks alimentaires sont faibles ou inexistants? Oui Non Go Go Si oul, quel type de bétail, combien et a quelle fréquence en moyenne? 4, Est-ce qu'un ou des membres de I'UP se servent de nuisibles tels que sauterelles ou termites comme source alimentaire? Oui Non Si oui, qui? SVP indiquez le quantité totale vedue (parcelles individuelle en 1994), Culture le quantité Culture le quantité Culture le quantité 172 Appendix C: Representation of One of the Programmed Model Scenarios in GAMS GAMS 2.25.055 386€/486 DOS 08/27/96 20:49:46 PAGE General Algebraic Modetling System Compilation 1 * ZNARIO 3A: Market Access/Wage Employment - Average PU 2 * Sorghum and Millet Consumption and Production 2 * 5 kaa Kaa KKK KKK KKK KK KKK KKK KKK KK KK KK Kk KR kK kk kk kkk kk Kk kk 6 * Sirakorola Model 7 * General Algebraic Modeling System (GAMS) 8 * Linear Programming Model 9 * for a USAID sponsored Integrated Pest Management 10 * Project in Mali ll * Gender-Differentiated-Constraints in Malian Subsistence 12 * Production: Implications for IPM and Food Security i3 * Masters Thesis 14 * Department of Agricultural and Applied Economics 5 * Virginia Polytecnnic Institute and State University ié6é * Adam D. Russ 18 kkk kk kk kk Kk kkk Kk KK KKK kkk kK kkk kkk kkk KKK Kh KKK KK kk KK i9 * 20 SETS 21 JC AREA CULTIVATED COMMON /ACC1*ACC12/ 22 JM AREA CULTIVATED MEN /ACM1*ACM12/ 23 JW AREA CULTIVATED WOMEN /ACW1*ACW12/ 24 KA CROPS CONSUMED /CC1*CC12/ 25 LA BOUGHT CROPS /BC1*BC12/ 26 MC INPUT AMOUNT LABOR COMMON /IALC1*IALC12/ 27 MM INPUT AMOUNT LABOR MEN /IALM1*IALM12/ 28 MW INPUT AMOUNT LABOR WOMEN /IALWI1*IALW12/ 29 OC INPUT AMOUNT PESTICIDE COMMON /IAPC1*IAPC12/ 30 OM INPUT AMOUNT PESTICIDE MEN /IAPM1*IAPM12/ 3u OW INPUT AMOUNT PESTICIDE WOMEN /IAPW1*IAPW12/ 32 PC INPUT AMOUNT SEED COMMON /IASC1*IASC12/ 33 PM INPUT AMOUNT SEED MEN /IASM1*IASM12/ 34 PW INPUT AMOUNT SEED WOMEN /TASW1*IASW12/ 35 QC INPUT AMOUNT MANURE COMMON /IAMC1*IAMC12/ 36 QM INPUT AMOUNT MANURE MEN /IAMM1*IAMM12/ 37 QW INPUT AMOUNT MANURE WOMEN /IAMW1*TIAMW12/ 38 RC INPUT AMOUNT FERTILIZER COMMON /TAFC1*IAFC12/ 393 RM INPUT AMOUNT FERTILIZER MEN /IAFM1*IAFM12/ 40 RW INPUT AMOUNT FERTILIZER WOMEN /IAFW1*IAFW12/ 41 SC INPUT AMOUNT X COMMON /IAXC1*IAXC12/ 42 SM INPUT AMOUNT X MEN /TAXM1* TAXM12/ 43 SW INPUT AMOUNT X WOMEN /IAXW1*IAXW12/ 44 TC EQUIP COUNT PLOW COMMON /ECPC1*ECPC12/ 45 TM EQUIP COUNT PLOW MEN /ECPM1*ECPM12/ 46 TW EQUIP COUNT PLOW WOMEN /ECPW1*ECPW12/ 47 C EQUIP COUNT HARROW COMMON /ECHC1*ECHC12/ 48 UM EQUIP COUNT HARROW MEN /ECHM1*ECHM12/ 49 UW EQUIP COUNT HARROW WOMEN /ECHW1*ECHW12/ 50 VC EQUIP COUNT CART COMMON /ECCC1*ECCC12/ D4 VM EQUIP COUNT CART MEN /ECCM1*ECCM12/ 52 VW EQUIP COUNT CART WOMEN /ECCW1*ECCW12/ 173 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 2 General Algebraic Modeling System om ompiilation 53 WC EQUIP COUNT DABA COMMON /ECDC1*ECDC12/ 54 WM EQUIP COUNT DABA MEN /ECDM1*ECDM12/ 55 WW EQUIP COUNT DABA WOMEN /ECDW1*ECDW12/ 56 ZM LABOR FOR EMPLOYMENT MEN /LFEM/ 57 ZW LABOR FOR EMPLOYMENT WOMEN /LFEW/ 58 YA LAND CONSTRAINT /CON1/ 59 YB LABOR CONSTRAINT /CON2/ 60 YC CAPITAL CONSTRAINT /CON3/ 61 YD MARKET CONSTRAINT /CON4/ 62 YE CALORIC CONSTRAINT /CON5/ 63 * YF MAX CALORIC CONSTRAINT /CON6/ 64 YG PROTEIN CONSTRAINT /CON7/ 65 YH FAT CONSTRAINT /CON8/ 66 YI CARBOHYDRATE CONSTRAINT /CON9/ 67 YJ IRON CONSTRAINT /CON10/ 68 YK VITAMIN A CONSTRAINT /CON11/ 69 YL FOLATE CONSTRAINT /CON12/ 70 YM VITAMIN C CONSTRAINT /CON13/; 71 PARAMETERS 72 Ci(JC) COEFFICIENTS FOR COMMON LAND 73 /ACC1l 29891, ACC2 381975, ACC3 9800, ACC4 3125, 74 ACC5 23700, ACC6 186300, ACC? 15200, 75 AcCC8 57916, ACC9 382720, ACC10 28059, 76 ACC1l1 220275, ACC12 21156/ 7 C2(JM)} COEFFICIENTS FOR MENS LAND 78 /ACM1 29891, ACM2 381975, ACM3 9800, ACM4 3125, 7 ACMS 23700, ACM6 186300, ACM? 15200, 80 ACM&S 70629, ACMY 382720, ACM1O 49487, i ACM11 220275, ACM12 23404/ 82 C3(JW) COEFFICIENTS FOR WOMEN'S LAND 83 J/ACWi 29891, ACW2 381975, ACW3 4263, ACW4 2213, 84 ACW5 23700, ACW6 186300, ACW7 15200, 85 ACW8 48522, ACW9 169928, ACW10 28851, 86 ACW11i 220275, ACW12 18216/ 87 C4(KA) CROPS CONSUMED COEFFICIENTS 88 /CCl 71, CC2 825, CC3 70, cCC4 25, cc5 75, Bg cc6é 900, CC7 76, CC8 39, CC9 299, CC1O 7i, 90 CCli 225, ccl2 41/ 91 C5(LA) BOUGHT CROPS COEFFICIENTS 92 /BC1l 71, BC2 825, BC3 70, BC4 25, BCS 75, 93 BC6 900, BC? 76, BCB 33, BCS 299, BC10 Ti, 94 BC11 225, BC12 41/ 95 C6(MC) HIRED LABOR COMMON COEFFICIENTS 96 /IALC1 500, IALC2 500, IALC3 500, IALC4 500, 7 IALC5 500, IALC6 500, IALC7 500, IALC8 9500, 98 TALC9 500, TALC10 500, IALC11 500, IALC12 500/ 99 C7 (MM) HIRED LABOR MEN COEFFICIENTS 100 /IALM1 450, IALM2 450, IALM3 450, IALM4 450, 101 ITALM5 450, IALM6 450, IALM7 450, IALM8 450, 102 IALM9 450, IALM10 450, IALM11 450, IALM12 450/ 103 C8 (MW) HIRED LABOR WOMEN COEFFICIENTS 104 /IALW1 450, IALW2 450, IALW3 450, IALW4 450, 105 TALW5 450, IALW6 450, IALW7 450, IALW8 450, 106 IALW9 450, IALW10 450, IALW11 450, IALW12 450/ 174 GAMS 2. 25.055 386/4 86 DOS 08/27/96 20:49:46 PAGE 3 ene ral Al gebraic Modeling system Og omp la t- tio n 107 C9 (OC) PESTICIDE COMMON COEFFICENTS 108 /IAPC1 287.5, IAPC2 287.5, IAPC3 287.5, 109 IAPC4 287.5, IAPC5 287.5, IAPC6é 2 87.5, 110 IAPC? 287.5, ITAPC8 287.5, TAPC9 2 87.5, 11i TAPC10 287.5, TAPCil 287.5, IAPC12 287.5/ 112 C10 (OM) PESTICIDE MEN COEFFICIENTS /IAPM1L 287.5, IAPM2Z 287.5, IAPM3 287.5, 214 IAPM4 287.5, IAPM5 287.5, IAPM6 2 87.5, 115 IAPM7 287.5, TAPM8 287.5, IAPM9 2 87.5, 116 TAPM10 287.5, ITAPM11 287.5, IAPM12 287.5/ 117 C11 (OW) PESTICIDE WOMEN COEFFICIENTS 118 /TAPW1 287.5, TAPW2 287.5, IAPW3 287.5, 119 IAPW4 287.5, TAPWS5 287.5, TAPW6 2 87.5, 120 IAPW? 287.5, IAPW8 2 87.5, TAPW9 28 7.5, 121 IAPW10 287.5, IAPW11 287.5, IAPW12 287.5/ 122 C12 (PC) SEED COMMON COEFFICIENTS 123 /IASC1 0, IASC2 @, IASC3 0, IASC4 0, IASCS5 0, 124 TASC6 O, IASC? O, IASC8 O, TASC9 125 IASC10 840, IASC11 O, IASC12 0Q/ 12€ C13 (PM) SEED MEN COEFFICIENTS 27 /TASM1 O, IASM2 0, IASM3 0, TASM4 O, IASM5 0, 128 IASM6 O, IASM?7 QO, IASM8 0, IASM9 129 IASM10 840, TASM11 QO, IASM12 O0/ 1306 C14 (PW) SEED WOMEN COEFFICIENTS 131 /IASW1 0, IASW2 0, IASW3 O, ITASW4 O, IASWS 0, 132 TASW6 QO, IASW7 0, IASW8 OQ, IASW9 133 ITASW10 840, IASW11 0, IASW12 0/ 134 C15 (QC) MANURE COMMON COEFFICIENTS 135 /IAMC1l 0, IAMC2 0, IAMC3 0, IAMC4 0, IAMC5 0, 136 IAMC6 0, IAMC7 0, TAMC8 oO, IAMC9 O, IAMC10 0, 137 IAMC11 0, IAMC12 0O/ 138 C16 (QM) MANURE MEN COEFFICIENTS 139 /IAMM1 ©, IAMM2 0, IAMM3 0, TAMM4 0, TAMM5 0, 140 IAMM6 0, IAMM7 0, IAMM8 O, IAMM9 0, IAMM10 0, 144 IAMMii 0, IAMM12 0/ 142 C17 (QW) MANURE WOMEN COEFFICIENTS 143 /IAMW1 0, IAMW2 0, IAMW3 0, IAMW4 0, IAMWS5 0 144 IAMW6 0, IAMW7 0, IAMWS oO, TAMW9 0, IAMW10 0, 245 IAMW11 0, IAMW12 0/ 146 C18 (RC) FERTILIZER COMMON COEFFICIENTS 147 /IAFC1 8750, IAFC2 8750, IAFC3 8750, IAFC4 8750, 148 IAFCS 8750, IAFC6 8750, IAFC7 8750, IAFC8 8750, 149 IAFC9 8750, IAFC10 8750, IAFC11 8750, 150 IAFCi2 8750/ 151 C19 (RM) FERTILIZER MEN COEFFICIENTS 152 /IAFM1l 8750, IAFM2 8750, IAFM3 8750, IAFM4 8750, 153 IAFM5 8750, IAFM6 8750, IAFM? 8750, IAFM8 8750, 154 IAFM9 8750, IAFM1C 8750, IAFM11 8750, 155 IAFM12 8750/ 156 C20 (RW) FERTILIZER WOMEN COEFFICIENTS 157 /IAFW1 8750, IAFW2 8750, IAFW3 8750, IAFW4 8750, 158 IAFWS 8750, IAFW6 8750, IAFW7 8750, IAFW8 8750, oO ps IAFW9 8750, IAFWiO 8750, IAFW11 8750, ow q IAFW12 8750/ he 175 G AMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 4 G eneral Algebraic Modeling System c ompiilation lei C21(SC) % COMMON COEFFICIENTS 162 /TAXC1l 0, IAXC2 0, IAXC3 OO, IAXC4 0, IAXC5 O, 163 TAXC6é 0, TAXC? O, IAXC8 0, IAXC9 0, IAXC10 OO, 164 TAXC1i 0, IAXC12 0/ 165 C22 (SM) X MEN COEFFICIENTS 166 /TAXM1 0, IAXM2 0, IAXM3 OO, IAXM4 0, IAXMS5 0, io? TAXM6 O, IAXM? 0, IAXM8 0, IAXM9 0, IAXM10 0, 16 TAXM11 0, IAXMi2 0O/ 169 C23 (SW) X% WOMEN COEFFICIENTS 170 /TAXW1 0, IAXW2 0, IAXW3 O, IAXW4 0, IAXW5S 0, 171 IAXW6 0, IAXW?7 0, IAXWS 0, IAXW9 OQ, IAXW10 OQ, 172 IAXW11 0, IAXW12 0/ 173 C24 (TC} PLOW COMMON COEFFICIENTS 174 /ECPC1L 0, ECPC2 0, ECPC3 0, ECPC4 0, ECPCS 0, 175 ECPC6 O, ECPC? QO, ECPCB O, ECPCO OO, 176 ECPC10 2500, ECPC11 0, ECPC12 O/ 177 C25(TM) PLOW MEN COEFFICIENTS 178 /ECPM1 2500, ECPM2 0, ECPM3 0, ECPM4 0, 179 ECPM5 0, ECPM6 0, ECPM? 0, ECPM8 2500, 180 ECPM9 0, ECPM10 2500, ECPM11 0, ECPM12 2500/ 181 C26 (TW) PLOW WOMEN COEFFICIENTS 182 /ECPW1 O, ECPW2 0, ECPW3 0, ECPW4 O, ECPWS 0, 183 ECPW6 0, ECPW7 0O, ECPW8 7500, ECPW9 2500, 184 ECPW10 2500, ECPW11 0, ECPW12 0O/ 185 C27 (UC) HARROW COMMON COEFFICIENTS 186 /ECHC1 O, ECHC2 0, ECHC3 0, ECHC4 0, ECHC5 OQ, 187 ECHC6 0, ECHC? OQ, ECHC8 0, ECHC9 0, ECHC10 0, 188 ECHCi1l 0, ECHC12 0O/ 189 C28 (UM) HARROW MEN COEFFICIENTS 190 /ECHM1 0, ECHM2 0, ECHM3 0, ECHM4 0, ECHMS5 0, 191 ECHM6 0, ECHM? 0, ECHM8 0, ECHM9 0, ECHM10 0, 192 ECHMi1 0, ECHM12 Of 193 C29 (UW) HARROW WOMEN COEFFICIENTS 194 /ECHW1 0, ECHW2 0, ECHW3 0, ECHW4 0, ECHW5 0, 195 ECHW6 0, ECHW? 0, ECHW8 0, ECHW9 0, ECHW10 0, 196 ECHW11l 0, ECHW12 0/ 197 C30(VC) CART COMMON COEFFICIENTS 198 /ECCC1 0, ECCC2 0, ECCC3 0, ECCC4 0, EcccsS oO, 199 ECCC6 C, ECCC? OQ, ECCC8 OQ, ECCC9 0, ECCC1O 0, 200 ECCCl1l 0, ECCC12 O/ 201 C31 (VM) CART MEN COEFFICIENTS 202 /ECCMi 0, ECCM2 0, ECCM3 0, ECCM4 0, ECCM5 0, 203 ECCM6 0, ECCM? 0, ECCM8 0, ECCM9 0, ECCM10 0, 204 ECCM11 0, ECCM12 O0O/ 205 C32 (VW) CART WOMEN COEFFICIENTS 206 /ECCW1L 0, ECCW2 0, ECCW3 0, ECCW4 0, ECCW5 0, 207 ECCW6 OO, ECCW? 0, ECCW8 O, ECCW9 0, ECCW10O 0, 208 ECCW11l 0, ECCW12 O/ 209 C33 (WC) DABA COMMON COEFFICIENTS 21 /ECDC1L 0, ECDC2 0, ECDC3 0, ECDC4 0, ECDCS5 O, 211 ECDC6 QO, ECDC? 0, ECDC8 0, ECDCS 0, ECDC10O oO, 2.2 ECDC11i 0, ECDCi2 0O0/ 213 C34 (WM) DABA MEN COEFFICIENTS 176 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE General Algebraic Modeling System Compilation 214 /ECDM1 0, ECDM2 0, ECDM3 6, ECDM4 0, ECDM5 0, 215 ECDM6 0, ECDM? 0, ECDM8 0, ECDM9 0, ECDMiQ 0, 216 ECDM11 0, ECDM12 0/ 217 C35 (WW) DABA WOMEN COEFFICIENTS 216 /ECDW1 0, ECDW2 0, ECDW3 0, ECDW4 0, ECDW5 oO, 219 ECDW6 0, ECDW7 O, ECDW8 0, ECDWS 0, ECDW10 OQ, 220 ECDW11 0, ECDW12 0/ 221 C36(ZM) LABOR FOR EMPLOYMENT MEN COEFFICIENT 222 /LFEM 500/ 223 C37({(Z2W) LABOR FOR EMPLOYMENT WOMEN COEFFICIENT 224 /LFEW 450/ 225 D1 (YA) RHS OF LAND CONSTRAINT 226 /CONi 8.3/ 227 D2(YB) RHS OF LABOR CONSTRAINT 228 /CON2 2251 229 D3/YC) RHS OF CAPITAL CONSTRAINT 230 /CON3 0O/ 231 D4(YD) RHS OF MARKET CONSTRAINT 232 /CON4 1012950/ 233 DS5(YE) RHS OF CALORIC CONSTRAINT 234 /CONS5S 8734663/ 235 * De6(YF) RHS OF MAX CALORIC CONSTRAINT 236 * /CON6 10483378/ 237 D7 (YG) RHS OF PROTEIN CONSTRAINT 238 /CON7 183790/ 239 D8 (YH) RHS OF FAT CONSTRAINT 240 /CONB 246912/ 241 D9{YI) RHS OF CARBOHYDRATE CONSTRAINT 242 /CON9 3330952/ 242 DLOfYI} RHS OF TRON CONSTRAINT 244 /CONIO 58823/ 245 Dil(YK) RHS OF VITAMIN A CONSTRAINT 246 /CON11 2024822/ 247 D12(YL) RHS OF FOLATE CONSTRAINT 248 /CON12 555210/ 249 D13(YM) RHS OF VITAMIN C CONSTRAINT 250 /CON13 105485/; 251 TABLE A(YA,JC) USE OF INPUTS PER A 252 ACC1 ACC2 ACC3 AcC4 ACC5 253 CON] 1 1 1 1 1 254 255 + ACC6 ACC7 ACC8 ACC9 ACC10 256 CON 1 i 1 i 1 257 258 + ACC1i1 ACC12 2549 CON] 1 1 ; 260 26) TABLE B(YB, JC) USE OF INPUTS PER B 262 ACC1 ACC2 ACC3 ACC4 ACC5 263 CON2 120.3 139.5 101.2 14.2 102.4 264 265 + ACC6 ACC7 ACC8 ACC9 ACC10 266 CON2 101.2 220.2 102.4 101.2 120.3 177 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 6 General Algebraic Modeling System Compilation 267 268 + ACC11 ACC12 . 269 CON2 95.4 108.4 & 270 271 TABLE C(YC,JC) USE OF INPUTS PER C 272 ACC1 ACC2 ACC3 ACC4 ACCS 273 CON3 i 1 1 1 1 274 275 + ACC6 ACC7 ACC8 ACCc3 ACC1i0 27 CON3 1 1 1 1 1 277 278 + ACCil ACC12 279 CON3 1 1 ; 280 281 TABLE D(YD,JC) USE OF INPUTS PER D 282 ACC1 ACC2 ACC3 Acc4 ACC5 283 CON4 120.3 139.5 101.2 14.2 102.4 284 285 + ACC6 ACC? ACC8 ACC9 ACC10 286 CON4 101.2 220.2 102. 4 101.2 120.3 288 + CCli ACCl2Z 289 CON4 95.4 108.4 290 291 TABLE E(YA, JM) USE OF INPUTS PER E 292 ACM1 ACM2 ACM3 ACM4 ACM5 293 CON] 1 1 1 1 294 295 + ACM6 ACM? ACM8 ACM9 ACM10 296 CON1 1 1 1 1 297 298 + ACM11 ACM12 299 CON 1 i 1 i 300 301 TABLE F(YB,JM) USE OF INPUTS PER F 302 ACMI ACM2 ACM3 ACM4 ACM5 303 CON2 120.3 139.5 101.2 14.2 102.4 304 305 + ACM6 ACM7 ACM8 ACM9 ACM10 206 CON2 101.2 220.2 102. 4 101.2 120.3 307 308 + ACM11 ACM12 309 CON2 95.4 108.4 r 310 311 TABLE G(YC,JM) USE OF INPUTS PER G 312 ACMI ACM2 ACM3 ACM4 ACMS 312 CON3 1 1 1 1 1 2i4 315 + ACM6 ACM7 ACM8 ACM3 ACM10 31€ CON3 1 1 + 1 1 318 + ACMi1 ACM12 319 CONS 1 1 178 5 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE General Algebraic Modetling System Compilation 320 321 TABLE H(YD,JM)} USE OF INPUTS PER H 322 ACMI ACM2 ACM3 ACM4 ACM5 323 CON4 120.3 139.5 101.2 14.2 102.4 324 325 + ACM6 ACM? ACM8 ACM9 ACM10 326 CON4 101.2 220.2 102.4 101.2 120.3 327 328 + ACM11 ACM12 329 CON4 95.4 108.4 33C 331 TABLE I(YA, JW) USE OF INPUTS PER I 332 ACW. ACW2 ACW3 ACW4 ACW5 333 CONI 1 ZL 1 1 1 334 335 + ACW6 ACW7 ACW8 ACW9 ACW10 33€ CON 1 i 1 1 1 337 338 + AcCWl1 Acwi2 329 CONL 1 1 340 344 TABLE J(YB, JW) USE OF INPUTS PER J 342 AcWwl ACW2 ACW3 AcW4 AcwW5 343 CON2 120.3 139.5 101.2 14,2 102.4 344 345 + ACW6 ACW7 ACW8 ACW9 ACW10 346 CON2 101.2 220.2 102.4 101.2 120.3 347 348 + ACW11 ACW12 349 CON 95.4 106.4 3506 351 TABLE K(YC, JW) USE OF INPUTS PER K 352 ACW] ACW2 ACW3 ACW4 ACWS5 353 CON3 1 1 1 1 1 354 355 + ACW6 ACW7 ACW8 ACW9 ACW10 356 CON3 1 1 1 1 1 357 358 + ACW11 ACW12 353 CON3 1 1 ; 360 362 TABLE L(YD, JW) USE OF INPUTS PER L 362 ACWl ACW2 ACW3 ACW4 ACWS5 363 CON4 120.3 139.5 101.2 14.2 102.4 364 365 + ACW6 ACW? ACW8 ACW9 ACW10 366 CON4 101.2 220.2 102.4 161.2 120.3 367 368 + ACWi1 ACW12 369 CON 4 95.4 108.4 370 371 TABLE M(YC,MC) USE OF INPUTS PER M 372 IALC1 IALC2 IALC3 IALC4 ITALC5 179 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE General Algebraic Modeling System Compilation 373 CONS 1 i 1 1 1 374 375 + ITALC6 IALC? ITALCS& ITALC9 IALC10 B7€ CON3 1 1 1 1 1 3974 378 + ITALCi1 IALC12 379 CON3 1 1 ; 386 381 TABLE N(YC,MM) USE OF INPUTS PER N 38z LALM1 IALM2 IALM3 IALM4 383 CON3 1 1 i 1 384 365 + IALM6 IALM? IALM8 IALMS 86 CON3 1 1 1 1 387 388 + TALM11 TALM12 389 CON3 1 1 ; 3906 394 TABLE O(YC,MW) USE OF INPUTS PER © 392 IALW1 ITALW2 TALW3 IALW4 IALWS 393 CON3 1 1 1 1 1 394 395 + TALW6 IALW?7 IALWS8 IALWY TALW10 396 CON2 i 1 1 i 1 397 398 + IALW1i TALW12 399 CON3 1 1 ; 400 401 TABLE MMMM(YB,MC) USE OF INPUTS PER M 402 IALC1 TALC2 IALC3 TALC4 IALCS 403 CON2 1 i 1 1 1 404 405 + IALC6 TALC7 IALC8 IALCY IALC10 406 CON2 1 1 1 1 1 4Q7? 408 + ITALCi1 TALC. 409 CON2 1 ; 4.0 4ii TABLE NNNN(YB,MM) USE OF INPUTS PER N 412 IALM1 IALM2 IALM3 TALM4 TALM5 id CON2 1 1 1 1 1 414 415 + IALM6 TALM? ITALM8 ITALM9 IALMi0O 416 CON2 1 1 1 1 1 a7 418 + IALM11 IALM12 41g CON2 1 1 ' 420 42] TABLE OOOO (YB,MW) USE OF INPUTS PER O 422 IALWI ITALW2 TALW3 IALW4 IALWS5S 4 423 CONZ A i i 1 aL 424 425 + IALWE TALW? TALW8 TALWY IALW10 180 aaa 426 oo & ~n CON3 CON3 CON3 TABLE CON? TABLE TABLE CON3 CON3 CON3 CON3 CON3 CON3 CON3 TABLE CON2 CON3 TABLE CON3 CON2 CON2 25.055 + + + + + + + + + + ilation ral T(YC,PM) R(YC,OW) S(YC,PC) Q(YC,OM) P(YC,0C) 386/486 Algeb IASM6 IASC6 IASC1l TASC1i IAPM1i TASMi TAPW11 IAPW1 IAPWO6 IAPM6 IAPM1 IAPC11 IAPC1 IAPC6 TALW11 i i 1 1 1 i 1 1 1 1 1 1 z 1 1 1 USE USE USE USE USE DOS OF OF OF OF OF raic TASM? INPUTS IASC? TASC3 IASC2 IASM2 INPUTS IAPW2 INPUTS IAPM7 IAPM2 TAPC7T TAPW?7 INPUTS IAPC2 INPUTS i 1 TASC12 1 1 1 1 1 1 1 i IAPM12 1 1 IAPW12 1 L TAPC12 1 1 IALW12 1 1 1 1 i 1 ° Modeling PER PER PER PER PER ; ; ; ; ; ; . i i 18] IASMB IASC8 IAPW3 AM IAPM9 IAPM8 IAPM3 TASM3 IAPW8 IAPC8 TAPC3 T R S Q i 1 1 1 P i 1 1 1 1 1 1 IASM3 IASC9 IAPWS IAPW4 IAPM4 IAPCS IAPC4 08/27/96 TASM4 IASC4 1 1 1 1 1 1 System 20:49:46 TASM10 IAPC10 IASMS IASC10 IAPW10 TAPWS IAPM10 TAPMS IAPCS IASCS5 1 + 1 1 1 1 1 1 1 1 iL PAGE 5.055 386/486 DOS 08/27/96 20:49:46 PAGE 10 ral Algebraic Modeling System ilation + TASM11 IASM12 CON3 1 i ; TABLE U(YC,PW) USE OF INPUTS PER U TASW1 IASW2 IASW3 ITASW4 CON3 1 1 1 + TASW6 IASW7 TASWS8 IASWS CONS 1 1 1 + ITASW11 IASW12 CON3 1 1 ; TABLE V(YC,OC) USE OF INPUTS PER V IAMC1 ITAMC2 TAMC3 ITAMC4 IAMC5 CON3 1 1 1 + ITAMC6 IAMC7 TAMC8 TAMC9 TAMC10 CON3 1 1 i + IAMC11 IAMC12 CON 3 1 1 ; TABLE W(YC,QM) USE OF INPUTS PER W TAMM1 TAMM2 TAMM3 TAMM4 ITAMM5 CON3 1 i i + TAMM6 IAMM? ITAMM8 IAMM9 TAMM10 CON3 1 1 1 + TAMM11 TAMM12 CON3 1 1 ; TABLE X(YC,QW) USE OF INPUTS PER X IAMW1 IAMW2 TAMW3 IAMW4 IAMWS CON3 1 1 1 b + TAMW6 TAMW7 IAMWS IAMW9 TAMW10 CON3 1 1 1 + TAMW11 ITAMW12 CON3 1 1 ; TABLE AA(YC,RC) USE OF INPUTS PER AA IAFCI IAFC2 IAFC3 IAFC4 IAFCS CON3 1 1 1 1 + TAFC6 IAFC? TAFC8B IAFCS TAFC10 CON3 1 1 1 1 on Ro OO G1 bh (oO + TAFCIilL IAFC12 cA 0d. to CONS i 4 ; t2 cn we 182 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 11 General Algebraic Modeling System Compilation 532 TABLE BB(YC,RM) USE OF INPUTS PER BB 533 IAFM1 IAFM2 TAFM3 IAFM4 IAFM5 534 CON3 1 1 i 1 1 535 536 + TAFM6 TAFM7 IAFM8 IAFM9 TAFM10 537 CON3 1 1 1 1 1 538 539 + IAFM11 TAFM12 540 CON 3 1 1 ; 541 TABLE YY{YC,RW} USE OF INPUTS PER CC S4z IAFW1 TAFW2 TAFW3 TAFW4 IAFW5 543 CON3 1 1 i 1 1 544 545 + TAFW6 IAFW? TAFWS8 IAFW9 IAFW10 54€ CON2 i i i‘ a i 547 54& + IAFW1L2 TAFWL2 549 CON3 1 > ; 550 551 TABLE DD(YC,SC} USE OF INPUTS PER DD 552 IAXC1 TAXC2 IAXC3 IAXC4 IAXC5 553 CON3 1 1 1 1 1 554 555 + TAXC6 TAXC7 IAXCB IAXC9 TAXC10 556 CON3 1 1 1 1 1 557 558 + TAXC11 TAXC12 559 CON3 1 1 ; 56C Sol TABLE EBE(YC,SM) USE OF INPUTS PER EE 562 ITAXM1 TAXM2 IAXM3 TAXM4 IAXM5 563 CON3 i 2 1 1 1 564 565 + TAXM6 IAXM7?7 IAXM8 ITAXM9 TAXM10 S66 CON3 1 i 1 i 1 567 568 + IAXM11 ITAXM12 563 CON3 i 1 ; a7¢C 571 TABLE FF(YC,SW) USE OF INPUTS PER FF 57 IAXW1 IAXW2 IAXW3 TAXW4 IAXWS 573 CON 3 1 1 1 i 1 Sv 4 575 + TAXW6 IAXW7 TAXWS8 TAXW9 TAXW10 S76 CON3 1 1 i 1 1 577 5768 + IAXW11 TAXW12 O79 CON 3 1 1 ; 580 581 TABLE GG(YC,TC) USE OF INPUTS PER GG 582 ECPC1 ECPC2 ECPC3 ECPC4 ECPC5 583 CON3 1 1 1 1 1 584 183 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 12 General Algebraic Modeling System compilation 585 + ECPC6 ECPC? ECPC8 ECPC9 ECPC10 586 CON3 1 1 1 1 i 587 586 + ECPC11 ECPC12 389 CON3 1 1 ; 5930 591 TABLE HH(YC,TM) USE OF INPUTS PER HH 592 ECPM1 ECPM2 ECPM3 ECPM4 ECPM5 593 CON 3 1 i 1 1 1 594 595 + ECPM6 ECPM7 ECPM8 ECPM9 ECPM10 596 CON3 i 1 1 1 1 59? 598 + ECPMil ECPM12 599 CON3 1 1 ; 600 601 TABLE II(YC,TW) USE OF INPUTS PER II 602 ECPW1 ECPW2 ECPW3 ECPW4 ECPW5 603 CON3 1 1 1 1 1 004 605 + ECPW6 ECPW7 ECPW8 ECPW9 ECPW10 606 CON3 1 1 1 i 1 O07 6608 + ECPW1i ECPW12 609 CON3 i 1 ; 610 il TABLE JJ(YC,UC) USE OF INPUTS PER JJ 612 ECHC1 ECHC2 ECHC3 ECHC4 ECHC5 613 CON3 1 1 1 1 1 614 615 + ECHC6 ECHC7 ECHC8 ECHC9 ECHC10 616 CON3 1 1 1 1 1 617 618 + ECHC11 ECHC12 619 CON3 L 1 ; 620 621 TABLE KK(YC,UM) USE OF INPUTS PER KK O22 ECHM1 ECHM2 ECHM3 ECHM4 ECHMS 623 CON3 1 1 1 i 1 625 + ECHM6 ECHM?7 ECHM8 ECHM9 ECHM10 626 CON 3 1 1 1 1 1 627 628 + ECHM11 ECHM12 629 CON3 i 1 ; 630 631 TABLE LL(YC,UW) USE OF INPUTS PER LL 632 ECHW1 ECHW2 ECHW3 ECHW4 ECHWS5S 633 CON3 1 1 1 1 1 634 635 + ECHW6 ECHW? ECHW8 ECHWSY ECHW10 E36 CON3 i a 1 i i 184 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 13 General Algebraic Modetliong System compilation 636 + ECHW? i ECHW1i2 039 CON 3 i 1 ; 64% 641 TABLE NN(YC,VC) USE OF INPUTS PER NN 642 ECCC1 ECCC2 ECCC3 ECCC4 ECCC5 643 CON3 1 i 1 1 i 644 645 + ECCC6 ECCC? ECCC8 ECCC9 ECCC10 646 CON3 1 1 1 1 1 647 648 + ECCC11 ECCC12 649 CON3 1 1 ; 6514 TABLE OO(YC,VM) USE OF INPUTS PER OO 652 ECCM1 ECCM2 ECCM3 ECCM4 ECCM5 653 CON 2 1 1 1 i 1 654 655 + ECCM6 ECCM? ECCMB ECCM9 ECCM10 656 CON3 1 1 1 1 1 657 658 + ECCMi1 ECCM1zZ 659 CON3 1 1 ; 660 661 TABLE PP(YC,VW) USE OF INPUTS PER PP 662 ECCW1 ECCW2 ECCW3 ECCW4 ECCW5 663 CON3 1 1 1 1 1 664 665 + ECCW6 ECCW7 ECCW8 ECCW9 ECCW10 666 CON3 1 1 1 1 1 66? 068 + ECCW11 ECCW12 6E9 CON 3 1 1 ; E79 671 TABLE QQ(YC,WC) USE OF INPUTS PER QQ 672 ECDC1 ECDC2 ECDC3 ECDC4 ECDC5 673 CON3 1 1 1 1 1 674 675 + ECDC6 ECDC? ECDC8 ECDCY ECDC10 676 CON3 1 1 1 1 1 677 678 + ECDCLIi BECDC12 67g CON3 i 1 ; 680 681 TABLE RR(YC,WM) USE OF INPUTS PER RR 682 ECDM1 ECDM2 ECDM3 ECDM4 ECDM5 083 CON3 i 1 1 1 1 064 685 + ECDM6 ECDM7 ECDM8 ECDM9 ECDM10 686 CON3 i 1 1 i 1 687? 688 + ECDM11 ECDM12 689 CON3 1 1 ; 696 185 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 14 General Algebraic Modeling System Compilation 691 TABLE SS(YC,WW) USE OF INPUTS PER SS 692 ECDW1 ECDW2 ECDW3 ECDW4 ECDWS 693 CON3 1 1 1 1 1 694 695 + ECDW6 ECDW7 ECDW8 ECDW9 ECDW10 696 CON2 i 1 1 1 1 697 698 + ECDWi1 ECDWi2 699 CON3 i L ; 700 701 TABLE TT (YC,KA) USE OF INPUTS PER TT 702 CCl CcC2 CC3 CC4 ccs 703 CON3 1 1 1 1 704 705 + CCe cC7 ccs ccg CC1LO 706 CON3 1 1 1 1 707 708 + Ccclil CC12 703 CON3 i 1 ; 720 T1i TABLE UU(YE,KA) USE OF INPUTS PER UU 712 CCl CC2 CC3 CC4 CC5 713 CONS 2587.5 3200 3200 3200 714 "15 + CCo Cc? cC8 Ccg CC1O a TIEbt CONS 3200 3456 3200 283.5 3990 74 4 a 71J + cell CCl2 719 CONS 3350 3350 ; 720 721 Ft TABLE UUU(YF,KA) USE OF INPUTS PER UUU 722 + CCl CC2 CC3 CC4 CC5 723 + CON 6 2587.5 3200 3200 3200 724 t 725 * + CCE CC? Cc8 CC9 CC10 726 CON6 3200 3450 3200 283. 5 3990 728 + CCcll CCi2 fen CONG 3350 3350 ; 7 4 TABLE VV(YG,KA) USE OF INPUTS PER VV 732 cel CC2 CC3 CC4 CCS 733 CON? 142.5 230.0 56.0 56.0 734 735 + CCE CC7 CCB CC9 CC10 736 CON7 220.6 100.0 56.0 17.0 175.0 737 738 + cell CC12 739 CON7 70.0 95.0; 740 741 TABLE XX(YH,KA) USE OF INPUTS PER XX 742 Ci CC2 CC3 cc4 CCS 71423 CONS 46.5 14.0 14.0 14.9 186 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 15 General Alge braic Modeling System Compilation 744 + V4 cc7 CCc8 Q CC10 746 CON8 45.0 14.0 315.0 747 7148 + CC12 749 CON8 28.0; 750 751 TABLE AAA(YI, KA) USE OF INPUTS PER AAA 752 CC2 Cc3 cc4 CC5 CONS 25 570.0 750.0 750.0 + CC? CCB CCg CC10 CONY 0 670.9 750.0 56.7 261.0 + CClLe CONS JC 740.0; TABLE BBB! YJ, KA} USE OF INPUTS PER BBB ccl CC2 CC3 cc4 CCS CON10 90.0 50.0 50.0 50.0 + CCO cc? ccs CC9 CC10 CON1O 100. 0 25.0 50.0 26.6 + CC1l1 CC12 CON10 17.0 45.0; TABLE CCC (YK, KA) USE OF INPUTS PER ccc CCL CC2 CC3 Ccc4 ccs CONil 15.0 30.0 40.0 40.0 + CCEé Cc7 CC8 CC9 CC10 CONIT 270. 0 46.0 259.2 21.0 + Cccll CC12 CONi1 30.0; TABLE DDD (YL, KA) USE OF INPUTS PER DDD CCl CC2 CC3 CC4 CCS CON12 4390 + CCE CC? CCc8 CC9 CC10 CON12 330. 0 186.3 770.0 + CON12 0 ; TABLE ERE (YM, KA) USE OF INPUTS PER EERE CCl CC2 CCc3 CC4 CCS CON13 20.0 + CCé CC? CC8 CC9 CC10 CON13 380.7 7.0 187 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 16 e ne ral Algebraic Modetliui ng System Qa Oo mp i1ilation 797 + ccil CCi2 798 CON13 f 799 BOC TABLE FFF (YC, LA) USE OF INPUTS PER FFF B01 BCi BC2 BC3 BC4 BCS 802 CON 3 1 1 1 1 8C3 B04 + BC6 BC? BC8 BCS BC10 SUS CON3 1 1 i 1 i 806 BO? + BCil BCiz BOR CON3 1 1 ; B09 810 TABLE GGG(YE,LA) USE OF INPUTS PER GGG 811 BCi BC2 BC3 BC4 BCS 812 CONS5 2587.5 3200 3200 3200 13 Bi4 + BC6 BC? BC8 BC3 BC1O 815 CONS 3200 3450 3200 283.5 3990 G16 817 + Bell BCl2 818 CONS 3350 3350 ; B15 820 TABLE GGGG(YF,LA) USE OF INPUTS PER GGGG 8 om ad Bcl BCZ BC3 BC4 BCS 4 822 CONG 2587.5 3200 3200 3200 £ 823 + 824 + BC6 BC7 BCc8 BCS3 BC10 825 * CON6 3200 3450 3200 283.5 399C 826 * 827 + BCil BC12 B28 CON6 3350 3350 i B29 630 TABLE HHH(YG,LA) USE OF INPUTS PER HHH B32 BCL BC2 BC3 BC4 BCS &3zZ CON? 142.5 230.0 56.0 56.90 833 834 + BC6 BC? BC8 BC9 BC1O 835 CON7 220.0 100.0 56.0 17.0 175.0 B3E B37 * BC1i BC12 838 CON7 70.0 95.0; B39 840 TABLE III(YH, LA) USE OF INPUTS PER III 847 BC1l BC2 BC3 BC4 BCS a42 CONS 46.5 14.0 14.0 14.0 8423 B44 + BC6 BC7 BC8 BC9 BC10 845 CON8 11,0 45.0 14.0 1.6 315.0 846 847 + BCil BC12 B48 CONE 8.0 28.0; x4ay 188 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE i7 Ge eral Al gebraic Modeling System CcCompii lation TABLE JJI(YI, LA) USE OF INPUTS PER JJJ BCl BC2 BC3 BC4 BCS CONS 457.5 570.0 750.0 750.0 mtn + BCE BC? BCc8 BCS Say CONS 560.0 670.0 750.0 56.7 161.0 + BC1li BC12 CONS 800.0 740.0; TABLE KKK (YU, LA) USE OF INPUTS PER KKK BCl BC2 BC3 BC4 CONG 90.0 50.0 50.0 + BCO6 BC7 BCB Bc10 CON1O 100 5.0 50.0 ZE.E + BCll BClL2 CONIC 17.0 45.0; TABLE LLL(YK, LA} USE OF INPUTS PER LLL BCl BC2 BC3 BC4 BC5 CONI1 15.0 30.0 40.0 40.0 + BC6 BC? BC8 BC9 BC10 CON11 270. 40.0 259.2 21.0 + BCil BC12 CONI11 30.0; TABLE MMM(YL, LA) USE OF INPUTS PER MMM BCl BC2 BC3 BC4 BCS CONT? 4390 + BC6é BC? BC8& BC9 BC10 CON12 330. 186.3 770.0 + BCl1 BC12 CON12 290. 0 TABLE NNN (YM, LA) USE OF INPUTS PER NNN BCcl BC2 BC3 BC4 BC5 CON13 20.0 4 WL aw + BC6 BC? BC8 BC 9 BC10 1 oO CON13 380.7 Ys OM wT wT ~)} WOO + BCll BC12 mM mM CONLS ‘ ww ww DO, DO, oO oO Owo Owo TABLE Z2ZM(YB, 2M) USE OF INPUTS PER Z2M LFEM Ooo Ooo MF MF WOW WOW CON2 1 189 ome) GAMS 955 954 948 947 946 945 944 943 952 952 951 950 949 942 940 939 G47 938 932 931 930 929 928 927 926 937 936 935 934 933 924 923 922 920 919 916 911 910 907 905 925 917 916 S15 914 912 912 908 906 904 903 924 ICY Oo oO 3.5 2.25.05 CON CON4 CON2 OD VARIABLES TABLE TABLE TABLE Hey X35 X34(WM) X%28(UM) X27{UC) X26(TW} X25{(TM) AZ0 X33 X32/VW) X3i(VM) EQUIF X30(VC) X23(UW) X24(TC: X23 X22 X21 X19(RM) X18(RC) X12(PC) X11 X10 X9(OC) XS Al? 416(0M) X15 X14 X13(PM) X7 X4(KA) X2(JM) XA1L(JC) X6 XS(LA} X3(JW) 24 23 22 Zl 4 a (MW) (MM) (MC) (SM) (WW) (RW) ation 1 5 (WC) (SW) SC) (OW) (OM) (OW) (QC) (PW) OBJECTIVE OBJECTIVE OBJECTIVE OBJECTIVE ZYW(YD, Z2W(YB, ZYM(YD,ZM) 386/486 Algebraic AREA AREA AREA BOUGHT CROPS INPUT INPUT INPUT INPUT EQUIP EQUIP EQUIP EQUIP EQUIP EQUIP EQUIP EQUIP EQUIP EQUIP EQUIP INPUT INPUT INPUT INPUT INPUT INPUT INPUT INPUT INPUT INPUT INPUT INPUT INPUT INPUT LFEW FEW LEFEM ZW) ZW) 2 1 i CULTIVATED CULTIVATED CROP AMOUNT AMOUNT AMOUNT AMOUNT CONSUMED FUNCTION FUNCTION FUNCTION FUNCTION DOS AMOUNT AMOUNT AMOUNT AMOUNT COUNT AMOUNT AMOUNT AMOUNT AMOUNT MANURE AMOUNT AMOUNT AMOUNT AMOUNT AMOUNT COUNT COUNT COUNT COUNT AMOUNT COUNT COUNT COUNT COUNT COUNT COUNT COUNT CROPS USE USE USE ; ; ; COMMON OF OF OF CART HARROW HARROW COMMON HARROW DABA DABA DABA CART CART PLOW PLOW LABOR LABOR LABOR PLOW PESTICIDE MANURE X X X MANURE FERTILIZER FERTILIZER FERTILIZER SEED SEED SEED PESTICIDE PESTICIDE INPUTS INPUTS INPUTS VALUE VALUE VALUE VALUE MEN MEN WOMEN WOMEN COMMON WOMEN MEN WOMEN MEN WOMEN MEN COMMON COMMON COMMON WOMEN MEN M MEN WOMEN COMMON COMMON MEN WOMEN ALL MEN WOMEN MEN WOMEN COMMON odeling COMMON PER PER PER 190 COMMON WOMEN MEN WOMEN MEN COMMON ZYW ZYM Z2W 08/27/96 System 20:49:46 PAGE AMS 2.2 5.055 386/486 DOS 08/27/96 20:49:46 PAGE 19 e al ral Algebraic Modeling System AAD ° m ilation 956 436(ZM) LABOR FOR EMPLOYMENT MEN 957 X37(ZW) LABOR FOR EMPLOYMENT WOMEN; 958 POSITIVE VARIABLE X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, 96C X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, 961 X25, X26, X27, X28, X29, X30, X31, K32, X33, X34, X35, X36, X37; 962 BQUATIONS 963 REVENUE1 OBJECTIVE FUNCTION ALL 964 LAND1 (YA) LAND CONSTRAINT ALL 965 LABORI1 (YB) LABOR CONSTRAINT ALL 966 MLABOR]1 (YB) MENS LABOR CONSTRAINT ALL 967 WLABOR] (YB) WOMENS LABOR CONSTRAINT ALL 968 CAPITALI (YC) CAPITAL CONSTRAINT ALL 269 MARKET? (YD) MARKET CONSTRAINT ALL 970 CALORIC] (YE) CALORIC CONSTRAINT ALL 971 * MCALORIC1 (YF) MAX CALORIC CONSTRAINT ALL 972 PROTEIN] (YG) PROTEIN CONSTRAINT ALL 973 FAT1 (YH) FAT CONSTRAINT ALL 974 CARBOS1 (YI) CARBOHYDRATE CONSTRAINT ALL 975 TRONI (YJ) TRON CONSTRAINT ALL 976 VITAMINAL (YK) VITAMIN A CONSTRAINT ALL FCLATE] (YL) FOLATE CONSTRAINT ALL 978 VITAMINC1]1 (YM) VITAMIN C CONSTRAINT ALL 979 REVENUE2 OBJECTIVE FUNCTION COMMON 986 LAND2 (YA) LAND CONSTRAINT COMMON 981 LABOR2 (YB) LABOR CONSTRAINT COMMON 982 MLABOR2 (YB) MENS LABOR CONSTRAINT COMMON 983 WLABOR2 (YB) WOMENS LABOR CONSTRAINT COMMON 984 CAPITAL2 (YC) CAPITAL CONSTRAINT COMMON 985 MARKET2 (YD) MARKET CONSTRAINT COMMON 986 CALORIC2 (YE) CALORIC CONSTRAINT COMMON 9e7 *~ MCALORIC2 (YF) MAX CALORIC CONSTRAINT COMMON 988 PROTEIN2 (YG) PROTEIN CONSTRAINT COMMON 989 FAT2 (YH) FAT CONSTRAINT COMMON 39 CARBOS2 (YI) CARBOHYDRATE CONSTRAINT COMMON 991 TRON2 (YJ) IRON CONSTRAINT COMMON 992 ITAMINA2 (YK) VITAMIN A CONSTRAINT COMMON 993 FOLATE2 (YL) FOLATE CONSTRAINT COMMON 994 VITAMINC2 (YM) VITAMIN C CONSTRAINT COMMON 995 REVENUE 3 OBJECTIVE FUNCTION MEN 996 LAND3 (YA) LAND CONSTRAINT MEN 997 LABOR3 (YB) LABOR CONSTRAINT MEN 998 MLABOR3 (YB) MENS LABOR CONSTRAINT MEN 99% CAPITAL3 (YC) CAPITAL CONSTRAINT MEN 1000 MARKET3 (YD) MARKET CONSTRAINT MEN 2901 CALORIC3 (YE) CALORIC CONSTRAINT MEN 1002 * MCALORIC3 (YF) MAX CALORIC CONSTRAINT MEN 1003 PROTEINS (YG) PROTEIN CONSTRAINT MEN 1004 FAT3 (YH) FAT CONSTRAINT MEN 1005 CARBOS3 (YI) CARBOHYDRATE CONSTRAINT MEN 1006 IRON3 (YJ) IRON CONSTRAINT MEN 1007 ITAMINAS (YK) VITAMIN A CONSTRAINT MEN 1908 FOLATE3 (YL) FOLATE CONSTRAINT MEN 19] GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 20 General Algebraic Modeling System Compilation 1009 VITAMINC3 (YM) VITAMIN C CONSTRAINT MEN 1010 REVENUE4 OBJECTIVE FUNCTION WOMEN 1031 LAND4 (YA) LAND CONSTRAINT WOMEN 1012 LABOR4 (YB) LABOR CONSTRAINT WOMEN 1013 WLABOR4 (YB) WOMENS LABOR CONSTRAINT WOMEN 1014 CAPITAL4 (YC) CAPITAL CONSTRAINT WOMEN 1015 MARKET 4 (YD) MARKET CONSTRAINT WOMEN 1016 CALORIC4 (YE) CALORIC CONSTRAINT WOMEN 1017 MCALORIC4 (YF) MAX CALORIC CONSTRAINT WOMEN 1018 PROTEIN4 (YG) PROTEIN CONSTRAINT WOMEN 161¢ FAT4 (YH) FAT CONSTRAINT WOMEN 1020 CARBOS4 (YT) CARBOHYDRATE CONSTRAINT WOMEN 1021 TRON4 (YJ) TRON CONSTRAINT WOMEN 1022 VITAMINA4 (YK) VITAMIN A CONSTRAINT WOMEN 1023 FOLATE4 (YL) FOLATE CONSTRAINT WOMEN 1024 VITAMINC4 (YM) VITAMIN C CONSTRAINT WOMEN 1025 ALIALC8 (YC) LABOR COMMON MILLET 1026 ALIALC10 (YC) LABOR COMMON PEANUT 1027 ALIALC12 (YC) LABOR COMMON SORGHUM 1028 ALIALW8 (YC) LABOR WOMEN MILLET 1029 ALIALW10 (YC) LABOR WOMEN PEANUT 1030 ALIALW12 (YC) LABOR WOMEN SORGHUM 1031 ALIAMC8 (YC) MANURE COMMON MILLET 1032 ALIAMC12 (YC) MANURE COMMON SORGHUM 1033 ALIAFC2 (YC) FERTILIZER COMMON COTTON 1034 ALIAFC3 (YC) FERTILIZER COMMON COWPEA 1035 ALIAFC8 (YC) FERTILIZER COMMON MILLET 1036 ALIAFM10 (YC) FERTILIZER MEN PEANUT 1037 ALIAFM12 (YC) FERTILIZER MEN SORGHUM 1038 ALIAXC8 (YC) X COMMON MILLET 1039 CCGAAL A PRODUCED G CONSUMED1 1040 CCGAA3 A PRODUCED G CONSUMED3 1041 CCGAA4 A PRODUCED G CONSUMED4 1042 CCGAA5S A PRODUCED G CONSUMED5 1943 CCGAAG A PRODUCED G CONSUMED®6 1044 CCGAA7 A PRODUCED G CONSUMED? 2045 CCGBA& A PRODUCED G CONSUMED8 1046 CCGAA9 A PRODUCED G CONSUMED9 1047 CCGAA1OQ A PRODUCED G CONSUMED10 1048 CCGAAI11 A PRODUCED G CONSUMEDI1 1049 CCGAA12 A PRODUCED G CONSUMED12 1050 CCGACI1 C PRODUCED G CONSUMED1 1051 CCGAC3 C PRODUCED G CONSUMED3 1052 CCGAC4 C PRODUCED G CONSUMED4 1053 CCGACS C PRODUCED G CONSUMEDS5 1054 CCGAC6 C PRODUCED G CONSUMED6 1055 CCGAC7 C PRODUCED G CONSUMED7 1056 CCGAC8 C PRODUCED G CONSUMED8 105? CCGAC9 C PRODUCED G CONSUMED9 1058 CCGAC1O CC PRODUCED G CONSUMED10 1059 CCGACI1 C PRODUCED G CONSUMED11 L060 CCGACi2 C PRODUCED G CONSUMED12 1061 CCGAMi M PRODUCED G CONSUMED1 192 08/27/96 20:49:46 PAGE 21 Nn No -25.055 386/486 DOS eral Algebraic Model ing System AAD 35 008 pilation 1062 CCGAM3 M PRODUCED G CONSUMED3 1063 CCGAM4 M PRODUCED G CONSUMED4 1064 CCGAM5 M PRODUCED G CONSUMEDS LOGS CCGAM6 M PRODUCED G CONSUMED6 1066 CCGAM?7 M PRODUCED G CONSUMED? 1067 CCGAMB M PRODUCED G CONSUMED8& 1068 CCGAM9 M PRODUCED G CONSUMED 9 1069 CCGAM10 M PRODUCED G CONSUMED10 1070 CCGAM11 M PRODUCED G CONSUMED11 LO71 CCGAM12 M PRODUCED G CONSUMED12 L072 CCGAW1 W PRODUCED G CONSUMED1 1073 CCGAW3 W PRODUCED G CONSUMED3 1074 CCGAW4 W PRODUCED G CONSUMED4 1075 CCGAW5 W PRODUCED G CONSUMEDS5 1976 CCGAW6 W PRODUCED G CONSUMED6 1077 CCGAW7 W PRODUCED G CONSUMED? 1078 CCGAW8 W PRODUCED G CONSUMED8 1079 CCGAW9 W PRODUCED G CONSUMEDS 1080 CCGAW10 W PRODUCED G CONSUMED10 1081 CCGAW11 W PRODUCED G CONSUMED11 1082 CCGAW12 W PRODUCED G CONSUMED12 1083 COTTONO NO COTTON CONSUMPTION 1084 SORMILC1 (YE) SORGHUM MILLET CONSUMP TION ALL 1085 SORMILC2 (YE) SORGHUM MILLET CONSUMP TION COMMON 1086 SORMILC3 (YE) SORGHUM MILLET CONSUMP TION MEN 1087 SORMILC4 (YE) SORGHUM MILLET CONSUMP TION WOMEN 1088 SORMILPi (YA) SORGHUM MILLET PRODUCT ION ALL 1089 SORMILP2 (YA ) SORGHUM MILLET PRODUCT ION COMMON; 1090 REVENUE1.. SUM(JC, C1 (JC)*X1 (JC ))}+SUM(JM, C2 (JM)*X2 (UM) ) 1991 +SUM(OW, C3 (JW) *X3 (JW) )+SUM(2ZM, 1092 C36 (2M) *X36 (2M) ) 1093 (ZW, C37 (ZW) *X37 (ZW) ) -SUM (KA, C4 (KA) *X4 (KA) ) 1094 -SUM(LA, C5 (LA) *X5(L A) ) -SUM (MC, C6 (MC) *X6 (MC) ) 1095 -SUM(MM,( C7 (MM) *X7 (M M) )-SUM (MW, C8 (MW) *X8 (MW) ) 1096 -SUM(OC,( C9(OC) *X9(O C) }-SUM (OM, C10 (OM) *X10 (OM) } 1097 ~SUM (OW, 1098 C11 (OW) *X11 (OW) )-SUM(PC,C12(PC) *X12 (PC) ) 1099 -SUM (PM, 2106 C13 (PM) *X13 (PM) )-SUM(PW,C14(PW) *X14 (PW) ) 12014 ~SUM(QC, 1102 C15 (QC) *X15 (QC) ) -SUM(QM, C16 (QM) *X16 (OM) } 1103 -SUM (OW, ba C17 (QW) *X17 (QW) ) -SUM(RC,C18(RC) *X18 (RC) ) ob os bt be -SUM (RM, ts be OOO C19 (RM) *X19 (RM) )-SUM (RW, C20 (RW) *X20 (RW) ) be ts -SUM(SC, BY OTH C21 (SC) *X21 (SC) ) -SUM(SM, C22 (SM) *X22 (SM) ) be Fe be -~SUM (SW, OOD bs be C23 (SW) *X23 (SW) ) -SUM(TC, C24 (TC) *X24 (TC) ) OO ba be -SUM(TM, bs C25 (TM) *X25 (TM) }) -SUM(TW, C26 (TW) *X26 (TW) ) be be bt -SUM(UC, Wo Fo PY PRP ws C27 (UC) *X27 (UC) ) -SUM (UM, C28 (UM) *X28 (UM) ) 193 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 22 General Algebraic Modeling System Compilation 1115 -SUM (UW, 1116 C29 (UW) *X29 (UW) )-SUM(VC,C30(VC) *X30 (VC) ) 1117 -SUM (VM, 1118 C31(VM) *X31 (VM) )-SUM(VW, C32 (VW) *X32 (VW) ) 1119 -SUM(WC, 2120 C33 (WC) *X33 (WC) )-SUM (WM, C34 (WM) *X34 (WM) ) i121 -~SUM (WW, C35 (WW) *X35(WW)) =E= 21; L122 LAND1 (YA)... SUM{JC, 1123 AYA, JC) *X1 (JC) )}+SUM(JM,E(YA, JM) *X2 (JM) ) 14124 +SUM(IW, ICYA, JW'*x3 (JW!) =L= DIC(YA); 1125 LABORI (YB).. SUM (JC, 1126 B(YB, JC) *Xi (JC) )+SUM(JM, F (YB, JM) *X2 (JM) ) 1127 +SUM(JW, J(YB, JW) *X3 (5W) )+SUM(ZM, 7228 Z2M(YB,ZM) *X3€ (2M) } 2129 +SUM(ZW, ZZW(YB,ZW) *X37 (ZW) ) 1130 ~SUM (MC, MMMM (YB, MC) *X6 (MC) ) 1131 -SUM (MM, NNNN (YB, MM) *X7 (MM) ) 1132 ~SUM (MW, OOOO(YB,MW)*X8(MW)) =L= D2(YB); 1133 MLABOR1 (YB) .. SUM(2M, ZZM(YB,2M)*X36(ZM)) =L= 980; 1134 WLABOR1 (YB).. SUM(ZW, Z2W(YB,ZW) *X37(ZW)) =L= 921; 1135 CAPITAL1(YC).. SUM(JC, C1(JC)*X1(JC))+SUM(UM, C2(JM)*X2 (UM) ) 1136 +SUM(JW, C3 (JW) *X3 (JW) )+SUM(ZM, 1137 C36(ZM) *X36(ZM) ) 1138 +SUM(ZW, C37 (ZW) *X37 (ZW) ) -SUM (KA, C4 (KA) *X4 (KA) } L1i3¢ -~SUM(LA, C5(LA)*X5 (LA) ) -SUM(MC,Cé(MC) *X6 (MC) ) 1140 -SUM(MM, C7? (MM) *X7 (MM) ) -SUM (MW, CB (MW) *X8 (MW) ) 1141 -SUM(OC, C9 (OC) *X9 (OC) )-SUM(OM, C10 (OM) *X10 (OM) } i142 -SUM (OW, 1143 C11 (OW) *X11 (OW) ) -SUM(PC,C12(PC) *X12 (PC) ) 1144 ~SUM(PM, 1145 C13(PM) *X13 (PM) )-SUM(PW,C14(PW) *X14 (PW) ) 1246 -SUM (QC, 1147 C15 (OC) *X15 (QC) ) -SUM (QM, C16 (QM) *X16 (OM) ) 1148 ~SUM (QW, 1149 C17? (QW) *X17 (QW) )-SUM(RC,C18(RC) *X18(RC)}) 115C -SUM (RM, 1151 C19(RM) *X19 (RM) }-SUM(RW, C20 (RW) *X20 (RW) ) 1152 -SUM(SC, 1153 CzZ1{SC)*X21(SC) ) -SUM(SM, C22 (SM) *X22 (SM) ) 12.54 —-SUM (SW, 2155 C23 (SW) *X23 (SW) )-SUM(TC, C24 (TC) *X24 (TC) ) LLbe€ -SUM (TM, 1157 C25(TM) *X25 (TM) ) -SUM(TW, C26 (TW) *X26 (TW) ) 1158 -SUM(UC, 1159 C27 (UC) *X27 (UC) ) -SUM (UM, C28 (UM) *X28 (UM) } 1160 ~SUM (UW, 1161 C29 (UW) *X29 (UW) ) -SUM(VC,C30(VC) *X30 (VC} ) i162 -~SUM (VM, 1163 C31 (VM) *X31 (VM) )-SUM (VW, C32 (VW) *X32 (VW) ) 1164 ~SUM (WC, 1165 C33 (WC} *X33 (WC) )-SUM(WM, C34 (WM) *X34 (WM) ) 1.66 -SUM (WW, C35 (WW) *X35(WW)) =G= D3(YC); i116? MARKETI1(YD).. SUM(JC, D(YD, JIC) *X1(JC})*450 194 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 23 General Algebraic Modeling System A CS mpiilation +SUM(JM, H(YD, dM) *X2 (JM) )*450 fe fe fo @ @ pS pS pee +SUM(JW, L(YD, JW) *X3 (JW) ) *450 THAN THAN bo bo pa -SUM(Z2M, ZYM(YD,ZM) *X36 (ZM) } DY DY ~J fs fs fs -SUM(ZW, ZYW(YD,ZW) *X37 (ZW) ) fea =L= D4(YD); ~J fo fo ~i CALORICI1 (YE).. SUM(KA, UU(YE,KA) *X4 (KA) } fs fs pd J) J) WHE WHE -} +SUM(LA, GGG(YE,LA}*X5(LA)) =G= DS(YE); Fa Fa od od * MCALORICI(YF).. SUM(KA, UUU (YF, KA) *X4 (KA) ) po po * +SUM(LA, GGGG(YF,LA)*X5(LA)) =L= D6(YF); fo fo WS WS PROTEIN (YG)... SUM (KA, VV (YG, KA) *X4 (KA) } pe pe oe oe pes pes +SUM(LA, HHH(YG,LA)*X5(LA)) =G= D7 (YG); Ww} Ww} FATI (YH)... SUM (KA, XX (YH, KA) *X4 (KA) ) ee ee ee ee +SUM(LA, III(YH,LA)*X5(LA)) =G= D8(YH); OWDAKY OWDAKY © © fo fo fa CARBOSI(YI).. SUM (KA, Ne )) 0 0 be be ee ee © © Fe Fe +SUM({(LA, JJJ(YI,LA)*X5(LA)) =G= D9(YI); Cc Cc IRONL (YJ). SUM (KA, BBB IYI, KA) *X4 (KA )) WMP WMP he he rt +SUM (LA, ee dea ete ke A)) =G= D1O(Yd); VITAMINA1 (YK)... SUM(KA, CCC(YK,KA) *X4 (KA) ) +SUM(LA, SLLIYK, LA) *X5 (L A)) =G= D11(YK); FOLATEL (YL) SUM(KA, DDD(YL, KA) *X4 (KA) } +SUM(LA, MMM(YL, LA) *X5 { ma) =G= DI2(YL VITAMINCI1(YM).. SUM(KA, EEE (YM, KA) *X4 (KA) ) +SUM(LA, NNN(YM,LA)*X5(LA)) =G= D1I3(YM); REVENUE2.. SUM(JC, C1(JC) *X1(JC))+SUM(2M, C36 (2M) *X36(ZM) ) +SUM(ZW, C37 (ZW) *X37 (ZW) ) -SUM(KA, C4 (KA) *X4 (KA) ) -SUM(LA, C5(LA)*X5(LA) )-SUM(MC,C6(MC) *X€ (MC) }) -SUM(OC, €C9(OC}*X9 (OC) )-SUM(PC,C12(PC) *X12 (PC) ) -SUM(QC, C15 (OC) *K15 (QC) ) -SUM(RC, C18 (RC) *X18 (RC) ) -SUM(SC, C21 (SC) *X21 (SC) ) -SUM(TC, C24 (TC) *X24 (TC) ) -SUM(UC, C27 (UC) *X27 (UC) ) -SUM (VC, C30 (VC) *X30 (VC) ) -SUM(WC, C33 (WC) *X33(WC)) =E= 22; LAND2 (YA) . SUM ( A(YA,JC) *X1(JC)) =L= .76*D1 (YA); LABOR2 (YB).. SUM(JC, B(YB, JC) *X1 (JC) }+SUM(2ZM, 22M (YB, ZM) *X36(ZM)} +SUM(Z2W, ZZW(YB,ZW) *X37 (ZW)) -SUM (MC, MMMM (YB,MC) *X6(MC)) =L= .76*D2 (YB); MLABOR2 (YB) SUM(ZM, ZZM(YB,2ZM)*X36(ZM)) =L= .76*980; WLABOR2 (YB) SUM(2ZW, ZZW(YB,ZW) *X37(2W)) =L= .76*921; CAPITAL2 (YC)... SUM(JC, C1(JC)*X1(JC))+SUM(ZM, C36(ZM) *X36 (2M) ) +SUM(ZW, C37 (ZW) *X37 (ZW) ) -SUM (KA, C4 (KA) *X4 (KA) ) ~SUM(LA, C5 (LA) *X5 (LA) )-SUM (MC, C6 (MC) *X6 (MC) ) -SUM(OC, C9(OC) *X9 (OC) )~SUM(PC,C12(PC) *X12 (PC) ) -SUM (QC, C15 (OC) *X15 (QC) ) -SUM(RC,C18 (RC) *X18 (RC) ) ~SUM(SC, C21. (SC) *X21(SC) )-SUM(TC, C24 (TC) *X24 (TC) ) -SUM(UC, C27 (UC) *X27 (UC) ) -SUM(VC, C30 (VC) *X30 (VC) ) -SUM(WC, C33 (WC) *X33(WC)) =G= D3(YC); MARKET2 (YD) SUM(JC, D(YD, JC) *X1 (JC) )*450 195 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 24 General Algebraic Modeling System Compilation 1221 -SUM(4M, ZYM(YD,ZM) *X36 (2M) ) 1222 -SUM(ZW, ZYW(YD,ZW) *X37(ZW)) =L= .76*D4 (YD); 1223 CALORIC2 (YE) .. SUM(KA, UU(YE,KA) *X4 (KA) ) 1224 +SUM(LA, GGG(YE,LA)*X5(LA)}) =G= .76*D5(YE); 1225 * MCALORIC2(YF).. SUM(KA, UUU(YF,KA) *X4 (KA) ) 1226 * +SUM(LA, GGGG(YF,LA)*X5(LA)) =L= .76*D6(YF); 1227 PROTEIN2 (YG)... SUM(KA, VV (YG, KA) *X4 (KA) ) 1228 +SUM(LA, HHH(YG,LA)*X5(LA)} =G= .76*D7 (YG); 1229 FAT2 (YH).. SUM (KA, XX (YH, KA) *X4 (KA) ) 1230 +SUM(LA, III(YH,LA)*X5(LA)) =G= .76*D8(YH); 1231 CARBOS2(YI).. SUM(KA, AAA(YI,KA) *X4 (KA) ) 1232 +SUM(LA, JJJ(Y1I,LA)*X5(LA)) =G= .76*D9(YI); 1232 TRON2 (YJ)... SUM(KA, BBB(YJ,KA)*X4 (KA) } 1234 +SUM(LA, KKK(YJ,LA)*X5(LA)) =G= .76*D10(YJ); 1235 VITAMINA2 (YK)... SUM(KA, CCC(YK,KA) *X4 (KA) }) 1236 +SUM(LA, LLL(YK,LA)*X5(LA)) =G= .76*D11 (YK); 1237 FOLATE2 (YL).. SUM(KA, DDD (YL, KA) *X4 (KA) ) 1238 +SUM(LA, MMM(YL,LA)*X5(LA)) =G= .76*D12 (YL); 1239 VITAMINC2 (YM)... SUM(KA, EEE (YM, KA) *X4 (KA) ) 1240 +SUM(LA, NNN(YM,LA)*X5(LA)) =G= .76*D13(YM); 1241 REVENUES... SUM(JM, C2 (JM) *X2 (JM) )+SUM(ZM, C36(ZM) *X36(ZM) ) 1242 ~SUM (KA, C4 (KA) *X4 (KA) ) 1243 -SUM(LA, C5(LA) *X5 (LA) ) -SUM (MM, C7 (MM) *X7 (MM) ) 1244 -~SUM (OM, C10 (OM) *X10 (OM) )-SUM (PM, 1245 C13 (PM) *X13(PM) } 1246 -SUM (QM, C16 (QM) *X16 (OM) )}-SUM(RM, 1247 C19(RM)*X19(RM) } i248 -SUM (SM, C22 (SM) *X22 (SM) ) -SUM(TM, 1249 C25(TM) *X25 (TM) ) 1250 ~SUM (UM, C28 (UM) *X28 (UM) )-SUM(VM, 125i C31 (VM) *x31 (VM) ) 1252 ~SUM (WM, C34 (WM) *X34 (WM) ) =E= 23; 1253 LAND3 (YA).. SUM(JM,E(YA, JM) *X2(0M)) =L= .17*D1 (YA); 1254 LABOR3 (YB) .. SUM (JM, F (YB, JM) *X2 (JM) )+SUM(2M, 1255 ZZ2M(YB,2M) *X36(ZM) ) 1256 -SUM (MM, NNNN(YB,MM)*X7 (MM) ) =L= .17*D2(YB); 1257 MLABOR3 (YB)... SUM(ZM, ZZM(YB,2M) *X36(Z2M)) =L= .17*980; 1258 CAPITAL3 (YC)... SUM(JM, C2 (JM) *X2(JM))+SUM(Z2M, C36(ZM) *X36 (2M) ) 1259 -SUM (KA, C4 (KA} *X4 (KA) ) 1260 ~SUM(LA, C5(LA)*X5(LA))-SUM(MM, C7 (MM)*X7 (MM) ) 1262 -SUM (OM, C10 (OM) *X10 (OM) )-SUM(PM, 2262 C13(PM)*X13(PM: ' 1263 -SUM (QM, C16 (QM) *X16 (QM) )-SUM(RM, 1264 C19(RM) *X19(RM) ) 1265 -SUM (SM, C22 (SM) *X22 (SM) }-SUM(TM, 1266 C25(TM) *X25 (TM) }) 1267 -SUM (UM, C28 (UM) *X28 (UM) )-SUM(VM, 1268 C31 (VM) *X31 (VM) ) 1269 ~SUM (WM, C34 (WM) *X34 (WM) ) =G= D3(YC); 12790 MARKET3 (YD).. SUM(JM, H(YD, JM) *X2 (JM) ) *450 1271 -SUM(ZM, ZYM(YD,2M)*X36(Z2M)) =L= .17*D4 (YD); 1272 CALORIC3(YE).. SUM(KA, UU(YE,KA) *X4 (KA) ) 2273 +SUM(LA, GGG(YE,LA)*X5(LA)) =G= .17*D5 (YE); 196 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 25 General Algebraic Modeling System Compiiation 1274 * MCALORIC3(YF).. SUM(KA, UUU(YF,KA) *X4(KA)) 1275 * +SUM(LA, GGGG(YF,LA)*X5(LA)) =L= .17*D6(YF); 1276 PROTEIN3(YG).. SUM(KA, VV (YG, KA) *X4 (KA) ) 1277 +SUM(LA, HHH(YG,LA)*X5(LA)) =G= .17*D7(YG); 1278 FAT3 (YH) .. SUM(KA, XX (YH, KA) *X4 (KA) ) 1279 +SUM(LA, III(YH,LA)*X5(LA)) =G= .17*D8(YH); 1280 CARBOS3(YI).. SUM(KA, AAA (YI,KA)*X4 (KA) ) 1281 +SUM(LA, JJJ(YI,LA)*X5(LA)) =G= .17*D9(YI); 1282 IRON3(YJ).. SUM(KA, BBB(YJ,KA)*X4 (KA) ) 2283 +SUM(LA, KKK(YJ,LA)*X5(LA)) =G= .17*D10(YJ); 1284 VITAMINA3 (YK).. SUM(KA, CCC (YK, KA) *X4 (KA) ) 1285 +SUM(LA, LLL(YK,LA)*X5(LA)) =G= .17*D11(YK); 1286 FOLATE3(YL).. SUM(KA, DDD(YL,KA) *X4 (KA) ) 1287 +SUM(LA, MMM(YL,LA)*X5(LA)) =G= .17*D12(YL); 1288 VITAMINC3(YM).. SUM(KA, EEE(YM,KA) *X4 (KA) ) 1289 +SUM(LA, NNN(YM,LA)*X5(LA)) =G= .17*D13 (YM); 1290 REVENUE4.. SUM (JW, C3 (JW) *X3(JW)) 1291 +SUM(ZW, C37 (ZW) *X37 (ZW) )-SUM(KA, C4 (KA) *X4 (KA) ) 1292 ~SUM(LA, C5 (LA) *X5(LA))-SUM(MW, C8 (MW) *X8 (MW) ) 1293 -~SUM(OW, 1294 Cli (OW) *X11 (OW) )-SUM(PW, C14 (PW) *X14 (PW) ) 1295 ~SUM (OW, 1296 C17 (QW) *X17 (QW) )-SUM(RW, C20 (RW) *X20 (RW) ) 1297 -SUM (SW, 1298 C23 (SW) *X23 (SW) )-SUM(TW, C26 (TW) *X26 (TW) } 1299 -SUM (UW, 1300 C29 (UW) *X29 (UW) )-SUM(VW, C32 (VW) *X32 (VW) ) 1301 -SUM(WW, C35 (WW)*X35(WW)) =E= 24; 1302 LAND4 (YA).. SUM(JW, I(YA, JW)*X3(JW)) =L= .07*D1(YA); 1303 LABOR4 (YB)... SUM(JW, J(YB, JW) *X3 (JW) )+SUM(2W, 1364 Z2ZW(YB,ZW) *X37(2W) ) 1305 ~SUM (MW, 0000 (YB, MW) *X8 (MW) ) =L= .07*D2 (YB); 1306 WLABOR4 (YB)... SUM(Z2W, ZZW(YB,ZW) *X37(ZW)) =L= .07*921; 1307 CAPITAL4(YC).. SUM(JW, C3 (JW) *X3(JW)) 1308 +SUM(ZW, C37 (ZW) *X37 (ZW) )-SUM (KA, C4 (KA) *X4 (KA) ) 1309 ~SUM(LA, C5 (LA) *X5 (LA) )~SUM (MW, C8 (MW)*X8 (MW) ) 2310 -~SUM(OW, 1311 C11 (OW) *X211 (OW) )-SUM(PW, C14 (PW) *X14 (PW) ) 1312 -SUM (OW, 1313 C17 (QW) *X17 (QW) )-SUM(RW, C20 (RW) *X20 (RW) ) 1314 -SUM (SW, 1315 C23 (SW) *X23 (SW) )-SUM (TW, C26 (TW) *X26 (TW) ) 1316 -SUM (UW, 1317 C29(UW) *X29 (UW) )-SUM(VW, C32 (VW) *X32 (VW) ) 1318 -SUM(WW, C35 (WW)*X35(WW)) =G= D3(YC); 1316 MARKET4 (YD)... SUM(JW, L(YD, JW) *X3(JW))*450 -320 -SUM(ZW, ZYW(YD,ZW) *X37(ZW)) =L= .07*D4 (YD); 1321 CALORIC4(YE).. SUM(KA, UU(YE,KA)*X4(KA) ) 1322 +SUM(LA, GGG(YE,LA)*X5(LA)) =G= .07*D5(YE); 1.323 * MCALORIC4iYF!.. SUM(KA, UUU(YF,KA)*X4(KA)) 1324 * +SUM(LA, GGGG(YF,LA)*X5(LA)) =L= .07*D6(YF); 1325 PROTEIN4(YG).. SUM(KA, VV (YG, KA) *X4 (KA) } 1326 +SUM(LA, HHH(YG,LA)*X5(LA)) =G= .07*D7(YG); 197 O55 386/486 DOS 08/27/96 20:49:46 PAGE 26 @ @ on on h h fe fe al Aigebraic Modeling System M M OO OO lation Oo Oo FAT4 (YH) SUM (KA, XX (YH,KA)*X4 (KA) ) +SUM(LA, III(YH,LA) *X5(LA)) =G= .07*D8(YH); CARBOS4 (YI). SUM(KA, AAA(YI, KA) *X4 (KA) ) +SUM(LA, JJJ(YI,LA)*X5S(LA)) =G= .O7*D9(YI); TRON4 (YJ)... SUM (KA, BBB(YJ, KA) *X4 (KA) ) +SUM(LA, KKK{(YJ,LA)*X5(LA)) =G= .O7*D10O(YJ); VITAMINA4 (YK)... SUM(KA, CCC(YK, KA) *X4 (KA) ) +SUM(LA, LLL(YK, LA} *X5(LA)) =G= .07*D1I1(YK); FOLATE4 (YL).. SUM(KA, DDD(YL, KA) *X4 (KA) } +SUM(LA, MMM(YL,LA) *X5(LA)) =G= .07*D12 (YL); VITAMINC4 (YM)... SUM‘KA, EEE(YM,KA) *X4 (KA) ; +SUM(LA, NNN(YM,LA) *X5(LA)) =G= .0O7*D13 (YM); ALIALC8 (YC) SUM(MC, M(YC,MC)*X6(MC)S(ORD(MC} EQ B)) =G= 15; ALIALC10(YC SUM(MC, M({(YC,MC)*X6(MC)S(ORD(MC) EQ 10)} =G= 2; ALIALC12 (YC). SUM(MC, M(YC,MC)*X6(MC)$(ORD(MC) EO 12)) =G= 10; ALIALW8 (YC).. SUM (MW, O(YC,MW)*X8 (MW) S(ORD(MW) EQ 8)) ALIALW1IO0(YC).. SUM(MW, O(YC,MW)*X8 (MW)S(ORD(MW) EQ 10))} =G= 25.3; ALIALW12 (YC)... SUM(MW, O(YC,MW)*X8 (MW)S(ORD(MW) EQ 12)) =G= 4; ALIAMC8 (YC)... SUM(QC, V(YC,OC)*K15 (OC) S$ (ORD(OC) EO 8)) =G= 1138.5; ALIAMC12 (YC). SUM ( V (YC, QC) *X15(QC)$ (ORD(OC) EO 12)) =G= 300; ALIAFC2 (YC)... SUM(RC, AA(YC,RC)*K18 (RC)S(ORD(RC) EO 2)) =G= 3; ALIAFC3(YC).. SUM(RC, AA(YC,RC)*X18(RC)$(ORD(RC) EQ 3)) ALIAFCB (YC)... SUM(RC, AA(YC,RC)*X18(RC)S(ORD(RC) EO 8)) =G= 1.5; ALIAFM10(YC).. SUM(RM, BB(YC,RM) *X19(RM)S(ORD(RM) EQ 10)) =G= 5; ALIAFM12(YC).. SUM(RM, BB(YC,RM)*X19(RM)$(ORD(RM) EQ 12)) ALIAXC8 (YC) SUM(SC, DD{YC,SC)*K21(SC})S$(ORD(SC} EO 8)) =G= 3; CCGAA].. SUM (KA, C4 (KA) *X4 (KA) S(ORD(KA) EQ 1)) ~(SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EO 1)) +SUM(JIM, C2(JM)*X2(JM)$(ORD(JM}) EQ 1)} +SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 1))) =L= 0; CCGAA3.. SUM(KA, C4 (KA) *X4(KA)S{(ORD(KA) EQ 3)) -(SUM(JC, Cl(JC)*X1(JC)S(ORD(JC) EQ 3)) +SUM(JM, C2(J0M)*X2(JM)S(ORD(JM) EO 3)) +SUM{(JW, C3 (JW) *X3 (JW) S(ORD(JW) EQ 3))) =L= 0; CCGAA4.. SUM (KA, C4 (KA) *X4 (KA) S(ORD(KA) EQ 4)) ~(SUM(JC, C1(JC)*X1(JC)S(ORD(JC) EQ 4)) +SUM(JM, C2(JM)*XK2(JM)S(ORD(JM) EQ 4)) +SUM(JW, C3 (JW) *X3 (JW)S(ORD(JW) EQ 4)))=L= Q; CCGAAS. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 5)) 198 GAMS 2.25.055 3 486 DOS 08/27/96 20:49:46 PAGE 27 General gebraic Modeling System Compidlat nN 1380 ~(SUM(JC, C1(JC) *X1 (JC) S(ORD(JC) EQ 5)) 1381 +SUM(JM, C2(JM)*X2(JM)$(ORD(JM))$ EQ 5)) 1382 +SUM(JW, C3(JW)*X3(JW)S(ORD(JW))$ EQ 5))) =L 1333 CCGAAG6.. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 6)) 1384 -(SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EQ 6)) 1385 +SUM(JM, C2(JM)*X2(JM)$S(ORD(JM) EO 6)) 1386 +SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 6))) =L= 0; 1387 CCGAA7T.. SUM (KA, C4 (KA) *X4 (KA) $ (ORD(KA) EQ 7)) i388 -~(SUM(JC, C1(JC)*X1(JC)S(ORD(JC) EQ 7)) 1389 +SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 7)) 1390 +SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 7))) =L= QO; 1391 CCGAAB8B SUM (KA, C4 (KA) *X4 (KA) $ (ORD(KA) EQ 8)) 2392 -(SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EQ 8)) 1393 +SUM(JM, C2(JM)*X2(JM)S(ORD(JM) EQ 8)) 1394 +SUM(JW, C3 (JW) *X3(JW)$(ORD(JW) EQ 8))) =L= 0; 1395 CCGAAY SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 9)) 1396 -(SUM(JC, C1 (JC)*X1(JC)S(ORD(JC) EQ 9)) 1397 +SUM(JM, C2(JM)*X2(JM)S(ORD(JM) EQ 9)) 1398 +SUM(JW, C3 (JW) *X3(JW)S$(ORD(JW) EQ 9))) =L= 0; 1399 CCGAAI10.. SUM (KA, C4 (KA) *X4 (KA) $ (ORD(KA) EQ 10)) 1400 -~(SUM(JC, C1(JC)*X1(JC).$ (ORD(JC) EQ 10)) 1401 +SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 10)) 1402 +SUM(JW, C3 (JW) *X3(JW)S(ORD(JW) EQ 10))}) =L= 0; 1403 CCGAAI1.. SUM (KA, C4 (KA) *X4 (KA) $ (ORD(KA) EQ 11)) 1404 -~(SUM(JC, C1(JC)*K1(JC)$(ORD(JC) EQ 11) 1405 +SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 11)) 1406 +SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 11)}) =L= 0; 1407 CCGAA12.. SUM (KA, C4 (KA) *X4(KA)$(ORD(KA) EQ 12)) 1408 -(SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EQ 12)) 1409 +SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 12)) 1410 +SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 12))) =L= 0; 141i CCGACI. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 1)) 1412 -SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EQ 1)) =L= 0; 141 CCGAC3.. SUM (KA, C4 (KA) *X4 (KA)S (ORD(KA) EQ 3)) 14714 -SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EQ 3)) =L= 0; 1415 CCGAC4.. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 4)) 1416 -SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EQ 4)) =L= 0; 1417 CCGACS.. SUM (KA, C4 (KA) *X4 (KA) S(ORD(KA) EQ 5)) 1418 -SUM({JC, C1(JC)*X1(JC)S{ORD(JC) EQ 5)) =L= 0; 1419 CCGACE6. SUM (KA, C4 (KA) *X4 (KA)$(ORD(KA) EQ 6)) 1420 -SUM{JC, C1(JC)*X1(JC)$(ORD(JC) EQ 6)) =L= 0; 1421 CCGAC?.. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 7)) 1422 -SUM(JC, Ch LIE) FX (IC) $ CORD (IC) EQ 7)) =L= 0; 1423 CCGAC8.. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 8)) 1424 ~SUM(JC, C1150) ¥X1 (30) $ (ORD LIC) EQ 8)) =L= 0; 1425 CCGACY.. SUM (KA, C4 (KA} *X4 (KA) 5 (ORD(KA) EQ 2) 1426 -SUMi JC, C1(JC)*X1(JC)$ (ORD(J C) EQ 9)) =L= 0; 1427 CCGACIO.. SUM (KA, C4 (KA) *X4 (KA) S$ (ORD(KA) EQ 10 )) 1426 -SUM(JC, C1(JC)*X1(JC)$(ORD(JC) EQ 10)) =L= 0; 1429 CCGACII1.. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 11))} 1430 -SUM(JC, C1l(JC)*X1(JC)$(ORD(JC) EQ 11)) =L 1431 CCGAC12.. SUM (KA, C4 (KA) *X4 (KA) $(ORD(KA) EQ 12)) 1432 ~SUM{JC, C1(JC)*X1(JC)$(ORD(JC) EQ 12)) =L 199 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 28 General Algebraic Modeling System Compilation 1433 CCGAM1.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 1)) 1434 ~SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 1)}) =L= 0; 1435 CCGAM3.. SUM(KA,C4 (KA) *X4(KA)$(ORD(KA) EQ 3)) 1436 -SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 3)) =L= 0; 1437 CCGAM4.. SUM(KA,C4 (KA) *X4(KA)$(ORD(KA) EQ 4)) 1438 ~SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 4)) =L= 0; 1439 CCGAM5.. SUM(KA,C4 (KA) *X4(KA)$(ORD(KA) EQ 5)) 1440 -SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 5)) =L= 0; 1441 CCGAM6.. SUM(KA,C4 (KA) *X4(KA)$(ORD(KA) EQ 6)) 1442 -SUM(JM, C2(JM)*X2(JM)S(ORD(JM) EQ 6)) =L= 0; 1443 CCGAM?.. SOM (KB, C4 (RB) 84 URA) § CORD EQ 7)) 1444 -SUM(JM, C2(JM)*X2(JM)$(ORD(JM) EQ 7)) =L= 0; 1445 CCGAMB.. SUM (Kh, C4 (KB. #4 (RAYS LORD IEA, EQ 833 1446 -SUM,IM, C2(JM}*X%2;(0JM)S(ORD(JM; EC §8., =L= 0; 1447 CCGAM9.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 9)) 1448 -SUM(JM, C2(JM)*X2(JM)S(ORD(JM) EQ 9)) =L= 0; 1449 CCGAM10.. SUM(KA,C4(KA) *X4(KA)$S(ORD(KA) EQ 10)}) 1450 ~SUM(JM, C2(JM}*X2(JM)$(ORD(JM) EQ 10)) =L= 0; 1451 CCGAM11.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 11)) 1452 -SUM(JM, C2(JM)*X2(JM)$(ORD(JM}) EQ 11)) =L= 0; 1453 CCGAM12.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 12)) 1454 -SUM(JM, C2(JM)*X2(JM)S(ORD(JM) EQ 12)) =L= 0; 1455 CCGAW1.. SUM(KA,C4(KA) *X4(KA)S(ORD(KA) EQ 1)) 1456 -~SUM(JW, C3(JW)*X3(JW)$(ORD(JW) EQ 1)) =L= 0; 1457 CCGAW3.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 3)) 1458 -SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 3)) =L= 0; 1459 CCGAW4.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 4)) 1466 -SUM(JW, C3(JW)*X3(JW)S$(ORD(JW) EQ 4)) =L= 0; 1461 CCGAW5.. SUM(KA,C4(KA)*X4(KA)$(ORD(KA) EQ 5)) 1462Z ~SUM (JW, C3 (JW) *X3(JW)S(ORD(JW) EQ 5)) =L= 0; 1463 CCGAW6.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 6)) 1464 -SUM(JW, C3 (JW) *X3(JW)S(ORD(JW) EQ 6)) =L= 0; 1465 CCGAW7.. SUM(KA,C4(KA) *X4(KA)S(ORD(KA) EQ 7)) 1466 -SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 7)) =L= 0; 1467 CCGAWS8.. SUM(KA,C4(KA) *X4(KA)$(ORD(KA) EQ 8)) 1468 -SUM(JW, C3 (JW) *X3(JW)S(ORD(JW) EQ 8)) =L= 0; 1469 CCGAW9.. SUM(KA,C4(KA)*X4(KA)S$(ORD(KA) EQ 9)) 1470 SUM TH, C3 (JW) *X3(JW)S(ORD(JW) EQ 9)) =L= 0; 1471 CCGAW10.. SUM(KA,C4(KA) *X4(KA)S$(ORD(KA) EQ 10)) 1472 -SUM (J C3 (JW) *X3(JW)S(ORD(JW) EQ 10)) =L= 0; 1473 CCGAW11.. SUM(KA, oa TRA, *X4(KA)S$S(ORD(KA) EQ 11)} 1474 -SUM(TW, C3(JW}*X3(JW}S(ORD{JW) EQ 11)) =L= 0; 1475 CCGAW1Z2.. SUM(KA,C4(KA)*X4(KA)S(ORD(KA) EQ 12)} 1476 -SUM(JW, C3(JW)*X3(JW)S(ORD(JW) EQ 12)) =L= 0; 1477 COTTONO.. SUM (KA, C4 (KA) *X4 (KA) $ (ORD(KA) EQ 2)) =E= 0; ~478 SORMILCL(YE).. SUM(KA, UU(YE,KA) *X4(KA)$(ORD(KA) EQ 8)) 2479 +SUM(KA, UU(YE,KA) *X4(KA)$(ORD(KA) EQ 12)) 1486 =G= .47*DS (YE); 1481 SORMILC2(YE).. SUM(KA, UU(YE, KA) *X4(KA)S$(ORD(KA) EQ 8}) 1482 +SUM(KA, UU (YE,KA) *X4(KA)$(ORD(KA) EQ 12)) 1483 =G= .47*.76*DS (YE); 1484 SORMILC3(YE).. SUM (KA, UU(YE,KA) *X4(KA)S(ORD(KA) EQ 8)} 1485 +SUM(KA, UU(YE,KA) *X4(KA)$(ORD(KA) EQ 12)) 200 GAMS 2.25.055 386/486 DOS 08/27/96 20:49:46 PAGE 29 General Algebraic Modeling System compilation 1486 =G= .47* .i7*D5S(YE) ; 1487 SORMILC4 (YE).. SUM (KA, UU(YE,KA) *X4(KA)$(ORD(KA) EQ 8)) 1488 +SUM(KA, UU(YE,KA) *X%4 (KA) $(ORD(KA) EQ 12)) 2489 =G= .47* .O07*DS (YE) ; 1490 SORMILPItYA).. SUM(JC, A(YA,JC)*X1(JC)$(ORD(JC) EQ 8)) 1492 +SUM (JC, er My ato CIMN S fORD CaM) EC 12))} ~492 +SUM(JM, E(YA, JM) *X2(JM)S$(ORD(JM} EQ 8))} 2493 +SUM(JM, E(YA, JM) *X2 (JM) $ (ORD (JM) EQ 12)) 1494 +SUM(JW, I (YA, JW) *X3(JW)S(ORD(JW) EQ 8))} 1495 +SUM(JW, I(YA, JW) *X3(JW)S(ORD(JW) EQ 12)) i496 =G= .75*D1 (YA); 1497 SORMILP2 (YA).. SUM(JC, A(YA,JC)*X1(JC}$(ORD(JC) EQ 8)) 1498 +SUM(JC, A(YA, JC) *X1(JC)$(ORD(JC) EQ 12)) i499 =G= .85*.76*D1 (YA); 1500 MODEL SIRAKROLA1 /REVENUE1, LAND1, LABOR1, MLABORI, 1501 WLABOR1, CAPITAL1, MARKETI, 1502 CALORIC], PROTEINI, FAT1, CARBOS1, 1503 TRON1, VITAMINA1, FOLATE1, VITAMINCI, 1504 ALIALC8, ALIALC10, ALIALC12, ALIALW8, ALIALW10, 1505 ALIALWi2, ALIAMC8, ALIAMC12, ALIAFC2, ALIAFC3, 150€ ALIAFC8, ALIAFM1C, ALIAFM12, ALIAXC8, CCGAAI1, 1507 CCGAA3, CCGAA4, CCGAA5, CCGAA6, CCGAA?, CCGAA8, 2508 CCGAAS, CCGAA10, CCGAAI11, CCGAAI12, 15909 COTTONO, SORMILC1, SORMILP1/; 1510 MODEL SIRAKROLA2 /REVENUE2, LAND2, LABOR2, MLABOR2, 1511 WLABOR2, CAPITAL2, MARKET2, 1512 CALORIC2, PROTEIN2, FAT2, CARBOS2, 1513 IRON2, VITAMINA2, FOLATE2, VITAMINC2, i514 ALIALC8, ALIALC10, ALIALC12, ALIAMC8, ALIAMC12, 1515 ALIAFC2, ALIAFC3, ALIAFC8, ALIAXC8, CCGACI, 1516 CCGAC3, CCGAC4, CCGAC5, CCGAC6, CCGAC7, CCGAC8, 1517 CCGAC9, CCGAC10O, CCGAC11, CCGAC12, 1518 COTTONO, SORMILC2, SORMILP2/; 1519 MODEL SIRAKROLA3 /REVENUE3, LAND3, LABOR3, 1526 MLABOR3, CAPITAL3, MARKET3, 1521 CALORIC3, PROTEIN3, FAT3, CARBOS3, 2022 IRON3, VITAMINA3, FOLATE3, VITAMINC3, 1522 ALIAFM10, ALIAFM12, 1524 CCGAM1, CCGAM3, CCGAM4, CCGAM5, CCGAM6, CCGAM7, 1525 CCGAM8, CCGAM9, CCGAM10, CCGAMi1, CCGAM12, 1526 COTTONO, SORMILC3/; 1527 MODEL SIRAKROLA4 /REVENUE4, LAND4, LABOR4, 1528 WLABOR4, CAPITAL4, MARKET4, 1529 CALORIC4, PROTEIN4, FAT4, CARBOS4, 1530 IRON4, VITAMINA4, FOLATE4, VITAMINC4, 1531 ALIALW8, ALIALW10, ALIALWi2, 1532 CCGAW1, CCGAW3, CCGAW4, CCGAW5, CCGAW6, CCGAW7, 1533 CCGAW8, CCGAW9, CCGAW10, CCGAW11, CCGAW12, 1534 COTTONO, SORMILC4/; L535 OPTION LIMROW 0; 1536 OPTION LIMCOL = 0; 15237 SOLVE SIRAKROLA1 USING LP MAXIMIZING 21; 2538 SOLVE SIRAKROLA2 USING LP MAXIMIZING 22; 201 ND BO OW © uo 5 PAGE 30 ~ ~ 386/486 DOS 08/27/96 20:49:46 1 9 Algebraic Modeiing System + aa PR be ook ao0 ao0 om L ton TO: TO: 1539 SOLVE SIRAKROLA3 USING LP MAXIMIZING 23; 1540 SOLVE SIRAKROLA4 USING LP MAXIMIZING 24; 202 Appendix D: Nutritional Composition of Foods* Edible Energy | Protein Fat Carboh Iron Vitamin | Folate | Vitamin Portion (kcal)* (g)* (g)* ydrates (g)* A (ug)* C (%) (g)* (Re)* (mg)* Bambara Nut 75 2587.5 142.5 46.5 457.5 90.0 15.0 0.0 0.0 Cowpea 100 3200.0 230.0 14.0 570.0 50.0 30.0 4390. 20.0 0 Dah 100 3200.0 56.0 14.0 750.0 50.0 40.0 0.0 0.0 Fonio 100 3200.0 56.0 14.0 750.0 50.0 40.0 0.0 0.0 Garden Pea 100 3200.0 220.0 11.0 560.0 100.0 270.0 330.0 0.0 Maize 100 3450.0 100.0 45.0 670.0 25.0 0.0 0.0 0.0 Millet 100 3200.0 56.0 14.0 750.0 50.0 40.0 0.0 0.0 Okra 81 283.5 17.0 1.6 56.7 9.7 259.2 186.3 380.7 Peanut 70 3990.0 175.0 315.0 161.0 26.6 21.0 770.0 7.0 Rice 100 3350.0 70.0 8.0 800.0 17.0 0.0 290.0 0.0 Sorghum 100 3350.0 95.0 28.0 740.0 45.0 30.0 0.0 0.0 Cow/Goat/ 100 1150.0 220.0 19.0 0.0 46.0 0.0 150.0 0.0 Sheep Meat Chicken/ 67 938.0 134.0 43.6 0.0 7.4 569.5 53.6 0.0 Poultry * Nutrition for Developing Countries. King and Burgess, 1993. 203 Appendix E: Average Annual Meat Consumption in Mali* Meat Consumed From: National Consumption Per Capita Consumption (kg) (kg) Cows 35,451,167 4.10 Goats and Sheep 20,092,020 2.32 Chicken and Poultry 14,080,000 1.63 * JER Annual Report. Annuaire Statistique Du Mali: 1994. 204 Vita Adam David Russ was born in Decatur, Georgia on February 1, 1971. He attended Virginia Polytechnic Institute and State University, in Blacksburg, Virginia, where he received a Bachelor of Science degree in Agricultural and Applied Economics in 1994. He continued on at Virginia Polytechnic Institute and State University where he received his Master of Science degree in Agricultural and Applied Economics in October 1996. / vided U>. Kiet 205