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Journal of farming systems research-extension

Material Information

Title:
Journal of farming systems research-extension
Running title:
Journal for farming systems research-extension
Abbreviated Title:
J. farming syst. res.-ext.
Creator:
Association of Farming Systems Research-Extension
Place of Publication:
Tucson Ariz. USA
Publisher:
Association of Farming Systems Research-Extension
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Language:
English
Physical Description:
v. : ill. ; 23 cm.

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Subjects / Keywords:
Agricultural systems -- Periodicals -- Developing countries ( lcsh )
Agricultural extension work -- Research -- Periodicals ( lcsh )
Sustainable agriculture -- Periodicals -- Developing countries ( lcsh )
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serial ( sobekcm )
periodical ( marcgt )

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Dates or Sequential Designation:
Vol. 1, no. 1-
General Note:
Title varies slightly.
General Note:
Title from cover.
General Note:
Latest issue consulted: Vol. 1, no. 2, published in 1990.
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Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.

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1051-6786 ( ISSN )

Full Text


Volume 4, Number 2 1994










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for Farming Systems Research- Extension



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Joep Nkwi S S






A. W. St~ an R. B. Smith



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Brc Pe.c an n Mt Plesan W. A.l. Shh Rushn Yamn talKrm M.A ai
93 ~I Roeo amr nte Evlato ofa mpoe






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Journal
for Farming Systems Research -Extension


Volume 4, Number 2, 1994


Published by
the Association for Farming Systems Research-Extension

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Journal for Farming Systems Research-Extension


Editor
Timothy R. Frankenberger
Office of Arid Lands Studies
The University of Arizona, Tucson

Associate Editors
Claude Bart, Daniel Goldstein, Jennifer Manthei, and M. Katherine McCaston Office of Arid Lands Studies
The University of Arizona, Tucson

Production and Layout
Claude Bart, Jennifer Manthei, M. Katherine McCaston, and Sonia Telesco
Arid Lands Design, Office of Arid Lands Studies The University of Arizona, Tucson

Sponsors
Ford Foundation
The University of Arizona




The Journal for Farming Systems Research-Extension is published by the Association for Farming Systems Research-Extension (AFSRE), an international society organized to promote the development and dissemination of methods and results of participatory onfarm systems research and extension. The objectives of such research are the development and adoption through participation by farm household members of improved and appropriate technologies and management strategies to meet the socioeconomic and nutritional needs of farm families; to foster the efficient and sustainable use of natural resources; and to contribute toward meeting global requirements for food, feed, and fiber.
The purpose of the Journal is to present multidisciplinary reports of on-farm researchextension work completed in the field, and discussions on methodology and other issues of interest to farming systems practitioners, administrators, and trainers. The Journal serves as a proceedings for the annual international Farming Systems Symposium from which selected and refereed papers are included. It also welcomes contributed articles from members of the AFSRE who were unable to attend the symposium. Contributed articles will be judged by the same review process as invited articles.


ISSN: 1051-6786

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Journal for Farming Systems Research-Extension Volume 4, Number 2, 1994


CONTENTS

1 Sustainability in Agricultural Development: Trade-Offs Between
Productivity, Stability, and Equitability
Gordon R. Conway

15 The Socioeconomic Role of Rural Kom Women in the Farming System of Cameroon Joseph Nkwain Sama

27 Analysis of the Competition for Labor by Dryland and Irrigated Crops: The Case of Rice and Millet in Niger
Ziyou Yu, Robert Deuson, Eric Bomans, and Jess LowenbergDeBoer

45 Participatory Needs Assessment: A Key to FSRE
A. W. Etling and R. B. Smith

57 Women Farmers' Role in Managing Cassava Production in Bandundu, Zaire
Mbongolo-Ndundu Mputela and Steven E. Kraft

71 Farmer-Controlled Diagnosis and Experimentation for Small Rural
Development Organizations
Bruce Petch and Jane Mt. Pleasant

83 Participation of Rural Women in the Homestead Vegetable
Farming Systems of Bangladesh
W. A. Shah, Rukshana Yasmin, Rezaul Karim, and M. M. A.
Karim

93 Role of Farmers in the Evaluation of an Improved Variety: The
Case of S35 Sorghum in Northern Cameroon
Mulumba Kamuanga and Martin Fobasso

111 Household Food Security and Environmental Sustainability in
Farming Systems Research: Developing Sustainable Livelihoods
Michael Drinkwater and Margaret A. McEwan

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Sustainability in Agricultural Development:
Trade-Offs Between Productivity, Stability, and
Equitability

Gordon R. Conway 1



ABSTRACT
Agroecosystem Analysis (AEA) provides a theoretical and practical context for useful definitions of productivity, stability, sustainability, and equitability. Productivity may be defined as any combination ofinput and output in which the output is a valued agricultural product. Stability measures the constancy of such productivityin the face ofsmall disturbing forces in the environment, while sustainability measures the ability of productivity to withstand major disturbing stress or shock. Sustainability can be expressed in terms of the inertia, elasticity, amplitude, hysteresis, or malleability of an agroecosystem. The inevitable trade-offs between these properties are illustrated quantitatively, using data from British manorial records ofcereal production, and qualitatively, with a contemporary village survey from Ethiopia. The Javanese home garden provides
an example in which the trade-offs are minimized.


INTRODUCTION
The phrase "sustainable agriculture" has acquired diverse meanings. To the agriculturalist it means maintaining the momentum of the Green Revolution. To the ecologist it is a way of providing sufficient food without degrading natural resources. To the economist it represents an efficient long term use of resources, and to the sociologist and anthropologist it embodies an agriculture that preserves traditional values. Almost anything that is perceived as "good" from the writer's perspective can fall under the umbrella of sustainable agriculture--organic farming, the small family farm, indigenous technical knowledge, biodiversity, integrated pest management, self-sufficiency, recycling, and so on (Conway and Barbier, 1990).
This diversity of interpretation is to be welcomed as part of a process of gaining consensus for radical change. But it results in concepts and definitions of little practical value. The often-quoted definition of sustainable develop1 Representative, Ford Foundation, New Delhi, and Professor, Centre for Environmental Technology, Imperial College of Science, Technology, and Medicine, London.

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ment proposed by the World Commission on Environment and Development-"development that meets the needs of the present without compromising the ability of future generations to meet their own needs" (Brundtland Report, 1987)-is valuable as a policy statement but is too abstract for farmers, research scientists, or extension workers trying to design new agricultural systems and develop new agricultural practices. For them, a definition is needed that is scientific, open to hypothesis testing and experimentation, and practicable.
In this paper I elaborate on a definition of sustainable agriculture that I and my colleagues have been using in recent years. The definition arises within a new paradigm for agricultural development that goes under the name of Agroecosystem Analysis (AEA). The origins of AEA lie in efforts to improve the analysis of natural ecosystems (Walker, et al., 1978), but most of the concepts and techniques were developed at the University of Chiang Mai in Thailand beginning in 1978 (Gypmantasiri et al., 1980) and were refined and elaborated at the University of Khon Kaen, also in Thailand, by a group of university and government research workers in Indonesia (KEPAS) and by the Southeast Asia Universities Agroecosystems Network (SUAN). The first major workshop on sustainable agriculture was held in Indonesia in 1982 (KEPAS, 1984).
Agroecosystem Analysis (AEA) uses the concepts of agroecosystems, agroecosystem hierarchies, and agroecosystem properties and their trade-offs to stimulate interdisciplinary analysis. It can be viewed as a form of Farming Systems Research; however, it has the capacity to extend the analysis, using the same concepts and techniques, to systems in the agricultural hierarchy above and below the farm. AEA may involve detailed quantitative analysis based on experiments or secondary data. But, more commonly, it takes the form of a rapid field appraisal utilizing a variety of simple descriptive diagrams, prepared in farms and villages from direct observations and from interviews with rural people. These diagrams, developed as part ofAEA, have subsequently become popular techniques of Rapid Rural Appraisal (RRA) (Khon Kaen University, 1987). They have proven to be powerful means of communication, especially when prepared and analyzed by farmers themselves (Mascarenhas et al., 1991). Today, AEA lies within a cluster of overlapping approaches that provides a rich array of concepts and methods for agricultural and rural analysis.2


2 The literature on AEA concepts includes a number of books and papers [Conway, 1985, 1986,
1987; Conway and Barbier, 1990; Soemarwoto and Conway, 1991; Sajise and Rambo, 1986; Rerkasem and Rambo, 1988 ]; most of the field-based analyses are described in published reports [KKU-Ford Cropping Systems Project, 1982a, b; Conway, et al.,1985, 1989; KEPAS, 1985a, b, 1986; Conway and Sajise, 1986; Ethiopian Red Cross, 1988]. Readers are also referred to the Gatekeeper Series on Sustainable Agriculture and to RRA Notes, both produced by the International Institute of Environment and Development in London.


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SUSTAINABILITY IN AGRICULTURAL DEVELOPMENT 3

CONCEPTS AND DEFINITIONS
Based on the literature cited above, an agroecosystem may be defined as an ecological and socioeconomic system, comprising domesticated plants and/ or animals and the people who husband them, intended for the purpose of producing food, fiber, or other agricultural products. Agroecosystems defined in this way fall into a hierarchy. At the lowest level is the individual plant or animal, its immediate microenvironment, and the people who tend and harvest it. The next level is the crop or herd, contained within a field or paddock, or in a swidden, home garden, or range. These systems, alone or in various combinations, together with the farm household, comprise a farming system. The hierarchy continues upwards in a similar fashion, each agroecosystem forming a component of the agroecosystem at the next level.
Agroecosystems, so defined, are also cybernetic (Wiener, 1948; Ashby, 1956). They have recognizable goals, and strategies to attain them. I suggest that the primary goal ofan agroecosystem is increased "social value." Broadly, it is composed of the amounts of goods and services produced by an agroecosystem, the degree to which they satisfy human needs, and their allocation among the human population. It also has a time dimension, since we seek not only increased benefits in the immediate future but also a degree of security over the longer term.
Thus social value has several measurable components: the present production of the agroecosystem, its likely level in the future, and its distribution among the human population. These are expressed in four agroecosystem properties: (1) productivity-the output of valued product per unit of resource input; (2) stability-the constancy of productivity in the face of small disturbing forces arising from the normal fluctuations and cycles in the surrounding environment; (3) sustainability-the ability of the agroecosystem to maintain productivity when subject to a major disturbing force; and (4) equitability--the evenness of distribution of the productivity of the agroecosystem among the human beneficiaries.


THE MEASUREMENT OF AGROECOSYSTEM PROPERTIES


Productivity
The productivity of plants and animals can be measured as the amount of new biological material, or biomass, produced per unit of time. But agriculture is only concerned with the portion of the biomass that is useful-the harvest. Productivity is thus more appropriately measured either as yield or income, or in terms of the other benefits that derive from the harvest.


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Commonly, yield is measured per hectare or as the total production of agricultural goods and services per household, region, or nation; a large number of different measures are possible depending on the nature of the product and of the resource input being considered. The yield may be expressed in terms of kilograms of grain, tubers, meat, fish, or any other consumable or marketable product. Alternatively, it may be expressed in calories, proteins, or vitamins, or as its monetary value in the market. In the last case it is measured as income over expenditure (i.e., as profit).
The three basic resource inputs to productivity are land, labor, and capital. Strictly speaking, energy is subsumed under land (solar energy), labor (human energy), and capital (fuel energy). Similarly, technological inputs, such as fertilizers and pesticides, are components of capital, but both energy and technology can be treated for many purposes as separate inputs. Productivity can be expressed as tons of grain per hectare, kilograms of nitrogen fertilizer per hectare, grams of active ingredient of insecticide per hectare, or any other conceivable combination of input and output.

Stability
Over time, productivity may rise, fall, or remain constant. It will also exhibit a pattern ofvariability relative to the dominant trend line. The yield of a crop, for example, is likely to mirror the variability in the climate. Income may also fluctuate, reflecting not only changes in yield but also variations in the market price ofinputs, such as labor, fertilizers and pesticides, and of the product. The latter, in turn, is a function of supply and demand.
Productivity may be defined in any of the ways described above and its stability measured by the coefficient of variation in productivity.

Sustainability
Stability measures the behavior of an agroecosystem in response to the normal fluctuations in the surrounding environment. Productivity goes up and down, but is not seriously threatened. However, agroecosystems are also subject to major disturbing forces that can cause productivity to fall well below its previous level. If productivity does fall, it may recover either to its original level or to a new lower level or, in extreme situations, it may cease altogether. Sustainability is the ability of an agroecosystem to withstand such disturbing forces.
The simplest case is an individual crop plant or animal. How does it withstand forces that threaten its survival? If we consider a plant strictly as a physical structure, the concepts of mechanics and Newton's laws of motion apply. A force acting on the plant, such as a low temperature, elicits a physical or chemical change. The force is termed a stress and the change a strain. If the change is reversible, the strain is regarded as elastic; if irreversible, the strain is plastic. When a maize plant is cooled from 300C to 5oC it stops growing, but


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SUSTAINABILITY IN AGRICULTURAL DEVELOPMENT 5

normal growth resumes when the temperature rises again. If wheat is cooled to 50C growth continues, although at a slower rate. Both plants have suffered elastic strain, although the strain is greater in the case of maize. At an even lower temperature a plant dies; it suffers a plastic strain.
But this physical model is too simple. The responses of biological systems are more than mechanical. Plants and animals may counter stress through either evolutionary change or adaptation during the organism's life. A common example of the latter is when plants of temperate regions harden to the freezing temperatures of winter through gradual exposure to the increasing cold of the autumn.
More dynamic still are the responses to nonphysical forces, such as attacks by pests or competition from weeds. Also, the variety of forces and responses becomes even greater as we move up the agroecosystem hierarchy through the crop, farm, village, and nation. Productivity may collapse, for example, under the pressure of economic forces, such as a steep rise in input costs or increased levels of indebtedness. Social disturbances, such as communal conflict or political revolution, may also pose a threat.
We can distinguish two kinds of disturbing forces. First, there are relatively small and predictable forces that act on a regular and sometimes continuous basis and produce a large cumulative effect. Salinity, toxicity, erosion, pest or disease attack, indebtedness, and declining market demand are examples. Such a force constitutes a stress. The other kind of force is very large, infrequent, and relatively unpredictable, and produces an immediate, large disturbance or perturbation. This I refer to as a shock. Examples are rare floods or droughts, an outbreak of a new pest, or the sudden rise in an input price. In practice, it is usually not difficult to distinguish between a stress and a shock, providing it is clear which level in the agroecosystem is being considered. A shock to an individual plant (i.e., being destroyed by a pest) may be only part of a stress to the whole crop.
There is a continuum between stability and sustainability, but they are usually distinguishable by qualitatively different patterns of behavior. Stability refers to the dynamics of an agroecosystem when subject to relatively minor and commonplace disturbing forces. Typically, the impact of these forces is small because, out of long association, agroecosystems have developed adequate defenses. By contrast, sustainability is concerned with forces that are rarer and less expected, so that agroecosystems are likely to have fewer or less developed defenses. Often the defense mechanisms are qualitatively different. An animal experiencing small changes in the outside temperature may respond with minor changes in circulation, but a sudden shock, such as immersion in freezing water, may elicit a qualitatively different response, such as the secretion of adrenaline.
Stability is easily measured from a time series of productivity. However, the measurement of sustainability is more complex because of the range of forces and responses that may be encountered. The strength and nature of the shock


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or stress has to be assessed, the pattern of response to disturbance has to be described, and the degree of resistance or resilience must be quantified. How far is the productivity depressed, how quickly and to what level does it return, and in what fashion?
Various responses are possible. First, productivity may remain unaffected if the agroecosystem adequately resists the disturbing force. One mechanism of resistance is to escape. For example, the seeds of wild cereals lie dormant in the ground throughout the Mediterranean summer, thus avoiding the effects of the extreme heat. Nomadic pastoralists may move their cattle to escape an impending drought. Alternatively, the agroecosystem may be protected. Farmers may build a bund to prevent flooding of crop fields. In analogous fashion, a tariffwall, as has been erected by the European Economic Community (EEC), may protect crop production from falling world prices. A measure of resistance is inertia (i.e., the amount ofstress or shock that can be withstood without the productivity being affected). For example, where a bund is built to guard a crop, the inertia can be expressed as the maximum height of flood against which the bund provides protection.
If resistance is weak, productivity may fall below the usual range of variation. The question then is: How resilient is the agroecosystem? Will it return to the previous level or trend of productivity, how quickly, and in what manner? The return may be smooth, or the disturbance may continue with violent aftereffects before the previous level or trend of productivity is regained. Sometimes the productivity does not recover; it may remain at a new lower level or trend line or, in extreme situations, continue to fall and disappear altogether.
Various measures of this pattern of resilience are possible (Table 1). Elasticity, amplitude, and hysteresis measure the speed, likelihood, and pattern of recovery of productivity following a stress or shock. Malleability measures the difference between the mean productivity before and after the disturbance.
Studies of the resilience of ecological systems gained widespread attention following the work of C.S. Holling (1973, 1985). He stressed the importance of distinguishing, as I do here, between the behavior of systems under normal environmental conditions (i.e., their stability), and when subject to a disturbance that has the potential to change their state dramatically. He suggested that ecological and other systems could potentially exist in more than one steady state and, when subject to disturbance, could "flip" from one state to another. There is some dispute as to whether multiple steady states exist in natural ecological systems (Connell and Sousa, 1983), but they commonly occur in agroecosystems. An example is the collapse of swidden cultivation from a regular cropping/fallow cycle to a persistent Imperata grassland cover following the stress of increased crop harvesting. Here the amplitude of the sustainability can be measured as the maximum number of crops that can be taken in the cropping phase before collapse occurs. In an example described


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SUSTAINABILITY IN AGRICULTURAL DEVELOPMENT 7

Table 1. Measures of agroecosystem sustainability. All measures relate to the sustainability of the productivity trend.
CHARACTERISTIC DEFINITION MEASUREMENT
Inertia resistance of productivity level of stress
trend to change or shock that can be resisted
without affecting trend line
Elasticity rapidity of recovery of time taken for trend
productivity trend line to be recovered
following disturbance
Amplitude zone from which recovery maximum amount of
occurs following disturbance following
stress and shock from
which recovery is possible
Hysteresis degree to which path of difference between disturbance
recovery is exact and recovery paths
reversal of disturbance path
Malleability degree of difference difference between
between system state mean productivities
before and after disturbance
Source: Orians, 1975 and Westman, 1978.

by Trenbath, et al. (1990), when up to eight crops were grown the system returned to mature forest, but after eight crops it collapsed to permanent grassland. Eight crops is thus a measure of the amplitude. Another example is the behavior of grazing systems, investigated by Noy-Meir (1975). Here, increasing livestock raises productivity but also stresses the vegetation. At a certain intensity of stress, which is often very close to the maximum livestock carrying capacity, the vegetation collapses and the grazing system moves to a new level of productivity, much lower than before. The amplitude can be measured in terms of the maximum livestock density before collapse.
Finally, the assessment of sustainability requires a measure of the effectiveness of internal adjustments that agroecosystems make in response to stresses and shocks. The variety of such adaptations is considerable. Hardening is one example. Another is the construction of a bund to protect against flooding. Weeds may be countered by hoeing or by more vigorous crop growth. At the farm level, the response to growing debt may be to switch to a less risky crop/ livestock combination or to one requiring lower inputs. A village stressed by the loss of young people emigrating in search of more lucrative work may respond by adopting more labor-saving techniques of cultivation. Similarly, a district may counter rising transport costs by a switch to higher value, lower volume products; a region may respond to a widespread drought by establishing a network of famine relief stores as a protection against future droughts; and a nation may respond to increasing competition by changing the nature of its productivity so as to exploit its comparative advantage.


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Equitability
Productivity, stability, and sustainability adequately measure how much an agroecosystem produces and is likely to produce over time. An African village agroecosystem that produces a high, stable yield of sorghum using practices and varieties that are broadly resistant to pests and diseases will have a higher social value than another village producing lower, less stable or sustainable yields. However, social value depends not only on the pattern of production but also on the pattern of consumption. Who benefits from the high, stable, and sustainable production? How is the harvested sorghum, or the income from the sorghum, distributed among the people of the village? Is it evenly shared or do some villagers benefit more than others?
Equitability describes this pattern of distribution of productivity. Again, productivity may be measured in any of the ways described above. Thus the product per person-hour of a farm may be shared between the tenant and the landowner. More commonly, equitability refers to the distribution of the overall production-that is, the total goods and services produced by an agroecosystem. In subsistence farming the producers are all consumers, but the higher the agroecosystem in the hierarchy, and the greater the degree of commercialization, the more nonproducers benefit.
The most straightforward way of measuring how evenly products or income are distributed is to construct a histogram. However, this can give a misleading impression because histograms tend to overemphasize the middle of the distribution at the expense of the extremes (Atkinson, 1970). At the left, beyond the origin, are those with negative incomes-bankrupt farmers or those close to starvation-while to the right the histogram has to be impossibly extended to incorporate the rich.
There are a number of alternative ways of representing equitability that are intended to be positive (i.e., objective measures of distribution). The most commonly used are the Lorenz Curve and the Gini Coefficient (Lorenz,1905; Gini,1912; Atkinson,1970). However, these measures suffer from several drawbacks (see the review in Sen, 1973). For example, they tend to give relatively greater weight to changes in different parts of the range. A more important criticism is that they refer to distribution of income or product among people as though everyone were alike. This is clearly untrue; people differ in their endowments and derive different amounts of individual utility from their income. A kilogram of rice is valued much more by a person who is starving than by one who is rich. Furthermore, individuals compare what they receive with what others receive. Measures of equitability, if they are to reflect the actual decisions made by social groups (whether they be households, villages, or nations), must incorporate these notions of social justice. But in practice this is hard to accomplish because measurement of individuals' needs and utilities is difficult and laborious. The most satisfactory measure, so far, is that of Atkinson (1975), who weights the individual incomes before adding them.


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SUSTAINABILITY IN AGRICULTURAL DEVELOPMENT 9

TRADE-OFFS
Each agroecosystem, at each level of the hierarchy, has a social value. Also, one kind ofagroecosystem may have a greater social value than another and hence is more sought after. I assume that people seek to maximize social value and, to this end, will adopt strategies consisting of various combinations of productivity, stability, sustainability, and equitability. Inevitably, there are trade-offs between the levels of the properties. For example, a large-scale irrigation project may achieve greater overall productivity at the expense of sustainability and equitability. Similarly, too much emphasis on equitability may inhibit productivity.

Quantitative Analysis
To date, there has been very little quantitative analysis of the trade-offs between agroecosystem properties. A notable example is the analysis carried out by Pretty (1990) of the properties of manorial agriculture in England during the Middle Ages. The study was based on a remarkable set of records for individual manors extending from 1283 to 1349 A.D. (Titow, 1972).
Three main cereals were grown--oats, wheat, and barley-and their productivity, stability, and sustainability are summarized in Table 2. Productivity was measured as the number of seeds harvested per seed sown or as net yield per hectare (gross yield minus seed retained for the next sowing). On both measures, wheat and barley outyielded oats but the productivity of oats was notably more stable. Pretty (1990) has also shown that the price of oats was more stable than that of wheat and barley.
Sustainability of the cereals was assessed by plotting the pattern of yields following a significant fall in yield caused, for example, by a very wet year. After poor harvests, oats recover more rapidly than wheat-they are more elastic (Table 2). Its greater stability and sustainability meant that the oat crop was more reliable and hence often provided the mainstay of the peasants' diet, particularly in the more marginal lands.

Table 2. Yields of cereals grown on English manors from 1283-1349.
WHEAT OATS BARLEY
Productivity
Net yield (kg/ha) 385.0 300.0 540.0
Seeds/seed sown 4.0 2.3 3.5
Stability (coefficient of variation)
Net yield 38.8% 31.3% 39.9%
Seeds/seed sown 36.9% 33.6% 37.3%
Sustainability (elasticity)
Time to return to average 4 7+ yrs 1 5 yrs
Source: Pretty, 1990.


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The manorial agroecosystem of England was extremely long-lived and Pretty (1990) has concluded that the high degree ofsustainability was marked by relatively high stability and equitability, but these were obtained at the expense of productivity. Yields had not significantly increased since Roman times and remained low for virtually all of the manorial period.

Qualitative Analysis
Most analyses of the trade-offs between agroecosystem properties are qualitative. They typically consist of rapid assessments of the likely impact of innovations proposed during AEA exercises. An example is the AEA carried out in the Wollo province of Ethiopia in 1986 (Ethiopian Red Cross, 1988). The exercise lasted just under two weeks and was carried out by a multidisciplinary team comprising headquarters staff of the Ethiopian Red Cross, the Ministry of Agriculture, and field staff from the province. Two villages were analyzed using RRA techniques with an emphasis on semistructured interviewing, analytical games such as preference ranking, and constructing maps, transects, and seasonal calendars. From these, problems and opportunities were identified and a shortlist produced of innovations or "best bets" that the team felt would be appropriate for development in the two villages. These best bets were then prioritized in terms of their cost, feasibility, and likely impact on development in terms of the system properties (Table 3).
The best bets were chosen for their potentially high stability and sustainability, but analysis by the team suggested considerable differences in potential productivity and equitability. Two of the best bets with the highest productive potential-lowland irrigation and the introduction of rainfed crops-were judged to have little effect on improving equitability, primarily because they

Table 3. Assessment of best bets for Abicho, Wollo province, Ethiopia.

FEASIBILITY
BET/INNOVATION PRODUCT- STAB- SUSTAIN- EQUIT- COST TECH- SOCIAL
IVITY ILITY ABILITY ABILITY NICAL
1. Lowland irrigation ++ + ++ 0 X XX XXX
2. Gully cropping + + ++ ++ XX XXX XXX
3. New rain-fed crops ++ ++ ++ 0 XX XX XX
4. Upland revegetation + ++ ++ ++ X XXX X
5. New forage crops + ++ ++ + XX XX XX
6. Household water
supply + ++ ++ + XX XX XXX
7. Home garden
development ++ ++ ++ ++ XXX XXX XXX
Key: negative impact, 0 no impact, + positive impact, ++ very positive impact, X high cost or poor feasibility, XX medium cost or feasibility, XXX low cost or high feasibility. Source: Ethiopian Red Cross, 1988.


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would only benefit certain better-off farmers. Other bets with a high potential for benefiting poor farmers were considered relatively less productive. The innovation that scored consistently high across the board was the development of home gardens for the new village sites that the government was in the process of creating. One outcome of the exercise was the establishment, by the Ethiopian Red Cross in the weeks that followed, of a nursery of home garden plants.
Because of the short time of the exercise and the pressing need to decide on appropriate innovations for the impoverished villages, no detailed quantitative analysis was possible. Assessment was based on the collective judgement of the team members, based on their experience and intuition. Although the results lacked precision or rigor, they had the advantage of being agreed upon collectively, which facilitated the subsequent action that was taken.

Minimizing the Trade-offs
The social value of an agroecosystem is the product of the levels of the four different system properties. However, the product is not a simple arithmetic addition or multiplication because people in each place and at each period of time weight the properties differently (Conway, 1985). In the Middle Ages, high stability and sustainability appear to have been given preference over high productivity. In the Ethiopian AEA, the team engaged in a fierce dialogue over the relative weighting between productivity and equitability when deciding on their final priorities. Nevertheless, in most situations, there is a preference for agroecosystems in which all properties are high and the trade-offs are explicitly minimized. In the Ethiopian case, the most promising innovation in this respect was home-garden development.
Home gardens are one of the oldest forms of farming systems and may have been the first agricultural system to emerge in hunting and gathering societies. Today, home or kitchen gardens are particularly well developed in the island of Java in Indonesia, and these have been explicitly analyzed in terms of agroecosystem properties by Soemarwoto and Conway (1991).
The immediately notable characteristic of home gardens is their great diversity relative to their size-in one Javanese home garden, 56 different species of useful plants were found. Also commonly present is a diversity of livestock-cattle, goats, chickens, fish in fish ponds, and so on. Closer analysis shows the high diversity to be matched by high levels of productivity, stability, sustainability, and equitability (Table 4). Part of the reason for this minimal trade-off is the inherent diversity, which helps stabilize production, buffers against stress and shock, and contributes to a more valued level of production. But equally important is the intimate nature of the home garden. The close attention that is possible from family labor ensures a high degree of stability and sustainability and the link between the garden and the traditional culture leads to an equitable distribution of the diverse products.


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Table 4. System properties of the home garden when compared with a rice field.

HOME GARDEN RICE FIELD

Productivity Higher standing biomass Higher gross income

Stability Higher net income Seasonal production
(lower inputs)
Greater variety of production Vulnerable to climatic and
(food, medicines, fuelwood) disease variation

Year-round production
("living granary")

Higher year-to-year stability

Sustainability Maintenance of social fertility Heavy pest and disease attack
Protection from soil erosion

Equitability Home gardens in most households Product to land owners

Barter of products
Source: Soermarwoto and Conway, 1991.


CONCLUSION

In many respects, the home garden is a unique agroecosystem. But there are, undoubtedly, other systems that have similarly high levels of productivity, stability, sustainability, and equitability. More important, there are technologies and innovations that can potentially help reduce the trade-offs between the properties. The methods of analysis described above provide a rigorous yet straightforward approach to assessing agroecosystem properties and their trade-offs. Experience in an increasing number of field projects has demonstrated that this approach is practical and easy to use in the hands of people with a range of disciplinary skills and backgrounds. As the Ethiopian case study demonstrates, it can be easily incorporated in day-to-day decision making.
Defining sustainability in terms of preservation or duration, as is commonly done, has little practical value. Long-term experiments to measure persistence (i.e., of different cropping systems) are of research interest but take too long to constitute a practicable analytical method. By contrast, measuring the ability of an agroecosystem to withstand stress and shock is a subject for experiments using classical agricultural methods.
The current danger of using sustainability as a loosely defined term to encompass a wide range of systems and technologies is that benefits may be obtained at the expense of other, less obvious costs. High sustainability is not


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SUSTAINABILITY IN AGRICULTURAL DEVELOPMENT 13

the only desirable aspect of agricultural production and in many situations it may be necessary to trade a degree of sustainability for higher levels of productivity or equitability. Choices and decisions are necessary and are best made when the options are clearly apparent.


REFERENCES

Ashby, W.R. 1956. An introduction to cybernetics. London: Chapman and Hall. Atkinson, A.B. 1970. On the measurement of inequality. Journal of Economic Theory
2:244-263.
Atkinson, A.B. 1975. The economics ofinequality. Oxford: Clarendon Press. Brundtland Report. 1987. Our common law. World Commission on Environment and
Development. London: Oxford University Press.
Connell, J.H., and W.P. Sousa. 1983. On the evidence needed to judge ecological
stability or persistence. The American Naturalist 121:789-824.
Conway, G.R. 1985. Agroecosystem analysis. Agricultural Administration 20:31-55. Conway, G.R. 1986. Agroecosystem analysis for research and development. Bangkok:
Winrock International.
Conway, G.R. 1987. The properties of agroecosystems. Agricultural Administration
24:95-117.
Conway, G.R., and E.B. Barbier. 1990. After thegreen revolution: Sustainable agriculture
for development. London: Earthscan.
Conway, G.R., Z. Alam, T. Husain, and M.A. Mian. 1985. An agroecosystem analysis for
the northern areas ofPakistan. Aga Khan Rural Support Programme, Gilgit, Pakistan. Conway, G.R., P.E. Sajise, and W. Knowland. 1989. Lake Buhi: resolving conflicts in a
Philippine development project. Ambio 18:128-35.
Ethiopian Red Cross Society. 1988. Rapid RuralAppraisal: A closer look at rural life in
Wollo. Ethiopian Red Cross Society, Addis Ababa, Ethiopia, and International
Institute for Environment and Development, London, UK. Gini, C. 1912. Variabilita e Mutabilita. Bologna, Italy. Gypmantasiri, P., et al. 1980. An inter-disciplinary perspective of cropping systems in the
Chiang Mai Valley: Key questions for research. Faculty of Agriculture, University of
Chiang Mai, Chiang Mai, Thailand.
Holling, C.S. 1973. Resilience and stability of ecological systems. Annual Review of
Ecology and Systematics 4:1-24.
Holling, C.S. 1985. Perceiving and managing the complexity of ecological systems. In
The Science and Praxis of Complexity. Tokyo: United Nations University.
KEPAS. 1984. The sustainability of agricultural intensification in Indonesia: A report of
two workshops of the research group on agroecosystems. Agency for Agricultural
Research and Development, Jakarta, Indonesia.
KEPAS. 198 5a. The critical uplands of Eastern Java: An agroecosystem analysis. Agency
for Agricultural Research and Development, Jakarta, Indonesia.
KEPAS. 1985b. Swampland agroecosystems of Southern Kalimantan. Agency for Agricultural Research and Development, Jakarta, Indonesia.
KEPAS. 1986. Agro-ekosistem daerah kering di Nusa Tenggara Timur. Agency for
Agricultural Research and Development, Jakarta, Indonesia.


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Khon Kaen University. 1987. Proceedings of the international conference on Rapid Rural
Appraisal. Khon Kaen University, Khon Kaen, Thailand.
KKU-Ford Cropping Systems Project. 1982a. An agroecosystem analysis of Northeast
Thailand. Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand.
KKU-Ford Cropping Systems Project. 1982b. Tambon and village agricultural systems
in Northeast Thailand. Faculty of Agriculture, Khon Kaen University, Khon Kaen,
Thailand.
Lorenz, M.O. 1905. Methods of measuring the concentration of wealth. Journal of the
American Statistical Association 9.
Mascarhenas, et al. 1991. Participatory Rapid Appraisal. RRA Notes 13. London:
International Institute for Environment and Development.
Noy-Meir, I. 1975. Stability of grazing systems: An application ofpredator-prey graphs.
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Orians, G.H. 1975. Diversity, stability and maturity in natural ecosystems. Pages 64-65
in W.H. van Dobben and R.H. Lowe-McConnel, eds., Unifying concepts in ecology.
The Hague: Junk.
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Agricultural History Review 38:1-19.
Rerkasem, K, and T. Rambo, eds. 1988. Agroecosystem research for rural development:
Selected papers from the Third SUAN regional research symposium on agroecosystem
research held at Chiangmai, Thailand. University of Chiang Mai, Thailand.
Sajise, P., and T. Rambo, eds. 1986. Agroecosystem research in rural resource management and development: Selected papers from the second SUAN regional research symposium on agroecosystem research held at Baguio City, the Philippines. University of the Philippines, Philippines.
Sen, A. 1973. On economic inequality. New York: W.W. Norton. Soemarwoto, 0., and G.R. Conway. 1991. The Javanese home garden. Journal for
Farming Systems Research-Extension 2(3):95-118. Titow, J.Z. 1972. Winchester yields. Cambridge, UK Trenbath, B.R., G.R. Conway, and I.A. Craig. 1990. Threats to sustainability in
intensified agricultural systems: Analysis and implications for management. Pages 337-365 in S.R. Gleissman, ed., Agroecology: Researching the ecological basis for
sustainable agriculture. New York: Springer-Verlag.
Walker, B.H., G.A. Norton, G.R. Conway, H.N. Comins, and M. Birley. 1978. A
procedure for multidisciplinary ecosystem research: With reference to the South
African Savanna Ecosystem Project. Journal ofApplied Ecology 15:481-502.
Westman, W.E. 1978. Measuring the inertia and resilience of ecosystems. BioScience
28:705-710.
Wiener, N. 1948. Cybernetics. Cambridge, Mass.: MIT Press and New York: John Wiley.


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The Socioeconomic Role of Rural Kom
Women in the Farming System of Cameroon

Joseph Nkwain Sama'



ABSTRACT
Women's access to appropriate resources increases their productivity.
This is true especially in the women-dominated agriculture of Kom society. Kom, an ethnic group ofCameroon's North West Province, faces frequent food shortages because its expanding population and increased cultivation of perennial crops have drastically reduced food-crop land.
Inefficient food usage and corn-based eating habits aggravate the food deficit. This paper examines women's unrecognized socioeconomic contribution to the Kom economy and identifies their sociocultural and physical constraints, and suggests the importance of recognizing the socioeconomic role ofwomen. It also proposes measures for minimizing these constraints. Such measures include improved farm-to-market roads, improved rural schools, intensified rural extension services with greater focus on the productivity of women, the use of more female extension agents, and improved inputs and farming systems to increase
income and facilitate food production for Kom farm families.


INTRODUCTION
Women have been observed to do more than half the agricultural work in female-labor-dominated farming systems (Boserup, 1970). Gladwin and McMillan (1989) suggest that unless ways are found to help farm women increase productivity in the short run, a turn around in the socioeconomic development of developing countries will not be possible. To this end, specific constraints to women's production and potentials for improvements in female-labor-dominated farming systems need to be identified. Interventions to assist them in any given society require prior knowledge of the available alternatives and of the constraints on such alternatives. Such is the case with the Kom people, a small, dynamic ethnic group strongly bound by a culture in which the women's role in agricultural production is crucial.



1 Joseph Nkwain Sama, is an assistant professor in the Department of Agricultural Economics, Dschang University, Cameroon.

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SAMA


Location and Sociocultural Background of Kom
The Kom are located in the Mentchum Division of the North West Province of Cameroon (Figure 1). Their population numbers over 130,000 and covers an area of less than 100 sq. km of hilly land. The Kom are one of the four principal ethnic groups of that province. Access to the area is difficult, but a variety of crops are produced in the valleys, hill slopes, and plains of the province.
The Kom have a matrilineal inheritance system. Descent is traced through mothers, but women have no rights to inheritance. Instead of sons inheriting from fathers, brothers inherit from brothers. If the deceased has no brother, inheritance passes through his sister to her son. It is thus possible for nephews to inherit from their uncles.
Each native of Kom can trace his or her heritage to one of three principal extended families-the Ikui, Itinalah, and Achaff. Marriages can be contracted within and among these families and each offspring takes his or her lineage identity from the mother's family. Landed properties in Kom are shared within the extended families and are jealously preserved and protected from generation to generation. This complicates the land tenure system (Sama, 1989).




Area: 475,000km2
Population: 9,933,823 (1985)
-NORD




NIGERIA


ADAMAOUA
NORD-OUEST
*Kom
CENTRAL
OUEST AFRICAN
u REPUBLIC





suD
EQUATORIAL GACONGOON
GUINEA CONGO
Figure i. Map of Cameroon showing provinces and the location of Kom in the North West Province.


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Statement of the Problem and Objectives of Research
Women in general, and African women in particular, play an important role in food production and in the nutrition and health of the family. Cloud (1986) pointed out that the access of women to resources such as land, seeds, fertilizers, technology, credit, and information greatly affects their productivity. For example, access to appropriate household technology (related to milling and cooking) can greatly facilitate and improve household efficiency. Nevertheless, Moock (1986) emphasized that the appropriateness of new technology depends on who performs what task, controls what resources, and performs what kinds of family responsibilities. From a recent study in Cameroon, Koons (1988) concluded that women benefited little and received less attention than men from the extension staff of the North West Development Authority (MIDENO) in the North West Province because of social, cultural, economic, attitudinal, and circumstantial differences between men and women. Kom is one of the regions covered by MIDENO.
This region is threatened with acute food shortage resulting from the reduction of its corn fields by the expanding population and the extension of perennial crop production (notably coffee and plantains) into the corn fields. Additionally, food management practices and the corn-based eating habits of the natives result in frequent and disturbing hungry seasons usually signalled by the shortage of corn. Potential solutions to this problem lie in the hands of women because they play a dominant role in household food production.
The objectives of this paper are: (1) to reveal the unrecognized socioeconomic contribution of women in the Kom economy; (2) to identify the constraints imposed on Kom women by the physical and sociocultural environment; and (3) to suggest ways to reduce some of these constraints.

Methodology
The Kom are divided into three geographical zones commonly referred to as the Njinikom, Belo, and Abassakom valleys. Each of these valleys is made up ofvillages, which in turn are made up of smaller units called quarters. From a list of the 57 quarters that constitute the Kom ethnic group, 25 were randomly sampled in order to represent proportionately the three valleys based on population and number of quarters. Consequently, nine quarters were sampled from the Belo valley and eight each from the Njinikom and Abassakom valleys.
From each quarter, a random sample of five households was made and the head or representative of each household interviewed with the aid of a structured questionnaire. Two of the five respondents from each quarter were women. Of the 125 households sampled, 112 representatives were reliably interviewed-70 men and 42 women. The questionnaires were pretested and administered by the heads of the agricultural posts in the three valleys after a short training.


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Supplementary general information was obtained by the author from a panel of 10 volunteer farmers from one of the villages and from the agricultural delegate (the official representative of the Minister of Agriculture) and other technical staff of the agricultural delegation. Additional information on the culture and practices of the area was also gathered from the head of each of the sampled quarters. The personal experience of the author himself, a Kom native familiar with its problems and culture, constituted an additional source of information for the study.
The data were summarized into tables and simple averages and/or frequencies expressed in percentages were used in the analysis.


RESULTS


The Farming System of the Kom People
Land tenure and use. The traditional ruler of Kom (the Fon) is the custodian of all land, which is shared among the villages and subsequently among the extended families. Over 90 percent of all individual land acquisitions are through family gifts. The remainder is acquired through borrowing and gifts from friends, quarter heads, and the Fon. The sale of land is restricted. Parts of the Kom territory are grazed by cattle owned by Fulani nomads, and some of the steep and rocky hills are unsuitable for agriculture. The remaining land is used for agriculture and buildings.
Settlement pattern. Settlement is in homesteads, groupings of which constitute quarters, which in turn constitute villages. On the average, each household has a homestead of 1.5 hectares occupied by 15 persons. The household is the basic production and consumption unit and is mostly polygamous. The family head has on average two wives and 12 children. Each household also has on average three food-crop plots with a total area ofabout 1.6 hectares, excluding the homestead. The food-crop plots are dispersed at distances that average about 4.5 km from the homestead.
Cropping systems. Both crops and livestock are raised in the homesteads, while staple food crops are grown in the cornfields located away from the homesteads. The following cropping systems are dominant:
(1) A coffee-based, mixed cropping system in the homesteads, where coffee (the principal crop), plantains, and a few fruit trees are grown in association. In the older homesteads, one can also find crops such as maize, coco yams, and pineapples grown under coffee and other tree crops. In the new homesteads, other arable crops such as beans, sweet potatoes, cassava, pumpkin, and assorted vegetables are grown in mixture under the tree crops.
(2) A corn-based, mixed cropping system in the cornfields where corn (the principal crop) is mix-cropped with beans, groundnut, pumpkin, yams, coco yams, and vegetables in various combinations and intensities. The cornfields,


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which are located in the valleys, hill-slopes, and plains, are shared by several households. They are divided into plots of less than halfofa hectare per farmer. Each cornfield is cultivated at the same time by those who share it, and then left to fallow for usually two to three years. The length of the fallow period is rapidly decreasing as the population expands. Two crops of maize--early and late maize-are grown per year.
Current changes in the settlement pattern are affecting the cropping systems. Homestead sizes are decreasing as the population increases, and buildings are increasingly encroaching on the agricultural land within the homesteads. This decreases the agricultural production potential of the homesteads. Also, new homesteads encroach on the cornfields as the villages expand, diminishing the area available for arable crops.
Sources and uses of household revenue. Traditional livestock production constitutes an important source of household revenue. It involves the raising of goats, sheep, and poultry within the homestead. Although such animals are kept mostly for household consumption and for death, birth, and marriage celebrations, occasionally some are sold to provide needed income. Other household income is raised from the sale of coffee, plantains, fruits (mangoes, pears, and kola nuts), and handicrafts, and from trade. Occasionally, staple food crops are sold when perceived to be in excess of household needs.
Household income is spent on farm inputs, food supplies, health, education of children, traditional ceremonies and celebrations, clothing, bride prices, and other household needs.

Women's Contribution to the Socioeconomic Life of their Households
The gendered division of labor in the household. The household obligations that the spouses expect from each other are presented in Table 1. It is clear from the table that both spouses recognize child care, food production, and cooking as the duties of a wife to her husband and to the household. This was indicated by more than 50 percent of the respondents of either sex. The other obligations were recognized by less than 20 percent of the men and 10 percent

Table 1. Wives' obligations to husbands as perceived by respondents. SUGGESTED OBLIGATIONS PERCENTAGE OF AFFIRMATIVE RESPONSES
MEN WOMEN
Child care 57.5 62.9
Food production 60.0 52.9
Cooking 65.7 59.5
Housekeeping 40.0 19.0
Financial assistance 11.4 44.8
Assistance during sickness 7.1 7.1
Bearing children 15.7 4.8
Source: Survey data.


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Table 2. Husbands' obligations to wives as perceived by respondents. SUGGESTED OBLIGATIONS PERCENTAGE OF AFFIRMATIVE RESPONSES
MEN WOMEN
Education of children 47.1 47.6
General care of household 44.3 40.6
Assistance during sickness 54.3 54.8
Provision of shelter 17.1 27.4
Food production 12.8 16.7
Financial assistance 20.0 27.4
Reproduction 12.8 4.8
Source: Survey data.

of the women. The housekeeping role of women was controversial. Whereas 40 percent of the men expected this service from their wives, only 19 percent of the women accepted it as a woman's obligation to her husband.
In contrast, Table 2 shows the expected obligations of husbands to their wives. The table confirms that food production in this community is the affair ofwomen. Only 13 percent of the men and 17 percent of the women considered men as food producers. From Tables 1 and 2, it is curious to observe that over 54 percent of both sexes expected husbands to assist sick wives but only seven percent expected the same obligation from wives to their husbands. Also, Table 1 shows that 45 percent of women respondents as opposed to 11 percent of the men felt that women should render financial assistance to their husbands, whereas less than 25 percent of both sexes feel that the husband should render financial assistance to his wife. This could be explained by the fact that women are not traditionally expected to own and use money and therefore should not be expected to receive money or be responsible for any transaction or obligation that involves the use ofmoney. The Kom traditionally frown on the idea of women earning cash income and/or taking loans. Over 85 percent of the respondents indicated that Kom people are against cash crop production or the undertaking of any income-earning activities by women. Men control the finances of the household and are therefore concerned mostly with finance-related responsibilities. Food production as the dominant role of the women is evident from the distribution of farming tasks among the household members. These are presented in Table 3. The table confirms that men are mostly active in income-generating activities. They are involved mostly in coffee, plantains, and livestock production. All other agricultural production activities of the household are the responsibility of women. The women also actively assist their husbands in the tedious and labor-intensive aspects of maledominated production activities.
Participation of children (boys and girls between 10 and 15 years old) in household tasks is also gender oriented. Judging from the type of activities they perform (Table 3), male children tend to assist their fathers whereas female children assist their mothers. It is also clear from Table 3 that parents rarely engage their children in activities such as the sale of crops and livestock that involve the handling of money.


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Table 3. Farming activities and their executors. ACTivITIEs ExECUTORS AND IDENTIFICATION FREQUENCY (%)
MEN WOMEN Boys GIRLS
Farm clearing 86.5 5.4 48.2 0.8
Planting coffee 91.0 8.9 25.9 1.7
Planting plantains 86.6 11.5 25.0 3.6
Ridging 8.8 88.5 3.3 35.7
Planting food crops 25.8 85.7 13.3 33.0
Weeding coffee 78.6 87.4 17.8 21.4
Weeding food crops 30.0 58.0 13.3 25.9
Harvesting coffee 80.3 86.6 46.4 43.9
Harvesting plantains 87.2 46.5 30.4 22.3
Harvesting food crops 32.9 78.5 19.6 27.7
Selling cash crops 85.2 11.0 0.8 0.8
Selling food crops 28.5 70.5 4.5 6.3
Selling livestock 88.4 36.7 0.8 0.0
Raising livestock 86.2 40.0 0.8 0.0
Source: Survey data.

Women's contribution to household income. At first sight, the direct contribution of women to household cash income seems relatively small. Almost all the household income is farm generated. From Table 4, of the 192,980 francs average farm income, the contribution from women-controlled crops is about 16 percent. However, this does not mean that the remainder is the contribution of men. Women also greatly contribute in the production of the malecontrolled farm enterprises. This is true especially for the coffee, plantains, and livestock enterprises, which provide over 75 percent of the farm income.
The non-cash contribution of women to the total well being of the household completely overwhelms that of the men, judging from the time they put into farm work. It is estimated that each woman puts in an average of 1,643 hours offarm work annually (at the rate of 7.9 hours/day and 4 days/ week), as against the man's 1264 hours (at the rate of 5.4 hours/day and 4.5 days/week). Moreover, the man's work is usually restricted to the supervision of the other members of the household.
Cash income from food crops (Table 4) represents the value of the surpluses sold after meeting the household consumption requirements. If the total production of these crops were valued in monetary terms, the household's total annual cash income could more than double the present estimates, and the women's contribution in that case would increase immensely.
Other contributions to household welfare. Although the education of children is purported to be the concern of their fathers, this obligation is shown to be shared by their mothers. The women are responsible for the sociocultural and moral education of their children. The men do, however, play the major role in the formal education of children. Even here, however, 52.4 percent of the women participate in the payment of the children's school fees.


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Table 4. Average annual farm income by source. SOURCE OF INCOME AMOUNT IN FCFA
Coffee 93,100
Plantains 12,050
Pears 8,540
Kola nuts 12,180
Mangoes 5,445
Vegetables* 5,445
Maize* 4,800
Coco yams* 8,525
Beans* 8,245
Irish potatoes* 3,860
Fowl 8,030
Goats/sheep 22,760
Total 192,980
Source: Survey data.
*Food crop.

The women are also very active in every aspect of family life. In fact, virtually their whole day is occupied and devoted to household activities: cooking and child care in the morning, trekking long distances to work and/or fetch food in the food-crop fields, and cooking and childcare in the evenings. This makes women especially indispensable members of households. Actually, household functioning depends more on women than on men. This is confirmed by the fact that over 50 percent of the women expressed confidence in their ability to manage the household in the absence of their husbands, while only 46 percent of men believed they could do the same in the absence of their wives.

Constraints Faced by Kom Women
In the performance of their numerous, demanding, and all-important obligations, Kom women are seriously constrained by lack of education and resources, and by sociocultural and technological factors that significantly reduce their effectiveness and retard the development of the household, community, and nation.
Educational constraints. Only 38 percent of the respondents were literate, and the majority of these respondents were men. In the past, the education of Kom females was considered uneconomical. At the time of marriage girls fetch bride prices for their parents and exit the household. Today, some parents still consider investment in their female children as an unnecessary cost that yields no future returns.
Although today the majority of the girls attend school, priority is placed on the education of boys. The education of children renders them employable in the urban areas and therefore speeds up rural-to-urban migration. As a consequence, the average age of agricultural producers has been increasing. This means that the health, nutrition, child care, and food production responsibilities of the Kom community are being left in the hands of an older

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Table 5. Sources of land by sex of recipient. SOURCE RESPONSE FREQUENCY (%)
MALE RECIPIENTS FEMALE RECIPIENTS
Father 93.8 15.0
Husband 0.0 40.2
Quarter head 29.5 5.4
Village head 4.5 4.5
Fon (chief) 14.3 7.1
Uncle 33.0 14.3
Mother 2.7 33.0
Friend 3.4 5.4
Source: Survey data.

and less-educated fraction of the rural population. Old age and illiteracy are known to work against efficient use of modern technologies in production, household nutrition, health, and child care. Among the Kom, therefore, the results of low investment in female children in the past is affecting agricultural production today. At the same time, increased access to education tends to encourage the migration of youths to urban centers. It could be concluded that failure to invest in female education in the past seriously constrains the productivity of Kom women today, and that increased investment in female education today has similar effects on productivity today and in the future because of the youth migration incentive.
Resource constraints. Land, labor, and capital, the traditional production resources, are either in short supply or available under very difficult conditions to Kom women. Food-crop land is scarce, fragmented, and dispersed, and its possession and use are uncertain. As indicated earlier, each household has an average of 1.6 hectares of food-crop land divided into several tiny plots located at average distances of 4.5 km from the homesteads. The hilly topography renders daily trekking difficult and slow, and further reduces the time and energy available to women for farm work.
The farm land available to the individual or household is fragmented and dispersed because the individual acquires it from several persons whose lands are also geographically dispersed (Table 5). Because most land is family property, the individual cannot undertake any meaningful or permanent development on it. This is a drawback to modern agricultural development in the area. Table 5 also brings out the current land acquisition trends operating in Kom. Fathers generally provide land for their sons, who alternatively may obtain family land from their uncles. Husbands usually acquire and make available farm land to their wives. Village or quarter heads may provide both building and farm land to those male children of the village or quarter whose parents do not have enough land, and female children may acquire some of their farm land by sharing land with their mothers.


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Out of the 15 members ofan average household, eight constitute the active labor force and include the husband, two wives, and five children. The man's role in food production is mostly limited to farm clearing (the initial stage of land preparation for planting), and he is aided in this task by the male children. The rest of the food production labor is supplied by the women. Farm labor is further reduced when the female children attend school and also by the women's other household tasks.
Additionally, as discussed above, women have no easy access to the money necessary to pay for labor and other production inputs. Women must therefore depend on their own labor and the use of traditional production methods that require little capital. As a consequence, efficient tools and modern inputs are not available to support improvement in productivity.
Technological constraints. The author's personal knowledge and understanding of the situation in Kom is consistent with Koons' (1988) conclusion that sociocultural, economic, and attitudinal conditions work against women's ability to benefit equitably from MIDENO's extension staff in the North West Province of Cameroon. However, this study did not attempt to verify whether women equitably receive such benefits. The facts are that women are less literate than men and that the extension agents carrying the extension packages are mostly men. Also, these agents are sometimes handicapped in their ability to communicate by language barrier. Most of the women understand only the Kom language, whereas the non-Kom extension agents use either English or pidgin English, understood by most of the men. Furthermore, most of the modern technology to be delivered involves a cost that the women, given their disadvantaged position in the household, cannot directly bear. Consequently, they have little access to improved farming methods.
The absence of home economics extension agents and of modern kitchen equipment deprives the women of the technology they so desperately need for child care, family health, and household nutrition. The absence of modern technology in the household means that life within the household is poor and tedious. Also, the absence of good farm-to-market roads and oftransportation to facilitate the daily treks to the farms and the markets further robs the women of much needed time and energy for farming.


CONCLUSION AND RECOMMENDATIONS
It is essential to recognize the economic importance of women in Kom society and to take steps to enhance their productivity in agriculture and other household activities. The men and women of the Kom community, as well as policy makers at all levels, need to focus attention on the total activities of the farm households and to discuss needed steps publicly. A fuller appreciation of the constraints women face in performing their responsibilities will help


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participants focus on what is needed to begin relaxing these constraints in order to make women even more effective in their many important roles. The increased efficiency of women will not only increase the productivity of the economy but will also enrich the material well-being of families in the rural sector.
Several items need explicit attention to improve the effectiveness and quality of life in rural households. These include: (1) greater access to electrical power and clean water, (2) improved farm-to-market roads, (3) increased and improved rural schools with smaller class sizes, (4) intensified rural extension services employing more women extension agents with greater focus on the productivity of women in their household and farm production activities, and
(5) improved inputs (including tools and equipment) and farming systems to facilitate both food and income production for farm families.
An improved quality of life in the rural sector will also provide greater encouragement for rural youth to remain in rural areas and engage in agriculture and other production activities. Such encouragement is very important for the long-term economic well-being of the rural sector in Cameroon.


ACKNOWLEDGMENTS

The author sincerely thanks Dr. Max Langham of the Food and Resources Department of the University of Florida for editing a draft of this paper and for the useful comments and suggestions he made to improve its quality.


REFERENCES
Gladwin, C.H., and D. McMillan. 1989. Economic Development and Cultural Change
37(2):346-369.
Boserup, E. 1970. Women's role in economic development. New York: St. Martin's Press. Cloud, K. 1986. Women's productivity in agricultural households: How can we think
about it? Pages 11-18 in Women as food producers in developing countries. Africa Studies Center, University of California at Los Angeles, OEF International, Los
Angeles.
Koons, A.S. 1988. Being rural women in the North West Province: A presentation of
more ways in which women are to men. Paper presented at the Conference on Development in Cameroon: The role of food and agriculture. University of Florida.
Gainesville, April 7-9.
Moock, J.L. 1986. Understanding Africa's rural households and farming systems. Boulder: Westview Press.
Sama, J.N. 1989. A socio-economic survey of agricultural related problems in the North
West Province of Cameroon. Department of Agricultural Economics, Dschang
University Center. Mimeo.


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Analysis of the Competition for Labor by
Dryland and Irrigated Crops:

The Case of Rice and Millet in Niger

Ziyou Tu, Robert Deuson, Eric Bomans, and Jess Lowenberg-DeBoer



ABSTRACT

The efforts to develop rice production in the irrigated perimeters along the Niger River have not lived up to expectations. A key problem is that farmers appear to neglect rice production in favor of allocating labor to dryland crops, especially millet, particularly when millet weeding and rice transplanting occur simultaneously. A hypothesis that considers the marginal value product of labor as the main determinant of labor allocation is tested. The production functions of rice and millet are estimated individually with ordinary least squares from the farm survey data collected in the rainy season of 1985 from five villages. The results indicate that the marginal product of weeding millet is not higher than that of transplanting rice. Four examples are used to illustrate the structure ofthe agronomic and economic tradeoffs in the labor allocation decision. These examples indicate thatin order to achieve high total grain yields and revenues, rice must be transplanted as early as possible at the beginning of the rainy period. Because of the high demand for labor in a short period, it would be difficult to achieve the highest rice yields with only family labor. The data support the hypothesis that Nigerien farmers allocate their labor to maximize millet yields, rather than to maximize revenue. The loss of revenue linked to this focus on millet production
appears to be small.


INTRODUCTION

Rice production in the irrigated perimeters along the Niger River is an important part of the food self-sufficiecy and agricultural development plan of the government of Niger (Rep. du Niger, 1987). But in spite of large investments in infrastructure, research, and extension, the production on these perimeters has not lived up to expectations. One of the key problems has been the competition for labor between rainfed and irrigated crops (Anders, 1 Ziyou Yu is a graduate student, and Robert Deuson and Jess Lowenberg-DeBoer are assistant
professors in the Department of Agricultural Economics, Purdue University. Eric Bomans is
agronomist and team leader of the SASEAC Project in Burundi.

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et al.,1984; Maikor6ma,1986; Rassas and Loute,1989; Rep. du Niger,1989a). During the rainy season, farmers are observed to neglect rice production in favor of allocating labor to dryland crops, primarily millet. The hypothesis that is tested in this analysis is that millet production is favored because the marginal value product of labor is higher for millet than it is for rice in the critical periods. The analysis uses production functions estimated with ordinary least squares (OLS) from farm survey data. The results are useful to policy makers, researchers, and extension workers concerned with the Niger River region and similar areas in West Africa.
The Republic of Niger is a landlocked country in the Sahelian region of West Africa. It shares borders with Nigeria and Benin on the south, Chad on the east, Burkina Faso and Mali on the west, and Algeria and Libya on the north. More than six percent of Niger is in the Sahara Desert zone with less than 200 mm of rain per year. Most of the country's agricultural land is in a narrow band within 150 km of the Nigerian border. The average annual rainfall in this band is in the 300-800 mm range. Millet is planted on over seven percent of the dryland crop area, often intercropped with cowpeas and sorghum. The average millet yield in the 1980s was 409 kg/ha (Rep. du Niger, 1989b).
Currently, less than one percent of the crop area of Niger is irrigated. The predominant irrigated crop is rice, with smaller areas devoted to cotton, sorghum, onions, and other crops. Two rice crops are produced per year with the crop calendar determined by climate and river flow. A rainy season crop is transplanted in July or August and harvested in November or December. A dry season crop is transplanted in December or January and harvested in April or May. In 1988 irrigated rice occupied about 5000 ha in each season (Rep. du Niger, 1989a), but this area is expected to rise rapidly with the construction of new perimeters. Potentially 140,000 ha could be irrigated in the rice growing area of the Niger River valley (Rep. du Niger, 1989a). Average yields for irrigated rice have been about 4000 kg/ha in the 1980s (Rassas and Loutte, 1989), substantially less than the average of 6000 kg/ha commonly observed in Asia.
Farmers in Niger also grow about 12,000 ha of traditional rice varieties in naturally occurring swamps and depressions without the benefit of controlled irrigation. Traditional rice can be very profitable, but the naturally suitable areas are limited and, because water is not controlled, crop failure is common. Because of the small cultivated area and simple labor methods, there is relatively little competition for labor between traditional and irrigated rice (Bomans, 1986). This article does not concern itself with traditional ricegrowing practices.
Currently, rice makes up about four percent of total cereals production of Niger, but it is more important in the national strategy than the level of production would indicate. Because rice yields can be good even in drought years when the millet crop fails, irrigated rice has the potential of stabilizing


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the national grain supply. In addition, domestically produced rice can help satisfy the growing demand for rice in urban areas, and at the same time provide a source of cash income for farmers. Domestically produced rice now satisfies only about 50 percent of the demand in Niger (Rep. du Niger, 1989a).
The competition for labor affects rice yields in several ways. In the rainy season, late planting of nurseries and transplanting of seedlings can push the rice maturity period into the cool season when night temperatures can drop as low as five degrees centigrade. The low temperatures cause rice grains to abort. In some cases, rice seedlings are left in the nursery too long before transplanting, which reduces tillering and hence yield. Weeding may be done late and incompletely, resulting in heavy weed pressure. An indirect effect of labor competition is that farmers choose lower yielding but flexible rice varieties, instead of the higher yielding varieties that are more sensitive to transplanting date, nursery time, and weed competition.
Alternative hypotheses about why farmers allocate labor to millet instead of rice focus on food preferences, relative profitability, and risk (Anders, et al., 1984; Maikor6ma, 1986; Rassas and Loutte, 1989). Relatively little of the irrigated rice is consumed by farm families. Millet is the staple food of their diet. Rice is consumed mainly on holidays and ceremonial occasions, such as weddings. For these events, the traditional rice varieties are preferred. Irrigated rice is often treated as a cash crop produced for urban consumers. In this situation, if the farmer's primary objective is to secure the family millet supply, with cash income as a secondary goal, labor would be allocated first to millet and only to irrigated rice when subsistence needs for millet are satisfied.
Still, several budgeting studies have shown rice to be moderately more profitable than millet (Anders, et al., 1984; Bomans, 1986; Maikor6ma, 1986; Rassas and Loutte, 1989), but it is not clear that the additional profits are sufficient to compensate for the extra financial and marketing risk. With irrigated rice, yield risk is relatively low, but financial risk is high because of the need for purchased inputs. Traditionally, millet production in Niger uses few purchased inputs, so the financial risk is negligible. Rice is purchased by the parastatal company Riz du Niger (RINI) at official prices that are often higher than world market prices, but RINI does not buy all of the production. About one-third of the irrigated rice production must be sold through private marketing channels that are not well developed for this product. In contrast, marketing channels for millet and the traditional rice varieties are well developed. Thus, we argue that farmers allocate labor to millet production because rice is not sufficiently more profitable than dryland crops to justify risk and marketing problems.


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DATA AND METHODS

The data for this study were collected during the 1985 rainy season in five villages on the east bank of the Niger River, about 50 km north of the capital city of Niamey. They are: Koutoukal6 Kado, Koutoukal6 Kourthey, Koutoukal6 Tegui, Koutoukal Zeno, and Zamakwara Zeno. The Koutoukal6 perimeter has been in operation since 1966. These villages were chosen on the basis of the following criteria: (1) isolation from main urban centers, (2) experience with irrigated agriculture to avoid the "learning curve" bias common with introduction of a new technique, and (3) relative ease of access, to permit regular and frequent supervisory visits.

Table 1. Land, labor, crop mix, dates of field operations, yields, costs and returns,
Koutoukalk perimeter, Niger, rainy season, 1985.

ITEM AND UNITS MEAN1 MINIMUM MAXIMUM


FAMILY SIZE:
Men 1.90
Women 2.10
Boys 2.30
Girls 2.10
Elderly 0.30
LAND PER FARM:
Monocrop millet, ha 2.20
Millet intercrop, ha 1.12
Other dryland crops, ha 0.20
Irrigated rice, ha 0.50
YIELDS:
Monocrop millet, kg/ha 367.80
Irrigated rice, kg/ha 3,789.90
CASH COSTS FOR IRRIGATED RICE:
Water use, FCFA/ha 58,171.00
Fertilizer, FCFA/ha 15,059.80
Hired labor, FCFA/ha 7,618.30
TOTAL LABOR USE:
Monocrop millet, PDE/ha 63.10
Irrigated rice, PDE/ha 364.60
GROSS RETURNS PER HECTARE:
Millet, FCFA/ha 29,560.50
Rice, FCFA/ha 373,858.00
GROSS RETURNS PER DAY:
Millet, FCFA/ha 782.50
Rice, FCFA/ha 2,452.10


3.00 3.20 3.10 3.00 1.00


0.60 0.30 1.00 1.00 0.00

0.10 0.09
0.02 0.03


104.40 2,343.70

58,171.00
0.00 1,250.00

8.20
206.90


990.10 5,207.30

58,171.00 26,556.30 39,750.00

296.40 812.80


2,077.70 171,053.00 108,355.10 272,727.20


49.80 706.60


2,795.50 8,635.50


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1 The arithmetic means reported here may differ from the geometric means reported in Table 4. 2 Gross returns were calculated by Bomans, 1986. Costs include hired labor, fertilizer, and
cooperative charges, which include water use and rice seed. The opportunity cost of family labor is not subtracted. The money unit is measured in Francs CFA (FCFA). The value of the FCFA at the time of this analysis was about 300 FCFA per US$1.

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The sample size was 50 farmers, with a total of 1890 hectares in 272 fields. In each village, 20 volunteers were identified and a sample of 10 farmers per village was chosen at random from among the volunteers. A completely random sample was not used because experience in the study area indicated that farmers forced to participate in such a study provide data of dubious quality. Nevertheless, this approach yielded a very complete data set. Out of the 516 farmer-months of data, only 10 farmer-months of data were lost, and that was due to enumerator error. One field enumerator was assigned to each village.
All data collected were identified by field and farm. The data include both physical inputs and output, and some accounting data ofexpenses. Labor time records include working hours, type of worker, operation, and salaries for hired labor. Worker types include family labor, hired labor, and community labor. Labor is divided into three groups: adult male, adult female, and child. The worker type "communal labor" is used for unpaid labor provided by friends and neighbors.
The descriptive statistics (Table 1) show a farming system in which the majority of the crop area is devoted to millet and other dryland crops, but the majority of the labor use, grain production, and income comes from the irrigated area. Only 12 percent of the farm area is in irrigated rice, but it uses 65 percent of the labor, yields 62 percent ofthe grain, and produces 69 percent of the total value of crops.
Most of the work on both dryland crops and rice is done by family labor. Overall, about 16 percent of all labor is hired. About 62 percent of all hired labor is used in rice production, with about 72 percent of all hired labor for rice production being used in harvest and postharvest operations. Most of the hired labor for dryland crops is used in millet weeding. Except for millet planting and rice threshing, women seldom do field work at Koutoukal6 .
The planting, transplanting, and weeding date statistics (Table 2) show that the periods for these operations overlap. The labor bottleneck that causes the most problems is in late July and early August, when both millet weeding and rice transplanting should be done (Bomans, 1986; Maikor6ma, 1986). The date of millet planting is determined primarily by the onset of rains. Though millet planting and replanting extends over a long period, most of the millet area is planted for the first time within a few days of the onset of the rains, and thus the conflict with rice operations is relatively small. Rice weeding is done mainly during a period before millet harvest, but after millet weeding.
It can be assumed that all farmers in the sample use the same technologies, though with varying input levels. They all used the traditional millet production system. All large perimeters in Niger are managed by ONAHA (Office National des Am6nagements Hydro-Agricoles), which prescribes recommended production practices. Though these recommendations are not followed to the letter, they create a certain consistency in rice production technology among farmers, for instance, in variety choice.


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Table 2. Critical periods in rainy season rice and millet production, Koutoukak, Niger, 1985.
OPERATION JUNE JULY AUG. SEPTr. OCT.

Millet planting and replanting June 18-Aug. 9.
mean = June 29
Millet first weeding .July 8-Aug. 13.
mean = July 28
Rice transplanting .July 24-Sept. 20.
mean = Aug. 22
Rice weeding .Aug. ?7-Oct. 24.
mean = Sept. 24
1 The mean reported here is the weighted average in which the weights are the proportions of the
total labor allocated to an operation expended on a given day.

The Model
A production function approach was chosen as the simplest means to test the hypothesis posed. The popular dual approach was not used because the hypothesis concerns the magnitude of the marginal value of the physical production response to labor allocation and because the available data were primarily in the form of physical inputs and outputs. The dual approach would estimate the production function characteristics implied by the structure of monetary costs and returns found in accounting data. In general, relatively good data on physical inputs and output can be collected in Niger. Accounting data on expenses and returns are usually less reliable than physical input and output data. The primary input, family and community labor, is hard to value. A large part of the production is consumed at home. Farmers are reluctant to discuss their cash transactions. Also, the dual approach often entails multiequation estimation.
A programing model analysis would be a useful approach, but it would not be the simplest method to test the hypothesis. Development of a representative farm programing model would have required substantially more time, data, and expertise than were available. A complete analysis of the management effects of the production response to labor timing would require a representative farm programing model. Estimation of the yield responses to various inputs, as demonstrated in this study, is a step toward a more complete analysis.
The decision framework for this analysis assumes the maximization of expected results in terms of food value or monetary value. It is commonly observed that farmers in Niger want both food self-sufficiency and cash income; thus, both aspects are considered. In mathematical terms, the problems are identical. In one case, the inputs and outputs are measured in terms of food value (kilograms of grain, calories, etc.), and in the other case they are measured in terms of money.


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Zellner, et al. (1966) show that simple, single-equation production function estimates with survey data can be consistent and unbiased if it can be assumed that the production process is risky and that this fact is recognized in the choice of input levels-assumptions that seem to hold for Niger.
Two equations are used to describe rice and millet production separately. The assumption is that the production of rice and millet are statistically independent while economically related. Statistical independence of the two crops is plausible because they are grown in substantially different conditions. Rice is irrigated and millet is rainfed. Rice is grown on clay soil near the river and millet on sandy uplands, even though the same insects and diseases do not affect both crops. Labor allocation also affects both production systems in the busy season. Particularly, in July/August, rice transplanting and millet weeding are crossed. Allocating more labor to rice transplanting must reduce the labor used in millet weeding. However, once the labor is allocated in the rice production, statistically, it will not have an effect in millet production.

Functional Form
A Cobb-Douglas production function is used for simplicity and to reduce the number of parameters to be estimated. This functional form is used as a convenient summary representation of the production process. Real production systems are, of course, much more complicated. The form is assumed to be adequate for the range of data observed, but not to represent the entire possible range of input levels. The properties of this functional form are well known and have been discussed in detail by Heady and Dillon (1961), and by Beattie and Taylor (1985). All data are transformed to natural logarithms; thus linear estimation using OLS is possible. The estimated equations were:
YRi = alphao + alpha1*RHAi + alpha2*RLABi + alpha3*FERi + alphaz*RPDATEi + alpha,*WDATE, + epsilon r,
YMj = betao + beta1*MHAJ + beta2*MLABj + beta3*MPDATEi + beta4*WDATEJ + epsilon m,
where:
YRi is the rice yield (kg/ha) from field i,
YM. is the millet yield (kg/ha) from field j,
RHAi is the area (ha) of rice field i,
MHAi is the area (ha) of millet field j,
RLABi is the total labor (PDE) used in rice field i, MLABj is the total labor (PDE) used in rice field j,
FERi is the total fertilizer expense for rice field i, RPDATEi is the rice transplanting date for field i,
MPDATE. is the millet planting date for field j,
RWDATEi is the date of first weeding for rice field i, and
MWDATE, is the date of first weeding for millet field j,
epsilon r, and epsilon m. are error terms for the rice and millet functions, respectively.


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The total labor per field is the sum of family, hired, and communal labors in units of PDE (person-day equivalent). The PDE is defined as an adult male working eight hours per day. An adult female working eight hours is counted as 0.85 PDE and a child as 0.65 PDE.
The weighted average date (WAD) of a field operation is used as a single valued index of the timeliness of that operation. The point of reference for calculating the WAD is the earliest date for a given operation in the data set. The earliest date is counted as day 1 of the period suitable for that operation. The weights used in calculating the WAD are the proportions of total labor for that operation used on a given day. For example, if the earliest rice transplanting date in the data set is July 24 and the farmer started transplanting the rice on July 31, the first day of work is counted as day 8. If the field was finished on August 3, that could be counted as day 11. If on July 31 and August 2 and 3, one man and nine boys worked (6.85 PDE/day), the total labor is 28.8 PDE. The weighted average date of transplanting is:
WAD = 8*(7.55/28.8) + (*(7.55/28.8) + 10*(6.85/28.8) + 11*(6.85/ 28.8) = 9.45
In general, the calculation formula of weighted average date is:
WAD = Dx*L/TL + D2*L2/TL + + D *L/TL = I Dk*Lj/TL
TL=L1+L2 + L =XLk
where:
Dk = the Julian date of kth operation day, k=1, 2, ., n;
Lk = the labor used on kth day for a given operation;
TL = the total labor used for that operation.
The labor variables were chosen to embody the maximum information in a parsimonious form. Based on the results reported by Maikor6ma (1986) and Bomans (1986), completion of millet planting, rice transplanting, and weeding of both crops were identified as the most important factors in timing of field operations. Measuring the planting, transplanting, and weeding dates from the earliest planting, transplanting, and weeding dates in the sample controls for environmental conditions, such as the onset of rains, that determine the cropping calendar in any given year. It is expected that the estimated coefficient for total labor input (RLAB, MLAB) will be positive. It is unlikely that farmers would use labor beyond the point of positive marginal product, even if that labor is low cost. The planting, transplanting, and weeding variables are expected to have negative signs. Agronomic reasoning suggests that delays in these operations in the range of dates observed in the sample will have negative effects on yield. Very early planting dates, which could have negative effects because of high temperatures, are not observed in the sample.
Other variables were chosen so as to control for as much of the remaining variation as possible, given the data collected. The field size variable is intended to capture management problems associated with labor utilization and water distribution. Field observations suggest that both labor and water


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management become more difficult with larger field sizes. Thus, the field size coefficient is expected to have a negative sign for both millet and rice. The fertilizer coefficient is expected to be positive. The fertilizer coefficient was estimated only for rice because no fertilizer use was observed for millet.
Only the millet monocrop data were used in the estimation of the millet production function. Monocrop millet occupied 65 percent of the total millet area in the sample. Intercropping with sorghum, cowpeas, and sesame introduces complicating factors and, unfortunately, the small number of observations on each crop association (millet-cowpeas, millet-sorghum, millet-sorghum-cowpeas, etc.) does not permit reliable estimates for these combinations.

Interpretation
Analysis focuses on the millet weeding and rice transplanting coefficients with respect to both date and labor usage. Other estimates will be considered to the extent that they complete the labor allocation picture. The marginal physical products (MPP) and marginal value products (MVP) are calculated. The MPP can be interpreted as the yield or food value impact of a one-unit change in a factor of production. Mathematically, the MPP is the first derivative of the production function with respect to the factor. The MVP can be interpreted as the monetary value of the yield change due to a one-unit change in a factor. The MVP is equal to the MPP multiplied by the output price. Consistent with the logic of the logarithmic transformation of the data, the central tendency of the marginal products is calculated at the geometric means instead of the arithmetic means (Heady and Dillon, 1961).
Accordingly, the MPPs and MVPs of millet weeding date and rice transplanting date can be directly observed from the first derivative of the production function. They should be negative because in the MPP and MVP calculation the denominator (expected yield or monetary value of the expected yield) will decrease as time passes and the numerator (the weighted average date) increases. However, comparison of the MPPs and MVPs calculated on a calendar date basis is of limited relevance because the amount of labor represented by a one calendar day change in date differs between the operations. Therefore, the MPPs and MVPs of labor used in millet weeding and rice transplanting are calculated by dividing the MPPs and MVPs of millet weeding and rice transplanting date by their respective labor requirements per hectare of crop. In this study, these requirement are 12 PDE/ha for the first weeding of millet and 59 PDE/ha for rice transplanting.
The MPP and MVP of millet weeding date and rice transplanting date are used to measure the yield/ha or food value/ha change due to the change of operation date such as delay (or advance) of one calendar day. The implication of MPPs and MVPs of labor used in millet weeding and rice transplanting are the crop yield or food value change due to the change of one unit labor (one PDE) used in weeding or transplanting.


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In the typical one-crop profit maximization problem, the optimality conditions require that the MVP of an input be equal to the cost of that input. In the two-crop case, the additional requirement is added that the MVPs of the two factors be equal. These typical conditions do not hold true with respect to the millet weeding and rice transplanting date variables, because they are expected to have a negative impact on yield; a larger date value results in a lower yield. Mathematically, this means that the function is convex in the transplanting and weeding dates, not concave as is required for maximization. Hence, in this case, the equality of the MVP is a condition for a minimum.
The maximum for this type of problem often takes an all-or-nothing form (corner solution). This type of solution is found interactively by checking candidate solutions and choosing the maximum. For example, if during the overlap period the marginal value product of the millet weeding date is consistently higher than that of rice transplanting, a candidate for the maximizing solution may be to allocate all labor to millet weeding until it is finished and then start on rice transplanting. If the opposite situation occurs and the rice transplanting marginal value product is higher, then a candidate solution would be to allocate all labor to rice early in the overlap period and weed the millet only after the rice is in the paddy.
These maximization results can be used in interpreting the magnitudes of the estimated coefficients. For the variables with positive coefficients, such as fertilizer and labor, average marginal products that are approximately equal to the opportunity cost of the input indicate that input levels are such that they maximize expected results. Given the tendency of the Nigerien farmer to allocate labor to millet weeding instead of to rice transplanting, one would expect to find that the marginal products of millet weeding are larger than those office transplanting, ifmaximization of food or monetary value is indeed the objective. If the magnitudes of the marginal products do not correspond to the hypothesis, it may be that they reflect an optimization, but not of the total food or monetary value. The alternative hypotheses suggest that the objective may be either a maximization of millet production or a risk-adjusted optimization.

RESULTS AND DISCUSSION
The estimation results are reported in Table 3. The F statistics are significant at the five percent level for both crops. All of the estimated coefficients have the expected signs, except for the coefficient ofthe total labor used in rice production, and that coefficient is not significantly different from zero, even at the 10 percent level. For rice, the coefficient estimates for field size, fertilizer, and transplanting day are statistically significant at the five percent level. For millet, field size and total labor use coefficients are significant at the five percent level. The planting day and weeding day are significant at the 10 percent level for millet. Because of the logarithmic


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transformation, the estimated coefficients can be directly interpreted as elasticities, that is, as the percentage change in yield for a one percent change in the input level. For example, a one percent change in either the millet weeding date or the rice transplanting date results in about a 0.14 percent change in yield. Because of the higher rice yields, the per calendar day response of rice to transplanting date appears more dramatic than that of the response of millet to weeding date (Figure 1), but the percentage response is almost the same for the two.
The average MPP and MVP of rice transplanting date are greater than the average MPP and MVP of millet weeding date (Table 4). Each day translates into a 17.8 kg/ha yield loss for rice, but only a 3.6 kg/ha loss for millet. In monetary terms, this is about 1387 FCFA per calendar day for rice transplanting and about 307 FCFA per day for millet weeding. These impacts reflect only the change in yield and revenue due to labor allocation.
For both crops, labor appears to be near the point of zero marginal return. In the case of rice, the coefficient is negative and not significantly different from zero. For millet, the marginal physical product of about 1.5 kg per hectare per day approaches the amount of grain needed to feed a laborer for one day. The marginal value product of labor in millet is only 128 FCFA/day, substantially less than the average wage rate for adult males in the area in 1985 of about 400 FCFA/day (Bomans, 1986).

Table 3. Elasticity estimates for rice and millet production functions in terms of yield
per hectare, Niger, 1985.

INDEPENDENT VARIABLE DEPENDENT VARIABLE
RICE MILLET
(T-STATISTIC) (T-STATISTIC)
Intercept 9.2099 5.6683
(16.373)** (11.333)**
Field size -0.2564 -0.1537
(-2.184)** (-2.078)**
Labor -0.1491 0.2511
(-1.448) (2.584)**
Fertilizer 0.0270
(2.352)**
Planting or transplanting date -0.1396 -0.2513
(-2.322)** (-1.722)*
Weeding date -0.277 -0.1389
(-0.499) (-1.612)*
F 2.80 6.822
MSE 0.038 0.2747
N 54 113
*Significance at the 0.10 level.
** Significance at the 0.05 level.


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It should be remembered, however, that for some of the family labor used in millet production the opportunity cost may be lower than the adult male wage. Women and children in the study area seldom work for wages, but some evidence indicates that earnings from traditional activities, such as basket making, may be in the range of 100 to 200 FCFA per day. The near zero marginal return to labor at the mean input levels does not necessarily indicate inefficient use of labor, but may simply be a reflection of the low marginal cost of family labor.
The MPPs and MVPs of labor (per PDE) used in rice transplanting and millet weeding were calculated by dividing the MPPs and MVPs by their labor coefficients (59 PDE/ha for rice transplanting and 12 PDE/ha for millet weeding). At the geometric means, the MMPs and the MVPs of millet weeding are about equal (Table 5). If feasible dates are defined as those that allow enough time to complete both tasks given the labor supply, for many feasible combinations of weighted average millet weeding and rice transplanting dates the absolute marginal value product of millet weeding is smaller than that of rice transplanting (Figure 2). In Figure 2, the MVPs are increasing (decreasing absolute value) and approaching zero as the operation date becomes later. This is because of the relatively short optimal period for these field operations. Biologically, after a certain date, the damage to the crop's yield due to the delay of operation is already made. For example, the yield of millet can be increased substantially by weeding on July 8 rather than 9, but there is only a small difference between weeding on July 28 or July 29.

6

5

4


8-Jul 18-Jul 28-Jul 7-Aig 17-Aug 27-Aug 6-Sep 16-Sep
[] rice O millet
Figure 1. Rice response to transplanting date and millet response to weeding date for
Koutoukal6, Niger, 1985, estimated with a Cobb-Douglas production function.


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Feasible date combinations depend on the amount of labor available, the work rate, and the area. In creating a mental image of the distribution of weeding or transplanting dates, it is useful to assume that the labor per day in a given activity is about equal; thus about half of the labor needed to accomplish a task would have to occur before the weighted average date. For example, ifan average farmer has 2.16 hectares ofmonocrop millet and it takes 12 PDE to weed one hectare, the weeding will require almost 26 days. If the farmer has one PDE per day of labor available, millet weeding would have to start about 13 days before the weighted average date. It should be noted that the PDE concept assumes an eight-hour workday, but in fact the farmers at Koutoukal1 often work four to five hours in the fields and spend the rest of the day in household chores, taking care of livestock, and other activities. The relatively short field work day can also be linked to poor health and nutrition. Thus, the one PDE labor used in the example may represent the work of two individuals.
To illustrate the structure of the agronomic and economic tradeoffs in the labor allocation decision, the MPPs, MVPs, yields, and returns for several feasible combinations of weeding and transplant dates are given in Table 6. The calculations assume 0.54 ha of irrigated rice and 2.16 ha of monocrop millet. It is further assumed that one PDE of labor is available each calendar day for either millet weeding or rice transplanting. All inputs other than the millet weeding or rice transplanting day are held at their geometric mean levels. The work schedule for Example 1 has all labor allocated to millet until millet weeding is finished, and then it allocates labor to rice transplanting.


8-Jul 18-Jul 28-Jul 7-Aig 17-Aug 27-Aug 6-Sep 16-Sep
[] rice O millet
Figure 2. Marginal value product of the rice transplanting date and millet weeding
date on a per-person day basis for Koutoukal6, Niger, 1985, estimated with a Cobb-Douglas production function.


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Table 4. Input means, estimated marginal physical products, and estimated marginal
value products for rice and millet, Koutoukal6, Niger, 1985.

MEANS AND MARGINAL PRODUCTS BY FACTOR UNIT RICE MILLET

GEOMETRIC MEAN INPUT LEVELS:
Field size ha 0.46 0.81
Labor PDE/ha 346.45 52.87
Fertilizer FCFA/ha 8,059.71 0
Planting or transplanting date day 29.12 10.95
Weeding date day 28.69 12.19
MARGINAL PHYSICAL PRODUCTS:
Field size kg/ha -2,088.81 -59.43
Labor kg/ha -1.60 1.49
Fertilizer kg/ha 0.01
Planting or transplanting date kg/ha -17.79 -7.20
Weeding date kg/ha -3.58 -3.57
MARGINAL VALUE PRODUCTS:2
Field size FCFA/ha -162,927.00 -5,110.99
Labor FCFA/ha -124.60 128.09
Fertilizer FCFA/ha 0.97
Planting or transplanting date FCFA/ha -1,387.62 -619.03
Weeding date FCFA/ha -278.99 -307.15
1 The marginal physical products are calculated as dY/dX=e*MY/MX, where Y is the yield, X is
the input, e is the estimated coefficient, MY is the mean yield in the sample, and MX is the mean
input for the sample.
The marginal value products are the marginal physical products multiplied by the average local market price in 1985: paddy rice, 78 FCFA/kg; millet, 86 FCFA/kg. The markets used were
at Karma and Namaro.

Table 5. Marginal physical product and marginal value product of labor (per PDE)
used in each operation.

OPERATION MPP (KG/PDE) MVP (FCFA/PDE)
Millet weeding -0.30 -26
Rice transplanting -0.31 -24

Example 1 has weighted average millet weeding and transplanting days close to those found in the sample. Example 2 is like Example 1 except that the beginning of rice transplanting is moved up one day from July 24 to July 23, and July 24 is allocated to millet weeding. Example 3 is like Example 2 except that instead of moving up rice transplanting one day, it is moved up 10 days. Example 4 shows the effect of giving priority to rice so that rice is transplanted at the earliest transplant day and millet operations are done only in between times.
The results listed in Table 6 indicate that as labor is allocated to rice transplanting earlier in the season, total grain production and net revenue rise modestly, but expected millet yields fall. The marginal products of rice


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Table 6. Expected marginal products, yields, and returns from millet and rice production under alternative weeding and transplanting schedules.'

SAMPLE EXAMPLE EXAMPLE EXAMPLE EXAMPLE
ITEM AVERAGES 1 2 3 4
DATES:
Rice transplanting Aug. 22 Aug. 19 Aug. 18 Aug. 15 Aug. 9
Millet weeding July 20 July 21 July 21 July 24 Aug. 2
YIELDS:
Millet, kg/ha 314 311 311 299 284
Rice, kg/ha 3,711 3,770 3,791 3,859 4,035
MPPs:
Rice, kg/PDE -0.31 -0.35 -0.36 -0.42 -0.60
Millet, kg/PDE -0.30 -0.28 -0.28 -0.22 -0.13
MVPs:
Rice, FCFA/PDE -24 -27 -28 -33 -47
Millet, FCFA/PDE -26 -24 -24 -19 -11

TOTAL GRAIN, KG 2,682 2,708 2,719 2,736 2,792
NET REVENUE,2 FCFA 178,871 180,799 181,684 182,876 186,945

1 Calculated with the estimated rice and millet production functions, assuming 0.54 ha of irrigated rice, 2.16 ha of millet monocrop, and labor available is 1 PDE per calendar day. The
transplanting and weeding dates used to calculate the weighted average dates are:
Example 1: millet weeding July 8-Aug. 2, rice transplanting Aug. 3-Sept. 3
Example 2: millet weeding July 8-Aug. 1 and on Aug. 3, rice transplanting on Aug. 2 and
from Aug.4-Scpt. 3
Example 3: millet weeding July 8-23 and Aug. 3-12, rice transplanting July 24-Aug. 2 and
Aug. 13-Sept. 3
Example 4: millet weeding July 8-23 and Aug. 25-Sept. 3, rice transplanting July 24-Aug.
24.
2 Net revenue is value of grain produced minus the cash costs. Subtracted costs are fertilizer for
rice (8,060 FCFA/ha) and the cooperative charges for water use and other costs (58,171
FCFA/ha). It is assumed that all labor is family or community labor. Millet seed is produced by the farmer; rice seed is included in the cooperative charge. In addition, because there is no
fertilizer used in millet production, cash costs for millet were considered as zero.

transplanting are above those of millet weeding for all the examples. Because of the negative sign of the millet weeding and rice transplanting response, the equality of the marginal products of millet weeding and rice transplanting suggests that the observed labor allocation (the sample averages that are the results from Table 5) may be minimizing total grain production and net revenue while maximizing millet production. Although the examples support the idea that the sample labor allocation is at a minimum production and net revenue, they also indicate that the cost of choosing the maximum millet production is relatively small under the assumed labor constraints. The difference in total grain production between Example 1 and Example 4 is only 110 kg and in the net revenue is 8074 FCFA. But the proportion of millet in the total grain supply between Example 1 and Example 4 is reduced from 25


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percent to 22 percent while the rice supply is increased from 75 percent to 78 percent. The strategy observed in farmers fields appears to maximize the proportion of millet in the total grain supply.
An alternative labor allocation only shows a substantial advantage if rice can be transplanted the first week of the production period. For example, if28 days of labor are hired so that rice can be transplanted in the period from July 25 to 28, then the estimated total grain production rises by 736 kg to 3418 kg and the expected net revenue is up 46,064 FCFA, after subtracting the labor cost. As in all the examples, this estimate assumes that total labor and other inputs are held constant.

Other Variables
Because the fertilizer variable is measured in terms of money spent on this factor, the marginal value product is a cost-benefit ratio. It estimates the return for each franc spent on nitrogen fertilizer. The estimate indicates that at the margin, farmers receive about 0.97 franc for each franc invested in fertilizer. Thus, the MVP is approximately equal to the input cost, and returns are maximized.
The field size variable marginal products are included in Table 3 for completeness, but because it is, in effect, a proxy for management factors, that coefficient must be interpreted with more than the usual caution. In particular, the marginal cost of the field size variable is not clear. It is not the cost of land or the land rental rate, because the area assumed by the production function is fixed at one hectare. In any case, it may be said that these management effects appear to be important for both crops, but more important for rice than for millet. This may be due to the role of field leveling in irrigation water management and to the fact that it is hard to level a larger field.


CONCLUSIONS AND IMPLICATIONS
The hypothesis that farmers in Niger allocate labor to millet production instead of rice production because the marginal product of labor is higher in millet is not supported by this research. At the average input levels, the absolute marginal products of labor for millet weeding and rice transplanting are about equal. It is difficult to find a feasible combination of millet weeding and rice transplanting dates for which the MPP and the MVP of millet weeding is substantially above those of rice transplanting.
The alternative hypothesis that Nigerien farmers allocate labor to maximize millet production is supported by the results. Examples using the estimated production functions suggest that it is easy to find alternative labor allocation patterns that modestly increase total grain production and net revenue, but most involve lower expected millet yields. The examples also suggest that yield


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increases due to alternative labor allocation would be modest unless labor hiring could permit rice to be transplanted very early in the transplanting period. Without hired labor, it would be difficult for most families to transplant rice early enough in the period to achieve the highest yields. The examples indicate that with family labor alone, even if the farmer puts an absolute priority on rice transplanting, the yield gains over the current system are modest. To achieve the high yields, the estimates suggest that the rice must be transplanted quickly at the beginning of the period.
Analysis of the structure of the labor allocation problem during the millet weeding and rice transplanting period at Koutoukal6 suggests that food preferences for millet may play a modest role in reducing rice yields and returns, but the heart of the problem is that for a short period there is a high demand for labor used in transplanting irrigated rice. Even if there were no competition from millet production, this demand could create labor problems.
Two suggestions have been made to reduce this problem: (1) enlarge the supply of labor or increase the effectiveness of the labor supply, or (2) change the cropping system to reduce labor needs. It would not be easy to greatly enlarge or improve the efficiency of the overall labor supply in rural Niger. Only the second suggestion has been seriously considered. Potential changes in the cropping system range from direct seeding of rice to switching crops. Sorghum has been mentioned as an alternative crop in the Niger River perimeters (Bomans, 1986). Because it has labor requirements similar to millet, sorghum production would substantially reduce the overall labor requirement in the critical millet weeding period. Rotation of rice with sorghum could break pest cycles. Potentially, improved sorghum could exceed current rice yields, but it is not clear that the market could absorb a large increase in production. Sorghum is part of the traditional Nigerien diet. Increased sorghum production would help meet the goal of food selfsufficiency, but the growing urban market does not demand sorghum as it does rice.

ACKNOWLEDGMENTS
This research was supported by the European Development Fund (FED); Commission for the European Economic Community (EEC), project No. 5605.33.40.007; and by the U.S. Agency for International Development (USAID), Science and Technology Bureau, Technology of Soil and Moisture Management Project, under USDA PASA No. BST-4021-P-AG-108D-00. The data were collected with the help of the National Office of Irrigated Perimeters (ONAHA), Ministry of Agriculture, Republic of Niger. The views expressed by the authors in this document do not necessarily represent those of the organizations cited.


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The authors are grateful to Drs. J. Binkley and D. Brown for their helpful comments, to Dr. Patrick Jomini for his help in using the SAS procedures, and to Mr. Abdoulaye Bonkoula for sharing his insights on the agronomic mechanisms for the yield effects of delayed planting and weeding.

REFERENCES

Anders, G., W. Firestone, M. Gould, E. Malek, E. Simmons, M. Versel, T. Ware, and T.
Zalla. 1984. Niger irrigation subsector assessment, volumes I and 2. USAID, Niamey,
Niger.
Beattie, B., and C.R. Taylor. 1985. The economics ofproduction. New York: John Wiley
and Sons.
Bomans, E. 1986. Temps de travaux et revenues des exploitations agricole de la Vall6 e du
Fleuve Niger, tome II, Koutoukal6, Hivernage, 1985. Cellule Suivi-Evaluation, Division de Mise en Valeur d'Office National des Amenagements Hydro-Agricole,
Ministdre du D6veloppement Rural, Rpublique du Niger.
Heady, E.O., and J. Dillon. 1961. Agriculturalproductionfunctions. Ames, Iowa: Iowa
State University Press.
Maikor6ma, Z.B. 1986. Gestion de deux syst6mes de culture dans les exploitations
agricoles du fleuve Niger. M6moire de fin d'6tudes, Ecole Nationale Sup~rieure
d'Agronomie de Rennes, Chaire d'Economie Rural.
Rassas, B., and T. Loutte. 1989. Niger: Rice and cotton policy. Technical Report No. 106
(Draft). USAID, Niamey, Niger.
Rpublique du Niger. 1987. Plan de d6veloppement economique et social du Niger.
Ministire du Plan.
Rpublique du Niger. 1989a. Seminaire national sur le d6veloppement de l'irrigation au
Niger, Rapport de Synth6se. Birni N'Konni, Niger.
Rpublique du Niger. 1989b. Rapport annuel des statistiques de l'agriculture et de
l'environnement. Minist6re d'Agriculture et de l'Environnement, Direction de la
Statistique de l'Agriculture et de l'Environnement.
Zellner, A., J. Kmenta, and J. Drize. 1966. Specification and estimation of CobbDouglas production function models. Econometrica 34(4):784-795.


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Participatory Needs Assessment:

A Key to FSRE

A.W. Etling and R.B. Smith



ABSTRACT

Needs assessment is a critical factor in Farming Systems ResearchExtension (FSRE) projects. Participation of local farmers and villagers is essential to identifying their needs accurately, planning, and securing the commitment necessary for successful implementation. However, needs assessment is often neglected in rural development work. When included, the needs assessment is usually done superficially by local field staffor by
outside "experts" using research methodology.
This article reviews two needs assessment methodologies, "rapid rural appraisal" and "participatory rural assessment," which balance the field perspective with academic rigor. These methodologies have been used in FSRE to help communities develop natural resource management plans.
Advantages and disadvantages of the methodologies are discussed, and specific steps in conducting a participatory rural assessment are described.
The example used is an adaptation of participatory rural assessment that was piloted in Costa Rica to identify the training needs of rural development workers. Various data-collection techniques-previously used to assess natural resource (technical) needs-were used to assess training (human) needs. This new needs assessment methodology is easy to implement and has the potential to strengthen training programs for development workers so that they can do a better job of guiding FSRE
projects.

INTRODUCTION

Needs assessment is on the verge of establishing itself as a promising international development tool. Achievement of this status depends, to a great extent, on the abilities of academics and practitioners to understand and interpret the opportunities and realities faced by developing nations and donors. In our development work experience, needs assessment is rarely given the recognition it deserves, and its potential contributions are seldom considered early or frequently enough (Butler and Butler, 1987).

1 Assistant Professor and graduate student, respectively, Department of Agricultural and Extension Education, The Pennsylvania State University at University Park.

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According to Butler and Butler (1987:262), Farming Systems ResearchExtension (FSRE) focuses on the farm family's goals and resources, and the interrelationships among these goals and resources. The primary aim of FSRE is to increase farm productivity in ways that are useful and acceptable to the farm family. This aim can only be realized if the farmer, along with other members of the local community, participates in problem identification through a practical yet reliable needs assessment.
Needs assessment, the "systematic process whereby relevant needs are documented" (Fear et al., 1978), has often been conducted in one of two "extreme" approaches, according to Chambers (1981). The brief site visit by an outside expert is the first approach. At its worst, this approach is unsystematic, incomplete, and more reflective of the expert's biases and experience than of local needs. The second approach is the academic or research study. Use of appropriate research methods to ensure validity and reliability makes this approach expensive in terms of time and other resources. The data often do not get summarized or read by the decision-makers. Also, farmers' needs have often changed by the time the research is completed.
Both of these extreme approaches to needs assessment tend to neglect the participation of client groups that is desirable in FSRE. Neither approach depends on the local community, which can be important in providing personal support to farmers as they interact with outside experts. Furthermore, local community support is often critical to implementing outsiders' recommendations.
A middle approach to needs assessment was needed to complement the holistic rural development emphasis embodied in FSRE. This middle approach evolved through "rapid rural appraisal" (Smith, 1991).

RAPID RURAL APPRAISAL
Also known as "rapid reconnaissance," "exploratory survey," and "sondeo," rapid rural appraisal (RRA) uses a wide range of needs assessment techniques. Mainly, it emphasizes careful observation coupled with semistructured interviews of farmers, local leaders, and officials during one or more brief visits. RRA has been used for agricultural marketing appraisal (Holtzman, 1986), natural resource appraisal (Stocking and Abel, 1981), participatory research for small farmers (Swift, 1981), as well as FSRE (Bartlett and Ikeorgu, 1981; Collinson, 1981; Hildebrand, 1981; Conway, 1986; Abalu, et al., 1987; McCracken, 1988).
McCracken (1988:164) lists five characteristics that qualify a needs assessment as RRA: (1) quick--will be completed within a few weeks; (2) team effort-two or more researchers are involved; (3) multidisciplinary-team members come from different disciplines; (4) interactive-team members share their different disciplinary perspectives during the appraisal; and (5) repetitive-techniques are repeated when doubts or inconsistencies arise.


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According to Butler and Butler (1987:264), weaknesses may arise if team members fail to appreciate each others' disciplines, if team members insist on rigid control of variables, or if the process is allowed to become too openended.

PARTICIPATORY RURAL ASSESSMENT:
NEEDS ASSESSMENT BY THOSE IN NEED
In response to these and other criticisms of RRA, modifications were introduced and a new needs assessment methodology-participatory rural assessment (PRA)-was developed. PRA is RRA with full participation of the community.
According to Ford (1989), PRA is designed to focus on rural communities, systematize rural participation, and help communities establish resource management plans. PRA is useful in remote rural communities that are often ignored by macrodevelopment strategies of national planning offices. Focusing on natural resource management, PRA involves specialists from various disciplines and representatives of different organizations who may not otherwise come together for a needs assessment focused on a particular community.

Theoretical Steps of PRA
Local villagers cooperate actively in each ofthe steps ofPRA, which include:
(1) site selection, (2) preliminary visits by the PRA team, (3) data collection,
(4) data synthesis and analysis, (5) ranking problems, (6) ranking opportunities, (7) adopting a village resource management plan, and (8) implementation of the plan. The PRA team is usually composed of four to six specialists. At least half are technical officers assigned to the community or area to be studied. The specializations may include plant science, animal science, community development, forestry, health, etc., based on the characteristics of the local area. In Kenya, where PRA has worked effectively, the needs assessment focuses on a particular village.
Data collection emphasizes spatial, temporal, social, and technical data. Spatial data come from a village sketch map compiled in cooperation with village leaders, a village transect (depicting land uses), and simple farm sketches. Six to eight farms are identified and sketches are prepared by team members and household heads to show distances, land use on typical farms, ecological variety, income variation, and ethnic distribution around the village. Temporal data include a time line of events important to local residents, trend lines ofa 40-year pattern ofchanges in resources (rainfall, crop production, soil loss, deforestation, health, population), and a seasonal calendar (land use, food surplus, food shortages, disease, cash availability). Social data are derived from farm interviews and discussions of village institutions. The interviews are carried out at those households where the farm sketches are compiled. Village institutions are described in diagrams that


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express their relative importance and relationships to each other. These diagrams result from discussions among groups of residents. Technical data show the economic and technical potential of resources (soil, water, etc.) needed for agriculture.
After the data are collected, the PRA team works with community representatives to organize the data and compile lists of problems and opportunities for possible action. Then villagers are assembled to discuss and rank the listed problems and create a priority list. Next, village groups rank the opportunities that seem to address the most severe problems. In ranking the opportunities, villagers are encouraged to consider the feasibility of implementing the opportunities and their likelihood of contributing to stability, equity, productivity, and sustainability.
The highest ranking opportunities are written into a plan that describes each action to be taken, the committee or individual responsible, resources needed, and a deadline for completion. This plan becomes the basic work plan for all elements of the community. It can also take the form of a contract among village groups, technical officers, and external groups such as donors or international agencies. Implementation of the plan is usually guided by a village leader. The actual work is performed by the community's self-help groups.

Advantages and Disadvantages of PRA
Advantages of PRA include: (1) use of visual materials that are easy for villagers to understand; (2) promotion of systematic participation of villagers, village groups, and interested agencies; (3) provision for interactive problem analysis and interdisciplinary problem solving; (4) identification of villagebased priorities; (5) application in the field quickly and inexpensively; (6) strengthening of rural institutions; (7) helping communities prepare organized proposals for external support; and (8) motivation of participants to action.
One disadvantage of PRA is that it ends with implementation and omits evaluation, which could easily be added as the last step. A village meeting could be called annually to discuss progress in implementing the village resource management plan and update priorities for the coming year. Another disadvantage of PRA, until recently, is that it has focused primarily on technical and resource needs. The lack of leadership and program management skills of those implementing FSRE programs are often greater problems than the lack of resources or technical expertise. For this reason, a recent adaptation of PRA to assess the training needs of extension workers in Costa Rica may have great importance for future applications of FSRE.


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USING PRA TO ASSESS TRAINING NEEDS: AN EXAMPLE FROM COSTA RICA
When the training staff of the United States Agency for International Development (USAID) in Costa Rica decided to organize a training program for youth development professionals, they recognized the importance of needs assessment. For several years, USAID had been providing nine-month scholarships for rural Costa Rican high school students that were members of 4-S (similar to 4-H) to study in the US. These students, called Central American Peace Scholars (CAPS), lived with 4-H host families in several states in the US. They returned to Costa Rica with many ideas for improving their communities through the 4-S organization. Such innovation was encouraged by the CAPS program. The 4-S professionals, however, seemed unprepared to encourage and support the returning high school students' new ideas. In some cases, the 4-S professionals perceived the new ideas as threatening; in others, these ideas were not priorities. Sometimes the professionals simply lacked the knowledge, skills, and experience needed to help the students implement new ideas for youth development.
Noting the frustrations that resulted, USAID decided to provide a twomonth professional development workshop in the US for 20 4-S agents. Through competitive proposals, the National 4-H Council was chosen to (1) conduct a needs assessment of the 4-S agents to determine the content of the training, and (2) design, implement, and evaluate the training program.
In Costa Rica, the needs assessment process adapted from PRA included seven of the eight theoretical steps (step 2 was not possible in this case).

Step 1
In Step 1, the site was determined by USAID to be the entire country. Twenty participants were chosen to represent the geographic and social diversity of Costa Rica. In the US, a needs assessment team was selected according to USAID guidelines; it was interdisciplinary and featured diverse yet complementary experience. Of the five individuals comprising the team, four had work experience in cooperative extension including 4-H, community development, family living, and agriculture. One member had not worked for extension before. Team members had 4-H experience at the national, state, county, and local levels. One was a 4-H parent and another a former 4-H member. Functional expertise of team members included administration, staff development, coordination of international exchange programs, experience in extension methodology, and work with extension in Central America. Three of the five team members were fluent in Spanish.

Step 2
Due to the nature of this needs assessment, a preliminary team visit was not possible. This PRA step was skipped without any apparent negative results.


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Figure 1. Logic model of the training program.


Fm-AG MEP 4-S Professionals

SAID


Rough Draft of Training Itinerary
in US


PLANNING
1. Needs
2. Priorities
3. Objectives
4. Resources Inventory


PROGRAM
5. Plan


- -


* Assess Needs
* Determine Objectives
* Conduct Orientation for 4-H


TRAINING 6. Program in
US


7. Evaluate


INCORPORATE
CAPS
Ideas into 4-S Clubs



Begin some Community
Clubs



Train Peers


DEVELOP
Youth Life
Skills


IMPROVE Costa Rican
Society


DEVELOP stronger 4-S







SAbility to Generate Funds


* Support MEP
& MAG


Ir 1

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PARTICIPATORY NEEDS ASSESSMENT


Step 3
In Step 3, data collection was adapted to determine the training needs of the 4-S professionals. Data were collected in six stages. Stage I was the construction of a "logic model" (Figure 1) to show the flow of events that brought about the need for the training, the training plan, and projected effects of the training. This logic model, used in "evaluability assessment" (Smith, 1989), another needs assessment methodology, is useful in understanding the chain of causes and effects in complex organizations.
The logic model begins with the 4-S professionals who are hired by the Ministry of Agriculture (MAG) and the Ministry of Public Education (MEP). They, along with USAID and the National 4-H Council needs assessment team, engaged collaboratively in planning (including the needs assessment) for the training program. The numbered items in the planning box indicate the first four phases of a standard program planning process. As a result of the


NICARAGUA


O


COSTA RICA


Figure 2. Costa Rica: Work sites of
the 4-S professionals who participated in the training.


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planning process, an outline for the training program was developed. Implementation of the training program in the US would lead to increased competence and ability, on the part of the 4-S professionals, to incorporate new ideas from returning CAPS youth into the 4-S clubs located where the 4S agents worked (Figure 2). In turn, these ideas would develop a stronger 4S organization with greater capacity to develop life skills in Costa Rican youth.
In Stage II of data collection, characteristics of the participants were described through bio-data (application) forms and individual interviews with each participant. They were asked about their 4-S responsibilities, communities, future plans for 4-S, and conditions that inhibited their work. The clear impression that emerged from this stage of the data collection was that the group was very heterogeneous. Those employed by MEP worked in schools and had programs that were quite different from the MAG employees, who worked out of county or area offices and tended to focus on community groups outside the schools. Furthermore, each participant had a unique mix of projects and activities underway. These facts indicated that individualized training would be necessary to accommodate individual differences. The training would also need to allow for considerable interaction so that participants could share ideas.
Stage III of the data collection involved a systematic review of documents pertinent to the organizational structure in which the participants worked. Three documents were especially helpful at this stage: (1) a manual for organizing 4-S Clubs, (2) a 4-S information brochure, and (3) the 4-S newsletter, "Intercambio," which is sent to each 4-S agent (Fundaci6n Nacional de Clubes 4-S, n.d., 1989, 1990). These documents gave the needs assessment team an idea of the resources and relationships pertinent to the participants. From these sources emerged an understanding of the institutional linkages that were important to the success of the individual participants in promoting 4-S clubs.
Stage IVofdata collection was a nominal group process conducted with the participants to identify their perceptions of their own training needs. The question posed was: What do you hope to gain from this training program (including skills, knowledge, and attitudes)? Each participant was given a 4" x 6" index card to record answers to the question silently. A Spanish-speaking member of the needs assessment team led participants through the nominal group process while a Costa Rican recorded the answers on newsprint. After each participant's answers were recorded (round-robin style), the answers were discussed and debated. Then each participant was given another index card and instructed to vote for the top three priorities of all the answers on the newsprint. The secret ballots were tallied and a priority list of training needs was identified (see Results below). The priority list was presented to the participants and their comments were solicited.


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Only the participants were allowed to discuss training needs. Aside from the needs assessment team member who conducted the session and the 4-S administrator who recorded answers, only three observers were allowed in the room: two representatives of USAID and a 4-S administrator who wanted to learn how to conduct a nominal group process. Discussion was lively and spontaneous and participants seemed happy with the training priorities identified. USAID and 4-S officials were also satisfied because the priorities complemented (yet did not exactly duplicate) USAID and 4-S expectations for the training program. This was the single most critical stage of the needs assessment. However, the overall strategy of complementary needs assessment techniques used in the other five data collection stages strengthened the nominal group process.
Stage V of data collection consisted of structured discussions among representatives of the organizations that were stakeholders in the participant training. Top administrators of 4-S, USAID in Costa Rica, and the Ministries of Agriculture and Public Education were all interviewed in groups. In each, the primary question was: What is your (organization's) view of the training needs of 4-S agents?
Stage VI of data collection was observation of the home and work environments of the participants. Visits were made to the schools where two participants worked, and the PRA team met families in their homes.

Steps 4-8
In Step 4, data were analyzed and synthesized by team members into a list of training needs (Step 5). This list was discussed, refined, and prioritized (Step 6) in a group discussion with the 20 participants and USAID officials responsible for funding the training.
A training plan was then developed (Step 7) that addressed the needs identified. The training consisted ofa two-month program (Step 8) that took the participants from Washington, DC to North Carolina A&T State University, Clemson, University of Tennessee, University of Georgia, the annual conference of the National Association of Extension 4-H Agents in West Virginia, Ohio State University, and Penn State University.

RESULTS
The needs identified for the professional development training of 4-S professionals were: (1) leadership skills (communication, motivation, program planning, curriculum development, and teaching skills); (2) competence in conducting leadership training for new 4-S professionals (including recruitment and management of volunteers); (3) community development skills; (4) subject matter in agriculture and family living; (5) skills in promoting entrepreneurship; (6) ideas to promote the personal development of 4-S members; (7) skills in promoting interorganizational cooperation at the


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community level; (8) organizational development skills to expand the impact of 4-S; (9) networking skills for sharing ideas among other youth development professionals; apd (10) skills in integrating 4-S members' individual projects with the dub projects.
These clearly identified needs were consistent with the expectations of the cooperating organizations. As a result of this participatory needs assessment process, an efficient and cost-effective training program was designed that satisfied both the participants and the donor (USAID). The needs identified provided the basis for evaluating the training program at its conclusion. Commitment to the training was high not only among the participants, but also at the National 4-H Council, which implemented the training, and at USAID, which funded it. Expectations among participants, trainers, and the donor were always clear and consistent.
At the end of the training, evaluation was conducted using a written questionnaire and group interview. Ayearlater, the questionnaire and group interview were repeated in Costa Rica, and a nominal group process was conducted to determine future training needs. This evaluation documented that the participants had improved markedly the skills, knowledge, and attitudes necessary to help returning CAPS youth implement community development projects. A year after the training, most of the participants had implemented new community projects of their own. Perhaps the most significant result of the training was that the participants formed their own "professional association of4-S agents" to help each other in their professional improvement.
Nine months after the final evaluation, however, the professional association was inactive due to lack of support from the participants' supervisors, who cancelled a national meeting of the association. Although this fact may have many causes, perhaps the agents' training and the association that resulted were considered a threat to supervisors who did not receive the training.

RECOMMENDATIONS
The needs assessment team's experience in Costa Rica indicates the importance of flexibility in carrying out a needs assessment. The stages of data collection are not necessarily sequential; team members were involved in several stages of data collection at the same time. Multiple techniques and sources for data collection mean that inconsistencies in the data can be expected and can be the focus of follow-up or subsequent stages in the data collection.
Time is needed for the needs assessment team to analyze data each day and to discuss the progress of the needs assessment and the activities for the coming day. Time is also needed to respond to unanticipated requests that may be irrelevant to the needs assessment process but important to the donor or participants. In Costa Rica, the needs assessment team was asked to provide participants with orientation to the training sites in the US.


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Team members who had never worked on formal needs assessment were able to understand the process and perform each of the steps without special training. Skills needed to assess the training needs of local 4-S agents were: (1) ability to conduct interviews, (2) ability to conduct group discussions with diverse individuals, and (3) experience in group dynamics. In this instance, fluency in Spanish and experience in conducting the nominal group process were also important.

EDUCATIONAL IMPORTANCE
Potentially the greatest benefit of this needs assessment is the new methodology developed in response to the request from USAID Costa Rica. PRA has been used successfully in several developing countries, involving collaboration with local villagers in their communities in order to identify problems and opportunities for FSRE projects (Ford, 1989). Now a participatory needs assessment has been adapted from PRA to identify the training needs of professional educators in developing countries. This new adaptation should further strengthen FSRE projects that often depend on local professionals to help the villages to carry out their plans. The participatory needs assessment process is easy to understand and implement. It can result in plans that all stakeholders help to develop and that they therefore have a greater commitment to implement.
The particular data collection techniques used in Costa Rica included techniques tested over decades (group discussion, individual interviews, nominal group process, and observation), as well as a new technique--construction of a logic model that clarifies complex relationships among individuals and organizations in local communities. These techniques were combined in a methodology that emphasized the collaboration of the local beneficiaries in their communities.
The advantages of the participatory needs assessment in Costa Rica confirmed and expanded the list of advantages of PRA. Active participation by those whose needs were being assessed was achieved. The donor and sponsoring organizations were sensitized to the particular needs of the individuals most involved in the program. The needs assessmentprovided for interactive problem solving. All ofthe stakeholders participated in defining problems, analyzing alternatives, and prioritizing solutions. The assessment was inexpensive and completed quickly.
The total time on-site in Costa Rica was one week. An objective research effort, needed to produce the same results, would have taken much longer and cost much more. Some of the steps (site selection, participant selection, and review of documents) were started prior to the site visit. Finally, this participatory needs assessment led to action. The two-month training program was designed, collaboratively, around the needs identified, and completed within three months of identifying the needs.
In addition to these advantages, a selection process was used that ensured a diverse, interdisciplinary assessment team, an important factor in conducting this collaborative needs assessment. The model was easily understood by all team members, easy to implement, and flexible. It has great promise for use by


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institutions planning development work in other countries where collaboration is desirable. It is particularly appropriate for FSRE projects that depend on the participation of all stakeholders (professional educators as well as villagers) in identifying pertinent needs in order to increase farm productivity and strengthen local communities.


REFERENCES

Abalu, G.O.I., N.M. Fisher, and Y. Abdullahi. 1987. Rapid rural appraisal for generating
appropriate technologies for peasant farmers: Some experiences from Northern Nigeria.
Agricultural Systems 25(4):311-324.
Bartlett, C.D.S., and J.E. Ikeorgu. 1981. A project to identify suitable innovations for small
farmers in Nigeria. Agricultural Administration 8(6):451-462.
Butler, L.M., and R.O. Butler. 1987. Needs assessment in international development. In
Johnson, D.E., et al., (eds.), Needs assessment: Theoryand practice. Ames, Iowa: Iowa State
University Press.
Chambers, R. 1981. Rapid rural appraisal: Rationale and repertoire. Public Administration
and Development (1):95-106.
Collinson, M. 1981. A low cost approach to understanding small farmers. Agricultural
Administration 8(6):433-50.
Conway, G.R. 1986. Agroecosystem analysis for research and development. Winrock International, Institute for Agricultural Development, Bangkok.
Fear, F.A., et. al. 1978. Needs assessment in community development: A resource book.
Department of Sociology and Anthropology, Iowa State University, Ames, Iowa.
Ford, R 1989. An introduction to participatory rural appraisal for rural resource management.
Program for International Development, Clark University, Worcester, Massachusetts. Fundaci6n Nacional de Clubes 4-S. (n.d). Manual para establecer clubes 4-S. San Jos6, Costa
Rica.
Fundaci6n Nacional de Clubes 4-S. 1989. Informaci6n bsica sobre 4-S. San Jos6, Costa Rica. Fundaci6n Nacional de Clubes 4-S. 1990. Intercambio 1. San Jos6, Costa Rica. Hildebrand, P. 1981. Combining disciplines in rapid appraisal: The sondeo approach.
Agricultural Administration 8(6):423-32.
Holtzman, J.S. 1986. Rapid reconnaissance guidelines for agricultural marketing and food
system research in developing countries. Working Paper 30. Michigan State University, East
Lansing, Michigan.
McCracken, J. 1988. A working framework for rapid rural appraisal: Lessons from a Fiji
experience. Agricultural Administration and Extension 29(3):163-184.
Smith, R. 1991. Rapid rural appraisal: A promising needs assessment paradigm for grassroots
development. In Proceedings of the 7th Annual Conference of the Association for InternationalAgricultural and Extension Education, St. Louis, Missouri, March 28-30.
Smith, M.F. 1989. Evaluability assessment: A practical approach. Boston, Massachusetts:
Kluwer Academic Publishers.
Stocking, M., and N. Abel. 1981. Ecological and environmental indicators for the rapid
appraisal of natural resources. Agricultural Administration 8(6):473-484.
Swift, J. 1981. Rapid appraisal and cost effective participatory research in dry pastoral areas of
West Africa. Agricultural Administration 8(6):485-492.


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Women Farmers' Role in Managing Cassava
Production in Bandundu, Zaire

Mbongolo-Ndundu Mputela and Steven E. Kraft1


ABSTRACT

Women farmers in many developing countries contribute more than 60 percentofthe effort involved in production of food crops. Consequently, in countries such as Zaire, it is important to analyze the contribution of women to food production and to relate it to national policies of food
security.
To enhance the productive capability ofwomen farmers, it is necessary to provide them with information that they can use in making better allocative and technical decisions. These decisions relate to the use ofland, labor, and capital. In addition, there are decisions related to credit, market access, and the use of "improved inputs" such as fertilizer and seed. Data derived from a farm-level survey of women farmers in Bandundu are useful in assessing what women farmers are doing now and for making recommendations for the future. In this study, a Cobb-Douglas production function is estimated, based on cross-sectional data collected from women farmers in three subregions of Bandundu during the summer of 1990. The production ofcassava is analyzed using data on eight variable inputs alongwith information of the women's use ofcredit and extension
training.
Female labor, male labor, seed, tools, market access, credit, and training were found to have a positive impact on cassava production in Bandundu.
Results are interpreted in terms offarm-level managerial decisionmaking
and macropolicy.


INTRODUCTION

Zaire is a vast country with social, cultural, and economic diversities. Because of the vastness of the territory and the diversity of resources, Zaire has a complex food problem. The annual growth rate of food production (2 percent) is lower than the annual growth rate of the population (3.3 percent).

1 Mputela Mbongolo-Ndundu has a MS in agribusiness economics from Southern Illinois
University. She has worked on promoting women's economic issues in Zaire. She has also worked on the marketing of agricultural products for the Government of Zaire. Steven Kraft has a Ph.D.
in agricultural economics from Cornell University with an emphasis in land-resource economics.
He currently works in the areas of soil and water conservation policy and implementation.

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In addition, although wages and salaries remain very low, there is a continuous increase in the price of food, especially in urban areas. The result is a spread of malnutrition. Furthermore, Zaire's capacity to import food is hampered by even lower prices for exported raw materials, and a heavy debt burden. Finally, the share of foreign exchange earned by the agricultural sector is declining.
One of the main constraints on food production is the low productivity of traditional farming. The labor division between sexes in Zaire indicates that women farmers play an important role in traditional farming. Hence any effort to improve food production needs to concentrate on women farmers and one of their main crops, cassava (Fresco, 1982; Tshibaka, 1990).
Women farmers in Zaire have little control over the economic and social environment in which they work. Nonetheless, most women farmers decide to produce cassava, and they decide how to allocate their limited resources in their farm operations to achieve their goals. How they allocate these resources is frequently determined by whether they have access to information about alternative production processes and the socioeconomic environment in which they operate.
A major step toward the resolution of the current food crisis in Zaire is enhanced availability of accurate information about the relationship between farm inputs such as land, labor, seed, and fertilizer, as well as from outputs. Information from the analysis of production functions allows women farmers to make choices regarding the best use of their limited resources. This paper gives the result of the estimation of a Cobb-Douglas production function using data from our survey of 360 women farmers in Bandundu, Zaire, in 1990. Based on the results, we offer suggestions to improve women farmers' management of cassava production.


BACKGROUND
The Zairian agricultural sector accounts for a large percentage of that nation's gross domestic product (29.5 percent), compared to 3 percent of GDP in the developed countries. But food production in Zaire has been increasing at an estimated rate of 2 percent per year. This increase has not kept up with the population growth rate of 3.3 percent per year (Mputu and Elengesa, 1987). The index of per capita food production (86) is far below the index of the developed countries such as the US (111), France (118), and even Niger (92), an important producer of cassava (FAO, 1984:51).
Food production in Zaire continues to utilize traditional agricultural practices, which means that labor is a main determinant of production. The percentage of the economically active population engaged in agriculture still remains very high (74 percent) compared to the active population engaged in agriculture in developed countries such as the US, where it represents only 2.1 percent (FAO, 1984:8). Labor is rarely enhanced by use of enough traditional


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and modern inputs such as improved seed, improved tools, and chemical fertilizer to increase productivity. For example, Zaire has an abundance of available land, but land available for permanent crop production per farm operator is less than 1 hectare; the average size in the US is 85.52 hectares per farmer (FAO, 1984). Furthermore, Zairian fertilizer consumption per hectare of available land is only 1.3 kg of plant nutrient, whereas in a developed country such as Belgium fertilizer consumption has reached a level of499 kgs.
Increasing agricultural exports is one of the few means available to Zaire for earning essential foreign exchange to acquire the technology necessary for the modernization of its agricultural sector. However, the share of foreign exchange earnings contributed by farming has declined from 38.9 percent in 1959 to 16 percent in 1987 (Tshibaka, 1986). In 1980, Zaire's main export food crops were coffee and palm oil. The world prices for these exports have been very low, limiting the earning capacity of the country. In addition, tariffs and other barriers put up by the developed countries hamper the efforts of small traders to sell Zairian food crops such as cassava.
Increased food needs and poor performance in the domestic food production sector have led the Zairian government to increase food imports. But the ratio of the value of exports to the value of imports in Zaire's food trade is very low (14.4 percent) compared to the percentage in developed countries such as the US (343 percent). In addition, if food imports are sold in domestic markets at prices below domestic foods, this direct competition hampers the ability of farmers to sell their products. As a result, Zaire is far from reaching its objective of food self-sufficiency.
Women play an instrumental role in the Zairian agricultural sector as significant actors in traditional agricultural production. This study is limited to the role ofwomen in the production of the main food crop in Zaire, cassava. The producer role of women is very pertinent for cassava, one of the most widely consumed crops in Zaire (Tshibaka and Lumpunga, 1983). However, a woman's role as farm manager is invisible because of many constraints. First, the concept of "family farm," with its implicit assumption that men are the heads of farm production, and married women are considered "helping workers" that are equivalent to "unpaid workers," reduces the impact of women in the food sector because married women farmers do not have independent access to the farm resources. The second constraint is the concept of "cash crop" versus "food crop," with the assumption that women farmers produce only food crops. As a result, the impact of development in terms of the expansion in trade, technological progress, and returns to labor do not benefit women, and reduce their economic role (Norris, 1990).
When women are indeed considered farm managers, they have been found to be more technically efficient than men in food production (Mosch, 1976). The analysis of production function seems a good approach to helping African women farmers with their decisions for allocating their available resources. Previous analyses of production functions focused on the labor-productivity


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in Africa's agricultural sectors. One study found that there is a possibility of increasing agriculture output per worker through adequate investment in education, research, and the supply of modern technical inputs (Kawagoe, et al., 1985). Another study found that it is possible to raise yields by increasing labor inputs per hectare. The study recommended a land reform giving the rural poor access to more land. Land redistribution would, if thoroughly implemented, have immediate beneficial effects in terms of enhanced output (Giovani, 1984). The result of a Cobb-Douglas production function analysis in the Zaire region, using cross-sectional data, demonstrated that easy access to land, along with education, research, and extension services such as weather forecasting, improved the productivity of labor (Tshibaka, 1990). Another application of the Cobb-Douglas production function showed a positive and significant relationship between yield per hectare and credit in kind (Kante, 1989).


METHODOLOGIES
Bandundu, the study area selected, is one of the largest agricultural regions near Zaire's capital city, Kinshasa, and is a region with social, cultural, and economic diversity. Bandundu has three subregions, two located in a savanna area and the third in a forest area. The three cities were chosen for the survey based on agricultural development programs for the Bandundu region.
The sample size for this study consisted of a total of 360 women farmers randomly selected. The sampling unit of the study is the adult woman farmer. We did not use the family farm because of the assumption that the husband is the head of the family farm. The 120 women farmers were selected in each location from the list of family farms compiled by the local agriculture service.

The systematic random sampling procedure involved selecting every

Ith = Total family farms
120

For example, if there were 840 farms, every seventh family farm on the list would be drawn. However, only the woman farmer was designated to be interviewed.
The instrument used for data collection was a questionnaire specifically designed to collect quantitative data about women farmers in Bandundu. The questionnaire had three main parts. The first involved the identification of female farmers according to social and economic characteristics such as age, marital status, spouse information, education level, and services received from agricultural projects in the area. The second part involved the production decisions made by women farmers, especially regarding which food crops to


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produce and the use of inputs such as labor, land, seed, fertilizer, tools and equipment, training, credit, and market access. The last part dealt with the quantity and value of food crops produced and marketed. The basic questionnaire in English was translated into three languages-French, Lingala, and Kikongo--to assure that everyone interviewed was asked the same questions and to avoid misinterpretations by the interviewers.
The interviewers were recruited from the local women of the survey area. The criteria used to select the interviewers were: (1) sex: only females were selected as interviewers in order to solicit freer responses from women farmers;
(2) locality: four interviewers were selected in each location to gain the confidence of women farmers and to explain the questionnaire in the local language, which is sometimes different from the official language of the location; (3) level of education: only interviewers with 12 years of education were selected to be sure that they understood the ideas underlying the study; and (4) experience: experience in school teaching or agricultural extension services was required. The investigator used three days to train the interviewers and one day to pretest the questionnaire. Each interviewer arranged the interview schedules with the women farmers, and ten days were used for the administration of the questionnaire in each location. The interviewers were motivated by a ten-day salary that was high compared to their monthly salary. Every day, when they brought back the questionnaire of the day, the investigator immediately checked for completeness before giving them the questionnaires for the next day.
Two methods of analysis were used in this study. The first was the descriptive statistics that classify and summarize the data of women farmers in Bandundu. The second method used was multiple regression to estimate the Cobb-Douglas production function. Dillon and Handaker (1980:106) suggest that Cobb-Douglas production function is appropriate for small farms, for the case of more than one variable input, and is also supported by the previous applications of the Cobb-Douglas production function in Africa, specifically in Zaire (Upton, 1987, Kante, 1989, Tshibaka, 1990, ).
The equation for the Cobb-Douglas production function is:
Y= aXI blX2 b2x3 b3 -X bn
where the a, bl, b2, b3, -bn are the coefficients to be estimated, and the Xs are a vector of independent variables. The Cobb-Douglas production function is a multiplicative relationship between output and the various inputs. It becomes a linear function in its logarithmic form, and can be easily analyzed by using multiple regression analysis.
Relationships in the model to verify in this study were whether the productivity of land (yield of cassava or value of cassava per hectare) was positively or negatively related to women's labor per hectare, husband's labor per hectare, seed per hectare, cost of tools per hectare, natural fertilizer, market access, training, association of credit, and the location of women's farms. In this study, we were interested in the productivity per unit of land


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because land is considered the most limiting factor of production for women farmers and because this form has the advantage of reducing the problems of multicollinearity among the independent variable (Intriligator, 1978:269).

Social and Economic Characteristics of Women Farmers in Bandundu
The descriptive statistics indicate that the average age of women farmers in Bandundu is 44 years, and 90 percent are married. The female farmer has, on average, a low level of education (i.e., three years of primary school).
The results of the study show that 53 percent of harvested cassava is consumed and 47 percent is sold. In short, cassava is not only a subsistence crop, but also a cash crop.
The allocation of time among farming operations indicates that transporting (57 days/year), and processing (30 days/year) are the most timeconsuming operations. The other operations require 17 to 26 days/year, except the tree felling operation, on which women farmers spend less time (6 days/year).
There is great variability in the husbands' time allocation among the three study sites. For example, in Feshi, women farmers work alone in the various farming operations, while in Inongo, located in the forest zone, husbands work with their wives in each operation, and especially in land clearing and tree felling.
The average size of a cassava field for a woman farmer is less than one hectare, and 82 percent of women farmers have one hectare or less of land for cultivation. Most women farmers are not the owners of the lands they cultivate. However, they report that it is possible to buy land. Unfortunately, they don't have enough money for that. Most women farmers in Bandundu use cuttings from the mature plants of their fields for the next planting. New varieties of cutting, such as F100 produced by the national cassava program, are used only in the demonstration fields of the extension services.
Most women farmers in Bandundu use only natural fertilization from the fallow period. Fifty-nine percent of women farmers leave land idle for less than six years, because with the fallow system a woman farmer needs to change the location of her field and this implies a negotiation with the traditional landowners and the potential for an increased distance between her fields and the market. A woman farmer in Bandundu needs to walk an average of nine km to sell her cassava. Many women farmers say that they are not able to replace their old manual tools after many years of utilization because they are not available in the rural area, and the cost of tools is high relative to their cash income. Unfortunately, women farmers in Bandundu have no access to commercial banks, and use credit associations to finance their farming activity. Better trained female farmers may increase their ability and willingness to allocate resources efficiently, but there was a variation among the study sites. In two locations, only 30 percent of women farmers have access to the advice of extension workers.


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WOMEN FARMERS IN ZAIRE 63

Table 1. Descriptive statistics: Distribution of output and inputs per hectare in Bandundu in 1990.


VARIABLE


1. Kg HAPA (in kgs)






2. TOTPA (in Z)







3. LABWPA (days) 4. LABHPA (days)



5. Colla H
(LABHPA) (LABWPA)




6. BOTTPA (cutting)








7. A Cost PA (z)


1 1000 1,001 2,000 2,001 3,000 3,001 4,000 4,001 5,000 5,001 6,000 More than 6,000
1 50,000 50,001- 100,000 100,001 150,000
150,001 200,000 200,001 250,000 250,001 300,000 300,001 350,000 more than 350,000 0- 200 201 400 401 600 601 800 801 1,000 more than 1,000 0- 100 101 200 201 300 more than 300
0 -0.3
0.31 0.6 0.61 0.9 0.91- 1.2 1.21 1.5 1.51 1.8 more than 1.8
1 10 11 20 21 30 31 40 41 50 51 60 61 70 71 80 more than 80
1 3,000 3,001 6,000 6,001 9,000 9,001 12,000 12,001 15,000 15,001 18,000 more than 18,000


PERCENT
52.0 21.0 10.5 4.0 3.5
4.0 5.0 51.0 17.0 9.0 5.0
4.0 3.0 2.5 8.5
34.5 32.0 15.0 9.0 6.0 3.5 77.0 9.0 6.0
8.0 74.0 12.0 5.0 4.0 3.0 0.5 1.5 31.0 39.0 10.0 6.0
4.0 3.0 2.0 2.0 3.0 58.5 20.0 12.0 3.5 3.0 1.0 2.0


Source: Survey data Vol. 4, No. 2, 1994


CUMULATIVE PERCENT
52.0 73.0
83.5 87.5 91.0 95.0 100.0
51.0 68.0 77.0 82.0 86.0 89.0 91.5 100.0
34.5 66.5
81.5 90.5 96.5
100
77.0 86.0 92.0 100.0 74.0 86.0 91.0 95.0 98.0 98.5 100.0
31.0 70.0 80.0 86.0 90.0 93.0 95.0 97.0 100.0 58.5 78.5 90.5 94.0 97.0 98.0
Joo.0

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Production Inputs and Outputs
The average yield per hectare of cassava for women farmers in Bandundu is estimated at 1,243 kgs/ha. There is a variation among the three locations. The productivity of cassava in Bandundu is very low compared to the 4t/ha found in other cassava producing countries (Cock, 1985). Women farmers in the sample averaged 362 woman farmer days per hectare of cassava. This average is slightly greater than the 200 man days per hectare in Nigeria and 300 man days per hectare in Indonesia (Cock, 1985). Total husband labor is 103 days per hectare, which is less than the 200 man days per hectare in Nigeria.
Table 1 summarizes the distribution of output and inputs per hectare in Bandundu in 1990. Pearson correlation coefficients indicate a positive correlation between cassava harvested per hectare and inputs such as women labor per hectare, husband labor per hectare, number of packages of cuttings per hectare, cost of tools per hectare, and market access.

Production Function
The estimated model of the relationship between the quantity of cassava harvested per hectare and the set of inputs is:
In Kghapa = 1.71+.17 In LABWPA + .09 Ln LABHPA + .34 Ln BOTTPA
- .18 Ln Fall + .29 Ln ACost +.21 Ln Dist + .56 Ln Train + .30 Ln Likelem +.13 Ln Loc.
The dependent variable Kghapa is the quantity of cassava harvested per hectare, and the independent variables are LABWPA = labor per hectare, HABHPA = husband labor per hectare, BOTTPA = number of packages of cutting per hectare, Fall = fallow period, Acost = cost of tools per hectare, Dist = access to market, train = training, LIKELEM = association of credit, and Loc = location.
The model to estimate the relationship between the value of cassava harvested per hectare and the same set of inputs is:
Ln tot PA = 4.59 + 0 14 Ln LABWPA + .13 Ln LABHPA +.21 Ln Bottpa
- .29 Ln Fall + .56 Ln Costpa +.08 Ln Dist + .42 Ln Train + .41 Ln Likelem +.06 Ln LOC.
Table 2 shows the regression solution of the models.
Each of the coefficients in the model is positive except the fallow period with value and quantity of cassava per hectare. The coefficient of determination for the overall model with the value of cassava per hectare is R2 = 0.58 percent and the adjusted coefficient of determination R2 = 0.57 percent may be considered as a good fit for a cross sectional study (Johnson, 1987). However, the model with quantity of cassava harvested per hectare as the dependent variable has R2 = 0.52 percent and R2 = 0.51 percent implying that the result must be interpreted with caution (Upton, 1987).


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Table 2. Cobb-Douglas production function of women farmers in Bandundu in 1990.

VARIABLES OrrPUT: LN TOTPA
PARAMETER STANDARD ERROR T FOR HO PROB
ESTIMATE PARAMETER = 0
Intercep 4.593 0.700 6.559 0.00
LN LABWPA 0.141 0.086 1.638 0.10
LN LABHPA 0.137 0.034 4.023 0.00
LN BOTTPA 0.214 0.104 2.047 0.04
LN FALL -0.293 0.196 -2.146 0.03
LN A Cost 0.567 0.102 5.544 0.00
LN Dist 0.088 0.092 0.951 0.34
TRAIN 0.423 0.143 2.942 0.00
LIKELEM 0.419 0.137 3.054 0.00
LOC 0.062 0105 0.592 0.55
R = 0.58; R =0.57; N = 356

VARIABLES OUTPUT: LN KoHAPA
PARAMETER STANDARD ERROR T FOR Ho PROB
ESTIMATE PARAMETER = 0
Intercep 1.711 0.633 2.701 0.00
LN LABWPA 0.171 0.077 2.201 0.00
LN LABHPA 0.098 0.039 3.179 0.00
LN BOTTPA 0.347 0.094 3.662 0.00
LN FALL -0.185 0.123 -1.502 0.19
LN A Cost 0.293 0.092 3.173 0.00
LN Dist 0.212 0.083 2.548 0.01
TRAIN 0.562 0.130 4.322 0.00
LIKELEM 0.305 0.124 2.463 0.03
LOC 0.137 0.095 1.449 0.14
R2 = .52; R = .51; N = 355, LABWPA= women labor/ha, LABHPA=husband labor/ha, BOTTPA= cutting/ha, FALL- fallow period, A Cost = annual cost of tools/ha, Dist = market access, TRAIN= training, LIKELEM = association of credit, LOC = location, TOTPA =
value/ha, KgHaPA = quantity/ha.
Source: Survey data.

T tests were performed to assess whether the individual regression coefficients were significant. The coefficients for women labor per hectare, husband labor per hectare, number of cutting per hectare, cost of tools per hectare, training, and credit association are all statistically significant in the two models at 10 percent level.
The coefficient fallow period is significant in the model with the value of cassava harvested per hectare, while the coefficient for access to market is significant in the model with the quantity of cassava harvested per hectare.
However, the coefficients for location are not significant in the two models. This result suggests that despite the ecological, social, economic, and cultural diversity ofBandundu, the similarities of the factors affecting the productivity of land for women farmers are more striking than the differences. The results of the study indicate a diminishing marginal productivity of factors, which is relevant with the principle of economic efficiency (Dillon and Handaker,
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Table 3. Cobb-Douglas type of production function of women farmers in Bandundu
in 1990.

VARIABLES OUTPUT: LN TOTPA
PARAMETER STANDARD ERROR T FOR Ho PROB
ESTIMATE PARAMETER = 0
Intercep 4.572 0.708 6.410 0.00
LN Colla H 0.100 0.032 3.607 0.00
LN BOTTPA 0.306 0.101 3.021 0.00
LN FALL -0.262 0.138 -1.898 0.05
LN A Cost 0.706 0.092 7.654 0.00
LN Dist 0.063 0.093 0.687 0.49
TRAIN 0.463 0.144 3.197 0.00
LIKELEM 0.487 0.137 3.556 0.00
LOC 0.15 0.102 1.474 0.14
R =.57; R2 =.56; N =356

VARIABLES OUTPUT: LN KGHAPA
PARAMETER STANDARD ERROR T FOR Ho PROB
ESTIMATE PARAMETER = 0
Intercep 1.558 0.641 2.585 0.01
LN Colla H 0.062 0.029 2.122 0.03
LN BOTTPA 0.436 0.091 4.744 0.00
LN FALL -0.155 0.125 -1.241 0.21
LN A Cost 0.428 0.083 5.123 0.00
LN Dist 0.189 0.084 2.250 0.02
TRAIN 0.601 0.131 4.577 0.00
LIKELEM 0.371 0.124 2.993 0.00
LOC 0.223 0.092 2.412 0.00
R2 = .50, R = .49, N = 356, Colla H = husband labor/ha, BOTTPA = cutting/ha, FALL=
fallow period, A Cost = annual cost of tools/ha, Dist = market access, TRAIN = training,
LIKELEM = association of credit, LOC = location, TOTPA = value/ha, KgHaPA = quantity/ha.
Source: Survey data.

1980). The sum of the coefficients for woman labor/ha, husband labor/ha seed/ha, and cost of tools/ha is .06 (value/ha) .91 (quantity/ha), suggesting constant return to scale. This means that, for one hectare of land, increasing these inputs by the fixed proportion will increase cassava harvested by the same fixed proportion.
The adjusted coefficient for the categorical variable training indicates that increasing the participation of women farmers irb training will increase the productivity of cassava by 53 percent to 75 percent, all other inputs held constant. The specific result of both models is that women farmers' association of credit is positively and significantly related to the productivity of cassava. An increase in the participation of women farmers in credit association will increase the quantity or the value of cassava harvested per hectare by 35 percent to 53 percent, all other inputs held constant.


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To correct for some degree of multicollinearity between female labor and male labor, and to test for the importance of collaboration of male labor in cassava production in Bandundu, the ratio of husband labor per hectare and woman labor per hectare (Colla H) was used in the model. The result suggests that increasing the collaboration of male labor may increase the productivity of cassava in Bandundu in terms of quantity or value of cassava harvested per hectare. Table 3 shows the regression solution.


CONCLUSIONS
Despite its enormous resources such as land, labor, raw materials, and hydroelectric energy, Zaire is still far from reaching its objective of selfsufficiency in food. The ratio of food self-sufficiency defined as number of calories domestically produced as a percentage of total calories supplied is estimated at 96 percent, which is very low compared to the ratio in developed countries such as the US with 131 percent. Food self-sufficiency entails food security, defined as the ability to produce an adequate quantity of food throughout the year, and remains a challenge for Zaire. The key to designing an effective food security program is to have an empirical understanding of the impact of various factors on the food production sector. Production functions analysis provides a valuable tool for making suggestions and recommendations to farmers and government concerning food production decisions.
The hypothesis of this study is that women farmers in Bandundu can be more productive if they have access to agricultural inputs. The problem was to determine what inputs are more effective in increasing the production of cassava, which is the basic food crop in Zaire, providing 75 percent of daily calorie intake.
The first step for this study was to define a framework in which to collect data about women farmers. A questionnaire was designed to collect the information for the estimation of a production function. The questionnaire was administered in three locations of Bandundu (Zaire) in a limited time period. The results of the descriptive statistics indicate that women farmers in Bandundu have a low productivity defined as the quantity or the value of cassava harvested per hectare. For example, 73 percent of women farmers have a yield of less than 2t/ha of cassava, which is very low compared to the world average of about 9t/ha (Cock 1985). This low productivity can be explained by the lack of control over land, the limited land cultivated by female farmers in Bandundu, and the limited use of agricultural inputs.
Land, the basic factor of production in agriculture, is the most limited resource for women farmers who have no right to the lands they cultivate; 82 percent of women farmers use less than one hectare, which is very small compared to 20 hectares in other cassava growing countries (Cock 1985). Women farmers, though not landowners, are still able to sell 47 percent of


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cassava harvested. The results indicate that women farmers in Bandundu use more time in transporting and processing cassava than in the productive operations such as preparing and hoeing, planting seeds, and weeding. They have limited access to male labor. Seventy-seven percent of husband farmers contribute less than 100 days/ha. Women farmers do not use improved varieties and the number of cuttings per hectare used are less than in the other countries that grow cassava (Cock 1985). The lack of availability of new tools and cash credit to purchase inputs also explain this low productivity.
The result of analysis of variance shows that the means of cassava harvested (kg/ha) in different locations were significantly different from each other at 10 percent level.
A Cobb-Douglas production function was used in this study of 360 women farmers because it is the best form for small farm operations, convenient for more than three independent variables, and allows the marginal productivity of a given input to depend on the levels of all inputs employed.
Regression results demonstrate that the coefficients for woman labor, husband labor, number of cuttings, cost of tools, training, and credit are all significant and positively related to the value and the quantity of cassava harvested. This means that women farmers in Bandundu not only need to have the right to the land they cultivate, but they also need husband labor, improved seed, fixed capital, access to extension services and access to credit to make the land produce. The significant negative coefficients of fallow period with yield and income suggest that women farmers also need chemical fertilizer to use with natural fertilizer because increasing the number of years of fallow after seven years might not improve the productivity of cassava. Access to market is significant only for quantity of cassava.
This case of cassava production analysis confirms the conclusion of the study of Gittinger et al. (1990) that increasing women's economic opportunities is not a "zero sum game," because not only women farmers and their husbands gain, but these gains also contribute to national food security.
The differences in the ecological, social, cultural, and economic diversities of Bandundu are among the factors affecting women farmers' contribution to cassava production. Direct access to traditional as well as modern factors of production may improve the economic role of women farmers in Bandundu. This research has to be extended to other regions in order to draw any general conclusion for the country. Nevertheless, the results of the study suggest the following recommendations:
* more attention has to be paid to women farmers in agricultural programs
of food self-sufficiency;
* each woman farmer needs to be secured with more land for food
production;
* further research is needed to reduce the time women use in cassava
processing;


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* more women farmers have to be trained, and one of the solutions is to
train and motivate women extension agents to advise women farmers;
* women farmers' credit associations should be used as channels to finance
women's activities, and thus to facilitate their access to inputs; and
* means of transportation have to be available for women farmers and
extension services should be used to reduce time women farmers use to transport their cassava from their fields to the local market or the local
storage facilities.


REFERENCES

Cock, J.H. 1985. Cassava: New potential for a neglected crop. Boulder, Colorado:
Westview Press.
Dillon, J.L., and J.B. Handaker. 1980. Production function and analysis. Farm Management Research 13:103-118.
Food and Agriculture Organization (FAO). 1984. Socio-economic indicators relating to
the agricultural sector and rural development. Economic and social development
paper. Rome: FAO.
Fresco, L. 1982. Women and cassava production: An approach to improving agricultural
productivity in rural Zaire. USAID/Kinshasa.
Giovani, A.C. 1984. Farm size, land yields and the agricultural production function: An
analysis for fifteen developing countries. World Development 13(4):513-534.
Gittinger, J.P., S. Chernick, N.R. Horensters, and K. Salts. 1990. Household food
security and the role of women. World Bank Discussion Paper 96:1-37.
Intriligator, M.D. 1978. Economic models, techniques, application. Englewood Cliffs,
New Jersey: Prentice Hall.
Johnson, A., C. Martin, J.B. Johnson, and R.C. Buse. 1987. Econometrics: Basic and
applied. Mecheillon, Inc.
Kante, B. 1989. Production and estimation of production functions. In Effectiveness of
agricultural credit in Mali, Case study: The office du Niger. The Department of
Agribusiness, Economics SIU-C.
Kawagoe, I., Y. Hayani, and V.W. Rulton. 1985. The intercountry agricultural production function and productivity differences among countries. Journal of Development
Economics 19:113-131.
Mosch, P.R. 1976. The efficiency ofwomen as farm managers: Kenya. American Journal
of Agricultural Economics 58:832-835.
Mputu, D., and M. Elengesa. 1987. La femme Zairaise et l'autosuffisance alimentaire.
Pp. 1-7.
Norris, M.E. 1990. The impact of development on women: A specific factors analysis.
Research paper. Pp. 1-27.
Tshibaka, B.T. 1986. The effects of trade and exchange rate policies on agriculture in
Zaire. Research Report 56:11-54. Washington, DC.
Tshibaka, B.T. 1990. Prospects for increasing agricultural labor productivity under the
current resource base and technology. In Division and allocation of labor in a rural household: Economy and the implications for the productivity of agricultural labor.
Washington, DC: IFPRI.


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Tshibaka, B.T., and K Lumpungu. 1983. Trends and prospects for cassava in Zaire.
Working Paper on Cassava 4:1-45. Washington, DC: IFPRI.
Upton, M. 1987. Estimation of production function. In African farm management.
Cambridge: Cambridge University Press. Pp. 137-173.


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Farmer-Controlled Diagnosis and

Experimentation for Small Rural

Development Organizations

Bruce Petch and Jane Mt. Pleasant '



ABSTRACT

Small rural development organizations working in highly variable agroecosystems must develop multiple technologies appropriate to the complex conditions of the different settings in which they work. With little or no access to institutional facilities or expertise, and few resources for conducting conventional agronomic research, new approaches are required. This paper describes efforts to meet this challenge. New methods to facilitate farmer participation in the generation and transfer of agricultural knowledge were developed and tested in coordination with five rural development organizations in seven remote upland areas in Indonesia and the Philippines. Three topics are discussed: (1) farmer diagnosis of crop production constraints; (2) farmer-controlled experimentation; and
(3) redefining roles of agriculturalists.
The diagnosis of crop production constraints focused on helping farmers analyze their own situations rather than bringing in a multidisciplinary team ofexperts. Not all ofthe techniques were successful,and someraised additional problems. Involving farmers in agricultural experimentation is extolled in the farming systems literature, but implementation can be extraordinarily difficult. When farmers were involved in the planning and design of experiments, the resulting heterogeneous set of treatments greatly complicated efforts to synthesize information and draw conclusions. Additionally, development organizations had to choose between introducing new technologies and encouraging the development of local methods. The complexity and implications of both of these issues are illustrated with examples from farmer-controlled experiments in several villages in one province. Agriculturalists working in areas where farmers, rather than experts, have primary responsibility for technology development are forced to redefine their roles. Several ways in which agriculturalists can contribute effectively in these situations are suggested.


1 Former graduate student, International Agriculture Program, and Assistant Professor, Department of Soil, Crop, and Atmospheric Sciences, respectively, Cornell University, Ithaca, New
York, USA.

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INTRODUCTION
Critics of conventional research-extension approaches have called for "reversals" in relationships between scientists and resource-poor farmers (Chambers and Jiggins, 1987). Expanding the participation of these farmers in the generation and transfer of agricultural knowledge requires fundamental changes in the fabric of the agricultural research-extension system (Farrington and Martin, 1988). The implications of increased farmer participation are not trivial and successful adoption of new models of researcher-farmer interaction is likely to be some time in coming. Even if such reversals are possible on a large scale, the ratio of "converted" scientists to resource-poor farmers would still be very small. This is an especially relevant concern in hilly and mountainous areas where the diversity of agroecosystems complicates extrapolation of experiences gained in one or two locations.
In Southeast Asia, small rural development organizations (mostly nongovernmental) are at the forefront of efforts to develop improved farming systems for upland areas. These organizations in remote upland areas of Indonesia and the Philippines cannot effect fundamental changes in the research-extension system, nor can they wait for others to implement them. Without regular access to scientists or research facilities, they must diagnose agricultural production problems in a range of agroecosystems and assist farmers in generating locally appropriate technologies.
This paper describes some of the issues faced by small rural development organizations in upland areas of Indonesia and the Philippines in developing techniques that can be used by farmers and field workers to address systematically the constraints they face, with minimal input from outsiders. The experiences and approaches described herein reflect the situation faced by small NGOs in remote areas generally far from (or lacking vehicle access to) research institutions, universities, and other institutional sources of expertise. In other locations and in different types of organizations in both countries, access to such facilities is much better and therefore the options for facilitating technology development are greatly expanded.

Responding to the Variable Impact of Contour Hedgerows
Until recently, many organizations in the region built their programs around contour planting of widely spaced, dense rows of leguminous trees (also known as hedgerow intercropping, alley cropping, or sloping agricultural land technology [SALT]). The appropriateness of this technology has been proven in many areas, and thousands of upland farmers in the region have adopted it. However, in some locations adoption has been very slow. Some adopters complain that the effect on crop production has been negligible or even negative. The reasons for such variability have not been assessed systematically in the region. A review of research conducted in various locations in the tropics indicates that competition between crops and hedg-


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erows for nutrients and moisture is often a problem. Furthermore, although the hedgerows contribute ample quantities of nitrogen to the cropping system, deficiencies in other nutrients (notably phosphorus) are not necessarily overcome (Petch and Mt. Pleasant, 1991).
The realization that the effectiveness of contour hedgerows is highly variable has led to attempts to develop complementary or alternative crop production technologies. As has already been learned, the heterogeneous nature of the agroecosystems of the region works against the "discovery" of any single technology or combination of technologies that would be appropriate and effective throughout an organization's area of service. Yet these small organizations lack the capability to carry out conventional agronomic research in a range of locations. This fact, combined with the desire to enhance the self-reliance of local communities, necessitates that farmers be largely responsible for technology development. The challenge addressed by the work described herein is to develop procedures that small rural development organizations can use to facilitate the generation of effective crop production technologies by farmers.


STUDY AREA AND METHODS
This paper is based on work done in the provinces of Nusa Tenggara Timur (Indonesia) and Cebu and Cavite (Philippines) from June to December 1990. The locations of these provinces are illustrated in Figure 1. The climate in the areas studied is generally subhumid. Topography is hilly to mountainous; calcareous soils predominate. Major food crops are maize, upland rice, and cassava. Seasonal food shortages are common in many locations, and famines occur occasionally.
Five rural development organizations participated in the work described herein. Fieldwork was done in seven villages served by the organizations. Emphasis was on developing new approaches and procedures, rather than on formal research. Discussions were held with farmers, fieldworkers and program leaders regarding existing procedures for problem analysis and technology development, focusing on food-crop production. Existing technology-testing programs were modified, and new ones were established. Some new procedures were designed and, in a few cases, tested. A detailed report (Petch, 1991) was compiled as part of the first author's graduate studies.

Participatory Diagnosis of Crop Production Constraints
Typically, diagnostic methods are applied to enhance researchers' understanding of local farming systems. As Gubbels (1988) points out, farmers may already have the capability to analyze their own agricultural problems. He suggests that the focus of "diagnosis" of farming systems by outsiders should


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Lowland Rice Areas ] Uplands


INDONESIA





NUSA TENGGARA TIMUR Figure 1. Location of study sites in the Philippines and Indonesia.
Source: Garrity and Sajise, 1990.


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be to "learn only what is essential to know in order to effectively guide peasant farmers to undertake their own analysis" (1988:11). We sought to develop methods in which researchers facilitate farmers' analysis of their own situation and problems. The primary goal was to help farmers overcome the constraints they face; enhancing outsiders' understanding was secondary. In the event, we were often perceived to be gathering information for our own purposes and we found it difficult to avoid playing the role expected. Furthermore, especially when outsiders are new to an area, a minimum understanding of local farming systems is necessary before meaningful dialogue can take place. Nevertheless, there were times when we were truly facilitators helping farmers to analyze the problems they face.
Many of the techniques used were modifications of methods described in recent farming systems research literature. One important difference from most approaches was that the activities were carried out not by a multidisciplinary team of specialists, but by a pair offield workers and a group offarmers in each location. The methods used included: (1) discussion of changes over time in farming systems with older farmers; (2) tour of local farms to stimulate analysis of issues; (3) discussion of constraints to crop production while working with farmer groups; and (4) community meeting to consider jointly possible solutions to the problems raised. Some progress was made in helping farmers assess crop production constraints systematically. Table 1 provides an example of the constraints identified in one location.

Difficulties Encountered
Many problems were encountered in applying these participatory techniques. Two notable difficulties were: (1) influential individuals tended to dominate discussions, despite efforts made by facilitators to constrain them; and (2) in one instance, raising the issue of unclear land tenure led to a proposal by landowners to adopt arrangements that would have been clearer but less fair. Because the landowners are also the village leaders, the proposal was difficult to reject and created a new problem that program staff had to address.
The capacity of the community and the rural development organization to address the problems raised is also a matter of concern. Establishing priorities is essential (Lightfoot, et al., 1987). Even so, there is some risk of engendering disappointment and cynicism when a wide range of problems are put forward but follow-up action is limited to only one or two issues.

Table 1. Crop production constraints in an Indonesian village.
PROBLEM SUGGESTED SOLUTIONS
infertile soil intensified use of Ssbaniagrandiflora; alley cropping
livestock as pests fence pastures; keep animals in pens
tenure not clear fixed percentage of crop for landowners
untimely weeding establish a fixed weekly schedule for group work


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In addition, the value ofspending time on diagnostic activities was questioned by program staff in some locations. They felt that local farmers and field staff (who are often local farmers themselves, or at least village based) already know what the problems are. This perspective results in a tendency, rightly or wrongly, to attempt to solve apparent problems directly, rather than to diagnose or analyze them. In a sense, the assumption is made that farmers in an area have diagnosed their problems through years of experience. To suggest that outsiders can better appraise the situation in a few days smells of "expert arrogance."
In a different institutional environment (national agricultural research services in Botswana and Cameroon), Baker states that "large investments in formal diagnostic surveys or participatory activities such as group treks, farmer groups, or village meetings" are not necessarily a prerequisite to "farmerbased experimentation" (1991:144). Nevertheless, farmer-participatory approaches have focused more on diagnosis than on technology development.
However, there are at least two factors that could affect the relevance of nonsystematic diagnosis made by outsiders or by a small number of local field workers and/or farmers: (1) The diagnosis may reflect the situation and perceptions of only one segment of the community, for example only relatively wealthy or productive farmers, or only male farmers; (2) Especially in isolated locations, farmers are often unaware of the potential for improvement of a particular parameter and thus may not perceive a problem to exist. For example, a tenant farmer who pays 60 percent of his harvest to the landowner may not see it as a problem if all other tenants in the area do the same.
In both cases, the perspective ofan outsider can contribute to ensuring that the problems being addressed are indeed relevant to a large portion of the farmers in an area. If there is widespread agreement on the nature and priority of problems, diagnostic activities may be superfluous.


FARMER- CONTROLLED EXPERIMENTATION
The abundant rhetoric about farmer participation in research found in recent farming systems literature is not matched by serious consideration of the logistical issues involved. In the case of small rural development organizations working in heterogeneous environments, the relative participation of researchers and farmers in experimentation is determined more by lack of research staff than by ideological considerations. The main challenge confronting these organizations is not to get more farmer participation but rather to expedite the generation and dissemination of knowledge and technologies with a very small number of technical and professional staff.
Heinrich and Masikara (1991), based on several years of experience in Botswana, provide some useful guidelines for organizing farmer-managed trials when the researcher-to-farmer ratio is very low: (1) work with groups of


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farmers rather than individuals; (2) do as much preparatory work as possible before the cropping season; (3) use trial designs that can be implemented by farmers and readily assessed by both farmers and researchers; (4) minimize the quantitative data collected and find efficient ways of collecting relevant qualitative data; and (5) make the data analysis simple enough to be handled by available staff and equipment. They also caution that such a participatory approach to experimentation "is not well-suited to collecting large amounts of empirical, technical data on specific technologies" (1991:8). The primary objective of Heinrich and Masikara was to include the perspectives of farmers in developing new technologies.
Gubbels (1988) reports a similar approach to farmer experimentation that World Neighbors has used in Mali. In the case described, each village selected "pilot farmers" to test a new variety of seed on their own land. Results were analyzed in a meeting of representatives from all the villages involved. The recommendations for extension that were compiled at the meeting were not based on "scientifically rigorous experimental data," but rather on the observations of participating farmers and their knowledge of local conditions.
In the programs described in this paper, two key issues complicate efforts by program staff to facilitate the process of technology development by farmers and to synthesize information gained for the benefit of farmers in other areas: (1) heterogeneity of experimental design and treatments due to farmer control of the research process; and (2) the "dilemma" between introducing new technologies or facilitating indigenous technology development. These issues are illustrated by experiences in the province of Cebu.

Effect of Farmer Control on On-farm Trials in Cebu
In Cebu, in-row tillage (cultivation of 50 cm planting strips, leaving interrow space uncultivated) and herbaceous green manure crops were selected for testing by a local rural development organization, based on experience with the technologies in Central America. Initially, a conventional agronomic approach was planned in which identical trials would be implemented in the three districts in which the organization worked. But when farmer-cooperators in each district were involved in planning and design, there was considerable variation in treatments and experimental designs between districts, and to a lesser degree between cooperators in each district. The variation reflected both the agroecological variability of the area and the heterogeneity of farmer preferences and interests. The result was a rather ad hoc experimental program (summarized in Table 2) that did not adhere to the norms of agronomic research (a statistician's nightmare!).
In the program area of Guba, where soils are mostly stone free, friable, and slightly acidic to neutral, herbaceous green manures were enthusiastically included in the experimental program. The green manures were perceived by farmers as a means of providing nitrogen and organic matter as a supplement


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Table 2. Adaptation of experimental program by farmers in three locations in Cebu,
the Philippines (simplified for purposes of illustration).
LOCATION TECHNOLOGIES INTRODUCED TECHNOLOGY COMBINATIONS
FOR TESTING SELECTED BY FARMERS
FOR TESTING
Guba green manures, green manures +
in-row tillage alley cropping + fertilizer
Argao green manures, green manures +
in-row tillage animal manure + fertilizer
Pinamungajan green manures, in-row tillage + goat manure,
in-row tillage in-row tillage + green manures

to alley cropping, which had already been widely adopted in the area. They hoped that the green manures would reduce the amount of chemical fertilizer they needed to get acceptable maize yields. There was little interest in in-row tillage among participating farmers.
In the program area ofArgao, soils are shallow over limestone, and alkaline. Alley cropping has never been as effective or as widely adopted as in Guba. Farmers in Argao had been experimenting with different combinations of goat manure, chicken manure, and various fertilizer formulations. They were interested in trying green manures as another element in their mix of manures and fertilizers. As in Guba, in-row tillage generated little interest.
In the third program area, Pinamungajan, the dominant soil type is comprised of more than 50 percent stones. Maize yields are generally far lower than in the other two sites, and the potential for improving production seemed limited given the soil condition. Attempts at alley cropping had produced small, spindly hedgerows that contributed nothing to the soil in the alleys. But when stones were removed from and animal manure added to narrow strips in an in-row tillage system, maize yields were spectacular (compared to past experience). The experimental plan thus became focused on various modifications to in-row tillage.

Introduce New Technologies or Facilitate Indigenous Technology Development?
Variability in the modification of introduced technologies is only one dimension of the complexity of farmer experimentation. An additional dimension is the experiments that farmers do on their own initiative. Although the current fashion is to reject the technology transfer approach and instead facilitate development of indigenous technologies, provision of new ideas and improved access to resources remains important to farmers. As Baker states, "what most farmers want and need is new technological options, not catalysts to their own innovation processes" (1991: 128). Nevertheless, the folly of ignoring farmers' knowledge and experiments is well documented (e.g.,


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Richards, 1985). Furthermore, the objectives of rural development organizations are not only technological. They also aim to enhance the self-reliance and confidence of farmers who, in many locations, have long been led to believe that they are backward, lazy, or both. Provision of new ideas should at least be accompanied by understanding of, and respect for, existing farming methods and farmer innovation. Ideally, an approach combining technology transfer with support for local knowledge seems desirable.
In practice, the holistic alternative of incorporating introduced technologies with indigenous technologies and farmer experimentation is difficult to monitor. For example, while visiting the introduced green manure trials of one farmer-cooperator in Cebu, we passed by the following experiments (among others): fertilizer + prunings of Gliricidia sepium + Cassia siamea versus fertilizer alone; prunings versus fertilizer; and goat manure + fertilizer versus fertilizer alone.
For staff already overburdened and confused by the range of modifications to the technologies they introduced, the addition of a whole set of farmerspecific experiments is difficult to take into account. In a different institutional environment, the "FSR team" might be able to put together a coherent picture of on-farm technology development processes. However, for one or two practitioners working with tens or even hundreds of farmers, a different approach is necessary.

REDEFINING ROLES
The agriculturists hired to "do research" in such situations are confused. None of the experimental approaches they learned at university or research stations seems to fit the rather ad hoc and site-specific trials that result from involving many farmers in different locations in the planning process. Furthermore, because farmers are supposed to be taking primary responsibility for technology development on their own farms, the role of and need for researchers from outside is unclear. The conventional role of pretesting technologies that are then disseminated among farmers is inappropriate. This lack of clear direction and purpose has affected the motivation of the agriculturists. In some cases they have slipped into routine program administration tasks; in others they have set up neat randomized block variety trials in their "backyards," and elsewhere they have undertaken surveys peripheral to program direction.
There is, however, a gradual discernment of how professional agriculturists can most effectively contribute to a technology-development program in which farmers have primary responsibility. The following roles seem to be appropriate:
1. Assisting farmers and village-based program staff in analyzing the problems theyface and assessingpossible solutions. Given the doubts raised above regarding diagnostic activities, this role may not always be considered useful.


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2. Introducing new technologies toprogramstaffandfarmers. This role is not greatly different from the conventional role of extension agents, except that the researcher has neither the time, the facilities, nor the mandate to pretest new technologies. Therefore, the research must provide farmers and field staff with balanced information on the likely advantages and disadvantages of a particular technology and avoid the promotional approach that has prevailed in the past. The agriculturists should also share insights resulting from their broader exposure to the world outside of the village, for example by providing information on the likely trend in fertilizer prices.
3. Enhancing local capacity to test and develop technologies. If agriculturists can see through the plethora of experimental designs that they have been told constitute "research," they can provide useful advice on technology development and basic principles of experimentation. A simple example was observed on a steeply sloping field. The farmer planned to have treatments running parallel to the contour. The agriculturist recommended that the treatments run perpendicular to the contour, given that the soil on the upper slope was much thinner than on the lower slope.
4. Providing farmers and program leaders with insights on variation in the performance of different technology combinations between locations (Kirkby, 1981). Such insights are essential to avoid "reinventing the wheel" in each new program area. To gain these insights, the research advisors have no alternative but to synthesize the incomplete and largely qualitative data that are being generated. As described above, such data will come both from testing of introduced technologies and from farmer-initiated experiments, as well as from observations of "normal practice." Initially, efforts will have to focus on procedures for collecting and recording such information in a systematic manner. The increasing use of laptop computers by small rural development organizations in Southeast Asia should facilitate this process. So far, the computers are used almost exclusively for word processing. Setting up a simple database to keep track of the diverse information available may be a useful first step. The use of nonparametric statistics and simple techniques such as modified stability analysis (Hildebrand, 1990) might be considered at a later stage.


CONCLUSION
The experiences described above represent a very preliminary attempt to develop technology development procedures that do not rely on "experts." Although the geographic scope was limited, the general situation is far from unique. In the tropics, many farmers are seldom if ever reached by national agricultural research services, international centers, or bilateral aid projects.


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While waiting for "reversals" of scientists' attitudes to occur, perhaps more farming systems research practitioners could focus their attention on developing approaches that can be implemented where there are no "experts."


ACKNOWLEDGMENTS

The Canadian International Development Agency provided financial support for the research upon which this paper is based. Cuso (a private Canadian development organization) provided logistical and financial support in Southeast Asia. The examples described from Indonesia and the Philippines are based on the work of the Geo Meno Foundation and the Mag-Uugmad Foundation, respectively. Discussions with Larry Fisher and John Jackson of World Neighbors Southeast Asia were of particular importance to the evolution of the ideas presented. The assistance provided by the above organizations and individuals is gratefully acknowledged.


REFERENCES

Baker, D. 1991. Reorientation, not reversal: African farmer-based experimentation.
Journal of Farming Systems Research-Extension 2:125-147.
Chambers, R., and J. Jiggins. 1987. Agricultural research for resource-poor farmers, Part
I: Transfer of technology and farming systems research. Agricultural Administration
and Extension 27:35-52.
Farrington, J., and A. Martin. 1988. Farmer participation in agricultural research: A
review of concepts and practices. London: Overseas Development Institute. 79 pp. Garrity, D.P., and P.E. Sajise. 1990. Sustainable land use systems research in Southeast
Asia: A regional assessment. Paper presented at the workshop on sustainable land use
systems research and development, New Delhi, India.
Gubbels, P. 1988. Peasant farmer agricultural self-development. ILEIA Newsletter
4(3):11-14.
Heinrich, G.M., and S. Masikara. 1991. Trial designs and logistics for farmer-implemented technology assessments with large numbers of farmers: Some approaches used in Botswana. Paper presented at the 11th Annual Symposium of the Association for Farming Systems Research-Extension, Michigan State University, East Lansing,
Michigan, October 5-10.
Hildebrand, P.E. 1990. Modified stability analysis and on-farm research to breed specific
adaptability for ecological diversity. Pages 169-180. In M.S. Kang, (ed.) Genotype-byenvironmentinteraction andplant breeding. Baton Rouge: Louisiana State University
Press.
Kirkby, R.A. 1981. The study of agronomic practices and maize varieties appropriate to
the circumstances of small farmers in highland Ecuador. Unpublished Ph.D. dissertation, Cornell University, Ithaca, NY.


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Lightfoot, C., O. de Guia, Jr., A. Aliman, and F. Ocado. 1987. Participatory methods for
identifying, analyzing, and solving systems problems. Paper presented at the 7th Annual Farming Systems Research-Extension Symposium, University of Arkansas,
Fayetteville.
Petch, R.B. 1991. Working with upland farmers to grow more food: Strategies from
Indonesia and the Philippines. Unpublished MPS project report, Cornell University,
Ithaca, NY.
Petch, R.B., and J. Mt. Pleasant. 1991. Agronomic limitations ofalley cropping:A review.
Department of Soil, Crop and Atmospheric Sciences. Research Series R91-8, Cornell
University, Ithaca, NY.
Richards, P. 1985. Indigenous agricultural revolution: Ecology and food production in
West Africa. London: Hutchinson. 192 pp.


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Participation of Rural Women
in the Homestead Vegetable Farming

Systems of Bangladesh'

W.A. Shah, Rukshana Yasmin, Rezaul Karim, and M.M.A. Karim2



ABSTRACT
Due to increasingly limited access to land and highly inadequate nutrition among rural women in Bangladesh, the analysis of and strategies to improve women's participation in vegetable production activities on farmer homesteads are urgently needed. This report is based on research carried out by the On-Farm Research Division of the Bangladesh Agricultural Research Institute from 1987 to 1989. The nature and extent ofwomen's participation in vegetable farming systems is a function of various social and socioeconomic factors, which suggest a number of
policy implications.


INTRODUCTION
The word "homestead" brings to mind two different images: urban homesteads (homes and gardens in city settings) and rural homesteads (with homes, sheds for cattle and poultry, and fruit and vegetable gardens). Rural homesteads are common in many developing countries. In Bangladesh, homesteads occupy about five percent of the total cultivable land (Taherunnesa, 1986). Homestead areas vary with farm size and have a highly skewed distribution (Hussain, et al., 1988). Of the 12 million rural homesteads in Bangladesh, 56 percent have only 0.004 to 0.04 ha, approximately 17 percent have 0.45 to 0.81 ha, and less than 10 percent own more than 0.81 ha (Islam and Rahman, 1989). The remaining 17 percent own no land. Thus two million people do not own their own homesteads; however, they usually reside on someone else's land as sharecroppers or wage laborers.
During the past few decades, the thrust of agricultural research and development has been on field crops, and the importance ofsmallholdings was mostly ignored. Only a few contemporary studies (Hussain, 1980; Gill and Sultana, 1982; Islam and Ahmed, 1986-87) highlight the importance of the 1 Paper presented at the Tenth Annual Association for Farming Systems Research-Extension Symposium, Michigan State University, East Lansing, MI, October 14-19, 1990. Farming Systems Research team members, On-Farm Division, Bangladesh Agricultural Research Institute, Ishurdi, Bangladesh.

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use of homestead land for agricultural production. In Bangladesh, population pressure has caused the percentage of landless families to increase, posing a serious threat to rural development. The technologies generated for field crops are of little use to large groups of landless families. Therefore, these farm households need appropriate strategies for income generation and/or expenditure savings.
The nutritional status of rural people is of great concern to the government of Bangladesh. The present average food intake of rural women is deficient by almost 40 percent (INFS, 1977) and 85 percent of rural women suffer from iron and protein deficiencies (Khan, 1988). Ensuring a sustainable and balanced diet has become one of the primary concerns of policy makers. However, there are several controversial issues relating to nutrition and income. Income elasticity and demand for nutritionally rich food by poor families are extremely low (Lipton, 1988). Bouis and Haddad (1988) observed that a rise in household income of 20 percent for poor farm families led to a rise in nutritional food intake of only one percent. These propositions were not tested in this study. However, we assumed that increased ingestion of vegetables increases nutritional status and health, and thus labor supply (Lipton, 1988).
Researchers at the Bangladesh Agricultural Research Institute (BARI) OnFarm Research Division (OFRD) have performed a series of vegetableproduction research activities on farmer homesteads. The results demonstrate that it is possible to increase the cultivation of different types of vegetables and that this can generate a considerable amount of income (Shah, et al., 1990).
Women in rural areas of Bangladesh perform a broad spectrum of agricultural activities, including fruit, vegetable, poultry, livestock, and fisheries production. Therefore, there is a need to develop technologies that benefit rural women. Although OFRD has studied homestead vegetable production systems, the role of women has not been assessed. The purpose of our study has been to analyze the nature and extent of women's participation in homestead production systems and includes the following objectives:
1. To describe the socioeconomic characteristics of the study's respondents;
2. To determine the relationship between the nature and extent ofwomen's participation in homestead farming systems; and
3. To derive a development policy for homestead farming systems.


RESEARCH METHODS
From 1987 to 1989, OFRD initiated a program to improve homestead areas by introducing and testing different vegetable and fruit species. The farm sizes of cooperating families were initially categorized as landless (less than 0.2 ha), marginal (0.21-0.50 ha), small (0.51-1.0 ha), medium (1.01-1.99 ha), and


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large (above 2.0 ha). Each family grew vegetables in five I x 6m beds. The homestead vegetable farming systems (HVFS), locally known as the "Kalikapur Model," comprised five vegetable cropping patterns:
1. Red amaranthus (Amaranthus gangeticus)-Red amaranthus-Indian spinach (Basella alba and B. rubra)-Radish (Raphanus sativus)-Tomato (Lycopenican esculentum Mill);
2. Okra (Abelmoschus esculentus)-Brinjal (Solanum melongena)-Red amaranthus-Red amaranthus;
3. Red amaranthus-Radish and Indian Spinach-Garlic (Allium sativum); 4. Kang kong (Ipomoea reptans)-Red amaranthus-Batisak (Brassica chienensis)-Orion-bitter gourd; and
5. Red amaranthus-Brinjal-Red amaranthus-Cabbage (Brassica oleracea var. capitata).
In a 1989 questionnaire survey, all 50 female farmers from the cooperating households were interviewed by a female enumerator to determine their perceptions of the usefulness of the model and the extent of their participation in the different activities of vegetable production.

RESULTS AND DISCUSSION

Socioeconomic Distributions
The majority of cooperating households in the program fall into the landless (30 percent) and marginal (24 percent) categories. Women farmers make up 18.9 percent of household members in both the small- and mediumsize farm cooperators. Furthermore, if the farm categories are regrouped into small (less than 1.0 ha), medium (1.0-2.0 ha), and large (more than 2.0 ha), 73 percent of the women are in the small-farm category. We conclude that technology-transfer programs in Bangladesh should focus on such small farms in order to reach the greatest number of women (Table 1).
For the survey sample, family size averages 6.24, which is close to the national average. The majority of women respondents (70 percent) have between five and nine people in their families (Table 2). The mean number of years ofschooling for the women is about one and a half. Only 16 percent have between one and five years of schooling, and 73 percent of the women have

Table 1. Distribution of farm categories among the respondents.
FARM CATEGORY FREQUENCY % OF TOTAL
Landless (less than 0.20 ha) 11 29.8
Marginal (0.21-0.50 ha) 9 24.3
Small (0.51-1.00 ha) 7 18.9
Medium (1.01-1.99 ha) 7 18.9
Large (above 2.00 ha) 3 8.1
TOTAL 37 100.0


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Table 2. Distribution of family size among the respondents.
FAMILY SIZE FREQUENCY % OF TOTAL
Less than 5 members 7 19
5-9 members 26 70
Above 9 members 4 11

Table 3. Distribution of years of schooling among the respondents.
YEARS OF SCHOOLING FREQUENCY % OF TOTAL
No schooling 27 73
1-5 years 6 16
6-10 years 4 11
Above 10 years 0 0
TOTAL 37 100
MEAN 1.51

no schooling (Table 3). In 1974, about 89 percent of women had no formal schooling (Khan, 1988); thus our study indicates a possible rise in women's literacy rates in the rural areas of Bangladesh.

Social Obligations
Certain social customs discourage active participation of women outside of the home and potentially limit participation of women in HVFS activities. Purdah, the seclusion of women from public observation, is practiced widely by women from Moslem families, which make up about 80 percent of the families in Bangladesh. In the purdah system, women are not allowed to work outside the home. Nevertheless, 62 percent of the women interviewed in this study felt no social obligation restricting their participation in HVFS activities. A total of 29.7 percent reported that some social obstacles existed, but that they participated because of financial need. About eight percent did not participate due to social dictates (Table 4). This suggests that the traditional social barriers have changed substantially and more women have access to work outside of their homes.

Husband's Attitude
The government's second five-year plan (1980-85) for development emphasized the importance of congenial socioeconomic conditions to ensure greater participation of women in economic activities. This called for a change in traditional social attitudes; more specifically, a change in men's attitudes toward women.
The results of our study support the generalization that, in rural families, the husband dictates the activities of his wife. The majority of husbands (78.4 percent) allowed their wives to participate in HVFS. However, about one-


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Table 4. Distribution of social obligation of women respondents.

SOCIAL OBLIGATION FREQUENCY % OF TOTAL
No obligation 23 62.16
Obligation exists, but is
not followed due to necessity 11 29.73
Obligation exists and is always observed 3 8.11

TOTAL 37 100.00

Table 5. Husband's attitude toward wife's participation.

ATrITUDE FREQUENCY % OF TOTAL
Does not allow to participate 6 16.22
Sometimes allows to participate 2 5.41
Always allows to participate 29 78.37

TOTAL 37 100.00

Table 6. Women's level of awareness about homestead vegetable production systems.

LEVEL OF AWARENESS FREQUENCY % OF TOTAL
Not aware 7 19
Moderately aware 11 29
Aware 19 52

TOTAL 37 100.00

sixth (16 percent) of the women were not allowed by their husbands to participate (Table 5) because of religious restrictions such as purdah.

Awareness
The study assessed the degree of awareness of women about newly introduced homestead technologies. The series of on-farm activities promoting different vegetable production possibilities directly affected the awareness and motivation of women with regard to HVFS. The results indicate that, after the program, more than 50 percent of the women were aware of the importance of homestead vegetable production (Table 6).

Type of Participation
Three categories characterize the participation of women in homesteading activities: (1) no participation, (2) participation as initiator or motivator (no physical participation in any of the activities), and (3) physical participation in activities. The majority of women respondents participated physically in harvesting, irrigating gardens, and storing seeds (Table 7). The women were


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Table 7. Nature of women's participation in homestead vegetable production.

ACTIVITIES No PARTICIPATION As INITIATORS PARTICIPATED
/MnTIVATORS PHYSICALLY
Land preparation 54 5 41
Weed management 46 0 54
Fertilization 65 3 32
Irrigation 16 11 73
Harvesting 11 8 81
Storing seeds 16 0 84
Others (e.g., like fencing) 57 0 43


Table 8. Nature of participation in relationship to selected variables.

SELECTED VARIABLES CORRELATION COEFFICIENTS
Age -0.2925
Family size -0.2106
Farm size -0.4345
Years of schooling -0.1177
Family income -0.2300
Social obligation -0.0203
Husband's attitude 0.48831
Awareness 0.84762
'Significant at 5% level.
2Significant at 1% level.

not as involved in fertilization and fence-making as they were in other activities. The results indicate that women participated very little as motivators.
Eight variables were correlated using Pearson's Product Moment with the level of women's participation in HVFS (Table 8). The results reveal that farm size, husband's attitude, and awareness about vegetable production technologies were significantly related to women's participation.
Among socioeconomic factors (including age, family size, farm size, and years of schooling), farm size was the only variable inversely related to women's participation (Table 8). A similar negative relationship was also observed in rice-farming areas in the Philippines (Shah, 1989) and India (Agarwal, 1985). This association may be related to family affluence: in wealthy families (with larger farms), family labor tends to decrease. This is particularly true for household women, who are replaced by hired women.
Two sociocultural factors (social obligation and husband's attitude) were included in the correlation. Social obligations and cultural values in the traditional Bangladesh society obstruct women's participation in farming activities. The correlation test suggests that no significant association exists between the level of women's participation and social obligations, whereas husbands' attitudes are significantly associated.


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The positive association between husband's attitude and the type of participation by his wife suggests that as the husband becomes aware of the need and usefulness of her participation, the wife's involvement in homestead farming systems tends to increase. Because the majority of rural households fall into the landless, marginal, and small-farm categories, their subsistence needs override cultural values and attitudes. Masood (1988) notes that perceived economic benefit is an important determinant in women's participation in rural development programs.
Furthermore, the level of awareness is significantly related to the level of women's participation in homestead farming. This positive association suggests that as the level of awareness about newly introduced HVFS increases, the participation of women will also increase. Awareness is endogenous in nature, reflecting individual behavior through psychological processes, and is influenced by cognition of what an individual thinks, feels, believes, and anticipates (Shah, 1989). Therefore, level of awareness can also be increased through personal contacts, radio programs, and other popular means of mass communication, and, to a lesser degree, through leaflets and booklets.

Extent of Participation
Participation was measured at three levels: (1) never, (2) sometimes, and
(3) always. More than 92 percent of the women respondents participated in some kind of activity in the HVFS (Table 9). This suggests that the employment of rural women in income-generating and/or expenditure-saving activities is possible in rural development programs. However, it may be necessary to estimate the willingness and ability of women to supply labor for homestead farming in order to determine the availability of women for labor, and also to determine the social, nutritional, and economic advantages of activities related to newly developed HVFS.
Statistical analysis (Pearson's Product Moment) indicates that age, husband's attitude, and awareness are significantly related to the extent of women's participation in homesteading activities (Table 10). The age of women was negatively related, suggesting that as the ages of rural women increase, the extent of their participation decreases. Younger women (25-34 years old) participated more than older women. Husband's attitude and the level of awareness correlated positively with the extent of women's participation.

Table 9. Extent of women's participation in homestead vegetable production.
EXTENT OF PARTICIPATION FREQUENCY % OF TOTAL
Never participated 3 8.11
Sometimes participated 14 37.84
Always participated 20 54.05
TOTAL 37 100.00


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Table 10. Relationship between the extent of women's participation and selected vari
ables.
VARIABLES CORRELATION COEFFICIENTS
Age1 -0.4259
Family size 0.0565
Farm category -0.2171
Years of schooling -0.0551
Family income -0.0893
Social obligation -0.1192
Husband's attitude 0.3208
Awareness 0.6814
ISignificant at 5% level of probability.


CONCLUSIONS AND POLICY IMPLICATIONS
The importance of sound plans and policies as a means of maximizing the use of scarce resources in socioeconomic development processes is obvious. However, rigorous analysis of data may not protect planners adequately against the pursuit of flawed policies or blindness to better alternatives. The difficulty in collecting reliable data and of monitoring programs and policies in the field often hinders the application of more appropriate alternatives.
Results generated in this study, which was conducted in a controlled setting, may not provide sufficient evidence for wider implications regarding the integration of women into homestead vegetable farming. Rather, the experience of the researchers involved highlights the possibilities for further research on such issues. However, based on our findings, the following recommendations are made:
1. In a broad sense, homestead vegetable farming not only maximizes the use of scarce resources, but also leads to increased income generation, improvement of nutritional status, and employment of rural women.
2. The study revealed that women are interested in adopting such technology. However, transfer of an innovative technology from one location to another is complicated. At this stage, similar tests should be carried out in representative farms households in other settings in order to determine how social and cultural constraints effect homestead vegetable gardening in other areas. Simultaneously, plans should be undertaken for technology dissemination.
3. Both husband's attitude toward his wife's participation and women's awareness of homestead vegetable farming technology were related significantly to the nature and magnitude of participation. Information dissemination regarding the importance of women's participation in homesteading activities may be accomplished through the mass media and existing extension services. Training programs for field-level extension workers may also increase the awareness of such programs.


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4. The HVFS may be extended to similar agroecological zones, which will require the involvement of existing extension services.
5. The HVFS technology needs further refinement in diverse agroecological conditions. Furthermore, the effect of supply of and demand for female labor should be studied carefully.
6. Marketing facilities for HVFS products, women's access to credit markets, and technology adoption need to be studied.


ACKNOWLEDGMENTS

The authors thank Mr. Kenneth D. Swann, Communication Specialist, PacMar, Inc.; Dr. M. Zainul Abedin, Chief Scientific Officer and Head, BARI OFRD; and Drs. Kamal Uddin Ahmad and R.N. Mallick, FSR Technology Specialists, Checchi and Company Consulting, Inc. for their suggestions, comments, and editing of this paper. We also thank Dr. M.H. Mondal, Director General, BARI, and Dr. Hamizuddin Ahmed, Director, Training and Communication, BARI, for allowing us to present this paper at the Tenth Annual AFSRE Symposium. Finally, we are grateful to the Symposium Committee for providing travel support.


REFERENCES

Agarwal, B. 1985. Rural women and high yielding rice technology in India. Pages 164171. Women in Rice Farming Systems, International Rice Research Institute, Los
Bafios, Philippines.
Bouis, H., and L. Haddad. 1988. Comparing caloric-income elasticities. IFPRI, Washington, D.C. Mimeograph.
Gill, G.J., and W. Sultana. 1982. Women's role in small farm production and resource
management in Bangladesh. The ADC Inc., Dhaka. Pages 15-21.
Hussain, A. 1980. Vegetable gardening in the homestead area of Bangladesh. Fifth
Workshop for the District Representatives of UNICEF, Dhaka.
Hussain, M.S., M.Z. Abedin, M.A. Quddus, S.M. Hossain, Banu, and Ahmed. 1988.
Women's contribution to homestead agricultural production systems in Bangladesh.
Bangladesh Academy for Rural Development, Comilla.
Institute of Nutrition and Food Science (INFS). 1977. Nutrition survey of rural
Bangladesh. University of Dhaka, Bangladesh.
Islam, M., and D. Ahmed. 1986-87. Analysis of homestead production and utilization
systems. BARI, OFRD Farming Systems Research Report. Mimeograph. 28 pp.
Islam, M., and Rahman. 1989. Homestead farming in rural Bangladesh: A case study.
Economic Affairs 34(2):89.
Khan, S. 1988. The 50percent: Women in development and policy in Bangladesh. Dhaka:
The University Press Limited.
Lipton, M. 1988. Attacking under-nutrition and poverty: Some issues of adoption and
sustainability. International Food Policy Research Institute, Washington, D.C.


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Masood, F. 1988. Women in traditional irrigated farming systems. Women in Rice
Farming Systems, International Rice Research Institute, Los Bahos, Philippines.
Shah, W.A. 1989. Women's participation in rice farming systems of Nueva Ecija,
Philippines. Unpublished Ph.D. dissertation, Central Luzon State University, Munoz, :hilippines
Shah, W.A., Rezaul Karim, and M.A. Karim. 1990. Economics of homestead vegetable
farming systems: Kalikapur model. BARI, OFRD Farming Systems Research Report.
Mimeograph. 5 pp.
Shah, W.A., Salima Jahan Nury, and M.A. Karim. 1990. Women's participation in
vegetable seed storage methods and practices in farm households. BARI, OFRD
Farming Systems Research Report. Mimeograph. 31 pages.
Taherunnesa, A.A. 1986. Home-based agricultural production in rural Bangladesh.
Association of Development Agencies in Bangladesh (ADAB) News 13(5):2-9.


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Role of Farmers in the Evaluation of an

Improved Variety:

The Case ofS35 Sorghum in Northern
Cameroon 1

Mulumba Kamuanga and Martin Fobasso 2


ABSTRACT

Efforts to develop high-yielding, stable, early-maturing sorghum varieties in northern Cameroon produced an apparently resounding success with on-farm tests of the variety S35 from 1983-1985. This caused great excitement and led to the recommendation of S35 to the regional extension agency in 1986. In subsequent years, under more favorable rainfall conditions, farmers reported serious agronomic problems with the variety. However, informal surveys revealed some farmer-initiated strategies for incorporating S35 into their traditional cropping systems.
In 1990 an adoption survey to measure research impact revealed that 13 percent of farmers had adopted the new variety. Results of regression models suggest thatfarmers tend to adopt S35 more on its own merit than as part ofa package ofrecommendations. Adoption rate is higher among farmers who planted improved sorghum three to five years ago, and location in drought-prone zone is determinant. Reasons for nonadoption, together with these results, now form the basis for revising breeding
objectives in order to respond better to farmers' needs.

INTRODUCTION

On-farm testing of improved varieties and agronomic practices developed by the Institute of Agronomic Research (IRA) at Maroua, northern Cameroon, has been conducted by two USAID-funded projects since 1979. The SemiArid Food Grain and Development Project (SAFGRAD) conducted on-farm trials on food crops until 1986. As SAFGRAD phased out, the National Cereals Research and Extension Project (NCRE), with technical assistance

1 Paper presented at the 11thAnnual Symposium of the Association for Farming Systems ResearchExtension, Michigan State University, East Lansing, MI, Oct. 5-10, 1991.
Agricultural Economist and Agronomist, Testing and Liaison Unit, National Cereals Research and Extension Project (NCRE/IITA/USAID), Institute of Agronomic Research, B.P. 33 Maroua, Cameroon. The authors are grateful to Doyle Baker and two anonymous reviewers for
comments received on an early version of this paper. Standard disclaimers apply.

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from the International Institute of Tropical Agriculture (IITA), took responsibility for on-farm testing in northern Cameroon in an effort to promote farming systems research and extension (FSRE) through its Testing and Liaison Unit (TLU). Maroua TLU is now responsible for farming systems diagnosis, on-farm research, and research-extension linkages.
The target zone of Maroua TLU is the region above the 10th parallel, which includes much of the cotton-growing portion of Far North Province and the Mayo Louti Division of North Province (Figure 1), an area frequently referred to as the Center North Zone. This region has about 210,000 farm families that grow cotton for cash and sorghum as the staple food.
Agronomic themes tested by Maroua TLU are jointly decided by IRA researchers and SODECOTON, a government parastatal in charge of cotton development and food crops extension in the North and Far North provinces. Between 1984 and 1988, nearly 840 on-farm tests were conducted success-


CHAD


NIGERIA


0-*" 0 0


Maroua 0 0


0 0


CHAD


Garoua


Figure 1. Northern Cameroon. Location of TLU Test Sites.


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S35 SORGHUM IN CAMEROON


fully, 56 percent of which were concerned with sorghum. Emphasis was on variety tests in response to constraints on increased sorghum production faced by farmers in the region (e.g., poor and erratic rainfall, weed [Striga hermonthica] infestation, low soil fertility, and labor scarcity), which could be globally addressed by developing short-cycle (85-95 day), drought-tolerant varieties.
The sorghum breeding program at IRA Maroua concentrated on the selection and multilocational trials ofshort-cycle, open-pollinated varieties, all medium in height (2.5m) with white grain, and resistant to diseases, insects, and Striga (Dangi, et al., 1989). Collaboration between TLU and the IRA/ NCRE breeding program resulted in some of these varieties being tested in farmers' fields over the 1984-1989 period. Ofparticular interest is S35, a new, high-yielding, early-maturing sorghum variety released to extension in 1986. Although S35 adoption has remained controversial, it has provided evidence of successful research efforts in developing and extending a new cultivar that is playing a particular role in a region characterized by a wide range of welladapted local materials. Measuring the impact of such efforts is an objective well in line with TLU orientation.
The purpose of this paper is: (1) to discuss the role collaborating farmers played in the evaluation and diffusion of S35, and (2) to examine the extent and patterns of its adoption in the region. We then propose revisions in sorghum breeding objectives at IRA in light of the feedback from farmers. Suggestions for extension, to further adoption of improved sorghum varieties in northern Cameroon, are also discussed.

BACKGROUND
The sorghum variety S35 originated in India. It was selected by the IRA/ NCRE sorghum breeding program from hundreds of lines sent to Cameroon in 1982 by Dr. N.G.P. Rao, then working in Zaria, Nigeria. These lines had general characteristics believed to be important for sorghum improvement in semiarid zones of Africa-earliness, high potential yield, medium height (22.5m), cream-colored grain, and resistance to diseases (Johnson, 1988; Dangi, et al., 1989). The variety S35 is nonphotosensitive, with a cycle of 90 days in northern Cameroon.

On-Farm Tests
On-farm testing ofS35 began in 1983 with 11 researcher-managed trials showing no significant difference in yield from the local varieties. From 19841987, the new variety was tested in 240 farmers' fields, in addition to multilocational trials in 17 locations. Results summarized in Table 1 indicate that S35 outyielded local varieties by a surprising 85 percent in 1984, a year of extremely low rainfall in northern Cameroon. During years of "normal


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Table 1. Grain yield (kg/ha) of introduced and local sorghum varieties in on-farm
tests across sites in Northern Cameroon (1984-1987).
YEA~ 1984 1985 1986 1987 ALL TESTS 1988
Rainfall' (mm) 528.7 729.0 772.6 614.0 830.5
Yields of S35 1,333.0 1,689.0 1,866.0 1,888.0 1,694.0
Yields of locals 719.0 1,539.0 1,721.0 1,825.0 1,451.0 1,4662
Yields of other
introduced
varieties 784.0 1,202.0 2,185.0 1,974.0 1,536.0 1,119
Number of sites 88 79 38 35 240 60
Yield difference:
S35 over locals
(%) 85.4 9.7 8.4 3.5 26.7
1 Mean total across selected sites.
2 Mean yield of the best local variety, Gueling. Mean yield of local Djigari varieties in 1988 was
1,336 kg/ha.
3 Mean yields of E35-1 and 38-3 in 1984; S34, S36, S20, and 82-S-50 in 1985; CS54 and CS61
in 1986 and 1987.
4 Mean yield of CS54.
Source: NCRE/TLU, IRA/SAFGRAD Annual Reports.

rainfall," as in 1986, S35 maintained its high yield but was not significantly different in yield from the local varieties.
Surveys conducted at the time of harvest in 1986 and 1988 revealed that 53 percent of collaborating farmers preferred S35 over other introduced cultivars. The majority (67 percent) cited the white-colored flour and taste of S35 as reasons for their preference (TLU, 1986). Despite mixed results in onfarm testing and farmers' assessment, by early 1987 researchers and extensionists alike believed that S35 was on its way to adoption. In 1986, for example, the Government's Seed Multiplication Project at Garoua produced more than 20 tons of S35 seed in response to increased demand. The same year, SODECOTON extended the variety on more than 600 hectares.

Farmer Feedback
Feedback from farmers who planted S35 over the 1983-1990 period as TLU adopters or collaborators in on-farm tests has been collected in various ways, including questionnaire surveys at time of harvest and informal or prearranged interviews with farmers. The following summarizes the most frequent observations (TLU, 1986; Johnson, 1988; Kamuanga et al., 1991).
Recommended planting dates (June 15-July 10) are considered late by farmers because the average date of planting traditional varieties is May 25 and much earlier in years when usable rains fall in May.
The short cycle of S35 is an obvious advantage, contributing to drought avoidance; the same characteristic, however, leads to increased susceptibility to grain mold when planting is too early in the season.


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