Citation
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
Publication Date:
Language:
English
Physical Description:
v. : ill. ; 23 cm.

Subjects

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

Notes

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.
Funding:
Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.

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sn 90001812 ( LCCN )
1051-6786 ( ISSN )

Full Text



Volume 3, Number 1 1992









o urnal


for Farming Systems



Research- Extension





I Mesrn S stanblt L. HarringtonI.
21 ~ Inienu Knweg an etlzrSrtge Da. errtM r an .F Weaver







R.N. Kuan. Ali 47 Casaa Faie nP esnlHne Arc Stve **.n I 71~ ~ p Deiig cmenain oan Stve *. 0*

..






Journal
for Farming Systems Research- Extension


Volume 3, Number 1, 1992


Published by
the Association for Farming Systems Research-Extension

..






Journal for Farming Systems Research-Extension


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

Associate Editors
Nancy Schmidt and Daniel Goldstein Office of Arid Lands Studies The University of Arizona, Tucson

Production and Layout
Nancy Schmidt, Eliza Cain, and Diedre Muns Arid Lands Design, Office ofArid Lands Studies The University of Arizona, Tucson

Sponsors
Michigan State University Foundation United States Agency for International Development The University of Arizona




The Journalfor Farming Systems Rtsearch-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 3, Number 1, 1992






CONTENTS


1 Measuring Sustainability: Issues and Alternatives
L. W. Harrington

21 Indigenous Knowledge and Fertilizer Strategies in Leyte, Philippines: Implications for Research and Demonstration Trials
Danile Perrot-Maitre and T.F. Weaver

35 Gender Issues in African Farming: I. A Case for Developing Farm Tools for Women
R.N. Kaul and A. Ali

47 A Regional Perspective on Cassava, Famine, and Seasonal Hunger in Humid and Subhumid Africa
Steven Romanoff

71 Extension Agent Surveys for Defining Recommendation Domains: A Case Study From Kenya
Steven Franzel

87 The Role of Farmers in the Jordanian Combined Sondeo Process D.L. Galt and A.F. Al-Kadi

105 Methodology for Desiging and Evaluating Comparative Cropping Systems
Luanne Lohr, Oran Hesterman, James Kells, Douglas Landis, and Dale Mutch

131 Some Aspects of Land Management by Small Farmers of Southwestern Nigeria
M.A. Adewole Osunade

145 Farming Systems Research Approach in Risky Agroecosystems
of North Bihar, India
S. Saran, P. Mishra, A. Kumar, and A. Salman

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Measuring Sustainability: Issues and Alternatives'

L. W. Harrington 2



ABSTRACT

There is widespread agreement among agricultural scientists on the importance ofsustainability as an objective for research. Many scientists acknowledge that the ability to quantify sustainability is critical to making the concept operational. There is less agreement, however, on appropriate methods for its measurement. This paper discusses alternative approaches to the measurement ofsustainability, given the differing ways in which the concept is used. Three major interpretations of sustainability are discussed: agroccology, equity, and sustainable growth. A number of nonquantitative and quantitative approaches to the measure of sustainability are then presented. It is concluded that work on measuring sustainability is still at an early stage and that much remains to be done by
FSRE practitioners in partnership with disciplinary specialists.


BACKGROUND AND OBJECTIVES

There is widespread agreement among agricultural scientists on the importance of sustainability as an objective for research. Many scientists acknowledge that the ability to quantify sustainability is critical to making the concept operational (e.g., Hildebrand and Ashraf, 1989). There is less agreement, however, on appropriate methods for its measurement. This is hardly surprising given the many ways in which "sustainability" has been characterized and defined.
The objective of this paper is to discuss alternative approaches to the measurement of sustainability, given differing uses of that concept. First, broad issues associated with measuring sustainability are discussed. Then three major interpretationsofsustainability (agroecology, equity, and sustainable growth) are presented, along with a classification of sustainability problems (internal vs. external, reversible vs. irreversible, agricultural produc1 Paper presented at the 11th Annual Association for Farming Systems Research-Extension
Symposium, Michigan State University, East Lansing, October 5-10, 1991.
2 Regional Economist for Asia, CIMMYT Economics Program, Bangkok, Thailand.

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HARRINGTON


tivity vs. public health). The implications of each of these for the measurement of sustainability are explored. A number of nonquantitative and quantitative approaches to the measurement of sustainability then are listed and discussed.


ISSUES IN THE MEASUREMENT OF SUSTAINABILITY

The notion of sustainability has been defined and characterized in vastly different ways-from the resilience of individual agroecosystems to food security in the face of global climate change. Approaches to measurement will be heavily conditioned by how "sustainability" is interpreted. Still, some general issues cut across all approaches to measuring sustainability.

General Issues
Predicting the future. Assessments of sustainability necessarily imply drawing conclusions-or at least stating probabilities-about future events. When an agroecologist warns of agroecosystem breakdown as a system becomes less diversified, a forecast is being made. Similarly, when farmers' soil-eroding practices are portrayed as unsustainable, predictions are implicitly being made about future levels of soil depth and fertility and crop productivity. More obviously, when LISA ("low-input sustainable agriculture") proponents advocate a switch to wholly renewable resources, tacit forecasts are being made about the future availability and prices of agricultural inputs.
Like all forecasts, these necessarily contain a degree of uncertainty. Forecasts about the future effects of rapid soil erosion on crop yields may be considerably more reliable than predictions about regional changes in temperature and rainfall due to global warming. Thus the degree to which sustainability can be measured and quantified depends very much on the ability of analysts to predict reliably relevant future events.
Time frame. Difficulties associated with measuring sustainability are exacerbated by the wide range of time frames that apply to different sustainability issues. Some problems are best studied in the medium-term, within a time frame of 5 to 20 years (e.g., problems of soil nutrient depletion; buildup of weeds, pests, and diseases; rapid soil erosion, etc.). Other problems are best studied within a longer time frame of 20 to 100 years. These include slower forms of land degradation (e.g., gradual processes of erosion, salinization, or desertification) and some expected changes in the external environment (e.g., initial effects of global warming). Still other problems are best "studied" (if that word still makes sense in this context) in terms of very long


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time frames of 100 to 1,000 years and beyond. These would include questions about the "ultimate" sustainability of agriculture.
State versus control variables-Problems and causes. In some approaches to sustainability measurement, only "state variables" (descriptors of environmental or resource quality) are quantified. In other approaches, both state variables and "control variables" (variables that directly influence the level of a state variable) are quantified. For example, the control variable "tillage practice" influences the state variable "soil depth remaining after erosion." There is typically a problem-cause relationship between control and state variables.
When only state variables are measured, considerable doubt can remain regarding causes of any observed changes. For example, it may be observed that per capita food production (a state variable) is declining over time. The causes (Increasing population? Lower input levels? A switch to nonfood cash crops? Declining yields?), however, cannot be ascertained unless appropriate control variables are also measured. Satisfactory measurement of sustainability, then, is likely to require the simultaneous measurement of a number of state and control variables, linking problems with their causes.
Continuous versus discrete measurement. There seems to be little discussion in the literature about continuous vs. discrete measurement of sustainability. Ifsustainability is conceived of as discrete, then an agroecosystem (in theory) can be described as being either sustainable or not. Measuring sustainability comes down'to ascertaining which of these two states prevails. Ifsustainability is conceived of as continuous, however, it is possible to visualize different degrees of sustainability, opening the way to quantitative comparisons between alternative systems. Most proponents of increased quantification seem to assume implicitly that continuous measurement is possible.
Level of measurement and possibilities for substitution. It is frequently assumed that sustainability is best measured at the plot level. Sustaining a particular cropping pattern in a specific location is often taken to mean maintaining the productivity of that pattern (and the quality of the resources devoted to the production of that pattern) indefinitely. Yet other cropping patterns may come along that are more attractive to farmers. At a more general level, it is not always necessary to insist on the sustainability of all system components-it is possible to employ some resources in excess of sustainable levels while maintaining the overall productivity of the resource base by using substitution possibilities among resources (Graham-Tomasi, 1990). A major issue, then, is deciding exactly what it is that we are trying to sustain.


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Sustainability of What? Three Interpretations
"Sustainability" and sustainable agricultural development have been conceptualized and defined in numerous ways. Indeed, there is an overabundance of definitions. Most of these, however, appear to fall into one or more of three distinct categories: agroecology, equity, and sustainable growth.
Agroecology. Some definitions focus on sustainability in terms of system resilience, or the ability of an agricultural system to "maintain its productivity when subject to stress or perturbation" (Conway, 1986). Sustainability in the agroecological sense is enhanced through system diversity. A diversity of enterprises over time and space fosters the recycling of nutrients, an increased efficiency in the use of moisture, nutrients, and sunlight, and a reduction in the incidence of weeds, pests, and diseases (Altieri, 1987). Modern monoculture, characterized by low levels of diversity, is viewed as having a fragile ecological equilibrium, with control coming from external inputs rather than internal feedback mechanisms (Ingram and Swift, 1989).
In this view, then, the sustainability of agriculture can be improved by increasing system diversity and by fostering nutrient and energy cycling (and thereby reducing the use of external inputs) through the development of suitable new farming systems (Francis, 1986; Altieri, 1987). Consequently, monitoring changes in system diversity and in the internal cycling of nutrients and energy is perceived as fundamental when measuring the sustainability of an agricultural system.
Equity. Other definitions focus on sustainability in terms of equity, especially intergenerational equity and the rights of nonhuman species (Batie, 1989). The emphasis is on stewardship, or the proper care and protection of resources (Barker and Chapman, 1988). This conceptualization is founded on the belief that future generations have the right to an environment and a stock of renewable and nonrenewable resources in no worse condition than that enjoyed by the current generation.
In theory, the efficient intertemporal use of resources can be assessed by means of benefit/cost analysis (Schmid, 1989). However, intertemporal efficiency can lead to extinction of renewable resources and exhaustion of nonrenewable resources (Clark, 1976). Discounting involves making judgements concerning the value of current versus future consumption. However, when these judgements are made by the current generation of humankind on the behalf of future generations, serious ethical questions emerge (Batie, 1989; IFPRI, 1989). Moreover, agricultural and economic development sometimes are seen as inherently unsustainable simply because geometric


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growth rates (e.g., in demand for food) are ultimately incompatible with absolute scarcities (e.g., of resources or of the environment to absorb pollution; Heilbroner, 1980; Batie, 1989).
In this conceptualization, the sustainability of agriculture can best be enhanced by slowing down economic development, stabilizing human population growth, and fending offthe exploitation of natural resources (especially common property resources; Barbier and McCracken, 1988; Durning, 1990). Proponents of the equity view, then, argue that measurements of sustainability must somehow capture the quantity and quality of natural resources expected to be available for future generations.3
Sustainablegrowth. A third major view ofsustainability focuses on the need for continued growth in agricultural productivity, while maintaining the quality of resources devoted to agriculture. It implies the following: using renewable resources at rates less than the rate at which they can be continuously generated; emitting wastes at rates less than the rate at which they can be absorbed by the environment; and optimizing the efficiency with which renewable resources are used (Barbier and McCracken, 1988).
This view of sustainability takes into account foreseen increases in the demand for food arising from continuing population and income growth. It is this view that has inspired the definition of sustainable agriculture proposed by the Technical Advisory Committee of the Consultative Group on Intemational Agricultural Research (CGIAR): sustainable agriculture "should involve the successful management of resources for agriculture to satisfy changing human needs while maintaining or enhancing the quality of the environment and conserving natural resources" (CIMMYT, 1989).
The sustainable growth perspective can be operationalized (and measured) at several different levels, among them the regional level (where sources of agricultural productivity growth are compared with expected growth in demand for agricultural products; e.g., Byerlee and Siddiq, 1989; Rosegrant and Pingali, 1991) and the plot level (where changes in yields and total factor productivity are explained in terms of changes in levels of inputs, technical change, and changes in resource quality; e.g., Lynam and Herdt, 1988). 3 Given the possibility of irreversible deterioration in resource quality, the nonlinear way in which
resource quality can vary (e.g., global climate change), and the very long time periods involved, sustainability measurement in the equity interpretation is clearly challenging--if not impossible. 4 Clearly, the levels are related: The ability of food supply to keep up with growth in demand
increasingly hinges on resolving plot-level constraints to increased yields. Similarly, it is not always necessary to insist on the sustainability of all system components-it is possible to employ some resources in excess of sustainable levels while maintaining the overall productivity of the
resource base by using substitution possibilities among resources (Graham-Tomasi, 1990).


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Plot-level issues can be further subdivided in accord with the importance of externalities or common property resources as causal factors.

Classes of Sustainability Issues
An immense number of issues are raised regularly in relation to the sustainability of agriculture. An incomplete listing of these issues might include soil erosion, global warming, salinization of irrigated areas, deforestation, deterioration of soil structure, reduction in biodiversity, exhaustion of soil nutrients, desertification, pest and disease buildup, environmental pollution from agricultural chemicals, and reduced future availability of agricultural inputs (including fossil fuels). Many of these issues, especially those having to do with land degradation or the maintenance of soil quality, have been studied in some depth by disciplinary and subject matter specialists. Other issues, such as global warming, are relatively new.
For simplicity, researchers should aim to group sustainability issues into categories. Different ways to measure sustainability may be needed for each of these categories.
External versus internal. External issues of sustainability are those associated with changes in farmers' external circumstances. Global warming and future climate change, future availability and prices of fertilizers and other purchased inputs, and changes in global biodiversity are examples. These issues are beyond the farmers' control, or exogenous. Internal questions of sustainability are associated directly with farming system operations and farmers' decision making, e.g., soil erosion, nutrient mining, buildup of pests and disease, salinization, environmental pollution from agricultural chemicals, etc. These questions may or may not be partly associated with issues of common property resources and externalities.5
Reversible versus irreversible. Sustainability problems may be distinguished as reversible and irreversible. The permanent effects of irreversible problems cause special concern. When future demands for a resource are uncertain and the effects ofan irreversible change are not well known, the present generation may perceive a value ("option demand") in maintaining an option for future uses of a resource (Johnston, 1988).6
5 Not all issues can be unquestionably classified as either internal of external. Farm operations, for
example, undoubtedly contribute (although in a relatively subordinate way) to global warming (Pretty and Conway, 1989). Moreover, most internal issues are conditioned to a certain extent by farmers' external circumstances. Nonetheless, the distinction helps by highlighting the
relative importance of farm-level decisions in addressing sustainability problems.
6 Irreversibility has been most studied in relation to species extinction (e.g., Krutilla and Fisher,
1975; Bishop, 1978). The concept of a "safe minimum standard" was developed to identify Journal for Farming Systems Research-Extension

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Some of the problems commonly associated with the sustainability of agriculture are not obviously irreversible, for example soil nutrient depletion, loss of soil structure, or buildup of pests and diseases. On the other hand, severe soil erosion or massive deforestation can often be considered reversible only under the most optimistic-and unlikely-assumptions about future land use over exceedingly prolonged periods of time.
Public health versus agricultural productivity. A few of the issues often included under the rubric of sustainable agriculture have little to do with the sustainability of agricultural productivity as such. Rather, they deal with the effects of agricultural practices (e.g., pesticide application) on public health. There is no doubt that these questions are important and that adjustments in agricultural technology can be effective in addressing them. However, they are different from other problems in that they do not deal with threats to future agricultural productivity or food security.

Interpretations and Categories: Implications in Measuring Sustainability
The measurement ofa complex notion like sustainability is, to say the least, challenging. It is unlikely that a single approach to measuring sustainabilityequally useful regardless of the concept interpretation or the category of problem under consideration-will ever be found. To this extent, the idea of "measuring sustainability" is not very meaningful.
More specifically, the measurement of the sustainability of agricultural productivity when threatened by externalproblems (climate change, reduced availability of external inputs) is likely to depend greatly on the work of disciplinary specialists. Within resource economics, for example, there are those who specialize in assessing future availability and prices of natural resources (e.g., Chapman, 1983; U.S. Department of Interior, 1989). Agricultural field scientists would do well to monitor (without feeling compelled to duplicate) the work of these specialists. Plausible farmer adaptations to expected increases in the prices of external inputs (e.g., adjustments in input use, shifts in enterprise selection, adoption of low-input technologies) can then be assessed 7 The work of outside specialists on global climate change
minimum resource quality levels (e.g., sufficient population and habitat of a species to assure survival) and to argue in favor ofa particular rule: "Extinction [and, presumably other irreversible forms of resource degradation] should be avoided unless social costs are unacceptably large"
(Bishop, 1978). These concepts (irreversibility, option demand, and safe minimum standards)
can be applied to other issues of resource degradation.
7 Developing adaptations to possible shifts in farmers' external circumstances is a worthwhile
activity. However, it should be distinguished from the broader theme of"measuring sustainability," which requires considerable information on the direction and extent to which those external
circumstances will, in fact, change.
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and implications for agriculture will take on a similar importance. Some studies of this issue have already been conducted (e.g., Arthur, 1988; Jodha, 1989) but much remains to be done.
Measuring sustainability, in the agroecological interpretation of system resilience, will depend on the development of reliable quantitative indicators of resilience and diversity that can be readily measured. To date, there has been little progress in formulating such indicators (Tisdell, 1988). In contrast, there has been considerable work, following the "sustainable growth" interpretation, on approaches to measuring the sustainability of agricultural productivity when threatened by internalproblems. A number of approaches have been proposed, typically relying in one way or another on trends in yields or total factor productivity (state variables), with or without complementary evidence on resource degradation. Discussion of some of these approaches (Table 1) constitutes the rest of this paper. Table 1. Approaches to Measuring Sustainability: Some Comparisons.'


Approach

Directional measurements

Forecasts on future availability and prices of external inputs Indicators of global climate change and effects on agriculture Indicators of system resilience

Indicators of groundwater pollution Production or yield trends

Trends in per capita production

Yield trends in relation to inputs applied


Interpretation All


Agroecology; equity


Problem category Potentially all


External, irreversible


Equity, External,
agroecology irreversible


Agroecology Internal,
plot-level,
productivity
Equity Internal,
public health

Sustainable External or
growth internal;
productivity
Sustainable Internal,
growth regional,
productivity
Sustainable Internal,
growth plot-level,
productivity


Comments

Assumes proportional relation between problems and causes Available from disciplinary specialists; uncertainty in forecasts

Available from disciplinary specialists; uncertainty in forecasts

Suitable indicators not clearly identified

May not deal with resource degradation, food security No information on causes; easily confounded

No information on causes; easily confounded

Approximation to total factor productivity; more information on sources


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METHODS FOR MEASURING SUSTAINABILITY Nonquantitative Approaches
Rejecting quantification. Before proceeding, it should be recognized that some scientists reject the very notion that sustainability can or should be quantified. For example, MacRae et al. (1989) argue that quantification tends to distort the research process, inducing researchers to choose quantifiable (but less relevant) variables at the expense of other nonquantifiable (but conceptually more important) ones. They are especially skeptical of numerical modeling of biological systems, arguing that the internal consistency of these models does not compensate for their lack of realism.
This rejection ofquantification is linked to a similar rejection of"reductionism." It is usually not possible, MacRae et al. (1988) maintain, to analyze complex systems by examining a few variables and then applying the results over a broad area, nor is it usually possible to find direct, single-cause-andeffect relationships between factors. Given that sustainable processes are


Table 1. Approaches to Measuring Sustainability: Some Comparisons (cont.)." Approach Interpretation Problem category Comments

Total factor produc- Sustainable Internal, Little information on
tivity trends growth plot-level, causes, less confounding
productivity
Trends in total Sustainable Internal, Decomposition may be
factor productivity growth plot-level, difficult or impossible
or yields, decomposed productivity in practice
into trends in input
use, technical change,
or resource degradation
Evidence from Sustainable Internal, Good information on
long-term trials growth plot-level, causes, does not take
on declining yields productivity account of possibilities
of substitution
Other physical Equity, Internal No clear link to issues
evidence on re- sustainable of food security
source degradation growth

(Measurement not Equity; Potentially all Claims sustainability
possible) agroecology cannot be measured

Listing of approaches to measuring sustainability is incomplete. For full description of interpretations of sustainability and categories of sustainability problems, see text. Note that few if any of these approaches take account of possible farmers' adaptations to sustainability problems, e.g.,
shifting to other enterprises or cropping systems, or substitution among inputs.


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location specific, they argue that these processes are inherently difficult or impossible to quantify.
There is undoubtedly a certain amount of truth to these arguments. Yet, as a practical matter, it is not humanly possible to deal with any problem (not just sustainability problems) in all of its real world complexity. In terms of science, "we have to simplify to survive" (McCall and Kaplan, 1985). In addition, the experience of farming systems research suggests that often it is possible to quantify and model complex biological systems without unacceptable loss of realism.
In contrast, lack of quantification can lead to circular reasoning, with the relative sustainability of systems being assessed in terms of the degree to which they use practices that have been defined a priori as "sustainable." This increases the probability of self-deception and virtually eliminates the ability to systematically compare alternative systems, examine sustainability-productivity trade-offs, or gauge progress being made towards specific goals.
Directional measurements. Most proponents of sustainable agriculture probably would not agree that measuring sustainability is utterly impossible, or that trying to measure it is a bad idea. Many, however, would be content with "directional" measurements. A directional measurement as used here is one that explicitly measures only the direction of change in the sustainability of a system, not the magnitude of that change.
Directional measurements may be most attractive when it is felt that a proportional relationship exists between control and state variables. The implicit assumption is that the sustainability of an agroecosystem is changed in rough proportion to changes in those practices felt to most strongly influence trends in the system's future productivity (and/or its ability to deal with stresses and perturbations). For example, in this approach an agroecosystem suffering from gradually declining levels of plant nutrients in the soil is thought to become more sustainable in rough proportion to the amount of these nutrients that are generated or recycled within the system or applied from external sources. Insofar as the levels of these nutrients are increased, the system is assumed to become proportionately more sustainable.
Note that in this approach, current levels of sustainability need not be measured. In fact, cardinal units of measurement are unnecessary. This approach implicitly agrees that "sustainability" is a continuous, not a discrete, variable but finds that measuring levels of sustainability in cardinal terms is unnecessary. Insofar as the assumption of proportionality between control


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and state variables is incorrect, of course, this approach can lead to thoroughly misleading results.

Quantitative Approaches
Purpose. Suitable methods of quantification are necessary in order for researchers to answer questions such as the following:
- Is System A sustainable or not?
- Is System A becoming more or less sustainable over time?
- Is System A more or less sustainable than System B?
- By what percent is System A more sustainable than System B?
- Is the relative sustainability of System A with respect to System B increasing or decreasing over time?
- What are the trade-offs between longer-term sustainability and current levels of productivity of System A?
- Is the current productivity of System A more or less sensitive than that of System B to technical changes aimed at enhancing sustainability?
Analysis of regional trends.s Trend analysis uses time series information from the recent past to forecast likely events for the near future. Most of the examples of trend analysis described below focus on a particular state variable: output, yields, total factor productivity, per capita production, per capita RAVC, etc. The aim of much of this analysis is to measure the extent to which a system has already become less sustainable, and the corresponding debate focuses on choice of state variable. Which ones most precisely and most accurately capture a decline in system sustainability? Which ones tend to confound trends in system sustainability with other factors? Which ones are relatively easy and economical to use in practice?
Aggregate trends in output and yields. There is an understandable temptation to measure levels of system sustainability in terms of trends in production or yields. These trends typically can be estimated through published data at the aggregate (e.g., district or provincial) level. When maize yields show a declining trend, for example, maize researchers understandably become apprehensive about possible degradation of resources devoted to maize production.
However, problems of sustainability can be present-and worseningeven when published data indicate a rising trend in output and yield at the regional level. Similarly, sustainability problems may be entirely absent when 8 Apart from the conceptual issues associated with trend analysis that are discussed below, there
are other issues of statistical estimation (e.g., how many years of data to use, choice of functional
form, etc.) that are not discussed in this paper.


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aggregate data show declining trends in these variables. Changes in aggregate production or yields may be due to changes in input levels, input quality, or in the enterprises assigned by farmers to different land types, not to changes in resource quality (Harrington et al., 1989). For example, yields of a particular crop may appear to be declining over time because more attractive crops have replaced it in the more favorable production environments, or because farmers are using lower levels of purchased inputs? Researchers should be careful not to confound trends in the productivity of a particular crop or enterprise with trends in system productivity, nor productivity trends with trends in resource quality.
Total factor productivity. Lynam and Herdt (1988) suggest that sustainability be measured in terms of trends in total factor productivity (TFP), where
(1) TFP = O / I
with O being the total value of all outputs and I being the total value of all inputs. A sustainable system would feature a non-negative trend in TFP.
Monteith (n.d.) notes, however, that the "total value of all inputs" can be a somewhat arbitrary quantity with diverse components whose relative value may be hard to assess. A declining trend in TFP (as defined above) might be due to resource degradation or to declining product prices and higher input prices caused by gradual shifts in government policy. Note that this approach takes no explicit account of changes in the quality of the agricultural resource base and does not feature hypotheses on technical factors that may be causing an observed decline in TFP. As a practical matter, measuring the total value of all outputs and all inputs used in a farming system is likely to be expensive, considering that input/output measurement may be needed at several times during the year (to minimize recall error), for a reasonably large number of farmers (to minimize sampling error), over an indefinite number of years.
Finally, Monteith notes that this approach focuses on sustainability at the plot level, while avoiding assessment at the regional level. Increasing levels of TFP may mean little if population increase outruns productive capacity. Similarly, past gains in TFP may mean little if made at the expense of system resilience or in ways that ultimately degrade farmers' resources. "Turning points" in trends have always been the bane of those who would predict the future from the record of the past.

9 Induced, for example, by changes in input or product price policies. The confounding effect
described here stems directly from the ability of farmers to switch resources among enterprises
and to substitute one input for another.


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Trends in per capita production. Monteith (n.d.) proposes a somewhat different variation on trend analysis. He argues that to be sustainable, a system should maintain per capita benefit levels from year to year (and in principle from generation to generation) and should not itself deteriorate as a consequence of being used. He summarizes this in the following rule: "A system is sustainable over a defined period if outputs do not decrease when inputs are not increased."
This conceptualization is intuitively attractive and is fairly transparent as long as farmers use the same cropping patterns and associated livestock enterprises year after year on the same fields without increasing input levels. It seems aimed at subsistence economies with few opportunities for enterprise diversification or use of external inputs-that is, it ignores opportunities for substitutions within farming systems.
With input levels held constant, per capita production is a function ofyields, harvested area, and population density, where yield changes are driven by "sustainability" factors (resource quality), not by varying input levels or land use shifts; thus
(2) C = Y (A/P)
where C = per capita production, Y = yield per unit area, A = harvested area, and P = population density. By differentiating with respect to time, percentage changes become additive in the following manner:
(3) dC/dt)/C = (dY/dt)/Y + (dA/dt)/A (dP/dt)/P where (dC/dt)/C is the percentage change in per capita production with a small increment in time. In other words, the percent increase in per capita production is the sum of the percent increase in harvested area and the percent increase in yields, less the percent increase in population density.
For example, if yields are growing at 3.1% per year, with harvested area declining at 0.2% per year, and population increasing at 2% per year, then per capita production is increasing by 3.1 + (-0.2) 2.0 = 0.9% per year. Note that this approach assumes that parameter values do not vary over time.
Because this approach defines sustainability as the maintenance of per capita net benefits from year to year (net benefits vary in direct proportion to gross benefits because inputs are held constant), declining trends in per capita production are used to identify sustainability issues. However, this approach, like the previous two approaches, provides little information on the technical dimensions of any decline there may be in the quality of the agricultural


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resource base-the "causes of unsustainability."
This approach can conceivably be applied to whole systems. However, given the emphasis in the analytical model on harvested area and yield, its use in enterprise-specific analysis seems virtually inevitable. Indeed, Monteith himself uses enterprise-specific examples in his paper. However, as noted in the discussion of the first approach ("aggregate trends in output and yields"), this increases the danger of confounding "declining trends due to resource degradation" and "declining trends due to other factors," e.g., shifts of the enterprise by farmers from one land type to another (less productive) land type.
Finally, the feasiblity of this approach hinges on an ability to hold input levels constant. This may be difficult in practice. In controlled trials, input levels can be kept from increasing. At the farm level, however, input levels are usually found to vary. An approach that cannot assess the sustainability of systems where both product and inputs are increasing does not seem terribly helpful.
TFP revisited. The definition of total factor productivity described above is not the only one-it's not even a standard one. The concept of TFP can be strengthened by explicitly linking it to a production function.
An acceptable method of quantifying sustainability should be capable of distinguishing between: (1) yield changes due to changes in levels of purchased inputs (movements along a production function), (2) increases in total factor productivity due to technological change (e.g., upward shifts in the production function due to farmer adoption of an improved variety, or earlier planting), and (3) reductions in total factor productivity due to resource degradation (e.g., downward shifts in the production function due to nutrient depletion). This latter case is hypothesized to take the form of stagnant yields despite continuously increasing input levels, or actual yield reductions given constant input levels. Note that in this sense, resource degradation can be regarded as "technological change" with a negative sign.
Total factor productivity has been widely used for the purposes of empirical measurement of "the effect of technological change." TFP has been operationalized in several different forms. Samuelson and Nordhaus (1985), for example, use the following:
(4) TFP = Q SL(L) SK(K)
where TFP = total factor productivity (% change per year), Q = output growth rate (% per year), L = labor input growth rate (% per year), K = capital input


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growth rate (% per year), SL = (constant) labor factor share, and SK = (constant) capital factor share.
TFP as defined here, then, is a residual after accounting for the effects of increased input levels on increased output. As noted above, it includes (and thereby confounds) positive effects of technological change as well as negative effects of resource degradation. If it were possible to explicitly identify shifts in the production function attributable to technological change, then, after subtracting these from TFP, the new residual would estimate the specific effect of resource degradation on productivity.
A farmer monitoring program recently begun in Nepal by the National Agricultural Research Center, International Maize and Wheat Improvement Center, and International Rice Research Institute takes this approach to measuring sustainability. A farmer panel is monitored by local research and extension workers twice per year. Input/output data are obtained, along with information on field-level productivity problems and assessments of resource quality. Yields and TFP are then explained (through a set of recursive regressions) in terms of changes in input levels, technical change, and changes in resource quality (with weather information included to reduce unexplained variability). This project is still at an early stage of development. However, this approach, like the "trends in per capita production approach," focuses on the plot level, not the regional level. It says nothing about the race between increased demand on the one hand and sources of productivity growth on the other.
Yield trends in relation to inputs applied. Not everyone is captivated by the idea of estimating TFP. Direct estimation of the contribution of different factors to yield increase might be an unpretentious approximation.
Cardwell (1982), for example, estimated the relative contributions of a number of factors, both positive and negative, affecting Minnesota corn yields from the 1930s to the present. Each factor was assessed separately. First, the contribution of a particular factor in kg/ha or kg/ha/year was estimated synthetically. Area and numbers of years corresponding to that factor were used to estimate its current year contribution to yield change, which was then expressed as a percentage of the current yield.
Cardwell found that the switch from open-pollinated varieties to hybrids, improved weed control through herbicide use, increased plant densities, and earlier planting (allowed by a combination offall plowing, better soil drainage, and herbicide use) accounted for most of the increase in yields. An increase in nitrogen fertilizer use also accounted for part of the yield increase, but much


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of this was merely a substitute for lower levels of manure and reduced levels of N from mineralized organic matter. Soil erosion was found to have reduced yield potential by 8 percent over the 50-year time horizon studied.
Byerlee and Siddiq (1989) followed a more elaborate variation of this approach in their assessment of sources of growth in wheat production and yields in the Pakistan Punjab. They identified three major sources of growth: Increased irrigated area relative to rain-fed area, adoption of HYV's (highyielding varieties) in both irrigated and rain-fed areas, and increased HYV yields in irrigated areas. They also identified factors tending to depress yields: earlier planting, declining groundwater quality and increased field salinization, an increase in problem weeds, and lower fertilizer efficiency. Secondary factors also were explicitly included and measured. For example, the increase in irrigated wheat area relative to rain-fed wheat area was found to be partly due to an increased share of wheat in total cropped area--a shift in cropping patterns.
This approach is powerful in that it explicitly measures trends in both state and control variables, controls for land type changes and cropping pattern changes, controls for changes in input use levels, and identifies both positive and negative factors affecting yields. This allows researchers more scope for reliable forecasting of future events.
At this level ofdisaggregation, for example, it may be clear that some of the past sources of yield increase (e.g., adoption ofHYVs) have been fully used and can no longer support further growth. In contrast, it may be found that some of the negative factors (e.g., salinity) are increasing in importance, threatening future productive capacity. By integrating all of these factors into a single model, a powerful tool is forged for assessing yield and production growth for the near future. These can then be compared with expected demand changes to develop regional-level assessments of sustainability.
A disadvantage of this approach, however, is that it is extremely data intensive. It requires a combination of time series data from secondary sources, and microlevel data from farm surveys and from on-farm and onstation experiments. In many cases this data will not be available and the approach will be unusable without a substantial investment in data generation. Moreover, the approach is even more difficult to apply to complex farming systems, at the level of the system. The example given, focusing on wheat in the context of a relatively simple system, was already somewhat elaborate.
Finally (and this is a comment that applies equally to all of the approaches to analysis of trends), it interprets sustainability in terms of efficiency, not


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"resilience," and shows little sensitivity to the possible virtues of diversity as a generalized solution to the negative trends that were identified. That is, principles of agroecology seem to have little place either in the analysis or in the conclusions.
Linear programming. Where trend analysis forecasts the near future based on information from the recent past, linear programming can be used to simulate possible future events given parametric changes in farmers' access to land and other assets. When farmers can choose between several activities that have differential effects on sustainability (resource quality), the conditions under which they might shift from a more to a less sustainable activity become interesting.
One recent study examined this very question (Hildebrand and Ashraf, 1989). Several alternative cropping activities were assessed, with some of them assumed to have more beneficial carryover effects than others. Farm size was parametrically reduced to reflect likely changes arising from population pressure. An estimate was made of the minimum farm size needed to meet family food requirements, while maintaining soil fertility through fallow, alley-cropping, or fertilizer application strategies. Not surprisingly, it was found that minimum allowable farm sizes were larger when soil fertility maintenance depended on traditional fallowing and alley-cropping activities. Activities featuring the use of chemical fertilizer allowed farms sizes to decline much further without reducing soil fertility below critical levels. Results specifically highlighted a trade-off between fertilizer application and bush fallow area. It was not possible, however, to compare the sustainability of one strategy versus another, due to a lack of time series data.


SUMMARY
In order to operationalize the concept of sustainability, it is necessary to develop ways to measure it. This concept, however, has taken on a vast array of definitions and interpretations. In this paper, three distinct interpretations of sustainability have been discussed: agroecology, equity, and sustainable growth. Moreover, threats to the sustainability of agriculture take on many forms. Problems may be internal or external; they may be reversible or irreversible; or they may affect public health as opposed to the quality of resources devoted to agriculture.
Approaches to measuring sustainability are sensitive to the interpretation given to sustainability and the category of sustainability problem under


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consideration. Approaches may be nonquantitative (e.g., directional) or quantitative. The measurement of most external problems (e.g., reduced
availability of external inputs, global climate change) is likely require considerable input from disciplinary specialists. Measurement of sustainability from the agroecology perspective is hindered by lack of indicators of system resilience and diversity.
Considerable attention recently has been given to measuring sustainability at the plot-level, focusing on resource quality and productivity (as opposed to public health), in the context of the sustainable growth interpretation. This paper has discussed several methods that have been used, including the use of production or area trends; trends in total factor productivity (with or without decomposition into the effects of changes in input use, technical change, and resource quality); trends in per capita food production; yield trends in relation to inputs applied; and others. Some advantages and limitations of these different approaches have been discussed.
Work on measuring sustainability is still at an early stage. Much remains to be done by FSRE practitioners in partnership with disciplinary specialists.


REFERENCES

Altieri, M. 1987. Agroecology: The scientific basis of alternative agriculture. Boulder,
Colo.: Westview Press.
Arthur, L. 1988. The greenhouse effect and the Canadian prairies: Simulation of future
economic impacts. In Natural resource and environmental policy analysis: Cases in
applied economics. Boulder,Colo.: Westview Press.
Barbier, E., and J. McCracken. 1988. Glossary ofselected terms in sustainable economic
development. IIED Gatekeeper Series SA7. International Institute for Environment
and Development.
Barker, R., and D. Chapman. 1988. The economics ofsustainable agriculturalsystems in
developing countries. Agricultural Economics Working Paper 88-13. Cornell University, Ithaca, N.Y.
Batie, S.S. 1989. Sustainable development: Challenges to the profession of agricultural
economics. Presidential Address, AAEA Summer Meeting, Baton Rouge, La., July 3August 2.
Bishop, R. 1978. Endangered species and uncertainty: The economics of a safe minimum
standard. American Journal ofAgricultural Economics 60(1):10-18.
Byerlee, D., and A. Siddiq. 1989. Sources of increased wheat production and yields in
Pakistan's irrigated Punjab, 1965-2000. CIMMYT Economics Working Paper 90/
04. International Maize and Wheat Improvement Center, Mexico City.
Cardwell, V.B. 1982. Fifty years of Minnesota corn production: Sources of yield increase.
Agronomy Journal 74(November-December).
Chapman, D. 1983. Energy resources and energy corporations. Ithaca, N.Y.: Cornell
University Press.


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International Maize and Wheat Improvement Center (CIMMYT). 1989. Toward the
21st century: Strategic issues and the operational strategies of CIMMYT. Mexico
City.
Clark, C. 1976. Mathematical bioeconomics. N.Y.: John Wiley. Conway, G. 1986. Agroecosystem analysis for research and development. Bangkok, Thailand: Winrock International.
Durning, A. 1990. How much is enough? World Watch 3(6). Francis, C. 1986. Resource efficient systems for Third World farmers. Seminar
presentation, Kansas State University, Manhattan, April.
Graham-Tomasi, T. 1991. Sustainability: Concepts and implications for agricultural
research policy. In Pardey, Roseboom, and Anderson, eds., Agricultural resource
policy: International quantitative perspectives. N.Y.: Cambridge University Press. Harrington, L., P. Hobbs, T. Pokhrel, B. Sharma, S. Fujisaka, and C. Lightfoot. 1990.
The rice wheat pattern in the Nepal Terai: Issues in the identification and definition of sustainability problems. Journal for Farming Systems Research-Extension 1(2):127.
Heilbroner, R. 1980. An inquiry into the human prospect. N.Y.: Norton. Hildebrand, P., and M. Ashraf. 1989. Agricultural sustainability as an operational
criterion. Paper presented at the Ninth Annual Farming Systems Research-Extension
Symposium, Fayetteville, Ark., October 9-12.
IFPRI. 1989. Environmental policy for agricultural sustainability: An IFPRI research
thrust for the 1990s. Unpublished draft.
Ingram, J., and M. Swift. 1989. Sustainability of cereal-legume intercrops in relation to
management of soil organic matter and nutrient cycling. Paper presented at the
Intercropping Conference, Lilongwe, Malawi, January.
Jodha, N. 1989. Potential strategies for adapting to greenhouse warming: Perspectives
from the developing world. In N. Rosenberg et al., eds., Greenhouse warming:
Abatement and adaptation. Washington, D.C.: Resources for the Future.
Johnston, G. 1988. The role of economics in natural resource and environmental policy
analysis. In Natural resource and environmental policy analysis: Cases in applied
economics. Boulder, Colo.: Westview Press.
Krutilla, J., and A. Fisher. 1975. The economics of natural environments: Studies in the
valuation of commodity and amenity resources. Baltimore, Md.: Johns Hopkins
University Press.
Lynam, J., and R. Herdt. 1988. Sense and sensibility: Sustainability as an objective in
international agricultural research. Paper presented at CIP-Rockefeller Conference
on Farmers and Food Systems, Lima, Per6.
MacCrae, R., S. Hill, J. Henning, and G. Mehuys. 1989. Agricultural science and
sustainable agriculture: A review of the existing scientific barriers to sustainable food production and potential solutions." BiologicalAgriculture and Horticulture 6:173219.
McCall, M., and R. Kaplan. 1985. Whatever it takes: Decision-makers at work. Englewood Cliffs, N.J.: Prentice-Hall. 25 pp.
Monteith, J., ed. n.d. Can sustainability be quantified? Conference Paper No. 538.
International Crop Research Institute for the Semi-Arid Tropics. Submitted.
Pretty, J., and G. Conway. 1989. Agriculture asaglobalpolluter. IIED Gatekeeper Series
No. SA1. International Institute for Environment and Development.


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Rosegrant, M., and P. Pingali. 1991. SustainingriceproductivitygrowthinAsia: Apolicy
perspective. Social Science Division Paper No. 91-01. International Rice Research
Institute, Manilla, the Philippines.
Samuelson, P., and W. Nordhaus. 1985. Economics. N.Y.: McGraw Hill. Schmid, A. 1978. Property, power and public choice. N.Y.: Praeger. Schmid, A. 1989. Benefit-cost analysis: A political economy approach. Boulder, Colo.:
Westview Press.
Tisdell, C. 1988. Sustainable development: Differing perspectives of ecologists and
economists, and relevance to LDC's. World Development 16(3):373-384.
U.S. Department of the Interior. 1989. Estimates of undiscovered conventional oil and
gas resources in the United States. U.S. Government Printing Office, Washington,
D.C.


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Indigenous Knowledge and Fertilizer Strategies in Leyte, Philippines: Implications for Research and Demonstration Trials'

Daniele Perrot-Maitre and T.F. Weaver 2



ABSTRACT

Indigenous resource taxonomies obtained by ethnographic interviews of lowland rice farmers in Leyte, Philippines are used to develop an understanding of farmers' fertilizer allocation decisions. The analysis shows that farmers have complex taxonomies of soils and paddy types and that a knowledge of these taxonomies is necessary for understanding farmers' rationale for fertilizer management. How farmers perceive fertilizer response, the rules of thumb they follow in allocating fertilizer, and their fertilizer-related concerns are addressed. The paper concludes with a series of suggestions for incorporating farmers' concerns and knowledge
into the design, management, and evaluation of fertilizer trials.


INTRODUCTION

Lightfoot (1985) has argued that indigenous research is "a viable resource for developing on-farm experimental methods that.promote farmer
participation.in adapting technologies to specific farming conditions and providing feedback on basic research needs." He stresses, however, that one of the main drawbacks of the approach is that indigenous knowledge is hard to elicit and that it will require researchers to develop new skills and new procedures for documentation and interpretation of this information.
This paper gives an example of how data from ethnographic interviews are useful for understanding farmers' knowledge of soils and fertilizer, which in turn is used to understand their rationale for making resource allocation 1 Paper presented at the 11 th Annual Farming Systems Research-Extension Symposium, Michigan
State University, East Lansing, October 5-10, 1991.
2 Research Associate and Professor, respectively, Department of Resource Economics, The
University of Rhode Island, Kingston. This paper is based on the first author's doctoral dissertation, "The use of indigenous knowledge in understanding resource allocation at the farm level: The case of lowland farms in Leyte Island, Philippines. "Rhode Island AES Contribution
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decisions. In the process, insights are gained that may be used in designing fertilizer trials and research that are relevant to farmers' concerns.


ETHNOGRAPHIC INTERVIEWS

Ethnographic interviews are a series of conversations for gathering information that describes the respondents' view of the world around them. The approach is based on the premise that naming and classifying things are fundamental principles of human thinking, enabling individuals to organize concepts and reduce them to a manageable set (Tyler, 1969). The categories of these classification systems or taxonomies are assumed to be operationally significant to an individual in day-to-day decison-making. Hence discovering them and the rationale behind their construct enables us to understand decisons regarding the allocation and the manipulation of the items that constitute the taxonomy. For example, if we know how a farmer classifies his or her land and the rationale behind the classification, we may gain considerable insight into his or her management decisions regarding this resource.
During ethnographic interviews, the task is to ask the "right" questions: those that will elicit relevant information from the informant about the areas of interest to the researcher. The sequence of questions is not predetermined as with conventional surveys, but follows directly from the informant's answers and knowledge. Discussion and examples of the types of questions used in this study are presented in Appendix A.


LOCATION OF THE STUDY SITE AND SAMPLE SELECTION

The research was conducted in Hibunawan, a barrio comprised of 131 households in the municipality of Baybay on the west coast of Leyte, Philippines (Figure 1). This part of Leyte lies in the humid typhoon belt with a climate characterized by abundant rainfall with no pronounced dry season.
Ninety percent of the 131 households in the barrio are involved in subsistence and semisubsistence agriculture. The main crops are rice, coconut palms, and corn, which occupy, respectively, 47, 41, and 12 percent of the total 254 ha of cultivated land in the barrio. Ethnographic interviews were conducted with 57 farm households selected by stratified random sampling.
Rice, which is double cropped by 97 percent of these households, is the economically dominant crop. Half the paddy area is rain-fed whereas the rest receives supplementary irrigation from the local pump-irrigation system.


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Tacloban


Reublic Lyte
C of the I
.4 Philippines N

I I
300 mi
Figure 1. Location of the Study Site in the Philippines
The rice farms in the barrio are small, averaging 0.9 ha (standard deviation of 0.98). The area devoted to paddy production ranges from 0.23 to 7.5 ha with 70 percent of the households in the sample cultivating 1 ha or less.


CLASSIFICATION OF PADDY LAND

For their rice lands, farmers have a taxonomy of paddy types that describes hydrological conditions, which reflect and are related to soil type, position in the toposequence, water source, and special features such as proximity to a canal, a creek, or a hillside. In this classification (Table 1) banika paddies refer to drought-prone upper paddies with low water-holding capacity. If rainfall is below normal, these fields may be unsuitable for rice cultivation unless irrigation is available. Binog refers to lower paddies with bad drainage. Many of these fields are prone to periodic flooding and/or waterlogging. The term lanoddescribes an intermediate situation. The risk of crop failure, considering both drought and flood, is the lowest in these fields. Generally fields lower in the toposequence are considered the best for growing rice, especially when the rainfall is not heavy and the risks of flooding are low. This is not only because of the better water situation but also because the fields benefit from nutrient runoff from higher elevations.


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Table 1. Characteristics of Paddy Types and Management Practices by Type of Paddy
According to Farmers in Hibunawan.

Paddy type Banikaa Lanodb Binogc


LITTLE RAINFALL Topography Highest/high
Drainage Dries quickly
Yield Poor
Tillers density Few tillers Planting
sequence Planted last
Land preparation technique Carabao only

Seedbed type Dapog Transplanting
method In rows w/marker
Weeding With knife only
Fertilizer Once or none


ABUNDANT RAINFALL Yield Good
Tillers density Good number Planting
sequence Planted second
Plowing
intensity Once or twice
Land preparation technique Hand-tractor/
carabao
Seedbed type Dapog Transplanting method In rows w/mai
Weeding Mechanical/
manual
Fertilizer Once or twice


rker


High/low Does not dry easily Good if irrigated Good number

Planted second

Hand-tractor/ carabao
Dapog

In rows w/marker Mechanical/manual Once or none


Good
Good number

Planted first

Once or twice

Hand-tractor/ carabao Dapog

In rows w/marker Mechanical/ manual Once or twice


Low/lowest Does not dry Good if no kohol snail Good number Planted first Hand-tractor if too deep
Wet bed if too deep Random Manual only None


Poor if floods Few tiller if floods Planted last None

None

Wet bed Random None or manual None


Generally pilit soils.
b Generally pilit, bukagay, or yutae soils.
C Generally yutae soils.
Source: 1987-88 Field Survey, N=57.


SOIL CLASSIFICATION AND FERTILITY DIFFERENCES

The farmers of Hibunawan distinguish differences in soil fertility both between and within fields. They associate differences with three identified soil types: (1) pilit (clay), (2) yutae (clay with silt), and (3) bukagay (clay with sand). As shown in Table 2, the concept of soil type encompasses texture, color, fertility, water-holding capacity, workability, and husbandry practices, including plowing requirements, weeding, and fertilization strategies. AcJournal for Farming Systems Research-Extension


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INDIGENOUS KNOWLEDGE AND FERTILIZER STRATEGIES 25

cording to the farmers, yutae soils are the best for growing rice and require very little or no fertilizer. Pilit soils generally are thought to require two applications of fertilizer whereas bukagay soils are intermediate to the other two in terms of their fertilizer needs. Operationally these differences in fertilizer need are associated as much with paddy types as with soil types. A Chi-square test of the association between soil and paddy types identified by farmers showed a strong relationship (Chi-square=29.7, 4 d.f., and 171 observations).'
Based on laboratory analysis, there was no significant difference across fields between the different types identified by the farmer in the level of organic matter (and therefore nitrogen (N)), phosphorus (P), potassium (K), and pH. This finding suggests that the terms for the three soil types (pilit, yutae, and bukagay) may be used by farmers in a relative sense based on their personal experience in their fields. Unlike paddy types that are identified by location variables that are directly observable and easily shared between individuals, soil types are dependent mostly on texture, which is evaluated by feel rather than by sight. Without the existence of a shared standard such as that taught to soil scientists for their textural classifications, the notion that farmers' soil types are relative concepts seems a reasonable hypothesis. The hypothesis is supported by the fact that no correlation was found between the farmers' and soil scientists' soil types4 based on textural classifications (Chisquare=2.630, 6 df, and 33 observations) and that attempts to produce a soil map based on the farmers' soil types resulted in a random mosaic. At the same time the soil map for the area produced by the soil scientists has the ordering typical of these sorts of efforts. Consistent with this line of reasoning, as already noted, there was no difference in fertility across fields identified as more fertile by farmers. However differences in relative fertility that farmers identified within the same field were supported by laboratory analysis.5




This association between soil and paddy types is illustrated by the fact that 60 percent of the binog
fields have yutac soils, 67 percent of the banika fields have pilit soils, and 56 percent of the lanod
fields have pilit soils, 30 percent have bukagay soils, and 14 percent have yutae soils.
4 These categories are clay, clay loam, silty clay loam, and sandy clay loam.
5 Approximately 83 percent of the sections of rice fields described as more fertile by the farmer
showed higher percentages of organic matter (six observations) than sections said to be less fertile. All had higher levels of P (although in only two-thirds of the sample was the difference significant enough to affect the P status of the soil) and two-thirds had significantly higher levels
of K.


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Table 2. Rice Soils and Associated Cultivation Practices According to Farmers in
Hibunawan.
Soil types
Bukagay Pilit Yutae
Texture Clay with sand Clay Clay with silt
Color Brown/yellow/black Red/brown/black Black/brown
Fertility Intermediate Exhausted soil; least Most fertile
fertile
WHCa Dries very easily Dries easily Does not dry easily
Workability Friable Hard if no water Soft and muddy
Plowing Can plow dry or wet Cannot plow without Can plow dry or wet
water
Weeding Can pull weeds even Need water to weed Intermediate
if dry
Fertilizer Intermediate Heavy None or very little
application (once or twice) (twice) (none or once)
a Water holding capacity
Source: 1987-88 Field Survey, N=57.

FARMERS' FERTILIZER STRATEGIES

Based on ethnographic interviewing, the fertilizer practices of the farmers are more related to fertilizer interaction with other inputs such as water and with the perceived risk of crop loss associated with different paddy types than with soil type itself.
Farmers do not allocate fertilizer according to the marginal productivity rule. Rather, they decide upon the quantity of fertilizer to apply by following what is known in the barrio as the ekonomia strategy. Using this strategy, farmers try to keep the crop "healthy" at a minimum cost. If growth is favorable, little or no fertilizer will be applied during the entire growing season. However, if growth is unfavorable and there is enough water in the field, then cultivators will tend to apply fertilizer in an attempt to have the crop "catch up." Farmers will not apply high doses of fertilizer on crops that are constrained from catching up, such as weed-infested fields.
The farmers' taxonomy of paddy land serves as the basis for understanding fertilizer-allocation decisions. As a rule, lower-lying fields (binog) are not fertilized because farmers say they are more fertile (partly because of the abundant water and nutrient runoff from upper paddies) and because the risk that the fertilizer will be washed away (hunob) is high. More fertilizer may be applied on upper fields (pilit and bukagay) to compensate for unfavorable water conditions over the entire growing season (provided there is enough water at the time of application and that the field is not infested byweeds). For


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example, one farmer applies the equivalent of 79 kg of N per hectare on an upper, drought-prone, less fertile field, and no fertilizer in the lowest, waterlogged field. The farmer's two other paddies, which present an intermediate situation, receive the equivalent of 33 to 73 kg of N per hectare. In summary, farmers' fertilizer allocation strategies are based on their paddy land taxonomy, which incorporates characteristics in addition to soil types.


TYPES AND CHARACTERISTICS OF COMMERCIAL FERTILIZERS
The most widely used commercial fertilizer is urea (grade 46-0-0) and, to a lesser extent, ammonium sulfate (21-0-0). Only one of the farmers applied complete fertilizer (14-14-14) in the dry season and, according to this farmer, this was an experiment.
All respondents indicated that too much fertilizer can be detrimental to plant growth and that diminishing returns occur if too much fertilizer is applied. Too much fertilizer, it is thought, will cause the plants to lodge (tomba), grow with fewer panicles, or bear empty panicles (tahopon). Fortyone percent of the farmers who usually apply fertilizer claimed they knew the maximum level of fertilizer to apply on their fields. These levels vary widely, averaging 69 kg of N per hectare (standard deviation of 45.5, 23 observations). This amount is close to official recommendations from the Philippines Council on Agriculture and Resources Research (PCARRD, 1979), which calls for 60-90 kg of N per ha depending on the season (and 30 kg of P and 0-30 kg of K depending on the soil type). But survey data indicate that respondents who applied N during the two cropping seasons studied applied on average respectively 40 and 34 kg of N per hectare for the two seasons (standard deviation of62.8 and 68.6), or 66.3 percent and 56.8 percent of the recommended 60 kg per hectare. These average levels are higher than usual because in the survey year farmers who cultivated irrigated land benefited from a government fertilizer subsidy6 (47.4 percent of the farmers in the first cropping season and 32.3 percent during the second cropping season benefited from this subsidy). Even with the subsidy, approximately 88 percent of the farmers applied less than the recommended 60 kg of N per hectare.
6 The goal of the subsidy program, referred to as "Buy One Take One," was to compensate farmers for the low harvest following the 1987 drought and to boost production levels by encouraging irrigated rice farmers to apply greater amounts of fertilizer. The program targeted irrigated rice farmers only and stipulated that for every bag of a type of fertilizer purchased (urea or ammonium sulfate), farmers would get one bag ofanother type of fertilizer free. In practice, depending on stock availability, farmers may have received two bags of the same type. Vol. 3, No. 1, 1992

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None of the farmers surveyed applied K and P to their fields. However results from a laboratory analysis of 33 soil samples indicated that 82 and 79 percent of the soils in the rice fields were deficient in P and K, respectively. On the other hand, 88 percent of the samples had adequate levels of organic matter, and therefore did not need N application. Because farmers do not recognize K and P deficiency as distinct from N deficiency and because complete fertilizer is more expensive, farmers are reluctant to apply Kand P.
There is almost total agreement among farmers concerning the appropriate use of the different commercial fertilizers. Urea is said to be good for "fruiting" (forming full panicles), whereas ammonium sulfate favors tillering. Huijsman (1986) reported similar findings among rice farmers in Iloilo province. Urea is considered best for rice because, although it is not immediately effective (it takes four to six days to see the effects compared to three days with ammonium sulfate), it has a longer lasting effect (about 90 days) than ammonium sulfate (about 21 days). Farmers in Hibunawan say that the long-lasting effect of urea means fertilizer need only be applied once, while ammonium sulfate, whose effect is believed to last only three weeks, must be applied at least twice during the growing season. Agronomists at PCARRD also reported that ammonium sulfate has a more immediate effect on crops than urea (PCARRD, 1979); however, none of their recommendations address that issue. Recommendations are worded in terms of kg of NPK per ha and give the optimal time and quantities for split application, without addressing how quantities, method, and timing of applications may vary depending on the type of fertilizer used.
According to all farmers surveyed, if only one application of urea is used, the optimal time to apply it is about a month after transplanting following the last weeding, because by then weed competition for nutrients has been eliminated. This is the reproductive stage (mabdos) and the rice plant needs to be "helped" to form panicles. This is partly consistent with recommendations from the International Rice Research Institute (IRRI, 1987), which suggest, among other practices, an application of N five to seven days before or at panicle initiation.
A widespread belief among Hibunawan farmers is that longer maturing (120 days) rice varieties need less fertilizer because they have more time to grow and recover from drought or pest infestation than do quicker maturing varieties. As a result, the optimal timing of fertilizer application differs according to the maturity of the variety. Some farmers claim that the best strategy is to use a split application with the second application earlier for the


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INDIGENOUS KNOWLEDGE AND FERTILIZER STRATEGIES 29

short-season varieties, but there is no consensus on the timing. Some advocate that the second application should be 45 days after transplanting for shortseason varieties and 60 days after transplanting for longer maturing ones. Others favor 21 days and 30 days in these two situations.
PCARRD recommendations are somewhat confusing regarding any relationship between rice varieties and fertilizer application. There are two sets of recommendations. One set (PCARRD, 1979) indicates the optimal level of NPK per hectare according to the percentage of organic matter, K, and P found in the soil, and the "group" to which rice varieties are assigned. The number and timing of applications or the effect of maturity on application strategies are not addressed. Also rice varieties are not classified by maturity, and the characteristics that distinguish them are not specified, so that it is impossible for the uninformed to extrapolate into which group new varieties will fall.
The second set of recommendations for rice (PCARRD, 1979) addresses the issue of timing and number of applications according to the method of crop establishment (transplanted versus direct-seeded), the water regime (irrigated versus rain-fed), the season (for irrigated fields only), and the soil types (clay versus loamy soils for the wet season only). The effect of rice varieties on the method of application is not addressed in these recommendations. All of these recommendations follow from basal application before the last harrowing without mentioning how recommendations might change when farmers do not apply fertilizer that early in the season. Survey data indicate that, in practice, no farmer in Hibunawan applies fertilizer before the last harrowing. Farmers explain that the risk of crop failure is too great that early in the season to invest in fertilizer, even in irrigated fields.
In summary, the recommendations, from both IRRI and PCARRD, do not address the issues raised by the farmers, such as the location of the fields, weed density, and risk factors associated with the quantity, timing, and number of fertilizer applications. Likewise, no recommendations address the possible effect of the type of fertilizer (urea or ammonium sulfate) on fertilizer strategies. The fact that the two sets of recommendations available during the research period did not match was a source of confusion for farmers and added to the fact that these types of recommendations are largely irrelevant to their concerns.
In general, the farmers of Hibunawan do not readily discuss abstract situations related to farming, i.e., situations they have not encountered in their fields. The levels of N application they are willing to discuss are the ones with which they have experience. Using ethnographic interviews, farmers were asked to recall, for specific fields, what fertilizer levels they had experience with


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and how different fertilizer levels affected yields. The descriptive experience question was worded with no specific quantity of fertilizer because, given the diversity of fertilizer levels and field areas experienced by farmers, it was impossible to word a general question suitable to all situations.
Twenty-one percent of the farmers reported that over the years they have experimented with more than one level of fertilizer application. From these farmers, an equal proportion (25 percent) reported that under higher levels of fertilizer they observed greater yields for all their fields, or higher yields in some fields and lower yields in other. A third of these farmers said their yields were lower in all their fields, and 17 percent reported no change in yield level. However, farmers with higher yields typically attributed them to the type rather than the quantity of fertilizer, the season, or a switch in rice variety. Farmers with lower yields explained it by the presence of tungro, and/or by application of too much fertilizer, so that in the end no farmers attributed the observed yield changes to the quantity of fertilizer itself.
Given that farmers who experimented with different levels of fertilizers did not explain the change in yield by the effect of fertilizer alone, an attempt was made to determine indirectly the perception of fertilizer response by asking farmers what were the best and worse yields they experienced in each field, and what they thought were the reasons for these yields. These results are given by order of importance in TalSle 3.
Water, rice variety, pests, and tungro infestation rank the highest in farmers' list of variables affecting yields. Good water and/or good rice variety were cited in 90 percent of the cases as the reason for high yield; inadequate water, tungro, and pests were mentioned in 77 percent of the cases as an explanation for poor yields.7 Fertilizer-related reasons were mentioned in only six percent of the cases as being responsible for high and low yields.
Again, fertilizer was never cited alone but always in combination with other inputs. Water, rice variety, and fertilizer levels interact and farmers recognize this interaction. According to the farmers, if there is little water during the reproduction stage, the crop is the most affected both because of the lack of water and because it is impossible to apply fertilizer. Therefore low application alone does not explain low yields. This reinforces the conclusion that farmers do not separate out the effect of fertilizer because they tend to apply this input in combination with water.
Other impressions and conclusions gleaned from ethnographic interviews were reinforced at a seminar given in May 1988 by an agronomist from the 7 This percentage does not include cases where water, pest, and tungro interact with other
variables.
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Table 3. Farmers' Perception of Factors Affecting Rice Yields, Hibunawan. Factors Percent mentioning

HIGHEST YIELDa
Good water 55
Good variety and good water 25
Good variety 10
Other 10

LOWEST YIELDc
Drought or not enough water 53
Tungro 18
Pest 6
Other 26
a Highest yields reported for 98 parcels.
b Includes 'right quantity of fertilizer and good seeds,' 'Good water and dean field,' 'Right type
of fertilizer and good rice variety,' 'good water, good seeds, and dean field,' 'Good water, good
seeds, and right quantity of fertilizer,' and 'Seedbed type, spacing, and good water.' c Lowest yields reported for 119 parcels.
d Includes 'Drought and pest,' 'Drought and not enough fertilizer,' 'Typhoon,' 'Drought and
tungro,' 'Drought and bad rice variety,' 'Waterlogged field,' 'Wind before harvest,' 'Weed infestation,' 'Several transplantings,' 'Tungro and other pests,' 'Field not well prepared,' and
'Drought, no fertilizer, and no time to weed.'
Source: 1987-88 Field Survey, N=57.

Visayan State College ofAgriculture about the use of fertilizer. The questions farmers raised revealed that they were not as concerned about optimal quantities of fertilizer as they were about the best way to apply a given quantity of fertilizer. The questions were of the type "How do I apply one sack of urea fertilizer if all I can afford is one sack?," "Is it okay to mix urea and ammonium sulfate together?," "Is it better to apply in two applications instead of one?," and "If there is no rain, how long should I wait for the second application of fertilizer?" Farmers also showed great concern about applying too much fertilizer, which they believe results in empty panicles, exhausted soil fertility, and increased soil acidity, and gets them caught in a vicious circle of needing to apply more and more fertilizer to maintain fertility.


IMPLICATIONS FOR FERTILIZER TRIALS

Overall, the ethnographic interviews with the farmers of Hibunawan provide information that strongly suggests that farmers' concerns are quite different from the issues traditionally addressed in fertilizer trials. Because recommendations are developed from the results of these trials, they bypass many of the factors the farmers consider to be relevant and are therefore of limited practical use. The concerns raised by farmers in this study suggest new directions for designing and


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evaluating fertilizer trials as well as for research. In addition, the ethnographic approach has provided an understanding of the cultivators' view of the production process that can provide the context for the discussion of current practices or the introduction of new ones through workshops and published materials.
The following are examples of the types of fertilizer trials and research that are suggested by the practices and concerns of the farmers of Hibunawan.
1. Farmers' knowledge ofmicrodifferences in their fields should be taken into account when locating fertilizer and demonstration trials. In Hibunawan, trial design might include the paddy types recognized by the farmers (banika, lanod, or binog).
2. The rules of thumb farmers use to manage their fields should be tested, such as applying more fertilizer on banika paddies although the marginal productivity of fertilizer is probably lowest on these fields and applying less fertilizer on the (supposedly more fertiles) low-lying binog paddies because of the high risk of flooding. At the present time there is insufficient information either to confirm or refute the farmers' rules of thumb relating to their perception of the marginal value product from the application of N to their rice crop.
3. Field trials or demonstrations might investigate farmers' perceptions that urea and ammonium sulfate have different effects on the crop. For example, the source of N is said to have an effect on tillering and panicle initiation with urea being better for panicle development and ammonium sulfate for promoting tillering. Fields trials also could demonstrate whether urea and ammonium sulfate require different timing of application.
4. Farmers' confusion over the implications of mixing types of N fertilizer might be clarified through field trials and/or published materials.
5. An analysis of Hibunawan farmers' apparently universal opinion that the application of fertilizer at planting time is too risky to recommend the practice might be instructive to a wide audience.
6. The effect of maturity of rice varieties on fertilizer strategies needs to be demonstrated and explained to the farmers.
7. A central issue to Hibunawan farmers facing budget constraints is how to make the best use of quantities of fertilizer that are less than the recommended optimal levels. For example, farmers can be instructed as to how they can best use one bag of urea and one bag of ammonium sulfate and under which circumstances they can be mixed together.
8 This view was not supported by the soil sample analysis for organic matter, K, and P, which showed
no difference on average for the three soil types. It is possible that differences in microelements not tested for, such as zinc, might explain the higher fertility attributed to the lower fields. Another possibility is that the risk of crop failure from flooding is great enough that indeed it does not pay
to fertilize the crop.
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INDIGENOUS KNOWLEDGE AND FERTILIZER STRATEGIES 33

CONCLUSION

This paper shows that the technique of ethnographic interviewing is useful in eliciting farmers' resource taxonomies that provide insights into the farmers' rationale for input allocation. The insights gained are relevant to farmers and can be directly incorporated into FSRE activities. The relevance to the design and evaluation of fertilizer trials and research has been addressed in this paper.
The discussion focused on how information gathered during ethnographic interviewing was useful in developing an understanding of the farmer's rationale for managing and applying fertilizer to their rice crops. The implications for fertilizer trials and research are important. Incorporating farmers' knowledge and practices not only into farmer-designed but also into farmer-managed trials would be a fruitful way to develop technologies that are truly relevant to farmers' circumstances. Because they are developed and tested according to the farmer's perspective, these technologies present a familiarity to the farmer that makes them more readily adoptable.
Perhaps of greater significance is the fact that by knowing the farmers' perceptions of their resources, a basis for communication between extension workers, scientists, and farmers has been established. This wll surely help facilitate the development of new technologies and management practices.


REFERENCES

Huijsman, A. 1986. Choice and uncertainty in a semi-subsistence economy. Wageningen, The
Netherlands, 336 pp.
International Rice Research Institute (IRRI). 1987. Rice production course. Volume 1,
Training and Technology Transfer Department, IRRI, Los Bafios, Philippines.
Lightfoot, Clive. 1985. Farmer participation in on-farm trials. Paper presented at the Fifth
Annual Farming Systems Research-Extension Symposium, Manhattan, Kans., October
13-16.
Perrot-Maitre, D. 1992. The use of indigenous knowledge in understanding resource
allocation at the farm level: The case of lowland farms in Leyte, Philipines. Unpublished
Ph.D dissertation, University of Rhode Island, Kingston.
Philippines Council for Agriculture and Resources Research and Development (PCARRD).
1979. The Philippines recommends for soil fertility management, 1979. Los Bafios,
Laguna, 108 pp.
Spradley, J.P. 1969. The ethnographic interview. N.Y.: Holt, Rinehart and Winston, 247
pp.
Tyler, S.A., ed. 1969. Cognitive anthropology: Readings. N.Y.: Holt, Rhinehart and
Winston, 521 pp.


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APPENDIX A: EXAMPLES OF ETHNOGRAPHIC QUESTIONS.

Ethnographic interviewing uses three main types of questions (Spradley, 1979): (1) descriptive questions that aim to identify classification systems, (2) structural questions that aim to verify taxonomic relationships, and (3) contrast questions that elicit the differences between folk terms. The primary varieties of questions that were employed in this study were four types of descriptive questions (grand tour, mini tour, experience, and native language questions), three types of structural questions (verification, cover term, and included term questions), and three types of contrast questions (verificationcontrast, dyadic, and rating questions). Examples of the different types of questions are given below.

Descriptive Questions
Grand tour question. "I would like to know what your farm is like. Could you please describe it for me?"
Mini tour question. "Could you explain to me what you do when you plow your field?"
Experience question. "Could you explain to me what happened when you applied fertilizer to your rice field?"
Native language question. "Could you explain to me what the word binog refers to?"

Structural Questions
Verification questions. "Are there different kinds of soils in your farm?" (domain verification question). "Are there other ways to plow a field?" (included term verification question). "You mentioned earlier that sandy soils were hot. Then could you say that clay soils are cold?" (semantic relationship verification question).
Cover terms questions. "Are there different kinds of fertilizers?"
Included terms questions. "Are there other kinds of soils in your farm?"

Contrast Questions
Contrast verification question. "Would you say that all binog paddies are fertile and all banika paddies unfertile?"
Dyadic contrast question. "What are the differences between pilit and yutae soils?"
Rating contrast question. "Which type of paddy is best for growing rice?"


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Gender Issues in African Farming:

I. A Case for Developing Farm Tools for Women'

R.N. Kaul and A. Ali 2



ABSTRACT

Women in Africa contribute up to an estimated 70 percent of the labor involved in food production and nearly 100 percent in rural food processing. Their range of daily activities (food preparation, water and fuel transportation, farm weeding, crop threshing, and dairy product preparation) is far wider than men's and demands high energy input.
Despite awareness of this, most technologies developed for small-scale farmers are geared to men with no concern for their appropriateness for women, who possess different physiques and energy capabilities in
comparison to men.
This paper examines farm tools and processes thatarepredominantly used by women in Africa and explains, with available research data, the inadequacies of such tools and processes when linked to women's physiques and energy levels. Specific examples of tool design-parameter selection for operations such as weeding and rural milk processing are highlighted to show the applications of the research. They also illustrate the need to design and develop farm tools specifically for female operators in order to improve overall ease of use, safety, and effective integration of
women in farming system innovations.


INTRODUCTION

Farming in developing countries is a complex interaction ofmany components that have evolved into various systems in different ecological regions. One thrust in farming systems research-extension (FSRE) is to understand and unravel these complicated systems so that, wherever possible, innovations may be introduced without disturbing the equilibrium of the systems.



1 Paper presented at the Ninth Annual Farming Ssytems Research-Extension Symposium,
University of Arkansas, Fayetteville, October 9-11, 1989.
2 Institute for Agricultural Research, Ahmadu Bello University, Zaria, Nigeria.

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Gender issues in farming have emerged as a strong focus in FSRE. It is reported that two-thirds of the work in the world is done by women. In return for their work they receive a mere 10 percent of all income and own only 1 percent of all means of production (Moller, 1987). Many studies have established that women perform nearly every possible task in rural African communities. Some of these activities, such as transportation and the drudgery associated with it, are not fully appreciated. An analysis of transport activities of able-bodied adult males and females, for example, revealed that an average female spends 1,000 hours in Ghana and 1,600 hours in Tanzania per year physically transporting items. This constitutes 70 percent of transport in terms of time and 80 percent in terms of effort (Harrison and Howe, 1989).
Studies by Adeyokunnu (1981) showed that 40 percent of all women studied in Nigeria could be classified as farmers, 28 percent as processors, and 52 percent as traders. In another study on Yoruba women in Nigeria, a time budget analysis showed that all village women spent some of their time farming (Spiro, 1981). Abalu (1984) identified the "cropping chain" as typical of farming systems in sub-Saharan Africa, where women constitute a vital link in the chain for optimal resource management.
The issue is not strictly one of gender differentiation but involves a much broader scope: Who does what, why, when, and where (Poats, 1988)? Women's work domain is far wider than men's. Agricultural modernization programs have tended to increase women's work load but seldom have attempts been made to introduce labor-saving devices for women's use in their traditional tasks. In fact, when a farm labor-saving gadget is developed, it is usually introduced for use by male workers. Sometimes, after a new gadget is introduced, a task formerly done by women is taken over by men, resulting in the loss of livelihood for women, who otherwise constitute a vital earning member of the family.
Upsetting gender roles in traditional tasks will have negative effects in the long run. Women's participation in rural development needs to be treated as a goal in its own right. This has become a topic of great concern in FSRE.


THE GENDER ISSUE

As has been briefly mentioned, the so-called gender issue is not only gender related-it involves looking at a typical system in which men, women, and children are integrated into a working unit with an unwritten understanding of the division of labor. The system, which is skewed towards greater total


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work hours by women, is operational although not fully efficient. When we introduce modernizations without taking gender issues into account, the system becomes more disadvantageous for women. For example, introduction of better seeds and fertilizer has increased the work load of women because of closer plant spacing and extra hours needed to weed and process the additional food produced. A similar pattern is observed in the animal ,production sector and involves farming and domestic activities that may necessitate a 16-hour work day (Hoskins and Weber, 1985). In the African context, there is a sexual distribution of farm labor that appears to be task and crop specific, as reflected in the work of Igben (1980) and Dixon-Mueller (1985). (See Tables 1 and 2, respectively, which are based on their work and which show the division of labor by activity and crop between the two genders.) The involvement of the whole family in the various tasks and the variations in labor participation for the different tasks and crops are significant. A different pattern may apply to other ecological areas but the division of labor prevails. It is therefore important that any technological improvement acknowledge this division of labor so that there is no imbalance created in the existing system by having only males benefit from new technology.
Most attempts to introduce better farming tools further complicate this issue. The tasks that were traditionally done by women are passed on to men, who with the new equipment find the task easy and remunerative. This leaves the more difficult tasks, such as grinding and internal transportation, for women to perform. Thus a new dimension is introduced into this system, displacing women from their traditional tasks and leading to subsequent loss

Table 1. Proportion of Labor Input Contributed by Family Members in Plateau State.
Person-hours contributed bya
Activity Men Women Children Relations Total
Land clearing 39.38 26.94 19.17 14.51 100.00
Land tilling 43.18 23.96 20.33 12.53 100.00
Planting 30.41 22.22 31.58 15.79 100.00
Weeding 39.00 27.76 18.00 15.25 100.00
Fertilizer
application 36.67 23.33 19.33 20.67 100.00
Harvesting 36.13 24.51 22.26 17.70 100.00
Transporting 35.77 30.66 18.97 14.60 100.00
Total average 38.21 25.72 20.75 15.32 100.00
a In percentages.
Source: Igben, 1980.


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Table 2. Female and Male Labor Contributions to Staple Crops Among the Tiv,
Central Nigeria.'

Crop Field Planting Weeding Harvesting Processing Storage
preparation

Yams F: 50 F: 80 F: 100 F:100 F: 100 F:100
M: 50 M: 20
Millet F: 20 F: 100 F: 50 F:100 F: 50
M: 100 M: 80 M: 50 M: 50
Sorghum F: 20 F: 100 F: 50 F: 100 F: 100
M: 100 M: 80 M: 50
Cassava F: 25 F: 75 F: 100 F: 75 F: 100 F: 100
M: 75 M: 25 M: 25
Maize F: 25 F: 90 F: 100 F: 90 F: 100 F: 100
M: 75 M: 10 M: 10
Rice F: 10 F: 100 F: 100 F: 50 F: 50 F: 100
M: 90 M: 50 M: 50
Sesame F: 50 F: 100 F: 50 F: 100 F: 100
M: 100 M: 50 M: 50
Water- F: 25 F: 25 F: 100 F: 25 F: 100 F: 100
melon M: 75 M: 75 M: 75
Cowpeas F: 25 F: 25 F: 100 F: 100 F:100 M:100
M: 75 M: 75


a All measurements are percentages. Source: Dixon-Mueller, 1985.


F = female; M = male.


of income. In the African context, this involves extra hardship for the family because women contribute greatly to family income and, in most cases, control their income for specific needs not shared with men.
The issue, therefore, is to find appropriate technology for women within the existing farming system so that they can perform their traditional tasks as well as upgrade male tasks. This approach would elevate the existing system to a higher level of efficiency without creating a gender imbalance in terms of tasks. The other strongly related issue in such an approach concerns social and governmental policies relative to rural development. This paper, however, will concentrate on appropriate technology aspects.


GENDER-SPECIFIC TOOLS

In view of the fact that nearly 8 0 percent ofThird World farmers are small-scale farmers, there is a global focus on developing appropriate and relevant technology for this group. However, even when developing "relevant" technology there is a tendency to ignore the well-established gender-based task division of small-scale farmers, as highlighted above. Therefore, ironical-


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ly, the so-called "relevant" technology usually is not acceptable to female users. Because of their socially underprivileged status, the women simply abandon the task to men or continue performing the task with the maleoriented equipment that is difficult for them to use. A few illustrations support this argument.
1. Governmental and other agencies have focused attention on rural water supply to "ease the hardship of women." However, in practice many pumps are not technically satisfactory for constant use and require increased energy beyond most women's capacities. Also, some unfamiliar movements, such as foot pumping, generally are not acceptable (Hoskins and Weber, 1985). Similarly, a pedal-operated grinding machine that was disseminated in central Africa was rejected because women had to sit astride it. This exposed part of their legs, a posture that is considered indecent for women in this area.
2. Although the introduction of the water pump may have eased lifting activities by women, little attention has been paid to the main chore of transporting the water after it is pumped. Also, the long line at the pumping point further delays women's work schedules.
3. While trying to encourage women to use a commercial maize sheller driven by a foot-operated pedal, it was noted that women's output was about half that of the male operators because women found the drive hard to use and also felt uncomfortable feeding the hopper, which was too close to chest level (Vermeulen and Kaul, 1979).
4. A hydraulic palm press in Nigeria was adopted initially by 72 percent of the villages but after a year the figure dropped to 24 percent because the machine was too taxing for the women to operate and because fiber obtained in the traditional extraction method, which was used as a source of heat, was not produced by the improved press. Also the new method upset the schedule for other tasks.
5. Many "improved stoves" were rejected by women because they did not cater to the prevailing pot sizes. Other varied cooking devices required an unacceptable standing posture or were difficult to light and maintain.
6. Any innovation introduced must ensure that it does not radically create an imbalance in the tasks allocated between men and women. For example, as a result of the introduction of a hard press for palm nuts, the sphere of activity shifted to men, whose work hours increased from 60 (before introduction of the device) to 1,050 while women's and children's work hours decreased from 1,450 to 992 (Afonja, 1984).


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7. Women have been relegated to using technologies with lower levels of efficiency, for example, manual transplanting, collecting firewood, and even walking to the market while men ride bicycles or drive carts.

Isolated Examples of Recognition of the Specific Needs of Women
Some projects have recognized the specific needs of women in developing new technologies. Concerned about depriving women of weeding as a source of income, the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) did not promote chemical weed-control measures in India. Instead, a rather labor-intensive double-cropping technology for vertisols was developed (Mueller and von Oppen, 1985). In developing smallscale threshing machines, the separation of chaff from grains was not incorporated into several prototypes because separation and winnowing are not tiring tasks. The cost of the machine also was reduced because incorporating a separation device would have involved sieves and fans and their regulated motions (Kaul, 1987).

Ergonomic Considerations in Developing Small Equipment
Any improved farming tool should involve a design criteria based on engineering and biological material (such as some crop or soil), the existing machine design, and the gender of the operator. The three factors (material, machine,'person) are interrelated and must be matched for optimal effectiveness. Often the role of operator is not fully addressed, i.e., the operator usually is assumed to be a man.
Ergonomics considers two main aspects:
1. The energy demand of the tool should be within the limits of both male and female operators' capacities. Energy assessment can be done by using a physiological index, such as oxygen consumption, heart rate, ventilation rate, etc. (Karpovich, 1971).
2. Anthropometric aspects use the dimensions of segments of the human body (arms, legs, etc.) and the tool configuration and dimensions to match the operator's dimensions for ease of operation. Broadly, it involves developing machines to fit the operator's convenience.
Such considerations are well established in the aircraft industry (cockpit and passenger seating), automobile design, and even workshop tools. Lately, this has been extended to agricultural equipment in the form of tractor cabins, tractor seats, location of controls, etc. Unfortunately, in the realm ofmanually operated and small equipment (motorized or animal drawn), this has seldom


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been recognized, even though most farmers in the developing countries use these tools. Blacksmiths, who typically fabricate the tools, seldom pay attention to the specific needs of women due to their own bias towards men as users. Women either avoid the improved tool or use it under duress, to the detriment of their health and with reduced performance.
Some efforts at the Institute for Agricultural Research (IAR), Samaru, Nigeria recently have been directed towards gender issues, and researchers are looking more specifically at the special tool needs of women.

Male-Female Body Segments and Their Use in Tool Design
In anthropometric terminology, most body segments can be expressed as a segment-link in terms of a person's overall height. Link parameters are used to build a segment-link ratio diagram for a known sample population. Basically, this diagram expresses the length of various linkages in the body (such as arm length) in terms of the total height.
Wagami (1983) studied the parameters of 450 male and 450 female workers in nine localities in the savannah region of northern Nigeria. Table 3 gives the difference in male and female workers based on an average of 450 persons in the nine locations. Taking a 95" percentile value of the user population, the dimensions suiting a male or female segment ratio can be established. This also can be related to appropriate energy levels and workperformance criteria-that is, a particular work posture will demand a specific energy level.
Just as sitting and standing posture demand different energy levels, the work posture induced by a tool (due to its shape, weight, etc.) can demand different human energy levels. For example, Nwuba and Kaul (1985, 1987) reported that long-handled manual tillage tools were less energy demanding than short-handled tools. However, by bending over, it was possible for

Table 3. Difference in Typical Segments of Male and Female Workers. Segment/parameter Male Female
Weight (kg) 63.18 53.69
Height (cm) 166.11 154.19
Arm reach (cm) 76.48 71.38
Hand length (cm) 19.08 17.72
Hand width (cm) 9.99 9.24
Forearm (cm) 28.87 26.93
Elbow to ground (cm) 100.58 91.69
Pelvic height (cm) 100.70 92.87


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farmers to be more effective in shaking weeds lose from the soil after they were uprooted by a hoe.
The optimal ratios of typical tool-handle dimensions (length, diameter, etc.) can be used to ensure convenience in operation. Proper tool-operator matching can indirectly reduce stresses on the back and other joints, which otherwise can result from bad work postures imposed by poorly designed farm tools. For instance, in a particular sample population it was observed that choosing a tool handle length of 0.60H gave convenience of operation. If the 95h percentile height (H) of the user population is 154.19 cm, the correct handle length is 92.5 cm (0.60H or 0.6 0 x 154.19). This would involve increasing the existing tool length by over 50 percent. Similarly, tool diameter can be related to the circumference of the circle that a hand (palm) traces when making an approximate circle while gripping the handle. This ensures a firm grip on the tool handle. Taking the length ofhand as 0.11H, the tool diameter can be calculated.
Using the above criteria, optimal values of existing and proposed dimensions have been worked for some tools (Table 4).
Table 5 gives comparative energy basis requirements for performing certain farm tasks for both male and female workers under three environmental regimes (Abdulkarim and Kaul, 1986). Using existing tools, the energy requirements for female operators are very high and, in fact, exceed the permissible limits of 4.5 kJ/min for most operations. Table 4. Typical Existing and Proposed Values for Selected Segments and Tools for
Female Workers.
Linkage Segment- Existing Proposed
link ratio (cm) (cm)
Handle length
for weeding hoe 0.6 Ha 59.98 92.51
Handle length
for sickle 2 x 0.06H 12.65 18.50
Length of
pounding pestle 0.6 H b 92.51
Diameter of weeding
hoe handle 0.11H 2.28 5.40
Diameter of sickle
handle 0.11H 2.59 5.40
Grip length of
grinding stone 0.33 x 0.11H b 5.60
a H = Height.
b 'Not included in survey.
Source: Abdulkarim, 1986; Bassi and Kaul, 1986.


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Table 5. Energy Requirement per Unit Output for Three Different Tasks With Male
and Female Operators Under Three Work Environment Regimes.
Men Women
Environment Output Energy/ Output Energy/ Higher
output output value for
(kJ) (kJ) women (%)
Task: Poundinga
20/48 42.6 1.03 30.3 1.30 20.8
25/85 35.2 1.01 22.8 1.65 38.8
36/63 26.5 1.50 17.8 2.07 27.5
Task: Weedingb
20/48 12.5 3.76 9.8 4.94 23.9
25/85 11.3 4.02 9.2 5.57 27.8
36/63 10.1 4.76 8.6 6.25 28.2
Task: Ridginga
20/48 2.0 32.85 0.8 68.66 52.2
25/85 1.8 35.14 0.9 63.82 44.9
36/63 1.6 40.99 0.9 62.02 33.8
a Outputin m3.
b Output in m2.

Efforts are underway to redesign the parameters of these tools for women and then to undertake field evaluations. A prototype of an improved weeding tool has been fabricated. Three female subjects were used for comparative evaluation between the new tool and the old conventional type. A summary of preliminary results is given in Table 6.
The results show that even though the traditional type covered more area, its energy expenditure was more than the improved type for the same work duration. Subjective responses confirmed that the improved tool was less fatiguing but that the output was lower because the tool was still unfamiliar to the users. Additional tools are being developed to test further the concepts.
Work scheduling and concepts of rest intervals are fairly well established in the industrial sector. Unfortunately in the agricultural sector these considerations are seldom viewed seriously. Preliminary farm studies (Abdulkarim and Kaul, 1986) have revealed that ridging drudgery was reduced by 16 and 8 percent, respectively, for male and female workers by introducing rest pauses and simultaneously gave an output advantage of 48 percent for male and 39 percent for female workers over the traditional work schedule.
In Nigeria most milk is produced by the Fulani, a nomadic tribe with about 9 million head of cattle. Milk yields are very low-about 0.74 1 per cow per day. The domestic milk production-to-total consumption ratio (self-sufficiency) has been declining, from, for example, 64.2 percent in 1970 to about


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Table 6. Comparative Evaluation of Modified Weeding Hoe and Conventional Hoe.
Conventional hoe Modified hoe
Subject Heart rate Area Subject Heart rate Area
(beats/min) (m 2) (beats/min) (m2)
1 140 1.8 1 144 1.2
2 168 2.6 2 152 1.6
3 128 3.2 3 120 1.6
Average 145 2.5 Average 138 1.47
1 132 1.8 1 124 1.6
2 160 2.8 2 156 1.8
3 136 2.6 3 132 2.4
Average 142.6 2.4 Average 137.3 1.93
1 144 2.0 1 140 1.6
2 156 2.6 2 120 1.8
3 126 2.2 3 120 2.0
Average 142 2.27 Average 126.6 1.8


35.6 percent in 1983 (Nwoko, 1986) due to neglect by Fulanis of milk production. Milk is processed mostly by Fulani women, who use very crude tools and techniques. Little fresh milk is sold due to low yields; instead it is stored and processed into cream and butter using traditional tools. For example, a bottle-like gourd (local calabash) is used for shaking cream collected from milk left overnight in another open vessel. The milk is shaken for 20-35 minutes depending on weather conditions. Shaking takes longer during the cold weather, and usually the Fulani women take 2-3 minutes for rest after 5 minutes of continuous shaking. The churned cream is then stirred with a primitive milk stirrer for collection of butter globules, which are pressed by hand into butter.
The whole process has a very low output and high energy demand. In a study by Eleseku (1989) on energy evaluation of a selected Fulani subject at IAR, the churning exercise required an energy level of 2.54 kJ/l of cream churned. An improved device using a simple barrel, which could be rotated axially to induce limited centrifugal action, was developed, reducing the energy level to only 0.89 kJ/l of cream, with an advantage of 64 percent over the traditional tool. Limited data on Fulani segment-link measurements was used to pick parameters for the improved device. An improved stirrer also was developed that gave a 47 percent time advantage over the traditional method and increased butter recovery by nearly 60 percent.


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CONCLUSIONS

The gender issue in small-scale farming as practiced in most developing countries involves a complex sex-based division of farm tasks that has evolved over a long period of time. Any innovation introduced into this system of farming must take into account the importance of gender so that improvements are planned simultaneously for both sexes.
Introduction of new farm tools is an innovation that affects the gender issue greatly. Past experience has shown that such introductions were skewed towards male workers, thereby upsetting the gender balance for farm tasks. Most tools introduced require high energy levels for women and are not operationally convenient based on body type.
Because women are the principal force for farm operations such as weeding, planting, harvesting, threshing, transportation, food storage, and food processing there is a vital need to ensure that tools are developed specifically to their needs. Women's roles in animal husbandry tasks, especially milk handling and processing, is no less important because the women (like the pastoral Fulani) who process most of the milk suffer from the lack of appropriate tools. Finally, farming operations are only some of the activities of typical rural women, who also perform domestic tasks and are responsible for child bearing. For effective upgrading and improvement of rural systems, the role of appropriate tools for women cannot be overemphasized.


REFERENCES
Abalu, G. 1984. Women, the cropping chain, and farming systems research in subSaharan Africa: Some experiences from Nigeria. Farming Systems Support Program
Seminars, University of Florida, Gainesville, October 16.
Abdulkarim, A.M. 1986. Comparative ergonomic studies of male and female operators
on selected tasks. Unpublished M.S. thesis, Department ofAgricultural Engineering,
Ahmadu Bello University, Zaria, Nigeria.
Abdulkarim, A.M., and R.N. Kaul. 1986. The effect of controlled work schedules on the
performance of a hoe farmer. Paper presented at the Annual Conference of the
Nigerian Society of Agricultural Engineers, Ife, September 20.
Adeyokunnu, O.T. 1981. Women and agriculture in Nigeria. Research Studies for
African Training, Rockefeller Foundation, N.Y. j
Afonja, S. 1984. The historical evolution of the sexual division of labour in Nigeria.
Pages 129-135 in Proceedings of the Meeting on Theoretical Framework and Methodological Approaches to Studies on the Role of Women in Africa. International Labour
Organization, Geneva.


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Bassi, S.Y., and R.N. Kaul. 1986. Selected anthropometric parameters of northern
Nigerian farmers. Paper presented at the Annual Conference of the Biotechnology
Society of Nigeria, University of Ilorin, May 16.
Dixon-Mueller, Ruth. 1985. Women's work in Third World agriculture: Concepts and
indicators. Women, Work and Development Paper 9. International Labor Organization, Geneva.
Eleseku, E.M. 1989. A study of milk processing tools used by Fulanis. Project report,
Department of Agricultural Engineering, Ahmadu Bello University, Zaria, Nigeria. Harrison, P., and J. Howe. 1989. Measuring the transport demands of the rural poor:
Experiences from Africa. German Appropriate Technology Exchange (GATE) No. 1,
May.
Hoskins, M., and F.R. Weber. 1985. Why appropriate technology projects for women
fail. ECOFORUM 10(2):April.
Igben, M.S. 1980. The economics ofpeasant maize production in Plateau State. Occasional Paper. National Institute for Socio-Economic Research (NISER), Ibadan, Nigeria. Karpovich, P.V., and W.E. Sinning. 1971. Physiology of muscular activity. Philadelphia:
W.B. Saunders Company.
Kaul, RN. 1987. Agricultural equipment prototypes. Report IAR/AM/87/10. Institute
for Agricultural Research, Samaru, Ahmadu Bello University, Zaria, Nigeria.
Moller, S. 1987. Women make up over half the work force in the informal sector.
German Appropriate Technology Exchange (GATE) March, pp. 3-5.
Mueller, R.A.E., and M. von Oppen. 1985. Developing agricultural technologies for
diverse target groups in the semi-arid tropics. Pages 41-57 in Women and agricultural technology: Relevance for research II. The Rockefeller Foundation, N.Y., and International Service for National Agricultural Research Council, The Hague, Netherlands. Nwoko, S.G. 1986. Dairyimports in Nigeria: Development and policies. Alpan African
Livestock Analysis Network.
Nwuba, E.I., and RN. Kaul. 1985. The effect of working posture on the Nigerian hoe
farmer. Journal of Agricultural Engineering Research 33:179-185.
Nwuba, E.I., and R.N. Kaul. 1987. Energy requirement of hand tools for wood cutting.
Journal of Agricultural Engineering Research 36.
Poats, S.V. 1988. Gender analysis in farming systems research and extension projects.
Keynote address at the Eighth Annual Farming Systems Research-Extension Symposium, University of Arkansas, Fayetteville, October 7-9.
Spiro, H.M. 1981. Thefifth world: Women's rural activitiesand time budgets in Nigeria.
Occasional Paper No. 19. Department of Geography, Queen Mary College, University of London, U.K.
Vermeulen, G.D., and R.N. Kaul. 1980. Maize sellers test report DAE 79/7. Institute
for Agricultural Research, Ahmadu Bello University, Zaria, Nigeria.
Wagami, S.Y. 1983. Selected ergonomic studies of commonly operated farm tools.
Unpublished M.S. thesis, Department of Agricultural Engineering, Ahmadu Bello
University, Zaria, Nigeria.


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A Regional Perspective on Cassava, Famine,

and Seasonal Hunger in Humid and

Subhumid Africa

Steven RomanoffI



ABSTRACT
Data from agricultural surveys by national scientists in eight countries of humid and subhumid Africa, conducted as part of the multidisciplinary COSCA project of IITA, yield the following information for each country: occurrence of famine, year of latest famine (food shortage causing death or movement), major cause of famine, month of planting of major crops, months of seasonal food shortage, relationship between planting and shortage seasons, relationship between latitude and hunger schedule, months ofconsuming cassava, and relationship between cassava schedule and shortage season. Conclusions include (1) famine is not limited to semiarid regions, where it occurs in different years than in subhumid areas; (2) seasonal food shortages are general and timed in relation to the agricultural cycle and natural factors; and (3) in tropical areas with more than 700 mm of annual rainfall, cassava is a major buffer to seasonal shortages. Besides cassava consumption, wage labor within the village may be a behavioral response to seasonal shortage. No evidence is found thatwage labor outside the village, importation offood, or hunting is more widespread during seasonal shortages. The organization of survey data from eight countries at the continental level by a geographic information system complements local studies and macroeconomic methods of studying agriculture and social phenomena.
Agricultural and social scientists are asked to code their data for latitude
and longitude.


INTRODUCTION

This article is intended to enhance the ability of agricultural scientists and planners to perceive regional patterns of food crises and local responses, to verify the role of cassava in food security, and to better focus agricultural

1 Consultant, Agricultural Sciences, The Rockefeller Foundation. A draft of this report was
presented at the 3rd meeting of the COSCA steering committee in London, August 13-15, 1990.

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research and nutritional interventions. The emphasis on cassava reflects that crop's general importance in the humid and subhumid regions of tropical Africa below the Sahara. In that area, cassava is the leading or second staple, providing more than half the calories in the diet of millions of people (FAO, 1988). Farmers value it for its high yields and as a reliable insurance crop (Jones, 1959; Longhurst and Lipton, 1989). The crop has many special agronomic traits that contribute to its rapidly growing importance in the region (Romanoff and Lynam, in press), especially in areas where market access is restricted, food-getting strategies are fewer, kinship groups are important, and rainfall is plentiful (Romanoff et al., in press).
This article draws on data from a continuing agricultural survey of cassavaproducing areas across the tropical belt of Africa. The survey began in 1988 and continues in 1992 with the third visit to sample villages. This study uses data from the first visits to sample villages in eight countries. It combines regional scope with village-level farmer interviews, and the method is designed to add perspective with moderate detail to the study of agricultural systems.
By covering hunger in the humid and subhumid zones, this article complements the more usual geographic focus of discussions of food security in the Sahel, the Sudan zone, and the drier parts of East and Southern Africa, where the problem is most severe (e.g. Nicholson, 1986; Harrison, 1988; Shipton, 1990). Other authors discuss whole countries using aggregated statistics that do not distinguish among zones (Jamal, 1988). The millions of people living in the wetter areas of the continent also face food scarcities, and their coping patterns are sometimes different from those of the drier areas. As a result, existing social science research is less relevant than it should be to the efforts of agricultural scientists and planners to improve the food security of people in humid areas.
This article also will document seasonal food shortages in ways that complement other reports. Seasonal deficits have received considerable attention (Glantz, 1989; Messer, 1989; Sahn, 1989; see also the description of the annual cycle of activities in most ethnographies). "While the evidence is scattered and somewhat inconclusive," says one researcher, "it does support the conclusion that [seasonal] food deficits are a priority concern for African smallholders" (Moris, 1989, p. 234). The contribution of this article is to move forward from scattered evidence to systematic consideration, showing that farmers in many villages consistently identify seasons of hunger that change in regular ways from one region to another.


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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 49

The role of cassava in mitigating seasonal shortages has been noted by several authors (Jones, 1959; Guyer, 1989; Moris, 1989). This article will attempt to prove that the function of cassava cultivation is nearly ubiquitous in the humid and subhumid belt. Other behaviors that have been proposed as adaptations to seasonal hunger will be shown to be less clearly patterned in the current sample.
Thus, with the advantage of new survey data from an unusually wide area, this paper documents the incidence of famine outside the Sahel, relates seasonal hunger in the humid and subhumid zones to natural and agricultural cycles, and shows the degree to which cassava consumption and other adaptive behaviors are related to seasonal food shortages.


METHODS

This report is part of the Collaborative Study of Cassava in Africa (COSCA), undertaken by national agricultural research institutions and the International Institute for Tropical Agriculture (IITA) in eight countries. COSCA is designed to discover and/or document broad differences in farming systems at the national or regional levels, in order to help agricultural scientists to plan research more effectively. COSCA is also designed to identify and weigh factors related to agricultural development. Focused primarily on cassava, the study covers farming systems based on other crops, food processing, marketing, consumption, and related social, cultural, or economic conditions (Nweke, 1988).
The data for this article are drawn from COSCA research in eight countries: Cote d'Ivoire, Ghana, Malawi, Nigeria, Tanzania, Uganda, Zambia, and Zaire. In each country, a stratified random sample ofplaces was used to choose between 30 and 70 villages; a few cases were also included from the Congo. The sampling frame, part of a geographic information system (GIS) devised by the Centro Internacional de Agricultura Tropical (CIAT),2 divided the map of Africa into 10 km2 grid cells. Grid cells were selected without weights within strata based on population density, road access, and climate type. The village nearest the center of the cell enters the sample (Carter and Jones, 1989). Most countries have excluded areas where cassava is not a staple, parks, areas that national agricultural scientists consider too dangerous to visit, and some considered to be uninhabited.
2 Both IITA and CIAT are international agricultural research centers of the CGIAR system. The
COSCA study is led by Dr. Felix Nweke of IITA.


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Survey Villages
The selected villages represent the broad mid-African belt where cassava is a primary staple: 75 percent of the continent's total cassava production occurs in the study countries (FAO, 1988). Most of the villages have less than six dry months (a "dry" month experiences less than 100 mm mean rainfall during the month); mean growing season temperature over 22 degrees Celsius; daily temperature range less than 10 degrees; and isothermic seasonality (Carter and Jones, 1989). In the current data, all villages have more than 700 mm of rain, although COSCA hopes to obtain data from Angola or Mozambique, where cassava production is important in drier areas. The sample does not include many villages in the drier zone of West Africa, where sorghum, millet, or cattle predominates. Culturally, the sample emphasizes rural settlements more than would be the case if it had been drawn with probability of selection proportionate to population.
COSCA survey teams, staffed by personnel from national research institutions in each country, make three visits to each village in the sample: first, for general group interviews; later, for observations of farmers' fields; finally, for a household survey. Each interview team includes at least two professionals, typically agricultural economists or breeders. In some countries, entomologists have participated to observe conditions in farmers' fields. Data from the first phase of the survey-group interviews, which have been completed in all eight countries-are the basis for this article.
In each village, the group interview actually consists of three concurrent sessions: one requires a small group of patient farmers to answer detailed questions about village agricultural practices; another asks more general agricultural questions; and the third asks about cassava processing and use, as well as other questions about the village. The sessions typically last between two and three hours. Earlier versions of the questionnaire had only two sections and exhausted the patience of most villagers.3
Questions on food scarcity and hunger refer to general patterns in the village, and interviewers tried to attain consensus responses. The questions included the following:

3 About 60 percent of the interview subgroups had between 10 and 24 adults; about 25 percent
had 25 or more adults; the rest had fewer than 10. In the median case, the adults of each subgroup represented 4 percent of the estimated adult population of the village. Most of the subgroups included both men and women, and in some cases the groups.split along gender lines on particular questions before reaching consensus. Although the written questionnaires are in English and French, most interviews were conducted in local languages or other non-European languages, such as Swahili, commonly used for trade and communication between different
ethnic groups.
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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 51

1. In which months of the year do people in this village suffer most from food shortage?
2. Has this village ever suffered from famine in the past? [Famine was defined as a shortage of food causing death or causing people to leave the village.]
3. In which years did famine occur in this village and what were the causes? [Both "major cause" and "other cause" were elicited] .
Other questions concerned the months when the villagers ate each staple, when they undertook specific agricultural tasks, when they were busiest, and when they migrated for work.

Climate
Information on climatic patterns is drawn from a digitized map of 4,000 meteorological stations. The computer map, another part of the geographic information system (GIS) created by CIAT's Agroecological Studies Unit with a presentation program written by IITA, affords researchers immediate access to monthly averages ofprecipitation and other climatic traits for any site in Africa.


ANALYSES

The incidence of famine and seasonal hunger are demonstrated by simple tables or calendars of occurrence, using unweighted data. Because sampling proportions varied from one country to another, the unweighted data are generally presented by country, except in a few general tables where little bias results from pooling the data.

The Database
The village-level COSCA interviews are the basis for an evolving database. The study began in Uganda, Tanzania, Zaire, Nigeria, Ghana, and Cote d'Ivoire. It was expanded when the East and Southern Africa Root Crops Research Network (ESARRN) adopted the COSCA methodology for diag4 Both the use of group interviews and presentation of the data by country affect some results.
Because they concern each village as a whole and are addressed to groups, the questions are unlikely to elicit information on such causes of hunger as illness or loss of employment, or on issues such as food allocation in individual households. There may be significant individual differences, for example, in agricultural and economic strategies among the households of a village (e.g., Alverson, 1990). Data on household variation and on events related to household production and shortages are being gathered in the second and third phases of COSCA. Scale of analysis also affects the results presented here: Seasonal patterns of hunger or behavior evident in countries considered severally may not coincide with patterns found in the region as a whole.


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nostic work. Malawi and Zambia have completed village-level surveys, and other ESARRN countries are in the field. When IITA conducted a diagnostic study in Sierra Leone and Liberia, it used many of the COSCA questions as well. Congo decided to use the COSCA questionnaire for diagnostic studies related to a development project, and some countries, such as Zaire and Tanzania, have undertaken to expand the original sample.
As new countries join the COSCA study, they determine sample size and whether or not to include areas without cassava production, while retaining the use of stratification by climate, population, and access. The Malawi sample, for example, covers the whole country, and Sierra Leone and Liberia have large samples relative to the size of each country.
Hence, this article presents descriptive results disaggregated by country and, for the moment, postpones statistical analysis of relationships among variables. Such studies (e.g., of the factors associated with susceptibility to famine or seasonal hunger) would be of great importance, but they await COSCA decisions on weighting cases to avoid bias and on other methodological issues. Future tables using weighted data may appear different from those presented in this article, which are based on unweighted data.

Graphs and Maps
Frequency graphs (by month and country) are used to illustrate calendarbased data. In a typical case, the graphs illustrate the variation in the number ofvillages reporting hunger in a given month. The graphs clearly demonstrate that the months when hunger is reported with the greatest frequency vary from one country to another.
Graphs also serve to reveal and verify functional relationships among behaviors, conditions, or natural events. Graphs demonstrate, for example, the relationships among date of planting, food scarcity, and cassava consumption. In this case, the graphs show that, in a given country or region, these variables are most common or concentrated in the same months.
Maps are used sparingly in this article, pending the availability of better maps as the COSCA data base continues to expand over the next several years. The seasonal map of hunger, included to illustrate the striking annual progression north and south of reported food shortages, should therefore be interpreted as showing only general patterns.


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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 53

BACKGROUND: SUBSISTENCE BASE OF THE SAMPLE VILLAGES
Residents of the sample villages ranked their crops by land area (Table 1). Because most villages in the sample were drawn from regions where cassava is a staple, it is not surprising to find cassava ranked first in more villages than any other crop. A large number of villages, however, report cassava as a secondary crop. Maize is reported as the dominant crop in Malawi, and in a substantial number of villages in many countries.
In terms of its importance as a staple food, rather than in terms of land area, cassava is even more frequently cited in first place in the COSCA sample. The difference may be due to cassava's very high yield compared to maize, even when measured on a dry weight basis. The sole exception is in western Nigerian villages (Table 2).
Irrigation is virtually unreported in the sample villages. Although ownership of goats or sheep is common, cattle herding is practiced in less than onethird of the villages of West Africa and Zaire. In East Africa, in contrast, cattle are herded in more than halfof the villages ofTanzania, Uganda, Zambia, and Malawi, where goats and sheep are correspondingly less important. The virtual absence of irrigation and the minor importance of cattle, together with the near absence of millet and sorghum as primary crops, confirm that the subsistence basis of the sample villages is sharply distinct from those of the Sahel or Sudan zones, in conformity with regional patterns observed by others (Davies, 1973).
The survey shows that residents of almost all the villages import some staple food, while farmers in almost all of the villages sell food. The most common food import is rice; cassava products are frequently traded, more often locally than in urban markets. Of the few villages that do not import food, most are located in zones of high rainfall and low population density. The degree to which the villages participate in the cash economy varies considerably.


RESULTS
The survey data show the incidence of famine in the villages, the patterns of seasonal hunger, and the relationship between cassava consumption and hunger.


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Table 1. Leading Crop Adcording to Land Area Planted, by Country.

Villages Leading Crop Total

Cassava Maize Yams Sorghum Rice Other

Cote # 2 6 4 28a 40
d'Ivoire % 5.0 15.0 10.0 70.0
Ghana # 8 3 8 1 10 30
% 26.7 10.0 26.7 3.3 33.3
Nigeria # 12 3 2 4 21
(west) % 57.1 14.3 9.5 19.0
Nigeria # 7 5 16 10 3 3 44
(east) % 15.9 11.4 36.4 22.7 6.8 6.8
Tanzania # 10 10 1 2 7 4 34
% 29.4 29.4 2.9 5.9 20.6 11.8
Uganda # 12 4 16 32
% 37.5 12.5 50.0
Zaire # 35 8 8 51
(west) % 68.6 15.7 15.7
Zaire # 5 1 4 10
(east) % 50.0 10.0 40.0
Zambia # 18 14 2 4 38
% 47.4 36.8 5.3 10.5
Malawi # 8 58 1 67
% 11.9 86.6 1.5
Congo # 6 6
% 100.0

a Principally cocoa
Source: COSCA database as of October 20, 1991; excludes Sierra Leone and Liberia; includes all of Malawi with non-cassava areas; unweighted data.

Famine
Although they are generally far from the Sahel and enjoy greater rainfall, most of the villages of the sample report experiences of famine: hunger resulting in mortality and/or migration from the village (Table 3). Of the four West African countries or areas, only western Nigeria reports that most villages experienced no famine. In Uganda, plagued by civil war, and in Malawi, with very low per capita income, almost all villages report famine. The famines reported are not long-ago or mythological events: two-thirds of the villages reporting famine (half of all villages) referred specifically to events of the last 15 years (Table 4).
Drought, distantly followed by insect damage, war, and plant diseases, was most frequently cited by villagers as the proximate cause of famine (Table 5). As contributory causes, insect pests were most frequently cited, followed by fire of natural or human origin (the questionnaire did not make the distinction), drought, and war. Overall, natural causes (including fire on the


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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 55

Table 2. Relative Importance of Cassava in the Diet, by Country.

Villages Rank'

1 2 3 4 5 6 7to 11
Cote # 4 20 7 5 2 1
d'Ivoire % 10.3 51.3 17.9 12.8 5.1 2.6
Ghana # 16 8 5 1
% 53.3 26.7 16.7 3.3
Nigeria # 8 10 1 1 1
(west) % 38.1 47.6 4.8 4.8 4.8
Nigeria # 15 8 2 5 5 5 4
(east) % 34.1 18.2 4.5 11.4 11.4 11.4 9.1
Tanzania # 24 4 2 1 2 1
% 70.6 11.8 5.9 2.9 5.9 2.9
Uganda # 16 6 6 1 2
% 51.6 19.4 19.4 3.2 6.5
Zaire # 47 2 1 1
(west) % 92.2 3.9 2.0 2.0
Zaire # 8 1
(east) % 88.9 11.1
Zambia # 18 5 4 2 1 1 4
% 51.4 14.3 11.4 5.7 2.9 2.9 11.4
Malawi # 9 15 6 10 10 8 2
% 15.0 25.0 10.0 16.7 16.7 13.3 3.4
Congo # 5
% 83.3
a 1 = most important; 11 = least important.
Source: COSCA database as of October 20, 1991; excludes Sierra Leone and Liberia; includes all of Malawi with non-cassava areas; unweighted data.

assumption that it was of natural origin or abetted by dry weather) were blamed in two-thirds of the reports, agricultural causes were cited in a quarter, and human causes (including the "other" category) in the remainder.
At least one famine was reported for almost each of the 60 years between 1930 and 1990 (Table 6). At least some famines, evidently, remain vivid in the memories of villagers (Shipton, 1990). Reports of only a single famine in many villages may reflect respondents' fatigue and/or the failure ofinterviewers to probe sufficiently. The actual incidence of famine therefore may be greater than reported.
Famine was particularly widespread in some years: 1945, even 45 years after the event, figures in 13 ofa total of 376 famine reports. In Malawi, 1949 was a hungry year in 54 villages, but not in other countries. More recently, famines in 1977 were remembered in 20 villages, most of them in Cote d'Ivoire; in 1980 in 27 villages, mostly in Uganda and Malawi; and in 1984 in 26 villages in many countries.


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Table 3. Villages Reporting Famine, by Country.

Village Famine

Not reported Reported Row total

Cote d'Ivoire # 0 40 40
% 0 100.0
Ghana # 2 28 30
% 6.7 93.3
Nigeria (west) # 13 7 20
% 65.0 35.0
Nigeria (east) # 6 38 44
% 13.6 86.4
Tanzania # 15 17 32
% 46.9 53.1
Uganda # 1 31 32
% 3.1 96.9
Zaire (west) # 18 30 48
% 37.5 62.5
Zaire (east) # 8 2 10
% 80.0 20.0
Zambia # 18 16 34
% 52.9 47.1
Malawi # 6 61 67
% 9.0 91.0
Congo # 4 2 6
% 66.7 33.3

Source: COSCA database as of October 20, 1991; excludes Sierra Ieone and Liberia; includes all of Malawi with non-cassava areas; unweighted data.

The worst year was 1983, cited in reports from 52 villages. Famine was most widely reported in that year in West Africa, but other regions were also affected. Fire was mentioned prominently as a contributing cause in 1983. In contrast, the years between 1968 and 1973, which were notable famine years in the Sahel, were reported as famine years by modest numbers of villages in the African cassava belt.


Seasonal Food Shortages
The hungry months, when exceptionally large numbers of villages report substantial or extraordinary food shortages, vary from country to country. The COSCA village data show the clear relationship between reported hunger and the agricultural cycle. In each country, the months when most villages are planting their main crop fall within a month either way of the onset of the hunger season. Graphically, this relationship appears quite strong (Figure 1), although variation in the data warrants further analysis at the village level.


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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 57

Table 4. Reported Year of Latest Famine, by Country.,

Villages Date of Latest Famine Total
Pre-1955 1955-64 1965-74 1975-84 1985-on
Cote d'Ivoire # 2 1 29 5 37
% 5.4 2.7 78.4 13.5
Ghana # 27 1 28
% 96.4 3.6
Nigeria # 3 1 3 7
(west) % 42.9 14.3 42.9
Nigeria # 6 2 1 12 12 33
(east) % 18.2 6.1 3.0 36.4 36.4
Tanzania # 3 1 3 5 4 16
% 18.8 6.3 18.8 31.3 25.0
Uganda # 2 2 23 4 31
% 6.5 6.5 74.2 12.9
Zaire # 1 4 12 9 26
(west) % 3.8 15.4 46.2 34.6
Zaire # 1 1 2
(east) % 50.0 50.0
Zambia # 6 3 2 1 4 16
% 37.5 18.8 12.5 6.3 25.0
Malawi # 39 1 14 7 61
% 63.9 1.6 23.0 11.5
Congo # 1 1
% 100.0
a Only villages reporting a famine and giving a year are included. Years refer to most recent famine
only, not to all famines reported.
Source: COSCA database as of October 20, 1991; excludes villages not reporting famine or not providing date; excludes Sierra Leone and Liberia; includes all of Malawi with non-cassava areas; unweighted data.

Table 5. Major Cause of Last Famine.
Cause Reported as Major Cause Reported as Contributory Total
(# villages) (# villages)
Drought 252 13' 265
Flood 15 15
Hail 2 1 3
Fire 7 19 26
Insect pests 49 23 72
Plant disease 17 5 22
Animal damage 7 8 15
Infertile soil 2 1 2
Land shortage 1 2 3
War 19 12 31
Government policy 5 4 9
Human disease 1 1 2
Marketing 1 1
Source: COSCA database as of October 20, 1991; excludes Sierra Leone and Liberia; includes all of Malawi with non-cassava areas; unweighted data. The causes refer to primary and secondary causes of all famines reported in the village.


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Table 6. Incidence of Famine, as Reported in Group Interviews.


Year

1935 1936 1937 1938 1939
1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962


Villages reporting famine Year Villages reporting famine


+
+++++++++++++
+++
+++++ ++++ ++++++++++++++++++//+++
+


1963 2 1964 7 1965 0 1966 2 1967 0 1968 3 1969 3 1970 4 1971 3 1972 2 1973 4 1974 9 1975 5 1976 5 1977 20 1978 6 1979 9 1980 27 1981 10 1982 2 1983 52
1984 26 1985 10 1986 9 1987 13 1988 12 1989 6 1990 3


++
+++++++

++

+++
+++ ++++ +++
++
++++ +++++++++ +++++ +++++
++++++++++++++++++++ ++++++
+++++++++ +++++++++++++++++++++++ ++++++++++ ++
+++++++++++++++++++//+++ ++++++++++++++++++++++++ ++++++++++ +++++++++ +++++++++++++ ++++++++++++ ++++++ +++


Source: COSCA database as of October 20, 1991; excludes Sierra Leone and Liberia; includes all of Malawi with non-cassava areas; unweighted data. Villages may report up to three events.

The planting season is related to the onset of the rainy season, which the IITA Agroclimatology Unit defines as the first month with more than 60 mm of rain. The CIAT meteorological data, which cover most of Africa, confirm the relationship of the rainy season to the hunger season, as graphed in Figure 1. Most villages begin to report hunger between 0 and 3 months after the first rainy month. At the country level, the average lag between rain and hunger in the COSCA data is 1.5 months.
While this general relationship between onset of rains and increased incidence of hunger is clear at the country level, important details remain to be added. The sample villages in the study are located largely in the humid and subhumid zones. The pattern found here-hunger at the beginning of the rainy season-may be somewhat distinct from that discerned by Moris


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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 59

(1989), who saw the "energy-deficit period overlapping the end of the long dry season and the beginning of a new cycle." Alternatively, it may be useful to redefine the onset of the rainy season as a month with 100 mm ofrain, since in practice villagers tend to plant somewhat later than the first month with 60 mm of rain.
In any case, the onset of rains proves to vary somewhat with latitude, and to a lesser degree with longitude, in that at a.given latitude the rains often begin a bit later toward the east. An unsystematic inspection of meteorological data reveals some general patterns with variations due to local factors, such as the proximity to seacoasts and their currents. Purely for illustration of the magnitude ofpatterns, one might say that at 70 N ofthe Equator, the rains tend to begin in June to the west or July to the east. At 50 N, depending on longitude, the rains begin in April or May. At 30 N, the starting dates are advanced to March or April.
At the Equator, the rains begin in September toward the west, while a bimodal pattern prevails to the east. At 30 S of the Equator, rains begin in the fourth quarter, between September and November. At 50 S, they advance nearer to year-end. At 70 S, the rain dates fall between October and December. These generalizations, inescapably, oversimplify reality, but they indicate the broad seasonal patterns that differ markedly with latitude.
The patterns suggest, for any date, where villages with adaptations like the ones found by COSCA are likely to be in their annual planting-hunger cycle. Patterns would be different for villages with different productive patterns, like those depending more on cattle or on cereals, and there may well be variation among the COSCA villages that depend on degree of market involvement on the cycle of special crops. Still, if the most marked differences in onset of rains, planting, and hunger are determined by latitude, broad similarities should exist in the hunger calendar within groups of northern countries: Ghana, Cote d'Ivoire, Nigeria, and Uganda. A group of southern countries-Tanzania, Zambia, Malawi, Zaire-should also display a pattern, but distinct from that of the north. In fact, as Figure 2 demonstrates, this is precisely the case.
Maps of the COSCA data clearly track the annual progression of hunger reports, starting in the south in February, moving north, and reaching the westernmost sites in June and July. By September, the reports center farther south, becoming increasingly concentrated in the south through February, when the cycle begins anew (Figure 3). This map series resembles those of rainfall and green vegetation.


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Jan. June Dec.

Ghana


June Zambia


Jan. June

West Nigeria


Jan. June Dec.

Tanzania


Hunger Plant


Jan. June Dec.

East Zaire


Figure 1. Planting Season and Hunger in Eight African Countries.
Source: COSCA.


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Jan. June Dec.

West Zaire


Jan. June

East Nigeria


Jan. June Dec.

Cote d'Ivoire


18 16
14
12 10
8
6
4
2
0
Jan. June Dec


Hunger

Pbnting


Uganda




Figure 1. Planting Season and Hunger in Eight African Countries (cont.).
Source: COSCA.


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50
40 r 30 ~20
10 C


June Malawi


Dec.


id

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60 30 30 15
bo IbO
0 50

Jan. June Dec. Jan. June Dec.
South Range North Range

Tanzania Zambia -- Ghana -- Uganda
Malawi -4 Zaire -- W. Nigeria Cote d'lvoire
-- E. Nigeria

Figure 2. Hunger Months for Northern and Southern Ranges.
Source: COSCA.

Adaptations to Hunger: The Role of Cassava and Other Crops
If the hunger season coincides with planting, the season of plenty begins with the harvests of such staples as plantain, banana, cocoyam, rice, potatoes, and sometimes beans. Between the planting season and the harvest of the local mix of multiple staples in each region, the only local production depends on a limited set of crops: first, the leafy vegetables, sometimes including cassava leaves; then cassava itself; then, in some cases, maize.
Except for cassava leaves, the leafy vegetables are secondary crops; presumably they do not yield enough food to eliminate hunger entirely. Their dietary value is enhanced, however, by their availability just when there is little else to eat. Cassava leaves, which begin to regrow on established plants at the onset of the rains, are important in East Africa, along with leafy vegetables, during the hunger period. In Zaire, cassava leaves are eaten regularly throughout the year.
Where cassava itself is not a year-round staple, cassava consumption is most widespread during the hunger season. Cote d'Ivoire is typical of the countries where most cassava is eaten as a hunger-breaker; East Nigeria is similar in this regard (Figure 4). In such countries, a minority of villages report cassava as the major crop. At the opposite extreme are countries or areas like western Zaire, western Nigeria, and Zambia, where cassava is consumed year-round (Figure 4) and where the majority ofvillages report cassava as their major crop by land area.


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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 63

Some countries exhibit some correlation between cassava use and hunger, but also include many villages that eat a lot of cassava in all seasons. These countries consume cassava in a pattern that is intermediate between that of countries where cassava is only a hunger breaker and that of countries where consumption is constant. Most of the East African countries-Uganda, Malawi, Tanzania, and East Zaire-are intermediate. The data for these countries are adjusted in Figure 5 to show the number of villages reporting high cassava consumption, minus a constant equal to the minimal number of villages reporting consumption in any month. In these countries, a substantial minority of villages, ranging between 29 and 50 percent (except in Malawi), report cassava to be the number-one crop. Cassava consumption in Ghana does not clearly conform to any of the patterns.
Despite their differences, all of the patterns described share a reliance on cassava during the hunger period. Other starchy staples, like banana, plantain, and yam, do not play this crucial role, and some, like sweet potatoes, are not widespread. In some areas, maize and/or one or another of the fast maturing legumes may also become available at about the same time as cassava. In West Africa, for example, leafy vegetables precede cassava during the hungry season and ground-nuts follow cassava by one month; in East Africa, beans peak a month before cassava, and ground-nuts and millet arrive in the same month.
Cassava is especially suited to provide protection against hunger because it can be harvested over a very long period, from about 7 to 36 months after planting. Because the period from the peak of the planting season to the peak of the hunger season varies from 12 to 18 months, cassava's indifference to time is important. Breeders often evaluate cassava for yield at 12 months, and precocity is valued; the patterns found here suggest that the ability to be available in the ground remains desirable.
Eating cassava is not the only alternative to going hungry mentioned in the literature. Villagers can import food into the village, work for wages within or outside the village, or harvest wild resources. In Cote d'Ivoire and Malawi, there is a substantial calendrical correlation between hunger and food imports into the villages; that is, most villages face food scarcity and import increased amounts of food in the same months. In none of the other COSCA countries, however, do COSCA data reveal a similar correlation. Other factors, such as the custom of importing rice into West African villages for holidays in December, account for spikes in food-import activities in some villages. The evidence does not suggest that imported food rivals cassava as a seasonal response to hunger.


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January


1.





6/

.4





. J

,a






i


February March






April May June


Figure 3. Annual Northward and Southward Progression of Hunger.
Source: COSCA.


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dwV
(S

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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA


July


N



# August


It



September






*. 4 October







November







December Figure 3. Annual Northward and Southward Progression of Hunger (cont.).
Source: COSCA.


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Jan. June Dec.
Cote d'Ivoire












Jan. June Dec.
West Zaire


Jan. June Dec.


30


20 10



Jan. June Dec.
East Nigeria


Jan. June Dec.
West Nigeria








-4
CHn


Zambia
Figure 4. Cassava and Hunger Months in Four African Countries.
Source: COSCA.

Demand for labor at the village level is strongly seasonal, peaking during or just before the months of food scarcity. COSCA data show that the busiest time is during land preparation and planting. Because we know from other COSCA data that some of the labor is hired, particularly for land preparation, there may be a peak of transactions, in cash or kind, during the months when hunger is most widespread.


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Jan. June Dec.
Uganda


Jan. June Dec.
Tanzania


40 20O 10

Jan. June Dec.
Malawi


East Zaire


Figure 5. Adjusted Cassava and Hunger in Four African Countries.
Source: COSCA.


Working outside the village is another possible strategy during the months of food shortage. The data show only a weak correlation between the two phenomena in Cote d'Ivoire and possibly Ghana, whereas in the villages of eastern Nigeria and Uganda, outside work appears actually to increase during the months of plenty. Other countries display no clear pattern. This lack of clear correlation suggests that the relationships between working outside the village and food scarcity, ifany, may be locally specific. Similarly, COSCA data show no correlation between food scarcity and consumption of bush meat.


CONCLUSIONS

The majority of villages of the sample, drawn from the cassava belt of Africa, report famine in the last 15 years. Village residents attribute the famines to


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drought, agricultural pests, or war. Because the sample is drawn mostly from areas with less than six months of dry season and rainfall above 700 mm annually, these results demonstrate that famine is not limited to the Sahel, Sudan, and dry parts of Southern Africa. Coping strategies such as slaughtering or selling cattle or relying on irrigation, both well known in the Sahel and Sudan, are unimportant in the sample villages simply because cattle are rare and irrigation virtually absent.
This has implications for researchers and planners. Among them are that breeders should maintain the traits of cassava that give it its special role in famine prevention. Indicators of famine should be regionally specific: sales of cattle would be an inappropriate indicator for the area studied here, and the price of cereals may or may not be appropriate. Menus of responses to drought or famine should also be as regionally specific as possible. Relief efforts may be needed in wetter areas, adapted to prevailing human ecological patterns. Studies in support of famine prevention should reflect the climatological and social diversity of the region, including both drier and wetter zones.
The data confirm that local patterns of seasonal hunger, linked to cropping patterns and thus to the onset of the rainy season, occur in a yearly cycle that moves north and south with the seasons. The pattern has been demonstrated to a degree that warrants further analysis to predict more precisely the usual onset of the hunger season in relation to natural, agricultural, and social factors. If that is done successfully, the patterns may be projected using meteorological and remote sensing data bases.
Eaten year-round or mostly during the hunger season, cassava plays a special role in the yearly calendar ofagriculture and nutrition in the vast region studied by COSCA. There may be additional functions of cassava production in evening out seasonal labor demand, but that is beyond the scope of this paper (see Stone et al., 1990). Maize also plays an important role at the end of the hunger season as well as generally, and the two vie for first place among African food crops. Other behaviors that may be related to seasonal hunger were not so clearly shown to be related to seasonal shortages. Importing food and seeking work outside the village may be alternative responses to food scarcity, but the analysis of COSCA data did not demonstrate that function.
Many authors have indicated dissatisfaction with using aggregate statistics to study problems of hunger and farming systems (e.g., Berry, 1984; Hill, 1985). Such studies do not perceive regional variations and may conjecture local dynamics from national data. Hill (1985) has suggested a taxonomic solution, and identification of domains of recommendations is standard


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CASSAVA, FAMINE, AND SEASONAL HUNGER IN AFRICA 69

practice in farming systems research. The volume of Huss-Ashmore and Katz (1989) suggests complementing "macroperspectives" with "microperspectives." This paper has used a wide regional perspective that incorporates and summarizes village-level data, an approach that is becoming more common (Downing, 1991).
We may be confident that eventually methods will be adopted to incorporate agricultural and social science survey data into geographic contexts. Such a process would be assisted by scientific surveys and trials that are coded by latitude and longitude.
In viewing human strategies for coping with famines or seasonal shortages, scientists should presume that each type of response is distributed differently in space and time. Beginning with rich ethnographic or farming systems descriptions and catalogues of responses to food scarcities, agricultural and social science researchers can develop continental data bases to map the occurrence of different strategies, in preparation for identifying their environmental and cultural correlates. The close-up focus of ethnography or farming systems research and the regional data base are complementary perspectives.


REFERENCES
Alverson, H. 1990. The class context of agriculture in Botswana: Some policy
implications. Culture and Agriculture 40:11-16.
Berry, S. 1984. The food crisis and agrarian change in Africa: A review essay. African
Studies Review 27(2):59-112.
Carter, S.E., and P.G. Jones. 1989. COSCA site selection procedure. Collaborative Study
of Cassava in Africa (COSCA) Working Paper No. 2. International Institute of
Tropical Agriculture, Ibadan, Nigeria.
Davies,H.R.J. 1973. TropicalAfrica:Anatlasforruraldevelopment. Cardiff: University
of Wales Press.
Downing, T.E. 1991. Assessing socioeconomic vulnerability to famine: Frameworks,
concepts and applications. Brown University, The Alan Shawn Feinstein World
Hunger Program, Providence, R.I.
Food and Agriculture Organization (FAO). 1988. FAO production yearbook. Food and
Agriculture Organization of the United Nations, Rome.
Glantz, M. 1989. Drought, famine, and the seasons in sub-Saharan Africa. In Rebecca
Huss-Ashmore and S.H. Katz, eds., African food systems in crisis. N.Y.: Gordon and
Breach Science Publishers.
Guyer, J. 1989. From seasonal income to daily diet in a partially commercialized rural
economy (southern Cameroon). Pages 137-150 in D.E. Sahn, ed., Seasonal variation in third world agriculture: The consequences for food security. Baltimore, Md.: Johns
Hopkins University Press.
Harrison, G.A., ed. 1988. Famine. Oxford: Oxford University Press.


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Hill, P. 1985. The practical need for a socio-economic classification of tropical agrarian
systems. Pages 117-130 in Socialanthropologyanddevelopmentpolicy. N.Y.: Tavistock
Publications.
Huss-Ashmore, Rebecca, and S.H. Katz, eds. 1989. African food systems in crisis. Part
One: Microperspectives. N.Y.: Gordon and Breach Science Publishers.
Jamal, V. 1988. Special issue: The African food crisis, food security and structural
adjustment. International Labour Review 127:6.
Jones, W.O. 1959. Manioc in Africa. Palo Alto: Stanford University Press. Longhurst, R., and M. Lipton. 1989. The role of agricultural research and secondary
food crops in reducing seasonal food insecurity. Pages 285-298 in D.E. Sahn, ed., Seasonal variation in third world agriculture: The consequences for food security.
Baltimore, Md.: Johns Hopkins University Press.
Messer, E. 1989. Seasonality in food systems: An anthropological perspective on
household food security. Pages 151-175 in D.E. Sahn, ed., Seasonal variation in third world agriculture: The consequencesforfood security. Baltimore, Md.: Johns Hopkins
University Press.
Moris, J.R. 1989. Indigenous versus introduced solutions to food stress inAfrica. Pages
209-234 in D.E. Sahn, ed., Seasonal variation in third world agriculture: The
consequences for food security. Baltimore, Md.: Johns Hopkins University Press.
Nicholson, S.E. 1986. Climate, drought, and famine in Africa. Pages 107-128 in A.
Hansen and D.E. McMillan, eds., Food in Sub-Saharan Africa. Boulder, Colo.: Lynne
Rienner Publishers, Inc.
Nweke, F. 1988. COSCA project description. Collaborative Study of Cassava in Africa
(COSCA) Working Paper No. 1. International Institute of Tropical Agriculture,
Ibadan, Nigeria.
Romanoff, S., and J. Lynam. n.d. Cassava and African food security: Some ethnographic
examples. Ecology of Food and Nutrition, in press.
Romanoff, S., S. Carter, and J. Lynam. n.d. Cassava production andprocessing in a crosscultural sample of African societies. Behavior Science Research, in press.
Sahn, D.E., ed. 1989. Seasonal variation in third world agriculture: The consequencesfor
food security. Baltimore, Md.: Johns Hopkins University Press.
Shipton, P. 1990. African famines and food security: Anthropological perspectives.
Annual Review of Anthropology 19:353-394.
Stone, G.D., R.McC. Netting, and M.P. Stone. 1990. Seasonality, labor scheduling, and
agricultural intensification in the Nigerian savanna. American Anthropologist 92(1):723.


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Extension Agent Surveys for Defining

Recommendation Domains:

A Case Study From Kenya

Steven Franzell



ABSTRACT
There is little consensus on when and how to define recommendation domains in FSR. Manyrecommend using secondarysources and informal farmer surveys; this paper evaluates a brief survey of extension agents in central Kenya for tentatively defining recommendation domains over a larger area than would normally be covered in an informal survey. The extension agent survey was evaluated by comparing the data obtained with those ofa formal survey of farmers in the same area during the same
year.
Compared to using an informal survey to define recommendation domains, an extension agent survey has three important advantages. First, it can provide information on recommendation domains for areas much larger than can be covered effectively in informal surveys and at much lower cost. Second, by delimiting boundaries between the defined domains, little time is wasted during the informal survey interviewing farmers not in the targeted domains. Third, byinduding extension agents in the information-gathering process, researchers can gain their confidence and active participation in on-farm research. However, as useful as an extension agent survey is for tentatively defining recommendation domains, it cannot serve as a substitute for an informal survey of farmers
for understanding farming systems.











1 Agricultural Economist, International Centre for Research in Agroforestry, Nairobi, Kenya.

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INTRODUCTION

The concept of "recommendation domain" has emerged as an important tool in farming systems research. A recommendation domain is defined as "a group of roughly homogeneous farmers with similar circumstances for whom we can make more or less the same recommendation" (Byerlee and Collinson, 1980). Defining recommendation domains in an area assists scientists to focus their efforts on the particular problems of different farmer groups, which may vary according to agroecological or socioeconomic variables. This approach contrasts with a more common one in which researchers seek to increase crop productivity without an awareness of the different categories of farmers they serve and the varying problems they face.
Defining recommendation domains in a particular area serves four purposes, as outlined below:
1. Helps researchers to decide where, and for which farmers, to focus their efforts (CIMMYT, 1979; Franzel, 1981; Kean and Singogo, 1988).
2. Assists researchers to assure that their activities are targeted towards the needs and circumstances of specific categories of farmers.
3. Makes the research process more efficient. By defining boundaries between domains, researchers can ensure that only farmers within the domains under study are included in the surveys and experiments, saving valuable time and resources.
4. Assists policy makers in development planning. For example, greater emphasis at the national level on foreign exchange earnings will require policies and resources to be shifted to those domains that have the greatest potential for producing export crops (Collinson, 1982).
There are varying opinions in the literature as to when and how researchers should define recommendation domains. Many recommend making hypotheses concerning domain boundaries at the beginning of the research process, based on reviews ofsecondary data or during the informal survey2 (Harrington and Tripp, 1984; Hildebrand and Poey, 1985). These hypotheses are then tested and refined during the surveys and experiments that make up the on2 Informal surveys, also called exploratory or rapid reconnaissance surveys, are surveys
in which researchers interview farmers using informal, unstructured techniques in order to encourage dialogue and probing of issues. This is often followed by a formal survey during which data are collected by means of a questionnaire, which is usually administered by enumerators to a randomly selected sample of farmers. The formal survey is used to verify the findings of informal surveys and to quantify important
parameters (Byerlee and Collinson, 1980).


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farm research process. Collinson (1982, 1987) proposes a distinct survey exercise preceding the informal survey in which researchers administer a brief questionnaire to extension agents or other key informants. The purpose of this exercise is to elicit information for tentatively defining recommendation domains over a larger area than can be effectively covered in an informal survey.
The objective of this paper is to evaluate a brief survey of extension agents in Kenya to define recommendation domains, similar to that proposed by Collinson. The method has several potential advantages over the two options discussed above for making a preliminary definition of recommendation domains, i.e., using secondary information or an informal survey. First, in many developing countries, secondary information is lacking, is of questionable reliability, or is aggregated across widely different agroecological zones. Second, using the informal survey to begin defining recommendation domains is not cost-effective, because only small areas can be covered and much researcher time might be wasted interviewing farmers who are not in the recommendation domains that are eventually chosen for focus. The extension agent survey can help to ensure that the recommendation domains are defined fairly accurately before the survey begins, thus ensuring that informal surveys are conducted more cost-effectively. Third, the area selected for the informal survey may not be representative of larger areas; the extension agent survey can provide information useful for selecting an area for the informal survey that is as representative as possible of larger areas.
However, the extension agent survey also has potential drawbacks. The technique is open to a wide range of error because prior to the informal survey, researchers might know little about the area and might have trouble formulating appropriate questions. Furthermore, the extension agents might give inaccurate responses. If the errors are significant, the exercise will be wasteful and misleading.
In this paper, the extension agent survey is evaluated by comparing the information obtained with that of a formal survey of farmers carried out in the same area during the same cropping season. First, the design and results of the extension agent survey are presented and sources of error are assessed. Next, data from the extension agent survey are compared to data from the formal survey. The comparison is used for evaluating the effectiveness of the extension agent survey for (1) providing preliminary information about farmers in each domain and (2) demarcating recommendation domain boundaries.


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DESIGN OF THE EXTENSION AGENT SURVEY
In 1981, the mid-altitude area (800 to 1,400 m) of Muranga, Embu, and Kirinyaga Districts in Kenya was selected as the study area for an extension agent survey to define recommendation domains by the Ministry ofAgriculture in collaboration with the International Maize and Wheat Improvement Center (CIMMYT). The study area covered roughly 4,000 m2 and was populated by about 400,000 people. The objective of the survey was to define recommendation domains and to select a target area to conduct surveys and on-farm trials.
The exercise took three weeks and involved four steps. First, secondary data on the area were assembled. These proved to be of limited use because most of the data had been averaged across entire districts, which ranged from several hundred to several thousand meters in altitude. Second, researchers, government officials, and local leaders familiar with the area were interviewed informally about agroclimatic differences in the area and socioeconomic differences among farmers. Third, changes in agroecological zones and cropping patterns were noted while touring the study area. Fourth, a brief two-page questionnaire was drawn up based on the interviews and observations and was administered to extension agents in each sublocation, the smallest administrative unit in Kenya. The respondents were asked about the principal characteristics of the farming systems in their area and characteristics that differentiated farmers between and within areas. Individual questions concerned such aspects as physical environment, cropping patterns and practices, livestock, and sources of income. Separate questionnaires were completed for low-income and high-income farmers.
The interviews were conducted by the researcher; each interview lasted about forty minutes. Twenty-two questionnaires were completed, most of these when extension agents attended monthly meetings at their district office.


RESULTS OF THE EXTENSION AGENT SURVEY
The results of the extension agent survey were used to select the medium altitude areas ofKirinyaga District (called Middle Kirinyaga in this paper) for further survey work and on-farm experiments. Middle Kirinyaga was selected because the area (1) had high potential for maize production, (2) had a fairly low adoption rate for improved technologies, and (3) was accessible throughout the year.
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Middle Kirinyaga has an area of about 170 km2 and a population of approximately 35,000. Principal agroecological and socioeconomic characteristics include 1,100 mm of rainfall distributed between two seasons; red loam soils and a flat to mildly sloping terrain; farm size averages of 2 to 4 ha; principal food and cash crops of maize and beans, which are almost always intercropped; and farmers who either own or rent oxen for plowing. The boundaries of Middle Kirinyaga represent fairly distinct changes in the agroecological and socioeconomic environment. For example, to the north of Middle Kirinyaga, altitude increases, rainfall is higher, and the terrain is steeper. The southern boundary of Middle Kirinyaga is a fairly distinct line representing a change in soil type from red loam to heavy, black clay. To the east and west of Middle Kirinyaga are dry, hilly areas with less fertile soils, lower population densities, and more uncultivated land.
The task of assessing socioeconomic differences among farmers in Middle Kirinyaga was more difficult than defining agroecological differences. Two factors were considered in the extension agent survey:
1. Characteristics of the farming systems. Researchers sought to determine whether there were important differences in the way farmers managed their farms, the resources they used, and their constraints. Important differences in management, it was felt, would probably indicate differences in research needs and development opportunities.
2. Potential. Researchers were concerned with the farmers' relative potential for adopting new technology, in terms of resource availability. Two farmers may be operating their farms in a similar manner, but have varying potential for adopting new technology because of differing resource availability.
In Middle Kirinyaga, it appeared that level of cash income was an important distinction among farmers determining both the characteristics of the farming system and the potential for change. For example, cash income influenced whether the farmer undertook certain high-cost, risk-increasing enterprises, such as owning exotic breed cattle, or used purchased inputs such as hybrid maize seed. These differences in the way farmers operate their farms affect the type of experiments to be planned for farmers of different income levels. For example, the number and range of experimental variables would generally be greater for high-income farmers, since low-income farmers lacked cash for purchasing improved inputs.
Another issue concerned the number of income categories to establish and how to differentiate among them. Before the extension agent survey, research-


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ers classified farmers into two recommendation domains-high income and low income. In subsequent informal and formal surveys, a set of proxies for income was drawn up to differentiate high-income farmers from low-income farmers. The proxies for high-income farmers included ownership of exotic breeds of cattle, house type, past land purchases, and type of off-farm income. A subjective weighting of these variables was used to allocate farmers between the two groups. In only a few cases was there any uncertainty as to which group a farmer belonged.


EFFECTIVENESS OF THE EXTENSION AGENT SURVEY

This section is divided into three parts. First, sources of error in the extension agent survey and measures taken to reduce their effect are examined. Next, the quality of data obtained in the survey is evaluated by comparing data obtained with data collected in the formal survey of farmers. Finally, the delineation of recommendation domains, based on the extension agent survey, is compared with the categories arrived at following the formal survey.

Sources of Error
Eight sources of error that were encountered in the extension agent survey are listed below. It is important to examine these sources so as to understand why some estimates were incorrect and to avoid or correct for these in future exercises. Five of the sources stem from the extension agents and three from the researcher.
Respondent-based sources of error.
1. Bias towards male, "progressive" farmers. Extension agents tend to frame their answers about farmer circumstances with the high-income, male, "progressive" farmers in mind. To reduce this bias in the extension agent survey, the respondent was repeatedly reminded of the difference between male, "progressive" farmers and the range of other farmers in the survey area.
2. Bias resulting from attempts to impress investigators. Extension agents tend to overestimate the number of farmers who have adopted recommended innovations, perhaps because they are trying to impress the researcher or because they are afraid their work is being evaluated. To counter this bias, respondents were reminded that the researcher's goal was not to evaluate their work, but to understand farmer circumstances. Respondents were also reminded that low adoption rates may be caused by many factors other than poor extension.


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3. Inaccurate information caused by inability of respondent to deal with percentages. Some extension agents have only primary school education and some are not comfortable dealing with percentages. Some progress was made by using a continuum of all-most-half-some-few instead of percentages.
4. Inaccurate information caused by respondent's lack of knowledge. The extension agents may think they know the answer to a question but are mistaken. In other cases, they are afraid to say they do not know the answer and give a false one.
5. Bias associated with answers that refer only to area that respondent knows best. Some extension agents may give an answer that applies to the area that they know best, usually the area around their home, but not to other parts of the area they serve. The effect ofthis bias was reduced by using a map during the interview and assuring that the responses were relevant to all areas within the respondent's jurisdiction.
Researcher-based sources of error.
1. Bias resulting from attempts to compensate for perceived respondent biases. In some cases, the researcher discounts or alters an extension agent's response because it is inconsistent with the responses of other respondents or because it contradicts the researcher's own observations. However, these compensatory biases may be incorrect or may overcompensate for respondent bias.
2. Improper phrasing of question or inappropriate narrowing of possible responses. In the early stages of an investigation, researchers lack sufficient knowledge to appropriately phrase questions; thus, they may inadvertently preclude realistic possibilities. For example, in this study, the researcher asked respondents to name the methods that farmers used to prepare their land, without considering the possibility that some farmers practice no-till. It is not clear that extension agents were aware that some farmers practice no-till, but the manner in which the question was phrased did not encourage them to offer this information.
3. Bias stemming from incorrect assessment of composition of a particular recommendation domain. The researcher believed that most high-income farmers were immigrants to the area and thus oriented the questions about high-income farmers towards immigrants. Later it became clear that most high-income farmers were natives of the area. Immigrants differ from other high-income farmers in some important respects; responses concerning such characteristics were biased towards immigrants.


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Evaluation of Data Describing Recommendation Domains
Table 1 presents the accuracy ratings and sources of inaccuracy for estimates' of 29 variables from the extension agent survey. The table shows that over one-third of the estimates made were highly accurate and threequarters were ofhigh or moderate accuracy for both income groups. Appendix 1 shows the estimates of the 29 variables made following the extension agent survey and those made from data collected in the formal survey.
The system of accuracy ratings was based on the degree of correspondence of an estimate in the extension agent survey to sample estimates from the formal survey. Formal surveys have inherent biases but are generally effective for collecting data on phenomena that change slowly (e.g., farm size) or onetime or infrequent occurrences (e.g., crops planted; Shaner et al., 1982). Nearly all of the variables examined in the extension agent survey fall into one of these categories (Appendix 1). Furthermore, the formal survey data are believed to be accurate on the grounds that the survey (1) was administered to a random sample of farmers, (2) used female enumerators to interview the approximately one-third of household heads who were female, (3) used a questionnaire, thus a set of standardized questions for all respondents, and (4) was preceded by a comprehensive informal survey, which was important for developing an understanding of the local terms of reference critical for questionnaire development
Correspondence was measured in three different ways, depending on the type of variable considered. If the variable estimated in the extension agent survey was the percentage of farmers with a particular characteristic, such as the percentage of farmers growing coffee, then the following system was used: A margin less than 10 percentage points away from the sample percentage in the formal survey was considered to be highly accurate, a margin of between 10 and 20 percentage points was considered to be moderately accurate, and a margin of greater than 20 percentage points was considered to be of low accuracy.
The particular boundaries between the ratings are arbitrary but broadly reflect the degree of closeness and the effects that an error would have on the understanding of the farming system and the defining of recommendation domains. For example, the formal survey indicated that 85 percent of low3 In the extension agent survey and the formal survey, "estimates" of quantitative
variables (e.g., maize yields) or percentage variables (e.g., percentage of farmers growing coffee) refer to the means of responses. For nominal variables (e.g., main method of land preparation), the estimate is the one most frequently mentioned by the
agents.
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Table 1. Accuracy of Estimates From Extension Agent Survey.

High-income farmers Low-income farmers
Number of Percent Number of Percent
Estimates Estimates

Level of accuracya
High 10 37 11 39
Moderate 10 37 10 36
Low 7 26 7 25
Total no. of estimates 27 100 28 100
Sources of inaccuracy
Respondent-based
Lack of knowledge 8 47 11 65
Progressive farmer bias 2 12 8 47
Researcher-based
Phrasing 1 6 2 12
Compensating for perceived
progressive farmer bias 1 6 2 12
Composition of
recommendation domains 8 47 1 6
Total of inaccurate estimates 17 100 17 100
a Accuracy means degree of correspondence with results from the formal survey.
Accuracy ratings and sources of inaccuracy are defined in the text.
b Figures do not sum to 100, since more than one type of bias may be encountered for
a particular estimate.
Source: Data are derived from the appendix.

income farmers intercrop their maize and beans. If the estimate from the extension agent survey had been off by 10 or less percentage points it would have made little or no difference in the understanding of the farming system or demarcation of recommendation domains. The conclusion would still be that the overwhelming majority of all low-income farmers intercrop. If the estimate from the extension agent survey had been off by 30 percentage points, i.e., if it had been estimated that 55 percent of farmers intercrop, the researcher may have decided to demarcate recommendation domains according to whether or not farmers intercrop. This would have been a fundamental error, because the number of farmers who do not intercrop is so small that this variable should not receive priority in defining recommendation domains.
For estimates of quantities, such as maize yields per hectare, an estimate within 10 percent of the sample estimate was considered to be accurate, 10 to 20 percent of moderate accuracy, and off by more than 20 percent of low accuracy. For nominal data, such as the two principal months in which farmers experience labor shortages, a "correct" estimate was considered to be highly accurate. A partially correct estimate, say estimating March and April to be the


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principal months of cash shortage when the formal survey indicates that April and May are the principal months, was considered to be moderately accurate. Estimates that were not even partially correct were considered to be of low accuracy.
Surprisingly, the levels of accuracy of the data from the extension agent survey are nearly identical for each income group (Table 1). Extension agents tend to have more contacts with "progressive" and high-income farmers (Leonard, 1977); thus it had been hypothesized that the accuracy ratings would be higher for high-income farmers than for low-income farmers. Indeed, extension agents were responsible for twice as many errors concerning low-income farmers as for high-income farmers. This result is as expected: extension agents showed a greater lack of knowledge and more progressive farmer bias in responses concerning low-income farmers than in responses for high-income farmers. Researcher-based errors were more numerous for highincome farmers than for low-income farmers because of the researcher's incorrect assessment of the composition of the high-income recommendation domain. Thus, whereas respondent-based biases were the principal factor lowering the quality of information about low-income farmers, researcherbased biases were the principal source of inaccuracy concerning high-income farmers. The net results were similar levels of accuracy in information concerning both groups.
Two other, less important, researcher-based biases also distorted information about farmers. First, inappropriate phrasing and formulating questions in a way to exclude certain appropriate responses were more of a problem in asking about low-income farmers than about high-income farmers. This indicates, not surprisingly, that the researcher knew less about low-income farmers' circumstances and practices than those of high-income farmers.
Second, modifying estimates to compensate for perceived respondent bias sometimes decreased the accuracy of the estimate. Five estimates concerning high-income farmers and seven concerning low-income farmers were revised for such reasons. In six cases the modification improved the accuracy rating, in four cases the modification lowered the accuracy rating, and in two cases there was no change. Two examples typify the advantages and disadvantages of this measure. First, extension agents reported that about half of the lowincome farmers purchased hybrid seed each year. However, a seed retailer assured the researcher that low-income farmers rarely, if ever, bought hybrid seed. In the report on recommendation domains the estimate was lowered, and formal survey results confirmed that the modification was correct. A


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second example shows how one can easily make mistakes using the compensatory bias tool. Extension agents estimated that one-third of low-income farmers grew coffee. This figure was revised downward because ofa perceived progressive-farmer bias on the part of the extension agents. In fact, the extension agents' estimates were accurate, as confirmed in the formal survey.
A further issue concerns the reasons underlying respondent inaccuracies. Estimates concerning crops grown, livestock, labor use, and income sources were generally fair to good; poor estimates were made on manure use, maize varieties, methods of land preparation, maize yields, and percentage of highincome farmers intercropping. In several instances, it is possible to explain why some estimates were poor. For example, estimates on manure use were poor because of respondents' lack of knowledge; manure use is not part of the extension agents' recommendations to farmers and thus they were unaware of farmers' manure practices. Progressive farmer bias and lack of knowledge were important sources of bias for the other four variables that were incorrectly estimated, and improper phrasing of questions contributed to inaccuracy in two of the cases.

Accuracy in Defining Recommendation Domains
Concerning agroecological differences, there were no important modifications of the preliminary set of boundaries established during the extension agent survey. The only modification following the exercise involved excluding a part ofRukanga sublocation, which included approximately 4 percent of the population and 10 percent of the area of Middle Kirinyaga. During the informal survey, it was apparent that rainfall was lower and farm size and livestock numbers were greater in Rukanga than in Middle Kirinyaga. Thus the area was excluded.
The attempt to define socioeconomic differences proved to be more complicated and somewhat inaccurate. Two recommendation domains were defined in the extension agent survey on the basis of income level and these domains were maintained throughout each ensuing stage of analysis. But the composition of the domains was modified following the informal survey. The correspondence between full-time farming and low-income was dropped, since some full-time farmers were found to have lucrative operations such as dairy or tobacco and were thus high-income farmers. Similarly, the correspondence between part-time farming and high-income was omitted because many low-income farmers were found to be farming only part-time; their off-farm activities were not profitable enough to make them high-income farmers.


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In summary, the exercise defining recommendation domains was reasonably accurate in defining and demarcating recommendation domains and in providing preliminary information about the farmers in each recommendation domain. This result is somewhat astonishing, because so little time and resources were allocated to this exercise. The primary source of inaccuracies were researcher-based biases concerning high-income farmers and respondent-based biases concerning low-income farmers.


IMPROVING THE ACCURACY OF THE EXTENSION AGENT SURVEY

By examining the sources of inaccuracy in the exercise it is likely that their effects can be minimized in future investigations defining recommendation domains. In addition, three important lessons arise from the exercise.
First, interviewing local leaders, e.g., sub-chiefs, in addition to, or in place of, agricultural extension agents, may be useful in obtaining more valid information about low-income farmers. This is also the finding of Njobvu (1985) who used a similar survey in Zambia to define recommendation domains. In Middle Kirinyaga, respondent-based biases were generally caused by extension agents' lack of orientation towards low-income farmers. In several cases, sub-chiefs were able to provide better information about their sublocation because (1) they were natives of the area and (2) they had less of a stake in over-reporting the use of recommended inputs than did extension agents. However, their education level was generally lower than those of extension agents. Thus, many had difficulties with such concepts as estimating the proportion of farmers having a particular characteristic. Moreover the fact that all were male and high-income may have contributed to biases against low-income and female-headed households.
Second, more effort should have been made to identify the possible range of responses to questions in the questionnaire, particularly concerning lowincome farmers. Because of the biases towards progressive, high-income farmers, there were more phrasing errors concerning low-income farmers than high-income farmers.
Finally, the exercise highlights the importance of correctly identifying socioeconomic differences among recommendation domains before designing questionnaires. In the case reported in this paper, a more thorough investigation of the composition of the high-income recommendation domain through discussions with key informants should have been carried out


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before administering the extension agent survey.


CONCLUSIONS
The extension agent survey described in this paper was found to be reasonably effective for:
1. Tentatively classifying farmers into recommendation domains. Few revisions were made in the domain boundaries as a result of the informal and formal surveys that followed the extension agent survey.
2. Developing some preliminary information about farmers in the respective recommendation domains. About three-quarters of the estimates of variables in the extension agent survey were moderately or highly accurate.
The extension agent survey also had important advantages over other methods (e.g., secondary information or the informal survey) fora preliminary demarcation of recommendation domains. Defining recommendation domains using secondary data was impossible, because what data was available was aggregated over a large area composed of several agroecological zones. Compared to using the informal survey to define recommendation domains, the extension agent survey had three important advantages. First, by defining recommendation domains in the study area before the informal survey began, researchers were able to decide which recommendation domains they wanted to target during the informal survey that followed. Second, by delimiting boundaries between the defined domains, little time was wasted during the survey interviewing farmers who were not in the targeted domains. Third, by including extension agents in the information gathering process, the researchers were able to gain their confidence and active participation in on-farm research activities. Furthermore, the cost of the extension agent survey was very low, lending further support for its use before beginning an informal survey. Only three weeks of one researcher's time was required to cover an area of 4,000 km2 and 400,000 people; this includes data collection, analysis, and completing a report on the findings.
Finally, it should be noted that an extension agent survey cannot be used to substitutefor an informal survey of farmers for understanding farming systems. The exercise described in this paper involved fairly simple questions about characteristics of farmers and areas that extension agents were capable of an-swering. To identify problems and propose solutions, researchers must care-fully observe local conditions and conduct detailed interviews with farmers.


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ACKNOWLEDGMENTS

The author wishes to thank the International Maize and Wheat Improvement Center (CIMMYT) and the Scientific Research Division, Ministry of Agriculture, Kenya, for support of the research reported in this paper. The advice and assistance of M. Collinson, E. Crawford, S. Haggblade, S. Mwaniki, N. Njeru, J. Murage, and S. Goldmark are also gratefully acknowledged. The views expressed in this paper do not necessarily reflect those of the institutions or individuals mentioned above.


REFERENCES

Byerlee, D., and M. Collinson. 1980. Planning technologies appropriate tofarmers: Concepts
and procedures. International Maize and Wheat Improvement Center, El Batan, Mexico.
71 pp.
International Maize and Wheat Improvement Center (CIMMYT), Eastern Africa Economics
Programme. 1979. Deriving recommendation domains for Central Province, Zambia.
Demonstrations of an Interdisciplinary Approach to Planning Adaptive Agricultural
Research Programmes No. 5. Nairobi, Kenya. 38 pp.
Collinson, M. 1982. Farming systems research in Eastern Africa: The experience of CIMMTT
and some national agricultural research services, 1976-81. Michigan State University International Development Paper No. 3. Michigan State University, East Lansing. 67 pp. Collinson, M. 1987. Farming systems research: Procedures for technology development.
Experimental Agriculture 23:365-386.
Franzel, S. 1981. Identifying farmer target groups in an area: Methodology and procedures.
Farming Systems Newsletter 4:13-25. International Maize and Wheat Improvement
Center, Eastern Africa Economics Programme, Nairobi, Kenya.
Franzel, S. 1983. Planning an adaptive production research program for small farmers: A case
study of farming systems research in Kirinyaga District, Kenya. Unpublished Ph.D.
dissertation, Michigan State University, East Lansing. 279 pp.
Harrington, L., and R. Tripp. 1984. Recommendation domains: A framework for on-farm
research. Economics Program Working Paper 2/84. International Maize and Wheat
Improvement Center, El Batan, Mexico. 27 pp.
Hildebrand, P., and F. Poey. 198 5. On-farm agronomic trials infarming systems research and
extension. Boulder, Colo.: Lynne Rienner Publishers, Inc. 162 pp.
Kean, S., and L. Singogo. 1988. Zambia organization and management of the adaptive
research planning team, Ministry ofAgriculture and Water Development. On-farm Client Oriented Research Case Study No. 1. International Service for National Agricultural
Research, The Hague, Netherlands. 302 pp.
Leonard, D.K. 1977. Reaching the peasantfarmer: Organization theoryandpractice in Kenya.
Chicago, Ill.: University of Chicago Press. 223 pp.
Njobvu, C.A. 1985. Factors influencing recommendation domain boundaries of the farming
systems and levels of agricultural development in Lusaka Province, Zambia. Paper presented at the Farming Systems Research-Extension Symposium, Kansas State University, Manhattan, October 13-16.
Shaner, W., P. Philipp, and W. Schmehl. 1982. Farming systems research and development:
Guidelines for developing countries. Boulder, Colo.: Westview Press. 414 pp.


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APPENDIX 1. COMPARISON OF ESTIMATES OF VARIABLES FROM EXTENSION AGENT SURVEY AND FROM FORMAL SURVEY, MIDDLE KRINYAGA, 1981.


High-income farmers
Extension Formal survey agent survey


Low-income farmers Extension Formal survey agent survey


Land and enterprises
Farm size (ha)
Area cultivated long
rains (ha)
Farmers who have
purchased land (%)
Main food crops Main cash crops
Farmers growing coffee
(%)
Farmers growing sunflower (%)
Area under maize (%) Area under beans (%) Farmers owning Zebu
cattle (%)
Farmers owning exotic
cattle (%)
Farmers owning cattle
(%)


40 23
maize-beans maize-beans maize-beans beans-maize


Maize-bean husbandry
Farmers intercropping
maize-beans (%) 20
Method of land preparation (%)
hire ox 60
own ox 20
borrow ox 0
rent tractor 20
no-till 0


Farmers growing long rains maize varieties (%)
hybrid 67 36
2nd gen. hybrid 10 28
katumani 33 49
local 0 59

Farmers growing short rains maize varieties (%)
hybrid 10 0
2nd gen. 0 38
katumani 90 51
local 0 51


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3.0 2.5


0
maize-beans maize-beans

15

5 90 90

71

15

80



88

50 45 0 5 0


0 5 10 55


0 30 30 50


2.4 1.4


2
maize-beans beans-maize

25

0 91
78

56

2

62



85

0 27 33 0 21


0 21 69 77


0 29 60 71

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APPENDIX 1. COMPARISON OF ESTIMATES OF VARIABLES FROM EXTENSION AGENT SURVEY AND FROM FORMAL
SURVEY, MIDDLE KRINYAGA, 1981. CONT.

High-income farmers Low-income farmers
Extension Formal survey Extension Formal survey agent survey agent survey
Farmers using fertilizer
(%) 20 18 5 2
Farmers using manure
(%) 30 82 10 49
Average maize yield
(kg/ha) 2,500 1,200 1,900 1,100

Income, labor, and other
Farmers getting income
from relatives (%) 0 0 10 11
Farmers having salaried jobs or businesses (%) 75 69 20 21
Busiest months March-May April-May March-May April-May
Activity at busiest time P, W W P, W W
Farmers hiring labor (%) 90 74 10 17
Farmers having zinc roofs
(%) 100 97 50 72
Main distinguishing factor
between domains part-time high income full-time low income
farming/off- farming/no
farm income off-farm income
Farmers in this domain
(%) 20 40 80 60
a P = planting; W = weeding. Source: Survey data, as reported in Franzel (1983).


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The Role of Farmers in the Jordanian Combined Sondeo Process'

D.L. Galt and A.F. Al-Kadi 2



ABSTRACT
A rapid rural appraisal (RRA) technique called the "combined sondeo"
evolved in Jordan to help the staffof the National Center for Agricultural Research and Technology Transfer (NCARTIT) of the Ministry of Agriculture (MOA) to prioritize their annual research and extension activities by directly incorporating farmers' priorities and problems on a subnational basis. The purpose of this paper is to explain how this RRA technique allows explicit incorporation of farmers' ideas, opinions, and suggestions into the annual national work plan process for research and technology transfer and to describe briefly how this process is evolving and becoming a sustainable part of the National Agricultural Research
System (NARS).


INTRODUCTION

The FSRE approach officially began in Jordan in January 1987, with a "training of trainers" workshop (Gaudreau et al., 1989), sponsored by the U.S. Agency for International Development (USAID)-funded Jordan National Agricultural Development Project (JNADP) and implemented by the Consortium for International Development (CID) through Washington State University (WSU) as the lead university. This training was followed by a second JNADP-sponsored training of NCARTT researchers and technology-transfer (i.e., extension) staff in August 1989 (Gaudreau et al., 1989). During both trainings, participants carried out socioeconomic field research.
The second research training included use of the classical sondeo technique of questioning farmers about their prevalent systems and problems (Hildebrand, 1981). The field work also included adaptations of relevant experiences from the Nepalese Samuhik Bhraman process (Mathema and Galt, 1988, 1 Paper presented at the Tenth Annual Association for Farming Systems Research-Extension
Symposium, Michigan State University, East Lansing, October 14-17, 1990.
2 Head, Monitoring and Evaluation Unit, MCARTI', Ministry of Agriculture, Jordan, and
Sociometric and FSRE Technical Assistance Advisor to NCARTT through JNADP, Jordan,
respectively.

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GALT AND AL-KADI


1989; Chand and Gibbon, 1990). The Jordanian combined sondeo is another example of how RRA techniques continue to be incorporated into full-scale FSRE approaches (Chambers, 1981; Chambers and Ghildyal, 1985; Mathema and Galt, 1988, 1989; Chand and Gibbon, 1990). Most importantly, however, the evolving combined sondeo in Jordan is a particularly costeffective way to collect and analyze rural data quickly (Kumar, 1987).
During the second training, participants interacted with farmers in Jordan's six regional centers. These six Regional Agricultural Service Centers, or RASCs, are located in Ramtha (in the north), Khaldieh (in the northeast), Deir Alla (in the Jordan Valley north of the Dead Sea), Mushaqar (just south of the Amman area), Rabba (between the southern most RASC and Mushaqar), and Shoubak (covering approximately one-half of the area of Jordan and located in the southern half of the Kingdom). Recently, a seventh region, Al-Balca, which includes the national research headquarters in the Al-Baqa'a Valley, has been added as a RASC. These seven RASCs represent the beginning of administrative decentralization of research and technology transfer in Jordan.
These interactions with area farmers lasted one week. During this week, individual farmer interviews occupied the first two or three days. Next, participants spent one or two days listing (1) priority farming systems and (2) farmer-identified problems in these systems. At the same time, they suggested tentative solutions to those problems judged to be most important.
The final day of the field week was set aside to host a joint farmerresearcher-extension agent meeting to discuss prioritized problems and tentative solutions to them. Finally, the research and extension participants were responsible for producing modified work plans for 1989/90 for each RASC, based on (1) 1988/89 work plans and (2) the assimilated input obtained from farmers. At some RASCs, a few trial design details were also included in the draft research work plans (Gaudreau et al., 1989).
During the spring of 1990, staff at each RASC (led by the RASC director, the research supervisor, and the extension supervisor and assisted by the socioeconomist) hosted follow-up meetings with groups of invited farmers. Also attending these meetings were representatives from appropriate regional Directorates of Agriculture and interested commodity section heads or representatives. More details concerning these joint farmer-researcherextension agent meetings are presented in Al-Kadi and Gait, 1990b.
The results of these joint meetings allowed RASC staff to plan research and extension activities for 1990/91, taking farmer priorities directly into account. Because it culminates with the annual NCARTT planning meeting, the


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process is institutionalized. The institutionalization of explicit farmer opinion in the annual NCARTr planning process makes the Jordanian FSRE approach sustainable into the future.
A key to the process is that neither research nor extension are able to ignore the priorities placed on activities in crop or livestock-specific areas, because these farmer-prioritized lists are used in follow-up meetings. This does not allow enough time for refiltering the priorities through normal research and extension disciplinary thought processes, so no unintentional reprioritization takes place when the combined sondeo process is followed. The rest of this paper describes the methodology of this process in more detail and presents some of its dynamics.


METHODS

Initial Farmer Meetings
NCARTT commodity and disciplinary research is organized around nine sections or units: (1) Stations and RASC Section, (2) Cereals Section (with responsibility for research on field crops), (3) Communications Section, (4) Fruit Trees Section, (5) Plant Protection Section, (6) Range and Livestock Section, (7) Soils and Irrigation Section, (8) Vegetable Section, and (9) Monitoring and Evaluation Unit. As the two major components of agricultural development, research and extension (or "technology transfer" in Jordan) are separated into their own divisions. Organizationally, these nine sections and units are in the research division.
During initial field site visits using the Jordanian combined sondeo process, researchers selected by Section Heads from each NCARTT commodity section join regional researchers and technology-transfer staff in farmer interviews. Interviews are carried out by two-person teams. The initial combined sondeo field activities take one week to complete.
The methodology used consists of three major components: (1) interviewing individual farmers during the first few days of the week; (2) listing predominant systems and problems within systems, discussing these lists, and advancing tentative solutions to them (during the latter part of the week); and
(3) convening a meeting at the end of the week that includes farmers, researchers, and extension agents. Output from the process includes (1) lists of predominant farming systems for the area, (2) lists of problems prioritized by the frequency with which they are mentioned by farmers, and (3) a draft RASC research and technology-transfer work plan.


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Individual farmer interviews. During two or three days of individual farmer interviews, farmers are interviewed where they are found--either in their homes or their fields. Lists offarmer's systems, problems within systems, and farmer's suggestions to overcome some of these problems are noted by the' interview team during and immediately after each interview.
The civil service working hours in Jordan are not conducive to completing field work. The Jordanian work week consists of six, six-hour work days (Saturday through Thursday), from 8:00 A.M. to 2:00 P.M. To extend this time to a meaningful length (so that either farmer interviews or group discussions can continue beyond 2:00 P.M.), it is necessary to provide a meal for the participants. There is no national mechanism in place to provide meals. Also, because governmental transportation leaves from each regional center at 1:55 P.M. each day, additional transportation must be ananged in advance to allow professionals to stay beyond 2:00 P.M. Thus, in the Jordanian context, use of outside funds has been necessary to finance part of the extra transportation costs, to purchase fuel for this transportation, and to pay for meals for professional staff. Therefore, the combined sondeo is not currently sustainable in Jordan unless outside donor funds are made available on a continuing basis to support it, or the appropriate national laws are changed.
Listing systems, problems, and solutions. After interview teams return to the RASC and finish lunch, their notes on predominant systems and farmer's problems within these systems are transferred to master lists on flip charts in an informal group setting. These lists, in turn, redirect research and technology-transfer plans for the following year.
These lists then are used by the professional staff to prioritize farmeridentified problems on a regional basis, using the simple method of farmerreported frequencies: The more often a problem is mentioned by farmers, the higher its priority is assumed to be. Problems then are divided into three subgroups: those to be addressed by (1) researchers, (2) extension, or (3) other policymakers.
The remaining time is spent in group brainstorming sessions to arrive at tentative solutions to the first two sets of problems. This consists of (1) proposing trials to solve research-related problems and (2) proposing extension activities to address problems for which research solutions now exist, but for which extension information has not yet been provided to farmers.
Final joint meeting and draft regional work plans. The mechanism for convening the farmer group meeting is as follows. During the first two to three days of individual farmer interviews, each farmer interviewed is invited


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to attend the closing group meeting. In our experience, an individual farmer's propensity to attend this final group meeting was a function of four factors: his or her (1) schedule for that particular day, (2) distance from the RASC, (3) access to public or private transportation, and (4) personal interest in the combined sondeo process. In 1989, across the six RASCs, a total of 238 individual farmers were interviewed during the three days devoted to this combined sondeo activity. Of these, 94 farmers (or 39%) participated in the final group meetings (Gaudreau et al., 1989).
During this final meeting, the summarized priorities and problems are reviewed for the farmers by means of RASC research and technology-transfer staffpresentations. RASC staff then outline next year's proposed research and technology-transfer draft work plans. Following these informal presentations, farmers offer their opinions on any part of the revised program. RASC staff take note of the suggestions that can be incorporated into improving their proposed activities. Occasionally, for problems that fall into the policy area outside of the mandates of both research and extension, the group is able to agree on a set of problems, tentative solutions, and a plan of action to solve them. Results of these discussions are noted by the socioeconomic staff, and this information is provided to other MOA decision-makers at a later date (Galt and Al-Kadi, 1989).
A practical example of farmer-generated policy issues occurred in Deir Alla. The farmer-researcher-extension agent group reached agreement on the following two points: (1) styrofoam containers (for transporting fruits and vegetables to market) are too expensive, pollute the environment to a great and very persistent degree, and should be replaced by an alternative (wooden or pasteboard) container and (2) farmer representatives must be included if and when fruit and vegetable grading standards are proposed and developed for the Kingdom. The group then proceeded to discuss how these problems could be addressed by other policymakers in the government. The results of this meeting were summarized, written up, and forwarded to the MOA (Galt and Al-Kadi, 1989).
This type of informal group meeting format allows for direct farmer input into the formation of research and technology-transfer activities, assisting in the fine-tuning of solutions that may already be formulated for the problems they identified earlier in the week. The output of the process is sets of annual work plans that have been modified to take high-priority, farmer-identified problems and constraints into account. Headquarters-based research and extension personnel have less chance to reprioritize these lists than personnel


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in the normal sondeo process, during which trial design and extension activity generation are physically separated from the farmer-interview process. This point, though subtle, is a key to incorporating real-not reinterpretedfarmer priorities into research and extension work plans. Finally, as shown above, the informal group setting provides a way for the whole group to forward concrete suggestions to other governmental decision-makers for solving policy issues that adversely affect agriculture in the region.

The Recurrent Nature of the Combined Sondeo
The initial combined sondeo represents only the first step of the process. It is followed by annual, recurring combined sondeos. The recurrent combined sondeo is the first part of a sustainable FSRE approach in Jordan. It is designed to accomplish two things: (1) to update the list of prioritized problems annually through a series of area meetings with farmers and (2) to allow each RASC to directly incorporate these reprioritized problems into a revised draft research and extension work plan for the following year.
Follow -up meetings withfarmers. The second part of the combined sondeo methodology involves updating work plans annually to capture new farmer concerns by fine-tuning on-going research trials and extension activities. These follow-up combined sondeos occur each spring at each RASC. A meeting is arranged by RASC staff to host farmers from each region. Those NCARTT commodity sections with sufficient time and interest assign staff members to participate in these meetings. Meetings last either one or two days, depending on the complexities of the systems and/or the frequency of problems in the particular area. More details of the dynamics of these meetings have been provided elsewhere (Al-Kadi and Galt, 1990b).
Follow-up survey meetings start with a brief introduction to the region and to the research and technology-transfer activities underway there. Next, a list of those problems identified by farmers during the previous combined sondeo are presented on flip charts (e.g., Gaudreau et al., 1989). An open discussion of these problem follows. During this discussion, existing problems are clarified, amplified, refined (divided into two or more subproblems), or eliminated if they no longer exist or if they are no longer considered important. Finally, new problems are discussed, clarified, and added to the master list.
Drafting revised annual RASC work plans. Following this discussion, two approaches have been used to move toward potential solutions to problems. These approaches are (1) prioritization of all problems and (2) prioritization of problems after separation into major commodity groups.


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Approach 1: Overall prioritization ofproblems. In 1990, all regions except Ramtha prioritized problems directly from the revised master problem list. Problems were not separated into major commodity groups. For example, the problem of "insufficient feed for livestock" was considered along with the problems of "excessive drop of citrus fruits," "not enough laborers," and "poor agricultural roads." While this is a less time-consuming method, it is not the preferred method as it involves combining unrelated topics.
Approach 2: Prioritization of problems by commodity type. The preferred method is to divide farmer-identified problems into lists by major commodities research sections; for example, "insufficient feed for livestock" would be considered along with all of the other problems being dealt with under the category of "Range and Livestock." Likewise, "not enough laborers" can be considered either a socioeconomic research problem (to be addressed by the Monitoring and Evaluation Unit), or it can lead to more research into those crop production processes that are hypothesized to be mechanized most easily in the Jordanian setting. Finally, problems such as "poor agricultural roads" are placed on the public policy list to be forwarded to upper-level governmental decision-makers.
The only region to separate problems by section commodity types in 1990 was Ramtha. Here, four commodity types (cereals, fruits, vegetables, and range and livestock) were selected, and problems were first grouped into separate master lists by these four groups. During the balloting process (detailed in the following paragraphs), votes were collected and analyzed separately from farmers, researchers, and technology-transfer staff for each group.
Although this second approach takes more time to complete (about four times as much time is required for data entry, for example), it leads to more logical lists of common problems. Indeed, when approach 2 was used in Ramtha in 1990, it was easier for the group to focus on tentative solutions to problems that should be addressed by either research or extension. This was because it was easier to concentrate on these more homogeneous groupings than it was to jump from topic to topic (e.g., from livestock to cereals to vegetables and back to livestock topics).
Once the master problem list was revised to everyone's satisfaction, simple ballot forms were distributed to farmers. These ballots were designed to allow each participating farmer to vote for the three most important problems that affect his or her system. After the ballots were distributed, the meeting facilitator gave a brief explanation of how to complete the ballots.


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At some RASCs, not enough farmers attended the meeting. At these RASCs (Khaldieh, Ramtha, and Deir Alla), both the researchers and the extension agents joined in the voting to prioritize problems. In such cases, each ballot cast was coded to identify the background of the voter: "Fl" = "farmer one;" "R3" = "researcher three;" "E4" = "extension agent four;" "A2" = "administrator two." No attempt was made to compare problem rankings between these subsets of voters to see if their perceptions ofproblem importance are similar or different, because the sample size was quite small.
It is significant that Jordan returned to a parliamentary democracy in November 1989, following 22 years ofemergency authoritarian rule. For this reason, the voting process, in addition to being extremely quick, was very popular with both farmers and NCARTT staff. Such a process should also be popular in other parts of the world now working in a more democratic mode.
By conducting an open meeting followed by individual problem prioritization through a secret ballot process, the combined sondeo allows a fullblown, open group discussion with minimized dominance of strong personalities, vocal farmers, and farmers of high local political standing. The over-dominant impact of these three subgroups of farmers has long been the major draw-back to holding farmer group meetings to obtain "averages" in regard to important rural issues.
When the ballots had been filled out, data from them were entered directly into a software program using a Zenith portable laptop computer. The software program used, Decision Pad (Apian Software, 1990),3 is one of several programs developed to facilitate the process of making difficult groupbased decisions. It allows the user to identify up to 250 alternatives for action, each ofwhich can be scored against up to 250 criteria. Criteria can be grouped together and/or assigned different weights. To make group decision-making easier, up to 60 different people can "vote" on the importance of each alternative. If the need arises, different weights can be assigned to different voters.
Depending on the number of voters and how data were input, data entry took between 30 minutes and one hour to complete. This time estimate does not include preparation time for data entry. Data entry template files were created and set up for each RASC in advance of each meeting. To record votes, very little time is required, because only the values 1, 2, or 3 are possible across a single criterion (or vote, in this case)w and because farmers were limited to

3 Names of products are included for the benefit of the reader and do not imply endorsement or
preferential treatment by the AFSRE.


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voting for their top three problems. Data entry time naturally varies also by the number of ballots cast per region and by the "keyboarding experience" of the person performing data entry.
In addition, there is the methodological difficulty of working with ranks of numbers where the top-ranked problem is 1, the next most important problem is 2, etc. Using this natural way of ranking means that any number greater than "3" must be entered into every cell for which a problem received no vote (i.e., was not ranked among the top three problems). This is because mathematically a "0," or a blank cell, is ranked higher than 1, whereas these values really mean that the problem is not in the voter's top three in importance. Thus, it was necessary to enter a "4" in each cell for problems that did NOT receive either a 1, 2, or 3 vote by each farmer.
Data entry was slightly more rapid if "4"s were pre-entered into all cells of the anticipated number of farmer voters during the early hours of the joint meetings. Where this procedure was followed, the staffmember entering data only had to change three of the 4s to 1, 2, and 3, corresponding to each farmer's vote for first, second, and third most important problems on a ballotby-ballot basis.
Decision Pad scores all votes and reranks problems automatically. After data entry, a single key-stroke produces a ranked listing of problems on the screen. A hard copy print-out of these reranked problems then is produced from the program's "report" menu. This report is used by the meeting facilitator to assign problem ranks to the problems on the master list, using a contrasting colored marker pen. The report is also given to RASC staffto assist them in documenting changes in their draft work plan.
When the master list of problems on the visual flip-chart pages has been
updated by indicating the new priorities based on frequency ofvote, a second open discussion follows. This time, tentative solutions to some of the most important problems are proposed by participants and discussed by the whole group.
If time permits, the research and technology-transfer staff reconvene late in the afternoon to work toward technical solutions to the most important research and extension problems. In situations where the complexity of systems was especially high (e.g., both Deir Alla and Ramtha) and/or there were a large number of problems (e.g., in Deir Alla)-approximately 30 or more-an additional day was required to complete this step of the follow-up combined sondeo process.


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RESULTS

Initial Combined Sondeo
In 1989, as in 1987, Jordanian farmer opinion had an impact on some planned research and technology-transfer activities of NCARTT. In 1989, those farmers who attended the final group meeting of the initial combined sondeo acted as an ad hoc advisory panel to the staffs of each of the six regional centers.
The lists of problems assisted RASC staff attending the FSRE training workshop to revise RASC work plans for 1989/90. The results of this combined sondeo were then summarized, published, and distributed to a wide range of MOA decision-makers on a Kingdom-wide basis (Galt and AlKadi, 1989; Gaudreau et al., 1989). In addition, problems were separated into those that have an impact on different commodity sections. These were summarized, published, and distributed to NCARTT commodity sections through a series of Research Information Bulletins (Al-Kadi and Galt, 1990a).

Follow-up Farmer Meetings
Overview of the process. Short and efficient follow-up combined sondeos were held in each region in the spring of 1990. Master problem lists from the initial 1989 combined sondeo were presented on flip charts to a group of invited farmers. This farmer group assisted NCARTT, RASC, and headquarters research and technology-transfer staff to reprioritize these problems according to 1990 realities.
It should be noted that the initial combined sondeos were carried out as the Jordanian dinar (JD) was undergoing a major devaluation to bring an overvalued currency more into line with other world currencies and to promote exports. Consequently, farmers interviewed during the initial sondeo in August 1989 expressed a great deal of uncertainty about the future of the JD and their own future farming plans. The follow-up combined sondeos carried out in the spring of 1990 took place after the JD had stabilized against the U.S. dollar at about US$0.68 = JD 1.000 (down from US$0.34 = JD 1.000 during the fall of 1988). In 1990, farmers expressed less concern about economic unknowns.
However, this experience reveals some of the difficulties of attempting to extrapolate from socioeconomic research that occur when there is a high degree of social uncertainty, unless such uncertainty can be considered "normal." Based on our experiences in Jordan, we recommend that follow-


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