indigenous knowledge systems, the cognitive revolution, and agricultural decision making (1989...

Indigenous Knowledge Systems, The Cognitive Revolution, and Agricultural Decision Making

Christina H. Gladwin

Christina H. Gladwin is Associate Professor in the Food and Resource Economics Department at the University of Florida, Gainesville. Her research interests include the cognitive relationship between norms, plans, and decision processes and large-scale shifts in norms and choice. The research for this paper was initiated while she was a Rockefeller post-doe assigned to the International Fertilizer Development Center.

ABSTRACT: Increasingly, it is accepted wisdom for agricultural scientists to get feedback from indigenous peoples--peasants--about new improved seeds and biotechnologies before their official release from the experiment station. What is not yet accepted udsdom is the importance of cognitive science to research on farmer decision making, especially of the type "Why don't they adopt." In this paper, the impact of the cognitive revolution on models of farmer decision making is described, and decision making models before and after the cognitive revolution are contrasted. An example of a decision model after the cognitive revolution is given by the Malawi farmer's decision whether to use chemical fertilizers or organic fertilizers or both. Results of testing the model show that in Malawi, smallholdsrs' lack of capital and credit are more important factors constraining use of chemical fertilizers than are indigenous beliefs in organic fertilizers or fears of a future dependency on chemicals.

The school of thought now dubbed IKS, indigenous knowledge systems, aims to elicit the expert systems of indigenous peoples--peasants---who are sometimes not thought of as experts. These knowledge systems are brought back to agricultural research centers and ministries of agriculture and used to educate agricultural scientists and policy makers so that they can design better technologies and policies to improve peasants' standards of living. The idea that local knowledge is a valuable resource to be understood and used to alleviate poverty is beginning to diffuse throughout the Third World in practical applications to development problems, and in the academic literature, as is evidenced by the papers in this volume and others (Brokensha, 1989). Starting with the collection of the same name, IKS scholars have described the expert systems of peasants to do any number of things: to farm, to fertilize, to manage their soils and natural resources in a sustainable way, to adopt or not adopt new '~improved" technologies, to take

risks, to make a living (Brokensha, Warren and Werner, 1980). Farming systems programs in international agricultural research centers have seen the value of knowing '~ow the farmers think" about their crops, pests, forests, and water resources (CIMMYT, 1984; Matlon, Cantrell, King, and Be- noit-Cattin, 1984), before agricultural scientists design improved technologies for them. Increasingly, it is accepted wisdom for an agronomist to get feedback from peasants about new improved seeds and biotechnologies before their official release from the experiment station.

It is also generally recognized that the goals of the IKS school go way back to the anthropologist Malinowski (1922) who aimed to "grasp the native's point of view, his relation to life, to realize his vision of his world." To see the insiders' world through the insiders' eyes has long been the aim of enthog- raphers, whose work describes a culture from the "native's" or insider's point of view and not from the researcher's or outsider's point of view (Sprad-

32

ley, 1979). To minimize the researcher's own ethnocentricity, i.e., the viewing of another culture through the lens of one's own cultural values and assumptions, the enthnographer seeks to learn from people, to be taught by them like a child is taught. The aim is to discover the cultural meaning of the insiders' relationships, native terms, rules, and way of life (Spradley, 1979: 3). This is a far different goal from that of collecting data about people and testing a model based on the outsider's view. Models of indigenous knowledge systems should thus contain "emic" categories, i.e., units of meaning drawn from the culture bearers them- selves, which can be contrasted with "etic" categories which may have meaning for researchers but need not have meaning for the people of the specific culture under study (Pike, 1954; Harris, 1979: 32- 45).

What is not yet accepted wisdom is the importance of cognitive science to this school of thought and agricultural development policies, and the in- fluence of '%he cognitive revolution," which, since the 1950s and 1960s, has shaken the foundations of six social sciences: artificial intelligence, anthropology, linguistics, neuroscience, psychology, and philosophy (Gardner, 1985). Perhaps agricultural scientists who are used to empirical laboratory ex- periments are uncomfortable in realizing that farmers' acceptance of their new biotechnologies partly depends on the mystery of how the human mind-- the ultimate black box--works. Yet as more and more agricultm~al scientists give up the naive notion that their technologies will automatically diffuse, more and more research on farmer decision making, especially of the type '~hy don't they adopt," is done. One lesson gleaned from the adoption literature, by now too vast to adequately cite, is that the success of efforts to predict farmers' decision making depends on the underlying assumptions that are made about cognition, i.e., the thought process itself. These assumptions changed drastically with the cognitive revolution. Before the cognitive revolution, agricultural decision models were mathemat- ically sophisticated, with risk-aversion often being measured by the sign of the second differential (Schoemaker, 1982); after the cognitive revolution, simpler decision rules and trees replaced optimiza- tion ofacontinuous, twice-differentiable function.

The Cognitive Revolution Scientific assumptions about cognition were

radically changed in 1956 with the publication of a seemingly-innocuous article entitled, "The Magic Number Seven, Plus or Minus Two, "by the psycho- logist George Miller. Miller's new idea was that the human computer is of limited capacity, unlike a real

Gladwin: Indigenous Knowledge Systems

computer; its short-term memory is limited to roughly 5 to 7 items at a time. Indeed, people seem to categorize or discretize variables, rather than deal with continuous quantitative variables as a real computer does. People use logic rather than perform complicated mathematical operations as a real computer does. The limited "rehersal buffer" of humans affects the way people organize their language and thought processes, such that they are "nierarchially" organized (Miller, Galanter, and Pribram, 1960).

This revolutionary idea was taken up by the linguist Noam Chomsky, whose '"crees" or transfor- mational grammars spread to most known languages around the world. In the field of artifical intelligence, it stimulated scientists to invent computer programs called "expert systems" which mirrored the way humans think, rather than expect humans to think like sophisticated computers. In psychology, the revolution stimulated new theories of problem solving (Neweli and Simon, 1972) and plans or scripts (Schank and Abelson, 1977), and caused the abandonment of decision making models of "expected utility" in favor of theories of "elimination- by-aspects" and "preference trees" (Tversky, 1972; Kahneman and Tversky, 1972, 1982; Tversky and Kahneman, 1981).

In anthropology, in resulted in the spread of cognitive models oftaxonomies, schematas, and decision processes, which replaced the use of psychological traits and raw-shock tests to explain cultural differences. Given the aims of ethnograp- hers to model the insiders' point of view and knowledge, it was not surprising that the cognitive revolution found the field of anthropology a fertile ground to grow in. The products of cognitive anthropology can now be seen in frame analysis (Frake, 1964), taxonomic analysis (Berlin and Kay, 1969), componential analysis (Romney and D'An- drade, 1964), schematas or folkmodels (D'Andrade, 1981; 1987; H. Gladwin, 1974; Quinn 1990), plans or scripts (Werner and Schoepfle, 1987), and decision tree models or tables. Before these models are described, a look at what decision models were like before the cognitive revolution is in order.

Agricultural Decision Making Before the Cognitive Revolution

In the field of agricultural decision making, long dominated by (agricultural) economists, decision models were quantitative, linear-additive, and often normative (e.g., linear programming models, expected value and expected utility models, sto- chastic dominance) but typically not empirically grounded. They were not usually tested against a set of choice data to see how well they predicted

33

AGRICULTURE AND HUMAN VALUES--SUMMER 1989

the choices of individuals in a group (see Anderson, 1979; Anderson, Dillon, and Hardaker, 1977). In- stead, they were either used as behavioral assumptions in a model of aggregate supply or demand, or as normative models to tell people how they should make decisions (Raiffa, 1968), or tested to see if they "fit" the observed behavior of one '~representative" individual (Benito, 1976).

They were not empirically tested against choice data because the test was usually so complicated that it was not worth the effort. For example, in a test of the expected utility model, the researcher had to measure each individual's "utility function," which could vary in shape across individuals de- pending on how risk-averse they were; and then independently measure each individual's "subjective" probability distribution which differed from the objective probability distribution. This proved to be such a job that it was rarely done; but text- books described how it could be done (Anderson, Dillon, and Hardaker, 1977). When it was done, errors arose due to inconsistencies (Officer and Hal- ter, 1968), or when midway through the experiment, riskless gambles were replaced by risky gambles, suggesting that even the same individual might have more than one utility function (Tversky, 1967).

Agricultural Decision Making After the Cognitive Revolution

Cognitive psychologists and anthropologists in the 1970s rejected the expected utility theory of choice, and searched for more cognitively-realistic models of the choice process. They claimed that people in real-life choice contexts don't make holis- tic assignments of utility or satisfaction to each alternative in the choice set, and separately formulate subjective probabilities (Quinn, 197~, and then pick the alternative with the most "expected utility" (Kahneman and Tversky, 1972, 1982). In line with Miller's results on the limitations of human compu- tational capacities, they felt that decisions are made in a decomposed fashion using relative compari- sons, because it is cognitively easier to compare alternatives on a piece-meal basis, i.e., one dimension at a time (Schoemaker, 1982). Indeed, people do not rank order alternatives holistically when they make a decision. They just chose one out of several alternatives without ranking them (Quinn, 1971), in which case the decision model is what Arrow (1951) calls a "choice function not built up from orderings," i.e., simply a set of rules. In some choice contexts, these rules may result in an incom- plete order (Gladwin, 1975) and intransitive perfer- ence structure (Tversky, 1969). In these cases, the

rules do not produce an ordinal utility function, as micro-economic theory assumes.

Cognitivists even objected to the linear-additive decision models called probit analysis and logit analysis, which have the advantage over expected utility and linear programming models ofbeingtest- able with data on choices made by many rather than one individual. Unfortunately, they also are not cognitively-realistic models of the choice process. No-one assigns weights to several variables and then adds them up to determine which of several outcomes is better; people compare alternatives one dimension at a time. But probit and logit analyses do have the advantage of providing a statistical test; they thus can be used alongside rule-based decision models to show whether there is a signifi- cant correlation between a particular independent variable (or decision criterion in the nile) and the decision outcome chosen. In this way, they can provide an indirect test of a rule-based model (Mukhopadhyay, 1984). Unfortunately, they can provide this test for only a few of the variables or criteria in an individual's decision process.

Cognitivists also rejected the quantitative nature of decision variables in a probit or Iogit analysis or linear programming model. Following Miller (1956), they claimed that decision makers use discrete decision criteriain real-life choices, even when faced with a variable amenable to quantification such as cost. The decision criteria used in decision tree models are thus not continuous quantitative variables; they are discrete constraints that must be passed or satisfied (e.g., Is cost of car < $4000?) or orders and semi-orders of alternatives on aspects (e.g., Is cost of carl < cost of car2?). An alternative is assumed to be a set of aspects or constraints (Lancaster, 1966; Tversky, 1972; Gladwin, 1980), but criteria are discrete. An algebraic form of choice model results. The decision process is also assumed to be deterministic rather than probabilistic: an alternative either passes the criteria or constraints with a probability of one or it does not. There are thus no probabilities otherthan 1 or 0--facing the individual on each branch, as in Raiffa (1968). A decision tree is thus a sequence of discrete decision criteria, all of which have to be passed along a path to a particular outcome or choice.

Decision Tree Modeling Ethnographic decision tree modeling starts

from the assumption that the decision makers them- selves are the experts on how they make the decisions they make. It uses ethnographic fieldwork techniques to elicit from the decision makers them- selves their decision criteria, which are then corn-

34

bined in the form of a decision tree, table, flowchart, or set of if-then rules or "expert systems" which can be programmed on the computer (Gladwin, 1989). There are thus two distinctive features about the method: its reliance on ethnographic fieldwork techniques to elicit the decision criteria, and its insistence on a formal, testable, computer-based model of the decision process which is hierarchical or treelike in nature.

Ethnographic decision tree modeling is not a black box technique like some quantitive methods (e.g., factor analysis, multidimensional scaling, cluster analysis). Because of its dependence on eliciting procedures, the model is culturally tuned by some specific group of individuals, and then tested against choice data from other individuals in the group. 1 The form of a decision tree model is amazingly simple, with the choice alternatives in a set at the top of the tree, denoted by { } and the decision criteria at the nodes or diamonds of the tree denoted by < >, and the decision outcomes denoted by [ ] at the ends of the paths of the tree. The decision maker starts at the top of the tree and, independently of other decision makers, is asked the set of questions in the criteria at the nodes of the tree, and based on his or her responses is "sent down" the tree on a path to a particular outcome.

Cognitive anthropologists in the 1970s and 1980s applied these ideas to real-life choice contexts in a number of different cultures. These included economic decisions made by Ghanaian fish sellers in deciding between markets (H. Gladwin, 1971; C. Gladwin, 1975; Quinn, 1978), farmers' adoption decisions in Puebla, Mexico (C. Gladwin, 1976, 1977, 1979a, 1979b), California families' decisions regard- ing the sexual division of labor within the family for daily routine tasks (Mukhopadhyay, 1984), farmers' cropping decisions (Barlett, 1977; C. Glad- win, 1983), peasants' choice of treatment forillness in Pichatero, Mexico (Young, 1980, 1981), U. S. car buyers' choice of cars (H. Gladwin and Murtaugh, 1984; Murtaugh and H. Gladwin, 1980), economic development decisions of the Navajo tribe (Schoepfle, Burton, and Morgan, 1984), and U. S. farmers' decisions to cut back production and sell land daring a farm crisis (Zabawa, 1984; C. Gladwin and Zabawa, 1984, 1986, 1987). In each case where the methodology of decision trees has been used, the predictability has been as high as 85 to 95 percent of the historical choice data used to test the model. These success rates are remarkable only because the pre-cognitive decision models of accepted wisdom (e.g., expected utility) could not even be tested to see how well they could predict a set of choice data.


A Real-Life Example: The Malawi Smallholder's Decision Between Chemical and Organic Fertilizers

How do small-scale farmers in the Third World decide whether or not to use chemical fertilizers? Why do or don't they use organic fertilizers (manure, compost, green manure) as substitutes for chemicals? Which constraints to chemical fertilizer use are more important: farmers' lack of capital or credit, or their indigenous beliefs in organic fertilizers (manure/compost) as the right way to fertilize their crops, or their fear of dependency on chemicals?

This decision is a crucial one for African govern- ments facing a '%od crisis" in their cities and trying to increase the food surplus produced by small farmers in the countryside, because food production is linked to quantity of fertilizer used on most food crops. It is even more important in a country like Malawi in southern A~ca that is land-locked, faces high transport costs to the sea (due to the cutting of the Beira railroad line in Mozambique) and im- ports all chemical fertilizer or its feedstocks. It is a key decision for those policy makers interested in the potential of sustainable agriculture or low- input agriculture (Brush, 1989). The decision model in figures la, lb, lc, and ld explains why some farmers use both chemical and organic fertilizer, while others use only chemical, while some use only organic (defined as animal manure, compost, or cer- tain kinds of green manure). It also tests whether the main reason for nonuse of either kind of fertilizer is simply farmers' lack of cash or credit, as opposed to an indigenous trust in local organic fertilizers or amore invidious fear of dependency on chemicals.

In April and May, 1987, the model was tested with choice data on fertilizer use of smallholders in Malawi during personal interviews; the test sample was comprised of 40 farmers in three agricultural districts: Lilongwe, Kasungu, and Salima. ~ Al- though farmers in this sample were on average big- ger, more experienced farmers than is the norm (with average landholdings of 3.02 ha.), in other respects the sample is fairly representative of Malawi smaUholders: 26 farmers were credit club members, and 14 were not; 22 farmers got credit for fertilizer in 1986/87, while 18 did not. Seventeen farmers were women household heads, three were couples interviewed together, and 20 were male household heads; the sample has a high proportion of female household heads because Dixon (1982) estimates that women in Malawi perform about 50 percent of the agricultural labor. Of the 40 farmers interviewed, 33 were household heads. Questions about the sexual division of labor and income within the family revealed that groundnuts is a woman's

35


cash crop while tobacco, cotton, and hybrid maize are men's cash crops, and local maize and beans are grown for the whole family's consumption.

Because the outcome chosen by the farmer is different for different crops, this model is specific to the local variety of maize, which constitutes 90 percent of maize production and is the staple food crop. Also for simplicity of modeling, it is here assumed that every farmer incorporates some crop residues of maize during the "banking up" of soil around the secondary roots aider the second weed- ing and fertilizing. This use of crop residues is no doubt beneficial to the soft, but is not the same terrific stuff as animal manure or compost. Hence organic fertilizer here means manure and/or compost and/or green manure, but not crop residues.

The model posits that farmers must first pass a set of simple "elimination-by-aspects" constraints (Tversky, 1972) in figure la. They then must have a need or motivation to use either or both kinds of fertilizer (figure lb). They then pass to a set of resource constraints specific to each kind of fertilizer, and will use that kind if they satisfy or pass each constraint (figures lc and ld). Farmers will use both kinds of fertilizer if they think the crop needs both, and they pass both sets of resource constraints.

Elimination Criteria Farmers must first pass a simple set of con-

straints in figure la which eliminate use of both organic and chemical fertilizers if: their type of soil doesn't need or respond to either kind of fertilizer (criterion 1), or their type of local maize seed doesn't need either kind of fertilizer (criterion 2), or they let most of their land lie fallow for two or more years so that after the fallow period it doesn't need either chemical or organic fertilizers (criterion 3), or they lack the cash or credit for either chemical or organic this year (criterion 4). If a farmer is eliminated at this first stage of the decision process, he or she doesn't have to decide between organic and chemical fertilizers because both are eliminated and the decision is simple.

Stage Two-Criteria Farmers who pass "stage 1" criteria do have a

complicated decision, however, and continue on to stage-two criteria in figure lb. If they think that their local maize variety needs both kinds of fer- tftizer to produce good yields (criterion 5) they are sent on to both sets of resource constraints in figures ic and ld. If they think local maize needs only organic fertilizer (criterion 6) and it is more profitable than chemical, they are sent only to figure lc. Simi- larly, if they think local maize needs only chemical

Figure la. The Decision Between Organic and Chemical Fertiliz- ers on Local Maize: Stage 1 Constraints.

40 cases $

{Apply organic; chemical; both}

,Does your soil need or respond to either chemical , Eliminate

or organic fertilizers? no Both I 0 cases

yes

l 2Does your local maize

variety need either chemical or Eliminate organic fertilizers? no Both

I 0 cases

yes

1 ~Do you let most of your land

lie fallow for two or more years . . . . at a time so that afterwards you - - *~nnunate don't need to apply chemical or yes Both

organic fertilizers? 0 cases

no, I need them afterwards

]

'Have the [cash or credit Eliminate to apply either chemical or no Both

organic fertilizers this year? 8 cases

I yes

Go on to figure lb

32 cases

fertilizer (criterion 6) and it is more profitable than organic, they are sent only to figure ld.

In figure lc, farmers will apply organic fertilizer to local maize if: they have enough animals to make enough manure or compost to use on their local maize (or a crop (e.g., tobacco) rotated with local maize) 8 every two or three years (criterion 8), or they can buy the manure/compost they need (criterion 9) and they have or can borrow/rent an oxcart and oxen to carry the manure/compost to their fields (criterion 10), and they have the time or (fun- or part-time) labor to carry it to their fields (criterion 12), which are not too far away to reach by oxcart (criterion 11). If all these constraints are passed, the model predicts that the farmer applies manure and/or compost to localize maize (or a crop rotated with local maize). If a farmer fails one constraint, the model predicts no manure/compost is applied.

In figure ld, farmers will apply chemical fertilizer to local maize if: there was chemical fertilizer available at the time needed, either to buy or get on credit (criterion 13), and the farmer had either

36

Figure lb. Motivations to Use Chemical or Organic or Both.

Given you've passed figure la

t 32 cases

qs there a necessity to use both chemical and

organic fertilizers on local maize?

no 2 6 cases

Apply both organic and ~ n o

chemical fertilizers if you also pass constraints

in figures lc and ld. 26 cases

~Which type of fertilizer does local maize

need more? Which is more profitable?

/ Organic emical

/ Apply only organic

fertilizer if you also pass constraints

in figure Ic.

I case Apply only chemical (1 error) fertilizer if you

also pass all constraints in

figure ld. 5 cases

the cash or credit at the time needed to get the fertilizer (criterion 14), and the farmer could take the risks associated with chemical fertilizers (criteria 15-17).

The Risk Subroutine The main risk of chemical fertilizer is what I call

the "dependency" of the land on chemical fertilizer. Farmers interviewed in Malawi claim that their land (and in some cases their traditional seeds or germplasm) get dependent on chemical fertilizer such that, if they stop applying it for one year, their yields decrease drastically. Whether or not this is due to a change in the land itself or merely in farmers' expectations of the yields they Should get from the land is irrelevant for the purposes of assessing whether farmers will take the risk of chemical fer-


Figure Ic. Resource Constraints to Use of Organic Fertilizer.

yes 17 cases

26 cases

SDo you have the animals to make enough manure/compost

to use on local maize (or crop rotated with local maize) every

two or three years?

\ no

9 cases \

~an you buy the manure/compost

you need? / \

yes no 8 cases

18 cases No Organic

~Can you get an oxcart and oxen

to carry manure to no--.. No Organic your g~rdens? 2

cases i

yes: 16 cases

1 ~qs all your local maize

planted too far away from No your house to carry manure-- yes--~ Organic

to?

i no: 16 cases

l ~Do you yourself have the time No

or can you hire labourers to carry---n~ Organic manure to your gardens? I case

i yes

1 Apply manure/compost

to local maize

15 cases (1 error)

t'flizer. What is relevant here is whether they con- sider chemical fertilizer to be risky (criterion 15), and whether they will take the risk (criteria 16-17). If farmers are worried about the dependency of the land on chemicals, the model predicts that they will take the risk if: either they have a farming practice or way to reduce the risk substantially (e.g., also apply manure) (criterion 16) or they feel they must take the risk anyway because th~.y cannot now re- turn to using only manure/compost without a drastic reduction in their yields (criterion 17). With the latter line of reasoning, they are weighing the risks of using fertilizer with the risks of dropping it al- together, which entails a possible drastic reduction in maize yields and consumption and maybe even

37


Figure ld. Resource Constraints to Use of Chemical Fertilizer.

31 cases

~Fnis year, is there chemical fe~ available No

to buy or get on credit mno-~Chemical at the time needed7

I yes: 31 cases

1 ~*Do you have the cash

or credit to apply chemical No on local maize? - - no-* Chemical

2 cases

yes: cases

1 ~Do you think that the

dependency of the land on chemical fertilizer is risky?

~no: 3 cases yes: 26 cases

/ , Apply Chemical

Fertilizer

~eDo you have a farming practice that

you can use to reduce the risk of dependency?

no: 7 cases

ApplYFertilizer 19 Chemicalcases <

17Risk Risk of dependency

farmer) did not have the time to transport and apply manure to local maize. One farmer is an error of the model because he did pass the organic constraints and should have applied organic but "didn't want to waste time with manure when chemical fertilizer was available nearby."

Thus results show that, in this sample of Malawi smallhoiders, only 15 out of 40 farmers apply manure while 28 of 40 farmers apply chemical fertilizer. Why? The majority of farmers claim that both kinds of fertilizer, organic and chemical, are necessary; but more of this sample of farmers can pass the chemical constraints than can pass the organic constraints, which are stiffer. I conclude that manure/ compost is a highly desirable kind of fertilizer, but farmers won't depend on it as their sole source of plant nutrients when chemical fertilizer is available at reasonable prices. What Malawi smallholders will do, given the resources, is apply both chemical and organic fertilizers to local maize as complementary inputs. In their indigenous knowledge system, chemical fertilizers give plants their nutrients and organic fertilizers build up the structure of the soil and more importantly, reduce the riskiness of the land's becoming too dependent on chemical fer- t '~r . For these farmers, application of organic fertilizer is a hedge against too strong a dependency on chemical fertilizer.

Conclusion

Of what use are decision trees and other cognitive models for applied agricultural scientists? The goal of decision studies is to model how people make real-world decisions and to identify the specific decision criteria used by most of the individuals in a group. An applied social scientist might use these results to identify intervention points in the decision process, whether they be important constraints blocking a desired action which could be alleviated by policy makers, or frequently-used reasons for a desired action which could be encour- aged by policy makers with new policies. For example, results from testing the Malawi farmer's decision to use chemical vs. organic fertilizers show policy makers that lack of capital/credit is the main constraint to farmers' use of chemical fertilizer, not an indigenous belief in organic fertilizers or low- input agriculture. Policy recommendations made to the Ministry of Agriculture thus focused on ex- panding credit use by smallholders so that more, not less, chemical fertilizer could be used by smali- holders in maize production. Similar policy recommendations can be drawn from any study of real-life choices people make. For example, previous results helped agronomists and planners ofarural development project to drop an agronomic recommendation


that was impeding spread of a '~package" of new technologies (Gladwin, 1976, 1979a, 1979b).

Decision tree models can also provide valuable feedback to social planners about why an applied project, aimed at helping some target clientele group do something, is fading. Experience with such projects in Third World settings show that the most frequent reason they fail is that they end up getting a very good answer to the wrong question. In Mexico, for example, planners of the Puebla Pro- ject designed fertilizer demonstrations so that farmers would fertilize at planting and increase their yields; most farmers did not adopt because with '%he traditional way," they applied fertilizer before the rains came anyway, and so the new technology would not increase their yields (Glad- win, 1979a).

In each case in which the development project failed, social scientists were involved in the project at the design stage as they should be (Matlon, Can- treli, King, and Benoit-Cattin, 1984), gatheringlots of good quantitative data. In the case of the Plan Puebla, for example, good socio-economic'%aseline data" was collected, as well as data about centimet- ers of rainfall, soil types, and communication net- works (CIMMYT, 1971). But still farmers did not adopt. What was missing? At the start of the project, social scientists did not find out why the farmers traditionally do what they do. They did not identify farmers' cognitive strategies and the decision criteria behind '%he traditional way" before they tried to improve on it (Gladwin, 1979a). They did not know the farmers' problems before they designed the solution, and so they ended up getting a good answer to the wrong question; no-one adopted, and the project failed.

An understanding of the knowledge systems of indigenous peoples and a modeling of the decision processes underpinning their farming systems can stop these failures of applied projects. If project designers understand the decision criteria and logic used by the target population, they can find the answers to the right questions. Clearly, the applica- bility of decision tree methodology and other cognitive models to development problems and issues is limited only by our imagination.

Notes

1. Decision tree models are similar to black-box techniques like factor analysis, multidimensional scaling, and cluster analysis in that all these methods are induc~/ve. When applied to elicited '~miders' "knowledge they make relatively few as- sumptious. Tree models are distinct from black-box techniques, however, because they require the modeler to elicit the reasons why insiders end up in different dusters. Factor and cluster analyses fail to do this, and the researcher ends up guessing what the factors that form the dusters are.

39


2. The model was tested in Malawi, after being elicited from farmers in Highland Guatemala in 1979, while the author was on a Rockefeller post-doc with the International Fertilizer Development Center. In Guatemala, the model predicted 90% of farmers' decisions. It was tested in Malawi simply to see fffarmers who similarly used chemical and organic fertilizers on maize would also use similar decision criteria in their decisions to use chemical and/or organic fertilizers. Could their reasoning as well as their farming practices be similar?

The author thus believes that'~indigenous knowledge systems" can be shared across quite different cultures. There is no need to assume that lbcal knowledge is solely "site-specific", as some IKS scholars claim (see McCorkle, this volume). Some local knowledge can be generalizable to other cultures, ifthe ethnographer tests to see if indeed it is shared. Towards this end, this model was tested in Malawi, to see how many decision criteria were shared knowledge.

Because the model is tested on this sample of 40 farmers, it is a predictive and not merely a descriptive model (Gladwin 1989: Chapter 3).

3. Farmers in Kasungu ADD normally rotate tobacco (the man's cash crop) with local maize every other year. Farmers that I interviewed apply quite a bit of manure on tobacco: 20 to 50 oxcarts per acre. Given that the effects of manure and/or compost last for two or more years because they are slow-release fertilizers, this manure is also considered to be applied to local maize for the purpose of testing this model.

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