Modeling Farmers' Decisions: a Comparison Between HDM and CART for Oats-Vetch Adoption in the Ethiopian Highlands

Modeling farmers' decisions: a comparison between HDM and CART for oats-vetch adoption in the Ethiopian Highlands I. Darnhofer, R. Gretzmacher and ~ Schneeberger Modellierung von bäuerlichen Entscheidungen: ein Vergleich zwischen HDM und CART anband von Hafer-Wicke-Anbau im Äthiopischen Hochland I.Introduction approaches seek to thoroughlyunderstand the farmers' situ ation and perspective and to integrate these factors into agri The rate ofadoption ofnew or improved agricultural tech cultural research (CHAMBERS, 1994). Their roots are found nologies in Third World countries has not been satisfying, in the recognition that a farmer's decision depends on, and as neither the size nor the distribution ofthe benefits have is influenced by, his/her own knowledge and perception of matched the expectations ofthe implementingagencies and a technology, rather than the researcher's knowledge ofthe national governments. This result is generally attributed to technology (GLADWIN et al., 1984). the neglect ofthe "human element" in farming systems in The underlying assumption is that the decision makers the traditional research approaches (NORMAN and BAKER, themselves are the experts on how they make the choices 1986; WALKER et al., 1995). Researchers increasingly real they make. With the focus on the farmer whose adoption or ized that the effect ofa new technology rarely depends sole rejection ofthe newtechnologycan make orbreaka project, lyon its technical importance, and that "human", "social" the researcher needs to know: (1) what decision the farm and "nontechnical" factors need to be taken into consider household is making, (2) what alternative he/she is consi ation, ifthe full potential ofa techno10gy is to be exploited. dering in each decision context, and (3) why helshe chooses As direct and creative farmer participation has been elu a particular outcome (GLADWIN, 1982). sivethe necessity for a two-waylinkage between various par The present study aims to elucidate the criteria influenc ticipants in the research process was recognized. New ing the adoption decision ofan improved feed for crossbred Zusammenfassung Die Entscheidung von Bauern ein verbessertes Futtermittel (Hafer-Wicke) anzubauen wird analysiert, und mit der Hilfe von zwei Methoden modelliert: hierarchische Entscheidungsbäume (HDM) und Klassifikations- und Regres sionsbäume (CART). Die Modelle werden dazu verwendet, zu erklären, warum Bauern im Äthiopischen Hochland die verbesserte Futtermittelbasis nicht im geplanten Ausmaß angenommen haben. Die Stärken und Schwächen der zwei Methoden werden verglichen. Beide Methoden werden als hilfreich befunden, um Informationen über die Meinung, Haltung und Taten der Bauern in sinnvollen Zusammenhang zu bringen. Schlagwörter: EntscheidungsmodelIierung, HDM, CAR'L Äthiopien, Hafer-Wicke. Summary Farmers' decision to adopt an improved feed (oats-vetch) is modeled with the help oftwo methods: Hierarchica1 deci sion trees (HDM) and classification and regression trees (CART). The models areused to help explain whyoats-vetch was not adopted by farmers in the Ethiopian highlands to the anticipated extend, and can be used to provide a guide for future extension efforts. The strengthsandweaknesses ofthe two methods are compared. Both are found useful for compiling information about farmers' opinions, attitudes and actions in a meaningful mannen Key words: adoption decision,HDM, CART, Ethiopia, oats-vetch, Die Bodenkultur 271 48 (4) 1997 I. Darnhofer, R Gretzmacher and W. Schneeberge;r cows in the Selale area of the Ethiopian highlands: inter criteria, aIl ofwhich have to be passed along a path to a par cropped oats-vetch, The studywas made in the context ofa ticular outeome or choice (GlADWIN, 1989). development project by the Ethiopian Ministry ofAgricul ture (MoA) and the FinnishInternational DevelopmentAid (FINNIDA) (MoNFINNIDA, 1986). The project, which 2.2 Classification and Regression Trees was implemented between 1987 and 1991, distributed crossbred eows against eredit to farmers, to enable them to The authors ofCART and developers ofits compurational raise milk produetion both for household eonsumption and algorithm are the statisticians Leo BREIMAN, Jerome FRIED as a source ofcash income. As crossbred eows require both MAN, Richard OLSHEN and Charles STONE (1984). Their quantitatively and qualitatively better feed than Ioeal zebu aim was to develop an easy to use statistical package for tree cows, improved feed produetionwas promoted. Therecom structured nonparametrie data analysis to tackle classifica mendations included intercropping oats and vetch. Oats is tion problems. already weIl known by the farmers in the area, and inter The elassification trees are also drawn in the form of an cropping the two species raises both the crude protein con inverted tree and are read like a flow chart, To decide how tent and the dry matter yield, without using supplementary to split anode, the CART software examines all possible erop land which is scarce. splits for all variables included in the analysis, and ranks The models presented hereafter examine reasons why them on a goodness-of-split criterion. The most common farmers will or will not plant vetch, and ifthey do, whether ly used eriterion is how weIl the splitting rule separates the or not theywill intererop it with oats, as was recommended classes contained in the parent node, i.e, deereases class by the MoA and FINNIDA. The decision criteria are impurity: assembled into decision models using two methods: Hier The CART output is thus composed ofseveraI elements: archical Deeision Models (HDM) as weIl as Classification the optimal tree, with detailed information for each node, and Regression Trees (CART). The juxtaposition of the such as the variable on which the node was split, the num 'manual' model and the computerized analysis should allow ber of eases going right and left, and the improvement in the comparison ofthe strengths and weaknesses ofthe two elass purity. Additional information on variables are also analytieal tools. Further details of the research reporred provided, such as possible competitor variables on which herein are presented in DARNHOFER (1997). the node could also have been split but with a less optimal result and surrogate variables whose split would have di vided the data similarIy and which can be used as alter 2. The methods native in case ofmissing data, 2.1 Hierarchica1 Decision Models 2.3 Data collection Christina GUDWIN (1976) developed a methodcalled hier archie decisionmodeling (HDM) to model the way people Following the method set forth by FRANZEL (1984), the make real...life decisions. Its distinctive features are: (I) a reli data for this study were collected in two stages: first all the ance on ethnographie fieldwork techniques to elieit deci decision eriteria were elicited from key informants and, in a sion criteria, and (2) combiningthese criteria in the form an seeond stage,the criteria were compiled into a formal ques irrverred tree which is read like a flow-chart. tionnaire and data gathered from 50 randomly selected The form ofadecision tree is simple: the possible outcome farmers, providing the data used in the models. ofthe decision to be modeled is formulated at the top ofthe The key informants for the interviews held in the first tree for easy reference. Each deeision criteria forms anode of stage were knowledgeable enumerators, farmers and MoA the tree, The decision variables or criteria have discrete values offieials. These were instrumental in acquiring a better (GlADWIN, 1975). These discrete criteriacan be either rejec understanding ofinfluencing faetors and the problems that red oraccepted.. After a number oferiteria have been passed, farmers may face with veteh. These first stage interviews the decision outcome is reached at the end ofa path ofthe took the form ofinformal eonversations, wirhont pre-for tree, These outcomesare ofthe general form: 'do X, 'do not mulated questions, During the interviews care was taken to da X ~ A decision tree is IDUS a sequence ofdiscrete decision follow ethnographie guidelines (SPRADLEY, 1979; ATIES- Die Bodenkultur 272 48 (4) 1997 Modeling farmers' decisions for oats-vetch adoption in the Ethiopian Highlands LANDER, 1993). This first stage is an iteration berween 2500 m to 2700 m and those living at an altitude range eliciting eriteria, buildingdraft deeision trees to organizethe between 2700 m and 3000 m, Above the 2700 m range, criteria, elieiting further eriteria and modifjring the draft frosts are likely to occur between November and December, tree. Onee the draft decision tree seemed reasonably com the period during whieh vetch flowers. These occasional plete, the questions underlying the decision criteria were night time frosts seem to hinder the seed production of written up and a formal questionnaire designed. vetch, In the seeond stage ofdata collection, 50 randomly sam In the view of the forage experts the impaired seed pro pled farmers who had partieipated in the MoAlFINNIDA duction is not a problem, as the oats-vetch mixture is meant projeetwere interviewed. The data eolleeted during this for to be harvested and fed green. But to farmers this is a major mal surveywere then used to build the final decision model. factor. On ehe onehand, a number offarmers remarked that For the CART analysis all questions were entered for the planting a crop which does not produce seeds is useless. On software to be able to seleet the most appropriate splitting

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