The Adoption of Agricultural Technology - Food Security Group

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have been developed to try to explain farmer decision-making. Suchmethods are beyond the scope of this manual, but one of the mostcommonly used tools, "decision trees," is illustrated in Box 26.Statistical ComparisonsAlthough farmers are experts regarding their own fields and practices,they may not be able to provide much information on factors that affectother farmers. In addition, the factors that influence the adoption of atechnology may be so diverse or complex that it is unreasonable to ask afarmer to account for them or to try to explain the pattern of adoption.For that reason, an understanding of adoption also can benefit from astatistical comparison of the farmers' adoption behavior with variouscharacteristics of the farmers' environment. In most instances it is thefarmers themselves who will be asked to describe these characteristics,but they will not be asked to actually articulate the causal linkagebetween the characteristics and adoption. That is the task of theresearcher, and it may draw upon a wide range of tools in statisticalanalysis.Techniques of survey analysis are well described in a number of texts,including Casley and Kumar (1988), Alreck and Settle (1985), andNorusis (1991). The purpose here is only to review briefly a few of themost common methods that are relevant to the analysis of an adoptionstudy.In attempting to explain adoption patterns by statistical analysis, themost common approach is to compare the characteristics of farmers whohave adopted a technology with those who have not adopted, to see ifsome of these differences might offer insights into the rationale foradoption. Thus if the proportion of farmers who have access to irrigationis much higher among adopters, we would be led to explore thepOSSibility that the technology is more appropriate or accessible if afarmer has irrigation.-68Just because these differences can be found does not of course constituteproof of an association. The differences must be explored within thecontext of all of the data available from the survey. Simple methods ofpresenting these data and statistical tests help in making the case, butthe major responsibility in interpretation lies with the researcher, whomust proVide a consistent, coherent, and logical explanation for theadoption patterns that are observed.
One of the simplest and most useful ways of examining differences inadoption patterns is with contingency tables, in which the cells or the tablecompare the proportion of adopters and non-adopters with a particularcharacteristic. This is particularly appropriate if the characteristic of interestis a nominal one, that is, one that is represented by non-numericalcategories, such as access to irrigation (yes or no) or previous crop in therotation (potatoes, barley, or other). Even in cases where the variable is acontinuous one (such as farm size or number or days between land preparationand planting), it is sometimes useful to divide it into a few simplecategories (large vs. small; low, medium, high) and develop contingencytables. The relevant statistical test or this association is the chi-square test.In the example below, the adoption of row planting for maize is examinedin light or land preparation practices. It can be seen that there is a strongassociation. Farmers who use a tractor for land preparation are much morelikely to adopt row planting; 84% of these farmers adopt row planting,while only 29% of the farmers using manual land preparation adopt rowplanting. The chi-square test shows that it is improbable that thisassociation could occur by chance.Planting method by land preparation-- - - ~ .,. - "', ~ ',' - .!........~ '., . -'.' ,:.•• • • I !!=. ..:.- -.....:..~ •• '. ">--",_ .. ~Random 177 22 199(71%) (16%)Row 71 114 185(29%) (84%)Total 248 136 384(100%) (100%)X'?- =105.0 d.t. = 1 P < .0001The contingency table does not tell us why these two factors are related,however. It could be that tractor preparation makes row planting easier; inthis case tractor preparation is a cause of row planting. It is possible(although unlikely) that row planting "causes" tractor use, in the sense thata farmer who decides to row plant will rent a tractor for land preparation.The point is that we must go beyond presentation of the association andseek an explanation.69
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I Chapter 1AdoptionStudiesWhy Study
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emphasis on participatory research
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of adoption study must be done with
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-7some idea of the degree to which
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80 2. tudl Used to Assess Different
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Another issue in measuring adoption
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Yt and we can estimate K (the maxim
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Measuring ImpactEarlier sections di
Page 25 and 26: -17The Role of Adoption Studies in
Page 27 and 28: Bo 3. Variable and Criteria __'_."C
Page 29 and 30: Farmers in Bumbogo, Rwanda grow bea
Page 31 and 32: -23The following graph shows the ac
Page 33 and 34: -25One valuable contribution to ass
Page 35 and 36: 80 1A study of mechanical wheat har
Page 37 and 38: Chapter 3UnderstandingAdoptionThe F
Page 39 and 40: small test of farmers' skills (for
Page 41 and 42: Wealth. Wealthier farmers may be th
Page 43 and 44: degree of adoption may depend on th
Page 45 and 46: conditions of the preceding or foll
Page 47 and 48: equired inputs, then the technology
Page 49 and 50: help in analyzing the degree to whi
Page 51 and 52: Checklist (cont'd.).....FactorBiolo
Page 53 and 54: Box 14. Gender Analysis and Adoptio
Page 55 and 56: Box 16. Labor Availability and Adop
Page 57 and 58: 80 1 te Trade-offsFarmers must ofte
Page 59 and 60: -51The adoption of technology is in
Page 61 and 62: -53Box 22. Source of Purchased Inpu
Page 63 and 64: Box 24. Knowledge of Recommendation
Page 65 and 66: services, or markets, it is importa
Page 67 and 68: - SamplingforcomparisonsTn many ins
Page 69 and 70: chosen for the survey will determin
Page 71 and 72: B. Measurements for input use may b
Page 73 and 74: 2. Where did you acquire the seed?1
Page 75: Cha ter 5MethodsforAnalyzingAdoptio
Page 79 and 80: For instance, a survey may show tha
Page 81 and 82: -73Revised[Oxen ownership (A) --.t~
Page 83 and 84: RevisedThe association is tested co
Page 85 and 86: The following example is a simple l
Page 87 and 88: where b o is the constant, b}' b 2,
Page 89 and 90: variables. And a high significance
Page 91 and 92: 1Decision trees are a method of rep
Page 93 and 94: ReferencesAcharya, M., and L. Benne
Page 95 and 96: Jha, D., B. Hojjati, and 5. Vosti.
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