The Adoption of Agricultural Technology - Food Security Group

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.