A model of farmers’ decisions to adopt the new sheep breeds

Abstract

This study examines the criteria used by farmers in their decisions to adopt or reject new sheep breeds. The method used Ethnographic Decision Tree Modelling to elicit decision criteria used by the decision makers when making real-world decisions.

The objectives of the research were to identify the main decision criteria or reasons for adopting the new breeds, and to identify the main decision criteria or reasons for rejecting the new breeds. Rather than using diffusion or economic models to study farmers' decision making, this research has presented an alternative approach to understand farmer's decisions from their point of view. The approach was called Ethnographic Decision Tree Modelling. Forty farmers consisting of 26 adopters and 14 non-adopters from different parts of the Canterbury Region were involved. Ethnographic interviews and participant observation were used to elicit decision criteria in order to build a decision tree. The decision tree model was also tested against an independent sample of 20 adopters and non-adopters in order to test the model's predictability.

It is concluded that ethnographic decision tree modelling can be used to elicit the main criteria used by farmers in their decisions whether to adopt or reject agricultural innovations such as new sheep breeds. These decision criteria were either specific aspects or constraints which the farmers used in the decision processes from their own perspectives. These criteria were presented in a decision tree. The results showed that those farmers who believed that the new breeds of sheep could improve the genetic merits of their stock and expect to produce financial returns and did not have the constraints of time, ram availability and its cost would adopt the breeds. Those who did not believe that the breeds could improve the genetic merits would not adopt the breeds. Other factors associated with disbelief in genetic improvement were export restrictions and health risk. Furthermore, although few farmers had indicated their belief in genetic improvement they did not adopt because of the constraints of time, ram availability and its cost. The decision model was tested and gave a predictability of 95 per cent.