Agriculture development efforts in the past have focused heavily on increasing aggregate output in less developed countries (LDC). In the last 2-3 decades, technology served as a key policy instrument used by developed countries to aid the less developed ones. However, such a policy had led to the neglect of certain farming groups, particularly small farmers. Recent trends in agriculture development tend to pay special attention to the needs of small farmers. It is the small farmers who form the backbone of agriculture in many LDC's. As their development receives priority, so does the need to gain a better understanding of their farming needs. Small farmer response to technology has been unevenly distributed. This has not only varied amongst the LDC's, but technology adoption rates differ within countries. Besides many socio-economic constraints experts consider risk and uncertainty to play a dominant role in technology adoption. The major issue is to what extent should risk and uncertainty be taken into account while evaluating potential technology. Farmers in many developing countries have shown preference for traditional technology over the improved one. The discussion of this paper focuses on the decision making strategies of farmers. Particularly those strategies adopted in choice of varieties and allocation of resources to agriculture. The interest here is to see to what extent does risk and uncertainty enter the farmers decision process. The focus here is not limited to the small farmers alone, however, they are of special interest in this paper. Usually a small farmer is taken to be synonymous with a poor farmer, but farm size alone is an inadequate criteria for distinguishing a poor farmer from those that are better off. Net per capita income appears to be a better proxy by which to define small farmers. This paper will deal with the decision making aspects of small farmers. Particular emphasis is on the choice of technology, (with emphasis on varieties) under conditions of risk. The interest is in gaining insight into variables which affect farmer decision making.


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