Focusing on agricultural development the chapter argues that the role of the communications system in the development process is through its impact on the adoption and use of inputs and through its augmentation of human labor. Attention is directed to the specific case of a group of agricultural households in Nepal and the direct and indirect effects of communications upon farmer productivity are examined. The data used in this case study were collected in 2 districts (Bara and Rautahat) of Nepal from October 1977 through May 1978. The districts are 2 of 75 administrative districts in Nepal and are located in the Nepal Terai. The sample is a stratified random sample of 683 households in 6 of 109 panchayats of Bara and 6 of 132 panchayats of Rautahat. All of the household heads in the sample are male. The higher incidence of extension contact in Bara most likely reflects the earlier introduction there of the Training and Visit System which stresses message saturation and the communication of appropriate recommendations via specifically trained single-purpose extension agents and carefully selected contact farmers. The structure of the extension service in Nepal is hierarchical. It was found that schooling and extension contact enhanced the relative technical efficiency of farmers in the production of late paddy and wheat. These 2 influences appeared to be substitute sources of technical information. Educational enhancement did not enhance the impact of extension exposure as might be predicted a priori. In the analysis of adoptive behavior it was found that a farmer is more likely to use chemical fertilizers on a crop the higher the farmers educational attainment. This effect of schooling was indirect with schooling affecting the farmers numeracy and numeracy affecting the likelihood of adoption. Holding constant the other determinants of adoption a farmer was more likely to grow wheat or use chemical fertilizers the greater the proportion of other farmers who do so in this farmers immediate area (his panchayat). This finding supports the diffusion of contagion model of innovation adoption.
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