Agricultural Economics (Amsterdam, Netherlands), The study aims to track adoption of improved chickpea varieties, and assess their on-farm benefits in some remote and backward tribal villages in Gujarat, India, where few newly developed varieties were introduced by a non-government organization. It also determines key factors which were influencing their adoption. The study found that adoption of improved chickpea varieties was gradually increasing by replacing a prominent local variety. Duration of crop maturity, farm size, yield risk, and farmers' experience of growing chickpea crop were significantly influencing their adoption. The on-farm benefits as a result of improved varieties were realized in terms of increased yield levels, higher income and labor productivity, more marketable surplus, price premium and stabilized yields in fluctuating weather. Breeding short duration varieties with stable yield levels under varying weather, and organizing seed multiplication and dissemination in regions, where moisture stress is a problem during maturity of chickpea, are the major suggestions.
Agricultural Economics (Amsterdam, Netherlands), GIS-derived measures of location and space have increasingly been used in models of land use and ecology. However, they have made few inroads into the literature on technology adoption in developing countries, which continues to rely mainly on survey-derived information. Location, with all its dimensions of market access, demographics and agro-climate, nevertheless remains key to understanding potential for technology use. The measures of location typically used in the adoption literature, such as locational dummy variables that proxy a range of locational factors, now appear relatively crude given the increased availability of more explicit GIS-derived measures. This paper attempts to demonstrate the usefulness of integrating GIS-measures into analysis of technology uptake, for better differentiating and understanding locational effects. A set of GIS-derived measures of market access and agro-climate are included in a standard household model of technology uptake, applied to smallholder dairy farms in Kenya, using a sample of 3330 geo-referenced farm households. The three technologies examined are keeping of dairy cattle, planting of specialised fodder, and use of concentrate feed. Logit estimations are conducted that significantly differentiate effects of individual household characteristics from those related to location. The predicted values of the locational variables are then used to make spatial predictions of technology potential. Comparisons are made with estimations based only on survey data, which demonstrate that while overall explanatory power may not improve with GIS-derived variables, the latter yield more practical interpretations, which is further demonstrated through predictions of technology uptake change with a shift in infrastructure policy. Although requiring large geo-referenced data sets and high resolution GIS layers, the methodology demonstrates the potential to better unravel the multiple effects of location on farmer decisions on technology and land use.
