Incentives for adoption of site-specific technologies under uncertainty: Economic and environmental implications

This thesis presents two methods to analyze the factors affecting adoption of site-specific technologies (SSCM) under uncertainty. The first part develops an option value model to analyze the impacts of output price uncertainty, high sunk costs of adoption, and site-specific conditions on the timing of adoption of two interrelated site-specific crop technologies, soil testing and variable rate technology (VRT). It incorporates the potential for adopting them jointly or sequentially. It also analyzes the implications of the pattern of adoption for nitrogen pollution and for the design of a cost-share subsidy policy to accelerate the adoption of these technologies to reduce nitrogen pollution. Ignoring the potential for stepwise adoption would tend to under-predict the adoption of soil testing and over-predict the adoption of VRT. It would lead to an underestimation of the required subsidy for inducing adoption of VRT and overestimation of that required for soil testing. It also shows that cost-share subsidies to accelerate the adoption of VRT would be most effective at reducing nitrogen pollution if targeted towards fields with low soil quality and relatively high spatial variability in soil quality or soil fertility.; The second part of the thesis develops a technology adoption model under uncertainty and risk aversion to examine the incentives for adoption of SSCM and its farm-level impacts. The theoretical model shows that ignoring the input availability uncertainty and risk preferences of farmers results in misleading estimates of impacts of SSCM on input use and yield, and overestimation of the impacts on quasi-rent differentials. Application of the model to adoption of SSCM in a watershed indicates that ignoring risk preference of farmers and uncertainties about SSCM equipment leads to over estimation of quasi-rent differentials of SSCM between 58%–76%. It also leads to an over estimation of the environmental benefits of SSCM by up to 90%. The results have policy implications for improving the benefits of SSCM and controlling pollution through encouraging the adoption of SSCM.