Nganje, William E.

By: Richards, Timothy J.; Nganje, William E.; Acharya, Ram N.
Despite the economic damage inflicted by a foodborne disease outbreak, firms at all points in the supply chain appear to be reluctant to invest in the necessary food safety technologies and practices. We argue that these investments are subject to both hysteretic and public good effects, and construct a theoretical model of food safety investment, calibrated to describe the 2006 E. coli outbreak in California spinach. Both effects are found to induce delays in food safety investments, but the public good effect dominates. We suggest a number of policy options that improve incentives to contribute to the public good.
By: Dahl, Bruce L.; Wilson, William W.; Nganje, William E.
Variety development and release decisions involve tradeoffs between yields and characteristics valued by end-users, as well as uncertainties about agronomic, quality, and economic variables. In this study, methods are developed to determine the value of varieties to growers and end-users including the effects of variability in economic, agronomic, and quality variables. The application is to hard red spring (HRS) wheat, a class of wheat for which these tradeoffs and risks are particularly apparent. Results indicate two experimental varieties provide improvements in grower and end-user value, relative to incumbents. Stochastic dominance techniques and statistical tests are applied to determine efficient sets and robustness of the results. A risk-adjusted portfolio model, which simultaneously incorporates correlations between grower and end-use characteristics, is also developed to compare the portfolio value of varieties.
By: DeVuyst, Eric A.; Johnson, D. Demcey; Nganje, William E.
Grain quality is typically measured via several attributes. As these attributes vary across shipments and time, grain quality can be described using multivariate probability or frequency distributions. These distributions are important in modeling blending opportunities inherent in various grain shipments. For computational reasons, it is usually necessary to represent these distributions with a small set of discrete points and probabilities. In this analysis, we suggest a representation method based on Gaussian quadrature. This approach maintains the blending opportunities available by preserving moments of the distribution. The Gaussian quadrature method is compared to a more commonly used representation in a barley blending model.