A mathematical programming formulation for long-term infrastructure investment planning in Small Island Developing States.

Mixed-integer programming is a common method used in electricity generation and transmission optimization models. However, the size of the problem can result in extraordinarily long run times. Solve time also increases exponentially with the number of variables to optimize. There is therefore a constant trade-off between a realistic representation of the network and computational tractability. Additionally, actual data and publicly available, real-world application are scare. This is particularly true for Small Island Developing States. This paper bridges these gaps by describing a customized mathematical formulation for co-optimizing generation and transmission infrastructure investments. Data from the island of Jamaica and program scripts are available for reproduction. Key customizations to a mixed-integer programming model for long-term generation and transmission infrastructure investment planning include:•Hours are treated as representative hour categories and multiplied by the number of hour types within a given period.•Simulated construction is limited to every other year.•While fossil fuel plants are treated as discrete variables, renewable energy plants are treated as continuous variables.

Economics of integrated insect management in stored corn.

Insects can cause substantial damage to stored grain. In addition, consumers and therefore food processors are increasingly interested in chemical-free products. Integrated pest management (IPM) may increase farmers' profits while reducing their use of pesticides. This study uses a stochastic dynamic programming framework to model the economics of optimal insect control in corn, Zea mays L., stored on-farm with multiple controls conditional on the biophysical conditions of the grain in the bin. We find that for farmers who have a contract with a food processor, where there are quality premiums, the optimal management strategy depends on monitoring the biophysical conditions of the grain and the time period under consideration. For farmers who deliver to the commodity market, their current practices are optimal.

Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions.

Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Such pixel level land use data can be used to investigate the impact of human activities on the environment. Validation exercises show that our approach is effective at downscaling cropland allocation to multiple levels of resolution.