The general objective of the proposed research is to improve the ability of the grain marketing system to respond to increased pesticide regulations and to consumer demands for wholesome, insect-free foods. The specific objectives are:<OL> <LI> Estimate costs and risks associated with chemical-based and IPM pest-control strategies in stored grain facilities. <LI> Identify economically optimal insect-management strategies for grain storage managers under alternative situations. <LI>Describe the structural, operational, and other insect-related characteristics of various types of grain and food processing facilities. <LI>Estimate costs and risks associated with chemical-based and IPM pest-control strategies in grain and food processing facilities
NON-TECHNICAL SUMMARY: Two major trends in the food industry are in conflict. On the one hand, consumers are increasingly demanding safer products. On the other hand, consumers also demand wholesome products, free of insects and other pests. Because of food safety, worker safety, and environmental concerns, many of the pesticides currently used to control pests in stored products such as grain are being either phased out or significantly restricted by regulations. The intent of the proposed research is to improve the ability of the grain marketing system to respond to increased pesticide regulations and to consumer demands for wholesome, insect-free foods by 1) estimating costs and risks associated with chemical-based and IPM pest-control strategies in stored grain facilities; 2) identifying economically optimal insect-management strategies for grain storage managers under alternative situations; 3) describing the structural, operational, and other insect-related characteristics of various types of grain and food processing facilities; and 4) estimating costs and risks associated with chemical-based and IPM pest-control strategies in grain and food processing facilities.
APPROACH: In a previous Hatch project, costs of IPM and chemical-based strategies were estimated using a partial-budgeting approach. These costs were considered in four categories: chemicals, labor, management, electricity, and equipment. Costs from this model and adaptations will be used to evaluate costs of insect control approaches. A second set of models will be used to predict insect growth under various environmental conditions and treatments. Flinn et al. (2004) and Flinn et al. (2007) describe an artificial intelligence insect growth simulator that will allow simulation of various pest management practices and their effect on insect populations. This model has been updated as new entomological data have become available (e.g. Flinn, Hagstrum, and Muir 1997), so the entomological basis for the proposed simulations is more than adequate. Second, economic risks associated with IPM and chemical-based strategies will be measured. Sources of risk considered are sampling risk, risk of inadequate insect control, and risk of pesticide residue leading to rejection by buyer. Sampling risk is the risk that sampling will fail to detect insects, and economic damage will result from failure to control them. It includes the risk that sampling will lead to overestimates of insect numbers, resulting in costly, unnecessary treatments, and the risk that sampling at point of sale will overestimate the number of insects in the population, causing economic loss to the seller. Inadequate insect control may result from improper choice of treatments, ineffective treatments (for example, fumigation of a leaky storage facility), treatment rendered ineffective by environmental conditions, or from insect resistance. Failure to control insects could result in insect-damaged kernels (IDK) and or other degradation, a grading designation of "infested," or simply rejection by a buyer. Detectible pesticide residue may result from failure of an applied pesticide to dissipate sufficiently, perhaps because of unfavorable environmental conditions or application too soon before sale, or from over-application of the pesticide. All of these likely will cause economic loss. Distributions for each relevant variable affecting costs and benefits for the firm will be estimated, and draws from these distributions will be used to estimate distributions of costs and benefits. These distributions will be evaluated using several approaches, including mean-variance, stochastic dominance, and value-at-risk (Value-at-risk measures could be used to answer the question, for example, "With 5% probability, what is the maximum loss the firm would incur using strategy y" Similar procedures will be used to accomplish Objective 2, except that individual sources of cost within the broad categories outlined above are likely greater in number and type than for grain storage firms because of the greater complexity of food processing firms. Also, field data will be more difficult to obtain because of the likely reluctance of food processing firms to allow observation of their practices and facilities. Cooperators on this project have established contacts that may provide representative observations and data.