In this paper, we propose a new family of premium calculation principles based on the use of prior information from different sources. Under this framework and based on the use of Ordered Weighted Averaging operators, we provide alternative collective and Bayes premiums and describe some approaches to efficiently compute them. Several examples are detailed to illustrate the performance of the new methods.

Policymakers are concerned about nitrogen and phosphorus export to water bodies. Exports may be reduced by paying farmers to adopt practices to reduce runoff or by paying performance incentives tied to estimated run-off reductions. We evaluate the cost-effectiveness of practice and performance incentives for reducing nitrogen exports. Performance incentives potentially improve farm-level and allocative efficiencies relative to practice incentives. However, the efficiency improvements can be undermined by baseline shifts when growers adopt crops that enhance the performance payments but cause more pollution. Policymakers must carefully specify rules for performance-incentive programs and payments to avoid such baseline shifting.

A mixed-integer linear programming model was formulated to minimize the cost of transport and processing of excess manure in the Chesapeake Bay watershed. The results showed that primarily poultry manure was moved out of surplus counties for land application or processing. In the base model, annual cost was more than $350 million, with the bulk of the cost arising from construction of energy facilities for poultry manure. Forestland application of poultry manure had the lowest average cost, and more forestland than agricultural land was used for manure application. The lowest cost scenario was $127 million annually when constraints were removed to expand manure application on agricultural land and allow unlimited construction of composting facilities. Such a low-cost solution could not realistically be implemented without further development of markets for compost.

Policies to mitigate greenhouse gas emissions are likely to increase energy prices. Higher energy prices raise farmer costs for diesel and other fuels, irrigation water, farm chemicals, and grain drying. Simultaneously, renewable energy options become more attractive to agricultural producers. We consider both of these impacts, estimating the economic and environmental consequences of higher energy prices on U.S. agriculture. To do this we employ a price-endogenous agricultural sector model and solve that model for a range of carbon-tax-based energy price changes. Our results show mostly positive impacts on net farm income in the intermediate run. Through market price adjustments, fossil fuel costs are largely passed on to consumers. Additional farm revenue arises from the production of biofuels when carbon taxes reach $30 per ton of carbon or more. Positive environmental benefits include not only greenhouse gas emission offsets but also reduced levels of nitrogen leaching.

Economic analysis was conducted on hypothetical agronomic research on new crop cultivars for Arkansas dryland soybean and wheat producers. In relation to farmers' attitudes toward risk, the microeconomic effects and level of adoption of yield variability reducing cultivars were analyzed utilizing a production management decision-making model formulated with mathematical programming techniques. The study indicated that negative covariance between crops continues to be an effective means of reducing production risk associated with yield variability. However, under varying circumstances, agronomic research on the breeding of new soybean and wheat cultivars with reduced yield variability is worthwhile if there is only slight concurrent reduction in expected yields.

Systematic approaches to validation of linear programming models are discussed for prescriptive and predictive applications to economic problems. Specific references are made to a general linear programming formulation, however, the approaches are applicable to mathematical programming applications in general. Detailed procedures are outlined for validating various aspects of model performance given complete or partial sets of observed, real world values of variables. Alternative evaluation criteria are presented along with procedures for correcting validation problems.

Payment arrangements among members of a cooperative play a critical role in the performance of the cooperative. The impact of three payment systems is assessed for Florida sugarcane cooperatives through a bi-level programming model which incorporates both individual and collective behavior.

A model with omitted resource constraints is suggested as an alternative to a risk aversion model for explaining economic behavior. This paper uses two standard mathematical programming models to further explore this issue. One model is a standard profit maximization linear programming model and the other is a risk averse quadratic programming model with part of the constraints deleted. Theoretical investigation of these models demonstrates that risk aversion can substitute for omitted resource constraints. A small empirical model is then solved under both formulations. With resource constraints deleted, positive risk aversion is necessary to obtain a similar enterprise organization as under profit maximization with complete constraints. These two solutions are then interpreted with the theoretical optimality conditions.

Exporting northwest Arkansas excess turkey and broiler litter to partially fertilize nutrient-deficient cropland in eastern Arkansas can be more cost effective than to supply all crop nutrients with chemical fertilizer only, given current high fertilizer prices. Cost savings are greater if litter is baled in ultraviolet resistant plastic and transported via truck, since backhaul opportunities reduce truck rates, or alternatively, if raw litter is shipped via a truck-barge combination. Rice is the crop that allows for greater savings according to a mathematical programming model implemented in General Algebraic Modeling System (GAMS).

Mathematical programming-based systems analysis is used to examine the consequences of alternative operation configuration for the agricultural operations within the Texas Department of Criminal Justice. Continuation versus elimination of the total operation as well as individual operating departments are considered. Methodology includes a firm systems operation model combined with capital budgeting and an integer programming based investment model. Results indicate the resources realize a positive return as a whole, but some enterprises are not using resources profitably. The integer investment model is found to be superior for investigating whether to continue multiple interrelated enterprises.

Farmers and taxpayers would benefit from more cost-effective agricultural nutrient pollution control measures. The objectives of our study are (1) to assess compliance costs and reductions in phosphorus loadings from implementation of nutrient management and riparian buffers; and (2) to estimate how the spatial scenario, which is the method of representing farms within the watershed, affects estimated compliance costs and reductions in phosphorus deliveries. Estimated compliance costs are quite sensitive to the spatial scenario. Buffers are more cost-effective than nutrient management under one of the two spatial scenarios, whereas nutrient management is more cost-effective under the other scenario. Shifts to more erosive crops reduce the effectiveness of both pollution control measures.

Mathematical programming formulations can yield faulty answers. Models can be unbounded, infeasible, or optimal with unrealistic answers. This article presents techniques for theory-based discovery of the cause of faulty models. The approaches are demonstrated in the context of linear programming. They have been computerized and interfaced using the General Algebraic Modeling System (GAMS), and are distributed free of charge through new GAMS versions and an online web page.

Mathematical programming results revealed that moving toward more flexible agricultural policies would generate substantial economic and environmental gains in a North Carolina diversified cropping region. But in a Washington-Idaho dryland grains region, only the use of relatively new and sometimes problematic alternative cropping systems permitted environmental and economic gains under policy reform. In both regions, a recoupling policy, which links government payments to resource-conserving farming practices, was needed to protect environmental quality when market prices for program crops were high.

Rotations have historically been used to alleviate pest problems in crop production. This paper considers methods of modeling rotations in linear programming models for Southeastern vegetable production. In such models, entering each possible crop rotation as a separate activity can be burdensome because of the large numbers of possible rotational alternatives. Conventional methodology for double crop rotations reduces the number of activities but must be adapted to accommodate triple crop rotational requirements in vegetable production. This paper demonstrates these methods both for a simple example and an empirical problem with numerous rotation alternatives. While the methods presented in this paper may have computational disadvantages compared to entering each rotation as a separate activity, they do have advantages in model design and data management.

Production risk includes yield and days suitable for fieldwork variability. Both were modeled using biophysical simulation and a mean-variance, chance-constrained mathematical programming formulation representing a Kentucky corn, soybean, and wheat producer. While crop diversification, planting date, and maturity group can be used to reduce the types of risk considered, interaction between the two influences how production practices are used to manage risk. For the conditions studied, plant population alterations were less effective for risk reduction of either component. The study provides evidence of the importance of the consideration of both elements of production risk in whole farm planning.

The value of an innovative seed technology is estimated in a discrete stochastic programming framework for a representative farm in the northern Corn Belt. Temperature-activated polymer-coated seed has the potential to increase net returns by increasing yields due to early planting and use of longer season varieties, as well as reducing yield loss due to delayed planting. A biophysical simulation model was used to estimate die impact of polymer-coated seed on corn and soybean yields and on field day availability for five planting periods, three crop varieties, and two tillage systems on two different soils under varying weather conditions.

A risk-averse irrigated corn producer would be better off choosing the more expensive subsurface drip irrigation (SDI) over center-pivot sprinkler (CPS), given limited aquifer life and swine effluent and urea fertilization. A stochastic optimization using EPIC data maximized expected utility of 100 years' worth of net revenues for a quarter section. Phosphorus accumulation was more likely with the CPS than with the SDI but soil nitrogen was constant under both systems. SDI conserves more water than CPS per acre but depletes the aquifer faster because a greater area is irrigated. These results were invariant in the sensitivity analysis.

Many areas of the US recently endured a severe drought and management strategies to cope with the lack of forage production varied. A multi-period mathematical model is presented that estimates the outcomes of two common producer responses to changes in precipitation, partial liquidation and purchasing hay, given fluctuating cattle prices over a long term planning horizon. Results were further summarized with regression analysis and selected elasticities were calculated to reflect the sensitivity of outcomes to variability in precipitation and livestock prices. Although little impact was seen from utilizing additional hay as a strategy during drought, producers who follow this strategy are in a position to market more animals immediately post drought in general, resulting in better long run financial outcomes. Elasticity estimates suggest that profitability is more sensitive to variability in prices but that optimal choices of management strategies are more sensitive to variability in precipitation.