Improved Program Planning Approaches Generates Large Benefits in High Risk Crop Farming

This paper examines whether there is room for the improvement of farm program decisions through the incorporation of mathematical optimization in the practical planning process. Probing the potential for improvement, we investigate the cases of four German cash crop farms over the last six years. The formal planning approach includes a systematic time series analysis of farmspecific single gross margins and a stochastic optimization model. In order to avoid solutions that simply exceed the farmer's risk tolerance, the apparently accepted variance of the observed program's total gross margin which represents an observable reflection of the individual farmer's risk attitude is used as an upper bound in the optimization. For each of the 24 planning occasions, the formal model is used in a quasi ex-ante approach that provides optimized alternative programs. The total gross margins that could have been realized if the formally optimized programs had been implemented are then ex-post compared to those that were actually realized. We find that the farmers could have increased their total gross margins significantly if - instead of using simple routines and rules of thumb - they had used the more sophisticated formal planning model. However, we also find that the superiority of formalized planning approaches depends on the quality of statistical analysis and the resulting forecasting model. Using our approach for practical decision support implies that farmers first specify their "own" production programs without the formal planning aid. Then, an alternative program can be provided which leads to superior expected total gross margins without exceeding the farmer's accepted total gross margin variance.production program planning, optimization, uncertainty, static distributions, stochastic processes, Crop Production/Industries, C1, C61, M11, Q12,

Modeling Agricultural Production Considering Water Quality and Risk

Environmental goals often conflict with the economic goals of agricultural producers. The Cottonwood River in Minnesota is heavily polluted with nitrogen, phosphate and sediment from agricultural sources in the watershed. Goals of profit maximization for producers conflict with those of effluent alleviation. We incorporate water quality goals and risk into a mathematical programming framework to examine economically efficient means of pollution abatement while considering a wide range of alternative production practices.Production Economics,

Modelling the Dynamics of Weed Management Technologies

An appropriate economic framework for valuing the benefits of weed management technologies is to treat weeds as a renewable resource stock problem. Consequently, the weed seed bank is defined as a renewable resource that changes through time due to management and seasonal conditions. The goal of decision-makers is to manage this (negative) resource so as to maximise returns over some pre-specified period of time. A modelling framework is presented for evaluating the biological and economic effects of weed management. The framework includes population dynamics, water balance, crop growth, pasture growth and crop/pasture rotation models for measuring the physical interactions between weeds and the environment. These models link in with numerical optimal control, dynamic programming and stochastic dynamic programming models for determination of optimal decision rules and measuring economic impact over time of policy scenarios.weeds, modelling, dynamic analysis., Land Economics/Use,

Impacts of risk aversion on whole-farm management in Syria

This article reports on a study of the impact of risk on farm management practices in northern Syria, focusing particularly on how these are affected by risk aversion and farm size. The study is based on production data from an eight‐year field trial and on prices from market surveys. A large linear programming model is built, representing the eight years as observations from a discrete probability distribution. Risk aversion is modelled by inclusion of a utility function with constant relative risk aversion, represented using the DEMP/UEP approach.Farm Management, Risk and Uncertainty,

THE EFFECT OF STOCHASTIC IRRIGATION DEMANDS AND SURFACE WATER SUPPLIES ON ON-FARM WATER MANAGEMENT

This study presents a procedure for simultaneously addressing stochastic input demands and resource supplies for irrigated agriculture within a linear modeling framework. Specifically, the effect of stochastic crop net irrigation requirements and streamflow supplies on irrigation water management is examined. Irrigators pay a self-protection cost, in terms of water management decisions, to increase the probability that stochastic crop water demand is satisfied and anticipated water supply is available. Self-protection cost is lower when increasing the probability that anticipated water supplies are delivered, ceteris paribus, than when increasing the probability that the crop receives full net irrigation requirement in the study region.Resource /Energy Economics and Policy,

CHANCE CONSTRAINED PROGRAMMING MODELS FOR RISK-BASED ECONOMIC AND POLICY ANALYSIS OF SOIL CONSERVATION

The random nature of soil loss under alternative land-use practices should be an important consideration of soil conservation planning and analysis under risk. Chance constrained programming models can provide information on the trade-offs among pre-determined tolerance levels of soil loss, probability levels of satisfying the tolerance levels, and economic profits or losses resulting from soil conservation to soil conservation policy makers. When using chance constrained programming models, the distribution of factors being constrained must be evaluated. If random variables follow a log-normal distribution, the normality assumption, which is generally used in the chance constrained programming models, can bias the results.Risk and Uncertainty,

On-Farm Costos of Reducing environmental degradation under risk

Farmers respond to environmental regulations by adjusting production practices so as to comply while minimizing their loss in expected income. Ultimately the cost of agro environmental regulation is determined by farm level adjust¬ments. Our farm level simulation framework assesses economic and environmental impacts of hypothetical pesticide restrictions in the context of continuing soil conservation efforts.

Combinatorial optimisation of a large, constrained simulation model: an application of compressed annealing

Simulation models are valuable tools in the analysis of complex, highly constrained economic systems unsuitable for solution by mathematical programming. However, model size may hamper the efforts of practitioners to efficiently identify the most valuable configurations. This paper investigates the efficacy of a new metaheuristic procedure, compressed annealing, for the solution of large, constrained systems. This algorithm is used to investigate the value of incorporating a sown annual pasture, French serradella (Ornithopus sativa Brot. cv. Cadiz), between extended cropping sequences in the central wheat belt of Western Australia. Compressed annealing is shown to be a reliable means of considering constraints in complex optimisation problems in agricultural economics. It is also highlighted that the value of serradella to dryland crop rotations increases with the initial weed burden and the profitability of livestock production.combinatorial optimisation, crop rotation, simulated annealing, Research Methods/ Statistical Methods, C63, Q15,

Assisting decision-making in Queensland barley production through chance constrained programming

A chance constrained programming model is developed to assist Queensland barley growers make varietal and agronomic decisions in the face of changing product demands and volatile production conditions. Unsuitable or overlooked in many risk programming applications, the chance constrained programming approach nonetheless aptly captures the single‐stage decision problem faced by barley growers of whether to plant lower‐yielding but potentially higher‐priced malting varieties, given a particular expectation of meeting malting grade standards. Different expectations greatly affect the optimal mix of malting and feed barley activities. The analysis highlights the suitability of chance constrained programming to this specific class of farm decision problem.Crop Production/Industries,