RESPONSES TO RISK IN WEED CONTROL DECISIONS UNDER EXPECTED PROFIT MAXIMISATION

Risk is an important characteristic of decisions about weed control in crops. In this paper it is shown that risk can affect weed control decisions even if the objective of the decision maker is to maximise expected profits: that is, even if the decision maker is ‘risk-neutral’ in the usual economic sense. This is shown for two decision frameworks: the optimal rate approach and the economic threshold approach. Empirical results are presented for control of ryegrass in wheat in Western Australia. It is found that, in general, risk reduces the optimal level of herbicide use under expected profit maximisation. Although individual sources of risk have a small impact on the optimal decision rules, combinations of uncertain variables can have a relatively large effect.     

AN ECONOMIC RESPONSE MODEL OF HERBICIDE APPLICATION FOR WEED CONTROL

A biologically realistic model of crop yield response to herbicide application is presented. It includes functions for weed mortality from herbicide application and yield loss due to surviving weeds. The optimal herbicide rate and two types of decision thresholds are derived theoretically and illustrated with empirical examples. Responses of the various decision criteria to changes in parameters are also examined theoretically and empirically. A multidimensional threshold for weeds based on weed density and weed-free yield is presented. The issue of farmers using other than officially recommended herbicide rates is discussed.     

Endogenous risk in weed control management

Weed control decisions are modeled in an endogenous risk framework where a producer invests in self-insurance and self-protection to reduce the severity of a realized pest infestation, or reduce the likelihood the infestation occurs. Self-insurance and self-protection are risk-reducing technologies that capture both the type and quantity of herbicides used. We supply conditions to unambiguously sign the effects of an increase in the probability of application or effectiveness failure and increased application or effectiveness uncertainty on optimal herbicide choices. If self-protection and self-insurance are stochastic substitutes, non-point source pollution policies targeted to reduce herbicide loadings can increase the use of more persistent herbicides. Policies that decrease loadings by reducing total mass may induce a substitution to herbicides more damaging or more likely to be transported to sensitive areas.     

The impact of climate and price risks on agricultural land use and crop management decisions

This article aims to investigate the impacts of climate change and of lower and more volatile crop price levels as currently observed in the European Union (EU) on optimal management decisions, average income and income risks in crop production in Western Switzerland. To this end, a bioeconomic whole-farm model has been developed that non-parametrically combines the crop growth model CropSyst with an economic decision model using a genetic algorithm. The analysis focuses on the farm level, which enables us to integrate a wide set of potential adaptation responses, comprising changes in agricultural land use as well as crop-specific fertilization and irrigation strategies. Furthermore, the farmer's certainty equivalent is employed as objective function, which enables the consideration of not only impacts on average income but also impacts on income variability.The study shows that that the effects of EU crop prices on the optimal management decisions as well as on the farmer's certainty equivalent are much stronger than the effects of climate change. Furthermore, our results indicate that the impacts of income risks on the crop farm's optimal management schemes are of rather low importance. This is due to two major reasons: first, direct payments make up a large percentage of the agricultural income in Switzerland which makes Swiss farmers less vulnerable to market and climate volatility. Second, arable crop farms in Switzerland have by law to cultivate at least four different crops. Due to these diverse cropping systems and high government direct payments risk does neither under climate change, market liberalization nor combinations thereof, play a very decisive role in arable farming in Switzerland.     

The U.S. Demand for Imported and Domestically Produced Foods: An Investigation of Intertemporal and Substitution Effects

This article extends the literature on time series estimation of U.S. consumer demand by presenting a coherent theoretical structure with a multi-period planning horizon for consumer choice and a more general treatment of the aggregation problem that allows the possibility that consumers' tastes change over time and vary across individuals. Based on our theoretical model, an econometric model is used to obtain estimates in a multi-period context of U.S. demand for imported foods and domestically produced foods. The hypothesis that current purchases depend on expected future prices is supported by the empirical results for imported foods.     

Determinants of herbicide use in rice production in the Philippines

This study identifies farm‐specific and market factors affecting the adoption of herbicides and the level of herbicide use by rice farmers in the Philippines. This requires the application of a modified version of Heckman's two‐step method to estimate a random‐effects double‐hurdle model for unbalanced panel data. The age of the farmer, household size, and irrigation use are significant determinants of the decision of farmers to adopt herbicides as an alternative to manual weeding, while economic variables such as the price of herbicides, total income, and access to credit determine the level of herbicide use. Determinants of both adoption and level are land ownership, farm area, and the method of crop establishment. These results are potentially relevant when designing policies to reduce excessive herbicide use or to encourage the adoption of alternative weed control methods such as integrated weed management.     

AN OPTIMAL CONTROL MODEL FOR INTEGRATED WEED MANAGEMENT UNDER HERBICIDE RESISTANCE*

The presence of weeds which have developed resistance to chemical herbicides is a problem of rapidly growing importance in Australian agriculture. We present an optimal control model of herbicide resistance development in ryegrass, the weed for which resistance is most commonly reported. The model is used to select the optimal combination of chemical and non-chemical control measures taking account of the trade off between short term profits and the long term level of herbicide resistance. Results indicate that given the threat of resistance there are benefits from integrating a combination of chemical and non-chemical control measures. The optimal strategy is found to include a declining herbicide dosage as resistance develops, with compensatory increases in the level of non-chemical control.     

Flat Earth Economics: The Far-reaching Consequences of Flat Payoff Functions in Economic Decision Making

Economists tend to emphasize the optimum, but in many cases, even large deviations from optimal decisions make little difference to the payoff. This has far-reaching implications that are under-recognized, including: ( a ) decision makers often have a wide margin for error in their production planning decisions, and flexibility to pursue factors not considered in the calculation of payoffs; ( b ) optimizing techniques are sometimes of limited practical relevance for decision support; ( c ) the value of information used to refine management decisions is often low; and ( d ) the benefits of using "precision farming" technologies to adjust production input levels are often low.     

DYNAMIC FARM—LEVEL PLANNING MODEL FOR BEEF FEEDLOT PRODUCTION AND MARKETING

The study is aimed at formulating and empirically applying a dynamic farm-level model for the planning of optimal beef feedlot production and marketing strategies. A dynamic programming (DP) model is used to calculate the optimal feeding schedule (i.e. liveweight gain sequence), market liveweight and stock replacement for a single bull calf over a one-year planning period, taking into account seasonal fluctuations of planning parameters such as beef prices, feeding costs, nutritional requirements, and stock replacement costs and obtainability. The DP model includes a linear programming (LP) subroutine for calculating least-cost feed rations. The planning model is empirically applied using data and assumptions representative offeedlots in the Coastal Plain region of Israel where most feedlot production and marketing decisions are made by ‘rule-of-thumb’ using set liveweight gain sequences, market standards and stock replacement decisions. The empirical findings indicate that the profitability of feedlot production and marketing could be increased by an average of 10% over that obtained from ‘rule-of-thumb’ decision rules.     

Multi-period stochastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain − A logistic case study in Midwestern United States

This work proposes a multi-objective optimization model to design a sustainable multi-period second generation biomass-to-bioethanol supply chain under multiple uncertainties. The objective is to simultaneously maximize the economic, environmental, and social performance. The strategic decisions such as land allocation for switchgrass cultivation, biorefinery locations and capacities, and the biomass-to-bioethanol conversion pathway are determined for each planning period which are staggered across the entire planning horizon. The augmented ε–constraint method is used to trade-off among the competing objectives and to obtain feasible solutions that achieve desired levels of sustainability. In order to solve the proposed stochastic optimization model efficiently and effectively, this work proposes a solution approach involving sequential application of a modified Sample Average Approximation method and Benders decomposition. A case study is presented to demonstrate the effectiveness of the proposed mathematical model and its impact on land usage and sustainability.     

A DYNAMIC PROGRAMMING MODEL OF EARLY POTATO HARVESTING

A vegetable producer often faces complex harvest decisions where yield increases through time and varies across crop area, price is determined in erratic seasonal markets, and harvest rate is constrained. In this paper, a simplified two-period potato harvest problem is developed to define a set of marginal conditions for an optimum. The model is then extended to the multi-period case and numerical solutions are generated for a representative farm in South Wales using dynamic programming. The model provides a general framework for analysing crop harvesting systems and prescribing marginal changes, such as adjustments in the harvest capacity, which improve profitability over myopic harvest patterns.     

PEST CONTROL DECISION MAKING: SUGAR BEET IN ENGLAND

This paper discusses approaches to the problem of pest control decision making. A farmer's pest control decisions are shown to depend on his subjective perception of the pest hazard, the controls he recognises as available to him, his subjective estimate of their net effect, and his individual decision rule or goal. A case study is discussed in which a decision model is used to compare objective pest observation with subjective perceptions of sugar beet growers. Applications of such a model for improving pest control decisions are suggested.     

Economic contribution of French serradella (Ornithopus sativus Brot.) pasture to integrated weed management in Western Australian mixed‐farming systems: an application of compressed annealing*

Sowing phases of French serradella (Ornithopus sativus Brot.) pasture between extended cropping sequences in the Western Australian wheatbelt can sustain grain production through restoring soil fertility and reducing selective herbicide use. The objective of this article is to investigate the profitability of rotations involving this pasture under a variety of weed management scenarios to obtain greater insight into its value for mixed farming systems in this region. A stochastic search procedure, compressed annealing, is used to identify profitable sets of weed management strategies in a simulation model representing a large number of potential combinations of chemical and non‐chemical forms of weed control. In contrast to a continuous‐cropping sequence, the inclusion of a serradella phase in a rotation is profitable at high weed densities and with increasing levels of herbicide resistance. A single year of pasture in the rotation is optimal if resistance to Group A selective herbicides is present at the beginning of the planning horizon, but a three‐year phase is required if resistance to multiple herbicide groups is observed. Sowing a serradella pasture twice over a two‐year phase is also shown to be economically attractive given benefits of successive high weed kills.     

A Stochastic Dynamic Programming Analysis of Farmland Investment and Financial Management

This paper develops a multiperiod investment portfolio model that includes risky farmland, risky and risk‐free nonfarm assets, and debt financing on farmland in the presence of transaction costs and credit constraints. The model is formulated as a stochastic continuous‐state dynamic programming problem, and is solved numerically for Southwestern Minnesota, USA. Results show that optimal investment decisions are dynamic and take into account the future decisions due to uncertainty, partial irreversibility, and the option to wait. The optimal policy includes ranges of inaction, states where the optimal policy in the current year is to wait. The risk‐averse farmer makes a lower investment in risky farmland reflecting risk‐avoiding behavior. We find that, in addition to risk aversion, the length of the planning horizon affects risk‐avoiding behavior in investment decisions. In contrast to a static model, changes in the riskiness of returns affect optimal investment decisions even when the decision maker is risk neutral. Finally, we find that higher debt financing on farmland is optimal when risky nonfarm assets can be included in the optimal investment portfolio and that the probability of exiting farming increases with the risky nonfarm investment. Dans le présent article, nous avons élaboré un modèle de portefeuille multipériode composé d'actifs agricoles risqués (terres), d'actifs non agricoles risqués et sans risque ainsi que de financement par emprunt de terres comprenant des coûts de transaction et des contraintes de crédit. Le modèle a été formulé comme un problème de programmation dynamique stochastique en temps continu et a été résolu de façon numérique pour le sud‐ouest du Minnesota, aux États‐Unis. Les résultats ont montré que les décisions optimales d'investissement sont dynamiques et qu’elles tiennent compte des décisions futures en raison de l'incertitude, de l'irréversibilité partielle et du choix d'attendre. La politique optimale inclut des plages d'inaction et des situations où la politique optimale de l'année en cours doit attendre. L'agriculteur risquophobe investit peu dans des actifs fonciers risqués ce qui traduit un comportement d'évitement des risques. Nous sommes arrivés à la conclusion que la longueur de l'horizon de planification, en plus de l'aversion pour le risque, influe sur le comportement d'évitement des risques au moment de prendre des décisions d'investissement. Contrairement au modèle statique, les changements dans les risques de rendements influent sur les décisions optimales d'investissement même si le décideur est indifférent au risque. Finalement, nous avons trouvé que le financement par emprunt pour des terres agricoles est optimal lorsqu’il est possible d'inclure des actifs non agricoles risqués dans le portefeuille optimal et que la probabilité de quitter l'agriculture augmente en présence d'investissements non agricoles risqués.     

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.     

Evaluating combined land conservation benefits from perennial pasture: lucerne (Medicago sativa L.) for management of dryland salinity and herbicide resistance in Western Australia*

The inclusion of perennial pasture phases in cropping rotations has been widely promoted throughout Australia for reducing the incidence of dryland salinity. To a lesser extent, they have also been promoted to enhance the management of herbicide‐resistant weeds. No previous economic analysis of perennial pasture has considered both of these benefits. This study combines a dynamic linear programming model to estimate the magnitude of salinity‐related benefits and a complex simulation model to assess the economics of herbicide‐resistance management. We present a case study of the perennial pasture lucerne (Medicago sativa L.) in the Wheatbelt of Western Australia, where the weed annual ryegrass (Lolium rigidum Gaudin) is resistant to multiple herbicide groups. Sequences incorporating lucerne are the most profitable land use at the standard set of parameter values if (i) annual ryegrass is resistant to all selective herbicides, (ii) the water table is so shallow (approximately < 3.5 m deep) that frequent rotation with perennials is required to avert soil salinisation, (iii) sheep production is highly profitable, or (iv) there is a combination of less extreme cases. The value of perennial pasture is sufficient under these circumstances to overcome its high establishment cost and the displacement of multiple years of crop. Consideration of dryland salinity and herbicide resistance are about equally important in evaluating the economics of lucerne; neither should be neglected.     

Optimal management of annual ryegrass (Lolium rigidum Gaud.) in phase rotations in the Western Australian Wheatbelt*

Lucerne (Medicago sativa L.) helps to prevent soil salinisation in the Western Australian Wheatbelt by reducing recharge to saline water tables. There is broad consensus, though, that it is not sufficiently profitable to motivate producers to plant it at the intensity at which considerable off‐site benefits would be conferred. This paper employs a multiple‐phase optimal control model to explore the value of this perennial pasture for the management of herbicide‐resistant annual ryegrass (Lolium rigidum Gaud.) in a crop–pasture rotation, given the difficulty of observing this value in practice. The availability of selective herbicides for efficient weed control is found to determine whether or not it is profitable to adopt lucerne pasture under optimal management. Herbicide resistance requires producers to employ costly, non‐selective treatments for in‐crop weed control. Thus, it motivates the adoption of perennial pasture in which cost‐effective forms of control can be implemented. Moreover, this result is robust to feasible changes in the current economic environment.     

Property portfolio allocation: A multi‐factor model

Summary

The objective of the paper is to describe a method of portfolio allocation currently being developed in the property sector. The model applies a multi‐index approach to portfolio decision making and enables investors to explore the effects of changing their allocational decisions. In estimating the parameters required for the model, we explain the method which can be used to adjust for smoothing effects on returns caused by the valuation process. Although the model is still at the development stage, the approach is general and can be applied both to allocating funds within a mixed‐asset portfolio or between different types of property within a wholly property‐orientated portfolio.     

Herbicide resistance and the adoption of integrated weed management by Western Australian grain growers

Extension programs to encourage farmers to reduce reliance on herbicides by adopting integrated weed management (IWM) practices have met with limited success. Studies aiming to understand the factors that influence farmers' choices of integrated control practices have faced difficulties in variable specification, and have not achieved high explanatory power. Using data from grain growers in Western Australia, where herbicide resistance in major crop weeds is common, this study tests the applicability of a framework for the IWM adoption decision in which herbicide efficacy is assumed to be a potentially exhaustible resource. Farmers' perceptions of multiple techniques and other variables are aggregated using principal components, and used in logistic regressions to explain the intensity of use of IWM practices. Eighty‐six percent of growers were correctly classified according to use of multiple IWM practices. Herbicide resistance and expectations of the future availability of effective new herbicides were significant in explaining IWM adoption. IWM adoption and herbicide‐resistance management are shown to be information‐intensive and involving an intertemporal resource management decision.     

Spatial Organization of Marketing Facilities in Developing Countries: A Case Study of Oilseeds in Sudan

Little attention has been devoted to the study of spatial organization of marketing facilities in developing countries, even though such studies would be most useful for a wide range of marketing problems. The results of such studies could be valuable to private and public decision-makers in developing countries whose policies and decisions determine the number, size and location of marketing facilities. The spatial organization model developed in this paper for application to the oilseeds industry in Sudan demonstrates the relevance of this research technique for developing country studies of marketing facilities. A linear programming transshipment model is utilized to determine the optimal spatial organization of oilseeds in Sudan when the costs of oilseed assembly, processing and distribution of oil and cake to final destinations are considered simultaneously. The optimal spatial organization of oilseed processing plants was determined for six alternative solutions. Model results indicate that the optimal organization of processing plants would be obtained with fewer and larger plants, resulting in lower transportation costs.