Measuring technical and environmental efficiency in a state-contingent technology

Most developed countries support farming activities through policies that are tailored to meet their specific social, economic and environmental objectives. Economic and environmental efficiency have recently become relevant targets of most of these policies, whose sound implementation can be enhanced by monitoring farm performance from a multidimensional perspective. This paper proposes farm-level technical and environmental efficiency measures that recognize the stochastic conditions in which production takes place. A state-contingent framework is used to model production uncertainty. An implementable representation of the technology is developed using data envelopment analysis. The application focuses on a sample of Catalan arable crop farms. Results suggest that technical efficiency is slightly lower in bad than in good growing conditions. Nitrogen pollution can decrease substantially more under good than bad growing conditions.     

Multiple objective linear programming for environmental farm planning

We present a multiple objective linear programming model developed to consider a wide range of farming situations, which allows optimisation of profit or environmental outcome(s) or both. The modelling considers the problem of planning a farming system within a world where environmental considerations are increasing. The objective is to identify the best cropping and machinery options which are both profitable and result in improvements to the environment, depending upon the farm situation of market prices, potential crop yields, soil and weather characteristics. In particular, the model uses a flexible approach to choosing the machinery, timing of operations, crop rotations and levels of inputs. We show for a UK scenario, that large reductions in environmental impact can be achieved for reductions in farm profit which are insignificant relative to the annual variation due to yields and prices.     

A stochastic programming based analysis of the field use in a farm

The paper is devoted to the use of the arable land of a farms. The farm want to have certain amount of productions from some crops. This policy of the farm has two purposes. First some products, e.g. silo, are used on further production levels. Furthermore the diversification of the finished product decreases the financial risk of the farm caused by the unknown future behavior of the markets. The aim is to determine that use of the fields, which guarantees the highest probability of the satisfaction of demand. Crop rotation determining that which crops can be produced in a field gives a natural condition to be satisfied. The problem is modeled and solved by stochastic programming. The results based on the data of a farm are provided.     

Energy crop supply in France: a min-max regret approach

This paper attempts to estimate energy crop supply using a linear programming (LP) model comprising hundreds of representative farms of the arable cropping sector in France. In order to enhance the predictive ability of such a model and to provide an analytical tool useful to policy makers, interval linear programming is used to formalize bounded rationality conditions. In the presence of uncertainty related to yields and prices, it is assumed that the farmer may adopt a min-max regret (MMR) criterion as an alternative to the classic profit maximization criterion. Recent advances in operational research are exploited, permitting an efficient implementation of the min-max criterion within an LP model. Model validation based on observed activity levels suggests that about 40% of the farms adopt the MMR criterion. Energy crop supply curves generated by the MMR model prove to be upward-sloped, like classic LP supply curves.     

Pesticide use,environmental spillovers and efficiency: A DEA risk-adjusted efficiency approach applied to Dutch arable farming

Pesticides are widely used by crop producers in developed countries to combat risk associated with pests and diseases. However, their indiscriminate use can lead to various environmental spillovers that may alter the agricultural production environment thus contributing to production risk. This study utilises a data envelopment analysis (DEA) approach to measure performance of arable farms, incorporating pesticides’ environmental spillovers and output variance as undesirable outputs in the efficiency analysis and taking explicitly into account the effect of pesticides and other inputs on production risk. The application focuses on panel data from Dutch arable farms over the period 2003–2007. A moment approach is used to compute output variance, providing empirical representations of the risk-increasing or -decreasing nature of the used inputs. Finally, shadow values of risk-adjusted inputs are computed. We find that pesticides are overused in Dutch arable farming and there is a considerable evidence of the need for decreasing pesticides’ environmental spillovers.     

A portfolio theory approach to crop planning under environmental constraints

This paper presents a multiobjective model for crop planning in agriculture. The approach is based on portfolio theory. The model takes into account weather risks, market risks and environmental risks. Input data include historical land productivity data for various crops, soil types and yield response to fertilizer/pesticide application. Several environmental levels for the application of fertilizers/pesticides, and the monetary penalties for overcoming these levels, are also considered. Starting from the multiobjective model we formulate several single objective optimization problems: the minimum environmental risk problem, the maximum expected return problem and the minimum financial risk problem. We prove that the minimum environmental risk problem is equivalent to a mixed integer problem with a linear objective function. Two numerical results for the minimum environmental risk problem are presented.     

Managing the spread of alfalfa stem nematodes (Ditylenchus dipsaci): The relationship between crop rotation periods and pest reemergence

Alfalfa is a critical cash/rotation crop in the western region of the United States, where it is common to find crops affected by the alfalfa stem nematode (ASN) (Ditylenchus dipsaci). Understanding the spread dynamics associated with this pest would allow growers to design better management programs and farming practices. This understanding is of particular importance given that there are no nematicides available against ASNs and control strategies largely rely on crop rotation to nonhost crops or by planting resistant varieties of alfalfa. In this paper, we present a basic host‐parasite model that describes the spread of the ASN on alfalfa crops. With this discrete time model, we are able to portray a relationship between the length of crop rotation periods and the time at which the density of nematode‐infested plants becomes larger than that of nematode‐free ones in the postrotation alfalfa. The numerical results obtained are consistent with farming practice observations, suggesting that the model could play a role in the evaluation of management strategies.     

Modeling Optimal Allocation of Deficit Irrigation: Application to Crop Production in Saudi Arabia

We develop an integrated dynamic programming—linear programming (LP) model to solve for optimal land exploitation for a given crop. The model applies deficit irrigation in order to increase the irrigated area at the expense of reducing the crop yield per unit area. The dynamic program guarantees that deficit irrigation is considered only when it is economically efficient. Moreover, it provides the best irrigation level for each growth stage of the crop, accounting for the varying impact of water stress overtime. The LP provides the best tradeoff between expanding the irrigated area and decreasing water share per hectare. The model objective is to maximize the total expected crop yield. The model is particularly applicable for regions suffering from irrigation water scarcity, such as Saudi Arabia. The implementation was made for crops in Al-Jouf Region, north of Saudi Arabia     

Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: The case of Dutch arable farms

Pesticides’ dynamic effects and production uncertainty play an important role in farmers’ production decisions. Pesticides have a current production impact through reducing crop damage in the current period and a future impact through impacting the farm biodiversity which alters the future production environment. This study presents the difference in inefficiency arising from models that ignore the dynamic effects of pesticides in production decisions and the impact of production uncertainty. A dynamic data envelopment analysis (DEA) model is applied to outputs, inputs, and undesirables of Dutch arable farms over the period 2003–2007. A bootstrap approach is used to explain farmers’ performance, providing empirical representations of the impact of stochastic elements on production. These empirical representations are used to adjust firms’ inefficiency scores to incorporate production uncertainty in efficiency evaluation. We find that efficiency increased dramatically when a production technology representation that considers both pesticides’ dynamic impacts, and production uncertainty is adopted.     

MODELING OF A LANDLORD—TENANT AGRICULTURAL SYSTEM IN THE ENVIRONMENTAL CONTEXT

A hierarchical economic–environmental model is formulated to analyze the sustainable management of farming production in a landlord–tenant system. The problem under study is interdisciplinary and combines various agricultural, economic, social, and environmental factors. To maximize profit, the landlord chooses a rental payment and the duration of a lease contract. Tenants invest into farmyard fertilizer in order to improve future crop growth and maximize their profit. It is shown that the qualitative behavior of optimal trajectories in the landlord–tenant problem is mostly affected by environmental conditions rather than by the end‐of‐horizon and delay effects. A range of model parameters is identified, where the optimal trajectory coincides with the first‐best environmentally and socially efficient solution.     

SOLVING A CROP PROBLEM BY AN OPTIMAL CONTROL METHOD

ABSTRACT. A system of ordinary differential equations coupled with a parabolic partial differential equation is studied in order to understand an interaction between two crops and a pathogen. Two different types of crops are planted in same field in some pattern so that the spread of pathogen can be controlled. The pathogen prefers to eat one crop. The other crop, which is not preferred by the pathogen, is introduced to control the spread of pathogen in the farming land. The “optimal” initial planting pattern is sought to maximize plant yields while minimizing the variation in the planting pattern. The optimal pattern is characterized by a variation inequality involving the solutions of the optimality system. Numerical examples are given to illustrate the results.     

Generalized bilinear programming: An application in farm management

In this paper we analyze a model which arises from considering manure and fertilizer aspects in the farm management modelling for cattle husbandry. Modelling these aspects may lead to a multi-extremal problem with bilinear terms. Solution techniques for this problem are discussed and a specific branch and bound procedure is outlined. An example illustrates the multi-extremal structure of the problem and the solution methods mentioned.     

An integer programming dynamic farm-household model to evaluate the impact of agricultural policy reforms on farm investment behaviour

We develop a multi-objective farm-household dynamic integer programming model to simulate investment behaviour in different policy and price scenarios, with a particular focus on the decoupling of the Common Agricultural Policy (CAP). The model takes into account the characteristics of individual assets, including ageing and fixity through the explicit consideration of transaction costs. A case study application in the context of arable farming in Northern Italy is provided as an example. The results emphasise different patterns of reaction of different farm-household types over time, as an effect of the varying opportunity costs of resources and initial asset endowments. Overall, this application highlights the potentialities and limits of the methodology. In particular, the approach proved to be effective in providing a variety of results depending on the individual features of each farm-household, such as the differences between: (a) a ‘no reaction’ attitude; (b) an adaptation of farm activity and assets; and (c) a radical reaction pattern guided by high-income alternatives to farming. This highlights the potential of this tool as a generator of ideas and working hypotheses. We argue that, in view of the further developments of the CAP, the use of instruments able to account for multiple objectives, dynamics and investment choices will become even more relevant in the analysis of EU agricultural policy.     

Simulation and optimization for crop water allocation based on crop water production functions and climate factor under uncertainty

In this paper, the uncertainty methods of interval and functional interval are introduced in the research of the uncertainty of crop water production function itself and optimal allocation of water resources in the irrigation area. The crop water production functions in the whole growth period under uncertainty and the optimal allocation of water resources model in the irrigation area under uncertainty are established, and the meteorological factor is considered in the model. It can promote the practical application of the uncertain methods, reflect the complexity and uncertainty of the actual situation, and provide more reliable scientific basis for using water resources economically, fully improving irrigation efficiency, and keeping the sustainable development of the irrigated area. This approach has important value on theoretical and practical for the optimal irrigation schedule, and has very broad prospects for research and development to other related agriculture water management.     

DEVELOPMENT OF AN OPTIMAL WATER ALLOCATION DECISION TOOL FOR THE MAJOR CROPS DURING THE WATER DEFICIT PERIOD IN THE SOUTHEAST UNITED STATES

ABSTRACT. We developed a dynamic economic model to optimize irrigation water allocations during water deficit periods for three major crops grown in the humid southeastern United States. Analysis involved the use of crop simulation models to capture (a) the yield water relationship and (b) soil moisture dynamics from one week to another week. A hy‐drological model was used to find the water supply; combinations of hydrological and simulation models were used to find the optimal water allocation during each week in corn, cotton and peanuts. Results indicated that farmers should irrigate the most valuable crop first (peanuts) before applying water to other crops (corn and cotton). Results also showed that, because of restriction on total water supply, an increase in crop acreage did not increase the net revenue of the farm in a proportionate amount. Results should provide guidelines to water managers, engineers, policy makers, and farmers regarding an optimal amount of water allocation that will maximize net returns when water shortage is a serious concern.     

GLOBAL WARMING,IMPACT ON AGRICULTURE AND ADAPTATION STRATEGY

Abstract Sensitivity of the Southeastern US agriculture sector to temperature increases will be based largely on accompanying changes in precipitation, extent of the warming, and relative impact on competing crops grown in the area. The impact of climate change in 10 Southeastern US counties was investigated under three different climate scenarios for two different reference years (2030 and 2090). Seven major crops grown in the area were selected to study the impact on crop yield, irrigation acreage, and optimal choice of crops in a representative farm in each of the southeastern states. If warming is moderate and also brings a considerable increase in precipitation—as indicated by the Hadley model—then, the effect on yields, water use, and income will be mostly benign. If warming is moderate without increased precipitation and the water for irrigation is available, then the effects on the agriculture sector are still mostly negligible. If warming is not moderate and no increased precipitation materializes, farmers could realize quite dramatic negative consequences for row crop agriculture in the Southeastern United States.     

A stochastic planning framework for the discovery of complementary,agricultural systems

One of the greatest 21st century challenges is meeting the need to feed a growing world population which is expected to increase by about 35% by 2050. To meet this challenge, it is necessary to make major improvements on current food production and distribution systems capabilities, as well as to adapt these systems to expected trends such as climate change. Changing climate patterns may present opportunities for unidentified, geographical regions with adequate climate patterns to produce high-value agricultural products in a profitable and sustainable manner.This paper focuses on the design and planning aspects of a discovery process to unearth agri-food supply chains capable of generating attractive return on investments. A stochastic optimization framework is used to develop planting and harvesting schedules for a set of identified regions with complementary weather characteristics. To address the high-level of variability in the problem context, a two-stage stochastic decomposition method is used to consider a larger number of scenarios. As part of the solution process, a modeling scheme is developed that learns past interactions between entering discretized, weather scenarios and optimal first-stage solutions. In this context, machine learning and dimensionality reduction techniques are used to iteratively estimate each region's probability of belonging to first-stage solutions based on previous solution-scenario results. The implementation of the stochastic framework is shown through a case study applied to multiple locations within the US southwest states of Arizona and New Mexico.     

RISK-BASED MULTIATTRIBUTE DECISION-MAKING IN PROPERTY AND WATERSHED MANAGEMENT

ABSTRACT. Determining best management systems for properties and evaluating their sustainability at the watershed scale are useful and important aspects of integrated watershed management. Multiattribute decision-making (MADM) is very useful for modeling the selection of best management systems for properties in a watershed. This paper reviews four MADM approaches including utility theory, surrogate worth tradeoff, free iterative search and stochastic dominance with respect to a function (SDWF). Emphasis is on determining how the first three methods could be used to determine the best (most preferred) combinations of attributes and associated management systems for a property. An application of the expected utility method with risk neutral preferences is presented in which farmer's preferences for five attributes are used to rank five farming systems for an agricultural watershed in Missouri. A framework is presented for assessing the sustainability of the best management systems for all properties in a watershed and the cost-effectiveness of policies for enhancing sustainable resource management at the watershed scale.     

Two Way Coupled Micro/Meso Scale Method for Wind Farms

Wind turbines extract energy from the approaching flow field resulting in reduced wind speeds, increased turbulence and a wake downstream of the wind turbine. The wake has a multitude of negative effects on downstream wind turbines. This includes reduced efficiency and increased unsteadiness resulting in vibrations and potentially in material fatigue. Moreover, the maintenance can increase compared to non-interfering wind turbines. The simulation of these effects is challenging. Computational fluid dynamics (CFD) simulations of these large and complex geometries requires exceedingly large computational resources. With present Reynolds Averaged Navier-Stokes (RANS) or Large Eddy Simulation (LES) based CFD methods it is virtually impossible to perform such simulations of the interaction between individual wind turbines in a complete wind turbine farm. Coupling to the mesoscale accounting for local weather situations becomes yet more challenging. This is due to the wide range of length and time scales that have to be considered for these simulations and therefore the tremendous computational power needed to perform such simulations. To investigate these effects we propose to combine ideas from existing methods, the Coarse-Grid-CFD (CGCFD) ( [1]) developed at the KIT and the meso-/ micro scale method developed at the University of Thessaloniki ( [2]). Goal of the proposed methodology is to provide a numerical method that allows to implement a wind farm in a meso-scale weather simulation which includes two-way coupling. Thus both the micro and the meso scale wind and energy production of wind farms can be addressed. This proposed multi scale coupling strategy can also be applied in two hierarchies reducing the numerical effort of the global approach yet more. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Stochastic Linear Programming Model for Corn Residue Production

This paper presents the results of a stochastic linear program for estimating the supply of corn residue for use as raw material in an ethanol plant. The model is based on the production capacity of an average Illinois farm, and considers the feasibility of three mutually exclusive residue harvesting alternatives-own baling, custom baling, and cob collection. Since the potential for residue use in animal feed may be even more promising, these results are directly useful for the feed industry. They also indicate the profitability of investing in residue harvesting equipment. From a methodological point of view, the paper contrasts the results of three OR approaches, a deterministic LP approach, a stochastic LP approach, and a chance-constrained approach. Because of the stochastic nature of the problem both Monte Carlo simulation and chance-constrained programming are found to be computationally viable, even though they differ in the way they incorporate risk information.