Are we risking too much? Perspectives on risk in farm modeling

Risk and uncertainty have been extensively studied by agricultural economists. In this paper we question (a) the predominant use of static frameworks to formally analyse risk; (b) the predominant focus on risk aversion as the motivation for considering risk and (c) the notion that explicitly probabilistic models are likely to be helpful to farmers in their decision making. We pose the question: for a risk‐averse farmer, what is the extra value of a recommendation derived from a model that represents risk aversion, compared to a model based on risk neutrality? The conclusion reached is that for the types of the decision problems most commonly modelled by agricultural economists, the extra value of representing risk aversion is commonly very little.     

Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies

A model of farmer decision making is developed to determine the extent to which uncertainties about soil fertility and weather affect the value of site-specific technologies (SSTs) using jointly estimated risk and technology parameters. Uncertainty can lead risk-averse farmers to apply more fertilizers and generate more pollution than in the certainty case. Ignoring uncertainty and risk aversion would overestimate the economic and environmental benefits of SSTs and underestimate the subsidy required to induce adoption. Accounting for uncertainties and risk preferences might explain the low observed adoption rates of SSTs. Improving the accuracy of SSTs would increase the incentives for adoption.     

Risk,learning, and technology adoption

This article explores how decision makers learn and use information, with an application to the adoption of biotechnology in agriculture. The empirical analysis relies on experimental and survey data measuring risk preferences, learning processes, and the adoption of genetically modified (GM) seeds among U.S. grain farmers. While controlling for risk aversion, we link individual learning rules with the cognitive abilities of each decision maker and their actual GM adoption decisions. We find evidence that very few individuals are Bayesian learners, and that the population of farmers is quite heterogeneous in terms of learning rules. This suggests that Bayesian learning (as commonly assumed in the analysis of agricultural technology adoption) is not an appropriate characterization. In addition, we do not find a strong relationship between observed learning styles and the timing of GM seed adoption. To the extent that learning is a key part of the process of technology adoption, this suggests the presence of much unobserved heterogeneity in learning among farmers.     

The impact of water price uncertainty on the adoption of precision irrigation systems

We examine whether a firm is more or less likely to adopt precision technology when input prices are stochastic. The results are important to determining whether programs and contracts that reduce input price uncertainty may deter the adoption of conservation practices. An economic model of the technology adoption decision shows that the net effect of input price risk is ambiguous and depends on several factors including the shutdown effect, the mean price effect, the precision expansion effect, and the risk aversion effect. An empirical implementation of the model relies on data on water price and irrigation technology adoption observed in a California irrigation district over the period 1999–2002. The results show that a stable input price increases the adoption of precision technology, but the impact depends on crop choice and land quality characteristics.     

Farmer responses to changing risk aversion,enterprise variability and resource endowments

The focus of this article is on assessing how risk aversion, enterprise variability and resource endowments affect farm land‐use decisions and economic returns. A theoretical model of a two‐enterprise, two‐constraint farm is developed, and then, an empirical illustration for an Australian farm is provided. The methodology used builds on previous expected mean‐variance (EV) models by incorporating land and budget constraints. The Kuhn–Tucker conditions of the EV model are examined to highlight that changes in resource endowments have larger effects on economic returns, than do changes in risk aversion or enterprise gross margin variability. It was also found that combinations of enterprise mixes that do not use all available resources can produce higher economic returns, relative to some enterprise mixes that use all available resources.     

Motivations to grow energy crops: the role of crop and contract attributes

Perennial energy crops are a promising source of bioenergy whose production involves production risks, long‐term commitment of land and need for crop‐specific investments without the coverage of crop insurance potentially available for conventional crops. We conduct a choice experiment in five states in the Midwestern and South‐central regions of the U.S. to examine the effect of crop‐contract attributes on the joint discrete‐continuous choice decisions to adopt an energy crop and convert acres to it from a status quo use, while controlling for the effect of various farmers’ risk and time preferences, sociodemographic characteristics, and availability of crop insurance for conventional crops. We find robust evidence that high discount rates, high upfront establishment costs and need for crop‐specific investments create disincentives for adoption and allocation of land to energy crop production. The effects of riskiness of returns and risk aversion are less robust across specifications. The effect of conventional crop insurance on the energy crop adoption decision differs across types of insurance; in particular, farmers with revenue insurance are statistically significantly less likely to adopt an energy crop. Our results have implications for the design of effective contracts and policy incentives to induce the production of energy crops.     

Incentive Perception in Livestock Disease Control

The economic incentives facing people making decisions about infectious disease control have been given due theoretical consideration in the literature, based on principles of economic rationality. Such deductive models provide important tools for generating hypotheses. However, the application of such models in a predictive capacity has been criticised. Simultaneously, empirical studies aimed at quantitative exploration of farmer behaviour have relied heavily on social cognitive models, such as the theory of planned behaviour, without exploration of the epidemiological consequences of variability in behaviour within populations. Advances in other social sciences have revealed systematic biases in human reasoning which cast doubt on the validity of the rational economic model as a generalisation of human decision making. We review the characteristics of infectious disease and disease‐control interventions and the potential for bias in implementation decision making at primary producer level. Specific focus is given to the generation of externalities, both positive and negative; the perception of risk, relating to disease incidence, technology adoption and time preference; and finally uncertainty, and its potential to be moderated by trust in information sources. This information is then used to summarise supplemental psychological constructs which taken holistically may strengthen our ability to quantitatively explore human behaviour in this complex decision‐making environment.     

Pests and pesticides,risk and risk aversion

Theoretical and applied literature on risk in decision making for agricultural pest control is reviewed. Risk can affect pesticide decision making either because of risk aversion or because of its influence on expected profit. It is concluded that risk does not necessarily lead to increased pesticide use by individual farmers. Uncertainty about some variables, such as pest density and pest mortality, does lead to higher optimal pesticide use under risk aversion. However, uncertainty about other important variables, such as output price and yield, leads to lower optimal levels of pesticide use. Neglect of these variables in most studies has led to the false assumption that pesticides are always risk-reducing inputs. Furthermore, there is evidence that, in general, the pesticide dosage which maximises expected profit is lower under risk than under certainty. Depending on the balance of forces to increase and decrease pesticide use under risk, in many circumstances the net effect of risk on optimal decision making for pest control may be minimal. The effect on risk of information about pest density and other variables (as in integrated pest management programmes) is discussed. Evidence on this issue is mixed. A range of analytical techniques for analysing risk in pest control is reviewed. Throughout the paper, gaps in the existing literature are identified.     

Social capital,risk preference and adoption of improved farm land management practices in Ethiopia

Many developing countries grapple with high rates of farmland degradation and low agricultural productivity amidst increasing climate variability. Considerable efforts have been exerted to promote the diffusion of improved farmland management to address these challenges. Despite these efforts, adoption rates, especially of soil conservation and water harvesting technologies, are still low, which has been the subject of investigation in several studies in Ethiopia and elsewhere. Most studies on the adoption of these technologies, however, tend to focus on economic incentives only, paying little attention to the role of social capital. This article provides evidence of the effects of different dimensions of social capital on innovation adoption across households holding different levels of risk aversion. We address this issue by using cross section and panel data from Ethiopia. Results show that social capital plays a significant role in enhancing the adoption of improved farmland management practices. We also find evidence that the effect of social capital across households with heterogeneous risk taking behavior is different.     

Non-parametric estimation of decision makers' risk aversion

A new non‐parametric method to estimate a decision maker's coefficient of absolute risk aversion from observed economic behaviour is explained. The method uses the expected value‐variance (E‐V) framework and quadratic programming. An empirical illustration is given using Norwegian farm‐level data.     

More reasons why farmers have so little interest in futures markets

The use by farmers of futures contracts and other hedging instruments has been observed to be low in many situations, and this has sometimes seemed to be considered surprising or even mysterious. We propose that it is, in fact, readily understandable and consistent with rational decision making. Standard models of the decision about optimal hedging show that it is negatively related to basis risk, to quantity risk, and to transaction costs. Farmers who have less uncertainty about prices and those with a diversified portfolio of investments have lower optimal levels of hedging. If a farmer has optimistic price expectations relative to the futures market, the incentive to hedge can be greatly reduced. And finally, farmers who have low levels of risk aversion have little to gain from hedging in terms of risk reduction, in that the certainty‐equivalent payoff at their optimal hedge may be little different than the certainty equivalent under zero hedging. These reasons are additional to the argument of Simmons (2002) who showed that, if capital markets are efficient, farmers can manage their risk exposure through adjusting their leverage, obviating the need for hedging instruments.     

Perverse supply response in peasant agriculture: A review

Abstract

The long‐running debate concerning the special characteristics of peasant production in less developed countries which may lead to perverse supply responses in their agricultural sectors is explored. Four stages in the debate are identified. The first was based on casual observation and the target income or fixity‐of‐wants hypothesis. The second took account of peasant own‐consumption and focused on the marketed surplus. The third addressed the possible effect of uncertainty and risk aversion on supply response. The fourth is embodied in modern farm household models of peasant behaviour. Although the predictions regarding supply response derived from these models vary, they all suggest that agricultural supply response may be negative. The bulk of empirical evidence, however, for both total production and marketed surplus tends to refute the notion, whether it is theoretically consistent or not, that supply response in peasant agriculture is negative.     

A conceptual framework of adoption of an agricultural innovation

In this paper we present a conceptual framework of individual farmers' decisions on adoption of a new innovation, using the example of a new crop species. This framework overcomes the shortcomings of a number of previous studies. It represents the adoption of an innovation as a dynamic decision problem spanning at least several years. The model allows for generation of potentially valuable information from trialing the innovation. The value of such trials is due to development of skills (e.g. in agronomic management of a crop) as well as reduction in uncertainty about the innovation's long-term profitability. The framework also includes the farmer's personal perceptions, managerial abilities and risk preferences in order to properly represent the adoption decision process. The influences of socio-demographic factors within the framework are discussed.     

Production risk,risk aversion and the determination of risk attitudes among Spanish rice producers

Agricultural production is subject to risk and the attitudes of producers toward risk will influence input choices insofar as these affect production risk. Risk attitudes in turn may be affected by certain socioeconomic characteristics of producers. Using 2004 survey data from a cross‐section of 130 Spanish rice farms, we estimate risk‐aversion coefficients of farmers and investigate the influence of a series of socioeconomic variables on their risk attitudes. Our results show that farmers exhibit risk‐averse behavior and that risk attitudes are related to a series of socioeconomic characteristics. In particular, the belief that the farm will continue after the producer retires is found to increase the degree of risk aversion, while age is found to have nonlinear effects on risk aversion. Off‐farm income, especially from nonagricultural activities, is found to reduce risk aversion. Neither the educational level of the producer nor the presence of dependents on the household is found to have an effect on risk preferences. Regarding the production technology, we find that land, labor, and fitosanitary products are risk‐reducing inputs, whereas capital, seeds, and fertilizer all increase risk.     

RISK-EFFICIENT THRESHOLDS FOR PEST CONTROL DECISIONS

Risk and uncertainty are incorporated in an economic model to develop risk-efficient pest control advice similar in character to advice currently being offered by researchers to farmers. An example to illustrate computation of risk-efficient advice is presented and implications of risk aversion for efficient pest control advice and average pesticide use are examined.     

Home Bias in Primary Agricultural and Processed Food Trade: Assessing the Effects of National Degree of Uncertainty Aversion

This study investigates the effects of national degrees of uncertainty aversion (unfamiliarity avoidance) on the magnitude of bias towards domestic products rather than imports. The empirical analysis is implemented for primary agricultural and processed food products, using a panel dataset covering trade between and within OECD countries. Primary agricultural products are often blended and associated with reference prices. Conversely, processed food products exhibit higher levels of product differentiation. The empirical results confirm expectations by emphasizing the magnifying effects of uncertainty aversion on home bias in the case of processed food products but not in the case of primary agricultural products. These magnifying effects are primarily associated with processed food products destined for final household consumption. Other results reveal significant variations between different countries (based on geo‐economic and national income categories). Our results also indicate that home bias and uncertainty aversion effects on home bias have not decreased over time. The empirical results remain robust under different estimation methods.     

Adoption of climate‐smart practices and its impact on farm performance and risk exposure among smallholder farmers in Ghana

Increased climate variability during the last four decades has made the agricultural environment in many developing countries more uncertain, resulting in increasing exposure to risk when producing crops. In this study, we use recent farm‐level data from Ghana to examine the drivers of individual and joint adoption of crop choice and soil and water conservation practices, and how adoption of these practices impacts on farm performance (crop revenue) and exposure to risks (skewness of crop yield). We employ a multinomial endogenous switching regression model to account for selectivity bias due to both observable and unobservable factors. The empirical results reveal that farmers’ adoption of crop choice and soil and water conservation leads to higher crop revenues and reduced riskiness in crop production, with the largest impact on crop revenues coming from joint adoption. The findings also show that education of the household head, access to extension and weather information influence the likelihood of adopting these practices. Thus, enhancing extension services and access to climate information and irrigation can reduce gaps in adoption of soil and water conservation and crop choice, considered as climate‐smart practices that will eventually improve crop revenues and reduce farmers’ exposure to climate‐related production risks.     

Adoption of weather‐index insurance: learning from willingness to pay among a panel of households in rural Ethiopia

In this article we examine which farmers would be early entrants into weather‐index insurance markets in Ethiopia, were such markets to develop on a large scale. We do this by examining the determinants of willingness to pay for weather insurance among 1,400 Ethiopian households that have been tracked for 15 years as part of the Ethiopian Rural household Survey. This provides both historical and current information with which to assess the determinants of demand. We find that educated, wealthier individuals are more likely to purchase insurance. Risk aversion is associated with low insurance take‐up suggesting that models of technology adoption can inform the purchase and spread of weather index insurance. We also assess how willingness to pay varied as two key characteristics of the contract were varied and found that basis risk reduces demand for insurance particularly when the price of the contract is high, and that provision of insurance through groups is preferred by female headed households and individuals with lower levels of education.     

Spatial Economic Analysis in Data‐Rich Environments

Controlling for spatial effects in micro‐level studies of consumer and producer behaviour necessitates a range of analytical modifications. These range from modest changes in data collection and the definition of variables to dramatic changes in the modelling of consumer and producer decision‐making. This paper discusses conceptual, empirical and data issues involved in modelling the spatial aspects of economic behaviour in data‐rich environments. Attention is given to established and emerging agricultural economic applications of spatial data and spatial econometric methods at the micro‐scale. Recent applications of individual and household data are featured, including models of land‐use change at the urban–rural interface, agricultural land values, and technological change and technology adoption.     

Moral Hazard and Risk Management in Agri‐environmental Policy

This paper develops the key finding of Ozanne, Hogan and Colman (2001) that risk aversion among farmers ameliorates the moral hazard problem in relation to agrienvironmental policy compliance. It is shown that risk averse farmers who face uncertainty in their production income are more likely to comply with such a policy as a means of risk management. In addition, it is shown that a principal who has control over both the level of monitoring and the size of penalty, if detected, can reduce non‐compliance by adjustments to these instruments which increase the variance of farmers' income but leave the expected penalty unchanged. It is concluded that risk management by both principals and agents has the potential to diminish the moral hazard problem, especially given proposed developments in agri‐environmental policy in the European Union.