Valuing seasonal climate forecasts in a state‐contingent manner

We applied state‐contingent theory to climate uncertainty at a farm level to assess the value of seasonal climate forecasts in the Central West region of NSW. We find that modelling uncertainty in a state‐contingent manner results in a lower estimate of forecast value than the typical expected value approach. We attribute this finding to a more conservative long‐term farm plan in the discrete stochastic programming (DSP) model, which is better balanced for climate uncertainty. Hence, a climate forecast, even though it still revises probabilities held by farmers, does not call forth such large changes in farm plans and associated farm incomes. We then use the DSP model to assess how attributes of a hypothetical forecasting system, particularly its skill and timeliness, as well as attributes of the decision environment, influence its value. Lastly, we assess the value of current operational forecast systems and show that the value derived from seasonal climate forecasts is relatively limited in the case study region largely because of low skill embodied in forecasts at the time when major farm decisions are being made.     

Drought policy: a graphical analysis*

The standard approach to modelling production under uncertainty has relied on the concept of the stochastic production function. In the present paper, it is argued that a state‐contingent production model is more flexible and realistic. The model is applied to the problem of drought policy.     

State‐contingent analysis of farmers’ response to weather variability: irrigated dairy farming in the Murray Valley,Australia

The agricultural sector is commonly regarded as one of the most vulnerable to climate change. Current understanding of the impact of climate change on this sector relies on the underlying assumptions about farmers’ possible responses to weather variability, including changes in crop choice, input combinations and land management practices. Many previous analyses rely on the implicit (and restrictive) assumption that farmers operate under a fixed technology set across different states of nature. This assumption, represented through stochastic production or profit functions, is commonly made but seldom tested and may understate farmers’ responses to climate change if state‐contingent production technologies are, in reality, more flexible. The potential for farmers to adapt production technologies in response to unforeseen events is at the core of the state‐contingent approach. Advanced in Chambers and Quiggin (2000), the theory contends that producers can manage uncertainty through the allocation of productive inputs to different states of nature. In this article, we test the assumption that farmers’ observed behaviour is consistent with the state‐contingent production theory using farm‐level data from Australia. More precisely, we estimate the milk production technology for a sample of irrigated dairy farms from the southern Murray–Darling Basin over the period from 2006–2007 to 2009–2010.     

Efficiency analysis under uncertainty: a simulation study

We model production technology in a state‐contingent framework assuming that the firms maximise ex ante their preference function subject to stochastic technology constraint; in other words, firms are assumed to act rationally. We show that rational producers who face the same stochastic technology can make significantly different production choices. Further, we develop an econometric methodology to estimate the risk‐neutral probabilities, efficiency scores and the parameters of stochastic technology when there are two states of nature and only one of which is observed. Finally, we simulate noiseless data based on our state‐contingent specification of technology. Our state‐contingent estimator recovers technology parameters and other economic quantities of interest without any error. But, when we apply conventional efficiency estimators to the simulated data, we obtain biased estimates of technical efficiency.     

Determinants of the Willingness of Belgian Farmers to Participate in Agri‐environmental Measures

This paper explores the willingness of Belgian farmers to participate in two voluntary agri‐environmental policies. Farmers' contingent behaviour is analysed on the basis of survey data. Derivations based on a conceptual micro‐economic model indicate that decision subject and decision maker characteristics are important for farmers' participation. The model is empirically tested through the specification and estimation of a probit model. Consistency is found between the theoretical framework and the empirical results indicating that both the expected effect on farm production and the farmers' environmental attitude, which is more positive among younger and better educated farmers, are significant determinants of the acceptance rate of agri‐environmental policies. Other variables which influence participation decisions are farm size and previous experience of farmers themselves or of neighbouring farmers with agri‐environmental measures.     

Firm behaviour under uncertainty: a simple parametric model

In this paper, we model production technology in a state‐contingent framework. We assume that all the firms use the same stochastic technology, but they may have different risk attitudes and information sets, and ex post they may operate in different production environments. Firms maximise ex ante their preference function subject to a stochastic technology constraint; in other words, they are assumed to act rationally, thereby leaving no room for either technical or allocative inefficiency. We provide a simple parametric functional form to represent the state‐contingent technology. Using simple numerical examples, we illustrate how optimal input–output choices are dramatically affected when firms have different preferences and information sets. Thus, we show that the observed disparateness of production choices among different firms can actually be attributed to the stochastic nature of the decision environment.     

Production economics in the presence of risk*

This paper provides an overview of the literature on production under the influence of risk. Various specifications of stochastic production function such as models with additive and multiplicative uncertainty, Just and Pope model, output‐cubical, state‐allocable and state‐general models are discussed. Further, criteria determining optimal producer behaviour are derived for deterministic production technology and for various kinds of state‐contingent technologies such as output‐cubical, state‐specific, state‐allocable and state‐general technologies. Finally, a brief discussion is presented about the drawbacks of each of these specifications of technology.     

The Role of Risk Mitigation in Production Efficiency: A Case Study of Potato Cultivation in the Bolivian Andes

Using a stochastic production frontier to model potato production in Bolivia, we quantify the costs of environmental and activity diversification (AD) in the form of efficiency losses and yield forgone. We find that efficiency decreases with the number of fields in a geographical cluster, distance between the dwelling and a particular field, discontinuity between fields, and off‐farm income. However, environmental diversification (ED) is more detrimental than AD. Using spatial analysis of field and household efficiency measures, we assess production vulnerability to climatic shocks and the potential of ED in mitigating shocks. We find important spatial clusters of low and high efficiencies at the field level suggesting that climatic shocks influence efficiency measures. Household‐level efficiency measures exhibit random spatial patterns suggesting that on average households can mitigate the adverse effects of shocks through ED.     

Farms’ technical inefficiencies in the presence of government programs*

We focus on determining the impacts of government programs on farms’ technical inefficiency levels. We use Kumbhakar's stochastic frontier model that accounts for both production risks and risk preferences. Our theoretical framework shows that decoupled government transfers are likely to increase (decrease) DARA (IARA) farmers’ production inefficiencies if variable inputs are risk decreasing. However, the impacts of decoupled payments cannot be anticipated if variable inputs are risk increasing. We use farm‐level data collected in Kansas to illustrate the model.     

Off‐farm work and technical efficiency on U.S. dairies

In this study, we investigate the impact of an increase in farm households’ off‐farm work on technical efficiency (TE) of U.S. dairies. We present a theoretical framework that implies that an increase in farm household off‐farm work decreases TE. We use two nationally‐representative samples of U.S. dairies (2010 and 2005 Agricultural Resource Management Surveys) and a parametric approach (stochastic frontier analysis) to empirically test the hypothesis. Results are generally consistent with the hypothesis and show that an increase in off‐farm work by the farm household is associated with a significant decrease in TE. In addition, results show that there is a statistically significant difference in TE between small, medium, and large farms. Small farms are associated with significantly higher off‐farm work and have lower TE than large farms, which implies that less off‐farm work by households with larger farms is at least partially responsible for the evidence of economies of scale in the U.S. dairy industry.     

The state‐contingent approach to production under uncertainty*

The central claim of this paper is that the state‐contingent approach provides the best way to think about all problems in the economics of uncertainty, including problems of consumer choice, the theory of the firm, and principal–agent relationships. This claim is illustrated by recent developments in, and applications of, the state‐contingent approach.     

Off‐farm work,technical efficiency,and rice production risk in Taiwan

This article investigates the differences in yield production, production efficiency, and yield risk for farmers both with and without off‐farm work. Using a nationwide survey of rice farmers in Taiwan, we estimate two stochastic production frontier models that accommodate technical inefficiency and production risk simultaneously for farmers both with and without off‐farm work. The stochastic dominance criterion is then applied to compare the differences in the distributions of the estimated technical efficiency and yield risk between groups. The empirical results indicate that these two groups of farmers use resources in different ways, and off‐farm work is not necessarily associated with lower technical efficiency. For farmers in the lower percentiles of the efficiency distribution, those with off‐farm work are more efficient than their counterparts without off‐farm work. In addition, farmers with off‐farm work face higher production risk and this result is robust for the entire distribution.     

Estimating Technical Efficiency and Production Risk under Contract Farming: A Bayesian Estimation and Stochastic Dominance Methodology

We investigate production risk, technical efficiency and risk attitudes amongst contract and independent farmers. We use a Bayesian parametric approach and stochastic dominance quantile regression methods to compare technical efficiency and risk attitude of smallholders in Nepal. Using farm‐level data, we find that contract farmers appear to show lower inefficiency and lower production risk. Additionally, contract and independent farmers can increase output by reducing the scale of operation. Regardless of the commodity produced and farming arrangement (contract or independent production), we find that labour, land and other inputs are risk‐augmenting, while the role of capital is mixed. We find a second order stochastic dominance (SSD) for lentils, and first order stochastic dominance (FSD) for tomatoes, ginger and HYV paddy seed commodities. Finally, contract farmers are more risk averse than independent farmers, regardless of the commodity produced.     

Event‐specific data envelopment models and efficiency analysis*

Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state‐contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition the state space in a fashion that permits DEA models to approximate an event‐specific production technology. The approach proposed in this article uses observed data on random inputs and is easy to implement. After developing the event‐specific DEA representation, we apply it to a data set for Western Australian barley production data. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.     

Modeling producer responses with dynamic programming: a case for adaptive crop management

Past research found agricultural producers’ conditional responses during the growing season are important adaptations to weather and other stochastic events. Failing to recognize these responses overstates the risks confronting producers and understates their ability to respond to adverse circumstances. Dynamic programming (DP) provides a means for determining optimal long‐term crop management plans. However, most applications in the literature base their analysis on annual time steps with fixed strategies within the year, effectively ignoring conditional responses during the year. We suggest an alternative approach that captures the strategic responses within a cropping season to random weather variables as they unfold, reflecting farmers’ ability to adapt to weather realizations. We illustrate our approach by applying it to a typical cereal farm in Karak, Jordan. The results show that including conditional within‐year responses to weather reduces the frequency of fallowing by 23% and increases expected income by 9%.     

Evaluating the Potential of Index Insurance Schemes to Reduce Crop Yield Risk in an Arid Region

We evaluate yield risk reduction through weather index, area yield index and farm yield insurance contracts for wheat farms in Kazakhstan by employing data from 1980 to 2002. We use the usual mean variance (MV) approach and also a second‐degree stochastic dominance (SSD) criterion. While MV is not necessarily consistent with the expected utility (EU) theory, SSD results only in a minimum but EU‐consistent benefit from insuring. Differences in the estimation results for both approaches underline the advantage of applying both criteria to analyse the risk‐reducing potential of crop insurance. Bootstrapping results show that none of the analysed insurance schemes provides statistically significant risk reduction for every single farm. In addition, weather‐based index insurance is found to provide less risk reduction than area yield insurance based on the rayon (county) yield. Moreover, rayon yield index insurance can reduce yield risk more effectively for Kazakhstan's wheat producers than farm yield insurance with a low strike yield.     

Measuring farm productivity under production uncertainty

This research introduces a novel empirical application to the assessment of farm productivity growth. While the existing research on productivity change has primarily focussed on ex post output observations, it has been shown that ignoring production uncertainty can lead to unreliable results. Using a state-contingent framework to represent the stochastic production environment, we extend the recent line of research that merged the state-contingent approach and efficiency measurement to productivity change using the Malmquist and Luenberger productivity indices. Using a balanced panel of 117 arable crop farms surveyed in 2011 and 2015, we show through the study results that productivity decreased, with technological regress being the major source of productivity change. Differences in productivity change between nonstochastic and stochastic modelling show the relevance to consider the state-contingent framework when assessing farms' productivity.     

Production Risk and Technical Efficiency in Organic and Conventional Agriculture – The Case of Arable Farms in Germany

This paper quantifies the importance of production risk and technical efficiency as two possible sources of production variability in German organic and conventional farming. Determinants of production risk and inefficiency are investigated based on a combination of Just and Pope’s stochastic production framework and a Stochastic Frontier Analysis. The empirical analysis is conducted using a balanced panel of farm records from 1999/2000 to 2006/2007 on 37 organic and conventional arable farms, respectively. Euclidian‐Distance‐Matching is used to identify for each organic farm a conventional counterpart with similar structural features. Results indicate that output variability in both production technologies is mainly caused by production risk. Land and labour are identified as risk‐increasing inputs in both farm types whereas higher capital endowment, seed costs and soil quality have risk‐reducing effects.     

The impact of decoupled subsidies on productivity in agriculture: a cross‐country analysis using microdata

The decoupling of direct payments from production introduced in the reform of the Common Agricultural Policy is expected to make production decisions more market‐oriented and farmers more productive. However, ex‐post analyses of the productivity of farms have yet to uncover any evidence of a positive impact of the decoupling policy on farm productivity. Using Irish, Danish, and Dutch farm‐level data, we identify whether the decoupling policy has contributed to productivity growth in agriculture and farm product adjustment behavior. We find some evidence that the decoupling policy had significant positive effects on farm productivity and behavioral changes related to farm specialization.     

Effect of changes in population density and crop productivity on farm households in Malawi

This study examines the ex‐ante farm‐household effects of changes in family size, yield potential, and yield gaps using a farm‐household simulation model that reflects the economic and biophysical conditions of central Malawi. Disparities between growth in human population and crop yields present challenges for farm‐household crop production and income in sub‐Saharan Africa. We focus on the effect of growth in yield potential and a more efficient use of livestock manure as approaches to improving crop production and incomes in the face of looming population pressures. Our results suggest that, even without considering climate change, expected changes in population density and crop prices in 2050 mean that per person crop production and income may fall by 21% compared to 2013 values if yield potential and yield gaps remain constant. However, per person crop production and income could increase in 2050 by 8% compared to 2013 values if (1) growth rates of yield potential rise for maize by 1.13% each year and for legumes rise by at least 0.4% each year, and (2) farmers use livestock manure more efficiently. Our foresight approach to considering crop production at the farm‐household scale supplements macro‐scale analyses of the production dimension of food security.