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

Pannell, David J.; Malcolm, Bill; Kingwell, Ross

doi:10.1016/s0169-5150(00)00058-x

Cited by 91 publications

(76 citation statements)

References 44 publications

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“…At the prevailing economic conditions, the main solution determinant was not the farmer's risk aversion, but other factors and constraints in the organic dairy system. These results support some previous studies that show the cost of ignoring risk aversion may be small in short-run decision problems in farming (e.g., Pannell et al, 2000;Lien and Hardaker, 2001). …”

Section: ( )supporting

confidence: 91%

“…For the farmer, the main issue raised by variability of price and production is how to respond tactically and dynamically to opportunities or threats to generate additional income or to avoid losses (i.e. how to respond after the outcome of a random variable is observed) (Pannell et al, 2000). Some studies of conventional farming systems have used stochastic programming with recourse to deal with this aspect (e.g., Kaiser and Apland, 1989;Kingwell, 1994;Torkamani and Hardaker, 1996;Pannell and Nordblom, 1998;Lien and Hardaker, 2001;Torkamani, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Stochastic utility-efficient programming of organic dairy farms

Flåten¹,

Lien²

2007

European Journal of Operational Research

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Opportunities to make sequential decisions and adjust activities as a season progresses and more information becomes available characterise the farm management process. In this paper, we present a discrete stochastic two-stage utility efficient programming model of organic dairy farms, which includes risk aversion in the decision maker's objective function as well as both embedded risk (stochastic programming with recourse) and non-embedded risk (stochastic programming without recourse). Historical farm accountancy data and subjective judgements were combined to assess the nature of the uncertainty that affects the possible consequences of the decisions. The programming model was used within a stochastic dominance framework to examine optimal strategies in organic dairy systems in Norway.

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Section: ( )supporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Stochastic utility-efficient programming of organic dairy farms

Flåten¹,

Lien²

2007

European Journal of Operational Research

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“…Since agricultural production occurs in a risky environment, there is a need to make decisions on how to manage the risk. Until mid1990s, priority with respect to analyzing risky decisions has been placed mostly on choice of farming strategy and on accounting for the effects of attitude to risk [32,33].…”

Section: Risk Managementmentioning

confidence: 99%

“…The benefits of including tactical response options in a farm model are often greater than the benefits of including risk aversion were hypothesized by Pannell et al [33]. The importance for strategic choice of accounting for the opportunities to tactically respond to outcomes of risk provided by each strategy has attracted attention [43].…”

Section: Assessing Risky Alternativesmentioning

confidence: 99%

“…The work by Pannell et al [33] hypothesized that the benefits of including tactical response options in a farm model are often greater than the benefits of including risk aversion. This is in line with studies by Kingwell et al [32] and Marshall et al [44], Kingwell et al [32] found that modeling tactical adjustments resulted in the identification of an optimal farming strategy expected to be 20.0% more profitable on average than the strategy that would have been identified considering a non-tactical approach.…”

Section: Embedded Riskmentioning

confidence: 99%

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Production and Marketing Risks Management System in Grazed Systems: Destocking and Marketing Algorithm

Gicheha¹,

Edwrads²,

B³

et al. 2017

Theory and Application on Cognitive Factors and Risk Management - New Trends and Procedures

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This study was carried out to explore potential approaches to managing production and market risks associated with climatic variability in dryland grazed systems. The methodology is novel in that it considers farmers' ability to make sequential adjustments to their production activities when information on uncertain events becomes available. Traditional approaches to evaluation of farmers' response to risk assume perfect knowledge of production resources and that risk emanates from uncertainty in yield returns. Strategic approaches are mostly considered in evaluating farmer's risk attitude implying that managing the variability (risk) assumes that different production activities resource requirements are known (non-embedded risk). In real farming systems, the producers make sequential decisions and adjust the timing and methods of their activities as a season progresses and more information on uncertainty becomes available (embedded risk). This chapter describes a platform adopted in making destocking and marketing decisions by simulating the impact of implementing alternative tactical adjustments. The algorithm was successfully tested in a research that investigated the physical and economic impact of incorporating tactical responses in risk management strategies in dryland sheep production systems in New Zealand. The algorithm can be integrated into existing grazing models and can also be used as a standalone system.

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Marketing contracts and risk management for cereal producers

Roussy¹,

Ridier

Chaib

et al. 2018

Agribusiness

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French specialized durum wheat farmers are subject to market risks (price volatility), production risks (fluctuating quality and yields), and an increasingly restrictive regulatory environment (limits on inputs).The purpose of this paper is to gain a more in-depth understanding of the strategies used by those farmers to manage these risks by analysing portfolio strategies in marketing methods and in production decisions. The findings show that three main categories of marketing methods can be adopted in different proportions: forward contracts, average price contracts and spot market selling. A hundred farmers are surveyed in South-West France. An empirical analysis of the determinants of their marketing methods show that risk perceptions and the farmer's level of education influence the choice to sign a preharvest contract for part of their production. In addition, agricultural diversification is negatively correlated with the choice to use forward contracts to hedge against market risk. [EconLit citations: Q13, L24, C24].

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

Cited by 91 publications

References 44 publications

Stochastic utility-efficient programming of organic dairy farms

Stochastic utility-efficient programming of organic dairy farms

Production and Marketing Risks Management System in Grazed Systems: Destocking and Marketing Algorithm

Marketing contracts and risk management for cereal producers

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