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Assessing the risk to the agricultural system is important for agricultural sustainability. The present study analyses agricultural drought risk with respect to different drought severities. Different drought indices – namely, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil moisture Index (SSI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI) – are used to evaluate the conditional probability. Non-stationary analysis is carried out for SPEI and SSI to incorporate the impact of large-scale oscillations and regional hydrological variability. Copula analysis is performed between drought conditions and various crop yield anomalies over Maharashtra, India, during 1998–2015. The outcomes suggest that SPEI is a significant drought indicator over the maximum number of districts in all the crops. Sea Surface Temperature (SST) and Indian Summer Monsoon Index (ISMI) are selected as suitable covariates to model the non-stationarity in the SPEI time series. The drought risk is estimated to increase with drought severity for all of the selected crops. It is observed that the exclusion of non-stationarity will underestimate the agricultural risk. [ABSTRACT FROM AUTHOR]

Securitization is an emerging alternative to transfer of insurance risk, especially in cases exceeding the capacity of reinsurance, thus extending the insurability of risks. The original subjects of securitization are the risks emerging from the aftermaths of natural disasters. The range of securitized risks has broaden rapidly over the past decade. The reason of securitization's feasibility in transfer of agricultural risks is the spatial correlation of harvests among the producers that can result in fatal loss suffered simultaneously by many producers and subsequent producer's insolvency to settle the insurance claims. The paper proposes the reduction of the insurer's risk exposure by its transfer to capital markets via catastrophe bonds. A catastrophic event is defined through the relative loss of the current national per hectare yield of the particular crop to the average yield from previous years. The number of years included in the average is subject to the minimization of the relative loss' fluctuation over the given period. The triggering probability of the catastrophe bond is calculated from the kernel estimation of the loss distribution, with the relative loss being the loss index. The general case is upgraded by the factor of soil quality. The insurer is proposed to offer the coverage according to the producers' soil. The soil classes are securitized separately, with the set of catastrophe bonds. Both cases are illustrated by the numerical example on the data set of wheat produced in the Slovak Republic over last 45 years. The outcome of the examples are the graphs of expected payoffs depending on various parameters. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to propose a general framework for modeling heterogeneous risk preferences of agricultural producers and identifying the underlying factors that affect risk preferences.Design/methodology/approach This paper nests the risk preference function in a general production decision framework to test and model producers’ risk preferences. The framework allows for both production and price risk, and accommodates potential inefficient behavior. Panel data and the GMM method are used in the empirical estimation.Findings The results in this study confirmed the hypothesis of heterogeneous risk preferences. Farmers are found to have decreasing absolute risk aversion. Both farmer characteristics and socioeconomic factors have significant impact on producers’ risk preferences. The results suggest that ignoring heterogeneity in risk preferences across individuals and how non-wealth variables could affect farmers’ risk preferences could result in biased economic behavior analysis.Originality/value It is generally assumed in the literature that risk preferences are homogeneous among farmers at given wealth. This is a strong assumption and there are abundant evidences that suggest otherwise. This paper makes contributions to the literature by proposing an approach to modeling heterogeneous risk preferences and identifying the factors that affect preferences. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for estimating the risk of agricultural damage caused by an invasive species when species-specific information is lacking. We defined the 'risk' as the product of the invasion probability and the area of potentially damaged crop for production. As a case study, we estimated the risk imposed by an invasive weed, Sicyos angulatus, based on simple cellular simulations and governmental data on the area of crop that could potentially be damaged in Miyagi Prefecture, Japan. Simulation results revealed that the current distribution range was sufficiently accurate for practical purposes. Using these results and records of crop areas, we present risk maps for S. angulatus in agricultural fields. Managers will be able to use these maps to rapidly establish a management plan with minimal cost. Our approach will be valuable for establishing a management plan before or during the early stages of invasion. [ABSTRACT FROM AUTHOR]

This paper aimed to evaluate impacts of risk management (RM) on sheep production. It is based on Solvsamp Modeling, to analyze empirical data (2003-2011) obtained in two marketplaces. Results proved that RM was sometimes ineffective against natural agent severity or market prices fundamental elements (supply, demand) which would have caused strongly risks within season independently of one another. With government policies producers based their RM tools on the use of concentrated animal feed input and herd size management, either this last or herd mobility (nomadic, transhumant) and animal feed diversification, but the RM strategies and returns for individual farmer looked like at a point in time. RM ensured for producers the margin profit rate of 15 percent in winter to 33 percent (summer) and conversely high seasonal coefficient (1.25) in winter, low (0.86) in summer. RM must consider the seasonal, economic or policy factors and specificity of each breeding system to be more efficient. [ABSTRACT FROM AUTHOR]

While agricultural risk and risk perception has received significant attention in the literature, few studies have explored the factors that influence the way farmers respond to particular risks. This paper uses the case of bovine tuberculosis (bTB), one of the most significant risks currently facing the English cattle farming industry, to explore these factors, with a particular focus on the role of social networks. A large scale postal survey distributed to beef and dairy farmers in the south west of England provides representative data which are subjected to factor and cluster analysis in order explore farmer views towards and responses to disease risk. Two groups of farmers are identified which can be distinguished from each other based on their attitudes towards bTB and the nature of their social networks. Farmers with wider, more externally focussed social networks are found to be more resilient than those whose social networks are restricted to family members and other farmers. However, while differences between the two groups are found in terms of their attitudes towards bTB, no differences are found in relation to their risk management behaviour, with few farmers taking clear action to reduce the risk of their herds contracting the disease. In order to address the identified disconnection between attitudes and behaviour, a number of potential interventions are put forward and discussed. [ABSTRACT FROM AUTHOR]

Soybean rust (SBR) is a disease of significant impact to Brazilian soybean production. Twenty-four locations in a major growing region in southern Brazil, where long-term (30 years) weather information was available, were selected to estimate the risk of SBR epidemics and identify potential predictors derived from El Niño 3.4 region. A rainfall-based model was used to predict SBR severity in an 'epidemic development window' (the months of February and March for the studied region) in the time series. Twenty-eight daily simulations for each year-location ( n = 720) were performed considering each day after 31 January as a hypothetical detection date (HDD) to estimate a severity index (SBRindex). The mean SBRindex in a single year was defined as the 'growing season severity index' (GSSI) for that year. A probabilistic risk assessment related GSSI and sea surface temperatures (SST) at the El Niño 3.4. region (here categorized as warm, cold or neutral phase) in October-November-December (OND) of the same growing season. Overall, the median GSSI across location-years was 34.5%. The risk of GSSI exceeding 60% was generally low and ranged from 0 to 20 percentage points, with the higher values found in the northern regions of the state when compared to the central-western. During a warm OND-SST phase, the probability of GSSI exceeding its overall mean (locations pooled) increased significantly by around 25 percentage points compared to neutral and cold SST phases, especially over the central western region. This study demonstrates the potential to use El Niño/Southern Oscillation information to anticipate the risk of SBR epidemics up to 1 month in advance at a regional scale. [ABSTRACT FROM AUTHOR]

• We reviewed the literature on the 5 major types of risk in agriculture. • 66% of the 3283 studies focused solely on production risk. • Only 15% of studies considered at least two types of risk. • Limited attention on multiple risks appears at odds with farmer realities. • A shift in research focus to multiple risks may help prioritize risk management. This study examines the scope and depth of research on the five major types of risks in agriculture, and the extent to which those studies have addressed the impacts of, and policies to mitigate individual types of risk as opposed to more holistic analyses of the multiple sources of risk with which farmers have to cope with. Risk is at the center of new paradigms and approaches that inform risk management initiatives and shape investments in many countries. Although the literature includes several substantive reviews of the methods available for risk analysis and their empirical applications have been extensively scrutinized, limited information exists about which types of risks have received sufficient attention, and which have not. This limited information is perplexing because farmers manage multiple risks at the same time and unanticipated events continue to have substantial impacts on farmers. We identify 3283 peer-reviewed studies that address one or more of the five major types of risk in agriculture (production risk, market risk, institutional risk, personal risk, and financial risk) published between 1974 and 2019. We conduct a literature search and then apply an eligibility criteria to retain eligible studies from the search. We then classify those eligible studies based on risk type and geographic focus. We placed no limit on the temporal scale, geographic focus, or study method for inclusion in our search. Results show that 66% of the 3283 studies focused solely on production risk, with only 15% considering more than one type of risk. Only 18 studies considered all five types of risk and those either asked how farmers perceived the importance of each risk or were focused on conceptual issues, rather than assessing how exposure to all the risks quantitatively affects farm indicators such as yields or incomes. Without more detailed analyses of the multiple types of risks faced by farmers, farmers and policymakers will lack the information needed to devise relevant risk management strategies and policies. A shift in research focus towards the analysis of multiple contemporaneous types of risk may provide a basis that gives farmers greater options for coping with and managing risk. We discuss some of the challenges for studying multiple risks simultaneously, including data requirements and the need for probability distributions and the role of simulation approaches. [ABSTRACT FROM AUTHOR]