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The purpose of this research is to develop a land suitability model for rice production based on suitability levels and to propose insurance premiums to obtain maximum returns based on the harvest index and subsidy dependence factor for the marginal and moderately suitable lands in the northern part of Bangladesh. A multicriteria analysis was undertaken and a rice land suitability map was developed using geographical information system and analytical hierarchy process. The analysis identified that 22.74% of the area was highly suitable, while 14.86% was marginally suitable, and 28.54% was moderately suitable for rice production. However, 32.67% of the area, which was occupied by water bodies, rivers, forests, and settlements, is permanently not suitable; 1.19% is presently not suitable. To motivate low-quality land owners to produce rice, there is no alternative but to provide protection through crop insurance. We suggest producing rice up to marginally suitable lands to obtain support from insurance. The minimum coverage is marginal coverage (70%) to cover the production costs, while the maximum coverage is high coverage (90%) to enable a maximum return. This new crop insurance model, based on land suitability can be a rational support for owners of different quality land to increase production.

Farmers' decisions about how much crop insurance to buy are not generally consistent with expected utility maximization. Taking into account both marginal risk benefits and marginal subsidy effects suggests that most farmers have chosen lower coverage levels than would be predicted by standard models. By modeling financial outcomes as gains and losses, cumulative prospect theory offers an alternative framework to perhaps better understand farmers' purchase decisions. The role of the reference point that defines outcomes as either a gain or a loss, the degree of loss aversion, curvature of the value function, and the probability weighting function in determining optimal crop insurance coverage levels are explored for three representative farms calibrated to 2009 conditions. Loss aversion and how crop insurance is framed through choice of the reference point are shown to be the key factors that determine whether predictions from prospect theory are consistent with observed crop insurance coverage choices. When crop insurance is framed as a tool to manage farm risk then optimal choices under prospect theory are not consistent with observed choices. If crop insurance is framed as a stand-alone investment where a loss is felt if the indemnity received is less than the premium paid, then prospect theory can generate optimal coverage level choices that are largely consistent with observed decisions. This result is shown to be robust to changes in parameterizations as long as loss aversion is maintained and if curvature of the value function is accompanied by decision weights that overweight low probability outcomes.

Bangladesh is one of the most vulnerable countries in the world to extreme climate events. With over 60% of its population living in rural areas, over a third of which lives under the poverty line and depends on agriculture, these climate stresses constitute a major challenge. The traditional financial instruments, e.g., microcredit and relief programs, continue today. However, how climate risk can be tackled through innovative financial instruments focusing on agriculture farms and farmers is crucial. Considering this issue, the Sadharan Bima Corporation and the Bangladesh Meteorological Department joined forces in 2014 to launch a $2.5 million three-year pilot project on weather-index-based crop insurance (WIBCI) executed by the Financial Institutions Division of the Bangladesh government’s Ministry of Finance. This study examined the basic strategy of this pilot project, the major challenges confronted, and possible solutions for creating a successful weather-index-based crop insurance scheme in Bangladesh. We relied on key informant interviews, informal discussions, focus group discussions, and in-depth interviews with the major stakeholders of the WIBCI pilot. These showed the WIBCI pilot to be a promising initiative that still faces problems from limited weather data, a costly business operations system, farmer insurance illiteracy, and fatalism, as well as problems with designing insurance products and recruiting qualified personnel. We compared this WIBCI pilot against the challenges of other projects, recommending best practices for a viable weather-index-based crop insurance system. The insurance mechanism of this study may apply to other vegetation sectors of Bangladesh, e.g., forest plantation or agroforestry for protecting natural resources from natural disasters.

The Agricultural Act of 2014 solidified insurance as the cornerstone of U.S. agricultural policy. The Congressional Budget Office (2014) estimates that this act will increase spending on agricultural insurance programs by $5.7 billion to a total of $89.8 billion over the next decade. In light of the sizable resources directed toward these programs, accurate rating of insurance contracts is of the utmost importance to producers, private insurance companies, and the federal government. Unlike most forms of insurance, agricultural insurance is plagued by a paucity of spatially correlated data. A novel interpretation of Bayesian Model Averaging is used to estimate a set of possibly similar densities that offers greater efficiency if the set of densities are similar while seemingly not losing any if the set of densities are dissimilar. Simulations indicate that finite sample performance—in particular small sample performance—is quite promising. The proposed approach does not require knowledge of the form or extent of any possible similarities, is relatively easy to implement, admits correlated data, and can be used with either parametric or nonparametric estimators. We use the proposed approach to estimate U.S. crop insurance premium rates for area-type programs and develop a test to evaluate its efficacy. An out-of-sample game between private insurance companies and the federal government highlights the policy implications for a variety of crop-state combinations. Consistent with the simulation results, the performance of the proposed approach with respect to rating area-type insurance—in particular small sample performance—remains quite promising.

Over the past 40 years, drought has affected more people in the world than any other natural hazard, affecting large segments of the population and destroying the natural resource base, livestock and livelihoods. Recent projections show that drought events are expected to increase in frequency and intensity due to climate change. According to studies conducted by the Food and Agriculture Organization of the United Nations (FAO), 83% of all damages and losses caused globally by drought between 2006 and 2016 have been absorbed by agriculture, putting a large part of the world’s population at risk of food insecurity. This study shows the advantage of scaling-up FAO’s agricultural drought monitoring and early warning system (ASIS) and building the bridge with the anticipatory action, drought financial mechanisms, social protection and other initiatives for preventing the deterioration of food security and strengthening resilience. The results of the methodology that is based on and supported by the digital innovation, machine learning, matured knowledge and experiences accumulated over the past 10 years are illustrated with practical examples from different countries, ecological environments and crops. A fused time series of Advanced Very-High-Resolution Radiometer (AVHRR) data from Meteorological Operational satellite (METOP) and National Oceanic and Atmospheric Administration (NOAA) was used to produce a consistent time series of a vegetation health index (VHI) at 1 km spatial resolution from 1984 to present. VHI is multiplied by the crop coefficient (kc) to provide more responsiveness to the VHI anomaly that occurs during sensitive phenological phases to water stress such as a flowering and grain filling. The weighted VHI (wVHI) is integrated from the start of the season (SOS) up to the end of season (EOS). Once the temporal analysis of wVHI is completed, the spatial average is calculated using the values of pixels within a specific crop mask and administrative unit. The system proposed different vegetation indices to assess the impact of drought in agriculture; including an agricultural drought forecast that provide more time to the decision makers for implementing anticipatory actions to mitigate the drought in agriculture. Next generation agricultural stress index system (ASIS) offers full capabilities to support: parametric crop insurance, social protection schemes, early action, national drought management plans and to guide public investments.

Purpose The purpose of this paper is to identify the factors that determine demand for crop insurance in Poland. Design/methodology/approach To examine the determinants of decisions regarding crop insurance, the authors used logistic regression. The base source of data for the analysis was the 2013 FADN sample. The scale of yield losses, the indemnities received and the Arrow-Pratt risk aversion coefficient were examined in a representative sample of farms in consecutive years in the period 2004-2013. Findings Losses are the major determinants of crop insurance uptake. Additionally, it was observed that the economic determinants are in line with the expected utility theory, while contrary to expectations, farmer’s characteristics such as education level, age or even risk aversion did not prove to have any influence on crop insurance uptake. Research limitations/implications The FADN sample is representative as regards the type of farming, economic size of farm and location of the farm. Every farm in the sample represents a specific number of similar farms in the population. However, it must be emphasised that the representativeness of the sample with respect to other determinants, e.g., yield losses in previous years, using crop insurance or the farmers’ age and education has not been verified due to lack of data characterizing the general population with regard to these factors. Practical implications It could be argued that the system of crop insurance subsidies should be targeted to encourage the farmers who previously had not used insurance to join the system. Originality/value The paper presents the analysis of crop insurance uptake in a country with a strongly polarised agriculture. The Polish farm sector consists of 1.4 million farms with sizes ranging from 1 ha to over a few thousands hectares. The research is based on a data set of 5,202 farms which contains data from ten years (2004-2013). The novelty of the methodological approach is that it includes information on the number of farms represented by every farm in the FADN sample in the Horvitz-Thompson estimator in order to achieve results which are valid for the general population of Polish farms.

'Catastrophe Risk Financing in Developing Countries' provides a detailed analysis of the imperfections and inefficiencies that impede the emergence of competitive catastrophe risk markets in developing countries. The book demonstrates how donors and international financial institutions can assist governments in middle- and low-income countries in promoting effective and affordable catastrophe risk financing solutions. The authors present guiding principles on how and when governments, with assistance from donors and international financial institutions, should intervene in catastrophe insurance markets. They also identify key activities to be undertaken by donors and institutions that would allow middle- and low-income countries to develop competitive and cost-effective catastrophe risk financing strategies at both the macro (government) and micro (household) levels. These principles and activities are expected to inform good practices and ensure desirable results in catastrophe insurance projects. 'Catastrophe Risk Financing in Developing Countries' offers valuable advice and guidelines to policy makers and insurance practitioners involved in the development of catastrophe insurance programs in developing countries.

PurposeThe purpose of this paper is to evaluate the Dual Use (DU) Option – a crop insurance policy created by the 2018 Farm Bill – relative to other policies available to dual-purpose annual forage producers. The new policy combines existing rainfall-based policies for annual forage crops and multi-peril policies for grain, allowing coverage for multiple crop uses on the same acres during the same growing season.Design/methodology/approachThe paper uses a simulation model to examine crop insurance choices for a typical Texas dual-purpose wheat farm. The certainty equivalent (CE) of wealth is used to rank choices within and between three insurance plans and to analyze the effects of those choices over a range of producer risk aversion levels and for three cases of yield expectations.FindingsThe DU Option is more preferred as risk aversion increases, but it is not universally preferred. Therefore, while the policy can be a viable risk management tool, certain restrictions may be limiting its effectiveness.Practical implicationsThe findings of this paper can help explain farm-level decision making related to dual-purpose annual forage crop insurance program choices.Originality/valueThis paper contributes to the literature by documenting a new crop insurance program made available in the 2018 Farm Bill and provides insights into producers' possible choices by evaluating extensive scenarios.