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The purpose of this paper is to identify the factors that determine demand for crop insurance in Poland.

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.

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.

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.

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.

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.

This paper examines the determinants of concurrent adoption of farm risk management strategies by rice growers in two different ecosystems of Telangana agriculture-irrigated and rainfed ecosystems.

The primary data have been collected from the rice growers in two different ecosystems, and after checking the variance inflation factor (VIF) for controlling multicollinearity, a multinomial logit model has been used to examine the determinants of concurrent adoption of coping strategies by rice growers.

The study finds that adopting one risk management strategy persuades farmers to embrace other strategies, reducing the risk in agriculture between the two ecosystems. Among the determinants, farmers' age, education, contact with extension services, irrigation sources, livestock income, total farm income, crop loss reasons, and crop insurance awareness significantly influence the adoption of various risk management measures. However, considerable heterogeneity is found among the driving forces across the rice ecosystems.

The major policy implications that can be drawn from the analysis are increased access to information through government-funded extension services and the provision of alternative risk management technologies, such as drought-resistant or flood-resistant seeds, farmers' field schools and increased provision of crop insurance, farmer-friendly agriculture extension services, and farm investment support, are critical for assisting farmers managing risks. In addition, however, there should be ecosystem-specific policies to tackle the ecosystem heterogeneity.

This paper is very timely and entails some relevant policy implications for the development of Indian agriculture.

The aim of this paper is to identify the factors that influence rice farmers' decisions to adopt crop insurance and premium payments (willingness to pay [WTP] amounts). The paper also demonstrates the usefulness of the complementary log-log (cloglog) truncated Poisson double-hurdle model as an alternative hurdle model.

The study first investigated the nature of the dependent variable, which had non-normal residuals and was overdispersed. The probit truncated normal regression double-hurdle model was tried but it failed the normality and homoscedasticity tests; hence, the cloglog truncated Poisson double-hurdle model was employed in the study.

An estimated 61% of respondents would purchase crop insurance, despite farmers not having prior experience with this product. Amongst others, the factors that influence insurance adoption amongst rice farmers are the share of rice in total income, reliability perception of crop insurance schemes and the probability of failure to achieve target yields. The latter helps the authors to address adverse selection, a central issue to the viability of such an insurance programme. The determinants of farmers' WTP are also identified.

Sampling was limited to farmers using irrigation and living in one region of Ghana, which may limit the study’s wider applicability.

As far as the authors are aware, this study is the first to select the appropriate hurdle model based on established properties of the dependent variable on this topic – crop insurance decisions.

The purpose of this paper is to identify the correlates of crop insurance adoption and estimate the impact on debt and farm income.

The authors used nationally representative data from National Sample Survey Office (NSSO), which consisted of 35,200 farming households. Logit and propensity score matching (PSM) (nearest neighbor, caliper and kernel matching) techniques were used.

With only around 5% of households insuring their crops and 87% of them not receiving claims, crop insurance in India has failed. Logit model estimates of correlates of adoption indicated that households with larger family size, lower social group, less education, lower standard of living and poor were more likely to be left out of the ambit of crop insurance. Further, propensity score estimates suggested that households with access to crop insurance had significantly lesser outstanding debt with positive effect on input costs and crop income. The authors’ results were in contrast to the risk balancing theory.

Results of our work encourage us to rethink and restructure the crop insurance policy design in India. With credit and insurance markets interlinked by design and as the risk balancing in the farm business found absent, policies to strengthen both the markets are the need of the hour. To encourage more farmers to take up crop insurance, revenue-based indemnity calculation could be tried in India.

Impact estimates from three different algorithms of matching were compared and tested for robustness. Consistent average treatment effect on treated (ATT) was considered for interpretation and policy implications. Since the data are from a nationally representative survey, results are believed to be of extreme value to policy makers and insurance providers as it can be generalized.

The purpose of this paper is to consider how the federal crop insurance (FCI) program influences farm debt use, one of the key financial decisions made by farm operators.

Using data from the nationally representative Agricultural Resource Management Survey, the paper implements a propensity score matching model of the impact of FCI participation on various measures of farm business debt use. To account for the simultaneity of financial decisions, the paper further tests this relationship using a seemingly unrelated regression model.

FCI participation is associated with an increase in use of short-term farm debt, but not long-term debt, consistent with risk balancing behavior and current trends in the farm sector.

In addition to risk balancing, the results are also consistent with credit constraints or lender preferences. The paper cannot fully establish causality between crop insurance participation and short-term debt levels. Future research should address these limitations.

Agricultural lending standards are generally conservative and the farm sector as a whole currently has historically low leverage, which implies that an increase in debt use may not be a threat to the financial health of the farm sector.

The results indicate that the reduction in total risk facing the farm sector is significantly less than the decline in risk provided by FCI, which is an important consideration for policymakers.

This is the first paper to use an econometric model to analyze the relationship between FCI and farm debt use decisions. This paper can inform future research on the FCI program and farm financial decisions.

This paper aims to provide a “biography” of sorts on Agricultural Finance Review. The paper tracks the evolution of Agricultural Finance Review from its introduction in 1938 to its current status.

The paper is based on a complete review of every paper and every issue. Not all papers were read by the author, but key papers of interest that in one way or another made significant contributions to the study of agricultural finance were reviewed.

The paper shows the evolution of agricultural finance from the early days of reporting financial data in the 1930s and 1940s, to its emergence as a major and significant sub discipline of the general field of agricultural economics.

As indicated, not all papers were fully reviewed or read. It is possible that papers identified as “firsts” may have been preceded by other papers. Nonetheless the paper identifies the basic evolutionary path of the journal and defines key points in time when a paradigm shift emerged to change the direction of this discipline.

As Agricultural Finance Review transitions from the Department of Applied Economics and Management at Cornell University to Emerald Group Publishing Limited, this “biography” provides readers with a general overview of the journal's and the discipline's historical development.

This paper is simply a review of the existing literature found in Agricultural Finance Review.

The purpose of this paper is to contribute to the aforementioned literature on the linkage between economic activity and human preference by estimating the cross-sectional determinants of farmers’ participation in participation in crop insurance programs (CIPs) and identifying the impediments preventing the remaining farmers from participating.

Using the unique data sets of risk preference experiments and maize producer surveys pertaining to the maize production areas of China, this paper explores the determinants of farmers’ CIPs and scrutinizes the role of risk aversion in farmers’ CIP purchase decisions under the expected utility maximization framework. And a “non-zero threshold probit model” is used for the analysis.

The results show that risk aversion plays an important role in CIP purchase decision-making, not only in the form of its direct effect but also with regard to the interaction term and expected loss. Furthermore, if the insured amount is high enough, then risk aversion will no longer affect insurance purchase. Additionally, purchase experience, CIP environment (village purchase ratio), and contract items (insured amounts) are significant determinates in these decisions. There is no significant evidence to suggest that serious adverse selection exists in the sampling areas.

One theoretical model is established which considered not only general variables like farmers’ production and household information, but also conditions and terms in the insurance policies. The unique experimental method is used to measure farmers’ risk aversion. Both the role of risk aversion and its’ interaction terms with others in CIP participation are scrutinized to identify complicated influences under the context of real society.

This research examines factors influencing the adoption of crop and revenue insurance. This is accomplished by estimating a multinomial logit model of insurance choices facing U.S. farmers. Results indicate significant differences in the probabilities of adoption of each insurance plan. The levels of selected explanatory variables, such as operator’s education level, debt‐to‐asset ratio, off‐farm income, soil productivity, participation in production and marketing contracts, and type of farm ownership, appear to be the determinants of the probability of having adopted each insurance plan.

Despite the susceptibility of cotton crops to pest attacks in the Malwa Region of Indian Punjab, no crop insurance policy has been implemented there– not even the Pradhan Mantri Fasal Bima Yojana (PMFBY), which is a central scheme. Therefore, this paper attempts to gauge the likely impact of the PMFBY on Punjab cotton farmers and assess the changes needed for greater uptake and effectiveness of PMFBY.

The authors have conducted a primary survey to conduct this study. Initially, the authors compared the costs of cotton production with the returns in two scenarios (with and without insurance). Additionally, the authors have applied a logistic regression framework to examine the determinants of the willingness of farmers to participate in the crop insurance market.

The study finds that net returns of cotton crops are conventionally small and insufficient to cope with damages from crop failure. Yet, PMFBY will require some modifications in the premium rate and the level of indemnity for its greater uptake among Punjab cotton farmers. Additionally, using the logistic regression framework, the authors find that an increase in awareness about crop insurance and farmers' perceptions about their crop failure in the near future reduces the willingness of the farmers to participate in the crop insurance markets.

The present study looks for the viability of PMFBY in Indian Punjab for the cotton crop, which can also be extended to other crops.

Punjab could also use crop insurance to encourage diversification in agriculture. There is a need for special packages for diversified crops under any crop insurance policy. Crops susceptible to volatility due to climate-related factors should be identified and provided with a special insurance package.

There exist very scant studies that have discussed the viability of a central crop insurance scheme in the agricultural-rich state of India, i.e. Punjab. Moreover, they do not also focus on crop losses accruing due to pest and insect attacks.

The purpose of this paper is to analyze the determinants of agricultural insurance adoption in Bulgaria, using a purpose-built survey of 224 farmers interviewed in 2011. The insurance decision is analyzed conjointly with other risk management decisions on the farm such as having contracts with retailers or processors, diversifying farm activities and using irrigation.

The agricultural insurance sector in Bulgaria is presented in the broader context of the transition to a market-oriented economy and integration of Bulgarian agriculture into the EU Common Agricultural Policy. The recent developments on the determinants of farm insurance adoption in the agricultural economics and finance literature are discussed. A multivariate probit model is used in order to determine the factors explaining the adoption or non-adoption of various risk management tools by the surveyed farmers, including farm insurance.

The authors find that farmers with diversified activities, using irrigation or having contracts with retailers or processors, are more likely to adopt insurance, after controlling for farms and farmers’ structural characteristics. Additionally, the authors find that the main characteristics distinguishing farmers who purchase agricultural insurance from non-users are farm size and farm location. The existence of strong regional effect suggests the importance of adapting the insurance products to the different regional contexts in Bulgaria.

This paper contributes to the (limited) literature on agricultural insurance adoption in transition countries, currently shifting from a system where compensation against natural hazards tended to come from a State damage mitigation fund, inherited from the centrally planned governments to private and voluntary agricultural insurance. This research provides a unique data source on the Bulgarian case study.