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The objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.

This study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.

It is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.

The primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.

This is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin.

This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin.

Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield.

The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13.

This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

This study investigates the effect of education on crop insurance knowledge within the context of noncompliance experiences. In addition, the study delves into the role of government endorsement in education, which is instructive for the implementation of future insurance promotions.

The study designs a randomized controlled trial (RCT) conducted in Jiangsu Province, China. A total of 518 sample farmers were randomly assigned to two experiments: The Education Experiment and the government’s Endorsement Experiment, respectively. After conducting a set of rigorous exogeneity tests, econometric analysis was conducted using baseline survey data and post experiment data.

Our results revealed that insurance education served as an effective tool in improving farmers’ insurance knowledge, especially their understanding of insurance mechanisms. However, this effect can be mitigated by the noncompliant insurance experience of farmers. Moreover, government-endorsed education proved to be more efficient in improving farmers’ insurance knowledge, thus highlighting the significance of building trust between insureds and insurers.

This study contributes to the literature by demonstrating that using a simple education tool, such as, brochures, can effectively improve farmers insurance knowledge. In addition, insurance mechanisms are now more urgently in need of universalization than policy information. Furthermore, by conducting the RCT, this study obtains unbiased causal inference on the effect of education on insurance knowledge and underscores the role of government endorsement in this process. In addition, the study illustrates the tradeoff between insurers’ efforts in enhancing education and regulating noncompliant insurance misconducts, which compromises education efforts. Overall, this study provides insights into the marketing strategies of insurers and government propaganda aimed at stimulating farmers’ incentives to purchase insurance.

The goal of this study is to predict the farmers’ concerns about agricultural index-insurance (AII) for weather risk mitigation of horticultural crops in hilly regions. The key impetus of analysis is to prioritize the AII requirements based on the farmers’ perspectives using the requirements prioritization approach.

The integrated approach has been applied in this paper. Initially, the MoSCoW prioritization technique has been employed to prioritize the AII attributes utilizing a four-dimensional agriculture insurance scale. Later, the rank sum weighting method was deployed to assign the ultimate rank to AII attributes based on the farmers’ responses.

Findings specified that out of 15 AII attributes, majority of 11 attributes were placed in “must have” and “should have” categories that related to claim, design, premium and grievance management dimensions. However, three AII attributes are placed in the “could have” category. Additionally, findings of rank-sum weighting method-based ranking can help insurers in redesigning farmers-oriented AII services for risk mitigation of horticulture crops by incorporating these ranks as per their priority level.

The prioritized AII attributes are helpful for insurers and managers in order to solve the problems associated with design, premium, claim and grievance management of AII.

Findings deliver significant insights to insurers to incorporate the prioritized AII attributes ranked by farmers.

This is the initial known analysis that integrated the MoSCoW and rank sum weighting method to prioritize the AII requirements prioritization among Indian farmers.

This study aims to measure the status of rural household food security across regions using multidimensional indicators. It also aims to identify the determinants of rural household food security in Ethiopia.

The study adopted descriptive and explanatory designs. It used data from the fourth wave of the Ethiopian socioeconomic survey that has 3,115 respondents. The authors constructed household food security index using variables that capture availability, access, utilization and stability dimensions of food security. The authors categorized households into relative food security groups, namely, alarming and moderately food insecure, as well as moderately and highly food secure. Beta regression model, which is widely used to analyze response variables that assume values between 0 and 1, is used to estimate the determinants of food security.

The study finds that 77.7% of rural households are food insecure. Of this, 90% are moderately food insecure. Regional variations in magnitude of food security showed that Harari, Gambella and Benshanguel Gumuz regional states are relatively better-off than other regions in Ethiopia. The study identified sex, education level, marital status, location and wealth status of households as significant determinants of food security.

This study sheds light on regional variations in multidimensional food security in Ethiopia. It thus challenged previous estimates of food security using uni-dimensional indicator. It highlighted the need for region-specific analysis of determinants and a follow up of tailored regional interventions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0139

This article aims to explore the impact of climate disasters on small-scale farmers’ willingness to cooperate and explore the mediating effect of social capital.

The study investigates farmers’ willingness to cooperate through a framed field approach and surveys the information of individuals and villages, including climate disasters and social capital, using a structured questionnaire from rural communities in Jiangxi and Sichuan, China.

The results show that climate disasters and social capital are significant and positive determinants of farmers’ willingness to cooperate. In specific types of climate disasters, drought is positively associated with farmers’ cooperation willingness. Moreover, the mediation effect of drought on farmers’ willingness to cooperate through social capital has been demonstrated to be significant although negative, whereas the mediation effect of flood on farmers’ willingness to cooperate through social capital is significant and positive.

First, given the limited studies focusing on the impact of climate disasters on small-scale farmers’ willingness to cooperate, the authors complement the existing literature through a framed field experiment approach by designing a scenario that every farmer may encounter in their production activities. Second, the study figures out the roles of drought and flood as different kinds of climate disasters in farmers’ decision-making of cooperation and sheds light on the positive impact of climate disasters on small-scale farmers. Finally, this paper provides empirical evidence of social capital as a potential channel through which climate disasters could possibly affect farmers’ willingness to cooperate.