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Natural disasters and human-made events occurring against the background of global climate change are directly causing an unexpected financial recession in agriculture. From this point of view, agricultural insurance is of special importance in financially mitigating the damage caused by climate change. The research analyzed the forecast parameters by selecting the amount of precipitation and the factors affecting cotton crop insurance, which is considered one of the agricultural crops. The research discusses the effective utilization of agricultural insurance opportunities in compensation for damage from climate change and the development of forecast development scenarios based on using econometric models in this direction. The authors explore the importance of further improving insurance protection measures in the future based on projection scenarios developed up to 2028 for crop and livestock insurance in agricultural insurance activities.

A correlation has been shown in the literature between credit constraints and the adoption of agricultural technologies, technical efficiencies and measures for adapting to climate change. The relationship between credit constraints, risk management strategy adoption and income, however, is not well understood. Consequently, the purpose of this study was to investigate how credit constraints affect the income and risk management practices adopted by Northern Nigerian maize farmers.

Cross-sectional data were collected from 300 maize farmers in Northern Nigeria using a multi-stage sampling technique. Descriptive statistics, seemingly unrelated regression and double hurdle regression models were the analysis methods.

The results showed that friends and relatives, banks, “Adashe”, cooperatives and farmer groups were the main sources of credit in the study area. The findings also revealed that the sources of risk in the study area included production risk, economic risk, financial risk, institutional risk, technological risk and human risk. In addition, the risk management strategies used to mitigate observed risks were fertilizer application, insecticides, planting of disease-resistant varieties, use of herbicides, practising mixed cropping, modern planning, use of management tools as well as making bunds and channels. Furthermore, we found that interest rate, farm size, level of education, gender and marital status were significant determinants of statuses of credit constraints while the age of the farmer, gender, household size, primary occupation, access to extension services and income from maize production affected the choice and intensity of adoption of risk management strategies among the farmers.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income. It therefore recommends that financial service providers should be engaged to design financial products that are tailored to the needs of smallholder farmers in the study area.

This paper incorporates the role of constraints in influencing farmers’ decisions to uptake credits and subsequently their adoption behaviours on risk management strategies. The researcher approached the topic with a state-of-the-art method which allows for obtaining more reliable results and hence more specific contributions to research and practice.

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 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.

The present study examines the determinants of households’ access to agricultural credit through institutional and non-institutional sources. The study evaluates the role of gender of the borrowers in accessing credit in rural India. Further, the paper also studies the impact of institutional variables in determining rural households’ access to credit.

The study used a multinomial logit model to identify the different factors that determine a farmer’s access to different credit sources.

The study reveals that substantial proportions of rural households do not access credit through any of the sources and the situation is very grim for the female-headed households (FHHs). The study highlights the importance of demographic, farm and institutional variables in determining households’ access to credit. Institutional variables significantly enhance rural credit access but favor male-headed households (MHHs). It highlights the need for policy intervention to target the specific needs of female borrowers. Further, the study also highlights the importance of adequate credit policy measures to address farmers’ vulnerability to natural disasters, mainly droughts.

The results of the study are based on recent unit-level data from the 77th Round of the National Sample Survey Organization (NSSO) survey. The survey covers a large number of farm households and reports information for the year 2018–2019.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-08-2022-0552

Though agriculture has the potential for job creation for the growing population; nevertheless, most Nigerian youth merely see the agricultural sector as a viable opportunity for livelihood. In the quest for food security, as encapsulated in sustainable development goals (SDGs), youth participation in agriculture is essential to unlock the agricultural sector’s potential and ensure adequate food production.

This study examined the factors influencing youth involvement in agriculture and its impact on food security in Nigeria, using Ekiti and Kwara States. The study engaged a multi-stage random sampling approach. The first stage involved a purposeful selection of the states among youth in agriculture-related activities. The second stage involved randomly selecting five Local Government Areas (LGAs) from each state. The third stage involved a random selection of five communities in the selected LGAs, making it a total of 25 communities for each state. Finally, 20 households were selected per community. In total, 500 respondents were selected from each of the two states, making it a total of 1,000 respondents for the survey. The Foster-Greer-Thorbeck (FGT) analysis uses the logit regression and the Propensity Score Matching (PSM) techniques.

The results showed that a large proportion (about 95%) of the youth farmers in the study area fell below the food security line (N6448.45) and are food insecure. Findings from the PSM showed that youth in agriculture has no significant impact on food security. The findings from the logit regression showed that gender, age, level of education, land ownership, income, safety net or social protection and value chain are significant determinants of youth participation in agriculture.

This study contributes to the literature by examining the determinants of youth in agriculture and its impact on food security in Nigeria, using Ekiti and Kwara States, by engaging the FGT, logit regression and PSM.

This study contributes to the literature by examining the determinants of youth in agriculture and its impact on food security in Nigeria, using Ekiti and Kwara States, by engaging the FGT, logit regression and PSM.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2021-0197

This study investigates the impact of board funds, banking credit, and economic development on food production in the context of South Asian economies (India, Pakistan, Bangladesh, Sri Lanka, and Nepal).

This study used data from the World Development Indicators covering the years 1991–2019. To investigate the relationship between the variables of the study, we employed the panel unit root test, panel cointegration test, cross-sectional dependence test, fully modified least squares (FMOLS), and panel dynamic least squares (DOLS) estimators.

The empirical results indicate that board funding significantly increase food production; however, banking credit had a negative impact. Furthermore, the findings indicate that economic development, Arable land, fertilizer consumption, and agricultural employment play a leading role in enhancing food production. The results of the Dumitrescu-Hurlin causality test also show substantiated the significance of the causal relationship among all variables.

South Asian countries should prioritize board funding, bank credit, and economic development in their long-term strategies. Ensuring financial access for farmers through micro-credit and public bank initiatives can spur agricultural productivity and economic growth.

This study is the first to combine board funding, banking credit, and economic development to better comprehend their potential impact on food production. Instead of using traditional approaches, this study focuses on these financial and developmental aspects as critical determinants for increasing food production, using evidence from South Asia.

The ailing agriculture sector in Pakistan demands a supportive financial sector. The low adoption of Salam financing by Islamic banks does not match the potential demand. Empirical studies identified demand-led issues that led to a low proportion of Salam financing, but the exploration of supply-side constraints is overlooked.

This study has applied Interpretive Phenomenological Analyses on 20 interviews with the experts in the Islamic banking industry who play a role in decisions on Salam financing to the agriculture sector. The purpose of the study is to explore the determinants of low adoption of Salam financing by Islamic banks.

The experiences led to the major reasons for the low adoption of Salam financing categorized as intentions, attitudes and behavior control which corresponds to the theory of planned behavior.

This study is instrumental in exploring the supply-side constraints to Salam financing and helps find aligning theory to intervene via Islamic banking regulations.

The purpose of this paper is to assess the adoption of climate-smart agriculture (CSA) and its implication on improving the farming household food security status, their resilience and livelihood risk management of farmers.

This systematic review has followed procedures to accomplish the review, including literature searches, screening studies, data extraction, synthesis and presentation of the data.

Based on the result of the review, the determinants of CSA adoption can be categorized into five categories, including demographic factors (age, sex, family size, dependency ratio, education), economic factors (land size, household income, livestock ownership), institutional factors (extension services, training access, credit services, farm input, market distance), environmental factors (agroecology, change in precipitation, slope of land) and social factors (cooperatives membership, farmers perception). The result also shows that applying CSA practices has an indispensable role on increasing productivity, food security, income, building resilient livelihoods, minimizing production risk and alleviating poverty. This concluded CSA practice has a multidimensional role in the livelihood of agrarian population like Ethiopia, yet its adoption was constrained by several factors.

This review mainly emphasizes on the most commonly practiced CSA strategies that are examined by different scholars.

This study aimed to identify the determinants of farmers' participation in agricultural information-sharing digital groups and their impacts on farm performance.

Primary data of the 2015/2016 crop year collected from 175 cattle farmers were analyzed using descriptive statistics and econometric models. Farmers who had smartphones and participated in social groups/applications, especially those created to exchange agricultural information, were considered adopters of the technology.

A Poisson hurdle model showed that farmers' decision to participate in agricultural information-sharing digital groups is determined by schooling, age (negative effect) and use of tools for planning production. The intensity of participation is affected by risk propensity, interaction with specialist advisors, use of tools for planning production and participation in cooperatives. The authors also found empirical evidence that farmers' participation in agricultural information-sharing digital groups positively affects farm income per hectare.

The results of this study are important for accelerating the diffusion of low-cost digital technologies, which are powerful tools for improving farmers' sharing and access to valuable information in real time and in locations far from urban areas.

To the best of the authors’ knowledge, this is the first empirical analysis of the adoption and impacts of agricultural information-sharing digital groups/applications by Brazilian cattle farmers. The diffusion of simple digital technologies is important for reducing heterogeneity and increasing the efficiency of cattle production.