Search Results (151)

Driven by climate change and stringent ecological conservation policies, arid and semi-arid pastoral areas face acute grassland degradation and forage–livestock imbalances. In Sunan County (Gansu Province, China), herders have increasingly turned to off-site grazing—leasing crop fields in adjacent oases during autumn and winter—to alleviate local grassland pressure and adapt their livelihoods. However, the interplay between the evolving land use system (L) and this emergent borrowed pasture system (B) remains under-explored. This study introduces a coupled analytical framework linking L and B. We employ multi-temporal remote sensing imagery (2018–2023) and official statistical data to derive land use dynamic degree (LUDD) metrics and 14 indicators for the borrowed pasture system. Through entropy weighting and a coupling coordination degree model (CCDM), we quantify subsystem performance, interaction intensity, and coordination over time. The results show that 2017 was a turning point in grassland–bare land dynamics: grassland trends shifted from positive to negative, whereas bare land trends turned from negative to positive; strong coupling but low early coordination (C > 0.95; D < 0.54) were present due to institutional lags, infrastructural gaps, and rising rental costs; resilient grassroots networks bolstered coordination during COVID-19 (D ≈ 0.78 in 2023); and institutional voids limited scalability, highlighting the need for integrated subsidy, insurance, and management frameworks. In addition, among those interviewed, 75% (15/20) observed significant grassland degradation before adopting off-site grazing, and 40% (8/20) perceived improvements afterward, indicating its potential role in ecological regulation under climate stress. By fusing remote sensing quantification with local stakeholder insights, this study advances social–ecological coupling theory and offers actionable guidance for optimizing cross-regional forage allocation and adaptive governance in arid pastoral zones. Full article

Climate change (CC) is a major threat to agriculture, the sector that supports the territorial economy in the Pays Haut Languedoc et Vignoble (PHLV) region (south France). In this region, farms have been facing the negative effects of CC for several decades. The implementation of agriculture adaptation policies requires a coherent and integrated tool that mobilizes approaches for territorial development, vulnerability assessments, and feasibility. The purpose of this research is to provide a multi-criteria assessment of farm vulnerability to CC in the PHLV region. An index of farm vulnerability was developed based on the classic model of vulnerability, which is the product of exposure and sensitivity divided by adaptive capacity. This assessment was conducted at the farm level, by combining biophysical variables (such as soil type and irrigation) and socioeconomic variables (such as agricultural experience and crop insurance), selected based on a literature review and interviews with local stakeholders and local experts. To solve the weighting problem, we differentiated between a “calculated vulnerability”, without any specific weighting of the vulnerability variables, and a “declared vulnerability” that integrates the scores assigned to the importance of each variable for each farmer surveyed, based on their awareness. Afterward, a discriminant analysis was used to identify the factors that determine the vulnerability classes. Our results show that (i) the majority of the surveyed farms have a relatively high vulnerability index, but wine farms and cereal farms are the most vulnerable; (ii) for all farms the “declared vulnerability” is lower than the “calculated vulnerability”, showing that farmers underestimate their vulnerability; (iii) there is an interesting link between the low level of vulnerability and the adaptation efforts already made over the past ten years by certain farms that have changed or introduced crops and improved their agricultural practices. Full article

Farm household pluriactivity has become increasingly prevalent in China; however, its influence on crop structure remains insufficiently explored. This study examines the impact of farm household pluriactivity on crop structure in China, focusing on factor input mechanisms. Based on survey data from 473 farm households in Sichuan Province, this study employs ordinary least squares (OLS), two-stage least squares (2SLS), and mediation analyses to systematically assess the impact of pluriactivity on crop structure through factor input mechanisms. The analysis reveals three key findings. First, rather than reducing the grain planting area, an increase in part-time farming is associated with a significant rise in the proportion of grain cultivation. Second, factor inputs partially mediate this relationship: while pluriactivity tends to reduce staple crop cultivation through mechanisms such as cultivated land transfer-out, land abandonment, and increased non-agricultural labor input, it simultaneously promotes staple crop expansion via enhanced agricultural technical services. Third, heterogeneity tests indicate that the positive effect of pluriactivity on staple crop cultivation is especially pronounced among households in hilly areas and those that have adopted agricultural insurance. These findings provide valuable policy insights for fostering sustainable agricultural transitions and enhancing food security in developing regions. Full article

Climatic events are expected to significantly impact global agricultural production, with China being particularly vulnerable. Research in China emphasizes the urgent need for sustainable agricultural practices that address climate change, implement effective management strategies to mitigate the impacts of climatic events, and ensure food security. Therefore, this study examines the impact of climatic events on agricultural production optimization in Northeast China. To complete this objective, this study uses Method-of-Moments Quantile Regression (MM-QR) and data from 2003 to 2020. The main findings reveal that climatic factors, such as the Standardized Precipitation Index (SPI) and High-Temperature Days (HTDs), have a more pronounced effect on agricultural outcomes at higher production levels, particularly for larger producers. In addition, machinery power (TPAM) enhances productivity. Its role is more focused on risk mitigation than on expanding production. Insurance payouts (AIPE) increase grain production capacity at higher quantiles, while fertilizer use (FEU) has diminishing returns on capacity but encourages planting. Granger causality tests further demonstrate that management factors—such as machinery, irrigation, and insurance—play a more significant role in shaping agricultural outcomes than extreme climatic events. To improve agricultural sustainability in the context of climate change, policy recommendations include promoting climate-resilient crops, investing in smart irrigation systems, expanding affordable agricultural insurance, and encouraging sustainable fertilizer use through incentives and training. These strategies can help mitigate climate risks, enhance productivity, and reduce the environmental impact of agricultural activities. Full article

Climate variability intensifies weather risks across smallholder rainfed farming systems in Africa. Farmers often respond by minimizing the use of modern inputs and opting for low-cost traditional practices, a strategy that decreases average yields and perpetuates poverty. While crop insurance could incentivize greater adoption of inputs, indemnity-based programs face market failures. Weather index insurance (WII), which utilizes objective weather data to trigger payouts while addressing traditional crop insurance market failures, is a viable solution. However, empirical evidence on the impact of WII remains limited, with most studies relying on controlled experiments or hypothetical scenarios that overlook real-world adoption dynamics. This study analyzed observational data from 400 smallholder farmers across diverse agroecological zones in Njoro Sub-County, Kenya, using instrumental variable regression to evaluate the impact of weather index insurance (WII) on input adoption and intensity of use. Findings indicated that WII significantly increased the adoption and intensification of improved inputs while displacing traditional practices, with effects moderated by gender, financial access, and infrastructure. Specifically, active WII users applied 28.7 kg/acre more chemical fertilizer and used 2.6 kg/acre more hybrid maize seeds while reducing manure and traditional seed usage by 27 kg/acre and 2.9 kg/acre, respectively. However, the effectiveness of WII was context-dependent, varying under extreme drought conditions and in high-fertility soils, which directly affected resilience outcomes. These findings suggest that policies should combine insurance with targeted agroecological practices and complementary measures, such as improved access to credit and gender-sensitive extension programs tailored to the specific needs of women farmers, to support sustainable agricultural transformation. Full article

The Black–Scholes model is a fundamental concept in modern financial theory. It is designed to estimate the theoretical value of derivatives, particularly option prices, by considering time and risk factors. In the context of agricultural insurance, this model can be applied to premium determination due to the similar characteristics shared with the option pricing mechanism. The primary challenge in its implementation is determining a fair premium by considering the potential financial losses due to crop failure. Therefore, this study aimed to analyze the determination of rainfall index-based agricultural insurance premiums using the standard and fractional Black–Scholes models. The results showed that a solution to the fractional model could be obtained through the Daftardar-Gejji Laplace method. The premium was subsequently calculated using the Black–Scholes model applied throughout the growing season and paid at the beginning of the season. Meanwhile, the fractional Black–Scholes model incorporated the fractional order parameter to provide greater flexibility in the premium payment mechanism. The novelty of this study was in the application of the fractional Black–Scholes model for agricultural insurance premium determination, with due consideration for the long-term effects to ensure more dynamism and flexibility. The results could serve as a reference for governments, agricultural departments, and insurance companies in designing agricultural insurance programs to mitigate risks caused by rainfall fluctuations. Full article

In Europe, climate change has a big impact on agriculture, due to an increase in the frequency and severity of extreme weather events. Many and prolonged droughts, heatwaves, floods, and hailstorms cause major economic losses that affect crop quality and generate instability in supply chains. In this study, we analyse the evolution of extreme weather events across Europe starting from the 1980s. The economic losses caused by extreme events were divided into three categories: heatwaves, frost, and fires; floods; and storms. In order to identify the trend and any shifts of the trend of the extreme weather events, we calculated moving averages over different periods: 5, 10, 20, and 30 years. The moving average analysis shows how climate change has altered from causing isolated and temporary economic losses to generate a consistent upward trend in losses, with an increasingly significant impact in the short, medium, and long term. In the second part of this study, we conducted a correlation analysis between the economic losses caused by extreme weather events and variations in property insurance premiums (fire and other property damage—which includes crop insurance premiums) and we calculated correlation coefficients directly, with a one-year lag, and with a two-year lag. Thus, we analysed whether insurance markets respond immediately to incurred losses or whether, depending on climate trends, there are delays in premium adjustments. Full article

Citrus cultivation represents one of the major economic pillars of the Valencian Community (Spain). Frost events pose a significant threat to these plantations, resulting in substantial economic losses. This study aims to assess the frequency and intensity of frost occurrences in the region from 2004 to 2023, using Meteosat Second Generation satellite imagery. These images provide daily land surface temperature data at 15 min intervals. Frost days were defined as those when temperatures fell below −2.3 °C, the threshold at which orange fruits become susceptible to damage, with different temperature thresholds applied to estimate varying levels of crop damage. Frost duration was also analyzed to classify event intensity and its potential impact on citrus crops. Annual comparisons revealed a decline in both the severity and frequency of frosts, particularly in cases of “moderate” and “intense” damage, supporting forecasts of increased regional aridity and suggesting new opportunities for expanding citrus cultivation to higher altitudes. When compared with farmers’ records, this study’s methodology proves effective in assessing frost impact and offers potential use for winter crop insurance. Validation was conducted using in situ data from the Spanish National Meteorological Agency (AEMET). Full article

Agricultural insurance is by far the most popular risk management tool used in Iran. Despite many years of experience, Iran’s current insurance policy has not managed to protect all producers in the sector. The basic principle of whole-farm insurance consists of pooling all the insurable risks of a farm into a single policy and overcoming most of the major impediments to existing policies. This study aimed to evaluate the benefits of whole-farm insurance (WFI) in Zanjan province of Iran. This study employed historical farm-level and county-level data from 1982 to 2021 to estimate yield and price density functions and predict future values. Parametric and non-parametric approaches were utilized to calculate farmers’ expected compensation and guaranteed and simulated revenues. The premium rates were then calculated using the PQH simulation and Cholesky decomposition and compared under three scenarios: the single-crop, double-crop, and triple-crop options. Finally, farmers’ welfare benefits were compared under the three scenarios with the no-insurance case. The results demonstrate that WFI provides lower loss ratios compared to yield insurance and crop-specific insurance. Furthermore, producer welfare can be improved when they insure at least one crop compared to no-insurance. For example, the welfare benefits of insuring wheat, barley, alfalfa, wheat–barley, wheat–alfalfa, barley–alfalfa, and barley–alfalfa in terms of cost reduction to producers at 75% coverage are 8.8, 1.8, 2.9, 1.2, 0.9, and 1.8, respectively. Therefore, we recommend that the Iranian Agricultural Insurance Fund adopts WFI as a new risk management tool. This policy has the potential to decrease insurance premiums and administrative costs while improving the certainty equivalents and benefits to farmers through crop insurance. Full article

Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims. Full article

Rainfall is significantly essential in the agricultural sector to increase productivity. However, rainfall instability serves as a potential source of risk, causing crop failure and negatively impacting the welfare of farmers. To mitigate this risk, rainfall index-based agricultural insurance offers financial protection to farmers. There is no information on how to set a reasonable premium in index-based agricultural insurance. Therefore, this research aimed to systematically explore a model for determining a rainfall index-based agricultural insurance premium, focusing on the methods used and their effectiveness in mitigating the risk of harvest failure in the agricultural sector. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method and a bibliometric analysis were used to collect and analyze articles from Scopus, ScienceDirect, and Dimensions databases. The results showed that there were 15 articles on determining a rainfall index-based agricultural insurance premium, where 4 used the Black–Scholes method and 11 applied other main methods. Meanwhile, no articles applied the fractional Black–Scholes method in determining agricultural insurance premiums based on the rainfall index, providing new opportunities for further research. The results contributed to the development of a model for agricultural insurance premium determination that could generate more diverse and flexible premium estimates as a sustainable method to mitigate the risk of harvest failure. This research is expected to serve as a reference for developing rainfall index-based agricultural insurance in the future and contribute to the Government of the Agriculture Department’s policy formulation regarding insurance programs for farmers. Full article

By the end of the decade, the world population is expected to increase by nearly one billion people, posing challenges to meeting global food demand. In this scenario, soybean production is projected to increase by 18% within this decade. Despite being the largest soybean producer, responsible for over 40% of soybeans produced worldwide, drought events often impair Brazilian production. The goals of the present research were to quantify soybean yield losses related to drought in Brazil from 1973 to 2023 at national, state, and municipal levels and to assess the spatial distribution of losses across the production areas. The hypothesis investigated is that year-to-year variations in soybean yield are closely related to water availability, considering that crop management practices are constant from year to year, while increments in soybean yield across time (more than five years) relate tightly to better crop management practices and breeding improvements. Thus, quantifying year-to-year yield losses might demonstrate the effects of water availability on soybean yield. Yield data from the 1976/1977 to 2022/2023 crop seasons from the 26 states and the Federal District came from the National Supply Company, while the Brazilian Institute of Geography and Statistics supplied yield data for the 1973/1974 to 2020/2021 crop seasons from 1998 municipalities with more than 14 crop seasons. Soybean drought yield losses were calculated for each cropping season individually at the municipal, state, and national levels, based on the deviation in the observed yield to the corresponding maximum yield in the five-year window, considering that crop management practices and genetics represent a regular increment in soybean yield, which means that production practices improved over time and deviations from year to year are mainly related to drought occurrence. Annual soybean yield loss (expressed in tons, USD, and percentage), frequency of yield loss, and severity of yield loss were calculated at national, state, and municipal levels for each cropping season. The Standardized Precipitation Index (SPI), acquired from the Brazilian Weather Forecast and Climate Studies Center at the National Space Research Institute, was used as a qualitative indicator to corroborate the assessed soybean yield losses related to drought. The results demonstrate yield losses in more than 50% of crop seasons at the national level, with a similar frequency across the five decades, albeit with lower severities in the last 30 years. The Central–West region was more stable than the South region, with yield losses of up to 74%. In five decades, yield losses related to drought events stand at 11.65%, corresponding to 280 million tons or USD 152 billion (considering the average soybean price in 2022 at the Chicago Board of Trade). At the municipal level, analogous behavior was observed across time and space. The outcomes from the present research might subsidize public and corporative policies related to agricultural zoning, farm loan programs, crop insurance contracts, and food security, contributing to higher agricultural, environmental, economic, and social sustainability. Full article

A protocol for detecting the potential occurrence of spontaneous combustion (SC) in stored cottonseeds and peanuts using a micro-incubator is described. The protocol indicates how to quantify CO₂ production rates and final CO₂ levels in wet versus dry cottonseed and peanut samples, which can provide crucial data for the early detection of SC risk in storage facilities. The experimental design utilizes a micro-incubator to simulate conditions found in large bulk crop storage. Parameters monitored include CO₂ concentration, temperature, and relative humidity. The protocol includes preparation methods, experimental procedures for both control (dry) and wet seed tests, and test termination criteria that allow for safe experimentation of likely pathogenic fungi. The protocol has three replicates for wet and dry conditions. The protocol is intended to facilitate future experimental studies and ultimately contribute to the development of a consistently reliable early warning fire detection system for SC in cottonseed and peanut warehouse facilities. A consistently reliable fire detection system would address a critical need in the cotton and peanut industry for improved fire risk management and insurability of storage facilities. Full article

China has invested substantial financial subsidies to promote the development of crop insurance; however, the insurance demand among farmers remains notably low, resulting in significant welfare loss. Based on a field survey conducted in 2021 in seven major grain-producing counties in Jiangsu Province, this study analyses the relationship between premium subsidy rates and the welfare effects of subsidies through theoretical model derivation and explores the impact of farmer heterogeneity on the results. This study innovatively introduces a power law distribution model to elucidate the distributional characteristics of farmers’ crop insurance demand, demonstrates the significant limitations of the linear demand model in welfare research, and effectively analyzes the welfare effects of China’s current crop insurance premium subsidy policy. The results indicate that: (1) the actual crop insurance demand of farmers aligns more closely with a power law distribution, and its long-tailed characteristics refute the assumption of linear distribution; (2) there exists an inverted “U”-shaped relationship between the subsidy ratio and the welfare effect, and an excessively high subsidy ratio produces substantial unnecessary losses; (3) variations in welfare effects exist among farmers in different regions, risk attitudes, and cultivation scales, but the range of differences between groups is limited. Full article

Climate change poses significant risks to natural and economic environments, particularly through its interaction with air pollution. As agriculture is vital for national production, and crop insurance supports social security, it is crucial to examine how air pollution affects crop insurance. Here, we quantify the impact of air quality on crop insurance claims from an actuarial perspective and evaluate the implications for the industry. Utilizing claims data from the U.S., we explore the potential of particulate matter (PM_2.5) as a predictor of insurance claims, building on literature that highlights its economic damage to crops. Through the application of a generalized additive model (GAM) and extreme gradient boosting, we found that PM_2.5 is indeed a factor influencing crop insurance indemnity in both models, with the GAM demonstrating superior predictive performance. Furthermore, we employed Bai and Perron breakpoint analysis to elucidate the relationship between PM_2.5 levels and crop insurance claims over time, alongside two-way fixed effects models to investigate its correlation with various crop types. Our findings highlight the need for crop insurance managers to integrate air quality considerations into their risk processes to ensure sustainability of the industry and pricing strategy in the face of evolving environmental challenges. Full article