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In response to climate change, many countries are introducing agricultural insurance. But will farmers buy more agricultural insurance as climate change deepens? The literature has shown that climate change has a significantly positive impact on agricultural insurance. Using data from China over the period 2012–2021, we examine the effects of climate change (CC) on agricultural insurance coverage (AIC). We find that CC does not always have a positive impact on AIC; instead, farmers' insurance choices depend on the difference between their total costs and earnings. Only when total earnings are greater than total costs will farmers choose agricultural insurance. Compensation and subsidies are components of total earnings and premiums are components of total costs. They are affected by the government's insurance policies and charging standards and insurance companies' compensation schemes. This study examines the factors that affect farmers' insurance choices and recommends strategies for improving agricultural insurance. [ABSTRACT FROM AUTHOR]

Agricultural insurance and agricultural credit are two important rural financial instruments, which play an important role in promoting rural poverty reduction. Based on the data of agricultural insurance and agricultural credit in 31 provinces, cities and autonomous regions in China, this paper makes an empirical analysis on the coordinated poverty reduction effect of agricultural insurance and agricultural credit and its differences in various regions in China by using the coupling coordination model and the static panel model. The results show that the development level of agricultural insurance and agricultural credit in China is low, and the development level of agricultural credit is higher than that of agricultural insurance. There is a coordinated development relationship between agricultural insurance and agricultural credit in China, and the coupling and coordination level of the two is constantly improving. The coordinated poverty reduction effect of agricultural insurance and agricultural credit and its regional differences in China are remarkable. This effect is more significant in South China, North China, northwest and Northeast China than in other regions. Based on this, it is suggested to vigorously develop agricultural insurance to improve the coupling and coordination level of agricultural insurance and agricultural credit. Create a good environment for promoting the coordinated development of agricultural insurance and agricultural credit. Pay attention to the regional differences of the coordinated poverty reduction effect of agricultural insurance and agricultural credit in China, and each region should choose its own development model. [ABSTRACT FROM AUTHOR]

Climate-smart agriculture restructures and reorients agricultural systems to improve food security in the face of climate change. This study aimed to examine whether climate-smart agriculture can enhance adaptation to climate change impacts and achieve food security. To fulfill this purpose, 180 agricultural specialists from Mazandaran Province, Iran, were chosen using the proportional stratified random selection approach, and a structured questionnaire was used to gauge their perspectives. The results showed that innovation, investment, and supportive institutions would be crucial in encouraging the use of climate-smart agriculture. Moreover, this study underscored the importance of improving farmers' awareness of the benefits of agricultural insurance. Enhancing insurance services specifically tailored to agricultural needs, both by the public and private sectors can significantly promote the adoption and implementation of climate-smart agricultural practices. Climate-smart agriculture may considerably increase food security by serving as a mediator between climate change adaptation and food security. It is indispensable to prepare the agricultural sectors in order to rapidly change the environmental landscape, utilize new technologies, increase the investment of public and private institutions, and provide more government support for climate-smart farming methods. The findings of this study could be employed to design suitable policies, investments, and institutional measures needed to properly monitor climate-smart agriculture and achieve food security. • This study aimed to explore the role of climate smart agriculture (CSA) in adaptation to climate change. • Innovation, investment, and supportive institutions would play an important role in accepting CSA. • CSA would be effective in increasing food security through the mediating role of adaptation to climate change. • Responsible and effective governance is critical for the long-term viability of both natural and human systems. • Low-income countries have additional infrastructure to move in a more sustainable direction. [ABSTRACT FROM AUTHOR]

We study the effects of health insurance coverage on agricultural production decisions, examining the causal relationships by exploiting a health care reform and providing a theoretical framework to elucidate underlying mechanisms. We find that the reform led to long-run increases in total cultivation investments and output, accompanied by a shift in households' cultivation portfolio towards riskier crops. We explain these findings using a model of agricultural investment, highlighting the important roles of health insurance in mitigating background medical expenditure risks and enhancing health. We also find that the reform improved households' financial well-being through reduced debts and defaults on loans. [ABSTRACT FROM AUTHOR]

Many countries have implemented premium subsidies for agricultural insurance to prevent the spread of natural disaster risks. However, whether such subsidies on agricultural insurance promoted the risk protection level remains unclear. In this study, we assessed the impact of premium subsidies on the risk protection level of agricultural insurance in China by combining the input-output (IO) model with the difference-in-difference (DID) model, using provincial panel data of China from 2003 to 2016. The IO model was employed to calculate direct and indirect economic losses from natural disasters. A risk protection level index was constructed by evaluating the loss compensation ability of agricultural insurance. The time-varying DID model was applied along with robustness tests to identify the effects of subsidies on the risk protection level. Heterogeneity analysis was conducted according to different levels of natural disaster losses. The results revealed that, on average, indirect losses accounted for 38% of annual total economic losses at a national scale. With the implementation of premium subsidies, the risk protection level significantly increased. Heterogeneity analysis indicated that the positive effect of subsidies was more evident in areas with higher economic losses as well as higher incidence of disasters. By evaluating the risk protection level of agricultural insurance and the effects of subsidies, this study aims to provide insights for subsidy design to mitigate economic losses from natural disasters and promote agricultural resilience. • Input-output model was combined with difference in difference model to assess the impact of premium subsidies. • Indirect economic losses accounted for up to 38% of total losses. • Premium subsidies have significantly increased the risk protection level index. [ABSTRACT FROM AUTHOR]

• Explain the economic value of the quadratic cost of MECM in terms of marginal cost and elasticity. • Adopt an opinion modification mechanism based on social network. • The MECMs with uncertain adjustment costs are developed under three uncertain scenarios. • The proposed models are applied to the agricultural insurance premiums subsidy policymaking in China. In the realm of group decision making (GDM), the maximum expert consensus model (MECM) emerges as a potent tool for consensus optimization. The complexity of decision-making environment leads to the uncertainty of adjustment costs and the intricate social relationships between decision makers (DMs). Therefore, this paper aims to develop the MECM that integrates both social relationships and uncertain adjustment costs to support social network group decision making (SNGDM) problems. Specifically, we propose a MECM with quadratic cost that can more accurately reflect DMs' sensitivity for opinion adjustment. Additionally, we adopt an opinion modification mechanism based on the information obtained from the social network. The paper also develops the robust MECM (RMECM) to handle the uncertainty of the unit adjustment cost under three uncertain scenarios. Finally, the efficiency of the proposed models is demonstrated by applying them to the agricultural insurance premiums subsidy policymaking in China, further substantiated by sensitivity analysis and comparative analysis showcasing their robust performance. [ABSTRACT FROM AUTHOR]

By using 1999 to 2016 agriculture and agricultural insurance data, building VAR model, this paper study the dynamic relationship among agricultural insurance and agriculture in China. We make fool use of VAR tools, such as granger casualty test, impulse response and variance decomposition. Empirical results show that agricultural insurance premium contributes to agricultural GDP and farmers' income in China about 12% in recent years while agricultural GDP is more important to agricultural insurance. 85% of agricultural insurance premium growth due to agricultural GDP. In addition, the increase of farmers' income can not improve agricultural insurance premium. Chinese government should develop agricultural insurance more steadily, help improve farmers' acknowledge about agricultural insurance and subsidizes agricultural insurance continually. [ABSTRACT FROM AUTHOR]

Effective risk control strategies require reliable risk assessment models to provide support. In recent years, the risk of extreme weather events has increased due to climate change. However, existing assessment models are not essentially used to explore short-term drastic changes in the environment, such as extreme heat or cold. Therefore, we proposed a climate-food risk assessment system that could detect drastic changes, such as extreme weather events and price fluctuations, in a timely and thorough manner. On the one hand, spikes were detected in the climate system. On the other hand, change points were detected in the food system. Finally, an innovative integrated analysis algorithm is carried out to obtain the ranking of highly impacted foods. Across all food supply chains, we found that sugar, bread and grains, and wine in grapes are most closely related to extreme weather events. This discovery needs to be treated with caution. To initiate a feasible control strategy for climate risk, a concrete policy implication was discussed in detail. If properly organized and supervised, the extreme weather agricultural insurance can become a transformative mechanism toward climate resilience. [Display omitted] • We study how extreme weather events may affect the price of food supply chains. • The top three food supply chains affected by extreme temperature are sugar, bread and cereals, and wine from grapes. • An innovative algorithm is provided to perform this integrated impact assessment. • A concrete policy application of extreme weather agricultural insurance is thoroughly discussed. [ABSTRACT FROM AUTHOR]

This study explores small-scale Vietnamese shrimp farmers' preferences for good aquaculture practices and proactive aquaculture development policy to encourage wastewater treatment and antibiotic use. Data were collected through a survey of farmers using two discrete choice experiments with a sample size of 450 respondents. The results of mixed logit and latent class models indicated that the farmers preferred good aquaculture practices in shrimp farming to conventional types, and the risk reduction and economic benefits of good aquaculture practices attracted shrimp farmers. However, many small-scale farmers did not prefer the integration of proactive aquaculture practices in development policy, despite the availability of a concessional loan to cover the initial investment cost, and they expressed less willingness to pay for wastewater treatment and antibiotic use regulations. To increase the adoption of good aquaculture practices, in addition to concessional funding policy, small-scale farmers also expressed a need for other support policies, such as the aquaculture insurance provision, subsidies for wastewater treatment, and compliance with regulations on antibiotic use. [ABSTRACT FROM AUTHOR]

• Shows how Index-Based Agricultural Insurance echoes colonial interventions. • Links climate vulnerability to history of finance, labour and migration in Senegal. • Develops Marxian concept of 'latent' surplus populations. Amidst growing concern about the displacement of rural populations exposed to climate change, new financial instruments are increasingly being presented as key tools for the management of climate risks. Index-based agricultural insurance (IBAI) is a particularly key example. Recent critical work has pointed to the deeply neoliberal character of IBAI and like interventions. However, the promotion of new financial instruments in response to crises of agrarian production has a much longer history in Africa and elsewhere. This article draws on Marx's concept of 'latent' surplus populations to trace out and explain parallels between IBAI and colonial interventions in Senegal's groundnut basin. Approaching the question in this way, the article highlights the long-run historical co-production and interdependence of the Senegalese state and a political ecology of groundnut production in which relations of indebtedness and the exposure to variable rainfall of a fragile relative surplus population has often been crucial to the mobilization of cheap labour for groundnut production. IBAI is thus positioned as part of a recurrent trajectory of emergency financial interventions aimed at smoothing out the contradictions implicit in this socio-ecological configuration. [ABSTRACT FROM AUTHOR]

Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [ABSTRACT FROM AUTHOR]

• Index Based Livestock Insurance has been sold in the Horn of Africa since 2010. • Patterns of purchases are not well explained by models of demand for insurance. • Supply appears to be uneven, restricting purchases in some areas. • Increasing coverage may require addressing both supply- and demand-side factors. To make a difference in lower-income countries, agricultural innovations must be adopted and ultimately diffused across diverse local environments. This study contributes to the ongoing debate about the factors limiting the spread of agricultural innovations by considering the role of heterogenous supply in determining observed demand for the Index-Based Livestock Insurance (IBLI) product, which is a commercial insurance product serving historically uninsured pastoralists in the Horn of Africa. Analysis of sales data from 2010 to 2020 in Ethiopia and Kenya shows that local conditions can reduce the likelihood of supply channels reaching prospective clients, effectively excluding them from accessing insurance, while other factors can work towards increasing supply of insurance while also decreasing demand for it. Surveys collected from insurance sales agents reveals considerable heterogeneity in their ability to and effort in suppling IBLI. Discussions with IBLI's providers confirms the role of supply constraints in observed demand; the firms consistently point towards the cost of last-mile extension and sales as their largest challenge to increasing sales, and emphasize that it is cost-prohibitive to provide equal access to well-trained insurance agents across the areas that they operate. These findings suggest that current investments aimed at increasing insurance coverage by increasing demand, for example through improved product design or by subsidizing premiums, should be accompanied by investments in developing more cost-effective marketing and distribution processes so that demand can be acted upon. On a broader level, the results highlight a need to consider non-random and incomplete supply as a factor when examining observed uptake of agricultural innovations. [ABSTRACT FROM AUTHOR]

The challenges faced by agricultural systems call for an advance in risk management (RM) assessments. This research identifies and discusses potential improvements to RM across 11 European Union (EU) farming systems (FS). The paper proposes a comprehensive, participatory approach that accounts for multi-stakeholder perspectives relying on 11 focus groups for brainstorming and gathering suggestions to improve RM. Data analysis is based on content analysis and coding of suggested improvements, and their assessment through the lenses of main challenges faced, farms' flexibility, and dependence on subsidies. First, the results show that necessary improvements differ depending on whether they have their origin in sudden shocks or long-term pressures. Second, farm dependence on direct payments determines a stronger need to improve financial instruments, whereas farm flexibility suggests a need for more accessible and tailored tools for low-flexibility FS, and increased know-what and know-how for high-flexibility FS. Third, our findings indicate a potential for extending stakeholder involvement in RM to new or unconventional roles. Underlying specific improvements, the paper suggests and discusses three main avenues to improve RM as a whole: i) a developed learning and knowledge network; ii) new forms of collaboration; and iii) integrated financial and policy instruments. • The conceptualization of risk management is extended including manifold actors. • Examples of new roles that stakeholders might play are given. • Long-term challenges calls attention to the need for longer-term strategies. • Improvements to learning networks and new forms of collaboration are proposed. • Integration of financial and policy instruments into long-term tools is required. [ABSTRACT FROM AUTHOR]

Insurance fraud detection has always been manual labor relegated to claim agents, who examine the facts and reach on an intuition- based conclusion. The following article proposes an automated solution to regulate the process of fraud detection of field fire-based insurance claims in the agricultural sector. The proposed work is an amalgam of computer vision, deep learning, and Internet of Things and aims to inculcate the positives of each of these technologies. To the best of our knowledge, a combination of the said technologies has never been used for insurance fraud analyses in the field of agriculture, making this a novel approach. The proposed model actively reads the input from the IR and temperature sensors of the IoT device, which further collects images of the field once the sensor values cross their respective thresholds. The collected images are then fed into a fire detection model trained using a variety of classifiers for performance comparisons. The results display that the proposed solution has an accuracy of 97%, which can be further increased with a refined dataset dedicated solely to fraud detection. [ABSTRACT FROM AUTHOR]

Coffee (Coffea arabica L.) is a climate-sensitive crop; rainfalls may trigger flowering event occurrences, and extreme rainfall during a flowering day can cause considerable yield reductions. Multiple flowering events can occur in the span of 12 months; the number varies from year to year. This paper introduces a Bayesian network model capable of inferring coffee flowering events in coffee areas in the Pacific Region of Central America based on observed data for coffee flowering and precipitation. The model structure was determined based on expert knowledge, and the model parametrization was learned from 53 years of data registered in the region. Data from four farms in the region were used for model validation. The model's performance in the inference of flowering intensity was good (spherical payoff of 0.78 out of maximal 1.00), and the model was able to depict expected behaviors for single and multiple flowerings. Further, comprehensive new details on the dynamics of multiple flowerings within a crop season were obtained, e.g., that a large flowering event tends to occur more quickly (8 to 10 days) after rain than a small flowering (10 to 13 days). We believe that this Bayesian network model has the potential to evolve and support the development of agricultural index-based insurance to deal with yield losses due to extreme rainfall during flowering. The use of longer farm records for model building may also serve to increase farmers' trust in the reliability of the tool. • Coffee flowerings are affected by extreme climatic conditions. • 53 years of farmers' data were used to build a Bayesian Network flowering model. • Multiple flowering events can be inferred in a crop year. • The model was validated and used to learn about the flowering dynamic. • Model suitable for Pacific Central American areas or others with unimodal rain patterns. • The model can serve as a basis for index-based insurance against rainfall on flowering. [ABSTRACT FROM AUTHOR]

The just-ended Sharm El-Sheikh Climate Change Conference in November 2022 brought to light the need to mitigate climate change adaptation related to risk and challenges through Climate financing, has made it very paramount the need to protect fish farmers' investments from climate change-related risk through the development of climate-smart insurance products and covers to help mitigate climate change effects on aquaculturists. As a result, this paper examines aquaculture farmers' preference for climate-smart aquaculture insurance products, challenges of climate-smart aquaculture insurance, and their preferred insurance cover. Survey data was collected from 140 fish farmers and analysed via Multinomial logistic regression, Bivariate Probit regression and Kendall's coefficient of concordance. Climate-Induced Aquaculture Stock Mortality Insurance (CIASMI) was the most-preferred climate-smart insurance product. Insurance Cover for Diseases (ICD) was the least preferred. The results further indicated that membership in a farmer organisation, farm income, credit access, climate-related peril and anthropogenic-related peril had a positive influence, whereas years of education had a negative influence on climate-smart aquaculture insurance participation. Moreover, production system had a negative relationship with mode of participation while number of employees, farm income and climate-related peril influenced mode of participation positively. The results also revealed that age, sex, stock size, awareness, income, climate and environmental-related peril, and anthropogenic-related peril significantly influenced the choice for climate-smart aquaculture insurance products, specifically climate risk related to stock mortality insurance and climate-related to consequential loss insurance. Finally, delay in claim settlement was the most pressing constraint. In contrast, lack of experts in disease diagnosis was the least ranked constraint of climate-smart aquaculture insurance. Therefore, this study provides insights for the Fisheries Commission, Ghana Agricultural Insurance Pool (GAIP), World Cover and other agricultural insurance companies globally to draft climate-smart insurance products for aquaculturists. • Majority of aquaculturists preferred individual aquaculture insurance participation. • Climate -induced aquaculture Stock Mortality Insurance (CIASMI) was the most-preferred climate-smart insurance product, while Insurance Cover for Diseases (ICD) was the least preferred. • Climate related factors had an influence on climate-smart aquaculture insurance participation. • We developed a framework to implement climate smart aquaculture insurance. [ABSTRACT FROM AUTHOR]

This study aims to investigate the effects of agricultural insurance on household economic resilience among rural households in China. To this end, this research derives data from the 2020 Guanzhong Plain Farm Household Survey and uses the Ordinary Least Squares (OLS) model, mediated effects model, and heterogeneity test. The results of the OLS model show that agricultural insurance exerts a substantial and positive influence on household economic resilience, with each unit of increase in the participation of farmers' households in agricultural insurance causing a 10.8% increase in household economic resilience. In addition, estimations of the mediated effects models reveal that agricultural insurance profoundly bolsters the economic resilience of rural households by effectively spreading risks, facilitating credit access, and augmenting income generation capabilities. Moreover, the heterogeneity analysis showed that agricultural insurance participation has a more significant effect on the economic resilience of cash crop households. The mediated effects result also indicate that agricultural insurance by large farmers plays a stronger role in promoting household economic resilience. These findings have policy implications for improving the agricultural insurance system, reinforcing the risk resilience among rural families, and advancing rural economic development. [Display omitted] • Agricultural insurance has a positive effect on household economic resilience. • Risk appetite, credit availability, and household income play a mediating role. • Agricultural insurance has a greater effect on cash crop and large farmers. • Helping to improve the agricultural insurance system and promote rural development. [ABSTRACT FROM AUTHOR]

Abstract China's fallow compensation policy is known to be relatively simple and unsophisticated, and in much need of improvement. In seeking such improvement, this paper designed some alternative compensation schemes that include recultivation insurance, priority right of participation in the fallowing work or free agricultural inputs, but lower income subsidy, and reported on a choice experiment of alternatives with 421 peasant-farming households in Chaling County, Hunan Province – a fallow area of heavy-metal-contaminated farmland. A mixed logit model was used to compare the choices made between pilot and non-pilot villages, and the influencing factors involved. The results showed that most farmers preferred the alternative compensation schemes to the current policy, and income subsidy was not the only factor affecting their choices, which indicated recultivation insurance and a longer period of free agricultural inputs were also important. However, the preferences differed between pilot and non-pilot village farmers, in which the pilot village farmers were more willing to accept less income subsidy in favor of increased recultivation insurance and an extended period of free agricultural inputs. By contrast, non-pilot village farmers preferred to adopt an alternative compensation scheme with a priority right of participation in the fallowing work and high level of income subsidy. The possible reason for the differences was that the publicity and training activities provided by the government had changed the preferences of pilot village farmers. Generally, the effects of various forms of economic compensation and single cash payment were different about fallow compensation policy, and the preferences of farmers from different backgrounds might also vary from person to person. Different regions needed to design the fallow compensation policy according to practical problems in the use of farmland and farmers' preferences. Highlights • Choice experiment is used to compare the preference between pilot and non-pilot village farmers. • The effects of various forms of economic compensation and single cash payment are different. • The preferences of farmers from different backgrounds may be different. • Extra attention should be given to works such as fallow publicity and training. [ABSTRACT FROM AUTHOR]

[Display omitted] • A sequentially integrated algorithm for crop lodging identification with low data acquisition costs, high automation and high accuracy. • An automatic method to extract lodged and non-lodged samples based on the isolation forest algorithm. • The automatic identification of crop lodging achieved an overall accuracy of 78%. • Exploring the practicality of Sentinel-1 and Sentinel-2 data for crop lodging identification. • Providing support for agricultural disaster emergency management and agricultural insurance loss assessment. Rapid identification of crop lodging has implications for optimizing disaster prevention and mitigation measures. This study proposes a sequentially integrated algorithm for crop lodging identification, which first automatically extracts lodged and non-lodged samples based on the isolation forest algorithm from the screened features dataset and then uses a supervised classifier for lodging identification. Taking the crop lodging disaster caused by the super typhoon "Maysak" in Bohetai Township as an example, we calculated 162 feature indices from Sentinel-1 and Sentinel-2 satellite data pre- and post-disaster and screened the optimal features. Then, the abnormal scores of the cultivated-land pixels were calculated using the isolation forest algorithm. After that, we calculated the coefficient of variation (CV) of the abnormal scores of each group, extracted the lodged and non-lodged samples based on the cumulative CV contribution rate, and input samples into three supervised classifiers (random forest, maximum likelihood, and support vector machine) to identify lodged crop pixels. The results showed that feature screening increased the identification accuracy of crop lodging by more than 10% and difference features of short-wave infrared (SWIR) and red edge bands contributed significantly to the identification. The accuracy of crop lodging automatic identification was verified using samples obtained from visual interpretation and digitization of UAV images as reference data and the overall accuracy reached 78%. The proposed method can simultaneously recognize lodging of different crop types without the need to select lodged samples and extract lodged ranges for each crop. With low data acquisition costs and high automation, the proposed method has promising applications in agricultural disaster emergency management and agricultural insurance loss assessment. [ABSTRACT FROM AUTHOR]

• The radar time series of maize populations was constructed. • The internal relationship between Synthetic Aperture Radar (SAR) features and lodging grades was explored. • The lodging severities of maize were monitored on the regional scale by Time-weighted Dynamic Time Warping (TwDTW). Lodging is one of the most important stresses in maize growth, which affects the yield, quality and mechanical harvesting ability of maize. Monitoring the lodging range and severity of large-scale maize by satellite remote sensing technology can support agricultural insurance claims, planting structure adjustment and field management. The purpose of this study is to investigate the application of Synthetic Aperture Radar (SAR) remote sensing images in monitoring the severity of maize lodging at the county scale. A time series feature dataset was obtained from multi-temporal Sentinel-1 SAR images before and after lodging. The sensitive features of maize lodging severity were screened by Jeffreys-Matusita (J-M) distance. The study compared the performance of three different methods for monitoring maize lodging severity, including Time-weighted Dynamic Time Warping (TwDTW), random forest (RF) and minimum distance (MinD). The results showed that all time phases of SAR images selected in the study had an important contribution to the differentiation of maize lodging severities. Among all features, the VH (Vertical Send and Horizontal Receive) time series features composed of five time-phase had better discrimination for different maize lodging severities. The accuracy of extracting maize lodging severities based on TwDTW was 76% and the Kappa coefficient was 0.70, which was higher than RF and MinD. Thus, by utilizing the multi-time phase features of Sentinel-1, we can effectively monitor the grades of maize lodging at the county scale. [ABSTRACT FROM AUTHOR]

• A lodging area detection model performance evaluation method was proposed. • Three indicators were introduced to assess model comprehensive performance. • Challenges were addressed in the presence of spatial and temporal heterogeneity. Fast and accurate assessment of wheat lodging holds significant importance for disaster prevention and agricultural insurance claim settlement. However, lodging detection is often influenced by the spatial and temporal heterogeneity of fields, leading to inconsistent lodging features in images captured at different times and locations. This inconsistency poses challenges for evaluating model performance and obtaining consistent results. In this study, we proposed a comprehensive lodging area detection model performance evaluation method for multi-year and multi-phenological periods. Three evaluation indicators—accuracy, potential, and stability—were introduced to assess model performance from different perspectives. Additionally, a weighted method is employed to combine the results of multiple experiments. To evaluate the performance of lodging area segmentation in wheat fields, we selected three feature adaptive models (CBAM-unet, SE-unet, and Swin-transformer) along with the Unet model. The experimental results are as follows: (1) Swin-transformer achieved the highest weighted average accuracy (Acc cp) of 83.53% among the four models evaluated. (2) Swin-transformer exhibited the highest upper limit of segmentation accuracy, reaching 97.98%. However, its average accuracy in the prediction experiment was 85% of the upper limit, suggesting potential for optimization. (3) CBAM-unet demonstrated the lowest overall weighted segmentation accuracy variance (35.92) compared to the other three models, indicating higher stability. Based on the experimental results it can be seen that our proposed method and model can address the challenge of evaluating lodging area detection models under spatial and temporal heterogeneity. [ABSTRACT FROM AUTHOR]

• The widely held view that smallholders in Africa are not willing to pay for agricultural insurance should be re-visited. • Agricultural insurance developers and providers should investigate farmers' most preferred insurance product type, duration, loss assessment criteria and mode of payment of premiums. • Farmers' subjective knowledge of and indigenous beliefs about agricultural risk and insurance negatively affect their decisions to insure their farms. • Hybrid (indemnity-index) insurance policy is mostly preferred by cashew growers. • Farmers are quite sensitive to agricultural insurance premiums, expected payouts and type of perils covered by the insurance. The development and uptake of agricultural insurance products by farmers in developing countries has been universally and disappointingly low. This paper investigates farmers' preferences and willingness to pay for a variety of agricultural insurance products, including indemnity insurance, index insurance, benchmark insurance, and hybrid (indemnity-index) insurance in the Bono and Bono East Regions of Ghana. We employed hybrid latent class and multiple indicators, multiple causes (MIMIC) models using discrete choice experimental data from 383 cashew growers. The results show that cashew farmers are heterogeneous in their preferences, with a majority advocating for agricultural insurance against key perils such as wildfires, high wind speed and excess rainfall. Hybrid (indemnity-index) insurance product is highly preferred and valued by cashew farmers advocating for agricultural insurance, followed by index insurance product. Farmers are quite sensitive to premiums, expected payout, type of perils covered by the insurance and loss assessment criteria. Social and behavioural constructs relating to trust in insurance companies, subjective knowledge about agricultural insurance, and perceived agricultural insurance benefits are significant determinants of farmers' preferences for agricultural insurance products. The findings imply that it has become very necessary for agricultural insurance product developers, underwriters, and insurers in developing countries to gain more insight on farmers' social and behavioural constructs related to agricultural risk, insurance knowledge and trust. We suggest that agricultural insurance product developers and policy-makers involved in agricultural insurance development should improve farmers' understanding of basis risk and the concept of agricultural insurance, as well as the potential benefits of farm insurance. In this way, we can improve the uptake of agricultural insurance products by farmers in developing countries. [ABSTRACT FROM AUTHOR]

This paper undertakes a critical assessment of the present state of fisheries insurance in India. Various factors that contribute to the poor performance of fisheries insurance in the country were identified and ranked based on the responses of fishermen and fish farmers in major maritime states. In the light of the findings, measures such as leveraging the potential of technological interventions, developing innovative institutional mechanisms, bridging the trust deficit amongst the stakeholders and bringing about attitudinal changes through awareness drives are suggested as way forward towards harnessing the potential of insurance as a safety net tool in fisheries sector of India. [ABSTRACT FROM AUTHOR]

Agricultural insurance programs are currently being championed by international donors in many developing countries. They are acclaimed as promising instruments for coping with climate risk. However, research on their impacts has mainly focused on economic considerations. Studies on broader social and ecological consequences are sparse and have produced ambiguous and inconclusive results. We address this knowledge deficit by (a) advocating for a holistic view of social-ecological systems and vulnerability when considering insurance impacts; (b) offering a systematic overview highlighting the potential beneficial and adverse effects of ‘climate insurance’ in agriculture, particularly where programs target intensifying agricultural production; and (c) suggesting preliminary principles for avoiding maladaptive outcomes, including specific recommendations for designing appropriate impact studies and insurance programs. Our synopsis brings together scientific knowledge generated in both developing and developed countries, demonstrating that agricultural insurance programs shape land-use decisions and may generate serious economic, social, and ecological consequences. If insurance is to be an appropriate tool for mitigating the impacts of climate change, it needs to be carefully developed with specific local social-ecological contexts and existing risk coping strategies in mind. Otherwise, it is liable to create long-term maladaptive outcomes and undermine the ability of these systems to reduce vulnerability. [ABSTRACT FROM AUTHOR]

Human capital theory identifies investments in health and health care policy as critical components to building human capital, however, there has been little research critically examining how health insurance policy factors into broader workforce development initiatives in the farm sector. In the U.S., the Patient Protection and Affordable Care Act (ACA), signed into law in 2010, represents the first major attempt to restructure the U.S. health insurance system, yet there has been little research examining how the ACA will affect the farm sector. This article seeks to broaden approaches to agriculturally based economic development by qualitatively and quantitatively: 1) establishing health insurance as an issue directly tied to human capital in the food and agriculture sector; 2) benchmarking farmer health insurance knowledge, the link between health insurance and farm finances, individual and employer attitudes towards the ACA, and health insurance decision-making shortly after the implementation of the ACA, and; 3) contributing research findings that inform national and state efforts to develop human capital in the food and agriculture sector. Farmers in this national sample tended to be well-insured, largely due to off-farm employment. However, a meaningful numbers of farmers reported being uninsured or underinsured and explained how their own personal health is linked to the health of the farm operation. Farmer attitudes towards the ACA and anticipated impacts varied across gender, race and ethnicity; age groups; farm scale; farmers who employ non-family members and state policy environments. Overall attitudes towards the ACA are largely mixed and differences are shades of grey rather than outright extremes. Young and beginning farmers tended to have the most positive attitudes towards the ACA and were most vocal in connecting health insurance to the viability, growth and development of their farm. [ABSTRACT FROM AUTHOR]

A stochastic model with endogenous and exogenous periodicities is proposed in this paper on the basis of metapopulation dynamics to model the crop yield losses due to pests and diseases. The rationale is that crop yield losses occur because the physiology of the growing crop is negatively affected by pests and diseases in a dynamic way over time as crop both grows and develops. Metapopulation dynamics can thus be used to model the resultant crop yield losses. The stochastic metapopulation process is described by using the Simplified Incidence Function model (IFM). Compared to the original IFMs, endogenous and exogenous periodicities are considered in the proposed model to handle the cyclical patterns observed in pest infestations, diseases epidemics, and exogenous affecting factors such as temperature and rainfalls. Agricultural loss data in China are used to fit the proposed model. Experimental results demonstrate that: (1) Model with endogenous and exogenous periodicities is a better fit; (2) When the internal system fluctuations and external environmental fluctuations are negatively correlated, EIL or the cost of loss is monotonically increasing; when the internal system fluctuations and external environmental fluctuations are positively correlated, an outbreak of pests and diseases might occur; (3) If the internal system fluctuations and external environmental fluctuations are positively correlated, an optimal patch size can be identified which will greatly weaken the effects of external environmental influence and hence inhibit pest infestations and disease epidemics. [ABSTRACT FROM AUTHOR]

A risk-based approach is more meaningful to quantify the effects of drought on crop yield given the randomness nature of past drought events, compared to the deterministic approach. However, the majority of these probabilistic studies are conducted at national or global scale to assess the yield loss probability under given drought conditions. There is still a lack of research combining droughts and crop yields in a probabilistic way at a local scale. Moreover, it is unclear how drought threshold triggering yield loss at a given conditional probability will vary in dryland cropping regions. Here, we used wheat yield data from 66 shires in New South Wales (NSW) wheat belt and meteorological data from 986 weather stations. A copula-based probabilistic method was developed to explore the yield loss probability to various drought conditions. We investigated the drought threshold under a given yield loss probability using the constructed copula function. We found that SPEI-6 in October was the optimal drought index to represent detrended wheat yield variation as this period covered the main growth stages of winter wheat in the study region. Our results show that as the severity of drought increased, the wheat yield loss probability also increased. Yield loss probability varied among the study shires, mainly due to the various climate conditions of each region. The drought threshold in subregion 1 (the northwest) was highest, followed by subregion 2 (the southwest) and subregion 3 (the eastern), indicating that wheat yield in subregion 1 was more sensitive to drought. The findings could provide important direction and benchmarks for stakeholders in evaluating the agricultural impact of drought, especially in those drought prone areas. We expect that the methodological framework developed here can be extended to other dryland areas to provide helpful information to growers, risk management policy makers and agricultural insurance evaluators. [Display omitted] • Wheat yield failure risk under drought conditions is quantified using copula functions in southeastern Australia. • Yield loss probabilities under drought conditions are much higher than those under non-drought conditions. • Western region in the study area is more vulnerable to drought than eastern region. • Drought thresholds vary across regions due to variable climate. [ABSTRACT FROM AUTHOR]

• Lodging severity of maize was described by canopy height and plant lodging angle. • Three methods of reconstructing the canopy structure of lodging maize were compared. • The lodging angle inversion model of crop was established. • UAV-LiDAR performed best for monitoring maize lodging severity. Lodging is one of the common stresses in maize production, which affects the benefit of farmers and impacts food security. Rapid monitoring of crop lodging can support loss assessment and agricultural insurance claims. Unmanned aerial vehicle (UAV) technology has been widely used in maize lodging monitoring in recent years. Compared with remote sensing monitoring based on spectral and texture features, the method based on crop three-dimensional structure data can accurately and quantitatively monitor the severity of maize lodging. The purpose of this study was to compare the effectiveness of UAV-LiDAR, backpack LiDAR, and 3D model reconstruction of UAV digital image (UAV-DIM) data on monitoring maize lodging. The canopy height model (CHM) was constructed using three kinds of data in the same conditions, and the canopy height of different lodging types of maize was extracted. The angle inversion model was proposed and the lodging angle of maize in the experimental plots was calculated. The results showed that UAV-LiDAR had the best performance in monitoring maize lodging. The determination coefficient (R2) of the canopy height was 0.956, while the RMSE was 0.097 m and NRMSE was 0.067. The correlation coefficient (ρ) of the lodging angle was about 0.9. It is the best method to monitor the lodging of maize based on three-dimensional structural information. The R2 , RMSE , and NRMSE of canopy height by the UAV-DIM method were 0.790, 0.482 m, and 0.336, ρ ≥ 0.8, respectively. It shows that the accuracy of maize lodging monitoring by this method is weaker than those of the UAV-LiDAR and backpack LiDAR methods. However, considering the advantages of low cost and high efficiency of data acquisition, the UAV-DIM method still has a wide application prospect in crop lodging monitoring. [ABSTRACT FROM AUTHOR]

This study focuses on an agricultural supply chain with a capital-constrained manufacturer, a bank and an insurer under three power structures: insurer-led Stackelberg (IS), bank-led Stackelberg (BS) and vertical Nash (VN). By establishing a two-level Stackelberg game, we examine the application condition and role of agricultural insurance under three channel structures and reveal the preferences of the supply chain and its participants for different channel structures. This study shows that, in either model, the manufacturer purchases agricultural insurance when the bank is highly loss-averse, but the probability of the manufacturer adopting agricultural insurance in the BS model is lower than that of the VN model but higher than that of the IS model. In these three models, the manufacturer's application of agricultural insurance increases the profits of the manufacturer, bank and insurer but reduces supply chain efficiency. In addition, from the perspective of the profit level and profit share of supply chain participants, for the manufacturer, the VN model of the two-level supply chain power structure betters than the BS and IS models of the three-level supply chain power structure, but the BS model is superior to the IS model; for the bank and the insurer, the higher their leadership positions in which power structure model, the better the power structure model. From the perspective of the overall profit and efficiency of the agricultural supply chain, the model preference of the supply chain is the same as that of the manufacturer. The findings provide new theoretical insights for the application of agricultural insurance and power relationship in the agricultural supply chain. • Application conditions and roles of agricultural insurance in IS, BS and VN models. • Profit relationship among the manufacturer, bank and insurer in IS, BS and VN models. • Preferences of the involved participants for different channel power structures. [ABSTRACT FROM AUTHOR]

In many low-income countries, agricultural producers face significant climate-related risks that undermine the resilience of their production and food supply systems. Agricultural insurance can help mitigate some of these risks, enabling farmers to increase farm investments, incomes, and food and nutritional security. This paper conducts a literature review to evaluate the extent to which agricultural insurance is gender-inclusive and gender-responsive (i.e., reaches and benefits both women and men), and whether there is potential for such insurance to empower women or even become gender-transformative. We find that existing agricultural insurance products are designed and delivered in ways that limit reach and benefits of insurance for women. Empirical research has focused less on the question whether insurance empowers women, let alone whether it has a transformative effect by changing institutional norms that perpetuate gender inequities. We present a case study of a crop insurance program in Kenya to discuss how agricultural insurance, if adequately designed, can have stronger impacts on gender-related outcomes. Empirically testing these approaches to agricultural insurance design is a key priority for future research. [ABSTRACT FROM AUTHOR]

Concerns about hydrological drought risk and irrigation water supply reliability have grown in recent years due to the increasing demand for water for irrigation and other uses, and the decline in water availability due to climate change. Hydrological drought insurance hedging against water supply gaps can be a key instrument for adapting irrigated agriculture to this new scenario, since it improves the resilience of the irrigation sector, which is having to cope with increasing uncertainty and vulnerability. The objective of this paper is to assess farmers' willingness to pay (WTP) for index-based hydrological drought insurance under different policy designs, considering several different amounts of insured capital, insurance deductibles, and contract terms. To that end, it uses a discrete choice experiment as the valuation method and the Sector BXII irrigation district (southern Spain) as a case study. The results show that farmers would be willing to pay for the proposed hydrological drought insurance, stating a higher preference for policy designs with lower amounts of insured capital, lower deductibles, and shorter contract terms. Moreover, the results also show the existence of heterogeneity among farmers' preferences, depending on their socio-economic characteristics. Finally, we compare the distribution of farmers' WTP for different policy design options of the proposed insurance with the commercial premium estimated using actuarial analysis. The comparison confirms that only the options with lower levels of insured capital present a mean WTP below the commercial premium, while the rest of the policy design options would need to be subsidized like other agricultural insurance schemes to make them attractive to most farmers. • Farmers are willing to pay for hydrological drought insurance. • Farmers prefer policies with low levels of insured capital, low deductibles, and short contract terms. • WTP is heterogeneous depending on farmers' socio-economic characteristics. • Only policy designs with low levels of insured capital are currently commercially viable. • Once the new drought insurance is implemented, it will become more attractive for farmers. [ABSTRACT FROM AUTHOR]

Farmers’ agricultural insurance decision-making is affected by behavioral factors. The paper using provincial panel data in Chinese 31 provinces and GMM dynamic panel data model examined the effect of five group variables on agricultural insurance decision making: risk perception and management, agricultural insurance recognition, trust, affordability and affection in agricultural production. The results show that agricultural insurance affordability, risk perception and management are the major influencing factors; agricultural insurance has the highest elasticity on household net income, which is 9.813, followed by elasticity on year-end total rural power machinery and cash expenditure on health care, which are 7.719 and 5.567 respectively, yet negative. Measures like raising household net income, building sound rural health care system, strengthening governments’ subsidy on agricultural machineries, improving the educational level of farmers can effectively boost agricultural insurance demands. [ABSTRACT FROM AUTHOR]

This paper considers the use of indices built on the basis of remote-sensing data for crop insurance purposes. In our analysis, we compare the capacity of two satellite-based vegetation health (VH) indices, the vegetation condition index (VCI) and the temperature condition index (TCI), measured for important periods of the crop vegetation to predict farmers’ wheat yields in two main grain producing regions of Kazakhstan. The selected indices are used for the design of index-based insurance contracts. The dependence of wheat yields on vegetation conditions as measured by VCI and TCI, is modeled by applying the copula approach. Our empirical results for 47 grain-producing farms in the Northern Kazakhstan show that insurance contracts built on VH indices can provide substantial risk reductions for a group of farms, though on average for the whole sample, risk reductions were found to be moderate. The study results suggest that the effectiveness of insurance contracts can be improved using satellite data of higher resolutions and measuring indices at more disaggregated levels. [ABSTRACT FROM AUTHOR]

• A deep leaning network was designed for farmland segmentation in satellite images. • Two-part framework of pseudo label generation and segmentation model fusion. • New block-wise metrics were introduced for parcel-level evaluation. • Experiments show significant performance improvements at the parcel level. The participation of insured smallholders and involvement of related requirements have been emerging and increasing with the popularization and promotion of agricultural insurance. Compared with traditional field surveys, remote sensing and deep learning technologies, with their rapid development in recent years, have provided an automatic and effective means for underwriting control in the agricultural insurance industry. Moreover, "smallholder-wise underwriting" is intensively expected by the local government and insurance companies to ensure precise claim settlements. In this process, farmland segmentation is a crucial and fundamental step for smallholder farmland claim settlements. However, segmentation of smallholder farmland parcels from satellite images remains a challenge, as the ridges and roads between farmland parcels are usually narrow and display confusing characteristics with their neighboring farmlands. This challenge causes undersegmentation on the farmland boundaries, leading to inaccurate and incomplete farmland parcel recognition. In this study, we aim to solve this problem by proposing a novel Deep Edge Enhancement Semantic Segmentation Network to refine parcels' boundary segmentation and improve the closure level of the farmland segmentation. We designed a framework for farmland edge enhancement through pseudo road label generation and model fusion. The proposed network aggregates feature extraction for ridge and road segmentation to improve its performance on farmland parcel recognition. Furthermore, we found that the commonly used image segmentation evaluation metric, such as pixel-wise–based mean Intersection over Union, cannot objectively reflect the effectiveness of the smallholder farmland segmentation. Therefore, we proposed two block-wise farmland evaluation metrics that are consistent with the practical evaluation rules and requirements of farmland segmentation at the parcel level. We implemented experiments in the study area of Jiaxiang County in northern China using the GaoFen-1 4-band multispectral images (red, green, blue, and near-infrared) at a spatial resolution of 2 m. Experimental results demonstrated the effectiveness of our method, which outperformed the baseline DeepLabv3+ network. [ABSTRACT FROM AUTHOR]

In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a crop model for a specific objective, location and implementation scale is a difficult task. A look inside the different crop and soil modules to understand how outputs are obtained might facilitate model choice. The objectives of this paper were (i) to assess the usefulness of crop models to be used within a crop insurance analysis and design and (ii) to select the most suitable crop model for drought risk assessment in semi-arid regions in Spain. For that purpose first, a pre-selection of crop models simulating wheat yield under rainfed growing conditions at the field scale was made, and second, four selected models (Aquacrop, CERES-Wheat, CropSyst and WOFOST) were compared in terms of modelling approaches, process descriptions and model outputs. Outputs of the four models for the simulation of winter wheat growth are comparable when water is not limiting, but differences are larger when simulating yields under rainfed conditions. These differences in rainfed yields are mainly related to the dissimilar simulated soil water availability and the assumed linkages with dry matter formation. We concluded that for the simulation of winter wheat growth at field scale in such semi-arid conditions, CERES-Wheat and CropSyst are preferred. WOFOST is a satisfactory compromise between data availability and complexity when detail data on soil is limited. Aquacrop integrates physiological processes in some representative parameters, thus diminishing the number of input parameters, what is seen as an advantage when observed data is scarce. However, the high sensitivity of this model to low water availability limits its use in the region considered. Contrary to the use of ensembles of crop models, we endorse that efforts be concentrated on selecting or rebuilding a model that includes approaches that better describe the agronomic conditions of the regions in which they will be applied. The use of such complex methodologies as crop models is associated with numerous sources of uncertainty, although these models are the best tools available to get insight in these complex agronomic systems. [ABSTRACT FROM AUTHOR]

Summary There are several studies on risk and insurance in village economies in the developing world. However, there is a lack of evidence for recent decades in spite of profound changes including structural transformation and widespread market liberalization in most regions. In this paper, I use primary panel data from five rainfed villages in a high-risk region of India and find evidence of considerable exposure as well as vulnerability to idiosyncratic and covariate risks. Further, there is significant wealth differentiated heterogeneity in vulnerability. The findings help identify the scope of public policy in improving risk management in rainfed agriculture. [ABSTRACT FROM AUTHOR]

An extension of guarantees related to rainfall-related risks in the insurance of processing tomato crops has been accompanied with a large increase in claims in Western Spain, suggesting that damages may have been underestimated in previous years. A database was built by linking agricultural insurance records, meteorological data from local weather stations, and topographic data. The risk of rainfall-related damages in processing tomato in the Extremenian Guadiana river basin (W Spain) was studied using a logistic model. Risks during the growth of the crop and at harvesting were modelled separately. First, the risk related to rainfall was modelled as a function of meteorological, terrain and management variables. The resulting models were used to identify the variables responsible for rainfall-related damages, with a view to assess the potential impact of extending insurance coverage, and to develop an index to express the suitability of the cropping system for insurance. The analyses reveal that damages at different stages of crop development correspond to different hazards. The geographic dependence of the risk influences the scale at which the model might have validity, which together with the year dependency, the possibility of implementing index based insurances is questioned. [ABSTRACT FROM AUTHOR]

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981–2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R 2 statistics (leave-one-year-out) for the overlapping period 2002–2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index. [ABSTRACT FROM AUTHOR]

• In a retrospective study of mishaps in degraded visibility the accident rate was undiminished (2010–2019). • In a prospective study of instrument-rated aircraft/owners, the minimum instrument approach count was not met by > 80% of aircraft/owners, with 24% of the deficient aircraft/owners departing into low clouds and 50% undertaking > 242 nmi flights despite the potential of diminished visibility. Instrument-rated private pilots (PPL-IFRs) are trained to fly in degraded visibility (where visual references are lost) and, towards maintaining proficiency, required to complete 6 instrument approaches in as many months. Nevertheless, operations in such weather carry an elevated risk of a fatal accident. Two hypotheses were advanced: (i) the majority of PPL-IFRs complete fewer than 6 instrument approaches/6 months and (ii) that a subset of such aviators still depart into such weather. Methods: Instrument approaches for a group of light aircraft in single PPL-IFR ownership (aircraft/owners) in the state with the largest count of single piston engine aircraft (Texas) was determined using Automatic Dependent Surveillance-Broadcast (ADS-B) data. Departure aerodrome weather flight category was per the Iowa State University database. IMC accidents were identified from the National Transportation Safety Board database and rates determined using GA fleet time. Statistical testing used Poisson Distribution, Chi-Square and Mann-Whitney U tests. Results: Over the Aug 2020-Mar 2021 period, 1,684 flights involving 106 aircraft/owners were ADS-B-tracked. Of these aircraft/owners, 81.2% completed < six approaches (median = 1.5). Importantly, for aircraft/owners completing < six approaches in the preceding 6 months, 24% departed into obscuration. Conclusions: This study demonstrates that, for the state most populous for reciprocating engine aircraft, the majority of aircraft in single PPL-IFR ownership fail to complete 6 in-flight instrument approaches/6 months. Nevertheless, some of these aircraft/owners elect to undertake flights in degraded visibility. From a practical perspective, PPL-IFRs unable to maintain instrument currency should be cautioned on the risks of operating in degraded visibility. [ABSTRACT FROM AUTHOR]

• LodgeNet was used to obtain the information of different lodging degrees in rice. • LodgeNet achieved 97.30% accuracy in rice lodging semantic segmentation. • LodgeNet has a better effect than other networks on a small data set. Rice lodging not only causes difficulty in harvest operations, but also drastically reduces yield. Therefore, it is very important to identify rice lodging efficiently. For unmanned aerial vehicle (UAV) remote sensing images, this paper combines the advantages of dense block, DenseNet, attention mechanism, and jump connection on the basis of U-Net network to propose an end-to-end, pixel-to-pixel semantic segmentation method to identify rice lodging. And the method can process the input multi-band image. The accuracy of the model proposed in this paper was 97.30% on rice lodging images, which performed better than other comparison methods in the test. At the same time, it has good effect on small sample data set. The results show that it is feasible to use the improved U-Net network model to extract the lodging area of rice, which provide a useful reference for rice breeding and agricultural insurance claims. [ABSTRACT FROM AUTHOR]

Highlights: [•] Four risk management policy options are analysed for Australia, Canada, and Spain. [•] We distinguish between farming risk and uncertainty in climate and farmer beliefs. [•] Climate change impact on yield variability affects robustness of different policies. [•] Extreme events and farmers’ misperceptions on climate can inflate a policy's budget. [•] Weather index insurance budget is less affected by extreme events and farmer beliefs. [ABSTRACT FROM AUTHOR]

Using agricultural household survey data and claim records from insurers in China, this paper analyzes hog producers' choice of the ways to prevent possible losses and identifies the relationships among biosecurity practices, vaccination, and hog insurance. By combining one probit and two structural equations, we adopt three-stage estimations by a mixed-process model to obtain results. The findings indicate that biosecurity practices provide the basic infrastructure for operating pig farms and complement both the usage of quality vaccines and the uptake of hog insurance. In addition, there is a strong substitution relationship between the quality of vaccine and the demand for hog insurance. Hog farmers that implement better biosecurity practices are more likely to seek high-quality vaccines or buy into hog insurance schemes, but not both. For those households with hog insurance, better biosecurity status, better management practices, and higher-quality vaccines significantly help to reduce loss ratios. However, we also find a moral hazard effect in that higher premium expenditures by the insured households might induce larger loss ratios. [ABSTRACT FROM AUTHOR]

Abstract: Lodging is one of the major factors contributing to crop yield and quality reduction. However, the current method for lodging rate evaluation (visual rating) lacks quantitativeness, objectivity, and precision. We have designed a low-cost, simple photogrammetric method to circumvent this problem. The efficiency of the method was evaluated in a field cultivated with a lodging-resistant variety and a standard variety of buckwheat (Fagopyrum esculentum Moench). Aerial images of the plant were acquired with a compact digital camera hung under a small balloon (2.2m in length, 0.56m3 in volume) from a height of 50–150m. Canopy height was calculated from the aerial stereo images with photogrammetric software. The photogrammetric analysis showed that digital canopy height data compared well with ground truth data at the flowering and seed-setting stages but were underestimated at the harvesting stage. The underestimation was probably caused by the change of plant structure due to flowers and leaves withering at harvest. Digital canopy height data revealed that the lower the sowing density, the less the lodging in the standard variety. This result would not be obtained by conventional visual rating and is essential for the development of cultivation technologies to reduce buckwheat lodging. The designed photogrammetric method can effectively estimate canopy height over a wide area and analyze lodging rate quantitatively. It would also have applications in other areas, e.g., the evaluation of damage by hail, the estimation of diseased areas, and assessment for agricultural insurance application. [Copyright &y& Elsevier]

Abstract: The potential risk for livestock breeding is much higher than other agricultural producer which affects income stability and welfare of households. Agricultural insurance is developed slowly in China. This paper analyzes farmers’ Willingness to pay (WTP) for cow insurance and factors influencing farmers’ participation in and WTP. It shows that the current premium is in the range that famers can accept. The acceptance of premium and farmers’ knowledge of allowance significantly influenced both of their participation and WTP. Policy makers should encourage farmers to enlarge their scale and make the premium as low as possible through subsidy farmers. [Copyright &y& Elsevier]

Abstract: This paper presents the results of an innovative use of the Delphi technique to obtain quantitative information for the evaluation of the Official Adjustment Rules for Damage Assessment in agricultural insurance. An efficient insurance system must guarantee that loss adjustment is performed fairly and transparently, so that the continual review and modification of adjustment rules becomes especially important. The present study develops and applies a methodology based on the subjective information compiled by experts to evaluate the modification of the Specific Loss Adjustment Rule for Fruit Crops under real market conditions. The results show that the method employed comes forward as a valid option to provide reliable information, as well as other economic and social advantages, in the absence of alternative statistical sources. The validity of the study has been proved by its utility, as the results were extremely useful for drawing up a new Official Adjustment Rule. This application opens up an interesting field of development for this technique, which will enable the proposed methodology to be applied to similar studies in the context of agricultural insurance loss adjustment. [Copyright &y& Elsevier]

Abstract: To establish a healthy and sustainable policy-driven agricultural insurance scheme, a discussion of the importance of Public-Private Partnerships (PPPs) is timely in light of the challenges that governments are facing against the backdrop of increasing scale and frequency of major agro-related natural disasters across the globe. Most recently in 2008, it is reported a total of 137 natural catastrophes led to overall economic losses of USD258 billion in the world. Moreover, as complexity and costs escalate, neither group - public nor private - can meet the challenge alone. This is particularly true for agricultural industry and rural areas in emerging economies that lack sufficient funds to deal with the increasing frequency and severity of natural disasters. Public private partnerships (PPPs), especially those involving reinsurance and capital market solutions, can improve risk management and prepare stakeholders for the consequences of uncertainties. A recent example is the agricultural reinsurance contract signed by the Beijing Municipal Government and Swiss Re. In 2007, Beijing imposed a new agricultural insurance policy for local farmers to promote the development of urban agriculture. Under the new policy, the Beijing municipal and district governments subsidized farmers up to 75% who buy agricultural insurances for certain crops/livestock, and 50% of management costs of the participating insurers are shouldered by governments. The municipal government also set up a protection funds for agro-related natural catastrophe events and regulated the loss ratio of 160% as an attachment point to start up the fund. In this paper, we apply the non-parameter information-diffusion model to stimulate possibility of different loss incurred in Beijing using time-series combined loss ratio data of historical agricultural insurance business. The results show that the current attachment point of protection fund, 160%, will impose additional financial burden to local government comparing to higher or lower points. It proves the necessity for government introducing reinsurance in the system to diversify risks and improve efficiency simultaneously. The paper also outlines the overall framework of Beijing agricultural insurance scheme for each stakeholder including direct insurers and reinsurers. [Copyright &y& Elsevier]

Abstract: In the new stage of rural reform, faced universal transfer of land contract rights, one of the most important tasks of rural system construction is accelerating development of agricultural insurance in China. However, agricultural insurance does not function efficiently enough to accord effective protection to farmers in land contract rights transfer, and agricultural insurance and transfer of land contract rights become a dual dilemma. To address this difficulty, agricultural insurance is premised to improve the implementation of land contract rights transfer, in which appropriate safeguards are acquired. Thus, the introduction of game theory is necessary and feasible for analyzing the strategy by which farmers select agricultural insurance and land contracts. Based on game analysis, the “players” are the agricultural insurance and land contracts encountered by farmers; “strategy” is composed of agricultural insurance and land contract selection behaviors. Farmer behavior can achieve equilibrium of anticipated profits. Equilibrium then yields the “outcome,” which is the cooperation and win-win solutions that may be achieved between agricultural insurance and transfer of land contract rights. Thus, in the rational selection process of farmers, agricultural insurance and transfer of land contract rights can facilitate positive support, interaction, and development. These will help realize overall progress in system innovation with respect to agricultural insurance. [ABSTRACT FROM AUTHOR]

Abstract: Agricultural insurance is an important component in the system of agriculture and the rural economy and plays an important role in the stability of rural society. Farmers as an important actor in agricultural insurance, the analysis of their economic behavior has important significance. Based on the basic assumptions of economics, this paper analyzes characteristics of farmer behavior in agricultural insurance and the factors affecting their behavior. Then using Von · Norman---Morgenstern Utility Model to analyze the risk preferences of individual farmer. The research result is that under the present stage, agricultural insurance behavior is influenced by many factors. In the voluntary insurance and nor a certain amount of subsidy, the vast majority of farmers would not choose insurance and the demand of agricultural insurance can only be regarded as a potential demand rather than effective demand. It needs joint work to ensure the farmer to attend the agricultural insurance. [ABSTRACT FROM AUTHOR]

Abstract: The CPC Central Committee Document No. 1 several times in the State Department stated that we will steadily push forward agricultural insurance policy the experimental work to speed up the development of various forms, multi-channel agricultural insurance. Agricultural insurance, however, due to natural disasters, leading to high-risk nature of the wishes of farmers and insurance companies, low purchase and show a high rate of “effective demand is insufficient, insurance products is in short supply” situation, the effectiveness of the agricultural insurance, risk compensation cannot fully achieve. In this paper, under the condition of bounded rationality analytical framework of evolutionary game model, from the farmers, insurance companies and mutual relations between the government proceed to analyze how to revise its strategy between the three options in order to achieve a balanced process. Then according to the balanced game model solution, we propose some policy recommendations to optimize the level of three well-beings. [ABSTRACT FROM AUTHOR]

Abstract: In 2004, China began to carry out policy-oriented agricultural insurance pilot experiment. By the end of 2009, the premium income scale of China''s agricultural insurance had ranked second in the world and first in Asia. In this article, through in-depth analysis on the experiment background of China''s policy-oriented agricultural insurance, the objectives of its implementation are firstly revealed. Secondly, based on clarifying the development process of China''s policy-oriented agricultural insurance, we analyze and summarize China''s policy-oriented agricultural insurance development effectiveness by taking the realization degree of policy objectives as the evaluation criteria, and also excavate out the main existence and potential problems. Finally, some proposals to perfect the system of China''s policy-oriented agricultural insurance are briefly put forward. [Copyright &y& Elsevier]