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The construction of digital villages has progressed in tandem with the transformation of traditional production methods, offering new perspectives for agricultural green growth and sustainable development. This study employs the entropy value method alongside the super-efficient global SBM (Slacks-Based Measure) mixed function model, which assesses efficiency by accounting for both inputs and outputs, thereby facilitating a comprehensive evaluation of agricultural green growth. This methodology facilitates the examination of the correlation between digital villages and agricultural green growth, as well as the influence of digital villages on this growth. Furthermore, the utilization of financial resources is employed as a mediating variable to elucidate the mechanism of action. The utilization of green finance and agricultural insurance can be facilitated by the establishment of digital villages, and that has been shown to promote agricultural green growth. Additionally, the promotion of agricultural green growth by digital village construction is stronger in middle-altitude regions, non-grain-producing regions, and regions where the digital literacy of the rural labor force is higher than average, as well as areas where the use of agricultural film is higher than average. Accelerating the construction of digital villages and promoting the utilization of rural financial resources while adapting the digital village development to local conditions are crucial for effectively fostering agricultural green growth and sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Agricultural insurance not only plays a significant role in disseminating agricultural risks and stabilizing farmers' income, but also constitutes an essential means of promoting the transformation of agricultural development from excessive reliance on resources to a green production mode. Based on the survey data of 978 rice farmers in Jiangxi Province in 2022, this paper explored the influence of farmers' participation in policy-based agricultural insurance on the adoption of soil testing and formula fertilization technology through the propensity score matching method. The results indicate that farmers participating in policy-based agricultural insurance exhibit significantly superior performance in adopting soil testing and formula fertilization technology compared to those not participating. Further analysis found that there were significant differences between farmers with different resource endowments participating in policy-based agricultural insurance, and that the policy-based agricultural insurance had a better effect on the adoption of soil testing and formula fertilization technology for farmers with more years of education, part-time employment, and more flat land than it did for farmers with fewer years of education, professional farmers, and more sloping land. The main conclusion is that the guarantee function of agricultural insurance in rural areas should be enhanced; the training and publicity of farmers' production technology and environmental awareness should be strengthened; rural characteristic industries and county economies should be vigorously developed; high-standard farmland should be vigorously constructed; and policy suggestions should be made on agricultural infrastructure construction. [ABSTRACT FROM AUTHOR]

Rainfall index insurance can enable farm households to manage production risk, but demand in developing countries remains low at market prices, in part because the insurance trigger may not correlate well with individual farm losses. Area‐yield crop insurance, which links payouts to average yield in a geographic zone, attempts to increase demand by more accurately targeting insurance payouts to production shortfalls. However, shifting from an exogenous weather‐based to an endogenous yield‐based index introduces concerns of asymmetric information, which can lead to market failures that constrain supply from providers. These features are inversely related: larger insurance zones inhibit index manipulation, but average yield is less informative about any individual plot. We quantify this tradeoff for maize in Ghana using a spatial yield model calibrated to match observed production. Insurers must demarcate zones of no more than 5000 farmers for area‐yield insurance to outperform weather insurance. The framework presented in this paper allows assessment of the relationship between index performance and asymmetric information in new crop insurance products. [ABSTRACT FROM AUTHOR]

In China’s agricultural catastrophe insurance market, issues of non-equilibrium are prominent. To understand the causes of non-equilibrium in agricultural catastrophe insurance and to develop prevention strategies, this study employs an Evolutionary Game Model, incorporating disaster and insurance data for three types of cereal crops in Henan Province to analyse the Evolutionarily Stable Strategies in the agricultural catastrophe insurance market. The research also considers government policies and disaster reinsurance as implicit participants in the model. The findings reveal significant differences in the impact of non-equilibrium in the agricultural catastrophe insurance market and the choice of game strategies, dependent on the scale of farm operations, the type of crops cultivated, and regional variations. Significantly, decision-making evolutionary paths vary between small and medium-scale farmers, with rice growers emphasising income insurance more. In regions prone to frequent catastrophes, the insurance rates for agricultural catastrophe insurance exhibit greater flexibility. By scientifically delineating agricultural catastrophe risk zones, appropriately expanding the scale of cultivation, reducing insurance rates, and adjusting agricultural catastrophe insurance products, a balanced development in the agricultural catastrophe insurance market can be promoted. [ABSTRACT FROM AUTHOR]

Agricultural household margin insurance (AHMI) is a crop insurance scheme that considers a farmer's trading capability. The scheme examines farmers' losses in terms of both agricultural input and output using the farmer exchange rate index. In this article, we improve the previous design of the AHMI [A. Ahdika, D. Rosadi, A.R. Effendie, and Gunardi, Household margin insurance of agricultural sector in Indonesia using a farmer exchange rate index, Agric. Finance Rev. 81 (2020), pp. 169–188.] proposed to be implemented in Indonesia – which only provided risk protection for companies – by broadening the formula of losses so that risk protection covers both farmers and companies. We also provide premium payment scenarios, both seasonally and non-seasonally. We employ a time-varying Student-t copula with the extended dynamic parameter to identify the dynamic dependency between the indexes involved in determining the loss variable. We also determine the premium rate for the insurance scheme with various payment scenarios and investigate the implications for farmer survival and company management. Examining the farmer exchange rate index data of Indonesia, the empirical results show that the improved AHMI using a time-varying copula approach produces more reasonable loss estimates and various premium payment options with low premium rates for farmers, compared to either the previous AHMI design or the current crop insurance program in Indonesia. [ABSTRACT FROM AUTHOR]

Due to the increased unpredictability and severity of weather patterns caused by climate change, traditional farming practices and risk management strategies are becoming increasingly inadequate. In this paper, we explore the literature to understand the potential of artificial intelligence (AI) in mitigating climate-related agricultural risks and the pivotal role that public institutions play in encouraging farmers to adopt such technologies. We propose a framework to integrate AI into government-subsidized insurance structures, focusing on reduced premiums through government intervention. We argue that AI's potential to reduce the uncertainty and severity of climate-induced damages could lower the overall risk profile of insured farmers, thereby justifying lower premiums in the long run. We further discuss the implications of such policies on insurance markets, agricultural sustainability, and global food security. Our initial exploration contributes to the literature by addressing a relatively underexplored intersection of two critical fields—agricultural insurance and artificial intelligence—suggesting directions for future research. [ABSTRACT FROM AUTHOR]

Based on the provincial panel data of the Yangtze River Economic Belt from 2012 to 2021, this article uses the OLS model to analyze the impact of agricultural insurance on the urban-rural income gap in the Yangtze River Economic Belt and various regions. The distribution mediation effect model is used to analyze the mediation effect of rural labor transfer in the impact of agricultural insurance on the urban-rural income gap. The results showed that: agricultural insurance has a significant narrowing effect on the overall income gap between urban and rural areas in the Yangtze River Economic Belt and the upper reaches, while it has a significant widening effect on the income gap between urban and rural areas in the middle and lower reaches, and has no significant narrowing effect on the lower reaches; agricultural insurance serves as a partial intermediary for the urban-rural income gap, and rural labor transfer plays a partial intermediary role in narrowing the urban-rural income gap. [ABSTRACT FROM AUTHOR]

Canada's agricultural business risk management (BRM) programs require significant public expenditure, with unclear consequences related to climate change adaptation and mitigation through the adoption of beneficial management practices (BMPs). This study examines the relationship between Canada's current suite of BRM programs and the adoption of practices that mitigate GHG emissions in crop agriculture. We review the impacts of agricultural insurance on climate adaptation and mitigation, identifying impacts on both the intensive and extensive margins of production. We consider five potential program modifications, including: (1) changes in producer insurance premiums in AgriInsurance for the adoption of practices that would decrease the actuarially fair insurance rate if they were properly incorporated in the calculation; (2) dedicated insurance products related to trials of specific BMPs; (3) adjustments to current programs to allow more whole‐farm considerations and intercropping; (4) cross‐compliance measures on AgriInvest tied to environmental education; and (5) reduced insurance coverage for unfavorable environmental practices. While the effects of these potential modifications remain uncertain, they will drive the data collection process necessary to ensure that Canada's BRM programs play an appropriate role in greenhouse‐gas reducing BMP adoption and climate change adaptation and mitigation. [ABSTRACT FROM AUTHOR]

Current climate issues pose a challenge to green development, so it is important to study the relationship between climate change and agricultural green total factor productivity (AGTFP). This paper constructs a panel smooth transition regression (PSTR) model using panel data from 2005 to 2019 in China to evaluate the relationship between climate change and agricultural green total factor productivity. Selecting agricultural operation scale, agricultural premium and green finance index as transfer variables, the PSTR model is constructed, respectively. The empirical results show that the impacts of different types of climate change on AGTFP have significant nonlinear characteristics. Smaller and larger agricultural operation scales can mitigate the negative impact of sunshine impact on AGTFP. Higher agricultural premiums and lower or higher green finance index can mitigate the negative impact of temperature shock and sunshine shock on AGTFP. Based on the research results, we put forward policy recommendations to encourage the expansion of agricultural operation scale, promote agricultural insurance and promote the construction of a green financial system. [ABSTRACT FROM AUTHOR]

Lending to the agricultural sector by commercial banks in Ghana is characterized by high credit risk. Empirical evidence suggests that commercial banks in Ghana have credit risk management (CRM) challenges. This study explores the credit risk mitigation strategies adopted by commercial banks to minimize credit risk in agricultural finance in Ghana. The study adopted a mixed-method approach using a survey questionnaire and interview instruments. The findings indicate that some of the strategies used by commercial banks to mitigate credit risk in agricultural finance do not meet commercial banks' CRM needs. In addition, Ghanaian commercial banks have not fully adopted some of the recommended strategies that are used to mitigate credit risk associated with agricultural lending. The study unveils some appropriate strategies used to mitigate credit risk exposure in agricultural finance among commercial banks. These strategies include agricultural value-chain financing, collaboration with off-takers, incentive-based and risk-sharing schemes, adoption of a holistic agricultural value chain financing, policy interventions, use of agricultural insurance pool, and the proper structuring of agricultural loans. [ABSTRACT FROM AUTHOR]

Excessive fertilizer input and inefficient utilization in agricultural production have caused significant negative environmental impacts. Based on provincial panel data in China from 2005 to 2021, this study adopts the full-cost insurance pilot launched in 2018 and uses the DID method to empirically analyze its impact on fertilizer application intensity and utilization efficiency. The study reveals the following findings: (1) Implementing full-cost insurance can reduce fertilizer application intensity by 21.761% and increase utilization efficiency by 1.915%. (2) Full-cost insurance reduces fertilizer application intensity and improves fertilizer utilization efficiency by expanding the land scale and reducing the agricultural labor force. (3) Full-cost insurance significantly improves fertilizer utilization efficiency in high-risk and low-risk areas. Nevertheless, while the policy significantly reduces fertilizer application intensity in high-risk areas, its effect on low-risk areas is not apparent. (4) Full-cost insurance has an environmental protection effect. It can significantly reduce 11.593% of nitrogen pollution emissions, 2.577% of phosphorus pollution emissions, and 35.400% of equivalent pollution emissions. The implementation of full-cost insurance plays an important role in reducing fertilizer use and improving utilization efficiency. So, we should continue to intensify the promotion of full-cost insurance policy to fully leverage the advantages of agricultural insurance and promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

The main objective of the research was to assess the risk management system and the agricultural insurance market in Italy. The subject of the study was risk management instruments and insurance products aimed at farmers. The source material primarily came from reports of institutions involved in the insurance system, such as ISMEA. The data were processed using selected descriptive statistical methods. The most important areas of the risk management system in Italy were compared to those in Poland. The research demonstrated that Italian agriculture is exposed to significant natural risks. The risk management system and insurance market are much more developed in Italy than in Poland, despite agriculture playing an important and similar role in both countries. The Italian system includes both traditional and modern risk management instruments, such as IST and mutual funds. Among EU countries, Italy allocates the largest funds under the Common Agricultural Policy to risk management instruments. The demand for the analyzed insurance products is highly regionally differentiated, with farmers in the north much more frequently using insurance. The main categories of insured crops are vineyards, fruits, and cereals, while in terms of livestock production, cattle, pigs, and eggs are the primary insured categories. Italy can serve as an example for Poland and many other countries on how to build a risk management system.

Background: The farming businesses play an essential role in contributing to Banyuwangi Regency's standard of living. Agriculture is also associated with food security. Rice plants are the most extensively cultivated food crop in the community. It is the background of why the Indonesian government established Asuransi Usahatani Padi (AUTP) or Rice Farming Insurance (RFI). RFI program is designed to help protect farmers from crop failure-related losses. This study intends to analyze the requirements that must be met to establish the RFI in Banyuwangi. Methods: Observation and research during 2020-2022. The focus of the investigation is farmers who use and do not use agricultural insurance in Kabat District, Banyuwangi Regency. Combining quantitative and qualitative approaches. 35 farmers and stakeholders were selected to collect data and 7 experts with knowledge and capacity to understand rice cultivation and social issues were involved in the evaluation using the analytical tool Interpretive Structural Modelling is a research method. Result: The indicated that systemically implemented extension services, the simplicity of RFI financing techniques and procedures and the strengthening of agricultural institutions and partnerships are essential for promoting RFI adoption. [ABSTRACT FROM AUTHOR]

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Agriculture is one of the major economic sectors in Africa, and it predominantly depends on the climate. However, extreme climate changes do have a negative impact on agricultural production. The damage resulting from extreme climate change can be mitigated if farmers have access to accurate weather forecasts, which can enable them to make the necessary adjustments to their farming practices. To improve weather prediction amidst extreme climate change, we propose a novel prediction model based on a hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), local mean decomposition (LMD), and artificial neural networks (NN). A detailed comparison of the performance metrics for the short- and long-term prediction results with other prediction models reveals that the three-phase hybrid CEEMDAN-LMD-NN model is optimal in terms of the evaluation metrics used. The study's findings demonstrate the efficiency of the three-phase hybrid CEEMDAN-LMD-NN prediction model in decision-system design, particularly for large-scale commercial farmers, small-holder farmers, and the agricultural index insurance industry that require reliable forecasts generated at multi-step horizons. [ABSTRACT FROM AUTHOR]

Increasing agricultural productivity and reducing carbon emissions are two major challenges that China faces. This paper explores how agricultural insurance affects agricultural green total factor productivity (AGTFP) that addresses these two challenges. First, we adopt DDF-GML to measure AGTFP, and the Dagum's Gini decompositions results show the large differences in AGTFP, with transvariation between subregions contributing the most. Second, we find the U-shaped effect of agricultural insurance on AGTFP in a large country context. The result remains robust after adjusting for time, sample and estimation method of AGTFP. We also construct instrumental variables from the minimum distance from the centroid of counties within a province to the 45 ports in China and two-stage lagged agricultural insurance penetration (AIP) for AIP, and their squares as instrumental variables for AIP2. The conclusion remains robust in 2SLS model. Third, the findings confirm three channels for agricultural insurance to influence the AGTFP: environmentally friendly production, labor inputs and operation scale. We further propose policy advice to promote China's agricultural modernization and point out further research directions in this field. [ABSTRACT FROM AUTHOR]

The heavy burden brought by the tax practices during the Second World War increased the reaction against the government on the producing class, the peasants and farmers. This reaction started to rise within the CHP and increased the number and influence of the MPs who formed the opposition group within the party. Post-war statements stating that Turkey was in favor of a liberal economy and multi-party democracy facilitated the establishment of opposition political parties. The Democratic Party showed itself as a strong opposition party in 1946. The party, which received great support especially from peasants and farmers, came to power in the 1950 elections. It has been observed that most of the political parties established against the DP, which based its development policy on agriculture and agriculture-based areas during its rule, could not show their presence in the elections and continued the agriculture-based development policies of the DP. Although opposition political parties established for ideological reasons had different solutions to problems such as agricultural insurance, seasonal unemployment, and migration from rural to urban areas, it was observed that these parties could not show a presence in the elections. In this article, the agricultural practices of the opposition political parties established against the DP government are examined according to their programs and declarations. [ABSTRACT FROM AUTHOR]

This study analyzed factors influencing poultry farmers' participation in agricultural insurance in Calabar Municipal Area of Cross River State, Nigeria using 136 poultry farmers in a multistage sampling strategy to choose respondents. Questionnaire administration and interview schedule were used to gather primary data. The study's findings indicate that 67% of the poultry farmers were men, with an average age of 43 years. Ninety (93) percent were married, with an average household size of five people. The survey also revealed that the mean stock size of the farmers was 1795 birds, 9 years of mean farming experience, and 50% of the farmers had postsecondary education. The results also indicate that 71% of respondents did not contact extension services, and 73% did not hold membership in any farmer's associations. The results also indicate that 79% of respondents had no access to loans for their farming business. Regarding knowledge of and participation in agricultural insurance programmes, 70% of respondents were aware that such programmes existed. However, only 31% of farmers took advantage by participating in the insurance scheme. According to the results of the logit regression, farmers' participation in agricultural insurance was found to be significantly influenced by age (-.0538), sex (-.7837), availability to credit (1.2366), and association membership (-1.2068). Among the difficulties poultry farmers encounter while attempting to enroll in agricultural insurance include; rigorous procedure in indemnity payment (79%), high cost of premium (74%), delay in assessment of losses (70.5), inadequate knowledge of insurance (40%), disbelieve in insurance companies (29%) and low compensation (26%). The study suggested that insurance schemes should ensure payment of indemnity (compensation) in a timely and efficient manner. In doing so, it will increase the trust and confidence of farmers who are unsure about the insurance plan. [ABSTRACT FROM AUTHOR]

Hail represents a major threat to agriculture in Switzerland, and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damage is challenging. Here, we build and systematically assess an open-source model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. In contrast, for grapevine, decreasing model resolution below 1 km tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about twice as often as observed (high frequency bias and false alarm ratio), but it also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %), while overall model skill, as measured by the Heidke skill score (HSS), remains largely unchanged (0.41–0.44). We argue that, ultimately, the best threshold therefore depends on the relative costs of a false alarm versus a missed event. Finally, the frequency of false alarms is substantially reduced and skill is improved (HSS = 0.54) when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skilful and is best done at 8 km resolution for field crops and 1 km for grapevine. [ABSTRACT FROM AUTHOR]

Turkiye is a country with significant production potential in the world beekeeping sector, being among the top four countries worldwide. In this study, aggregate claims based on data on the hive insurance policies of the companies operating in the beekeeping sector in Turkiye, covering the years 2014–2021, was modelled using a collective risk model and premium calculations for aggregate claims were determined according to different calculation principles. Cluster analysis was conducted to calculate the premiums, and similarities between provinces were revealed based on claims ratios. The results of the study revealed that the highest premiums are found in Eastern Anatolia while the lowest premiums are found in Central Anatolia. In the case of Muğla, Ordu and Hakkâri provinces, which differed in the cluster analysis, the highest premiums were found for Hakkâri province. [ABSTRACT FROM AUTHOR]

Copyright of Water & Landscape (WAL) / Agua & Territorio (AYT) is the property of Editorial de la Universidad de Jaen and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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As the main service mode and management mode of socialized service, agricultural production trusteeship services (APTS) are an important way to realize the tight link between farmers and modern agricultural development, which is highly important for ensuring national food security. By constructing a utility model of farmers, this paper deduces the decision-making mechanism of farmers' APTS from the dual mechanism of natural risk and non-agricultural employment income (NAEI) and uses the survey data of 956 farmers in the three northeastern provinces to verify the empirical results by using multivariate logit (Mlogit) and propensity score matching (PSM) methods. The research shows that natural risk hinders farmers' choice of the APTS; NAEI has a positive impact on APST, and an increase in NAEI weakens the negative effect of natural risk on the choice of the APTS, and has an enhanced moderating effect. In addition, the scale heterogeneity analysis revealed that the impacts of natural risk and NAEI on farmers of different scales are significantly different. The NAEI adjustment effect has the greatest impact on small-scale farmers, followed by medium-scale farmers. In view of this, the government should stabilize the nonagricultural employment market and improve the nonagricultural employment opportunities of farmers. APTS organizations should transfer natural risks and improve the agricultural insurance system. According to the differences of different scales of farmers, different promotion policies are formulated, and the expansion of management and deepening services is proposed to further promote the development of APTS and consolidate the foundation of food security in China. [ABSTRACT FROM AUTHOR]

Weather risk affects the economy, agricultural production in particular. Index insurance is a promising tool to hedge against weather risk, but current piecewise-linear index insurance contracts face large basis risk and low demand. We propose embedding a neural network-based optimization scheme into an expected utility maximization problem to design the index insurance contract. Neural networks capture a highly nonlinear relationship between the high-dimensional weather variables and production losses. We endogenously solve for the optimal insurance premium and demand. This approach reduces basis risk, lowers insurance premiums, and improves farmers' utility. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the Research Grants Council, University Grants Committee [Grants GRF 16502020, GRF 16504522, and T31-603/21-N], Singapore Ministry of Education Academic Research Fund Tier 1 [Grants RG143/19 and RG55/20], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2021-04144 and DGECR-2021-00330], the Research Database Matching Fund, and the School of Business and Management, Hong Kong University of Science and Technology. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4902. [ABSTRACT FROM AUTHOR]

Hog insurance and rural environmental protection are complementary to each other. Studying the environmental effects of hog insurance is imperative for safeguarding food safety and promoting the long-term development of the agricultural insurance industry. Informed by the risk management theory and sustainable development theory, this paper constructs a theoretical framework for the impact of policy-supported hog insurance on the green total factor productivity (GTFP) of hog farming. Utilizing panel data from China's hog-dominant production areas spanning from 2005 to 2021, the slacks-based measures of directional distance functions (SBM-DDF) model and multiple-time-point difference-in-differences (DID) approach were used to measure GTFP and explore the effects of hog insurance on GTFP and the underlying mechanisms. The findings indicate a substantial enhancement in GTFP due to hog insurance. The conclusion drawn was robust to various tests. The mechanism is that hog insurance fosters GTFP by expanding the breeding scale, adjusting the planting–breeding structure, and promoting technological progress. Furthermore, the environmental effects of hog insurance policy are more pronounced in economically developed regions, with significant effects observed on the GTFP of free-range, small-scale, and medium-scale hog-farming households. This study contributes new evidence to the field of assessing the environmental impact of agricultural insurance policies and provides valuable insights for furthering green transformation and development in the hog insurance-supported breeding industry. [ABSTRACT FROM AUTHOR]

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region. [ABSTRACT FROM AUTHOR]

Agricultural insurance is an important tool for promoting low-carbon agriculture and achieving the "Carbon Peaking and Neutrality" goal. Using panel data from 31 provinces in China from 2001 to 2020, this study analyzes the carbon-reducing effects of agricultural insurance in both crop and livestock sectors. The results show that: (1) Agricultural insurance can decrease agricultural carbon emissions. (2) For crops, agricultural insurance reduces carbon emissions through green technical efficiency, and for livestock products by green technological advances. (3) Agricultural insurance could lower carbon emissions from the livestock and crop sectors in the eastern region. The carbon-reducing benefits of agricultural insurance for the crop sector are seen in the agricultural, agro-pastoral, and pastoral domains; for the livestock sector, these impacts are only seen in the agricultural domains. [ABSTRACT FROM AUTHOR]

In Indonesia, where the agricultural insurance system has been in full operation since 2016, a new damage assessment estimation formula for rice diseases was created through integrating the current damage assessment method and unmanned aerial vehicle (UAV) multispectral remote sensing data to improve the efficiency and precision of damage assessment work performed for the payments of insurance claims. The new method can quickly and efficiently output objective assessment results. In this study, UAV images and bacterial leaf blight (BLB) rice damage assessment data were acquired during the rainy and dry seasons of 2021 and 2022 in West Java, Indonesia, where serious BLB damage occurs every year. The six-level BLB score (0, 1, 3, 5, 7, and 9) and damage intensity calculated from the score were used as the BLB damage assessment data. The relationship between normalized UAV data, normalized difference vegetation index (NDVI), and BLB score showed significant correlations at the 1% level. The analysis of damage intensities and UAV data for paddy plots in all cropping seasons showed high correlation coefficients with the normalized red band, normalized near-infrared band, and NDVI, similar to the results of the BLB score analysis. However, for paddy plots with damage intensities of 70% or higher, the biased numbering of the BLB score data may have affected the evaluation results. Therefore, we conducted an analysis using an average of 1090 survey points for each BLB score and confirmed a strong relationship, with correlation coefficients exceeding 0.9 for the normalized red band, normalized near-infrared band, and NDVI. Through comparing the time required by the current assessment method with that required by the assessment method integrating UAV data, it was demonstrated that the evaluation time was reduced by more than 60% on average. We are able to propose a new assessment method for the Indonesian government to achieve complete objective enumeration. [ABSTRACT FROM AUTHOR]

Agricultural microinsurance is a promising risk management tool for smallholder farmers. However, adoption rates remain low and only a small share of farmers renew their policy after the first period. With the increasing availability of cell phones, mobile-delivered insurance is gaining importance on the market. As for any agricultural microinsurance, it is essential for the longevity of a mobile-delivered insurance scheme to retain a solid customer base. To date, it is unknown what drives the decision to renew a mobile-delivered agricultural microinsurance policy. We address this question by performing mean comparisons and logistic regressions based on primary data collected from 479 smallholder farmers in Mali who purchased a mobile-delivered weather index-based insurance in 2020. Results show that the level of satisfaction with the insurance product was considerably higher among farmers who renewed. We found low levels of understanding of the product among all clients, but especially among those who did not renew. Both factors were confirmed as drivers for renewal. Consistent with previous findings, receiving a payout had the strongest effect on the decision to renew. We conclude that additional measures to foster client satisfaction as well as to promote understanding of agricultural insurance among smallholder farmers are highly recommended. [ABSTRACT FROM AUTHOR]

China's urban–rural dichotomy has resulted in a widening gap between urban and rural areas, posing significant challenges to rural development. This study aims to investigate the spatio-temporal differentiation and driving mechanisms of rural industry integration within the Chengdu–Chongqing Economic Circle in China. Using panel data from 2011 to 2020, we employed the entropy weight TOPSIS method to construct a comprehensive index that charts the evolution of rural industry integration across various districts and counties. Additionally, we utilized fixed-effect and spatio-temporally weighted regression models to analyze the underlying driving forces behind this integration. Our findings reveal a dynamic and varied landscape of rural industry integration, with different levels of depth and breadth across various subsystems. Spatially, we observed a transition from a dispersed to a more concentrated agglomeration pattern within the Chengdu–Chongqing Economic Circle. This shift suggests a diffusion effect emanating from core metropolitan areas, as well as an attracting force exerted by adjacent metropolitan circles. In terms of drivers, market demand, openness level, financial development, policy support, and agricultural insurance breadth significantly contribute to rural industry integration. However, technological progress and rural human capital exhibit a weaker correlation. Notably, our models identified pronounced spatial–temporal heterogeneity among these influencing factors, highlighting a nuanced and dynamic relationship between them. Overall, our study emphasizes the crucial role of rural industry integration in bridging the urban–rural divide and fostering sustainable agricultural development and rural revitalization. The insights gained from this research provide valuable guidance for policymakers and stakeholders seeking to optimize rural development strategies and unlock the potential of integrated rural industries. [ABSTRACT FROM AUTHOR]

IntroductionThe dependence of agriculture on environmental conditions has caused the activity in this sector to face natural and unnatural risks. After several years of agricultural insurance activity in Razavi Khorasan province, most of the pistachio farmers are not insured. Drought insurance is one of the methods that has become important to cover the risks of drought and lack of water resources in order to compensate part of the gardeners' losses. The main issue from a managerial perspective is risk management. The use of agricultural insurance, which is one of the risk management tools, will ensure financial security and stability for farmers. Given that insurance is a tool for risk management, and given the uncertainty and risks of climate change in agriculture, insurance can be a very adaptable tool to water scarcity. Agricultural insurance is considered as a useful and appropriate solution to deal with natural hazards. . Drought insurance is an important factor in off-farm drought risk management that can mitigate the effects of this inevitable phenomenon. Insurance as one of the risk management tools can increase the risk-taking of farmers and, consequently, increase the sense of security in farmers, the necessary ground for proper and efficient use of factors of production and investment in the use of new technology and thus increase productivity in agriculture provide. The effects of water scarcity can be summarized as follows; Loss of production and income, abandonment of busy crops (with high water demand) and decline in agricultural employment, on the other hand, intensifies the over-exploitation of groundwater aquifers, which has tempted many farmers to do so meet your water needs. Access to water in the study area is one of the important variables affecting pistachio yield and quality as well as the survival of pistachio trees. This variable directly affects the profitability of producers and gardeners may suffer losses from this vital input. For this reason, gardeners' behavior in relation to regular pistachio insurance can affect access to water and make more farmers inclined to drought insurance. Materials and MethodsThis research seeks to answer the question that with 5% reduction in available water, pistachio growers in Sabzevar city, whether these people are willing to accept pistachio drought insurance or not, and if so, what is the extent of this desire. The Probit pattern is one of the most suitable econometric patterns for censored observations. This model was first proposed by Tobin (1958) to estimate the demand for durable goods. Subsequently, Arab Mazar and Schmidt (1979), Brown and Mufit (1982), Madela and Nelson (1982), and Hard (1975) worked on and developed the model, validating its high capability. This pattern was named by Goldberger (1964) as the Tobit or Probin Tobin model. Assume that y is the level of activity or action desired and xi are factors that generally affect the level of activity or action in question, namely:Also assume that one group of the observed observations performs the desired activity and the other group (the rest) does not perform the desired activity. As mentioned earlier, the values of xi and yi are visible for the first group. While for the second group only xi values are available and yi values are zero.In Hackmann's proposed two-step method for estimating the Tobit model, it is assumed that one set of variables may influence the decision to participate in the activity and another set of variables may affect the amount of activity performed after the decision is made. Therefore, two different sets of variables can be included in the Probit model, which are not necessarily barriers to aggregation. Therefore, two different sets of variables can be included in the Tobit model, which are not necessarily barriers to aggregation. Because it does not have a one-step model of this flexibility, it assumes that the variables influencing a person's decision to engage in an activity are the same as the variables that determine the amount of activity, if this is not necessarily the case. Hackman's two suggested steps are:Step 1: In the first step, the variables that affect the decision of gardeners in accepting pistachio drought insurance are identified and placed in a model with a binary dependent variable (zeros and ones); This means that the positive values of the dependent variable that indicate the tendency to accept pistachio drought insurance become the number one, and the dependent variable that does not tend to accept the drought insurance is set to zero. The number one means the decision to perform the activity and zero means the non-performance of the activity. At this stage, in order to identify the factors influencing the individual's decision, the Probit Model is used and estimated by the maximum likelihood method. The first step is to create a new variable inverse of the Mills ratio to enter the second step. In other words, this variable is the first and second stage communication bridge.Step 2: In the second stage, the measures affecting the willingness to participate in drought insurance after the decision is made along with the inverse Mills ratio variable are placed in a classical regression model. The dependent variable in the second stage is the amount of garden area likely to be allocated to drought insurance.Reasons to use the Tobit model: Many econometric models face two types of errors, either due to the use of specific observation data or due to the structural features of the models: first, the error due to incorrect sample selection, which usually occurs in using classical regression models, and second, the same error Assuming effective variables in the decision stage and the amount of activity performed after the decision is made (decision and action or intention and action), which usually occurs in regression models with binary and multiple responses. The Tobit model has been developed to prevent the occurrence of these two types of errors in studies.The first error is the error of incorrect sample selection; in the sense that in many econometric models, information is obtained only from observations that have acted on the activity and omits observations that have refused to do that activity. Therefore, these models are not able to assess the reaction of observations that did not act on the independent variable changes. Tobit model (type one) solves this problem in terms of observations that have performed the desired activity as well as other observations. Under these conditions, the effect of changes in independent variables on both the total observations and on the observations of the activity can be calculated separately.The second error means that the factors that influence a person's decision to perform an activity are not necessarily the same as the factors that determine the amount and level of activity desired, and can be two different sets of variables. The Tobit model (type two, Hackett or Hackman two-stage) solves this problem by separating the factors influencing the decision and the amount of activity. Results and DiscussionThe data show that the response of pistachio growers to the reduction of available water in the next 2 and 5 years is that all gardeners will insure their pistachio orchards with a 5% reduction in available water, but in terms of area under cultivation, only 39% Gardeners will increase their arable land in the next 2 years and 33% of gardeners in the next 5 years. The reaction of gardeners who did not have a history of pistachio insurance to accept pistachio insurance and increase or decrease the area under pistachio orchard in exchange for a 5% reduction in available water in the next 2 and 5 years shows that about 51% of gardeners face a 5% reduction in water in 2 And in the next 5 years, they will insure their pistachio orchards, and about 60% of gardeners will increase their cultivation in the next 2 or 5 years in the face of a 5% reduction in available water. The results of the evaluation of gardeners' reaction to the continuation of the horticultural profession in the face of a 5% reduction in available water in the next 2 years will cause 34% of gardeners not to continue this profession and 51% of gardeners will not continue this profession in the next 5 years. In the long run, water shortages can reduce the incentive for gardeners to grow pistachios. The reaction of gardeners to pistachio insurance against the reduction of available water quality shows that only 1.38 percent of the total population in the face of reduced quality of available water reduce the level of their insured garden and about 30% of them faced with declining available water quality, they will increase the level of their insured garden; And the rest of the gardeners (about 68.6%) do not change their insured level in the face of declining water quality.ConclusionAccording to the obtained information, the variables as gardener's age, ownership, relationship between gardener's field of study and agriculture, location, variety of cultivation, existence of insured pistachio garden in the neighborhood, frequency of risk, total water available to each gardener and garden life of each gardener in the first stage (Probit Model) have positive coefficients; which indicates the positive effect of these variables on the probability of willingness to accept pistachio drought insurance. In the second stage (linear regression), the variables of pistachio horticulture history, frequency of risk, garden life and total number of water hours available to gardeners have positive coefficients, which indicate the positive effect of these variables on the dependent variable of the second stage, is the tendency to accept pistachio drought insurance.

Starting from the ‘four-factors’ theory of natural disaster risk formation, the study first selects indices that represent hazard, vulnerability, exposure, and emergency response and recovery capability to construct the drought risk assessment index system. Then the natural disaster risk index method is employed to evaluate the risk of maize drought disasters in Liaoning. Finally, the pure premium rate for maize yield insurance is determined based on the risk assessment results and the Copula function. The results show that: (1) The hazard, vulnerability, and exposure index in northwestern Liaoning are high, while emergency response and recovery capability is relatively low. Therefore, northwestern Liaoning is identified as a high-risk area for drought risk. (2) There exists a negative correlation between the drought risk index and maize yield in most areas of Liaoning; that is, higher drought risk is associated with lower maize yield. (3) Under the same coverage level, pure rates are higher in the northwestern and eastern Liaoning and relatively lower in the southwestern and southeastern Liaoning. Under different coverage levels, higher coverage corresponds to increased pure rates. The results can offer valuable guidance and benchmarks for relevant departments in Liaoning Province to assess the impact of drought risk on agriculture.

Meteorological disasters are one of the major risks to agricultural production, causing huge losses to agriculture. Agricultural insurance is an important part of the agricultural security system and can effectively spread agricultural risks and losses. Weather index insurance, which emerged in the late 1990s, has received a lot of attention, research, and applications due to its objectivity, standardization, and robust liquidity. In this paper, authors analyzed the advantages and disadvantages of weather index insurance compared to traditional agricultural insurance. The development of weather index insurance technology at home and abroad was reviewed. Authors introduced the development and practical application of weather index insurance model for Anhui grain crops (winter wheat, one season rice, summer corn), and discussed and prospected the future development of weather index insurance. [ABSTRACT FROM AUTHOR]

Extreme abiotic stress factors such as hail, defoliation, salinity, and moisture stress can affect the biosynthesis of essential oils. To mitigate this, most commercial farmers globally use agricultural crop insurance; however, this is not the case for most smallholder and emerging farmers in southern Africa, as agricultural insurance tends to be expensive when included in the production system. Therefore, this study aimed to investigate the comparative effects between two elicitors: abscisic acid (ABA) and methyl jasmonate (MeJA) on essential oil biosynthesis of simulated hail-damaged rose geranium (Pelargonium graveolens L’Hér.) as an alternative strategy to improve the essential oil biosynthesis following severe hail damage of rose geranium. The study was conducted in a 72 m2 temperature-controlled greenhouse, using a 4 x 2 factorial treatment design arranged in a randomized complete block design. Treatments comprised four levels of two elicitors at 75 µM (ABA), 150 µM (ABA), 10 mM (MeJA), and 20 mM (MeJA), applied in two different application periods (daily application for either 7 or 14 days). The simulation of hail damage was through 100% defoliation, with decapitation of the terminal buds. A significant interaction was observed between the elicitor treatment, and length of the period of application on essential oil yield (mass), where the highest yield was observed when MeJA (10 mM) was applied for seven consecutive days compared to the 14-day treatment. Geraniol esters were partially improved by the application of MeJA at 10 mM. Application of the two elicitors improved citronellol, geraniol, and linalool, regardless of the length of the period of application. The citronellol to geraniol ratio, a determining factor of essential oil quality, was 2.7:1 when MeJA was applied at 10 mM, significantly better than any application level of ABA. Therefore, it could be concluded that the application of MeJA at 10 mM may improve the essential oil biosynthesis of haildamaged rose geranium plants compared to ABA at any level, and this may be adopted as a possible mitigating strategy by the emerging farmers and growers in southern Africa for improving the biosynthesis of rose geranium post hail damage. On the other hand, where growers are interested in higher contents of linalool and citronellol, ABA may be used at any level. [ABSTRACT FROM AUTHOR]

A relatively high share of small-scale farmers in China is living in a precarious environment that limits their productivity. Moreover, small-scale farmers are among the most vulnerable population characterized by the lack of capital, therefore, they face difficulties in coping with the negative effects of climate risk. A good financial system is among the numerous solutions to help farmers to be more resilient to external risks. However, the traditional financial system often excludes the most vulnerable population. Therefore, this study attempts to understand the relationship between climate risk, digital inclusive finance, and the income of the farmers. Specifically, we want to verify if digital inclusive finance mitigates the impact of climate risk, thus increasing the income of farmers. For that, we used panel data from 288 prefecture-level cities in China from 2011 to 2020. We found that climate risk decreases the income of farmers, and the magnitude of the effect of climate risk is higher for middle and low-income farmers, for cities with a relatively low level of agricultural insurance, and in the northern region. Thus, climate risk increases the income gap between farmers and between urban and rural areas. Moreover, we found that digital inclusive finance, the coverage breadth, usage depth, and digitalization level of digital finance mitigate the adverse effect of climate risk on the income of farmers. This paper provides new perspectives for policymakers to mitigate the effect of climate risk on the income of farmers, improve the ability of farmers to adapt to climate change and reduce the income gap between farmers and between urban-rural areas. [ABSTRACT FROM AUTHOR]

This paper analysed the determinants of smallholder cattle farmers' willingness to adopt Index-Based Cattle Insurance (IBCI) being climate change adaptive strategy in the Polokwane Municipality of South Africa. A multi-stage purposive sampling procedure was employed to collect primary data using structured questionnaires from 110 smallholder cattle farmers. The results revealed that about 89% of the sampled farmers were willing to adopt IBCI, while about 11% weren't willing to adopt IBCI. The Probit regression model results revealed that marital status, farming experience, access to extension services, off-farm income and land ownership had a positive and significant influence on the willingness to adopt IBCI. However, educational level, household size, experience of cattle loss and farm size significantly influenced willingness to adopt IBCI negatively. Therefore, this study recommends workshops for raising IBCI awareness, government subsidy on insurance premiums and finally, improved market access and extension services for smallholder cattle farmers for sustainable livelihoods. [ABSTRACT FROM AUTHOR]

Climate change significantly impacts the livelihoods of farmer households. Particularly vulnerable areas, both economically and environmentally, face significant threats from climate change. This study developed a framework to assess household-level vulnerability to climate change by integrating the Exposure-Sensitivity-Resilience Analysis (ESRA) and Sustainable Livelihoods Analysis (SLA) frameworks. Using Gui-Zhou Province as the study area, the study examined whether livelihood vulnerability differs among various types of farmer households in economically and environmentally vulnerable areas and identified the main factors contributing to vulnerability. Results indicate significant differences in livelihood vulnerability among the three household types, with pure agricultural households (PAH) being the most vulnerable due to high exposure, sensitivity, and low adaptive capacity. Further analysis revealed minor differences in sensitivity but significant differences in adaptive capacity among the three farmer categories. In terms of sensitivity, all three farmer household categories exhibit high sensitivity to water, housing, and agricultural production. Regarding adaptive capacity, significant differences in human and financial capital exist among the three household types, with off-farm households (OFH) possessing the highest adaptive capacity due to their substantial human and financial capital. Further research identified high exposure and low adaptive capacity as the primary causes of livelihood vulnerability, noting no significant difference in the main contributing factors among the three types of farmer households. Common factors contributing to the livelihood vulnerability of farmer households include agricultural cooperatives, labor capacity, temperature changes, drought frequency changes, precipitation changes, agricultural insurance, and losses in agricultural production. Overall, the proposed livelihood vulnerability framework offers guidance for analyzing the vulnerability of farmer households in areas with both economic and environmental vulnerabilities under climate change. Concurrently, tailored measures to reduce farmer households' livelihood vulnerability should be developed for different household types, considering the local climatic, geographic, and socioeconomic conditions. [ABSTRACT FROM AUTHOR]

Ensuring national food security is a perennial topic, and securing the grain planting area is an essential solution. Cost savings at scale from agricultural insurance policy adjustments could be a powerful incentive for grain production. In this study, 527 data sets from 31 provinces in China from 2006 to 2022 were used as the sample, and the author applied a multi-stage DID model to measure the effects of agricultural insurance policy adjustments on the grain planting area and planting structure, as well as the influence mechanisms behind them. The results can be summarized as follows: Firstly, agricultural insurance policy adjustments can make a significant contribution to increasing the grain planting area, with some positive impact on the 'grain-oriented' planting structure. Secondly, agricultural insurance policy adjustments can significantly increase the grain planting area by increasing the application of agricultural machinery, but this mechanism does not affect the 'grain orientation' planting structure. Thirdly, agricultural insurance policy adjustments can have a significant positive impact on the grain planting area and 'grain—oriented' planting structure in both high- and low-risk areas, with low-risk areas being more affected than high-risk areas. [ABSTRACT FROM AUTHOR]

Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance. [ABSTRACT FROM AUTHOR]

This article is based on panel data of the Yangtze River Economic Belt from 2012 to 2021. The entropy weight method is used to evaluate the level of high-quality agricultural development in this economic belt, and the convergence model is used for convergence analysis. Finally, the Tobit model is used to analyze the impact of agricultural insurance on high-quality agricultural development. Research has found that:①the high-quality development level of agriculture in the entire Yangtze River Economic Belt is in the middle stage, and overall shows a trend of first increasing and then decreasing. Among various regions, the high-quality development level of agriculture in the upper reaches is higher than the middle and lower reaches, with the middle reaches being the lowest. Among various provinces (cities), Shanghai, Chongqing, and Guizhou have higher levels of high-quality agricultural development, with Shanghai having the highest level of development, while Hubei, Hunan, and Zhejiang Province have lower levels of development. ②The high-quality development of agriculture in the Yangtze River Economic Belt shows a trend of “divergence followed by convergence, and then divergence followed by convergence”. In each region, the development trend of the middle and downstream regions is generally similar, while there are significant differences in the upstream. ③The impact of the scale of agricultural insurance development on the high-quality development of agriculture in the Yangtze River Economic Belt is significantly negative, while the impact of guarantee level and compensation level on the high-quality development of agriculture in the economic belt is significantly positive. [ABSTRACT FROM AUTHOR]

The paper presents the development of a conceptual information model for optimizing land use and crop forecasting in agriculture, implemented using the PHP programming language, Javascript and the MYSQL database management system. The model is designed to solve optimization problems in various fields such as logistics, manufacturing, finance, etc. As part of the work, an information model was developed, which is a set of data, algorithms and software modules designed to solve optimization problems. The model is implemented based on PHP, Javascript, and MYSQL technologies. The model allows you to solve optimization problems of varying complexity using various optimization methods such as linear programming, nonlinear programming, artificial intelligence methods, etc. The model can be applied to solve optimization problems in various fields. In addition, the model allows you to solve optimization problems with a high degree of accuracy and in a short time, and also has a user-friendly interface that allows users to easily and quickly set optimization tasks. The current optimization problems and forecasting problems are not covered in the work. How this will be implemented is not indicated in the resulting information model. The relevance of scientific research – Every year farmers face the task of how to use the land. And they face many factors, including transportation costs, seed quality, cattle grazing and many other options for using the land. But using the simulation model or information system proposed by us, it is important to understand not only the actual use of the land, but also the economic profit. The system will indicate (advise) based on historical data as an expert, what kind of crop to plant so that there is maximum benefit. Agrotechnical indicators, allows you to create more accurate models and forecasts, which contributes to making informed decisions. Such a system allows farmers and agronomists to respond quickly to changing conditions, optimize land use and improve the efficiency of agricultural production in general. An information model has been developed that will make it possible to predict the harvest taking into account data on weather, soil, crops and historical yield data, as well as optimize land use taking into account data on soil type, climate, crop rotation and financial indicators. The developed information model can improve the efficiency and sustainability of agricultural production. The model can be used by agronomists and farmers to make more informed decisions. The model takes into account data on soil type, climate, crop rotation and financial indicators to optimize land use. [ABSTRACT FROM AUTHOR]

Introduction The dependence of agriculture on environmental conditions has caused the activity in this sector to face natural and unnatural risks. After several years of agricultural insurance activity in Razavi Khorasan province, most of the pistachio farmers are not insured. Drought insurance is one of the methods that has become important to cover the risks of drought and lack of water resources in order to compensate part of the gardeners' losses. The main issue from a managerial perspective is risk management. The use of agricultural insurance, which is one of the risk management tools, will ensure financial security and stability for farmers. Given that insurance is a tool for risk management, and given the uncertainty and risks of climate change in agriculture, insurance can be a very adaptable tool to water scarcity. Agricultural insurance is considered as a useful and appropriate solution to deal with natural hazards. Drought insurance is an important factor in off-farm drought risk management that can mitigate the effects of this inevitable phenomenon. Insurance as one of the risk management tools can increase the risk-taking of farmers and, consequently, increase the sense of security in farmers, the necessary ground for proper and efficient use of factors of production and investment in the use of new technology and thus increase productivity in agriculture provide. The effects of water scarcity can be summarized as follows; Loss of production and income, abandonment of busy crops (with high water demand) and decline in agricultural employment, on the other hand, intensifies the over-exploitation of groundwater aquifers, which has tempted many farmers to do so meet your water needs. Access to water in the study area is one of the important variables affecting pistachio yield and quality as well as the survival of pistachio trees. This variable directly affects the profitability of producers and gardeners may suffer losses from this vital input. For this reason, gardeners' behavior in relation to regular pistachio insurance can affect access to water and make more farmers inclined to drought insurance. Materials and Methods This research seeks to answer the question that with 5% reduction in available water, pistachio growers in Sabzevar city, whether these people are willing to accept pistachio drought insurance or not, and if so, what is the extent of this desire. The Probit pattern is one of the most suitable econometric patterns for censored observations. This model was first proposed by Tobin (1958) to estimate the demand for durable goods. Subsequently, Arab Mazar and Schmidt (1979), Brown and Mufit (1982), Madela and Nelson (1982), and Hard (1975) worked on and developed the model, validating its high capability. This pattern was named by Goldberger (1964) as the Tobit or Probin Tobin model. Assume that y is the level of activity or action desired and xi are factors that generally affect the level of activity or action in question, namely: yi = β' xi + ui Also assume that one group of the observed observations performs the desired activity and the other group (the rest) does not perform the desired activity. As mentioned earlier, the values of xi and yi are visible for the first group. While for the second group only xi values are available and yi values are zero. In Hackmann's proposed two-step method for estimating the Tobit model, it is assumed that one set of variables may influence the decision to participate in the activity and another set of variables may affect the amount of activity performed after the decision is made. Therefore, two different sets of variables can be included in the Probit model, which are not necessarily barriers to aggregation. Therefore, two different sets of variables can be included in the Tobit model, which are not necessarily barriers to aggregation. Because it does not have a one-step model of this flexibility, it assumes that the variables influencing a person's decision to engage in an activity are the same as the variables that determine the amount of activity, if this is not necessarily the case. Hackman's two suggested steps are: Step 1: In the first step, the variables that affect the decision of gardeners in accepting pistachio drought insurance are identified and placed in a model with a binary dependent variable (zeros and ones); This means that the positive values of the dependent variable that indicate the tendency to accept pistachio drought insurance become the number one, and the dependent variable that does not tend to accept the drought insurance is set to zero. The number one means the decision to perform the activity and zero means the non-performance of the activity. At this stage, in order to identify the factors influencing the individual's decision, the Probit Model is used and estimated by the maximum likelihood method. The first step is to create a new variable inverse of the Mills ratio to enter the second step. In other words, this variable is the first and second stage communication bridge. Step 2: In the second stage, the measures affecting the willingness to participate in drought insurance after the decision is made along with the inverse Mills ratio variable are placed in a classical regression model. The dependent variable in the second stage is the amount of garden area likely to be allocated to drought insurance. yi = β' xi + σλ + ui Reasons to use the Tobit model: Many econometric models face two types of errors, either due to the use of specific observation data or due to the structural features of the models: first, the error due to incorrect sample selection, which usually occurs in using classical regression models, and second, the same error Assuming effective variables in the decision stage and the amount of activity performed after the decision is made (decision and action or intention and action), which usually occurs in regression models with binary and multiple responses. The Tobit model has been developed to prevent the occurrence of these two types of errors in studies. The first error is the error of incorrect sample selection; in the sense that in many econometric models, information is obtained only from observations that have acted on the activity and omits observations that have refused to do that activity. Therefore, these models are not able to assess the reaction of observations that did not act on the independent variable changes. Tobit model (type one) solves this problem in terms of observations that have performed the desired activity as well as other observations. Under these conditions, the effect of changes in independent variables on both the total observations and on the observations of the activity can be calculated separately. The second error means that the factors that influence a person's decision to perform an activity are not necessarily the same as the factors that determine the amount and level of activity desired, and can be two different sets of variables. The Tobit model (type two, Hackett or Hackman two-stage) solves this problem by separating the factors influencing the decision and the amount of activity. Results and Discussion The data show that the response of pistachio growers to the reduction of available water in the next 2 and 5 years is that all gardeners will insure their pistachio orchards with a 5% reduction in available water, but in terms of area under cultivation, only 39% Gardeners will increase their arable land in the next 2 years and 33% of gardeners in the next 5 years. The reaction of gardeners who did not have a history of pistachio insurance to accept pistachio insurance and increase or decrease the area under pistachio orchard in exchange for a 5% reduction in available water in the next 2 and 5 years shows that about 51% of gardeners face a 5% reduction in water in 2 And in the next 5 years, they will insure their pistachio orchards, and about 60% of gardeners will increase their cultivation in the next 2 or 5 years in the face of a 5% reduction in available water. The results of the evaluation of gardeners' reaction to the continuation of the horticultural profession in the face of a 5% reduction in available water in the next 2 years will cause 34% of gardeners not to continue this profession and 51% of gardeners will not continue this profession in the next 5 years. In the long run, water shortages can reduce the incentive for gardeners to grow pistachios. The reaction of gardeners to pistachio insurance against the reduction of available water quality shows that only 1.38 percent of the total population in the face of reduced quality of available water reduce the level of their insured garden and about 30% of them faced with declining available water quality, they will increase the level of their insured garden; And the rest of the gardeners (about 68.6%) do not change their insured level in the face of declining water quality. Conclusion According to the obtained information, the variables as gardener's age, ownership, relationship between gardener's field of study and agriculture, location, variety of cultivation, existence of insured pistachio garden in the neighborhood, frequency of risk, total water available to each gardener and garden life of each gardener in the first stage (Probit Model) have positive coefficients; which indicates the positive effect of these variables on the probability of willingness to accept pistachio drought insurance. In the second stage (linear regression), the variables of pistachio horticulture history, frequency of risk, garden life and total number of water hours available to gardeners have positive coefficients, which indicate the positive effect of these variables on the dependent variable of the second stage, is the tendency to accept pistachio drought insurance. [ABSTRACT FROM AUTHOR]