Search Results (15)

This study aims to reconstruct NDI45 missing values due to cloud cover while outlining the importance of vegetation health for the climate–carbon cycle and the benefits of the NDI45 index for high canopy area indices. The methods include a novel hybrid framework that combines a deterministic Kalman filter (KF) and a clustering-based LSTM network to generate gap-free NDI45 series with 20 m spatial and 5-day temporal resolution. The innovation of the applied method relies on achieving a single-sensor workflow, provides a pixel-level uncertainty map, and minimizes LSTM overfitting through clustering based on a correlation threshold. In the northern Pampas (South America), this hybrid approach reduces the MAE by 22–35% on average and narrows the 95% confidence interval by 25–40% compared to the Kalman filter or LSTM alone. The three-dimensional spatio-temporal analysis demonstrates that the KF–LSTM hybrid provides better spatial homogeneity and reliability across the entire study area. The proposed framework can generate gap-free, high-resolution NDI45 time series with quantified uncertainties, enabling more reliable detection of vegetation stress, yield fluctuations, and long-term resilience trends. These capabilities make the method directly applicable to operational drought monitoring, crop insurance modeling, and climate risk assessment in agricultural systems, particularly in regions prone to frequent cloud cover. The framework can be further extended by including radar backscatter and multi-model ensembles, thus providing a promising basis for the reconstruction of global, high-resolution vegetation time series. Full article

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

Index-based insurance is an innovative concept for evaluating agricultural risks and payouts, which uses an index instead of traditional on-site loss assessment. Area yield insurance, as an index-based approach, is an effective strategy to mitigate moral hazard and adverse selection issues. This study aims to develop area yield insurance as a new insurance plan in Iran for two major crops: wheat and barley. It utilized kernel and joint kernel distributions to price the insurance and assessed producer welfare benefits by comparing the certainty equivalence (CE) of farmers’ utility with and without the policy. Data were collected from East Azerbaijan Province, including county-level yield data for irrigated and rainfed wheat and barley from 1975 to 2019 and 446 individual-level yield data from 2015 to 2019. A two-stage method was used to model yield risk: the first stage fits a trend model, while the second estimates the yield distributions with the detrended data. The results showed a significant difference in premiums calculated by the two distributions, with joint kernel distribution offering the best empirical fit and reasonable premiums. The findings indicate that area yield crop insurance provides positive welfare benefits and should serve as a viable alternative or complement to existing yield insurance plans. The successful implementation of this policy in various countries suggests it can be a suitable risk management program for developing countries like Iran. Full article

In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit from an accurate forecast of coconut production. Internet of Things (IoT) sensors are strategically positioned to continuously monitor the environment and gather production statistics to obtain accurate agricultural output predictions. To effectively estimate coconut prediction, this study presents an enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with the integrated Lévy Flight and Seagull Optimization Algorithm (LFSOA). LASSO feature selection is applied to eliminate the superfluous characteristics in the yield estimation. To further enhance the coconut yield estimate, the optimal set of hyperparameters for BILSTM is tuned by the LFSOA, which helps to avoid the overfitting issue. For the results, the BILSTM is compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), and LSTM. Similarly, LFSOA-based hyperparameter tuning is contrasted with different optimization algorithms. The outputs show that LFSOA-based hyperparameter tuning in BILSTM achieved accuracy, precision, recall, and f1-score of 98.963%, 99.026%, 99.155%, and 95.758%, respectively, which are higher when compared to existing methods. Similarly, the BILSTM-LFSOA accomplished better results in statistical measures, including the Root Mean Square Error (RMSE) of 0.105, Mean Squared Error (MSE) of 0.011, Mean Absolute Error (MAE) of 0.094, and coefficient of determination (R²) of 0.954, respectively. From the overall analysis, the proposed BILSTM-LFSOA improves coconut yield prediction by achieving better results in all the performance measures when compared with existing models. The results of this study are important to many stakeholders, including but not limited to policymakers, farmers, banks, and insurance companies. As coconuts are an important crop in developing countries, accurate coconut yield forecasting will lead to greater financial and food security in these regions. Full article

Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and handling complex data by adjusting the complexity of the model through the addition or reduction of terms. Moreover, quantile generalized additive regression was deliberated to evaluate relationships with lower quantiles, such as low-yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as conditional tail expectation (CTE), certainty equivalence of revenue (CER), and mean root square loss (MRSL). The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R² of 0.691. MRSL should be taken into consideration initially, as it is a farmer’s revenue assessment that distinguishes between those with and those without insurance. As a result, the GAM model indicates the least fluctuation in farmer income at the 90th percentile. Additionally, our study suggests that this type of insurance could apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production. Full article

Insurance purchase and organization participation in risk management is of great practical significance for stabilizing agricultural production and household income. The aims of this study were to analyze farm households’ choices of insurance purchase and organization participation, and their effects on crop revenue and its higher-order moments using the multinomial switching endogenous regression (MESR) model. The results showed that the adoption of insurance and organization was significantly affected by household head characteristics, farm household characteristics, and cropland attributes. Insurance purchase, organization participation, and their joint adoption contributed to the increase in crop revenue and decrease in crop revenue variance, and the benefits were larger when adopting two risk management tools in combination. When skewness was taken into account in risk management analysis, insurance purchase, organization participation, and their joint adoption resulted in a reduction in the probability of crop failure, of which, participating in organizations was the most effective. Efforts should be put forth to improve the functioning and effectiveness of agricultural insurance and organization to promote the adoption of risk management tools. Full article

This article substantiates the structure of the crop insurance system and describes the participants of the insurance system and their activities. The positive impact of crop insurance development on all participants of the system has also been clarified. The aim of the article is to present a methodology for assessing substantiated directs of activity for public authorities in order to make more active crop insurance system performance. The application of the proposed methodology can help to activate crop insurance processes and to expand farmers’ activities and achieve better commercial results of insurance companies. It has been proven that it is beneficial for the public authorities to reinsure farmers’ crops and to refuse to pay direct payments to farmers for the losses incurred. The criteria selected for the assessment of the development directions are relevant to all participants of the insurance system. The research was carried out in a region of Lithuania in which the composition of agricultural business entities corresponds to the situation in the whole agricultural sector of the country. The identification of the insurance system participants and their functions, as well as the use of an expert assessment method with the application of quantitative data processing, showed directions for the activation of crop insurance activities. Full article

India is home to over one-third of all undernourished children worldwide, and it ranks 94th out of 107 nations in the Global Hunger Index 2020. Instability in production and market risks make agriculture a risky business and directly affect farmers’ income levels, thereby impacting food security. This review aimed to understand various features of different crop insurance policies in India and to analyze the Pradhan Mantri Fasal Bima Yojana’s (PMFBY) impacts on Indian farmers. A literature search was performed in all popular databases, including Scopus, Web of Science, ProQuest, AGRICOLA, AGRIS, and Google search engines, as well as annual Indian government reports. The keywords “Crop Insurance” OR “Pradhan Mantri Fasal Bima Yojana” OR “National Agriculture Schemes” AND “India” were searched to obtain relevant articles. By using cumulative data, we conducted a multiple regression analysis and a model was developed to estimate the effects of insurance characteristics on farmer coverage for the years 2017–2018 and 2018–2019. Agricultural insurance coverage under PMFBY remained low in terms of the number of farmers insured, the area insured, claims paid, and total farmers benefited. Compared to other schemes, the beneficiary and claim premium ratios were substantially lower under the PMFBY. The multiple regression analysis showed that farmers’ premiums have a significant effect on the number of farmers insured over time, although the subsidies do not have a significant influence in farmers’ insurance participation. Delays in claim settlement, the complexity of the system, and a lack of awareness among farmers are the major weaknesses of the PMFBY. Greater use of digital media could help spread awareness of these schemes among farmers. Full article

Conventional crop insurance systems are complex and often not economically feasible. Farmers are often reluctant to be covered for their crops due to lack of trust in insurance firms and the fear of delayed or non-payment of claims. In this paper, a blockchain based crop insurance solution is suggested. The solution suggested in this paper is an affordable, efficient, low cost crop insurance solution which will ensure many farmers are insured and benefiting from timely crop insurance. Currently the cost of administering insurance is an essential barrier to accessing this facility. With the proper use of blockchain based on ethereum this expense can be reduced dramatically. We have conducted various tests on platforms such as Google Cloud and found that the least throughput is 165 transactions. Upon analysis we have found that the time taken by the block formation is directly proportional to the timing of processing. The end-to-end average latency of the system was achieved as 31.2 s, which was quite effective for the infrastructure what we are using. Upon conducting acceptance testing, we found that the system suggested in the paper is effective and we are planning to release the application on open source platforms for future improvements. Full article

Conservation agriculture (CA) involving zero tillage, crop diversification, and residue retention is considered a panacea for several interrelated problems in agricultural production. However, in the mixed crop-livestock production systems of the drylands, crop residues have great significance as sources of animal feed, posing a major challenge in the promotion of CA. While the economic benefits and the drivers of adoption of zero tillage and rotation have been well documented, the literature on the economics of residue retention (RR), especially in the drylands, is scanty. By applying the endogenous switching regression model to a case study of 2296 wheat fields in Morocco, this paper provides evidence on the socio-economic impacts of residue retention. Between 30% and 60% and above 60% of crop residues were retained respectively on 35% and 14% of wheat fields. These levels of residue retention led to 22% and 29% more yields, 25% and 32% higher gross margins and 22% and 25% more consumption of wheat, respectively. Retention of above 60% residue reduces both downside risk and variability of yield while lower levels of residue retention have mixed effects. Residue retention is economically and biophysically beneficial even for owners of livestock as the monetary value of the additional grain yield more than offsets the cost of purchasing an equivalent amount of feed from the market—all providing good economic justification for residue retention. Our findings show that economic reasons are not barriers for adoption of residue retention, but risk factors and absence of alternative feed sources might. The policy implication of our results is that there are high incentives for Morocco and other similar countries in North Africa and West Asia to invest in the development and/or import of alternative feed sources, introducing crop insurance, and raising the awareness of the economic, biophysical and environmental benefits of residue retention among farmers. Full article

Globally, agricultural soils are being evaluated for their role in climate change regulation as a potential sink for atmospheric carbon dioxide (CO₂) through sequestration of organic carbon as soil organic matter. Scientists and policy analysts increasingly seek to develop programs and policies which recognize the importance of mitigation of climate change and insurance of ecological sustainability when managing agricultural soils. In response, many countries are exploring options to develop local land-use carbon inventories to better understand the flow of carbon in agriculture to estimate its contribution to greenhouse gas (GHG) reporting. For instance, the Canadian province of Ontario does not currently have its own GHG inventory and relies on the Canada’s National Inventory Report (NIR). To address this, the province explored options to develop its own land-use carbon inventory to better understand the carbon resource in agricultural soils. As part of this undertaking, a gap analysis was conducted to identify the critical information gaps and limitations in estimating soil organic carbon (SOC) monitoring to develop a land-use carbon inventory (LUCI) for the cropland sector in Ontario. We conducted a review of analytical and modeling methods used to quantify GHG emissions and reporting for the cropland sectors in Canada, and compared them with the methods used in seven other countries (i.e., France, United Kingdom; Germany; United States of America, Australia, New Zealand, and Japan). From this comparison, four target areas of research were identified to consider in the development of a cropland sector LUCI in Ontario. First, there needs to be a refinement of the modelling approach used for SOC accounting. The Century model, which is used for Ontario’s cropland sector, can benefit from updates to the crop growth model and from the inclusion of manure management and other amendments. Secondly, a raster-based spatially explicit modelling approach is recommended as an alternative to using polygon-based inputs for soil data and census information for land management. This approach can leverage readily available Earth Observation (EO) data (e.g., remote sensing maps, digital soil maps). Thirdly, the contributions from soil erosion need to be included in inventory estimates of SOC emissions and removals from cropland. Fourth, establishment of an extensive network of long-term experimental sites to calibrate and validate the SOC models (i.e., CENTURY) is required. This can be done by putting in place a ground-truth program, through farmer-led research initiatives and collaboration, to deal with uncertainties due to spatial variability and regional climates. This approach would provide opportunities for farmers to collaborate on data collection by keeping detailed records of their cropping and soil management practices, and crop yields. Full article

Climate change will increase simultaneous crop failures or too abundant harvests, creating global synchronized yield change (SYC), and may decrease stability in the portfolio of food supply sources in agricultural trade. This study evaluated the influence of SYC on the global agricultural market and trade liberalization. The analysis employed a global computable general equilibrium model combined with crop models of four major grains (i.e., rice, wheat, maize, and soybeans), based on predictions of five global climate models. Simulation results show that (1) the SYC structure was statistically robust among countries and four crops, and will be enhanced by climate change, (2) such synchronicity increased the agricultural price volatility and lowered social welfare levels more than expected in the random disturbance (non-SYC) case, and (3) trade liberalization benefited both food-importing and exporting regions, but such effects were degraded by SYC. These outcomes were due to synchronicity in crop-yield change and its ranges enhanced by future climate change. Thus, SYC is a cause of systemic risk to food security and must be considered in designing agricultural trade policies and insurance systems. Full article

Early prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons’ harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named ‘bins’. Cloud free (<20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each ‘bin’ was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha^-1) achieved for the respective bin over the five growing years, producing strong correlations (R² = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha^-1) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R² = 0.87 and RMSE = 11.33 (t·ha^-1)) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach. Full article

Most U.S. farms today specialize in either crop or livestock production, failing to harness the potential economic and environmental benefits of integrated crop–livestock systems (ICLS). This specialization is particularly contradictory for organic operations, which aim to promote biodiversity and reduce reliance on outside sources of feed and fertility. This study investigated the challenges and opportunities experienced by farmers interested in integrating crops and livestock on organically managed farms in Iowa, Pennsylvania, and Minnesota. Qualitative methods, including focus groups and interviews, generated four categories of challenges: farming norms, complexity of management, biophysical conditions, and financial costs, and four categories of opportunities: increasing support for ICLS, financial and labor advantages, biophysical improvements, and animal welfare. Discussion of the data analysis demonstrates how most of the challenges of ICLS are mitigated by opportunities. For instance, increasing support for ICLS means there are growing communities of practice in which farmer-to-farmer knowledge exchange and peer support overcome obstacles to success in these systems. Unmitigated challenges that are beyond the control of farmers include regional infrastructure, financing and insurance, and long time horizon for returns. These three unmitigated challenges may require interventions such as policy support, economic incentives and social infrastructure to enable successful farm transitions to ICLS in this region. Full article

The purpose of this paper is to assess the gaps in the adoption of crop insurance in Thailand and suggest possible solutions relating to policy support and framework, implementation mechanisms, technology adoption, and awareness amongst farmers. The methodology includes a literature review, interaction with officials, rice experts and insurance experts, and discussion with farmers. A study was undertaken at province level to assess the impact of using rainfall index as a threshold. Additionally, focused group discussions (FGD) were conducted with rice farmers at the village level. Key issues targeted in the FGD were to understand the behavior and practices during droughts, impact of drought on crop yield, methods already in use to reduce the impact, such as plantation of drought-resistant rice, and the adoption of crop insurance. Data availability is a challenge and has led to withdrawal of Weather Index Insurance (WII) in 2015. WII have threshold levels based on historical rainfall. Adoption of coping mechanisms, such as drought-resistant rice and irrigation increases the chances of adverse selection. In absence of ground based weather data, a combination of satellite agriculture drought information can be used to make crop insurance more attractive as it would help in reducing basis risk and improving insurers and farmers’ confidence in the product. Discussion with farmers, insurance companies, and the Bank of Agriculture and Agricultural Cooperatives (BAAC) in Thailand suggested low awareness among farmers about the potential benefits of weather index insurance products. Relatively low compensation is also an obstacle. Proper marketing and awareness raising campaigns should also accompany the introduction of index-based insurance products. Full article