Search Results (315)

Promoting the adoption of climate-smart agricultural technologies (CSATs) is essential for food security and achieving the Sustainable Development Goals (SDGs). However, adoption rates remain low. The objective of this study is to reveal the associations between financial instruments and farmers’ adoption of CSATs. Using survey data from 1219 farmers in climatic transition zone of China, the Probit model, and mediation model were used to empirically test the associations of credit and insurance on farmers’ adoption of CSATs. The results show that both credit and insurance are positively associated with CSATs adoption, with insurance exhibiting a stronger marginal effect than credit. Mediation analysis reveals that credit and insurance are positively associated with farmers’ adoption behavior through attending technical training, strengthening subjective norms, and improving risk attitudes. Heterogeneity analysis indicates that these associations vary significantly across different climatic sub-regions. The findings provide evidence-based policy insights for leveraging targeted financial instruments to accelerate CSATs adoption among smallholders in China and other countries. Full article

The ecological efficiency of grain production has attracted growing scholarly interest in recent decades. Agricultural insurance alleviates operational risks embedded in grain cultivation, which further promotes improvements in ecological efficiency. Using provincial panel data covering 31 provincial-level administrative regions of China over the period 2001–2021, this study adopts the super-efficiency SBM model to quantify and analyze agricultural insurance development as well as the ecological efficiency of grain production nationwide. The empirical results yield three core conclusions: (1) The advancement of agricultural insurance significantly raises grain production ecological efficiency. (2) Carbon emission intensity exerts a significantly negative impact on ecological efficiency, demonstrating that carbon abatement policies are effective in boosting the green performance of grain production. (3) Carbon emission intensity exerts a statistically significant positive moderating effect. Specifically, the efficiency-improving effect of agricultural insurance on grain production eco-efficiency is more pronounced in regions with high carbon emission intensity. Based on the empirical findings, differentiated premium subsidy schemes for agricultural insurance should be formulated to promote grain production ecological performance. Policymakers are advised to introduce targeted ecological protection policies tailored to regional heterogeneous characteristics, and to advance coordinated linkage between agricultural insurance instruments and carbon reduction regulations. Full article

Long-duration flood inundation can substantially suppress crop growth and cause yield loss, particularly in semi-arid agricultural regions increasingly affected by extreme rainfall. Timely crop damage assessment is critical for disaster response and insurance-related decision-making, but direct yield-loss observations are often unavailable during or shortly after flooding. This study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations. The study was conducted on the Tumochuan Plateau, Inner Mongolia, China, where severe rainfall beginning on 23 July 2025 caused widespread cropland inundation. Sentinel-2 EVI time series from 2022 to 2025 were fitted using a Savitzky–Golay (SG) filter, and annual area under the EVI curve (AUC) loss in 2025 relative to the 2022–2024 historical mean was used as a proxy for flood-induced crop damage. Optical features from Landsat-8/9 and Sentinel-2, together with SAR backscatter features from Sentinel-1, Lutan-1, and Gaofen-3, were incorporated into machine learning regression models. SAR features improved pixel-wise prediction, with the Random Forest model achieving the highest $R^2$ of 0.62 using early-period features and 0.77 using later-period features. Village-scale aggregation further improved performance, yielding an early-period $R^2$ of 0.84 across 123 and 0.78 across 122 villages. These results demonstrate the feasibility of SAR–optical and phenology-guided regression for early crop damage assessment under long-duration inundation. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

The probability estimation of agrometeorological damage risk is the core technical support for consolidating the agricultural disaster prevention and reduction system and ensuring the sustainable development of national agriculture. It has been widely applied in agricultural production and agricultural insurance. Based on the crop planting area data, as well as the damaged crop area data (damage-affected, damage-stricken, and dead harvest) of 31 provinces and municipalities from 1980 to 2018, this study creatively builds a comprehensive damage strength index. After that, this study obtains accurate risk probability estimation results of five meteorological damage types by using the parameters of the nonparametric normal information diffusion model. The results show that the risk probability of comprehensive meteorological damage is the largest, followed by drought, flood, wind and hail, and freezing. Flood in Hubei, drought in NeiMenggol, windstorm and hailstorm in Qinghai, freezing damage in Hainan, and comprehensive meteorological damage in NeiMenggol have the highest risk probability. The regions where various meteorological damage occur show different distribution characteristics, which is closely related to the latitude and longitude and topography of China. These findings indicate that it is necessary to understand the overall patterns of agrometeorological damage risks and consider their internal heterogeneity, in order to take targeted prevention and control measures to avoid systemic risks in agricultural production and safeguard sustainable and high-quality agricultural development. Full article

This study evaluated a cost-effective UAV-based multi-sensor approach for characterizing river corridor conditions during and after moderate floods in two mountain river corridors in Northern Utah, USA: the Logan River and Blacksmith Fork River. These study reaches included urban, rural, and agricultural areas, hydraulic structures and bridges, and fish passage structures. A DJI Matrice 300 UAV was used with two separate payloads: an AgEagle Altum-PT multispectral camera and an R3 Pro V2 two-return LiDAR system. The workflow included UAV flight planning and data collection, post-processing of the multi-spectral and LiDAR sensor data, spatial resolution and accuracy assessment, and interpretation of the resultant data. The multi-spectral post-processing produced pansharpened orthomosaics with a spatial resolution of 0.0432 m, while the UAV LiDAR produced DSM/DTM products at 0.05 m resolution. LiDAR accuracy assessment showed vertical RMSE values of approximately 0.0602 m for the Blacksmith Fork and 0.0782 m for the Logan River. The results showed that multispectral imagery and 2-band LiDAR provided a cost-effective means for detailed remote sensing with each sensor providing complementary information for flood and river corridor assessment. Multispectral imagery supported interpretation of flood extent, vegetation condition, relative turbidity, and thermal patterns, while LiDAR captured terrain and surface features such as banks, levees, floodplain surfaces, channel modifications, and structures. The integrated datasets supported maximum flood extent mapping and flood-level estimation. These datasets can support reach-scale hydraulic modeling, catchment hydrology, river corridor ecology, floodplain conditions, and real-time monitoring of floods, in addition to quantification of flood hazards or post-flood impacts for municipalities and insurers. Full article

Climate risks and rural labor outflow are jointly challenging the sustainability of farm households in disaster-prone agricultural regions. Agricultural insurance, as an important risk management tool, may influence not only farm income but also farmers’ production and labor decisions. While previous studies on agricultural insurance have primarily focused on staple crops and income stabilization, the effects of labor allocation in facility-based intensive agriculture remain underexplored. This study addresses this gap by examining how participation in agricultural insurance affects farm household labor allocation in the context of greenhouse vegetable production. Using survey data collected from July to August 2025 from 247 greenhouse vegetable farmers in ShouGuang, China’s largest vegetable production base, we employ Probit and Two-Stage Least Squares (2SLS) models to address endogeneity and mediation analysis to explore the underlying mechanisms. The findings show that: (1) Agricultural insurance participation significantly increases the probability of farm households increasing agricultural labor input in the next planting season. (2) Agricultural insurance reduces farmers’ likelihood of exiting agricultural production through increased investment in agricultural technology, but this effect is constrained by the availability of investment capital. (3) The impact of agricultural insurance on labor allocation varies across different farmer groups, and is more pronounced among middle-aged and younger farmers with relatively small farm sizes, lower disaster risk, and higher levels of education. Several insurance-related policy recommendations are proposed to stabilize the agricultural labor force and mitigate rural labor decline—key dimensions of sustainable rural development. Given the single-region focus and sample size, the generalizability of these findings requires further validation in other agricultural contexts. Full article

Single-country livestock-mortality insurance programs in smallholder economies face a structural problem: under regime-switching mortality, the capital needed to stay solvent in a bad year can approach annual premium revenue, so public budgets become the insurer of last resort. This paper designs and prices a regional alternative for the smallholder cattle and buffalo systems of the ASEAN-5 (Thailand, Vietnam, Cambodia, Laos, and Myanmar): a sovereign risk pool with a catastrophe (CAT) bond that transfers the extreme tail to capital markets. A 100,000-iteration Monte Carlo simulates correlated regime-switching mortality, with cross-country dependence structured by epidemiological proximity and calibrated to Food and Agriculture Organization, national-statistics, and World Organisation for Animal Health data. Pooling cuts the 99 percent Value-at-Risk capital requirement by 41 percent relative to the sum of country buffers, a saving of USD 464 million per year, and the saving stays between 25 and 47 percent across plausible correlations. A parametric CAT bond attaching at the 95 percent and detaching at the 99.5 percent pool VaR transfers USD 353 million at an expected loss of 2.2 percent and a spread of 3.6 to 4.1 percent. The Southeast Asia Disaster Risk Insurance Facility provides a ready operational vehicle. For ASEAN agriculture and finance ministries, the practical implication is to build the national mortality-data systems and pooled financing arrangements, through SEADRIF, that would move this cover from design to issuance. Full article

This study examines the impact of agricultural insurance on farm household resilience and explores the underlying mechanisms through which this effect operates. Using survey data from 1242 farm households collected in 2021 across three major grain-producing provinces in China—Heilongjiang, Henan, and Hunan—this study constructs a multidimensional index of household resilience based on resistance, recovery, and regeneration using the entropy method. An instrumental variable approach is employed to address endogeneity concerns, and a mediation model is used to identify the mechanisms of influence. The results show that agricultural insurance significantly enhances farm household resilience, with the strongest effect observed in the recovery dimension. Mechanism analysis reveals that this effect operates through reducing risk aversion, improving income security, and promoting crop innovation. Heterogeneity analysis indicates that the resilience-enhancing effect is more pronounced among large-scale farmers and those who have not adopted climate adaptation measures. These findings suggest that agricultural insurance plays a critical role in strengthening farm household resilience through multiple channels. Policy efforts should focus on improving insurance accessibility, particularly for smallholder farmers, and promoting complementary measures such as risk management education and technological innovation. Full article

Agricultural production is increasingly threatened by climate variability, limited access to quality inputs, and market shocks in developing countries. Insurance–input bundles, which integrate crop insurance with inputs like fertiliser and seed, have emerged as a promising tool for improving productivity and resilience among smallholder farmers. This study adopts a structured (systematic narrative) literature review approach, synthesising evidence from 152 studies to examine farmers’ awareness, attitudes, willingness to pay, participation, satisfaction, and productivity outcomes associated with insurance–input bundles. The findings show that awareness remains uneven and often limited by weak extension systems and low financial literacy, while farmers’ attitudes are strongly shaped by past experiences, cultural perceptions, and institutional trust. Furthermore, affordability constraints and risk misinterpretation reduce willingness to pay, whereas perceived value and institutional credibility significantly enhance demand for bundled products. Across the reviewed literature, adoption is shown to be a non-linear and interdependent process influenced by behavioural, economic, and institutional factors, where breakdowns in trust, affordability, or information can limit participation. Evidence further indicates that insurance–input bundles promote the adoption of improved inputs, increase yields, and enhance income stability, although these impacts are highly context-dependent and mediated by implementation quality, including timely payouts and effective service delivery. The review contributed to the literature by advancing a systems-based understanding of bundled insurance adoption, highlighting the central role of institutional reliability, behavioural responses, and implementation quality. Lastly, the review underscores the need for strong institutions, integrated extension systems and farmer-centred design to ensure sustainable scaling. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

Plastic pollution is a problem for many municipalities, water authorities, and industries, including transportation, energy, agriculture, fisheries, real estate, tourism, hospitality, insurance, and healthcare. Efforts to understand and mitigate plastic pollution would benefit from a dedicated map satisfying basic criteria including traceability, scalability, spatio-temporal resolution, and data flexibility. This article details and demonstrates how several existing pollution maps satisfy these criteria and makes recommendations on their use for specific activities, including temporal monitoring, root-cause analysis (RCA), cleanups, and tourism guides. Advantages of using plastic density rather than piecewise logs as the primary data format are highlighted, in particular feasible memory requirements and access to cloud data. Environmental plastic mapping by passive optical sensors, which offer the potential of comprehensive qualified data, is also surveyed, including demonstration of an original shortwave infrared (SWIR) polarization imager, and dynamic plastic pollution monitoring is demonstrated through the application-programming interface (API) of the Google Maps platform utilizing both sensor and published survey data. Full article

The green transition in agriculture is a key issue for achieving sustainable development. Based on panel data from 30 Chinese provinces covering the period from 2011 to 2022, this paper examines the relationship between environmental enforcement and agricultural green total factor productivity (AGTFP), with a focus on analyzing the moderating effects of land transfer and agricultural insurance, as well as their synergistic threshold characteristics. The study employs two-way fixed-effects models, moderating effect models, and Hansen threshold regression methods for empirical analysis. The baseline regression results show a significant positive association between environmental enforcement and AGTFP. This conclusion remains robust after various tests, including truncation, replacement of core explanatory variables, difference GMM, and instrumental variables. The decomposition test shows that this positive correlation is mainly reflected through the channel of technological progress, rather than the improvement in technical efficiency. Heterogeneity analysis indicates that the positive association is more pronounced in regions with high GDP, strong law enforcement capacity, and in northern regions. Moderation analysis reveals that both the land transfer rate and insurance depth positively moderate the relationship between environmental enforcement and AGTFP, and the two exhibit a synergistic effect. However, this synergistic effect exhibits nonlinear characteristics and may weaken or even reverse at extreme value intervals. A threshold model further reveals an asymmetric complementary relationship between the two institutional conditions. The moderating effect of land transfer is activated only after insurance depth crosses a threshold value, while the moderating effect of insurance depth is most effective during the small-scale farming stage. These findings suggest that environmental regulation policies should be advanced in coordination with land transfer and agricultural insurance systems, with a focus on institutional alignment and coordination. Full article

Estimation of rice crop yield at the village level is essential because village is the Insurance Unit (IU) for rice crop in many regions in India, and timely and accurate yield information at this scale supports timely and transparent claim settlements for farmers and supports local agricultural planning. To achieve this, a multi-source satellite data-based machine learning approach was used to estimate rice yield at the village level using optical and SAR data, climatic data and land surface model-derived parameters in Kakinada of Andhra Pradesh, India. The predictor dataset included seasonal cumulative rainfall, seasonal Normalized Difference Vegetation Index (NDVI)-Max, seasonal NDVI-Mean, seasonal Land Surface Water Index (LSWI)-Max, seasonal LSWI-Mean, season total Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and season total Root Zone Soil Moisture (RZSM), and season total backscatter of the Sentinel-1 VH polarization were used to represent crop greenness, moisture status, photosynthetic activity, soil water availability, canopy structure, and seasonal water supply. For model development and validation, village-level rice yield data from 2017 to 2023 was used, which was collected through Crop Cutting Experiment (CCE) at the maturity stage of Kharif season. In this study, four machine learning models such as Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) were evaluated. The multi-source satellite data and yield data for the period 2017–2021 were used to train the models, which were independently tested on 2022 data and then applied to predict the rice yield in 2023. Leave-One-Year-Out (LOYO) cross-validation was also conducted on the 2017–2022 data to assess temporal robustness and generalization capability across years. Among the evaluated models, Random Forest exhibited the best overall performance. For the independent test year 2022, RF achieved an R² of 0.465, RMSE of 415.34 kg ha⁻¹, MAE of 322.22 kg ha⁻¹, and MAPE of 10.36%. For the prediction year 2023, RF achieved improved accuracy with an R² of 0.838, RMSE of 325.75 kg ha⁻¹, MAE of 262.21 kg ha⁻¹, and MAPE of 7.68%. Further, LOYO cross-validation also showed the robustness of RF, achieving the highest mean R² of 0.702 and mean RMSE of 384.73 kg ha⁻¹. The results illustrate that multi-source satellite data combined with machine learning can be a reliable and operationally useful tool in predicting village-level rice yield, which can be used for crop insurance claim settlement. Full article

Enhancing agricultural economic resilience (AER) is essential for global food security. As a key policy tool for stabilizing agricultural production, policy-based agricultural insurance lacks rigorous causal evidence on its impact on resilience. In this study, AER is operationalized as a composite index capturing resistance and recovery capacities across pressure, state, and response dimensions. Using 2012–2023 provincial panel data from China (31 provinces × 12 years = 372 observations), we measure AER via the entropy method and identify policy effects using a staggered multi-timepoint difference-in-differences (DID) model. We find that policy-based staple crop insurance significantly increases AER by approximately 2.5 percentage points, primarily by promoting agricultural technological innovation (ATI) and regional industrial structure upgrading (RIS). The improvement effects are more pronounced in central and western regions, non-major staple-crop producing areas, and regions with higher natural risks. Robustness is confirmed via event study, alternative weighting schemes (PCA and equal weighting), and placebo tests. This study provides reliable causal evidence for the resilience-enhancing effect of agricultural insurance and clarifies its internal transmission mechanisms, offering empirical support for the optimization of agricultural risk governance policies. Limitations include the use of provincial-level aggregate data and the lack of analysis of spatial spillover effects between regions. Our findings suggest that differentiated policy implementation can support more sustainable and targeted agricultural risk governance. Full article