Search Results (2,248)

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for 2000 and 2020 covering 420 relatively large lakes. We systematically examined the spatial restructuring of six major food and cash crops—wheat, rice, maize, soybean, peanut, and rapeseed—and evaluated their multiscale associations with lake total nitrogen (TN) and total phosphorus (TP) concentrations and how these associations changed over time. The results showed the following: (1) From 2000 to 2020, the spatial distributions of the six major crops underwent substantial restructuring. The dominant production areas of rice, wheat, and maize were maintained or further reinforced, whereas soybean, rapeseed, and peanut exhibited varying degrees of regional redistribution and localized concentration. (2) Lake water quality differed between the flood and non-flood seasons. TN exhibited pronounced seasonal differences between the two study periods, whereas temporal changes in TP were generally limited; both nutrients nevertheless showed marked regional heterogeneity among the five major lake regions. (3) The crop–water quality relationship exhibits significant scale dependence and crop-specific variations. The XGBoost model demonstrated a certain degree of out-of-field (OOF) predictive capability for both TN and TP, with OOF R² values of 0.448 and 0.447, respectively. For TN, the highest OOF R² values were observed in the 1000–2000 m buffer zone in both 2000 and 2020; the optimal prediction scale for TP shifted from 1000–2000 m in 2000 to 2000–5000 m in 2020. SHAP results showed that corn maintained a high and relatively stable predictive importance in the TN model, followed by wheat, peanuts, and rice; in the TP model, corn and rapeseed were the crop predictors with the highest relative SHAP importance. PDP results further indicate that there are generally nonlinear or non-monotonic relationships between different crop coverage proportions and TN and TP. (4) Pronounced spatial heterogeneity was observed across the five lake regions. The Eastern Plain Lake Region was characterized by associations involving multiple crops, whereas maize was the most prominent crop in the Northeast Plain and Mountain Lake Region. In the Inner Mongolia–Xinjiang Plateau Lake Region, maize predominated, with wheat and rapeseed also showing notable importance. In the Tibetan Plateau Lake Region, TN was associated with multiple crops, whereas TP was primarily related to maize and rapeseed. The Yunnan–Guizhou Plateau Lake Region exhibited particularly strong scale-dependent differences. This study provides a nationwide analytical framework for comparing the scale differences and regional variations in the statistical associations between the spatial distribution of crops and lake water quality at the specific crop level. The findings can provide a scientific basis for formulating differentiated agricultural nonpoint source pollution control strategies that are adapted to the evolving characteristics of crop planting structures. Full article

The Jianghan Plain is one of the important freshwater aquaculture regions in China. Accurate information on the spatial distribution and spatiotemporal dynamics of aquaculture ponds is essential for regional ecological management. However, large-scale and accurate identification remains challenging for aquaculture ponds because they have spectral and seasonal hydrological characteristics similar to those of rice fields, rivers, canals, and lakes. In this study, we developed a framework for mapping inland aquaculture ponds using multi-source remote sensing data from Sentinel-2, Sentinel-1, and PlanetScope. The framework integrated elevation-zoned Otsu thresholding for candidate water extraction, phenological features for rice field removal, and object classification based on a Gradient Boosting Decision Tree (GBDT) model using 12 shape and spatial-context features. It was applied to identify aquaculture ponds in the Jianghan Plain from 2016 to 2025. Overall accuracy exceeded 93%, and the F1-score exceeded 0.93 in all validations. Results from different sensors also showed high spatial consistency. In 2025, aquaculture ponds covered 2365.17 km² in the Jianghan Plain and were mainly concentrated in the central and eastern parts of the plain, especially near Honghu Lake and along the Yangtze and Hanjiang river meanders. Over the ten-year period, the aquaculture pond area fluctuated between 2033.58 and 2383.52 km², showing an initial decline followed by recovery. Lost aquaculture ponds were mainly located around lakes, while newly added ponds were mostly distributed along the margins of existing clusters. The decade dataset generated in this study can support freshwater aquaculture management and wetland conservation. Full article

Farmland abandonment has become an increasingly important challenge for the sustainable use of cultivated land worldwide. Although agricultural cooperatives can help mitigate farmland abandonment, their effectiveness has historically been limited in China due to their relatively weak organizational capacity and irregular development. This study argues that the County-wide Cooperative Quality Improvement Policy (CCQIP) improves the operational performance of agricultural cooperatives, thereby strengthening their capacity to mitigate farmland abandonment. Using geospatial data, we identify farmland abandonment at the county level in China and develop and test an analytical framework based on two mechanisms: increasing agricultural returns and improving service provision. The results show that the CCQIP significantly reduces the area of farmland abandonment. This effect is primarily achieved through three channels: promoting agricultural product branding, increasing the use of agricultural socialized services, and enhancing the vitality of the farmland transfer market. Heterogeneity analysis further indicates that the policy has stronger effects in major grain-producing regions and in rice-dominant areas. Furthermore, the inhibitory effect of the CCQIP on farmland abandonment becomes stronger as the region’s comparative advantage in agriculture increases. Full article

Precise spatiotemporal assessment of habitat suitability is essential for crop pest and disease risk warning and food security. However, most existing approaches focus on disease occurrence, overlook spatial heterogeneity and time series information, and therefore, struggle to capture the habitat dynamics from occurrence to epidemic. We propose a dynamic framework for rice sheath blight (RSB) habitat suitability assessment that integrates rice phenology and disease time series to reveal fine-grained intra-annual spatiotemporal variability via a spatiotemporally adaptive strategy beyond the reach of traditional static models. Remote sensing and meteorological time series data, together with RSB survey data and crowdsourced records from southern China, are integrated in this study. First, the study area is partitioned into sub-regions and sensitive time windows (STWs) based on climate and rice-cropping systems. MaxEnt, combined with natural breaks, is then used to assess RSB occurrence suitability and delineate multi-level suitable areas. Geographical and temporal weighted regression (GTWR) and the coefficient of variation (CV) are finally applied within moderate-to-high occurrence-suitability areas to reconstruct time series of intra-STW epidemic potential dynamics and quantify their temporal variation. Results indicate the optimal phenology-based scheme yields one STW for single-cropping sub-regions and three STWs for double- and mixed-cropping sub-regions. MaxEnt AUC ranges from 0.610 to 0.768, with natural-break thresholds at 0.312 and 0.473. GTWR produces generally robust intra-STW fits (most local R² > 0.4), and the CV highlights localized, time-varying high-fluctuation zones within occurrence-suitable areas that static maps do not reveal. Overall, our method extends crop disease habitat suitability assessment from a static paradigm to a spatiotemporally adaptive, dynamic one, providing useful habitat background constraints for monitoring, early warning, and forecasting of crop pests and diseases under complex cropping systems. Full article

India faces large, persistent yield gaps across eight major crops—wheat, rice, maize, soybean, pulses, mustard, cotton, and sugarcane—that collectively limit food security and rural income. This review synthesises evidence from over 100 peer-reviewed studies (1990–2024) to provide the first comprehensive, multi-crop, agro-climatically resolved yield gap analysis for India. Applying the three-tier framework of potential yield ($Y_p$), attainable yield ($Y_a$), and farmer yield ($Y_f$), exploitable yield gaps are quantified at national and agro-climatic zone level, ranked limiting factors are identified, and projected climate change impacts are assessed. Yield gaps range from 20 to 35% in irrigated rice of the north-western Indo-Gangetic Plain to 60–75% in rainfed cotton and pulses of the semi-arid tropics. Yield gaps of blackgram across 20 major districts average 515 kg ha⁻¹, driven by moisture stress, biotic pressure, and management constraints. Nitrogen management, irrigation access, variety adoption, and sowing-date optimisation collectively explain 80–85% of exploitable gaps, while nitrogen and irrigation alone account for 40–58%. Closing 50% of exploitable yield gaps could add approximately 100 million tonnes of food grain annually without expanding cultivated area, directly supporting Sustainable Development Goal (SDG 2). However, climate change is projected to widen yield gaps of rainfed rice by 15–30% by the 2040s under Representative Concentration Pathway (RCP) 8.5. Beyond this crop-by-crop synthesis, the review proposes a constraint-transition framework in which the dominant type of limiting factor shifts predictably from biophysical (water, climate) in the largest-gap rainfed systems to agronomic management (nitrogen, sowing date, variety) at intermediate gap levels and to institutional and socioeconomic constraints (credit, extension, land tenure, gender) as gaps narrow in the best-resourced irrigated systems, thus offering a conceptual lens for prioritising interventions by development stage rather than by crop alone. Full article

The valorization of agricultural and food-processing waste offers a promising pathway to develop cost-effective heterogeneous catalysts for biodiesel production. Herein, calcium-based catalysts were prepared by pyrolyzing mixtures of rice husk (RH) and eggshell (ES) at different RH:ES mass ratios. Structural, compositional, and catalytic properties of the resulting materials were investigated for soybean oil transesterification. This was conducted under mild conditions: 65 °C, 4 h, methanol-to-oil ratio of 7:1, and 1 wt.% of catalyst. In this sense, mixed RH-ES catalysts outperformed those synthesized from the individual components (P_RH and P_ES), achieving FAME yields of 81.2% and 71.2% for P_RH:ES_1:2 and P_RH:ES_1:1, respectively. Characterization by FTIR, XRD, SEM-EDS, and BET revealed that RH can act as a carbonaceous structural modifier, promoting the transformation of CaCO₃ into catalytically active species. It improves calcium dispersion and increases surface area from 0.73 to 49.56 m² g⁻¹. The P_RH:ES_1:1 material was selected for further evaluation to prioritize biomass loading while maintaining high catalytic performance and demonstrated good reusability with minimal activity loss. Moreover, GC-MS analysis of the resulting biodiesel confirmed that it resembles the FAME profile of conventional KOH-derived biodiesel. These results demonstrate that biomass-assisted calcium phase engineering enables efficient biodiesel production while promoting waste valorization. Full article

Balancing food production, ecosystem service value, and agricultural irrigation water consumption is a major challenge for sustainable agricultural development in China. However, quantitative evidence at the national scale remains limited on whether crop planting structure optimization derived from models can effectively achieve coordination among these three objectives. Existing studies mainly focus on individual crops or localized regions and rarely integrate the spatiotemporal evolution, influencing factors, and multi-objective optimization of major staple crops within a unified framework. This study developed a progressive framework integrating spatiotemporal evolution analysis, influencing factor identification, and planting structure optimization for wheat, rice, and maize. Spatial autocorrelation analysis, center-of-gravity shift analysis, and pixel-based image differencing were applied to reveal crop evolution patterns across China from 2000 to 2025. A five-dimensional indicator system comprising 17 quantitative indicators was developed through multiple experiments using four large language models, and the Random Forest algorithm was employed to identify key influencing factors and their relative importance. Based on these factors, optimization constraints were constructed, and a multi-objective fuzzy linear programming model combined with the NSGA-II algorithm was used to determine optimal crop area allocation across 28 provincial-level regions. The three staple crops exhibited a significant pattern of northward shift, eastward expansion, and southern contraction. Precipitation and market accessibility were common core influencing factors, ranking among the top five factors in all nine Random Forest models. Crop-specific factors, including soil available phosphorus for rice, accumulated active temperature for wheat, and soil pH for maize, explained differences in spatial responses among crops and provided a scientific basis for optimization modeling and coordinated improvement of food production, ecosystem service value, and irrigation water consumption. The optimized scheme increased total grain output by 3.6%, improved ecosystem service value by 11.0%, and reduced irrigation water consumption by 45.7% compared with the actual planting structure, all 28 provinces achieved improvement or stability in the three indicators simultaneously. Based on optimized crop allocation patterns, national planting structures were summarized into regional models, including a rice–maize dual-core system in Northeast China, wheat–maize rotation in the Huang-Huai-Hai Plain, rice-dominated systems in the middle and lower Yangtze River Basin and South China, water-efficient dryland farming in Northwest China, and a diversified balanced system in Southwest China. These findings provide a quantitative reference for optimizing China’s staple crop planting structure. Full article

Accurate discrimination between rice and artificial grassland remains challenging in regional agricultural monitoring because both herbaceous types share similar spectral signatures during vegetative growth, and existing land-cover products do not treat artificial grassland as a separate class. Using Jurong City, Jiangsu Province, as the study area, we propose a framework that optimizes red-edge vegetation index selection within crop-specific phenological windows to separate rice from grassland. Using Unmanned Aerial Vehicle (UAV) multispectral imagery and Sentinel-2 satellite data, we quantified spectral separability across eight phenological stages using Fisher ratios. We identified two optimal discrimination windows: early tillering (mid-June) and heading–flowering (early September). Within the heading–flowering window, a dual-index classification rule combining Normalized Difference Red-Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI) was transferred from UAV to Sentinel-2 and used to produce a 10 m rice–grassland map for the entire city. Spatial agreement with two publicly available rice datasets reached 75.2% and 79.5% for rice pixels, reflecting differences in spatial resolution, reference year, and class definition rather than classification error. Independent field validation using 200 samples yielded an overall accuracy of 92.50% (F1-score = 0.93), confirming the effectiveness of the VI–window optimization strategy. The framework offers an interpretable, physiology-driven alternative for crop-type mapping that relies solely on widely available multispectral bands. Full article

Agrarian cultural landscapes depend not only on the symbolic value of place but also on the viability of the producers who sustain landscape-bearing practices. This study asks which indigenous rice value-chain pathway is most supportable in the near term under current production, post-harvest, market, and coordination conditions, and what this implies for conservation planning. Using the Pak Phanang Basin, Thailand, as an area-based case, the study integrates evidence from 52 indigenous rice-farming households in four subdistricts, farmer meetings and focus group discussions, value-chain actors, relevant agencies, spatial and documentary sources, and a consumer survey used as supplementary demand-side evidence. Seven value-chain pathways were compared qualitatively using six criteria: downstream demand or final use, quality requirements, post-harvest burden, investment needs, coordination burden, and producer readiness. The findings show that farmer participation remains concentrated in low-risk channels, especially household consumption and mill sales. Paddy rice for game fowl and native chicken feed emerged as the most territorially supportable near-term pathway because it showed the strongest practical fit among identifiable downstream buyers and final-use clarity, moderate quality requirements, manageable post-harvest and investment burdens, coordination requirements, and producer readiness. Its feasibility, however, depends on reducing post-harvest bottlenecks, improving aggregation, managing quality, and coordinating with downstream buyers. The study concludes that feasible value-chain upgrading should be planned as conservation-enabling support for custodian viability, rather than as a simple shift toward higher-value markets. Full article

The growing demand for low-carbon building materials and the challenges of handling agroforestry waste and discarded EPS particles have spurred research toward developing novel building composites that utilize solid waste. Therefore, this study aims to develop a lightweight geopolymer composite incorporating these recycled materials to balance thermal insulation, mechanical strength, and waterproofing properties. In this work, geopolymer served as the binder, with various types of raw biomass (sawdust, rice husk, rice straw, and coconut fiber) as the primary aggregates and EPS particles as an additive to create a closed-pore structure. The microstructure of the raw biomass was characterized by SEM, while its specific surface area and average pore diameter were determined by BET analysis. Furthermore, the prepared composites were comprehensively evaluated in terms of their microstructure, pore structure (MIP), density, thermal conductivity, compressive strength, total water absorption, capillary water absorption, surface wettability, and UV aging behavior. The results showed that the prepared composites exhibited a porosity of 59.9–65.7%, a density of 492.9–586.3 kg/m³, a compressive strength of 7.3–10.9 MPa, a thermal conductivity of 0.115–0.142 W/(m·K), a total water absorption of 35.2–42.2%, capillary water uptake coefficients of 4.9–11 kg/m², and a water contact angle exceeding 140° (after modification). In addition, the developed composites offered significant environmental and economic benefits, with a low carbon footprint and an estimated cost of 100.6–150.3 USD/m³, making them more competitive compared to traditional insulation materials. Meanwhile, this study provides a scientific basis for developing high-strength building insulation materials from agroforestry waste, thus outlining a promising direction for future research and industry development. Full article

Accurate mapping of paddy rice is essential for agricultural monitoring, yield estimation, and food security assessment. However, optical imagery is often affected by clouds and spectral confusion, while SAR imagery suffers from speckle noise and weak spatial detail representation. Simple optical and SAR feature concatenation is therefore insufficient for complex agricultural landscapes. To address these limitations, this study proposes ACBE-CroFuseNet, an optical and SAR cross-fusion semantic segmentation network for paddy rice extraction using Sentinel-1 SAR and Sentinel-2 optical imagery in Yancheng, Jiangsu Province. ACBE-CroFuseNet introduces two task-oriented designs for paddy rice mapping. First, an attention cross-fusion module is developed to adaptively model modality contributions and spatial responses between optical spectral–textural features and SAR scattering–structural features. Second, a boundary enhancement module with boundary supervision is introduced to strengthen the delineation of fragmented paddy fields and field edges. Multimodal feature aggregation and multi-scale deep supervision are further used to improve feature utilization and segmentation stability. Compared with UNet++, Swin-Unet, CroFuseNet, and CMFFNet under five-fold cross-validation, ACBE-CroFuseNet achieves the best overall performance. The extracted paddy rice area in Yancheng in 2025 demonstrates the applicability of the proposed method for large-scale crop mapping. Full article

Flooding during the rice (Oryza sativa L.) jointing stage threatens yield stability in regions with concentrated rainfall and limited drainage. However, the extent to which the water regime before flooding modifies rice growth and yield formation remains poorly understood. A pot experiment was conducted over two years to compare controlled irrigation (CI) with conventional flooding irrigation (CF) across three flooding depths and two durations imposed at the jointing stage. Flooding increased tiller number, plant height, and total leaf area, but reduced net photosynthetic rate and grain yield. Yield loss increased with flooding depth and duration. Under corresponding flooding treatments, CI moderated vegetative expansion and maintained a higher net photosynthetic rate, greater root dry matter, and a higher root-to-shoot ratio than CF. Relative to the corresponding non-flooded controls, yield loss ranged from 4.11% to 39.33% under CI and from 5.63% to 52.50% under CF. The lower yield loss under CI was associated with greater effective panicle number, higher seed setting rate, and better maintenance of photosynthetic activity and root biomass. These findings indicate that the water regime before flooding can influence biomass allocation and yield formation during the jointing stage flooding. Controlled irrigation combined with timely drainage may help reduce yield risk in rice systems exposed to temporary flooding. Full article

To evaluate the yield and grain quality performance of rice varieties under rice-ratoon rice cultivation in northern Guangdong, a four-site field experiment was conducted from March to November of 2025 in Liannan and Lianshan counties of Qingyuan City and in Liucheng and Shangguan townships of Dongyuan County, Heyuan City, respectively. Seven rice varieties were evaluated: Qingxiangyou 19xiang (QXY), Nanjingxiangzhan (NJXZ), Jiangliangyouhuazhan (JLYHZ), Guiliangyou 6177 (GLY), Weiliangyouyuzhan (WLYYZ), Guiyouxinzhan (GYXZ), and Jiulixiang (JLX). The results showed clear differences in grain yield and grain quality among varieties and ecological sites. QXY generally showed the highest grain yield, especially in the ratoon crop, and ranked first in annual grain yield among the four sites, with values ranging from 10.26 to 11.63 t ha⁻¹. However, the quality advantage differed among sites and varieties. JLYHZ showed the best quality performance in Lianshan County and Dongyuan County (Shangguan Township), GLY showed the best quality performance in Liannan County, and JLX showed the best quality performance in Dongyuan County (Liucheng Township). These results indicated a yield–quality trade-off among the tested varieties. Therefore, QXY may be considered a promising yield-oriented variety for the tested sites in northern Guangdong, whereas JLYHZ, GLY, and JLX may be more suitable where grain quality is prioritized. Full article

Converting conventional rice monoculture to co-culture with fish is a common strategy to increase farmers’ incomes. The Chinese soft-shell turtle is a typical high-value species for paddy co-culture, but the effects of the co-culture system (RT) on greenhouse gas emissions (GHG) are poorly understood. This study evaluated the impacts of RT on economic benefits and greenhouse gas emissions. Our results showed that RT co-culture did not significantly increase rice yield, but it provided additional income from turtle sales, increasing net profits. GHG emission responses to RT differed between the two farms. In farm 1, RT increased CH₄ emissions from the rice-cultivated area but decreased them from the turtle culture area, resulting in no net effect on total CH₄ emissions. The reduction of CH₄ emissions in the turtle culture area was associated with lower DOC content and reduced methanogenic gene mcrA abundance. In farm 2, RT reduced CH₄ emissions from both areas, lowering total CH₄ emissions by 36.5%, which was linked to less water flooding and higher methanotrophic gene (pmoA) abundance. Total N₂O emissions were not significantly affected by RT at either farm. RT increased indirect GHG emissions, but these accounted for only 7.2–16.9% of total emissions. Overall, RT reduced total GHG emissions by 27.7% at farm 2, with no significant change at farm 1. These findings suggest that RT can enhance economic benefit while lessening total GHG emissions. Full article

Abandoned paddy wetlands (APWs) develop after rice cultivation ceases and may support carbon sequestration, soil conservation, and sustainable management of abandoned agricultural land. This study quantified soil and biomass carbon in six reference APWs in the central region of the Republic of Korea. Following the Intergovernmental Panel on Climate Change (IPCC) Good Practice Guidance for Land Use, Land Use Change and Forestry (GPG-LULUCF), soil samples were collected to a depth of 30 cm to estimate soil organic matter (OM), soil organic carbon (SOC), and soil organic carbon storage per unit area (SOCS). Land cover-based carbon absorption was evaluated within 300 m hydro-ecological impact zones for 2000, 2013, and 2021, and biomass carbon was estimated for woody, herbaceous, and mixed communities using field measurements and drone LiDAR-derived vegetation structure. The mean OM and SOC contents were 33.7 and 19.54 g/kg, respectively. The corrected mean SOCS was 61.20 ± 22.28 kg/m² (unadjusted mean: 67.97 kg/m²), approximately 8.5–40 times the reference values reported for South Korean forest soils and urban parks. The mapped mean carbon absorption was 20.45 tCO₂/ha in 2000, 35.30 tCO₂/ha in 2013, and 38.14 tCO₂/ha in 2021. Because only three mapped years were analyzed, these values describe observed differences and do not establish a continuous temporal trajectory or a stable plateau. The findings suggest that APWs may contribute to sustainable land management, wetland restoration, soil conservation, and climate-change mitigation through carbon retention in soils and vegetation. These field data provide region-specific evidence for evaluating sustainable management strategies and complementing broader-scale soil–vegetation carbon models. Full article