River catchments are critical to rice paddies and other croplands because they provide reliable supplies of safe water for irrigation and support essential ecosystem functions. However, pollution from illegal discharges of untreated wastewater threatens irrigation water quality, soil health, and food safety, for instance, often contain potentially toxic heavy metals including Cu, Zn, Pb, As, Ni, Hg, Cr, and Cd which can accumulate in crops and other biological and environmental systems. This review presents an overview of advanced pollution-tracing technologies, including heavy metal isotope analysis, resin capsules, fluorescence fingerprinting, microbial source tracking, and machine learning models. A comparative analysis of detection limits, operational costs, scalability, and technical constraints highlights both the strengths and limitations of each methods and underscores the value of multi-tracer approaches and integrated data analysis. By synthesizing recent case studies, the review identifies four core strategies for watershed-scale pollution monitoring: (1) integrating microbial, chemical, and geospatial tools within multi-tracer frameworks to improve source attribution, (2) standardizing tracing methodologies and improving cross-method comparability, (3) developing cost-effective and field-deployable tracing tools, and (4) integrating tracer-derived data with remote sensing observations, machine learning, and other artificial intelligence models. This synthesis provides a foundation for developing high-resolution, reliable, and scalable river pollution monitoring systems that support the protection of soil health and crop safety.
This study develops a Dynamic Irrigation Management Transfer (Dynamic IMT) framework to reinterpret Taiwan’s irrigation governance and its adaptive evolution under changing socio-environmental conditions. Building on Sasaki Nobuo’s governance typology, the framework conceptualizes governance transformation along two interacting dimensions: authority allocation and functional integration. It explains how irrigation institutions adjust between decentralization and centralization in response to internal organizational change and external environmental pressure. Taiwan’s trajectory demonstrates a gradual transition from farmer-led Decentralized-Integrated (DI) governance to state-led Centralized-Integrated (CI) management through intermediate stages of Decentralized-Separated (DS) and Centralized-Separated (CS) systems. This evolution was driven by internal factors including aging membership, fiscal dependence, and institutional ambiguity, as well as by external pressures such as urbanization, industrial water competition, and intensifying climate extremes. The 2020 reform that incorporated all Irrigation Associations into the Ministry of Agriculture was not a political reversal but an adaptive response intended to restore coordination, accountability, and water security. In comparative perspective, Japan’s Land Improvement Districts sustain participatory governance through fiscal autonomy, while Korea’s Rural Community Corporation maintains a hybrid balance between centralized efficiency and local responsiveness. Taiwan’s integrated model reflects demographic and climatic constraints that made hybrid governance unsustainable. The Dynamic IMT framework extends traditional IMT theory by emphasizing institutional adaptability, feedback learning, and cyclical adjustment. Sustainable irrigation governance depends on adapting authority, integration, and participation to changing demographic and environmental conditions.
Given the increasing need for accurate agricultural land monitoring due to the discrepancy between official records and actual land use in South Korea, an urgent demand is for automated monitoring systems. This study aims to develop a system that combines object detection and segmentation capabilities in high-resolution images using the YOLO-Seg model for paddy parcel monitoring based on drone imagery. We propose a centroid-based tiling-movement technique together with a systematic analysis of optimal overlap rates (0–90
Rice straw and medical non-woven fabric waste were co-pyrolyzed at mass ratios of 1:0, 1:1, 2:1, and 3:1 (rice straw/non-woven) at 450 °C to produce biochars (BC1R0N, BC1R1N, BC2R1N, BC3R1N). A 30-day pot experiment with four replicates per treatment examined the effects of soil amendment with 0.5
Soil detachment is a fundamental process in water erosion and a key concern in intensively cultivated rice systems. However, no studies have explored this process in rice paddy fields, where the poor structure makes the soil prone to detachment. This research evaluated the soil detachment capacity (hereafter “Dc”) across four sediment size fractions (0–0.1, 0.1–0.2, 0.2–0.3, and 0.3–0.5 mm) under variable hydraulic and morphological conditions. Dc was measured on soil samples collected in rice paddy fields of Northern Iran by flume experiments. Three soil slopes (1 to 3
Planting date is an important determinant of wet-season rice performance in monsoon Asia. However, it is influenced not only by rainfall but also by irrigation access, drainage conditions, and field operability. This study quantified how the spatial distribution and dispersion of planting dates were associated with provincial wet-season rice yields in Northeast Thailand. Planting dates at 1 km resolution for 2002–2023 were estimated from time series of the Enhanced Vegetation Index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) using the PhenoRice algorithm, and province-year indicators of mean planting date and within-province spatial standard deviation were derived. These indicators were analyzed together with June–August precipitation, June–October air temperature, and the Standardized Precipitation–Evapotranspiration Index (SPEI) for September–October. Satellite-derived monthly planted area for May–August showed moderate agreement with reported agricultural statistics, with a correlation coefficient of 0.65. Spatially, mean planting dates were earlier in the southern part of the region and later in the wetter northeast, suggesting that drainage and waterlogging constraints, rather than early-season rainfall availability alone, influenced establishment timing. Two-way fixed-effects regressions showed that planting-date dispersion was positively associated with yield, and that this association was stronger under wetter late-season conditions. These results suggest that spatial heterogeneity in planting date is a yield-relevant characteristic of wet-season rice systems beyond rainfall totals alone.
Rice fields are one of the most important and extensive agroecosystems in the world. In Europe, Italy is a major rice producer, with a significant proportion of the yield originating from a vast area within the Po Valley. In this study, we investigated the impact of adopting winter flooding (an established practice in Asia, but novel to Northern Italy) on the biodiversity, abundance, and functional organisation of aquatic invertebrate communities. In particular we attempt to: (i) describe and compare the biodiversity of aquatic invertebrates in the study area, (ii) assess how winter submergence affects the structural and functional composition of aquatic invertebrate communities (iii) provide recommendations to increase the sustainable management of rice fields. Preliminary results suggest that maintaining water in intensive agricultural areas during the cold season could serve as a substitute for lost natural wetlands and enhance local biological and functional diversity. Indeed, although our study pointed out that winter flooding does not lead to a significant increase in the richness and abundance of invertebrate communities, this practice can be potentially useful since flooded rice fields host a community that would otherwise disappear and which plays an important role in both spring recolonization and organic matter decomposition.
To obtain freshwater for irrigation, we collected water vapor in a cultivation greenhouse using underground pipes and film condensation collectors. Two underground pipe systems, each composed of four polyvinyl chloride pipes with a diameter of 50 mm, were buried 0.2 m below ground on both sides of the greenhouse. Warm and humid greenhouse air was introduced into the pipes using solar-powered fans, and the volume of water condensed in the pipes was measured. Four L-shaped aluminum frames were attached to the plastic film to collect droplets on the inner surface. Spinach and komatsuna (Japanese mustard spinach) were grown in the greenhouse from November 2023 to June 2024. The daily evapotranspiration rate in the greenhouse was measured by weighing the pots. The amounts of evapotranspiration and water collected using the pipes increased from Trials 1 to 3 and showed a strong correlation with the temperature difference between the air in the pipes and the inner pipe wall. The volumes collected using the frames were equal across all the three trials. This water collection showed a strong correlation with the temperature difference between the greenhouse air and the film during the cold period, but no correlation during the warm period. The water-vapor collection ratio of the frames decreased during the warm period, despite an increase in evapotranspiration. The total collection ratios using the pipes and the film were approximately 30
Mitigating methane (CH4) emissions from agriculture is critical for addressing climate change. This study aimed to evaluate the effects of organic (OF) and conventional farming (CF) on the community structure and characteristics of methane-cycling microorganisms (MCM) and their role in CH4 production and oxidation processes. A total of 16 soil samples were collected from paddy fields in Yangpyeong, Gyeonggi Province, in 2022 and five sites in Chungcheong Province in 2023. The microbial community distribution was analyzed using 16S rRNA gene sequencing, targeting the V3–V4 region. The relative abundances of methanogenic archaea (MA) were higher in CF (0.55 ± 0.27
Heavy metals, particularly chromium (Cr), poses serious concern regarding contamination and its accumulation in soil impacting crop growth, yield, food safety besides environmental safety and human health in Cr-contaminated areas. An experiment was conducted to evaluate application of various amendments (viz. biochar and vermicompost) besides irrigation practices for alleviating Cr toxicity in rice. The experiment was laid out in a factorial complete randomized design with three factors i.e. Cr stress, amendment application and irrigation practice. The elevated Cr levels significantly inhibited plant growth parameters, yield attributes, and ultimately grain (12
Rice is a critical global staple, yet its intensive cultivation generates significant environmental pressures, thereby creating tension between food security and ecological health. Despite growing interest in sustainable agriculture, empirical evidence on how farm-level practices align with international sustainability standards remains limited, particularly in production regions where integrated sustainability assessments remain scarce, such as Türkiye. This study addresses this gap by assessing the sustainability of rice production in Samsun, Türkiye, using the Sustainable Rice Platform (SRP) standard. Primary data were collected from 166 rice farms through face-to-face surveys to evaluate economic, environmental, and social performance indicators. The results reveal a clear sustainability imbalance: economic performance was highest, particularly among larger farms, while environmental sustainability recorded the lowest scores across all farm-size groups. Inefficient nitrogen and phosphorus use and high greenhouse gas emissions were identified as the primary barriers. Farm size was positively associated with economic and social sustainability, though environmental sustainability did not follow this pattern, suggesting that environmental performance may be more strongly shaped by management practices and technical capacity than by farm scale. Policy interventions should prioritise resource-use efficiency and support environmentally friendly practices through scale-sensitive measures that take into account differences in mechanization, education, land structure, and infrastructure across farm types. Improving the sustainability of rice farming in Türkiye requires not only scale-sensitive policies but also targeted efforts to strengthen environmental performance alongside economic viability.
Projecting the precise impacts of climate change is challenging due to uncertainties in climate trends. Adaptation planning could be done using the information provided on climatic risks together with assessing different irrigation systems and strategies that could be employed for increased agricultural resilience. This study focuses on the deltaic region of southern India, a highly climate-sensitive agricultural area and it evaluates climate risk at the village level. A Soil and Water Assessment Tool (SWAT +) model was used for watershed simulation, calibration, and validation with observed data. We analysed key water balance components, including runoff and evapotranspiration, to estimate risks of floods and droughts. The analysis showed there is a 46
Efficient irrigation management is crucial to achieve optimal rice yields, and crop modeling serves as a valuable tool for optimizing irrigation schedules. In this respect, a field experiment was carried out at Bangladesh Agricultural University, Mymensingh, from January to June 2023 and 2024, to evaluate the AquaCrop model's accuracy in simulating yield and irrigation for dry direct-seeded rice under various irrigation managements. Three irrigation managements: no irrigation (I1), always remain field capacity (FC) moisture (I2) and irrigation at 25
The excessive application of pesticides in rice cultivation leads to environmental contamination. In this study, farmers’ use of pesticides in rice fields and pesticide residues in water, soil, and sediment were investigated. The results showed that 23 active ingredients were used in rice cultivation. Among them, six insecticide active ingredients—diazinon, cypermethrin, dimethoate, permethrin, chlorpyrifos ethyl, and fenvalerate—were detected in water, soil, and sediment in rice fields, as well as in canal sediments. In the paddy field, the highest concentrations were observed for cypermethrin on the 60th day, with 31.13 ± 7.87 µg/L in water and 284.03 ± 88.26 µg/kg (dry weight) in soil. Dimethoate showed the highest concentration in sediment, reaching 254.90 ± 10.71 µg/kg (dry weight) on the 60th day after rice seeding. By the 75th day, only diazinon and chlorpyrifos ethyl were detected, while other insecticides had completely dissipated. This study also provides important information on the accumulation of insecticides in rice ecosystems. Chronic risk assessment using the risk quotient (RQ) in water showed that all insecticides posed a high potential ecological risk. In addition, hazard quotient (HQ) values for non-carcinogenic human health risks, calculated from average insecticide concentrations, indicated that dimethoate in paddy field water posed a risk to both children and adults via ingestion. The risk assessment offers insights into the effects of insecticides on human health and ecology, providing scientific guidance for managing pesticide application. Six active ingredients of insecticides were detected in a paddy field during a rice cultivation crop. Diazinon and chlorpyrifos ethyl were slower dissipated in water, soil and sediment in a rice field and sediment in canals compared to other insecticides. All insecticides accumulated in water, soil and sediment posed a high possible ecological risk based on the Risk Quotient assessments. Dimethoate in water in the paddy field posed risk to both children and adults via ingestion.
The study was conducted at the Norman E. Borlaug Crop Research Centre (CRC), G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India, to evaluate the effects of soil moisture regimes on wheat growth, yield, and water use efficiency under surface irrigation. Eight treatments (T1 to T8) were tested, each maintaining different soil moisture levels at three depths (0–15 cm, 15–30 cm, and 30–45 cm), ranging from 21 to 38
Reconciling biodiversity conservation with agroecosystems is a challenge for sustainable food systems. Organic rice production is increasing in Italy, promoting natural and agro-biodiversity. Rice fields can be considered wetland surrogates, crucial as biodiversity hotspots, and thus represent natural or semi-natural habitats within intensively farmed districts. The ecological role and value of rice fields increase when combined with agroecological farming practices. The aim of the current research was to assess the role of paddy fields in biodiversity promotion at different spatial scales in organic and conventional farming conditions, from surrounding environmental matrices to the sustainability of single farming practices. We developed a pool of scalable ecological indicators to assess wetlands-associated biodiversity according to the reference agro-system. Butterflies and dragonfly taxa were used as the main biodiversity and ecosystem services proxy. All low-impact rice farming practices on field banks such as improved soil cover and water level, reduced grass cutting and weeding, and increased botanical diversity prove to be effective for promoting biodiversity. We argue that insect monitoring can be integrated into rice farm appraisal to quantify crop and natural diversification levels. These data, correlated with biodiversity-friendly practices on a scalable field, farm, or district/landscape level, could become a useful tool to weigh the measured benefits of biodiversity-friendly rice management practices and related ecosystem services. Agro-ecological low-input rice farming practices can improve food system resilience against abiotic and biotic stresses caused by climate change, and enhance biodiversity and resilience at both farm and landscape scales.
Accurate prediction of paddy yield is essential for ensuring food security, optimizing agricultural management, and supporting policy decisions in regions characterized by intensive paddy cultivation. This study develops a comprehensive machine learning-based framework to model and explain paddy yield variability using a combination of meteorological parameters, MODIS-derived vegetation indices, and ancillary crop yield datasets for the lower part of the Ganga Basin. A series of preprocessing steps including spatial and temporal aggregation, standardization, and correlation analysis were applied to ensure data integrity and robustness. Four machine learning models, namely Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Multilayer Perceptrons (MLP), were trained using an 80:20 split and validated through 9-fold cross-validation. Model evaluation metrics such as R2 (0.818) RMSE (0.206) and MAE (0.143), demonstrated that RF achieved the highest predictive accuracy, outperforming other approaches due to its capability to capture nonlinear relationships and variable interactions. To enhance interpretability, SHAP (SHapley Additive exPlanations) analysis was employed, providing a transparent assessment of feature contributions across models. The results revealed that solar radiation, soil moisture, NDVI, and ET exerted the strongest influence on yield prediction, aligning with physiological drivers of paddy growth. Model robustness and reliability were further supported by consistent feature importance trends across multiple algorithms. By integrating remote sensing, meteorology, and machine learning, this study generates reliable yield estimates to support early warning systems and precision agriculture.
The present study shows a comprehensive modelling framework to estimate the infiltration rates in the permeable channels using various soft computing techniques, like Random Forest (RF), Distributed Random Forest (DRF), Deep Neural Network (DNN), Artificial neural network (ANN), Stacked Ensemble, and Gradient Boosting Machine (GBM). The models were developed using input parameters which included base width (b), channel side slope (m), water level (y), sand (
Aman rice, cultivated during the wet season in the region, is predominantly rainfed but often requires supplemental irrigation to close yield gaps. This study evaluated evapotranspiration-based irrigation intervals of 5 days (I5D), 10 days (I10D), and 15 days (I15D) on grain yield, water use efficiency (WUE), and groundwater recharge potential during the 2021 and 2022 Aman seasons. It also quantified the water balance during the last 10 years. Supplemental irrigation requirements were calculated as the deficit between crop water demand and water supply (rainfall plus root-zone residual moisture) at designated intervals. The reference evapotranspiration (ET0) estimated using the CROPWAT 8.0 software was multiplied by the crop coefficient to find actual evapotranspiration (ETc). The field-scale water balance gave the potential for groundwater recharge (percolation). The I10D irrigation produced the highest grain yield in both seasons; however, the treatments had no statistically significant effect on grain yield, straw yield, and other growth and yield components. Water input (rainfall and irrigation) matched well with the cumulative ETc in 2021, but was much higher than the cumulative ETc in 2022 due to higher rainfall. The groundwater recharge potential ranged 71–116 mm in 2021 and 406–484 mm in 2022 across the irrigation treatments, and averaged 286 mm during 2014–2023 under rainfed cultivation. The WUE of rice showed a minimal response to irrigation treatments. Annual variability in rainfall had significant effects on WUE and groundwater recharge potential. These results indicate the need for offering practical insights for water-efficient Aman rice production under fluctuating rainfall patterns.
Global climate change and subsequent increase in temperature and dry periods are expected to affect agricultural productivity and food security. Here, we modeled the impact of future climate on water yield and rice productivity across a range of elevations in two watersheds in Northern Iran using the AquaCrop model. After evaluating 19 Atmosphere-ocean general circulation models (AOGCMs) from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report (AR5) using statistical metrics, the models with better performance at six stations were selected in predicting historical air temperature (T) and precipitation (P). Our results showed that, despite a general increase in minimum and max air temperature during near future period (2020–2040), spring months had the highest increase in Tmax, while the lowest increase occurred during the winter months for all the stations. The greatest change in precipitation was observed in summer months. Increases in the future T and P were predicted to be larger at higher altitudes. The annual trend of rice yield was increasing based on RCP4.5 scenario and a gradually increasing pattern in rice yield was revealed from the lowlands to uplands. Under the RCP8.5 scenario, the annual rice yield in most areas was predicted to decline in the future, except for the upland area. In the lowland areas, most of the AOGCMs predicted a decrease in the water productivity, but an increase in water productivity was observed in the upland areas. The information on water productivity would help devise strategic water management plans during periods of both water shortage and excess under a changing climate.
