Search Results (340)

Tillage and crop rotation alter soil environments, thereby influencing both crop yields and methane-cycling microbiomes, yet their combined effects on microbial diversity, assembly, and interaction networks remain unclear. Using a two-factor field experiment, we assessed the impacts of raised seedbed vs. flat cultivation and rice–oilseed rape vs. rice–faba bean rotations on crop productivity and the ecology of methanogen (mcrA) and methanotroph (pmoA) communities. Raised seedbed cultivation significantly increased yields: rice yields were 7.6–9.6% higher in 2020 and 4.7–5.8% higher in 2021 than under flat cultivation (p < 0.05). Faba bean and oilseed rape yields were also improved. Flat rice–bean plots developed more reduced conditions and higher organic matter, with a higher NCM goodness-of-fit for methanogens (R² = 0.466), indicating patterns more consistent with neutral (stochastic) assembly, whereas the lower fit for methanotrophs (R² = 0.269) suggests weaker neutrality and stronger environmental filtering, accompanied by reduced richness and network complexity. In contrast, raised seedbed rice–oilseed rape plots improved redox potential and nutrient availability, sustaining both mcrA and pmoA diversity and fostering synergistic interactions, thereby enhancing community stability and indicating a potential for methane-cycle regulation. Overall, raised seedbed cultivation combined with legume rotation offers yield benefits and ecological advantages, providing a sustainable pathway for paddy management with potentially lower greenhouse gas risks. Full article

Climate change poses a threat to global rice production by increasing the frequency and intensity of extreme weather events. The widespread cultivation of genetically uniform modern varieties has narrowed the genetic base of rice, increasing its vulnerability to these increased pressures. Rice landraces are traditional rice varieties that have been cultivated by farming communities for centuries and are considered crucial resources of genetic diversity. These landraces are adapted to a wide range of agro-ecological environments and exhibit valuable traits that provide tolerance to various biotic stresses, including drought, salinity, nutrient-deficient soils, and the increasing severity of climate-related temperature extremes. In addition, many landraces possess diverse alleles associated with resistance to biotic stresses, including pests and diseases. In addition, rice landraces exhibit great grain quality characters including high levels of essential amino acids, antioxidants, flavonoids, vitamins, and micronutrients. Hence, their preservation is vital for maintaining agricultural biodiversity and enhancing nutritional security, especially in vulnerable and resource-limited regions. However, rice landraces are increasingly threatened by genetic erosion due to widespread adoption of modern high-yielding varieties, habitat loss, and changing farming practices. This review discusses the roles of rice landraces in developing resilient and climate-smart rice cultivars. Moreover, the Pantiange Heigu landrace, cultivated at one of the highest altitudes globally in Yunnan Province, China, has been used as a case study for integrated conservation by demonstrating the successful combination of in situ and ex situ strategies, community engagement, policy support, and value-added development to sustainably preserve genetic diversity under challenging environmental and socio-economic challenges. Finally, this study explores the importance of employing advanced genomic technologies with supportive policies and economic encouragements to enhance conservation and sustainable development of rice landraces as a strategic imperative for global food security. By preserving and enhancing the utilization of rice landraces, the agricultural community can strengthen the genetic base of rice, improve crop resilience, and contribute substantially to global food security and sustainable agricultural development in the face of environmental and socio-economic challenges. Full article

Accurate mapping of rice cultivation is vital for ensuring food security, reducing greenhouse gas emissions, and achieving sustainable development goals. However, large-scale deep learning–based crop mapping remains limited due to the demand for vast, uniformly distributed, high-quality samples. To address this challenge, we propose a Progressive Deep Learning Crop Mapping (PDLCM) framework for national-scale, high-resolution rice mapping. Beginning with a small set of localized rice and non-rice samples, PDLCM progressively refines model performance through iterative enhancement of positive and negative samples, effectively mitigating sample scarcity and spatial heterogeneity. By combining time-series Sentinel-2 optical data with Sentinel-1 synthetic aperture radar imagery, the framework captures distinctive phenological characteristics of rice while resolving spatiotemporal inconsistencies in large datasets. Applying PDLCM, we produced 10 m rice maps from 2022 to 2024 across the middle and lower Yangtze River Basin, covering more than one million square kilometers. The results achieved an overall accuracy of 96.8% and an F1 score of 0.88, demonstrating strong spatial and temporal generalization. All datasets and source codes are publicly accessible, supporting SDG 2 and providing a transferable paradigm for operational large-scale crop mapping. Full article

The worldwide population is anticipated to reach 10.12 billion by the year 2100, thereby amplifying the necessity for sustainable agricultural methodologies to secure food availability while reducing ecological consequences. Conventional synthetic pesticides, while capable of increasing crop yields by as much as 50%, present considerable hazards such as toxicity, the emergence of resistance, and environmental pollution. This review examines biopesticides, originating from microbial (e.g., Bacillus thuringiensis, Trichoderma spp.), plant, or animal sources, as environmentally sustainable alternatives which address pest control through mechanisms including antibiosis, hyperparasitism, and competition. Biopesticides provide advantages such as biodegradability, minimal toxicity to non-target organisms, and a lower likelihood of resistance development. The global market for biopesticides is projected to be valued between USD 8 and 10 billion by 2025, accounting for 3–4% of the overall pesticide sector, and is expected to grow at a compound annual growth rate (CAGR) of 12–16%. To mitigate production costs, agro-industrial byproducts such as rice husk and starch wastewater can be utilized as economical substrates in both solid-state and submerged fermentation processes, which may lead to a reduction in expenses ranging from 35% to 59%. Strategies for process intensification, such as the implementation of intensified bioreactors, continuous cultivation methods, and artificial intelligence (AI)-driven monitoring systems, significantly improve the upstream stages (including strain development and fermentation), downstream processes (such as purification and drying), and formulation phases. These advancements result in enhanced productivity, reduced energy consumption, and greater product stability. Patent activity, exemplified by 2371 documents from 1982 to 2021, highlights advancements in formulations and microbial strains. The integration of circular economy principles in biopesticide production through process intensification enhances the safety, quality, and sustainability of food systems. Projections suggest that by the 2040s to 2050s, biopesticides may achieve market parity with synthetic alternatives. Obstacles encompass the alignment of regulations and the ability to scale in order to completely achieve these benefits. Full article

This study evaluated the effects of applying fecal sludge-based co-compost (CC) integrated with chemical fertilizers on soil nutrient status, organic carbon (OC) storage, and economic returns in paddy soils. Ten integrated nutrient management (INM) treatments were tested, i.e., BRRI recommended dose of fertilizer (RDF), CC 5.0 t ha⁻¹, RDF + CC 2.0 t ha⁻¹, RDF + CC 1.5 t ha⁻¹, RDF + CC 1.0 t ha⁻¹, RDF + CC 0.5 t ha⁻¹, 75% RDF + CC 2.0 t ha⁻¹, 75% RDF + CC 1.5 t ha⁻¹, 75% RDF + CC 1.0 t ha⁻¹, and 75% RDF + CC 0.5 t ha⁻¹. Two rice varieties were cultivated over two consecutive seasons—winter rice (boro) and monsoon rice (aman)—in the experimental field. Soil samples (0–15 cm) were collected before and after the seasons and fractionated into labile particulate organic matter (>53 µm) and stable mineral-associated organic matter (<53 µm). Bulk soils and CC were analyzed for OC, nitrogen (N), phosphorus (P), potassium (K), sulfur (S), and heavy metals, while the fractions were analyzed for OC and N. Across both seasons, 75% RDF combined with 2.0 t ha⁻¹ or 1.5 t ha⁻¹ of CC consistently showed the highest OC, total N, and soil C stock, with moderate P, K, and S levels. Sole RDF produced the lowest OC and N. Among fractions, stable OC was the highest in the 75% RDF + 2.0 t ha⁻¹ CC treatment, statistically similar to 75% RDF + 1.5 t ha⁻¹ CC, and the lowest under RDF alone. Economically, sole RDF yielded the highest profit, while full RDF + CC achieved competitive returns. Reduced RDF + CC treatments (75% RDF + 1.5 or 2.0 t ha⁻¹ CC) offered slightly lower returns but improved soil sustainability indicators. Overall, applying 75% RDF + 1.5 t ha⁻¹ CC provided the most cost-effective balance of nutrient enrichment, soil C stock, and profitability. This CC-based INM approach reduces chemical fertilizer dependency, enhances soil health, and promotes sustainable waste management, supporting environmentally resilient rice production. Full article

Rice blast, caused by Magnaporthe oryzae, is a devastating disease that jeopardizes global rice production and food security. Precision agriculture demands timely and accurate monitoring tools to enable targeted intervention. This study introduces a novel deep learning framework that fuses time-series hyperspectral imaging with an advanced Autoformer model (AutoMSD) to dynamically track rice blast progression. The proposed AutoMSD model integrates multi-scale convolution and adaptive sequence decomposition, effectively decoding complex spatio-temporal patterns associated with disease development. When deployed on a 7-day hyperspectral dataset, AutoMSD achieved 86.67% prediction accuracy using only 3 days of historical data, surpassing conventional approaches. This accuracy at an early infection stage underscores the model’s strong potential for practical field deployment. Our work provides a scalable and robust decision-support tool that paves the way for site-specific disease management, reduced pesticide usage, and enhanced sustainability in rice cultivation systems. Full article

This study investigates the carbon footprint (CF) and eco-efficiency of rice cultivation in the Thessaloniki Plain, with the objective of identifying sustainable practices that mitigate greenhouse gas emissions while safeguarding productivity and farm income. Primary data were collected through structured questionnaires, and two complementary methods were employed: Life Cycle Assessment (LCA) for the quantification of CO₂e emissions and Data Envelopment Analysis (DEA) for the evaluation of technical and environmental efficiency. Results indicated a CF ranging from 6532 to 13,263 kg CO₂e/ha, largely shaped by residue management practices. Overall, the findings underline the importance of rational input use and the adoption of best practices to enhance sustainability. Full article

Clomazone is a widely used herbicide in rice cultivation, known for its high toxicity to aquatic organisms and its potential to contaminate water bodies. This study investigates the medium-term effects (after four and five years) of rice management practices on the environmental fate of Clomazone under semi-arid Mediterranean conditions. The practices investigated are tillage systems, irrigation methods, and compost application. A field experiment was conducted to compare the following treatments: sprinkler irrigation combined with no tillage (S-NT), sprinkler irrigation combined with conventional tillage (S-T), flooding irrigation with conventional tillage (F-T), and each of the above with a single compost amendment (S-NTC, S-TC, and F-TC, respectively). Compost application consistently enhanced the soil’s capacity to adsorb Clomazone, regardless of the irrigation or tillage regime. However, the use of sprinkler irrigation was shown to increase Clomazone persistence, regardless of the tillage method (S-NT and S-T), which may in turn elevate the risk of groundwater contamination. Compost addition significantly reduced Clomazone leaching losses, particularly under sprinkler systems; leaching decreased from 47% to 27% in S-NT and from 48% to 36% in S-T after five years. These findings highlight that the application of compost, particularly when combined with sprinkler irrigation, could be a sustainable agricultural approach to significantly reducing the environmental risks associated with Clomazone in rice cultivation, at least in the medium term. Full article

Accurate evaluation of plant-available phosphorus (P) in flooded paddy soils requires consideration of redox dynamics and soil-specific properties. This study evaluated five soil P extraction methods, such as Truog, Bray 2, Mehlich 3, Olsen, and ascorbic acid-reduced Bray 2 (AR Bray 2), using soils collected from 20 paddy fields in a cold region of Japan that have received long-term fertilization. All four methods, except AR Bray 2, were conducted under air-dried and flooded incubation conditions. Additionally, we conducted pot experiments with the two rice cultivars to measure P uptake. Bray 2 extracted the highest amount of P (543.6–1045.4 mg P kg⁻¹). Incubation increased extractable P by factors of 2.4–4.9 with the Mehlich 3 and Truog methods, indicating enhanced P solubility under reduced conditions. The Olsen method showed minimal sensitivity to redox changes (−31.4 mg P kg⁻¹). Principal component and cluster analyses suggested three patterns of soil P behavior under changing redox conditions: (1) stable P extractability regardless of redox status; (2) increased P availability after incubation; and (3) P extractability depending on the extraction method used. These patterns were not explained by regional or taxonomic classifications. A comparison of soil extractions and P uptake indicated that no single method consistently predicted shoot P concentrations across all soils, suggesting that conventional P extraction methods may have limited ability in long-term fertilized paddy soils. Our findings demonstrate that soil-specific redox behavior and cultivar-specific P demand critically influence the effectiveness of standard P tests. Therefore, selecting diagnostic methods tailored to soil characteristics and crop requirements is essential for accurate P evaluation and sustainable fertilizer management in rice cultivation. Full article

This study evaluates the influence of four unmanned aerial vehicle (UAV) spray nozzle geometries—flat-fan, hollow-cone, air-induction, and ultra-fine electrostatic—on water and pesticide use, canopy coverage, and greenhouse gas emissions in PB-112 rice under field conditions in Saharanpur, India. Across six farms (n = 6), ultra-fine nozzles achieved the greatest reductions in water (41%) and pesticide (43%) volumes, yielding direct pump energy savings of 737 kWh ha⁻¹ and 369 kg CO₂e ha⁻¹, plus further indirect savings from manufacturing. Paired t-tests confirmed highly significant differences (p < 0.001) with large effect sizes. Finer droplets also reduced run-off and evaporation losses by over 60%. These findings demonstrate that nozzle optimization markedly enhances resource efficiency and environmental protection in precision rice spraying. Full article

Coastal saline-alkali areas represent huge under-exploited land and water resources. Due to the high salinity, there exists a great discrepancy between the benefits derived from the cultivation of agricultural crops and the cost in terms of manpower and material resources. The mud crab Scylla paramamosain can survive across a wide range of salinity, making it an excellent aquaculture species in crop–fish co-cropping in coastal saline-alkali areas. However, detailed research concerning economic and ecological efficiency remains unclear. This study investigated the effect of stocking density of S. paramamosain co-cropping with salt-tolerant rice on the economic benefits, physiochemical parameters, and the microecological changes. By elaborate management of aquaculture and rice cropping, together with the comprehensive investigation of physiochemical influence on paddy water and soil, microbial community alteration, and functional gene dynamics, we found that an appropriate density of 6000 ind/ha generated the highest net profit, which is more than 9-fold higher than the rice monoculture. In addition, nutrient inflow increased the environmental burden of higher stocking densities. Microbial community composition and structure were altered, as shown by the 16S amplicon sequencing of water and soil samples. Functional gene chips confirmed that the carbon, nitrogen, sulfur, and phosphorus cycle genes in the microbial community contributed to the microecological function. This study proposes a new salt-tolerant rice–mud crab integrated culture mode, which is customized for the underdeveloped saline-alkali areas, and will be helpful in promoting aquaculture as well as sustainable development. Full article

The Northeast region of Thailand covers approximately 16.89 million hectares, with about 6.17 million hectares of seasonal rice cultivation and 2.85 million hectares affected by soil salinity—a major constraint to agricultural productivity in this region. This study develops an integrated data fusion framework combining multi-temporal Landsat-8 and Sentinel-2 imagery to train machine learning (ML) models for the prediction of rice yield and soil salinity, allowing for an analysis of their relationship. The field data comprised 380 rice yield and 625 soil electrical conductivity (EC) samples collected in 2023. Three ML models—Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Regression (SVR)—were applied for variable reduction and optimal predictor selection. RF achieved the highest accuracy for yield prediction (R² = 0.86, RMSE = 0.19 t ha⁻¹) and salinity estimation (R² = 0.93, RMSE = 0.87 dS/m) when using fused Landsat–Sentinel data. Spatial analysis of 5000 matched points showed a strong negative relationship between seedling stage EC and yield (R² = 0.71), with yields declining sharply above 5 dS/m and remaining below 1.5 t ha⁻¹ beyond 15 dS/m. These results demonstrate the potential of multi-sensor fusion and ensemble ML approaches for precise soil salinity monitoring and sustainable rice production. Full article

Sustainable recycling of organic residues and industrial byproducts is crucial for soil fertility and environmental sustainability. This study evaluated the effects of clinker-tea-waste compost (CTC) on rice growth, nutrient uptake, and soil chemical properties in a low-fertility paddy field over two years. In 2017, CTC was applied at 12, 18, and 22 Mg ha⁻¹, while chemical fertilizer (CF) served as control. In 2018, all treatments received equal CF to assess residual effects. The results showed a limited immediate nitrogen supply in 2017, with no significant differences in rice growth, yield, or soil ammonium nitrogen (AN) among treatments. However, significant residual nitrogen effects emerged in 2018, with higher soil AN concentrations, nitrogen uptake indices, and rice yields in plots with higher CTC rates than in 2017. Si availability from clinker ash was evident immediately after application in 2017, correlating positively with rice stover Si content and CTC application rate. However, its residual effect disappeared in 2018 when CTC was discontinued. These findings demonstrate the complementary nutrient supply of CTC, with delayed nitrogen availability from tea residues and short-lived silicon release from clinker ash. This study highlights the potential of CTC for enhancing soil fertility and crop productivity in rice cultivation systems. Full article

Groundwater depletion is among the most critical hydrological threats to sustainable agriculture and water security in semi-arid regions. This study presents a high-resolution, multi-sensor assessment of groundwater storage (GWS) dynamics across the Lower Chenab Canal (LCC) command area in Punjab, Pakistan—an intensively irrigated agro-hydrological system within the Indus Basin. We integrated downscaled GRACE/GRACE-FO-derived total water storage anomalies with CHIRPS precipitation, MODIS evapotranspiration (ET) and vegetation indices, TerraClimate soil moisture, land surface temperature (LST), land use/land cover (LULC), and population density using the Google Earth Engine (GEE) platform to reconstruct spatiotemporal GWS changes from 2002 to 2020. The results reveal a persistent and accelerating decline in groundwater levels, averaging 0.52 m yr⁻¹, which intensified to 0.73 m yr⁻¹ after 2014. Cumulative GWS losses exceeded 320 mm yr⁻¹, with severe depletion (up to −3800 mm) in northern districts such as Sheikhupura, Gujranwala, and Narowal. Validation with borewell data (R² = 0.87; NSE = 0.85) confirms the reliability of the remote sensing estimates. Statistical analysis indicates that anthropogenic drivers (population growth, urban expansion, and intensive irrigation) explain over two-thirds of the observed variability (R² = 0.67), whereas precipitation contributes only marginally (R² = 0.28), underscoring the dominance of human-induced stress over climatic variability. The synergistic rise in evapotranspiration, land surface temperature, and cultivation of high-water-demand crops such as rice and sugarcane has further amplified hydrological imbalance. This study establishes an operational framework for integrating satellite and ground-based observations to monitor aquifer stress at basin scale and highlights the urgent need for adaptive, data-driven groundwater governance in the Indus Basin. The approach is transferable to other data-scarce semi-arid regions facing rapid aquifer depletion, aligning with the global targets of Sustainable Development Goal 6 on water sustainability. Full article

Accurate identification and monitoring of rice cultivation areas are essential for food security and sustainable agricultural development. However, regions with frequent cloud cover, high rainfall, and fragmented fields often face challenges due to the absence of temporal features caused by cloud and rain interference, as well as spectral confusion from scattered plots, which hampers rice recognition accuracy. To address these issues, this study employs a Satellite Image Time Series Transformer (SITS-Former) model, enhanced with the integration of diverse phenological features to improve rice phenology representation and enable precise rice identification. The methodology constructs a rice phenological feature set that combines temporal, spatial, and spectral information. Through its self-attention mechanism, the model effectively captures growth dynamics, while multi-scale convolutional modules help suppress interference from non-rice land covers. The study utilized Sentinel-2 satellite data to analyze rice distribution in Wuxi City. The results demonstrated an overall classification accuracy of 0.967, with the estimated planting area matching 91.74% of official statistics. Compared to traditional rice distribution analysis methods, such as Random Forest, this approach outperforms in both accuracy and detailed presentation. It effectively addresses the challenge of identifying fragmented rice fields in regions with persistent cloud cover and heavy rainfall, providing accurate mapping of cultivated areas in difficult climatic conditions while offering valuable baseline data for yield assessments. Full article