Search Results (473)

Astragalus sinicus, a key leguminous green manure widely cultivated in Southern China’s rice-based cropping systems, plays a pivotal role in sustainable agriculture by enhancing soil organic matter sequestration, improving rice yield, and elevating grain quality. The symbiotic nitrogen-fixing association between A. sinicus and its matching rhizobia is fundamental to its agronomic value; however, suboptimal inoculant efficiency and field application methodologies constrain its full potential. To address these limitations, we conducted a multi-phase study involving (1) rhizobial strain screening under controlled greenhouse conditions, (2) an optimized lyophilization protocol evaluating cryoprotectant (trehalose, skimmed milk powder and others), and (3) seed pelleting trails with rhizobial viability and nodulation assessments over different storage periods. Our results demonstrate that Mesorhizobium huakuii CCBAU 33470 exhibits a superior nitrogen-fixing efficacy, significantly enhancing key traits in A. sinicus, including leaf chlorophyll content, tiller number, and aboveground biomass. Lyophilized inoculants prepared with cryoprotectants (20% trehalose or 20% skimmed milk powder) maintained >90% bacterial viability for 60 days and markedly improved nodulation capacity relative to unprotected formulations. The optimized seed pellets sustained high rhizobial loads (5.5 × 10³ cells/seed) with an undiminished viability after 15 days of storage and nodulation ability after 40 days of storage. This integrated approach of rhizobial selection, inoculant formulation, and seed coating overcomes cultivation bottlenecks, boosting symbiotic nitrogen fixation for A. sinicus cultivation. Full article

Weeds pose a significant challenge to agricultural productivity, requiring control strategies that are both effective and environmentally sustainable. Therefore, this study evaluated the inhibitory potential of aqueous extracts from Ricinus communis L. leaves to manage the weeds Oryza sativa L. (weedy rice) and Cyperus ferax. Extracts were obtained through pressurized liquid extraction using water as the solvent. Bioassays were conducted during pre- and post-emergence stages by foliar spraying 15 and 30 days after sowing (DAS). The effect of extraction time (1–30 min) on inhibitory efficacy was also assessed. Chemical profiles of the extracts were characterized using high-performance liquid chromatography. The extracts significantly inhibited seed germination, with suppression rates reaching 92.7%. Plant growth was also diminished, particularly with earlier treatments (at 15 DAS), resulting in reductions of up to 32% and 53% in shoot length, and 69% and 73% in total dry mass for O. sativa L. and C. ferax, respectively. Mortality rates of O. sativa L. and C. ferax reached 64% and 58%, respectively. Phenolic compounds were identified in the extracts, and higher concentrations were observed at shorter extraction times. These findings underscore the potential of R. communis L. leaf extracts as an ecologically sustainable alternative for weed management, providing an effective and natural approach that may reduce reliance on synthetic herbicides and mitigate their environmental impact. Full article

Traditional intensive agricultural system impedes ecological functions, such as nutrient cycling and biodiversity conservation, resulting in excessive nitrogen discharge, CH₄ emission, and ecosystem service losses. To enhance critical ecosystem services and mitigate environmental externalities in paddy fields, we developed a multi-objective agricultural system (MIA system), which combines two eco-functional units: paddy wetlands and Beitang (irrigation water collection pond). Pilot study results demonstrated that the MIA system enhanced biodiversity and inhibited pest outbreak, with only a marginal reduction in rice production compared with the control. Additionally, the paddy wetland effectively removed nitrogen, with removal rates of total nitrogen and dissolved inorganic nitrogen ranging from 0.06 to 0.65 g N m⁻² d⁻¹ and from 0.02 to 0.22 g N m⁻² d⁻¹, respectively. Continuous water flow in the paddy wetland reduced the CH₄ emission by 84.4% compared with the static water conditions. Furthermore, a simulation experiment indicated that tide flow was more effective in mitigating CH₄ emission, with a 68.3% reduction compared with the drying–wetting cycle treatment. The emergy evaluation demonstrated that the MIA system outperformed the ordinary paddy field when considering both critical ecosystem services and environmental externalities. The MIA system exhibited higher emergy self-sufficiency ratio, emergy yield ratio, and emergy sustainable index, along with a lower environmental load ratio. Additionally, the system required minimal transformation, thus a modest investment. By presenting the case of the MIA system, we provide a theoretical foundation for comprehensive management and assessment of agricultural ecosystems, highlighting its significant potential for widespread application. Full article

Rice lodging severely affects crop growth, yield, and mechanized harvesting efficiency. The accurate detection and quantification of lodging areas are crucial for precision agriculture and timely field management. However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. To address these issues, this study proposes SWRD–YOLO, a lightweight instance segmentation model that enhances feature extraction and fusion using advanced convolution and attention mechanisms. The model employs an optimized loss function to improve localization accuracy, achieving precise lodging area segmentation. Additionally, a grid-based lodging ratio estimation method is introduced, dividing images into fixed-size grids to calculate local lodging proportions and aggregate them for robust overall severity assessment. Evaluated on a self-built rice lodging dataset, the model achieves 94.8% precision, 88.2% recall, 93.3% mAP@0.5, and 91.4% F1 score, with real-time inference at 16.15 FPS on an embedded NVIDIA Jetson Orin NX device. Compared to the baseline YOLOv8n-seg, precision, recall, mAP@0.5, and F1 score improved by 8.2%, 16.5%, 12.8%, and 12.8%, respectively. These results confirm the model’s effectiveness and potential for deployment in intelligent crop monitoring and sustainable agriculture. Full article

The increasing presence of ECs in aquatic environments has drawn significant attention to the need for innovative, accessible, and sustainable solutions in wastewater treatment. This review provides a comprehensive overview of the use of agricultural residues—often discarded and undervalued—as raw materials for the development of efficient bioadsorbents. Based on a wide range of recent studies, this work presents various types of materials, such as rice husks, sugarcane bagasse, and açaí seeds, that can be transformed through thermal and chemical treatments into advanced bioadsorbents capable of removing pharmaceuticals, pesticides, dyes, and in some cases, even addressing highly persistent pollutants such as PFASs. The main objectives of this review are to (1) assess agricultural-residue-derived bioadsorbents for the removal of ECs; (2) examine physical and chemical modification techniques that enhance adsorption performance; (3) evaluate their scalability and applicability in real-world treatment systems. The review also highlights key adsorption mechanisms—such as π–π interactions, hydrogen bonding, and ion exchange—alongside the influence of parameters like pH and ionic strength. The review also explores the kinetic, isothermal, and thermodynamic aspects of the adsorption processes, highlighting both the efficiency and reusability potential of these materials. This work uniquely integrates microwave-assisted pyrolysis, magnetic functionalization, and hybrid systems, offering a roadmap for sustainable water remediation. Finally, comparative performance analyses, applications using real wastewater, regeneration strategies, and the integration of these bioadsorbents into continuous treatment systems are presented, reinforcing their promising role in advancing sustainable water remediation technologies. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

Urbanization is causing a decrease in agricultural land. This leads to changes in cropping patterns, irrigation water availability, and water allowance. Therefore, change in cropping pattern, irrigation water availability, and water allowance were investigated in the Multan region of Pakistan using remote sensing and GIS techniques. The multi-temporal Landsat images with 30 m resolution were acquired for both Rabi (winter) and Kharif (summer) seasons for the years of 1988, 1999 and 2020. The image processing tasks including layer stacking, sub-setting, land use/land cover (LULC) classification, and accuracy assessment were performed using ERDAS Imagine (2015) software. The LULC maps showed a considerable shift of orchard area to urban settlements and other crops. About 82% of orchard areas have shifted to urban settlements and other crops from 1988 to 2020. The LULC maps for Kharif season indicated that cropped areas for cotton have decreased by 42.5% and the cropped areas for rice have increased by 718% in the last 32 years (1988–2020). During the rabi season, the cropped areas for wheat (Triticum aestivum L.) have increased by 27% from 1988 to 2020. The irrigation water availability and water allowance have increased up to 125 and 110% due to decrease in agricultural land, respectively. The overall average accuracies were found as 87 and 89% for Rabi and Kharif crops, respectively. The LULC mapping technique may be used to develop a decision support system for evaluating the changes in cropping pattern and their impacts on net water availability and water allowances. Full article

This study presents an integrated spatiotemporal assessment of drought conditions in the Thung Kula Ronghai region of Northeastern Thailand from 2001 to 2023. Multiple satellite-derived drought indices, including SPI, SPEI, RDI, and AI, together with NDVI anomalies, were used to detect seasonal and long-term drought dynamics affecting rainfed Hom Mali rice production. The results show that dry season droughts now affect up to 17 percent of the region’s agricultural land in some years, while severe drought zones persist across more than 2.5 million hectares over the 20-year period. In the most recent 5 years, approximately 50 percent of cultivated areas experienced moderate to severe drought conditions. The RDI showed the strongest correlation with NDVI anomalies (r = 0.22), indicating its relative value for assessing vegetation response to moisture deficits. The combined index approach delineated high-risk sub-regions, particularly in central Thung Kula Ronghai and lower Surin, where drought frequency and severity have intensified. These findings underscore the region’s increasing exposure to dry-season water stress and highlight the need for site-specific irrigation development and adaptive cropping strategies. The methodological framework demonstrated here provides a practical basis for improving drought monitoring and early warning systems to support the resilience of Thailand’s high-value rice production under changing climate conditions. Full article

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural areas and rice fields surrounding the National Park (R = 0.3), contrasted by a strong negative ET trend in wetlands (R < −0.5). These opposing patterns suggest a growing diversion of water toward irrigation at the expense of natural ecosystems. The impact was especially marked during droughts, such as the 2011–2016 period, when precipitation declined by 16%. In wetlands, ET was significantly correlated with precipitation (R > 0.4), highlighting their vulnerability to reduced water inputs. These findings offer crucial insights to support sustainable water management strategies that balance agricultural productivity with the preservation of ecologically valuable systems under mounting climatic and anthropogenic pressures typical of semi-arid Mediterranean environments. Full article

Climate change poses significant challenges to temperate rice production, particularly affecting grain quality and market acceptance. This review synthesizes current knowledge of climate-induced quality changes, with a focus on the Australian rice industry as a case study with comparisons to other temperate regions. Environmental stressors such as extreme temperatures, variable rainfall, elevated CO₂, and salinity disrupt biochemical pathways during grain development, altering physicochemical, textural, and aromatic traits. Different rice classes exhibit distinct vulnerabilities: medium-grain japonica varieties show reduced amylose under heat stress, aromatic varieties experience disrupted aroma synthesis under drought, and long-grain types suffer kernel damage under combined stresses. Temperature is a key driver, with quality deterioration occurring above 35 °C and below 15 °C. Systems biology analyses reveal complex signalling networks underpinning these stress responses, although experimental validation remains limited. The Australian industry has responded by developing cold-tolerant cultivars, precision agriculture, and water-saving practices, yet projected climate variability demands more integrated strategies. Priorities include breeding for stress-resilient quality traits, refining water management, and deploying advanced phenotyping tools. Emerging technologies like hyperspectral imaging and machine learning offer promise for rapid quality assessment and adaptive management. Sustaining high-quality rice in temperate zones requires innovation linking physiology with practical adaptation. Full article

Weeds present a pervasive challenge in agricultural fields. The integration of herbicide-resistant crops with chemical weed management offers an effective solution for sustainable weed control while reducing labor inputs, particularly in large-scale intensive farming systems. Consequently, the development of herbicide-resistant cultivars has emerged as an urgent priority. In this study, we found that the G311E mutant gene of Arabidopsis MATE (multidrug and toxic compound extrusion) family transporter DTX6, designated DTX6m, confers robust resistance to bipyridyl herbicides paraquat and diquat in rice. DTX6m-overexpression lines exhibited marked resistance to these two herbicides, tolerating diquat concentrations up to 5 g/L, which is five-fold higher than the recommended field application dosage. Agronomic assessments demonstrated that grain yields of DTX6m-overexpressing plants were statistically equivalent to those of wild-type plants. Moreover, the plants displayed beneficial phenotypic changes, such as accelerated flowering and a slight reduction in height. Seed morphometric analysis indicated that in comparison with the wild-type control, DTX6m-transgenic lines exhibited altered grain dimensions while maintaining consistent 1000-grain weight. Nutritional assays further demonstrated that DTX6m increased the levels of free amino acids in seeds, while normal protein and starch contents were retained. Collectively, these results establish that DTX6m effectively boosts rice resistance to paraquat and diquat, validating DTX6m as a candidate gene for engineering plant herbicide resistance and also implying a potential role for DTX6m in amino acid homeostasis in plants. Full article

Salinity and heavy metal stress significantly reduce agricultural productivity in arable lands, particularly affecting crops like rice (Oryza sativa L.). This study aimed to evaluate the efficacy of heavy metal-tolerant plant growth-promoting rhizobacteria (HMT-PGPR) in mitigating the harmful effects of salt (NaCl), chromium (Cr), and combined NaCl + Cr stress on rice plants. Two pre-isolated and well-characterized heavy metal-tolerant epiphytic (Ochrobactrum pseudogrignonense strain P14) and endophytic (Arthrobacter woluwensis strain M1R2) PGPR were tested. The LSD test (p ≤ 0.05) was used to assess the statistical significance between treatment means. Stresses caused by NaCl, Cr, and their combination were found to impair plant growth and biomass accumulation through mechanisms, including osmotic stress, oxidative damage, ionic imbalance, reduced photosynthetic pigment, lowered relative water content, and compromised antioxidant defense systems. Conversely, inoculation with HMT-PGPR alleviated these adverse effects by reducing oxidative stress indicators, including malondialdehyde (MDA), hydrogen peroxide (H₂O₂) content and electrolyte leakage (EL) and enhancing plant growth, osmolyte synthesis, and enzymatic antioxidant activity under single- and dual-stress conditions. The application of HMT-PGPR notably restricted Na⁺ and Cr⁶⁺ uptake, with an endophytic A. woluwensis M1R2 demonstrating superior performance in reducing Cr⁶⁺ translocation (38%) and bioaccumulation (42%) in rice under dual stress. The findings suggest that A. woluwensis effectively mitigates combined salinity and chromium stress by maintaining ion homeostasis and improving the plant’s antioxidant defenses. Full article

Globally, agricultural irrigation accounts for the majority of freshwater use and 15% of annual agricultural greenhouse gas emissions, highlighting its critical mitigation potential amid climate change. While localized Chinese studies have analyzed the water–energy–carbon nexus, nationwide assessments of irrigation carbon-reduction potential, integrating crop water requirements, water use, and energy consumption, remain limited due to scarce longitudinal panel data. This study fills this gap by evaluating provincial-level potentials in China (2004–2020) using national/provincial statistical data on crop areas, irrigation water, energy use, and climate parameters. Findings reveal pronounced spatial–temporal variations: Henan, Heilongjiang, and Shandong exhibit the highest crop water demands (driven by rice/maize/wheat), while Heilongjiang, Jiangsu, and Guangdong show substantial water-saving opportunities. Xinjiang has the largest amount of irrigation-related carbon emissions, whereas the northeastern provinces offer the greatest reduction potential. A positive correlation between irrigation-carbon efficiency and groundwater utilization underscores the need for improved groundwater management. By linking crop water requirements to emission reductions through a nationally representative dataset, this study provides empirical evidence for region-specific strategies to enhance water-use efficiency and reduce irrigation’s environmental footprint. The findings inform policymakers on balancing agricultural productivity with sustainability goals, addressing both local water scarcity and global decarbonization imperatives. Full article

The fall armyworm (Spodoptera frugiperda) is a highly invasive agricultural pest that has caused significant damage to maize and other crops since its initial detection in China in 2019. Understanding its invasion dynamics, migration patterns, genetic diversity, and overwintering capacity is crucial for developing effective pest management strategies. This study investigates these aspects in Shaanxi Province, a critical transitional zone between northern and southern climates in China, from 2019 to 2023. We conducted field surveys in six cities across Shaanxi to monitor the initial infestation of FAW. Migration trajectories were simulated using the HYSPLIT model, integrating pest occurrence data and meteorological information. Genetic analyses were performed on 113 FAW individuals from 12 geographical populations using mitochondrial COI and nuclear Tpi genes. Additionally, an overwintering experiment was conducted to assess the survival of FAW pupae under local winter conditions. The first detection dates of FAW in Shaanxi showed significant interannual variation, with a trend of delayed infestation each year. Three primary migration routes into Shaanxi were identified, originating from Sichuan, Hubei-Chongqing, and Henan. Genetic analysis revealed a predominance of the rice-strain FAW in Shaanxi, with some corn-strain variants in northern regions. The overwintering experiment indicated that FAW pupae could not survive the winter in Shaanxi, suggesting that the region does not support year-round breeding of this pest. This study provides comprehensive insights into the spatiotemporal dynamics and migration patterns of FAW in Shaanxi. The findings highlight the importance of integrated pest management approaches, including monitoring migration routes and genetic diversity, to develop targeted control measures. The inability of FAW to overwinter in Shaanxi suggests that regional climate conditions play a significant role in limiting its year-round presence, which is valuable information for designing early warning systems and sustainable pest management strategies. Full article

Climate change has emerged as one of the most pressing global challenges in recent decades. Agricultural activities significantly influence climate dynamics, necessitating thorough investigation of their emission patterns. Using the FAO datasets, the objectives of this study were to assess agricultural GHG emissions, identify influencing factors, and explore potential mitigation strategies. The results show that emissions related to crop production are strongly correlated with the yields of predominant crops. Maize production had the largest impact on crop emissions (0.023), followed by potato (0.021) and rice (0.007). Notably, these three crops accounted for substantial portions of total crop-related emissions, with maize contributing 11.70%, potatoes (Solanum tuberosum L.) 10.21%, and rice 9.25%. In the livestock sector, cattle herds generated 10.75% of emissions, with pigs and sheep contributing 9.82% and 10.03%, respectively. Multivariate analysis revealed the cattle/buffalo population as the dominant emission driver (0.32), followed by sheep/goat (0.21) and swine (0.10) populations. Simultaneously, emissions from livestock operations were closely associated with the populations of key livestock species. Thus, from a climate mitigation perspective, prioritizing yield-optimized agronomic approaches for maize and potato cultivation, along with strategic population management of cattle and sheep, represents a critical pathway toward achieving emission reduction targets in global agricultural systems. Full article