Search Results (1,010)

Rice (Oryza sativa L.) grain quality is a critical determinant of market value, consumer acceptance, and nutritional security. This multifaceted trait is governed by the dynamic interaction of genotype (G), environment (E), and management practices (M). In this review, we synthesize recent advances in understanding these multifaceted determinants. We first delineate the genetic architecture, emphasizing key genes and quantitative trait loci (QTLs) such as Wx, ALK, Chalk5, and the GS3/GW families, which control starch composition, gelatinization temperature, chalkiness, and grain dimensions, forming the foundational blueprint for quality potential. We examine how this genetic potential is influenced by environmental factors, focusing on the detrimental impacts of abiotic stresses, particularly high temperatures during grain filling and drought, which impair milling yield, increase chalkiness, and modify starch and protein profiles. Furthermore, we discuss how optimized agronomic strategies—including precision water management (e.g., alternate wetting and drying), balanced nitrogen fertilization, and targeted micronutrient (e.g., silicon) application—can mitigate these adverse effects and potentially improve specific quality parameters. Post-harvest handling is identified as the final determinant of product quality. We conclude that achieving high and stable rice quality under climate variability requires an integrated G × E × M approach. Prospects include next-generation breeding for climate-resilient quality, precision agronomy guided by real-time sensing, synergistic soil health management, and the integration of systems biology with digital agriculture to design sustainable, high-quality rice production systems. Full article

Seed coating technology is regarded as one of the optimal strategies to promote sustainable agricultural development. It can effectively optimize the physical and physiological characteristics of seeds, improve germplasm quality, and enhance crop resistance to abiotic and biotic stresses. Saline–alkali soils, characterized by high salinity and alkalinity, severely restrict plant growth and development. However, alfalfa, a high-quality leguminous forage, faces substantial challenges in large-scale popularization and cultivation in saline–alkali regions. At present, research on the application of microbial seed coating technology in alfalfa production under saline–alkali conditions remains insufficient, and relevant techniques and formulations still require optimization. Under field conditions, this study used a randomized complete block design with alfalfa as the research material. Different coating treatments combining plant growth-promoting rhizobacteria (PGPR), rhizobia, and extracellular polysaccharides (EPSs) were established to systematically investigate the effects of various coating formulations on alfalfa yield, nutritional quality, root system architecture, and rhizosphere soil properties. Meanwhile, high-throughput sequencing was employed to analyze shifts in rhizosphere soil microbial community structure. The results demonstrated that all microbial coating treatments exerted significant growth-promoting effects on alfalfa grown in saline–alkali soils, among which the T8 treatment (combined coating of rhizobia + PGPR + EPS) performed the best. This treatment not only significantly improved alfalfa yield and nutritional quality but also modified root system architecture and enhanced soil enzyme activities, soil nutrient contents, and soil physical structure, thereby creating a favorable growth environment for plants. Among the single microbial coating treatments, the combined coating of rhizobia and EPS outperformed other single treatments and exhibited favorable application potential. Sequencing results revealed that microbial seed coating treatments significantly increased the relative abundance of beneficial soil bacteria, decreased the abundance of harmful fungi, regulated rhizosphere microbial community structure, and consequently promoted improvements in alfalfa yield and quality by optimizing the plant growth microenvironment. The findings of this study provide important theoretical support for the popularization and application of microbial seed coating technology in crop cultivation in saline–alkali soils, offer a key reference for optimizing alfalfa-specific seed coating formulations for saline–alkali conditions, and are of great significance for promoting the efficient utilization of saline–alkali land resources and the development of ecological agriculture. Full article

The safety of waterproof joints in concrete-faced rockfill dams (CFRDs) founded on deep overburden was determined during construction, impoundment, and sedimentation periods, employing the flexible FEM-NSBPFEM coupled method. Through eleven numerical scenarios, critical deformation zones are identified, and the effects of upper soil loads (upstream weighting and sedimentation) and cutoff wall design plans on the key joint between the connecting plate and the cutoff wall (J1) are systematically evaluated. The principal findings reveal that: (1) Joint deformation is dominated by vertical shear, primarily localized at J1, with the shear deformation at J1 reaching approximately 15 cm when the height of the upper soil load reaches 40 m. (2) Upper soil loads exert a greater influence on J1 shear deformation than hydrostatic pressure. (3) Increasing sedimentation loads cause J1 shear deformation to initially mirror impoundment trends before undergoing a sharp surge, and the effect is exacerbated by higher upstream weighting loads. (4) Shear deformation varies markedly between closed and suspended cutoff walls, whereas variations among different suspended wall designs are smaller. Based on these mechanical insights, two optimization schemes for the impermeable system are proposed, effectively constraining joint shear and opening displacements to within 4 cm. These findings provide critical guidance for the reliability analysis and design optimization of CFRD impermeable systems in deep overburden environments. Full article

Flax (Linum usitatissimum L.) is among the earliest domesticated crops and remains agronomically and economically important due to its dual use for fibre and seed (oil) production. In recent years, renewed interest in flax has emerged from its role in diversified and sustainable agriculture, human nutrition, and bio-based industrial applications. This review provides a comprehensive agronomic synthesis of global flax production, integrating worldwide production trends, genetic resource availability, and the main agronomic drivers governing crop establishment, growth, yield, and quality. Particular emphasis is placed on climatic requirements, soil and nutrient management, crop management practices, and water use, as well as on the contrasting requirements of fibre flax and seed flax. Despite growing research efforts, agronomic knowledge on flax remains fragmented across environments, production purposes, and management strategies, limiting the translation of experimental findings into robust, environment-specific crop management recommendations. Sustainable intensification of flax production will therefore depend on integrating optimized agronomic practices with breeding strategies that exploit existing genetic diversity to improve yield stability, quality, and resilience under increasing climatic variability. Full article

Optimizing planting density is a critical, cost-effective strategy for sustainable agricultural intensification, yet moving beyond static recommendations to environment-specific precision management remains a key challenge. This study establishes a three-step framework (comprising zoning, response extraction, and machine learning modeling) to determine optimum planting density (OPD) for rice (Oryza sativa L.). Utilizing a data-driven synthesis of 960 field observations from the Northeast Black Soil Region (NBSR) of China, we identified distinct spatial variability in OPD (16.6 to 37.4 × 10⁴ hills ha⁻¹). Northern regions computationally prioritized higher densities, aligning with agronomic strategies to offset thermal constraints, while southern regions favored lower densities to reduce canopy competition. Soil properties, particularly Soil Organic Carbon (SOC), pH, Cation Exchange Capacity (CEC), and Total Nitrogen (TN), were identified as the dominant predictive indicators, collectively surpassing climatic factors in their predictive importance. This highlights the foundational role of soil buffering capacity in estimating crop tolerance to density management. Based on model-derived estimates, optimized density management indicated potential yield improvements of 3.8% to 9.7% (up to 872.32 kg ha⁻¹) compared to conventional practices. By replacing uniform practices with dynamic, environment-driven strategies, this work contributes to Sustainable Development Goals (SDGs) 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action), offering a scalable solution for diverse rice production systems under climate change. Full article

Cutter soil mixing (CSM) is a widely adopted construction technique for forming waterproof diaphragm walls in underground engineering. However, cement slurry is prone to dispersion loss and performance degradation in moving water, making it difficult to meet engineering requirements. In this study, based on the characteristics of the CSM method in dynamic water environments, ordinary Portland cement is used as the main material, and hydroxypropyl methyl cellulose (HPMC), redispersible latex powder, polypropylene fiber and a polyether defoamer are added to improve it. The influence of each component on the performance of the new material is investigated, and a new CSM material suitable for dynamic water environments is developed. The material has good stability and suitable fluidity, controllable setting time, good anti-dispersion performance in dynamic water. The optimal mix ratio is as follows: water–cement ratio of 1; HPMC 1.4%; redispersible latex powder 3%; polypropylene fiber 0.4%; and polyether defoamer 0.8%. Field tests show that the new grouting material applied to CSM waterproof curtain construction results in a leak-free wall with excellent waterproofing performance, which verifies its engineering feasibility and provides a technical reference for the application of the CSM method in dynamic water environments. Full article

The development of effective remediation strategies for aniline-contaminated sites has become a significant research focus in environmental science. This study aimed to construct a highly efficient aniline-degrading synthetic microbial consortium guided by ecological niche information from contaminated soil. Microbial community analysis of aniline-contaminated soil from a typical industrial park revealed the significant enrichment and adaptability of Proteobacteria and its genus Pseudomonas in the polluted environment. Based on these ecological niche characteristics, a targeted screening strategy was employed to isolate two highly efficient degrading strains from heavily contaminated soil: Pseudomonas sp. RF and Acidovorax sp. PH. Both strains exhibited excellent aniline degradation performance in monoculture, with strain RF capable of completely degrading 1000 mg·L⁻¹ aniline within 24 h. Through orthogonal experiments to optimize the inoculation ratio, a synthetic consortium, RF-PH, composed of the two strains at a 3:1 ratio, was constructed. This consortium demonstrated significant synergistic effects, with degradation efficiency markedly surpassing that of the individual strains. Specifically, its degradation rate for 500 mg·L⁻¹ aniline within 12 h was 11.33–17.02% higher than that of the individual strains. This study confirms the effectiveness of a targeted screening and synthetic consortium construction strategy based on ecological niche information, providing efficient microbial resources and technical support for the bioremediation of aniline-contaminated sites. Full article

Over the past few decades, legume-based intercropping has emerged as a strategic agronomic practice to enhance the sustainability and resilience of agro-ecosystems, thanks to its ability to perform biological nitrogen fixation and store soil organic carbon. The present study, given the growing recognition of agroecological practices, aims to analyze through a global bibliometric analysis the research conducted between 1986 and 2025 on legume–non-legume intercropping, with particular emphasis on its ecological and agronomic benefits. The investigation, carried out according to the PRISMA protocol on the Scopus database, selected 167 original English-language articles, excluding reviews, conference proceedings, modeling studies, and meta-analyses. China and India are identified as the most productive countries. Co-occurrence and bibliographic coupling analyses highlight thematic clusters centered on soil fertility, microbial communities, productivity, and the mitigation of environmental impact. Furthermore, management practices such as integrated rotations, cover crops, and agroforestry systems amplify the benefits in terms of carbon accumulation and resilience to adverse climate conditions. The distribution of publications by journal highlights the centrality of journals such as Agriculture, Ecosystems & Environment and Plant and Soil. Overall, the data confirm the crucial role of intercropping as a pillar of the agroecological transition, underscoring the need for policies and research programs capable of amplifying its global adoption. The findings of this study may guide future interdisciplinary research and evidence-based policy decisions aimed at optimizing the design of resilient intercropping systems, tailored to address the challenges posed by climate change and the growing demands of global food security. Full article

Soil drought impact on irrigation areas is not merely a single reduction in crop yields, but rather a chain reaction that occurs from multiple dimensions including crop growth, water resource allocation, soil environment, operation of irrigation area projects, agricultural economy and ecosystems. The changing trend and mutation characteristics of soil drought are unclear in the People’s Victory Canal Irrigation District (PVCID). The Standardized Soil Moisture Index (SSMI) and the breaks for additive seasons and trend (BFAST) decomposition algorithm were adopted, combined with the eXtreme Gradient Boosting (XGBoost) model, to explore spatio-temporal evolution characteristics, driving factors and response to meteorological drought of soil drought. During the research period, the area percentage of SSMI showing a downward trend was 97.30%. The most severe soil drought occurred in 2019. In addition, the optimal trivariate combination is precipitation, evapotranspiration, and air temperature. This study has clarified the spatio-temporal evolution laws and driving mechanisms of soil drought in the PVCID, providing an important theoretical basis for the early warning, prevention and control of soil drought and the adaptive management of the ecosystem. Full article

The rapid development of the oil and gas industry has led to increasingly severe challenges for buried pipelines when crossing complex geological environments. Especially in fault zones induced by seismic action, the pipe–soil interaction mechanism and the rapid judgment of pipeline mechanical response urgently require in-depth research. This study conducted pipe–soil interaction tests on pipeline uplift under seismic-frequency loading, and for the first time, proposed a modified soil-spring method suitable for typical soft clay under seismic wave frequencies of 1–5 Hz. Through numerical simulation, the axial strain response of pipelines under normal fault movement was systematically analyzed. Considering comprehensively various variables such as fault dip angle, seismic wave frequency, internal pipeline pressure and wall thickness variation, this study extracted the maximum and minimum strain characteristics of the pipe top and pipe bottom, established a diversified intelligent prediction system for fault geological hazards, constructed the optimal machine learning model matching the type of normal fault geological hazards, and realized full-process intelligent modeling from model selection to parameter optimization. The research results can provide technical support for the seismic design and safety status prediction of pipelines under normal faulting conditions. Full article

Tillage–residue management is a controllable lever for improving maize yield and system resilience under climate variability. Here we propose a mixed-effects spatiotemporal learning framework (ME-LSTM) that integrates multi-source observations to enable robust yield prediction and management system evaluation across heterogeneous sites and years. First, we construct multi-year sliding-window inputs to represent legacy effects and cumulative influences of past management and environment. Second, a deep temporal encoder learns nonlinear dependencies from climate–soil–remote-sensing sequences to enhance interannual extrapolation. Third, a mixed-effects module explicitly separates management fixed effects from hierarchical random effects (e.g., source/study, site, year, and plot), absorbing source-specific biases and unobserved heterogeneity while improving interpretability. Finally, we parameterize management × climate/soil interactions to quantify system-specific sensitivities to environmental drivers and to support scenario-based comparison and recommendation of management options. Across multi-ecological maize datasets, ME-LSTM achieved an R² of 0.8989 with an RMSE of 309.83 kg ha⁻¹ on the test set. Ablation analyses show that removing remote-sensing features or ground-based temporal information substantially degrades performance, confirming the complementary value of multi-source fusion. Benchmarking against strong temporal baselines (LSTM, GRU, BiGRU, and Transformer) further demonstrates consistent accuracy gains of ME-LSTM, highlighting its suitability for small-sample, noisy, and hierarchically structured agricultural data. Overall, ME-LSTM provides an interpretable and scalable tool for climate-adaptive optimization of tillage–residue management and supports robust, actionable decision-making across diverse agro-ecological conditions. Full article

Soil heavy metal pollution caused by mineral resource extraction activities poses a serious threat to the ecological environment within and surrounding mining areas. As a highly concealed toxic heavy metal, arsenic (As) urgently requires the establishment of efficient pollution monitoring methods to achieve pollution prevention and control, as well as environmental remediation in mining areas. This study investigated the feasibility of hyperspectral remote sensing inversion for soil heavy metal arsenic based on ZY-1 02E hyperspectral satellite imagery, focusing on a mining area and its surrounding soils in Sanlisha’an, Wuxuan County, Guangxi. Full Constrained Least Squares (FCLS) was employed to separate mixed pixels and enhance soil spectral contributions in ZY-1 02E imagery, thereby mitigating vegetation interference. Six mathematical transformations, including RT, AT, FD, RTFD, ATFD, and SD, were applied to both the original and enhanced spectra to enhance spectral features. The correlations between the transformed spectra, as well as the original image spectra (S), and soil As concentration were analyzed; then the spectra strongly correlated with soil As concentration were selected to construct Ratio Spectral Index (RSI) and Normalized Difference Spectral Index (NDSI). Correlation matrices were calculated between RSI/NDSI indices and As concentration. Sensitive features were screened using an improved Successive Projection Algorithm (SPA). As concentration inversion was also performed with four models: traditional regression models, PLSR and MLR, and ensemble learning models (RF and XGBoost). In the soil contribution-enhanced spectral modeling results, the optimal transformation–index combination is ATFD-NDSI. The performance indicators of each model are as follows: MLR test set R² = 0.65, PLSR test set R² = 0.62, RF test set R² = 0.7, and XGBoost test set R² = 0.64. The results indicate that the ATFD-NDSI-RF ensemble model provides the best performance. By integrating multiple decision trees, RF effectively handles complex nonlinear relationships, thus enhancing the accuracy and generalization ability of predication. The analysis of NDSI–ATFD–RF inversion results based on sampling points indicates that model error correlates with the pollution intensity gradient, showing greater errors, especially in high-concentration areas, but still maintaining strong correlations (tailings reservoir: r = 0.92, forested areas: r = 0.96, and cropland: r = 0.83). The spatial distribution reveals that the inversion results are closely similar to the spatial distribution of IDW interpolation. Areas with high As concentrations are concentrated in the tailings reservoir and in the southeastern part of the study area. The correlation coefficient between the inversion results and IDW interpolation is 0.6, which further verifies that the inversion results effectively reproduce the spatial distribution trend of highly polluted areas. Full article

The structural stability of lignocellulosic fibers in crop straw presents a significant challenge to its short-term biodegradation in natural environments, particularly in the cold regions of northern China. To isolate low-temperature straw-degrading bacteria, we selectively enriched microorganisms from straw-amended soils using lignocellulose as the sole carbon source. Three strains were isolated and identified: Stenotrophomonas sp. X24, Flavobacterium sp. X26, and Erwiniaceae bacterium X27. These strains were capable of growth and maize straw degradation within a 4–20 °C range and exhibited key cellulolytic activities (CMCase, FPase, and β-glucosidase). A synthetic three-strain mixture was assembled by combining these isolates in equal proportions. Solid-state fermentation (12 °C, 45 days) was used to assess straw degradation efficacy, while separate enzyme production experiments (12 °C, 3 days) were conducted to evaluate key cellulolytic activities and subsequently optimize culture conditions. The three-strain mixture achieved a net straw degradation rate of 30.93 ± 1.05%. Furthermore, optimization of culture conditions enhanced the carboxymethyl cellulase activity (CMCase) to a maximum of 24.51 ± 0.97 U/mL. The study demonstrates that the three-strain synthetic microbial mixture effectively degrades straw at low temperatures, offering a promising microbial resource to improve straw utilization and soil fertility in cold regions. Full article

In the long-term operation of canals in loess areas, instability and landslides frequently occur due to the effect of wetting–drying cycles, which severely restricts the long-term safe operation of engineering projects. To reveal the evolution law of loess strength under wetting–drying cycles and establish a strength prediction model, this study conducted wetting–drying cycle tests and direct shear tests, analyzing the effects of different cycle times, dry densities, and initial water contents on the shear strength and its parameters. A combined model of improved adaptive genetic algorithm and backpropagation neural network (IAGA-BP) was adopted for shear strength prediction. An adaptive crossover and mutation operator based on the Sigmoid function, which combines the fitness value with the population iteration number, was proposed. By optimizing the parent selection strategy and the uniform crossover genetic method, the population diversity was effectively maintained, and premature convergence was avoided. The test results show that with the increase in the wetting–drying cycle times, both the shear strength and strength parameters of loess exhibit a trend of gradual attenuation and eventually tend to be stable. The increase in the dry density and initial water content can reduce the degradation amplitude of soil cohesion after five wetting–drying cycles. The model verification results indicate that all evaluation indicators of the IAGA-BP neural network model (MAPE = 3.75%, MAE = 0.95 kPa, MSE = 9 × 10⁻⁴, R² = 0.975) are significantly superior to those of the traditional BP and GA-BP models, with the comprehensive prediction performance improved by 62% and 46%, respectively. This model not only effectively overcomes the defect that traditional models are prone to fall into local extremum but also shows significant advantages in prediction accuracy and convergence speed. This study can provide a theoretical reference for the calculation of loess strength degradation and the prediction of long-term stability under the environment of wetting–drying alternation. Full article

Carbide lime (CL) and powdered glass (PG), as industrial by-products, possess significant potential as eco-friendly soil amendment materials. This paper presents a systematic investigation into the effectiveness and reaction mechanisms of a composite material comprising CL and PG for stabilizing dispersive soils. A systematic experimental program was designed with varying CL (0.5–6.5%) and PG (4–16%) contents, along with curing ages of 1, 7 and 14 days. Macroscopic properties, including dispersibility and permeability, were evaluated through pinhole, mud ball, and permeability tests, while phase composition and microstructural evolution were analyzed using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Results demonstrate a pronounced synergistic effect between CL and PG at optimal ratios: soil dispersibility is markedly improved when CL ≥ 2.5% and PG ≥ 8%, non-dispersive behavior is achieved at all curing ages with CL between 4.5 and 6.5% and PG between 4 and 16 permeability coefficient decreases significantly with increasing material content; for instance, increasing CL from 2.5% to 6.5% (at 16% PG) reduces the permeability coefficient by over 50%. Microstructural analysis reveals that CL supplies Ca²⁺ and an alkaline environment, whereas PG provides reactive SiO₂ and Al₂O₃. Their interaction facilitates ion exchange and pozzolanic reactions, leading to the formation of C–S–H and C–A–S–H gels. These cementitious products effectively fill pores and bond soil particles, thereby enhancing structural stability. This study confirms that the CL-PG composite is an efficient and sustainable soil stabilization material. It provides novel insights into the synergistic mechanisms and optimal dosage range, offering valuable theoretical and practical guidance for the resource utilization of industrial by-products in geotechnical engineering. Full article