Search Results (479)

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article

The long-term sustainability of Australian agriculture is fundamentally constrained by the capacity, condition, availability, and governance of soil resources. Australian soils are among the oldest and most weathered globally, highly heterogeneous, and often slow or effectively irreversible to recover once degraded. Traditional approaches centred on soil health, while valuable at paddock scale, are insufficient to address national-scale challenges related to spatial variability, data continuity, economic valuation, and policy integration. This paper examines soil security as a policy-relevant framework for supporting more sustainable Australian agriculture. Building on the dimensions of soil security (capacity, condition, capital, connectivity, and codification), we synthesise recent Australian case studies to show how soil security extends beyond soil health to integrate biophysical properties, digital soil infrastructure, socio-economic value, and governance mechanisms. Drawing on recent Australian case studies, this review identifies advances in digital soil mapping, national soil assessments, economic valuation of soil capital, stakeholder connectivity, and emerging policy frameworks, while also identifying persistent gaps in regulation, data standardisation, and institutional coordination. The paper argues that soil security can help operationalise 3-N agriculture—Net-Zero, Nature-Positive, and Nutrient-Balanced systems—by translating sustainability goals into spatially explicit, place-based decisions grounded in soil realities. By explicitly accounting for soil capacity limits, condition trajectories, capital value, information flows, and codified rules, soil security can support more realistic climate mitigation strategies, targeted nature-positive interventions, and durable nutrient security outcomes. We conclude that embedding soil security more explicitly within Australian agricultural research, policy, and governance would strengthen efforts to deliver productive, resilient, and socially legitimate food and fibre systems. Without soil security, sustainability frameworks may remain difficult to operationalise consistently; with soil security, they can be translated more effectively into measurable, place-based, and durable decisions. Full article

Drought is one of the major disasters constraining crop production. The accurate identification of the dominant environmental factors that drive drought stress at different growth stages of maize is essential for developing stage-specific and precise water management strategies, enhancing drought resistance, and ensuring food security. However, a key challenge is quantifying the nonlinear interactions among multiple environmental factors. This study focuses on the rain-fed agricultural region of Northwest China. To address the limited availability of drought event samples in this region and the inadequacy of traditional statistical methods in capturing complex inter-factor relationships, we integrate a small-sample modeling framework based on an improved Conditional Generative Adversarial Network (CGAN) with an attribution framework that employs SHapley Additive exPlanations (SHAP) for interpretability analysis. We incorporate ten environmental factors derived from multi-source remote sensing: temperature (T_max, T_min, T_mean), precipitation (P), evapotranspiration (ET), soil moisture at 0–10 cm (SM_0–10) and at 10–40 cm (SM_10–40), and solar-induced chlorophyll fluorescence (SIF_max, SIF_min, SIF_mean). Sample sets were established for different maize phenological stages. The CGAN model was employed to achieve high-precision estimation of maize drought severity levels, while the SHAP method was used to quantitatively analyze the dominant factors and their contributions at each phenological stage. The results show that the CGAN model achieved coefficients of determination (R²) of 0.963, 0.972, and 0.979 for the seedling, jointing–tasseling, and maturity stages, respectively, demonstrating excellent nonlinear modeling capability under small samples. SHAP analysis reveals a clear dynamic evolution of dominant factors across phenological stages. Evapotranspiration (ET) dominated in the seedling stage, reflecting the primary role of surface water–heat balance, while the jointing–tasseling stage transitioned to a co-dominance of ET, topsoil moisture (SM_0–10), and minimum SIF, indicating intensified crop transpiration and physiological stress under the meteorological drought framework, and the maturity stage shifted to an absolute dominance centered on mean temperature (T_mean), highlighting the critical impact of heat stress. This study provides a data-driven quantitative perspective for understanding maize drought mechanisms and offers a scientific basis for formulating differentiated drought management strategies for different growth stages. Furthermore, it demonstrates the potential of integrating CGAN with SHAP for agricultural remote sensing and drought attribution research in data-scarce regions. Full article

Soil contamination in South African agriculture poses escalating threats to food security and ecosystem integrity, yet the geospatial and machine learning evidence base addressing this problem has never been systematically synthesised. This scoping review, conducted within the PRISMA-ScR framework, applied SVM-assisted screening to 2000 retrieved records, yielding a final corpus of 228 eligible studies published from 2003 to 2025. To characterise temporal, thematic, and geographic patterns in the corpus, we applied machine learning-assisted topic modelling (LDA, k = 7), logistic growth modelling, keyword co-occurrence network analysis, and technology–contaminant evidence gap matrices. Remote sensing was the dominant methodology throughout the review period (n = 142; 62.3% of studies), with machine learning rising to the highest adoption rank from approximately 2020 onwards. Logistic modelling estimated a carrying capacity of K = 292.3 (95% CI: 269–324) studies and an inflexion year of 2020.2 (95% CI: 2019.4–2021.1), projecting 90% saturation by 2028. Research effort was highly concentrated in KwaZulu-Natal and the Eastern Cape, while Pesticides/Herbicides and acid mine drainage each comprised only three corpus studies. Deep learning registered zero entries across all cells of both the technology–contaminant and technology–province evidence matrices. Targeted investment in field validation, hyperspectral and deep learning deployment for underrepresented contaminants, and interpretable modelling for regulatory defensibility are identified as priority actions for the next research cycle. Full article

Drought, a major abiotic stressor affecting global agricultural productivity, significantly reduces crop yields and threatens food security worldwide. As the primary organ for perceiving soil moisture signals and absorbing water, the crop root system architecture plays a pivotal role in plant adaptation to drought conditions. With the development of high-throughput imaging technologies (i.e., 2D/3D image acquisition), high-throughput genotyping platforms, and gene-editing technologies, significant progress has been achieved in the characterization of root traits and the dissection of molecular genetic regulatory networks underlying these traits in crops. This review comprehensively synthesizes recent advances in the phenotypic characterization, underlying molecular regulatory networks, and functional roles of key root architectural traits, including the root length, angle, density, and root hair development, in enhancing drought resilience. Finally, we discuss the existing challenges in the current research and provide an outlook on the future trend of integrating multi-omics, high-throughput phenomics, and genome editing technologies to breed new drought-resistant crop varieties with ideal drought-resistant root architectures. Full article

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

Despite recent advances in artificial intelligence for agriculture, reliable crop recommendation remains constrained by limited access to soil diagnostics, insufficient integration of environmental context, and the absence of transparent, quantitative evaluation frameworks. This study addresses the research question: How can we integrate multiple indicators to generate accurate, explainable, and context-sensitive crop recommendations? To this end, we propose a multimodal decision-support framework that combines image-based soil texture classification with geospatial, and climatic information. A convolutional neural network was trained on a curated dataset of 3250 soil images aggregated from four publicly available sources, covering four primary soil texture classes, alongside tabular soil and nutrient data. The model was evaluated using 5-fold stratified cross-validation, achieving an average classification accuracy of 99.30% (standard deviation ≈ 0.66), and was further validated on an independent hold-out test set to assess generalization performance. To enhance practical applicability, the framework incorporates elevation, rainfall, temperature, and major soil nutrients, and employs a large language model to generate user-oriented, interpretable justifications for each recommendation. Crop recommendations were quantitatively evaluated using a novel Agronomic Suitability Score (ASS), which measures alignment across soil compatibility, climatic suitability, seasonal alignment, and elevation tolerance. Across six geographically diverse case studies, the framework achieved mean ASS values ranging from 3.76 to 4.96, with five regions exceeding 4.45, demonstrating strong agronomic validity, robustness, and scalability. A Streamlit-based application further illustrates the system’s ability to deliver accessible, location-aware, and explainable agronomic guidance. The results indicate that the proposed approach constitutes a scalable decision-support tool with significant potential for sustainable agriculture and food security initiatives. Full article

Extreme weather events such as higher temperatures, droughts, and soil salinization are projected to increase as atmospheric CO₂ concentrations rise and climate change progresses. These factors have a negative impact on global food security, the water supply, and ecosystem productivity. The focus of this review is on modern concepts, comparative studies, and our data on the mechanisms of adaptation of halophytes and glycophytes with different types of photosynthetic metabolism (C₃, C₄) to the individual and combined effects of climatic factors. The analysis revealed that C₃ and C₄ species and C₄-NAD-ME and C₄-NADP-ME species differ in terms of stability and photosynthetic plasticity. Under drought conditions, both individually and in combination with other factors, C₄ halophytes demonstrate the advantages of efficient photosynthesis and salt tolerance. Halophytes with C₄-NADP-ME are characterized by uniquely high levels of plasticity and variability in photosynthetic metabolism. This is reflected in their ability to mitigate the negative effects of elevated temperatures and drought through the use of elevated CO₂ (eCO₂). The mitigating effect of eCO₂ on photosynthesis at elevated temperatures was not detected in halophytes, regardless of photosynthesis type. Halophytes possess an augmented capacity for heat tolerance. Integrating fundamental scientific knowledge with urgent practical needs will enable us to predict changes in ecosystems and create new, sustainable agricultural systems. Full article

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance. Full article

Intercropping is a pivotal strategy for achieving Sustainable Development Goal (SDG) number 2—End hunger, achieve food security and improved nutrition and promote sustainable agriculture (SDG 2)—by enhancing food security agroecosystem resilience and sustainability. By integrating diverse species within the same plot, this sustainable approach takes advantage of the beneficial interactions between them. The simultaneous cultivation of multiple crop species within the same field increases agricultural diversification and contributes to a more resilient production system, breaking the uniformity of modern intensive agriculture. The objective of this review is to evaluate intercropping practices throughout the Mediterranean, specifically in Southern Europe (Portugal, Spain, Italy, and Greece), North Africa (Morocco, Algeria, and Tunisia), and the Middle East (Turkey, Israel, and Jordan). This review intends to show advantages and disadvantages of intercropping and crops used and also highlight how intercropping systems affect crop production and quality, soil quality and microbiome, and proliferation of weeds, pests and diseases. The literature suggests that diversification in agriculture supports biodiversity and ecosystem services by the cultivation of diverse crop species together and, hence, may reduce independence in external outputs such as nutrient supply, pesticides and soil amendment. Despite the potential benefits of intercropping, the major caveats of this practice are the competition between different crops on resources, potential risks of plant protection, technical challenges of integrating the different requirements of each crop used in the system, and culture-related restrictions or regulations. Full article

Accurate and stable acquisition of the spatial distribution of soybean planting areas is essential for supporting precision agricultural monitoring and ensuring food security. However, crop remote-sensing mapping for specific regions still faces critical data bottlenecks: high-precision, large-scale pixel-level annotation is costly, resulting in scarce available labeled samples that make it difficult to construct large-scale training datasets. Although parameter-intensive models such as FCN and SegNet can achieve sufficient end-to-end training on large-scale public remote sensing datasets like LoveDA, when directly applied to the data-limited dataset in this study area, the models are prone to overfitting, leading to a significant decline in generalization ability. To address these issues, this study proposes a lightweight U-shaped semantic segmentation model, SimSDRU-Net. The model utilizes a pre-trained VGG-16 backbone to extract shallow texture and deep semantic features. The pre-trained weights mitigate the impact of overfitting in data-limited settings. In the decoding stage, a parameter-free lightweight SimAM attention module enhances effective soybean features and suppresses soil background redundancy, while an embedded S-DRU unit fuses multi-scale features for deep complementary reconstruction to improve edge detail capture. A label dataset was constructed using Sentinel-2 images as the data source and Menard County (USA) as the study area. The USDA CDL was used as a foundation for the dataset, with Google high-resolution images serving as visual interpretation aids. In the context of the experiment, Deeplabv3+ and U-Net++ were compared with U-Net under identical conditions. The results demonstrated that SimSDRU-Net exhibited optimal performance, with MIoU of 89.03%, MPA of 93.81%, and OA of 95.96%. Specifically, SimSDRU-Net uses the SimAM attention module to generate spatial attention weights by analyzing feature statistical differences through an energy function, so as to adaptively enhance soybean texture features. Meanwhile, the S-DRU unit groups, dynamically weights, and cross-branch reconstructs multi-scale convolutional features to preserve fine boundary details and achieve accurate segmentation of soybean plots. The present study demonstrates that SimSDRU-Net integrates lightweight design and high precision in data-limited scenarios, thereby providing effective technical support for the rapid extraction of soybean planting areas in North America. Full article

Soil salinization and heavy metal pollution represent significant global challenges to farmland sustainability and food security. Globally, over 800 million hectares of land are affected by salinity, with approximately 17% of cultivated land exhibiting concentrations of at least one heavy metal exceeding established agricultural safety thresholds. Microbially Induced Calcium Carbonate Precipitation (MICP) is an innovative biogeochemical process that harnesses microbial metabolic activities to facilitate soil mineralization. The core mechanism involves ureolytic microorganisms hydrolyzing urea to produce carbonate ions (CO₃²⁻). These ions subsequently react with environmental calcium ions (Ca²⁺) to form insoluble calcium carbonate (CaCO₃) precipitates. This review synthesizes recent research progress on the application of MICP technology for the remediation of heavy metal pollution. It elucidates the mechanistic pathways by which MICP immobilizes heavy metal ions and critically evaluates its potential application for ameliorating heavy metal contamination specifically within saline–alkaline soils. Key challenges impeding the broader practical deployment of MICP are analyzed, particularly concerning salt-alkali stress tolerance and the management of ammonia emissions during urea hydrolysis. Emerging strategies, such as the synergistic integration of MICP with biochar amendments, offer promising solutions. Biochar can provide a protective microenvironment for microbial consortia and potentially mitigate ammonia volatilization, thereby enhancing the overall efficacy and feasibility of this remediation approach for contaminated saline–alkaline lands. Full article

Underground gas storage (UGS) in salt caverns is increasingly important for a flexible and secure energy supply and for stabilizing the gas market. However, cavern operations can induce surface ground movements that must be monitored to safeguard infrastructure integrity and environmental compatibility. This research analyzes horizontal (W–E) and vertical ground movements above the cavern field Gronau-Epe in northwestern Germany, using radar interferometry (InSAR), specifically the SBAS (Small Baseline Subset) approach, combined with clustering and multi-criteria analysis. The study was conducted in cooperation between Uniper Energy Storage GmbH, the Research Center for Post Mining at THGA Bochum, and the company EFTAS. Freely available Copernicus Sentinel 1 data were integrated with public soil maps and operational storage information. A multistage workflow quantified deformation patterns, classified coherent deformation zones via clustering, and evaluated geological and technical drivers using multi-criteria analysis to better distinguish operational (primary) from overburden (secondary) influences. Results reveal long term deformation trends closely linked in time and space to injection/withdrawal cycles. Locally confined vertical and horizontal movements near caverns are attributed to salt convergence triggered by cyclic pressure changes, but they are linked to (hydro)geological and pedological factors. The developed approach shows strong monitoring potential in addition to classic mine surveying. 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

Heavy metals in agricultural systems pose a significant challenge to food security, especially in regions with long-term intensive land use. While the Pannonian Plain represents Croatia’s primary breadbasket, accounting for a significant portion of the nation’s cereal production, data on the soil-to-grain transfer of heavy metals and the associated human exposure risk are limited. The objective of this study was (i) to determine the concentrations of arsenic (As), cadmium (Cd), and lead (Pb) in agricultural soils and corresponding grains (wheat, barley, and maize) across four principal counties within the Pannonian region of Croatia; (ii) to evaluate the soil-to-grain transfer factors that varied regionally and among cereal types; and (iii) to assess the potential non-carcinogenic health risks for both adults and children highlighting differences in exposure due to body weight and consumption patterns. Soil and cereal grain samples were collected in 2019 and 2020, and metal concentrations were determined by ICP-MS after microwave acid digestion. The transfer of metals from soil to grain was estimated using the transfer factor (TF), while exposure assessment was conducted by calculating the estimated daily intake (EDI), hazard quotient (HQ), and hazard index (HI). Due to the nonlinear distribution of the data and the lack of strictly matched soil and grain samples, median metal concentrations pooled across all studied regions were used for exposure assessment. For As, a conservative approach was applied, assuming that 50% of the total As is in inorganic form. Additionally, a probabilistic risk assessment using Monte Carlo simulations was conducted to account for variability in body weight and cereal intake, providing a more comprehensive evaluation of potential exposure. The results showed differences in metal accumulation among cereal species, with wheat and barley tending to accumulate more Cd than maize, while As and Pb concentrations in grains were low for all crops studied. Although soil metal concentrations in Međimurje County were generally low, elevated TF values for As and Pb were observed, indicating enhanced soil-to-plant transfer under specific local soil conditions. In contrast, high soil metal concentrations in Slavonski Brod–Posavina County were associated with low TF values, suggesting limited bioavailability and restricted transfer to cereal grains. Both deterministic and probabilistic assessments indicated that the HQ and HI for adults and children were below 1, suggesting low non-carcinogenic risk from cereal consumption. These findings highlight pronounced regional and crop-specific differences in soil-to-plant metal transfer and confirm that low soil contamination does not necessarily imply low transfer potential, emphasizing the importance of integrated soil–plant–grain monitoring for food safety assessment. Full article