Search Results (7,533)

Accurate tire–soil traction prediction is critical for agricultural and off-road vehicle design, yet rigorous comparisons of advanced discretization strategies under controlled-slip conditions remain limited. This study compares MM-ALE and Hybrid FE-SPH (H-SPH) discretization in LS-DYNA for SRTT (225/60R16) traction prediction on sandy loam (0.4% gravimetric moisture content) across 5–40% slip ratios. A CT-scan-based tire model using Yeoh visco-hyperelastic rubber (Material_2) was validated against experimental data, achieving CORA scores of 0.989 (radial deflection), 0.999 (loaded radius), 0.947 (footprint area), and 0.985 (contact pressure), outperforming the Mooney–Rivlin formulation (Material_1; CORA = 0.618). Soil moisture content (0.4%, 8%, 14%) was included as a design variable through a Latin Hypercube Sampling framework. Both methods reproduced a monotonic increase in traction; inter-method differences ranged from 29 to 36% at low slip, converging to a 7.8% coefficient of variation at 40% slip. A 27-run full-factorial DOE-I identified normal load as the dominant traction driver (90.1%), followed by velocity (8.6%) and inflation pressure (1.3%). An LHS-based DOE-II revealed moisture content as the primary driver of traction coefficient (67.5%), via a non-monotonic cohesion mechanism peaking at 8% gravimetric moisture. H-SPH reduced runtime by 38% versus MM-ALE. The validated framework provides reusable traction prediction protocols for variable conditions. Full article

Quantifying vegetation ecological quality and ecological water requirement is essential for understanding ecosystem sustainability in arid regions. However, large-scale assessments of vegetation ecological quality and ecological water requirement thresholds remain limited in Central Asia. In this study, we developed a Vegetation Ecological Quality Index (VEQI) for Central Asia based on fractional vegetation cover (FVC) and net primary productivity (NPP) and estimated vegetation ecological water requirement quota (VEWRq) and total vegetation ecological water requirement (VEWR) using the Penman–Monteith method, the soil moisture limitation coefficient (SMLC), and GIS-based spatial analysis. We further examined the spatiotemporal variations in VEQI and VEWR during 2001–2020 and identified VEWRq thresholds corresponding to different VEQI levels. The results showed that (1) the multi-year mean VEQI in Central Asia was 28.46 and exhibited a slight increasing trend during 2001–2020; (2) the annual mean minimum, maximum, and optimal VEWRq were 147.53, 179.71, and 162.52 mm, respectively, corresponding to mean annual VEWR values of 146.98 × 10⁹ m³, 179.04 × 10⁹ m³ and 161.91 × 10⁹ m³, respectively; and (3) VEQI was positively correlated with VEWRq in 89.48% of the vegetation area. The VEWRq threshold increased with vegetation ecological quality. The five VEQI levels in Central Asia, namely very poor, poor, moderate, good, and very good, corresponded to VEWRq thresholds of 28.62–35.96, 88.33–107.81, 190.69–233.32, 362.86–432.81, and 678.59–838.31 mm, respectively. This study provides a remote sensing-based framework for evaluating vegetation ecological quality and quantifying ecological water requirement thresholds in arid regions and offers scientific support for regional ecological management and water resource allocation. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

Under the dual constraints of global food security and ecological protection, conventional agriculture is hampered by low resource efficiency and sluggish environmental response. Crop digital twin technology establishes a dynamic virtual reality system that integrates crops, environment, and water to enable real-time interaction and optimization. Based on the existing literature, this paper reviews the concept, architecture, and core modules of this technology and summarizes its applications in precision irrigation and crop monitoring. There are three major bottlenecks that persist, including limited high-frequency multi-source sensing and spatiotemporal fusion, insufficient parameter calibration and dynamic updating, and weak cross-scale integration from plant to watershed. Water is increasingly recognized as the key constraint and control variable and acting as both the central physiological driver of crop growth and the mass-flow link that connects the soil–plant–atmosphere continuum. The spatiotemporal dynamics of crop water deficit, compensatory root water uptake, evapotranspiration feedback, and the hydraulic behavior of irrigation-district canal systems constitute the core hydrological processes that must be simulated within the digital twin. Synchronizing crop water demand, soil moisture dynamics, atmospheric evapotranspiration, and irrigation scheduling within a unified spatiotemporal framework establishes a complete sensing, diagnosis, prediction and regulation technical chain. This chain offers a core pathway for alleviating agricultural water scarcity, improving irrigation efficiency, and ensuring food security. Full article

Modeling crop development under different agrotechnologies is important not only for assessing the factors that affect their yields but also because of the role of vegetation in regulation of the hydrology regime. For this reason, interest in the plant module in the semi-distributed hydrological model SWAT is increasing. The model has to be supplied with a lot of information for running and testing, which can be achieved with ground-based, statistical and satellite data. The aim of the study is to determine the accuracy of the SWAT model to predict crop development by using ground-based and satellite data for LAI in the case of a 5-year field experiment. Two staple crops in rotation were monitored—winter wheat and maize—under different erosion control technologies (up-and-down conventional tillage, conventional contour tillage, and minimum contour tillage with inclusion of cover crop before maize) on sloping terrain on moderately eroded Epicalcic Chernozem in the region of Ruse, north Bulgaria. The remote sensing data from the Copernicus Sentinel-2 mission were used for estimation of LAI of both crops and verified against ground-based data in two ways—via a custom LAI script available through the Sentinel Hub cloud platform and as input to a machine learning quantile regression forests (QRF) model. The calibrated satellite-derived LAI, ground-based soil moisture and yields data were used to calibrate several SWAT model parameters (EPCO, ESCO, CN2, LAImax, HU, HI) and assess the model performance regarding these variables. Although a good temporal fit of the SWAT-modeled LAI data with the satellite data was achieved, the accuracy of predicted LAI is moderately high only in the last two years of the rotation (R² = 60.4%). The accuracy of calibrated yields (R² = 55.5%) is acceptable in four of the years. On average for the period, the applied erosion control agrotechnologies did not cause significantly different yields, but they are 14% higher compared to the up-and-down conventional tillage. The most sensitive SWAT parameters accounting for this effect are EPCO and ESCO. Full article

Estimation of rice crop yield at the village level is essential because village is the Insurance Unit (IU) for rice crop in many regions in India, and timely and accurate yield information at this scale supports timely and transparent claim settlements for farmers and supports local agricultural planning. To achieve this, a multi-source satellite data-based machine learning approach was used to estimate rice yield at the village level using optical and SAR data, climatic data and land surface model-derived parameters in Kakinada of Andhra Pradesh, India. The predictor dataset included seasonal cumulative rainfall, seasonal Normalized Difference Vegetation Index (NDVI)-Max, seasonal NDVI-Mean, seasonal Land Surface Water Index (LSWI)-Max, seasonal LSWI-Mean, season total Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and season total Root Zone Soil Moisture (RZSM), and season total backscatter of the Sentinel-1 VH polarization were used to represent crop greenness, moisture status, photosynthetic activity, soil water availability, canopy structure, and seasonal water supply. For model development and validation, village-level rice yield data from 2017 to 2023 was used, which was collected through Crop Cutting Experiment (CCE) at the maturity stage of Kharif season. In this study, four machine learning models such as Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) were evaluated. The multi-source satellite data and yield data for the period 2017–2021 were used to train the models, which were independently tested on 2022 data and then applied to predict the rice yield in 2023. Leave-One-Year-Out (LOYO) cross-validation was also conducted on the 2017–2022 data to assess temporal robustness and generalization capability across years. Among the evaluated models, Random Forest exhibited the best overall performance. For the independent test year 2022, RF achieved an R² of 0.465, RMSE of 415.34 kg ha⁻¹, MAE of 322.22 kg ha⁻¹, and MAPE of 10.36%. For the prediction year 2023, RF achieved improved accuracy with an R² of 0.838, RMSE of 325.75 kg ha⁻¹, MAE of 262.21 kg ha⁻¹, and MAPE of 7.68%. Further, LOYO cross-validation also showed the robustness of RF, achieving the highest mean R² of 0.702 and mean RMSE of 384.73 kg ha⁻¹. The results illustrate that multi-source satellite data combined with machine learning can be a reliable and operationally useful tool in predicting village-level rice yield, which can be used for crop insurance claim settlement. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article

Saline irrigation is increasingly practiced in semi-arid regions to cope with freshwater scarcity; however, it strongly affects crop growth, water use, and soil salinity. This study aims to calibrate and validate the AquaCrop model to simulate key growth parameters of winter wheat (cv. Achtar) under saline irrigation conditions in the Tadla Plain, Morocco, focusing on canopy cover (CC), actual evapotranspiration (ETa), soil water content (SWC), biomass (B), and grain yield (GY). The model was first calibrated using observed data from the 2023 growing season and subsequently validated using data from the 2022 growing season. Overall, AquaCrop effectively reproduced crop growth during both calibration and validation phases. During calibration, canopy cover was accurately simulated, with average RMSE values below 1%, while biomass and grain yield were also well reproduced, with low RMSE values (0.25 t ha⁻¹ for B and 0.10 t ha⁻¹ for GY), confirming the robustness of the calibrated parameters. The model also performed well in simulating ETa and SWC, capturing the seasonal dynamics of crop water use and soil moisture. During validation, ETa was satisfactorily reproduced, with an RMSE of approximately 0.80 mm day⁻¹, while SWC showed good agreement with observations, with NRMSE values ranging from 7.9 to 10.5%. Grain yield and biomass were reliably predicted, with NRMSE values below 4%. These results demonstrate that AquaCrop is a reliable tool for simulating winter wheat under saline irrigation and for assessing crop response under salt-affected conditions, providing an integrated evaluation of crop performance, water use, and soil salinity dynamics to support improved irrigation management and water-use efficiency under semi-arid conditions. Full article

High-resolution soil moisture monitoring in a lysimeter requires precise Ground-Penetrating Radar (GPR) systems that can provide clean time-domain data for a Full-Waveform Inversion (FWI) algorithm. Using high-speed Radio Frequency System-on-Module (RFSoM) devices provides flexibility in signal generation. To optimize such a system, an appropriate transmit waveform and processing pipeline need to be selected. This paper presents a performance evaluation of three GPR waveforms—impulse, Stepped-Frequency Continuous Wave (SFCW) and non-linear Frequency-Modulated Continuous Wave (FMCW/chirp)—on the same hardware setup. To ensure a fair comparison, all waveforms were tested under an identical total measurement time. Numerical simulations were performed using an electromagnetic model of the system. Physical validation was conducted in an anechoic chamber using a 4 GS/s RFSoM setup and planar elliptical dipole antennas. Simulations showed that both sinewave-based methods provide better signal-to-noise ratios (SNRs) than the impulse GPR, with the non-linear chirp achieving the best results (20.7 dB improvement compared to impulse). Experimental measurements supported these results, showing better SNR across the frequency band for the SFCW and chirp waveforms. Because of its high SNR and simple hardware implementation, the non-linear chirp was identified as the most suitable waveform for this RFSoM-based GPR system. Full article

Aridification and hotter droughts are triggering forest die-off events characterized by high mortality rates and declines in forest productivity. The western Mediterranean Basin is a climate change hotspot where many of these die-off events have affected several tree and shrub species in recent decades. Yet, the responses of canopy greenness and cover, radial growth, and gross primary productivity (GPP) to climate in these die-off sites remain poorly understood across species and biomes. Here, we examined 44 sites across Spain, covering humid, dry sub-humid, and semi-arid biomes, and including nine tree and one shrub species. We obtained and correlated monthly climate data, satellite-derived vegetation indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index), tree-ring metrics (basal area increment, ring-width indices), and GPP. We assessed climate trends and relationships between climate, vegetation indices, growth, GPP, and resilience after five extreme drought years in the period 1984–2023. Climate warming impacted all sites, increasing vapor pressure deficit and reducing soil moisture availability, with semi-arid sites warming the most. Vegetation indices and growth showed the largest declines during extreme droughts in dry sub-humid and semi-arid sites. Correlations with climate variables highlighted strong sensitivity to drought stress, particularly regarding growth metrics. During die-off events, GPP significantly declined in the growing season, but no legacy effects were observed afterwards. Vegetation indices and growth partially recovered one year after drought, with resilience peaking for GPP in semi-arid sites. Hotter droughts constrain GPP and growth, especially in dry sub-humid and semi-arid forests. Forests and shrublands experiencing die-off are diagnostic monitors of drought-induced thresholds in ecosystem productivity. Full article

Alfalfa (Medicago sativa L.) is widely recognized as a major forage crop, yet its role as a multifunctional biological input in sustainable horticultural production remains underexplored. This review evaluates alfalfa as a biological nitrogen source, organic fertilization resource, and biofertilizer-supporting crop within vegetable, medicinal, and perennial horticultural systems. Due to its high capacity for biological nitrogen fixation, alfalfa can supply substantial amounts of plant-available nitrogen, reducing dependency on synthetic fertilizers and supporting environmentally sound nutrient management. When used as green manure, cover crop, intercrop, mulch source, compost feedstock, or processed organic fertilizer, alfalfa enhances the soil organic carbon (SOC), improves soil structure, and increases the water-holding capacity properties particularly critical in intensive horticultural production. Higher SOC levels also contribute to the improved tolerance of horticultural crops to drought and heat stress through enhanced soil moisture retention and rhizosphere buffering. Alfalfa-based organic inputs stimulate rhizosphere microbial biomass, enzymatic activity, and functional genes associated with nitrogen cycling, strengthening plant–microbe interactions that underpin biofertilizer effectiveness. Evidence from vegetable and perennial systems indicates that alfalfa-derived amendments and rotations increase soil nitrogen availability, support yield stability, and improve soil health over the long-term. In orchards and vineyards, alfalfa cover cropping contributes to carbon sequestration, erosion control, and enhanced soil biological functioning. Overall, alfalfa emerges as a strategic species for integrating organic fertilization and biofertilizer-based approaches into modern horticultural systems, supporting reduced mineral fertilizer inputs while sustaining productivity, soil health, and environmental quality. Full article

The Loess Hilly Region of northern Shaanxi is one of the most erosion-prone areas in the world due to its porous, erodible loess, steep slopes, and seasonal rainfall. To address this, conversion of sloping farmland to terraces has been extensively conducted across China’s loess regions, as terracing can reduce soil and water loss and enhance soil fertility. However, disturbance of soil layers during terracing can also lead to short-term decline in farmland productivity. This study investigates the effects of terracing operations at two sites of different ground substrate configurations in the Loess Hilly Region. Utilizing geochemical and molecular biological analysis methods, we examined the changes in the physicochemical and microbial properties of the ground substrate after terracing, using adjacent sloping farmlands as control sites. The results show that when the ground substrate configuration remained intact, terracing increased the average water content (from 8.44% to 14.34%) and soil organic carbon (from 2.74 g/kg to 5.76 g/kg) by 70% and 110%, respectively, and increased soil microbial α-diversity by 90%. The microbial community structure was also enhanced with an increase in relative abundance of soil- and plant-benefiting genera such as Streptomyces and Nocardioides, thereby promoting plant growth. Conversely, when the ground substrate configuration was altered, terracing led to a decrease in soil nutrient and moisture content, which was detrimental to crop growth. Therefore, maintaining the integrity of the ground substrate configuration is crucial during the terracing process to achieve optimal soil and water conservation outcomes. Full article