Search Results (246)

Current agricultural irrigation management practices are often extensive, and traditional soil moisture content (SMC) monitoring methods are inefficient, so there is a pressing need for innovative approaches in precision irrigation. This study proposes a Multi-Feature Fusion Network (MFF-Net) for SMC inversion. The model uses a designed Channel-Changeable Residual Block (ResBlockCC) to construct a multi-branch feature extraction and fusion architecture. Integrating the Channel Squeeze and Spatial Excitation (sSE) attention module with U-Net-like skip connections, MFF-Net inverts root-zone SMC from summer maize leaf images. Field experiments were conducted in Zhengzhou, Henan Province, China, from 2024 to 2025, under three irrigation treatments: 60–70% θ_fc, 70–90% θ_fc, and 60–90% θ_fc (θ_fc denotes field capacity). This study shows that (1) MFF-Net achieved its smallest inversion error under the 60–70% θ_fc treatment, suggesting the inversion was most effective when SMC variation was small and relatively low; (2) MFF-Net demonstrated superior performance to several benchmark models, achieving an R² of 0.84; and (3) the ablation study confirmed that each feature branch and the sSE attention module contributed positively to model performance. MFF-Net thus offers a technological reference for real-time precision irrigation and shows promise for field SMC inversion in summer maize. Full article

This study investigates the effects of nitrogen application and sprinkler irrigation on winter wheat growth, water use efficiency (WUE), and yield formation under dry-hot wind stress. The primary aim was to understand how nitrogen levels influence canopy structure, soil water–nitrogen coupling, and yield components under varying irrigation conditions. Field experiments were conducted with different nitrogen rates (N1, N2, N3, N4, N5) and sprinkler irrigation under heat stress. Plant height, leaf area index (LAI), canopy interception, and stemflow were measured, along with soil moisture and nitrogen content in the root zone. Results indicate that moderate nitrogen application (212 kg N ha⁻²) optimized yield and WUE, with a significant enhancement in canopy structure and water interception. High nitrogen levels resulted in increased water consumption but decreased nitrogen use efficiency (NUE), while lower nitrogen treatments showed reduced yield stability under heat stress. The findings suggest that balanced nitrogen management, in combination with timely irrigation, is essential for improving winter wheat productivity under climate stress. This study highlights the importance of optimizing water and nitrogen inputs to achieve sustainable wheat production in regions facing increasing climate variability. Full article

This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. A Mamdani-type fuzzy controller is designed to approximate the optimal irrigation strategy, and an equivalent Takagi–Sugeno (TS) representation is derived, enabling a rigorous stability analysis based on Input-to-State Stability (ISS) theory and Linear Matrix Inequalities (LMIs). Online parameter estimation is performed using a Recursive Least Squares (RLS) algorithm applied to real IoT field data collected from a drip-irrigated orchard. To enhance prediction accuracy and long-term adaptability, the fuzzy controller is augmented with lightweight artificial neural network (ANN) modules for evapotranspiration estimation and slow adaptation of membership-function parameters. This work provides one of the first mathematically certified neuro-fuzzy irrigation controllers integrating ANN-based estimation with Input-to-State Stability (ISS) and LMI-based stability guarantees. Under mild Lipschitz continuity and boundedness assumptions, the resulting neuro-fuzzy closed-loop system is proven to be uniformly ultimately bounded. Experimental validation in an operational IoT setup demonstrates accurate soil-moisture regulation, with a tracking error below 2%, and approximately 28% reduction in water consumption compared to fixed-schedule irrigation. The proposed framework is validated on a real IoT deployment and positioned relative to existing intelligent irrigation approaches. Full article

Crop root development, and in turn crop growth, is strongly influenced by soil strength and the mechanical impedance of compacted layers, which restrict root elongation and exploration. Because the depth and thickness of compacted layers vary across a field, their identification is essential for site-specific tillage and sustainable root-zone management. A sensing approach that can support future real-time identification of compacted layers after soil-specific calibration, which would enable variable-depth tillage, reducing mechanical impedance and improving energy-use efficiency while maintaining crop yields. This study aimed to develop and evaluate prediction models that can support future real-time identification of compacted soil layers using soil electrical conductivity (EC) and moisture content as non-destructive indicators. A sandy clay soil (48.6% sand, 29.3% clay, 22.1% silt) was tested in a soil-bin laboratory under controlled conditions at three moisture levels (13, 18, and 22% db.) and six depth layers (C1–C6, 0–30 cm) identified from the penetration-resistance profile to measure penetration resistance, shear resistance, and EC. Penetration and shear resistance increased toward the most resistant depth layer and decreased with increasing moisture content, whereas EC generally increased with both depth layer and moisture content. Linear regression models relating penetration resistance ($R^2=0.893$) and shear resistance ($R^2=0.782$) to EC and moisture content were developed and evaluated. Field validation in a paddy field of similar texture showed that predicted penetration resistance differed from measured values by 3–6% across the three compaction treatments evaluated. Root length density and root volume decreased with increasing machine-induced compaction, confirming the agronomic relevance of the modeled patterns and supporting the suitability of the proposed indicators. Together, these results demonstrate that EC and moisture content can potentially be used as non-destructive proxies for compacted-layer identification and provide a calibration basis for future on-the-go sensing systems to support site-specific, variable-depth tillage in agricultural fields. Full article

In arid regions, the ecological restoration of limestone tailings requires sustainable strategies, yet the synergistic effects of substrate optimization and native plant selection remain poorly understood. In this study, we systematically evaluated substrate amendments and native species for rehabilitating limestone tailings in Northern China’s arid zone using a controlled pot experiment. An orthogonal L9(3⁴) experimental design was employed to test three factors: the soil-to-tailings ratio (1:2, 1:1, and 2:1), moisture level (30%, 45%, and 60% of field capacity), and nitrogen addition (0, 5, and 10 g N m⁻²). Five native grass species (Pennisetum centrasiaticum, Setaria viridis, Leymus chinensis, Achnatherum splendens, and Eleusine indica) were grown under these treatment conditions, and plant biomass and key soil nutrient variables were measured. Stepwise regression, structural equation modeling, and principal component analysis were applied to assess plant growth responses and soil nutrient dynamics. The results indicated that a 2:1 soil-to-tailings substrate maintained at 60% moisture content maximized biomass production across all species. Soil total potassium consistently correlated positively with biomass (Standardized β: 0.397–0.603), whereas available potassium showed a negative relationship (Standardized β: −0.825–−0.391). Nutrient dynamics ultimately governed biomass accumulation, accounting for 57.8–84.2% of the biomass variation. P. centrasiaticum ranked as the most effective species, followed by S. viridis, L. chinensis, A. splendens, and E. indica. We concluded that successful restoration under these experimental conditions hinged on key factors: using a 2:1 soil-to-tailings substrate, maintaining 60% soil moisture, and strategically combining deep-rooted P. centrasiaticum with shallow-rooted S. viridis to exploit complementary resource use. This work provides fundamental data and a conceptual framework for rehabilitating arid limestone tailings in similar ecological settings, based on controlled experimental evidence. Full article

Focusing on the ecotone from the Qinghai–Tibetan Plateau to the Loess Plateau (QPtoLP), this study firstly constructs a retrieval model of soil moisture in various depth layers based on multi-source remote sensing data by using the two-source energy balance (TSEB) model and soil–vegetation–atmosphere transfer (SVAT) model. And then, it uncovers how the soil moisture changes across various depths in the root zone and discusses the lagging effect of rainfall. This research indicated that the correlation between the retrieved soil moisture and field-monitored values in various depth layers ranged from 0.720 to 0.8414, demonstrating that it is suitable for the retrieval of soil moisture at various depths in the study area. During the growing season, soil moisture experienced a slight decrease from mid-May to mid-June, followed by a partial recovery in mid-June. After a dry spell in July, the soil moisture reached its lowest point, but surface and deep soil moisture levels rebounded to above 0.2 and 0.1 cm³/cm³, respectively, by mid-August. Spatially, the soil moisture was higher in the southern region, characterized by dense human activities, and lower in the northern region, which is dominated by alpine grasslands. Comparing different depths, the soil moisture at a 0–5 cm depth was generally the highest most of the time, except in July, when the 35–50 cm depth had the highest value. Additionally, the surface soil moisture at a 0–5 cm depth indicated frequent fluctuations at elevations above 4000 m. As the soil depth increases, the rainfall lag effect becomes more pronounced, and the lag effect in the 35–50 cm soil layer is three days. Full article

SoilMERGE (SMERGE) is a 0.125-degree root zone soil moisture (RZSM) product (0 to 40 cm depth) covering the contiguous United States. The study area included most of Oklahoma and Kansas, a region where SMERGE exhibited superior performance. The time frame examined was the warm season from 2008 to 2019. In this study, evaluation of a prototype downscaled (500 m) version of SMERGE was made using (1) Ranked correlation (R²) benchmarking against Normalized Difference Vegetation Index (NDVI) datasets and (2) Ranked correlation (R²) analysis of antecedent RZSM with storm-event streamflow across a range of precipitation intensities (5 to >35 mm/day) at a watershed scale. In the NDVI benchmarking, all three downscaled products outperformed (0.52 to 0.59) default SMERGE (0.44). EXtreme Gradient Boosting (XGB) and Gradient Boost recorded a higher ranked correlation (0.59) than Random Forest (0.52). Within the study area, ranked correlation analysis of antecedent RZSM with storm-event United States Geological Survey streamflow was examined in five watersheds. For the most intense storm events (>35 mm), antecedent XGB downscaled SMERGE (0.64) outperformed antecedent streamflow (0.43) and all other versions of SMERGE (0.52 to 0.56) as a predictor of storm event response. The results of this study demonstrated broad-scale benefits of Machine Learning-assisted downscaling, providing proof of concept for the development of state-based SMERGE products across the US Great Plains. Full article

Understanding soil respiration (Rs) dynamics in Mediterranean olive groves is crucial for quantifying carbon fluxes under climate change. Soil respiration represents the combined CO₂ efflux from root metabolic activity and microbial decomposition of soil organic matter, processes strongly controlled by soil moisture, temperature, and the quantity and quality of organic matter inputs in semi-arid Mediterranean environments. This study quantified the seasonal and spatial variability of Rs in a traditional rainfed olive orchard planted at a spacing of 11 m between rows and 9 m between trees (≈101 trees ha⁻¹). Continuous measurements were conducted in two contrasting zones, under-canopy (UC) and inter-row (IR), using automated soil CO₂ flux chambers. Annual Rs reached 3.68 Mg CO₂ ha⁻¹ y⁻¹ in UC and 2.21 Mg CO₂ ha⁻¹ y⁻¹ in IR, with substantially higher emissions per unit area beneath the canopy. However, due to its larger surface proportion, the IR zone contributed more to the orchard scale CO₂ budget. Soil water content emerged as the dominant environmental driver of Rs, moderating or suppressing the temperature response during dry periods. These findings highlight the importance of explicitly considering microsite heterogeneity when assessing soil CO₂ efflux and designing sustainable carbon-management strategies in Mediterranean olive agroecosystems. Full article

Soil profile moisture (0–200 cm) in agricultural fields is a critical variable determining root-zone water storage and irrigation scheduling accuracy, yet continuous deep-layer monitoring is constrained by equipment costs and installation difficulties. This study developed a near-real-time reconstruction model for soil moisture profiles across the 0–200 cm depth based on shallow-layer (0–20 cm, 20–40 cm) real-time monitoring data and multi-day accumulated meteorological features. Using field measurements from 2023 to 2025, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR) models were compared across different input scenarios and cumulative time windows. The results showed that using only surface moisture as input (Scenario A), prediction R² ranged from 0.87 to 0.93 for shallow layers (≤80 cm) but decreased to 0.58 for deep layers (140–200 cm). Incorporating multi-day meteorological accumulation (Scenario B) improved R² by 0.05–0.08. When dual-layer moisture and meteorological drivers were combined (Scenario D), shallow-layer R² reached 0.96–0.98 with RMSE < 7 mm, mid-layer performance maintained at 0.85–0.90, and deep layers still achieved 0.76–0.84. Optimal time windows exhibited depth-dependent patterns: 5–10 days for shallow layers, 10–15 days for mid-layers, and ≥20 days for deep layers. Rolling validation demonstrated high consistency between model predictions and observations in the 0–80 cm range (R² > 0.90, RMSE < 10 mm), enabling stable estimation of 0–200 cm profile dynamics. This approach eliminates the need for deep probes while achieving low-cost, interpretable, and deployable near-real-time deep moisture estimation, providing an effective technical pathway for precision irrigation and water management in semi-arid regions. Full article

Optimizing field cropping practices to improve nitrogen use efficiency is imperative to promote intensive and sustainable wheat production. As a cultivation method commonly adopted in arid and semi-arid regions globally, the ridge–furrow mulching system (RFMS) is capable of efficiently harvesting rainfall, reduce evaporation losses, enhancing soil moisture levels in the root zone, and boosting crop productivity. However, the combined effects of varying ridge–furrow ratios (RD), ridge heights (RH), and nitrogen application rates (RN) on nitrogen fertilizer bias productivity (PFPN) under the influence of climatic conditions, soil types, and field management practices remain poorly understood due to a lack of systematic evaluation. This study conducted a meta-analysis of 462 comparative datasets from 98 research projects to reveal the interactive effects of RFMS and nitrogen fertilizer across climatic gradients. The results showed that RH, RD, and RN increased by 23.78%, 22.37%, and 23.07% respectively (p < 0.05), with the most significant enhancement of PFPN being demonstrated by RH. The most significant improvement in PFPN was observed when RD = 1:1, R < 10 cm, and RN > 200 kg∙hm⁻², with PFPN increasing by 27.7%, 29.50%, and 29.32% respectively (p < 0.05). Climatic and soil physico-chemical factors and field management practices are the key factors influencing the RFMS. When average annual evapotranspiration (AE) < 1000, RN > 200 has the best effect on nitrogen utilization efficiency, while under the condition of AE > 1500, RN < 100 is more effective. In terms of mulching strategy, full mulching of ridges and furrows is recommended in areas with severe drought and low temperatures, while mulching only ridges or furrows is more appropriate in areas with relatively mild climate. The present study provides a scientific basis for the optimal design of ridge–furrow mulching configuration and nitrogen application level. This is achieved by considering climatic conditions, soil fertility, and field management in agro-ecosystems in arid and semi-arid areas. Full article

Flash droughts—characterized by their sudden development, severity, and short duration—impose considerable challenges on the soil–water complex of agricultural systems, especially under the Mediterranean climate. Though gaining increasing global significance, Mediterranean flash droughts are still understudied. This study examines the spatiotemporal variability of flash droughts in Greece for the period 1990–2024 using 5-day (pentad) ERA5-Land root-zone soil moisture (0–100 cm) at 0.25° resolution. A percentile-threshold approach detected flash drought events, and their main features—including frequency, duration, magnitude, intensity, decline rate, recovery rate, and recovery duration—were evaluated at the annual and seasonal levels. Findings indicate that Central Greece and Thessaly face the highest frequency and longevity of flash droughts, while Western Greece and Peloponnese and Western Macedonia are characterized by rapid development but intense recovery. An innovative empirical classification framework founded on decline and recovery rates indicated that Mild Fast Recovery events prevail in northern and central Greece, while Intense but Recovering events dominate in western and southern Greece. These results offer new perspectives on how flash droughts impact soil–water availability and agricultural resilience, providing a data-driven platform to aid sustainable water management, early warning systems, and adaptation strategies for Mediterranean agriculture in conditions of climate variability. Full article

In early initial tests, the Self-regulating Low-Energy Clay-based Irrigation (S.L.E.C.I.) has provided convincing results. During the DIVAGRI project, S.L.E.C.I. irrigation was plotted against reference drip irrigation and rain-fed control in order to compare soil moisture dynamics across different soil depths (30 cm, 60 cm, and 90 cm), irrigation water use, cherry fruit quality traits and yield, and irrigation water productivity (IWP). The data, collected between 2021 and 2023 at the Fruit Growing Institute–Plovdiv test site, reveals that S.L.E.C.I. system demonstrates a clear robustness from short-term climate fluctuations, maintaining root-zone moisture with greater consistency across depths. This contrasts with higher climate dependency observed in the reference variants. The average water productivity of S.L.E.C.I. irrigation is more than 12 times higher compared with the average IWP for drip irrigation. Probably, the superior ratio stems from two factors: first, S.L.E.C.I. delivered only the water that root tension demanded, and second, there is almost no loss of water to evaporation or deep percolation. Statistical analysis confirms that S.L.E.C.I. reduces variability within the crop, delivering significant improvements in both productivity and uniformity, essential traits for high-value commercial fruit production. Despite facing challenges, S.L.E.C.I. remains a promising sustainable irrigation technology, supporting efficient resource utilization while reducing environmental impact. Full article

Soil moisture plays a key role in the critical zone of the Earth and has extensive value in the understanding of hydrological, agricultural, and environmental processes (among others). Long-term (in situ) monitoring of soil moisture measurements is generally not practical; however, short-term measurements are often found. Limited soil moisture measurements can be employed to develop a numerical model for long-term and accurate soil moisture estimations. A key input variable to the model is precipitation, which is also not easily accessible, particularly at a finer spatial resolution; hence, publicly available remote sensing data can be used as an alternative. This study, therefore, aims to develop a numerical model HYDRUS-1D to estimate soil moisture in the data-scarce state of the Northern Territory, Australia, with a land cover of shrubland and a Tropical-Savannah type climate. The HDYRUS-1D is based on the numerical solution of Richards’ equation of variably saturated flow that relies on information about the soil water retention characteristics. This study utilized the van Genuchten model parameters, which were optimized (against measured soil moisture) through parameter optimization with initial estimates obtained from the HYDRUS catalogue. Initial estimates from different sources can differ for the same soil texture (e.g., loamy sand) and can induce uncertainties in the calibrated model. Therefore, a comprehensive uncertainty analysis was conducted to address potential uncertainties in the calibration process. The HYDRUS-1D was calibrated for a period between March 2012 and February 2013 and was independently validated against three different periods between March 2013 and October 2016. Root Mean Square Error (RMSE), Pearson’s correlation coefficient (R), and Mean Absolute Error (MAE) were used to assess the efficiency of the model in simulating the measured soil moisture. The model exhibited good performance in replicating measured soil moisture during calibration (RMSE = 0.00 m³/m³, MAE = 0.005 m³/m³, and R = 0.70), during validation period 1 (RMSE = 0.035 m³/m³ and MAE = 0.023 m³/m³, and R = 0.72), validation period 2 (RMSE = 0.054 m³/m³ and MAE = 0.039 m³/m³, and R = 0.51), and validation period 3 (RMSE = 0.046 m³/m³ and MAE = 0.032 m³/m³, and R = 0.61), respectively. Remotely sensed precipitation data were used from the CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now to assess their capabilities in estimating soil moisture. Efficiency evaluation metrics and visual assessment revealed that these products underestimated the soil moisture. The CHRS-CCS outperformed other products in terms of overall efficiency (average RMSE of 0.040 m³/m³, average MAE of 0.023 m³/m³, and an average R of 0.68, respectively). An integrated approach based on numerical modelling and remote sensing employed in this study can help understand the long-term dynamics of soil moisture and soil water balance in the Northern Territory, Australia. Full article

Modern agricultural production faces challenges, caused by soil degradation, declining natural fertility, and a lack of organic matter and productive moisture in the arable layer, which is especially relevant in the context of global climate change and rising prices for fuel and lubricants, mineral fertilizers, and plant protection products. Five tillage systems (moldboard, flat-cut, adaptive, shallow and surface) and three fertilization options (no fertilization, by-product, by product + N₆₅P₆₀K₇₀) were tested. The combination of adaptive cultivation and organic-mineral fertilization resulted in the highest input of crop by-products (up to 1.26 g cm⁻³), elevated humus reserves (69.2 t ha⁻¹ in the 0–40 cm layer), reduced bulk density in the root zone (down to 1.26 g cm⁻³), improved soil moisture conditions, and, consequently, the highest grain yield—4.34 t ha⁻¹, which is 7.4–21.4% higher than in other treatments. The use of adaptive cultivation with differentiation of the depth and type of loosening allowed the humus reserve to be increased to 66.4 t ha⁻¹, the productive moisture in the 0–40 cm layer to reach 86 mm, and ensured an increase in the yield of the grain units to 4.34 t ha⁻¹. The obtained results prove the validity of the efficient integration of the plant biomass on light-textured soils with low physicochemical parameters and humus content as a renewable resource in sustainable agriculture technologies, especially in conditions of climate instability and the rising costs of the resources. Full article

The Normalized Difference Vegetation Index (NDVI) is a fundamental metric for monitoring terrestrial ecosystem dynamics and assessing ecological responses to climate change. However, uncertainties persist across NDVI products, and a comprehensive assessment of their consistency is lacking. This study conducts a multi-faceted evaluation of three NDVI products, GIMMS V1.2 NDVI (NDVI_3g+), PKU GIMMS NDVI (NDVI_pku), and MODIS NDVI (NDVI_mod), to elucidate their performance across ecosystem applications. Our analysis encompasses a comparative analysis of NDVI values, trends, sensitivity to root-zone soil moisture (RSM), and performance in tracking photosynthesis benchmarked against solar-induced chlorophyll fluorescence (SIF). Our results reveal that NDVI_3g+ deviates notably from NDVI_pku and NDVI_mod over cold climates and Evergreen Broadleaf Forest (EBF). Additionally, NDVI_3g+ exhibits significant global browning, in contrast to the significant greening observed for NDVI_pku and NDVI_mod. Although their responses to RSM are generally uncertain, consistent positive responses appear in Drylands, with NDVI_mod showing the highest sensitivity. Additionally, the three NDVI products have high seasonality consistency with SIF, except over EBF, and NDVI_pku and NDVI_3g+ achieve the highest and lowest overall anomaly consistency with SIF, respectively. Furthermore, converting NDVI_3g+, NDVI_pku, and NDVI_mod to the corresponding kernel NDVIs improves seasonality consistency with SIF across 85% of the globe. Full article