Search Results (148)

Soil erosion on sloping farmland impairs cultivated land quality, food security, and watershed ecological sustainability across China. Vegetation serves as a core erosion buffer, yet how nitrogen–phosphorus (N-P) stoichiometry shapes coordinated maize growth to regulate multi-stage runoff–sediment coupling remains underexplored. This study reveals the complete mechanistic chain linking N-P ratios, crop growth synchrony, and hillslope erosion dynamics via 60 mm·h⁻¹ simulated rainfall experiments at three key summer maize stages, with six N/P gradients (0–3.75) in an eastern China brown soil zone. An N/P ratio of 2 optimized maize biomass, canopy cover, and root soil-binding capacity, yielding the lowest sediment concentrations. Excess nitrogen (N/P = 3.75) triggered spindly, mechanically weak maize growth, elevating runoff volume and sustaining high-variability sediment transport. High-frequency runoff–sediment signals maintained a consistent positive correlation, while mid/low-frequency components decoupled under imbalanced N-P supply. Moderate balanced N-P fertilization (N/P = 1–2) stabilized hillslope hydrological-erosion processes throughout the growing cycle, whereas surplus nitrogen induced asynchronous erosion responses. This research delivers quantitative evidence for coordinated high-yield and erosion-control nutrient management. Full article

Ginger (Zingiber officinale) fields in northern China are often covered by plastic mulch film and shade netting, so Sentinel-2 records management materials as well as the crop canopy. Because these materials and their deployment differ among production systems, models calibrated locally may transfer poorly. We defined three observation windows for the main study counties: spring film mulching, summer shade-net coverage, and autumn exposed-canopy greening. Within each window, spectral bands, vegetation indices, and gray-level co-occurrence matrix (GLCM) textures were extracted from single Sentinel-2 scenes across three northern Chinese counties with contrasting practices. A cross-regional Random Forest Gini ranking, weighted by the similar county model-sample totals, retained four variables per feature family per stage (36 of 120 variables across the three-stage stack). The spectral-index-texture scheme achieved within-county F1 scores of 95.00% in Changyi, 94.83% in Qingzhou, and 95.58% in Fengrun. Leave-one-county-out (LOCO) model fitting returned a mean F1 of 94.73% compared with 95.14% for the within-county splits. Because the fixed-feature protocol was selected using all three counties, this LOCO test evaluates county-held-out classifier fitting rather than a fully nested feature-selection pipeline. Independent field verification with Global Positioning System (GPS) points ranged from 85.80% to 89.95%, and village-level area estimates agreed with remote-sensing totals (R² = 0.869). The 36-variable protocol performed similarly to the full 120-variable input (95.14% vs. 95.24% mean F1), and the selected features were stable across the three tested weighting rules. In Funing County, where shade nets are absent, omitting the shading stage on the basis of local agronomic practice yielded 93.75% accuracy against 96 independent GPS points. The results support management timing as a practical guide for ginger mapping within the tested northern production systems; wider climatic validation and fully nested transfer tests are still needed. Full article

Accurate maize distribution information is critical for crop-area statistics, food-security assessment, and agricultural monitoring, but large-scale maize-mapping remains difficult in regions with limited reference samples, heterogeneous crop calendars, and frequent optical data gaps. This study proposes a phenology-adaptive maize mapping framework based on Sentinel-2 time-series imagery and an Enhanced Red-edge NDVI (ENDVIre). ENDVIre was constructed from the Sentinel-2 red-edge 4 and red-edge 2 bands to enhance the spectral response of maize during the silking-to-grain-filling stage, when maize develops a dense canopy and high chlorophyll content but is often confused with soybean. The framework first reconstructed the NDVI time series using an upper-envelope-constrained Whittaker smoother to identify key phenological stages, including sowing–emergence, vigorous growth, and maturity–harvest. NDVI, ENDVIre, and LSWI were then integrated into an interpretable decision-tree model with phenology-aligned time windows to distinguish maize from soybean, rice, wheat, and other non-maize backgrounds. The method was evaluated in six representative maize-growing regions across the United States, Brazil, China, Kenya, and Ukraine, covering different crop calendars, field sizes, and agricultural systems. The mean overall accuracy, F1-score, and Kappa coefficient across the six regions reached 93.27%, 93.14%, and 0.8652, respectively. Cross-year experiments in a winter-wheat–summer-maize rotation region from 2020 to 2024 achieved overall accuracies of 89.80–96.80%, while spatial-transfer experiments in six independent regions achieved overall accuracies of 87.40–95.40%. A comparison with existing high-resolution maize products in the Huang-Huai-Hai Plain further showed that the proposed method better balanced omission and commission errors. These results indicate that ENDVIre-based phenology rules provide an interpretable and transferable solution for maize mapping under limited-sample conditions, although persistent cloud contamination and fragmented smallholder landscapes remain important challenges. Full article

Understanding evapotranspiration (ET) partitioning into soil evaporation (E) and plant transpiration (T) is crucial for improving agricultural water use efficiency in water-scarce regions. The isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized. This study systematically compared these two methods using two consecutive years (2012–2013) of field isotopic observations in a summer maize field on the North China Plain, a core maize production area facing severe agricultural water scarcity. Stable isotope analysis showed that the local meteoric water line (LMWL) had a slope lower than the global meteoric water line. The 0–5 cm surface soil water evaporation lines had slopes of 5.84 (2012) and 8.06 (2013), confirming significant evaporative enrichment in the topsoil. Plant water isotopic composition closely resembled that of 40–100 cm deep soil water, indicating limited root uptake from the surface layer. IMB-estimated transpiration ratio (T/ET) exhibited distinct phenological patterns, increasing from 37 to 44% at jointing to a peak of 94–96% at filling, then declining to 84–85% at maturity. The two methods agreed well during filling to maturity (differences of 2–10%), but compared with the IMB method, AquaCrop substantially underestimated T/ET at jointing (0.9% vs. 43.8% in 2013) due to its canopy-cover-based transpiration algorithm. These findings identify the filling stage as the critical water demand period, providing a quantitative reference for precision irrigation management under similar climate and soil conditions. Full article

In southern Brazil, a large proportion of farmers maintain their fields under fallow conditions during the transition period between summer and winter crops. During this interval, mechanical practices such as chiseling or the introduction of cover crop species may contribute to improving soil management and conservation in no-tillage systems. Therefore, this study aimed to investigate the effects of mechanical soil chiseling and production system intensification on soil physical–hydric properties and soybean performance. The experiment was conducted in São José do Ouro, Rio Grande do Sul, Brazil, from September 2023 to April 2025. The experimental design consisted of three factors: soil management (spring 2023 chiseling, autumn 2024 chiseling, and a no-till control), post-maize cover (millet and fallow conditions), and winter cover crops (black oat, white oat, vetch, and radish) grown either as monocultures or in mixtures. A randomized block design with split plots and three replicates was used. The evaluated variables included dry biomass of winter cover crops, soil bulk density, total porosity, microporosity, macroporosity, soil water content at field capacity, soil penetration resistance, plant gas exchange, leaf area index, thousand-grain weight, and soybean grain yield. The results indicated that soil chiseling altered soil physical properties by reducing soil bulk density, penetration resistance, microporosity, and field capacity, while increasing total porosity and macroporosity. Soil chiseling promoted short-term increases in thousand-grain weight and soybean grain yield, with no persistent effects after 20 months. Production system intensification, through the use of cover crops and millet, did not affect grain yield but increased stomatal conductance and soybean leaf area index. Therefore, occasional tillage in high-clay subtropical Oxisols should be strategically applied and associated with long-term conservation agriculture practices to sustain improvements in soil physical quality. Full article

Agroforestry and perennial tree crop production systems, particularly in Mediterranean regions, exhibit a high degree of integration among trees, herbaceous, and soil components. They provide essential services including provisioning, regulation, support, and cultural services, which enhance human health, well-being, and economic stability. However, guaranteeing their long-term resilience in the face of environmental challenges, including drought and soil degradation, is essential for the sustainable management of these systems. We examine the impact of microsite conditions (soil and herbaceous layer) and their management on olive trees (Olea europaea L.) under varying levels of drought stress. A fully factorial design was implemented in a Spanish agroforestry system, combining two irrigation regimes (rainfed vs. summer irrigation) and two soil management practices (customary plowing vs. herbaceous layer conservation) across four independent and replicated zones. Twelve olive trees per zone were individually monitored, treating each tree as the experimental unit, with one 50 × 50 cm sampling plot per tree in which microsite conditions were characterized for each tree. Plowed areas (shallow tillage) showed lower industrial extraction yield (%), fat yield based on dry matter (%), olive maturity and phytosanitary status compared to areas conserving their herbaceous layer cover (0.81, 0.96, 0.92, and 0.65-fold lower, respectively). Rainfed areas (i.e., those without supplemental water supply) showed a reduction in both industrial extraction yield (%), olive yield (kg tree⁻¹) and oil yield (kg ha⁻¹) (0.77, 0.86 and 0.67-fold lower, respectively). Under combined tillage and water-deficit conditions, oil yield (kg ha⁻¹), industrial extraction yield (%), and total phenolic content (ppm) were considerably lower (0.50, 0.60, and 0.67-fold lower, respectively). Furthermore, low quality of the herbaceous layer dominated by nitrophilous invasive species were associated with decreased leaf nutrient content, lower industrial extraction yield, reduced olive maturity and poorer phytosanitary status of olives. These findings suggest that maintaining a spontaneous herbaceous layer with a high-quality species (legume incorporation) and well-managed herbaceous cover, i.e., repeated mowing of the herbaceous layer instead of customary plowing, can enhance sustainable olive production by improving soil resilience, reducing water stress, and optimizing nutrient use, thereby supporting long-term ecosystem stability and agricultural productivity. Full article

Soil degradation, nutrient depletion, and persistent weed pressure represent critical challenges in the adoption of sustainable agriculture practices in subtropical organic farming systems. Reliance on conventional inputs threatens long-term soil health and ecosystem resilience, highlighting the need for regenerative alternatives. Cover crops are widely recognized as multifunctional agroecological tools with the capacity to enhance nutrient cycling, perform weed suppression, and improve soil organic matter. To evaluate their effectiveness in South Florida's subtropical climate and organic raised bed systems, a field experiment was conducted as a Randomized Block Design (RBD) at the Florida International University Organic Garden during the 2024 summer season. The six cover crops species that were tested include green gram (Vigna radiata), hibiscus (Hibiscus sabdariffa), sorghum (Sorghum bicolor), soybean (Glycine max), sunn hemp (Crotalaria juncea), and pearl millet (Pennisetum glaucum). Data collected includes plant establishment, biomass accumulation, weed suppression, soil physiochemical properties, and plant nutrient composition. Sorghum and sunn hemp produced the highest fresh and dry biomass, with sorghum achieving the most effective weed suppression with the lowest weed biomass and weed population. Sunn hemp contributed to enhanced nitrogen content in plant tissues, while hibiscus promoted the highest soil P and N concentrations. Pearl millet exhibited the highest total carbon and organic matter content, indicating potential for enhancing soil carbon content and soil fertility. Results show that each cover crop species can provide a specialized or generalized ecosystem service depending on management goals. Full article

Summer cover crops can improve soil fertility and contribute to nitrogen (N) supply in temperate cropping systems, yet the effects of mixture composition and sowing timing remain insufficiently documented. This study evaluated biomass production and N accumulation of five multispecies cover crop mixtures grown in Estonia during 2024–2025 under two sowing dates per year. Aboveground biomass, botanical composition, and carbon (C) and nitrogen concentrations were measured to assess productivity, species contributions, and residue quality. Earlier sowing was generally associated with higher biomass and N accumulation, with first-sown mixtures producing, on average, 38.7% more biomass than later-sown mixtures. Mixture performance was strongly shaped by species composition and competitive hierarchies. Total N accumulation of the cover crop mixtures ranged from 42 to 275 kg N ha⁻¹ depending on mixture composition and sowing time, with mixtures dominated by common vetch (Vicia sativa L.) achieving the highest values. Oat (Avena sativa) dominated and contributed substantially to biomass in mixtures lacking competitive legumes, whereas sunflower (Helianthus annuus) and maize (Zea mays) performed less well under delayed sowing. Low-growing species such as Persian clover (Trifolium resupinatum) produced little biomass when grown with highly competitive species. Legumes exhibited lower C:N ratios than non-legumes, while mixture-level values remained moderate, suggesting residue quality with potential for favourable decomposition and nutrient release in summer cover crop systems under temperate conditions. Full article

This study investigates how cover crop management and soil tillage influence the development and yield of cucumber and cabbage crops. Three cover crop treatments—blue lupin, black oats, and their mixture—were evaluated during the autumn/winter season, while Stylosanthes capitata (Fabaceae), pearl millet (Pennisetum glaucum, Poaceae), and their mixture were assessed during the spring/summer season, under both conventional tillage and no-till (direct seeding) systems. Cover crops were established in spring/summer (October–November) and, after their management, cucumber (Cucumis sativus L.) was cultivated from December to February. Subsequently, winter cover crops were grown from May to July, followed by cabbage (Brassica oleracea var. capitata) cultivation from July to September. Drip irrigation was used, and organic practices were employed for weed, pest, and disease management. Germination, seedling survival rate, and plant growth (height, number of leaves, foliage cover, and fruit or cabbage size) were evaluated. Finally, crop yield is considered by comparing harvest weight and quality to determine which combination of soil cover and planting method maximizes crop productivity and quality. Obviously, management differences that influence yield will be associated with soil properties. To better understand the causes of these yield differences, the influence of soil chemical properties was explored using multivariate analysis techniques (discriminant analysis) and neural networks. Multivariate techniques allow for the exploration of complex relationships among multiple variables simultaneously, facilitating the identification of key patterns or factors that influence crop yield. On the other hand, neural networks, using machine learning models, allow for the prediction of outcomes based on the soil’s physicochemical properties, as well as the identification of optimal combinations of factors that maximize crop yield. Discriminant analysis and neural networks showed that soil variables such as pH, organic matter (OM), cation exchange capacity (CEC), phosphorus (P), and potassium (K) were key determinants in differentiating the yield groups. Cabbage yield was most strongly associated with pH and OM, while cucumber yield responded more strongly to potassium and CEC. Full article

The adoption of conservation systems in agriculture has been increasingly explored as a strategy to improve soil quality and potentially influence greenhouse gas (GHG) emissions. This study reports the first assessment of GHG emissions within a long-term (14 years) agroecological field experiment evaluating soil management systems for onion (Allium cepa L.) production in a Humic Dystrudept (Cambissolo Húmico Distrófico, Brazilian Soil Classification System) in Southern Brazil. Three management systems based on permanent soil cover and crop diversification were evaluated in an onion–maize rotation: conventional tillage (CT) without cover crops, no-till (NT) without cover crops, and a no-till vegetable system (NTV) with a summer cover crop mixture of pearl millet (Pennisetum americanum), velvet bean (Mucuna aterrima), and sunflower (Helianthus annuus). Short-term GHG emissions were monitored during one onion growing season (106 days), while soil chemical and physical properties reflect long-term management effects. Evaluations included (i) daily and cumulative GHG (N₂O, CH₄, and CO₂) emissions, (ii) soil carbon (C) and nitrogen (N) stocks, (iii) soil aggregation, porosity, and bulk density in different soil layers (0.00–0.05, 0.05–0.10, and 0.10–0.30 m), and (iv) onion yield and cover crop dry matter production. The NTV system improved soil physical and chemical quality and increased onion yield compared to NT and CT. However, higher cumulative N₂O emissions were observed in NTV, highlighting a short-term trade-off between increased N₂O emissions and long-term improvements in soil quality and crop productivity. All systems acted as methane sinks, with greater CH₄ uptake under NTV. Despite higher short-term emissions, the NTV system maintained a positive C balance due to long-term C accumulation in soil. Short-term greenhouse gas emissions were assessed during a single onion growing season, whereas soil carbon stocks reflect long-term management effects; CO₂ fluxes measured using static chambers represent ecosystem respiration rather than net ecosystem carbon balance. These results provide an initial baseline of GHG dynamics within a long-term agroecological system and support future multi-year assessments aimed at refining mitigation strategies in diversified vegetable production systems. Full article

Rainfall characteristics proven to trigger general erosive events (EE) and rill erosion events (RE) under reference experimental conditions of soil type, slope, and land use—previously established at a test site in central Italy—are applied as likely thresholds to characterize their spatiotemporal variability across Umbria using 24 years of semi-hourly data from 53 stations. Marked spatial patterns emerge, with mean EE frequencies per station ranging from 1.14 to 2.36 per month, while mean RE frequencies per station vary between 0.04 and 0.45 per season. No significant temporal trends are observed over the study period. Monthly and seasonal comparisons between EE and RE frequencies often deviate from the corresponding USLE R-factor dynamics, highlighting limitations of relying solely on this parameter. These findings are contextualized within common soil conservation practices—such as cover crops—to identify critical periods during which maintaining soil cover. For example, winter—when cover crops are typically present in Central Italian agroecosystems—is among the seasons with the highest EE frequency (4.45 yr⁻¹), second only to autumn (6.47 yr⁻¹). However, when focusing on REs, winter shows the lowest mean frequency (0.08 yr⁻¹). In contrast, the mean RE frequency increases in summer (0.24 yr⁻¹) and reaches its maximum in autumn (0.26 yr⁻¹), when bare soil or poorly developed cover crops are common. Overall, results provide actionable insights for aligning protective measures with high-impact erosive event probabilities. Full article

In the context of global warming, agricultural drought risks are exacerbated by increasing atmospheric aridity. This study pioneers the application of the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm at a provincial scale to detect change points in vapor pressure deficit (VPD), leveraging high-density meteorological station data from Hunan Province to delineate the nuanced evolution of VPD and its implications for early drought warning. Key findings reveal the following: (1) The VPD in Hunan exhibits a spatial pattern of “higher in the south than north, higher in the east than west” and a seasonal variation of “summer > autumn > spring > winter”. (2) BEAST identified abrupt changes in VPD coinciding with critical phenological periods, such as the early rice transplanting period in early April, with spatial and temporal gradient differences (up to 25 days) that can guide irrigation resource scheduling; moreover, the months of change points have been consistently advancing during the study period. (3) The dominant factors of VPD exhibit regional and seasonal differentiation. Annually, the maximum temperature (contribution rate 57.1–60.6%) is the primary factor. (4) Extreme events with VPD > 1.5 kPa for three consecutive days covered 92 stations in 2022. Combining this with the critical growth periods of double-cropping rice, it is recommended to set VPD = 1 kPa as the drought early warning threshold for the northern and southern regions. This study provides a scientific basis for the prevention and control of agricultural drought by integrating climate diagnostics and crop physiological needs. Full article

The diversification of cover crops grown in soils with granulometric variability may potentially enhance microbial community and enzyme activities. Thus, the main goal was to evaluate the effect of autumn/winter cover crop sequences and cash crops in spring/summer on soil microbial biomass and enzyme activities. The experiment was conducted in open-field microplots (10 m × 1 m × 0.7 m), containing soils from B horizon of five Oxisols with granulometric variability and clay content ranging from 17 to 80 dag kg⁻¹. The treatments were three cover crops and a winter fallow with a completely randomized experimental design with three replicates. Soil samples from the 0–10 cm layer were collected to analyze soil microbial biomass of carbon and nitrogen, enzyme activities of the acid phosphatase, arylsulfatase, urease, and fluorescein diacetate hydrolysis. The number of nodules in soybean roots was average 63 ± 14.42 nodules per plant and dry mass of nodules was 169 ± 13.74 mg plant⁻¹. Soybean nodulation and N uptake ensured the supply of nitrogen to the soybean plants with 331 ± 82 mg plant⁻¹. Overall, diversified autumn/winter cover crop sequences provide plant residue, containing nutrients, and different carbon/nitrogen, which alters microbial biomass, the ratio of C_mic/N_mic, basal respiration, and soil enzyme activities within each Oxisol with different particle size distribution. Full article

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions. Full article

This study investigates the combined impact of climate change and land use changes on water resources and soil conditions in the Litani River Basin (LRB) in Lebanon. The Mediterranean region, including the LRB, is highly vulnerable to climate change. This study utilizes the WiMMed (Water Integrated Management for Mediterranean Watersheds) model to assess hydrological variables such as infiltration, runoff, and soil moisture for the years 2009, 2014, and 2019. It considers 2019 climate conditions to project the 2040 scenarios for Representative Concentration Pathways (RCPs) 2.6 and 8.5, incorporating the unique characteristics of the Mediterranean watershed. Results indicate a concerning trend of declining infiltration, runoff, and soil moisture, particularly under the more severe RCP 8.5 scenario, with the most significant reductions occurring during summer. Land use changes, such as deforestation and urban expansion, are identified as key contributors to reduced infiltration and increased runoff. This study highlights the critical role of soil moisture in crop productivity and ecosystem health, showing how land cover changes and climate change intensify these effects. Soil moisture is highly sensitive to precipitation variations, with a 20% reduction in precipitation and a 5 °C temperature increase leading to substantial decreases in soil moisture. These findings highlight the urgent need for sustainable land management practices and climate mitigation strategies in the Litani River Basin (LRB) and similar Mediterranean watersheds. Protecting forests, implementing soil conservation measures, and promoting responsible urban development are crucial steps to maintain water resources and soil quality. Furthermore, this research offers valuable insights for policymakers, farmers, and environmentalists to prepare for potential droughts or flooding events, contributing to the preservation of this vital ecosystem. The data from this study, along with the recommended actions, can play a crucial role in fostering resilience at the national level, addressing the challenges posed by climate change. Full article