Spatiotemporal effects of soil and water conservation measures on soil organic carbon enhancement in China: a meta-analysis

Response of runoff-sediment relationship at flood event scale to soil and water conservation measures from 1960 to 2020 in a paired watershed on the Loess Plateau

Human-induced changes in fluvial regimes for organic carbon supply and burial off the Yellow River Estuary.

This study addresses the critical need for climate-resilient hydrological tools by developing Decision Support System (DSS). The DSS integrates a modified Soil Conservation Service Curve Number (SCS-CN) method with high-resolution geospatial datasets, such as Sentinel-2 Land-Use/Land-Cover (LULC), OpenLand soil properties, Shuttle Radar Topography Mission (SRTM)- Digital Elevation Model (DEM) topography and Climate Hazards Group Infrared Precipitation with Station (CHIRPS) precipitation, to simulate historical and near-real-time runoff. Furthermore, it incorporates CMIP6 climate projections to facilitate future runoff estimation. Validated across two contrasting Indian watersheds-the monsoon-driven Salebhata catchment (R² = 0.82) and semi-arid Venkatapur sub-watershed (R² = 0.78). The model performs well at capturing seasonal variability, including monsoon floods, runoff during dry spells, seasonal extremes and variations across different areas. Its user-friendly application enables stakeholders to create real-time, location-specific runoff estimates through polygon-based analysis, which directly facilitates flood forecasting and adaptive water resource management. By incorporating climate projections and high-resolution geospatial analysis, this framework provides a reproducible platform for climate-resilient water resource planning, especially in data-poor regions exposed to hydroclimatic extremes.

The degradation of land resources across East African watersheds, most acutely evidenced in Ethiopia, presents a substantial threat to agricultural viability and water security due to rampant soil erosion and nutrient depletion. This research introduces a rigorous, evidence-based prioritization framework for Sustainable Land Management (SLM). This framework synergistically integrates empirical field observations, nuanced local knowledge, and advanced SWAT+ modeling tethered to the Analytic Hierarchy Process (AHP). Our findings reveal substantial spatial heterogeneity in erosion and nutrient export, where croplands bear the disproportionate burden of degradation, while designated exclosures demonstrate the most pronounced mitigation effects. Crucially, the calibration and validation of the SWAT+ model were significantly refined through the incorporation of community-derived data and empirical field metrics, leading to substantially more accurate spatial identification of erosion hotspots and subsequent conservation prioritization. Furthermore, the analysis strongly suggests that the intrinsic quality and consistent maintenance of SLM/Soil and Water Conservation (SWC) interventions supersede the impact volume of spatial coverage alone. Economically analyzed, prospective SLM practices exhibit favorable cost–benefit ratios, providing justification for their strategic adoption as durable, long-term restoration strategies. This study ultimately contributes a scientifically robust, yet socially contextualized, bio-physical-social framework for comprehensive watershed stewardship, furnishing actionable scientific insights alongside pragmatic policy directives for ecological restoration. Keywords: Sustainable Land Management (SLM); Soil and Water Conservation (SWC); SWAT+; Analytic Hierarchy Process (AHP); soil erosion; nutrient loss; local knowledge.

Management of ecological functional zoning for land use along China's Pinglu Canal under ecological supply-demand balance and threshold regulation.

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.

Amid increasing climate variability and soil degradation, the need for soil and water conservation has become of utmost importance for India’s future, supporting sustainable agricultural production and the conservation of natural resources. Keeping this as a focal point, the present study conducted a two years’ field study on the effect of tillage and green manuring on soil moisture content cultivating maize (Zea mays L.) during the kharif (May-September) seasons of 2022 and 2023 in the experimental farm of the Department of Soil and Water Conservation, School of Agricultural Sciences, Nagaland University, Medziphema Campus. The field was laid out in a split-plot design with two factors: tillage and green manuring. The study revealed that in the main plot, the implementation of minimum tillage (TM) was found to be superior to conventional tillage (TC) in conserving soil moisture content by 0.73% at 30 DAS, 0.35% at 60 DAS, 1.37% at 90 DAS, and 0.92% at 120 DAS. In subplot G4, i.e., Green manuring with cowpea at 4 tonnes ha-1, the soil moisture content increased by 18.32%, 21.86%, 21.18%, and 25.07% at 30, 60, 90, and 120 DAS, respectively, compared to the control plots G0. In the interaction between tillage and green manuring, TMG4 i.e. minimum tillage + cowpea @ 4 tonnes ha-1 was found to be superior over all the other treatment combination resulting in an increase of 19.94%, 23.47%, 23.38% and 27.19% in 30, 60, 90 and 120 DAS respectively as compared to TCG0 i.e. conventional tillage + control, which exhibited the worst performing treatment combination to maintain and increase soil moisture content.

The highlands of Ethiopia are a well-known example of severely degraded landscapes in Africa, with concerns for food shortages. The Ethiopian Highlands Reclamation Study (EHRS) has documented the rates, underlying causes, and costs of land degradation, which resulted in intensive conservation and restoration efforts over the past decades. Despite the extensive effort devoted to the problem, the actual extent of conservation and restoration work has not been adequately quantified and documented. This prompted a crucial inquiry into the current environmental status to quantify the biophysical impacts of 20 years of Soil and Water Conservation (SWC) and restoration in the Ethiopian highlands. Quantitative data on the extent of conservation and restoration measures implemented were generated through a systematic analysis of metadata obtained from Ethiopia’s watershed database, maintained by the Ministry of Agriculture (MoA), with field validation in 56 sample watersheds. The analysis of change in land use/land cover (LU/LC) and the normalized difference vegetation index (NDVI) was employed to examine the extent of vegetation restoration and forest cover change in the Ethiopian highlands from 2002 to 2022. The Google Earth Engine (GEE) was utilized for acquiring and processing satellite image data. The potential soil loss across different land-use types was quantified using the InVEST-SDR (Integrated Valuation of Ecosystem Services and Trade-offs Sediment Delivery Ratio) model, combined with other biophysical data disaggregated by physiographic regions to capture diversities in the biophysical settings. Erosivity, erodibility, digital elevation model, vegetation cover, and land management factors were the input data used for the InVEST-SDR model. The results showed that Ethiopia has achieved unprecedented progress in reversing land degradation over the past two decades, with nearly 28 million hectares of degraded landscapes treated through community mobilization and flagship programs. The forest area has increased from about 2.6 million ha in 2002 to 5.9 million ha in 2022. Conversely, the total area of bare land was reduced from 2.5 million hectares in 2002 to 830,000 hectares in 2022. Similarly, the average soil erosion rate on cultivated fields declined from 69 ton/ha in 2002 to 35 ton/ha in 2022, reflecting the cumulative impact of extensive land restoration and soil and water conservation interventions implemented across the Ethiopian highlands. These benefits underscore the effectiveness of combining community-led restoration, bottom-up conservation efforts with top-down policy support. Numerous Sustainable Development Goals, such as Zero Hunger (SDG 2), Climate Action (SDG 13), and Life on Land (SDG 15), can be achieved in part thanks to Ethiopia’s experience. Future research should focus on detailed watershed-level studies to study agro-ecological variations in conservation outcomes, conduct cost–benefit analyses of water and soil conservation measures, and implement long-term vegetation monitoring and socio-economic surveys to enhance community-led restoration efforts.Supplementary informationThe online version contains supplementary material available at 10.1007/s10661-025-14914-6.

Water erosion causes soil fertility loss and land degradation, posing serious threats to agricultural production, soil and water conservation, and environmental protection. Accurately assessing the spatiotemporal dynamics and driving mechanisms of water erosion is essential for mitigating regional erosion risks. In this study, the Revised Universal Soil Loss Equation (RUSLE) was coupled with the Transport Limited Sediment Delivery (TLSD) to simulate water erosion in the Shiyang River Basin from 2001 to 2020. Additionally, the Random Forest (RF) algorithm was employed to quantify the contributions of different driving factors to net soil erosion. The results showed that the RUSLE-TLSD model reliably simulated soil erosion processes in the basin (NSE = 0.70). The annual mean net soil erosion rate varied between 2.28 and 9.68 t·ha−1·a−1, with an overall decline, and the most intense water erosion occurred during the summer (June-August). Areas of intense water erosion were primarily concentrated in the southern Qilian Mountains, characterized by steep relief, abundant rainfall, and strong sediment transport capacity. The RF model explained approximately 85% of the variance, indicating that LUCC, NDVI and slope had the most significant impact on the spatial pattern of water erosion in the basin, and the area with sparse vegetation and large topographic relief had a high risk of water erosion. It is hoped that the findings of this study will provide a reference for water erosion risk assessment and management planning in arid zone basins.

Different soil and water conservation (SWC) measures have been constructed on farmlands to control runoff surface water, erosion, sedimentation and conserve soil and water on agricultural fields. This study was entitled with: the effects of level soil bund structure on selected soil properties at Menentela watershed, Habru District North Wello Amhara Regional National State, Ethiopia. Objectives of the study includes: to assess the status of selected soil physico-chemical properties under the two adjacent treatments: treated with LSB (Deko) and none-treated (Doba), to observe the persons correlations between the studied soil properties and further to examine their variations with soil depth. The total of 48 composite soil samples (2 treatments * 3 replications*4 sample plots*2 depth layers: 0-15 cm and 15-30 cm) were collected and analyzed for soil (%STF), soil MC %, soil BD g/cm<sup>3</sup>, soil pH (1:2.5 soil: water ratio), SOC %) and CEC meq/100g. Also the total of 48 soil samples were separately collected for the determination of soil bulk density and soil moisture contents. Results had showed that sand, clay, MC and BD were significantly varied with treatments (P<0.001, P<0.001, P<0.001, P<0.001 and P=0.018, respectively). These parameters also showed significant differences with soil depth. Cation exchange capacity (CEC) had showed differences with soil depth (P=0.005), but not with treatment (P=0.63). Sand%, silt% and bulk density (BD g/cm<sup>3</sup>) were the higher in the non-treated farmlands (35.85±0.32, 33.35±0.25 and 1.13±0.02, respectively) than under LSB treated farmlands, while clay, and MC were lower in non-treated farmlands. Silt and clay were declined with soil depth, while, sand, soil MC and soil BD were increased with soil depth. The mean value of soil pH, SOC and CEC were the higher under LSB than none-treated farmlands. Moreover, soil pH, SOC and CEC were the higher (6.72±0.04, 0.58±0.04 and 34.59±1.18) in the top of 0-15 cm of the layers than in the lower of 15-30 cm of the layers. The differences in these selected soils physico-chemical properties were in general resulted from the soil and water conservation practices (LSB) implemented in the study watershed which has reduced runoff water, conserve soil and moisture, provide the better infiltration rate and reduce the removal of soil organic matter and soil colloidal. In general the studied soil properties had showed that well improves due to the farmlands treated with LSB while none treated farmlands had showed that decline of the soil properties.

Steep olive orchards in northwest Syria are experiencing severe land degradation as a result of unsustainable uphill–downhill tillage, which accelerates erosion and reduces productivity. To address this problem, three tillage systems, no-till natural vegetation strips (NVSs), contour tillage, and uphill–downhill tillage, were evaluated at two research sites, Yakhour and Tel-Hadya, NW Syria. The adoption of no-till NVSs significantly increased soil organic matter (SOM) at both sites, outperforming uphill–downhill tillage. While contour tillage resulted in lower SOM levels than NVSs, it still performed better than the conventional uphill–downhill practice. Contour soil flux (CSF) was lower in Yakhour, where mule-drawn tillage on steep slopes (31–35%) was practiced, compared to higher CSF values in Tel-Hadya, where tractor tillage was applied on gentler slopes (11–13%), which highlights the influence of slope steepness on soil fluxes. Over four years, net soil flux (NSF) indicated greater soil loss under tractor tillage, confirming that mule-drawn tillage is less disruptive. Olive trees with no-till NVSs benefited from protected root systems, improved soil structure through SOM accumulation, reduced erosion risk, and improved surface runoff buffering, which resulted in increased water infiltration and soil water retention. This study was carried out using a participatory technology development (PTD) framework, which guided the entire research process, from diagnosing problems to co-designing, field testing, and refining soil conservation practices. In Yakhour, farmers actively identified the challenges of degradation. They collaboratively chose no-till natural vegetation strips (NVSs) and contour tillage as key interventions, valuing NVSs for their ability to conserve moisture, suppress weeds and pests, and increase olive productivity. The farmer–scientist co-learning network positioned PTD not only as an outreach tool but also as a core research method, enabling locally relevant and scalable strategies to restore soil functions and combat land degradation in northwest Syria’s hilly olive orchards.

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R2 = 0.87) and artificial neural networks (ANNs) performed best for EFR (R2 = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R2 ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change.

This study explores the potential of hyperspectral imaging combined with machine learning techniques to provide accurate and non-invasive methods for analyzing soil nutrient content in precision agriculture. Data were collected from agricultural regions in Tamil Nadu, India, using conventional soil sampling methods that are labor-intensive and time-consuming. In contrast, hyperspectral imaging preserves soil integrity and enables rapid, remote assessment of soil health. The red fox optimization (FOX) algorithm was employed for spectral band selection, effectively reducing data redundancy while retaining the informative features. The partial least squares regression (PLSR) model achieved high prediction accuracy for organic carbon, with R2=0.93, a mean absolute error (MAE) of 16.4, and a root mean square error (RMSE) of 20.1, whereas for nitrogen, phosphorus, and potassium, the corresponding R2 values all exceeded 0.89. These results confirm the robustness and computational efficiency of the FOX-optimized models and demonstrate that integrating hyperspectral imaging with optimized machine learning can enable accurate, real-time soil nutrient estimation without destructive sampling, thereby supporting sustainable soil monitoring and protection in large-scale precision agriculture.

Soil quality is essential for sustaining agricultural productivity, land conservation, and ecosystem resilience, particularly in volcanic landscapes such as the Upper Brantas Watershed. This study assessed the impact of land management intensity (LMI) on the soil quality index (SQI) across different land-use types and slope gradients. Using a stratified random sampling approach, soil physical and chemical properties were analysed, while LMI was evaluated based on input intensity and soil conservation practices. Statistical analyses, including ANOVA and correlation analysis, were used to examine relationships among LMI, vegetation biomass, and SQI. The results showed a negative correlation between LMI and SQI. The highest soil quality occurred in protected forests and pine–coffee agroforestry systems, whereas upland farming and pine–horticulture agroforestry exhibited the lowest SQI values. Steeper slopes were associated with greater soil degradation, particularly under intensive land use. These findings demonstrate that intensive land management accelerates soil degradation, while agroforestry and conservation-oriented practices enhance soil health through increased organic matter inputs and reduced soil disturbance. The study highlights the importance of sustainable land management strategies, including mulching, minimum tillage, and terracing, to mitigate soil degradation and support sustainable watershed management.

Rainfall erosivity is a critical factor for quantifying the potential of precipitation to cause soil erosion, and it is expected to increase under projected climate change scenarios. This study presents a comprehensive assessment of the spatiotemporal variability of rainfall erosivity across the cross-border region of Bulgaria and Serbia over the 1961–2020 period. Using monthly precipitation data from 23 meteorological stations, the analysis is based on the Fournier Index (FI), Modified Fournier Index (MFI), and Angot Index (K). The results reveal a distinct north-south erosivity gradient. Northern stations consistently showed higher mean annual values and more frequent moderate-to-high erosivity events. Conversely, central and southern areas were dominated by very low and low erosivity, despite isolated extreme events. The Angot Index identifies May and June as the months with the highest potential for soil erosion, coinciding with intense convective rainfall. These findings confirm that northern areas of the region are more vulnerable to rainfall-induced soil erosion. This analysis provides critical data for developing targeted soil conservation strategies and land management policies to mitigate risks in the most susceptible areas during critical seasons.

Estimating runoff and erosion on forest roads under various climate projections in Florida Panhandle region, USA.

The loess hilly area of western Henan Province is one of the most serious areas of soil erosion due to rugged terrain, steep slopes and weak soil resistance ability. The prevention and control of soil erosion needs to know the rate, area and distribution of soil erosion in the region, so as to accurately plan and place the corresponding soil and water conservation measures. However, the study of temporal and spatial pattern, evolution and driving force of soil erosion in this region are far more enough. Therefore, this study conducted a quantitative evaluation of soil erosion in 2011 and 2022 in the loess hilly area of western Henan Province through sampling method and field investigation based on the Chinese Soil Loss Equation (CSLE). The spatial and temporal variation of soil erosion and the driving forces of soil erosion evolution were analyzed by using geographic detector method to reveal the key driving factors affecting soil erosion. The results showed that soil erosion in the loess hilly area of western Henan showed a “double decline” (decline of soil erosion rate and soil erosion area) trend in 2022. Compared with 2011, the average soil erosion rate of the investigation units was reduced by 25.5%, and the percentage of soil erosion area was reduced by 34.0%. In 2011, the areas with high soil erosion rates were mainly distributed in the southeast of Yiyang County, the southwest of Yichuan County, the north of Song County and the southeast of Luoning County. The distribution of high value areas was scattered, mainly in the west of Shangjie District, Xingyang City and Jiyuan City. Soil erosion mainly existed in forest land and cultivated land, followed by construction land, orchard land and grassland. High soil erosion rates were distributed in the area above 25° slope, and large percentage of soil erosion area was distributed at the slope range below 6° slope. The number and density of all land patches, except orchard land, increased significantly from 2011 to 2022. The results of geographical detector analysis indicated that population was the main factor affecting the change of percentage of soil erosion area. Shannon Diversity Index, GDP, and CONTAG were identified as the key factors influencing the distribution and variation of soil erosion rates.

Soil erosion threatens agricultural sustainability and water quality in Mediterranean watersheds, necessitating effective Nature-Based Solutions (NBSs) for mitigation. This study applied the InVEST Sediment Delivery Ratio (SDR) model to assess erosion patterns and evaluate NBS effectiveness in the Carapelle watershed (506 km2). The SDR model was calibrated and validated using measured sediment yield data from 2007 and 2008. Model validation achieved a 4.3% deviation from observed data after parameter optimization. Four NBS scenarios were evaluated: contour farming (CF), no-tillage (NT), cover crops (CCs), and combined practices (Comb). Baseline soil loss varied from 2.43 t ha−1 yr−1 (2007) to 3.88 t ha−1 yr−1 (2008), with sediment export ranging from 0.86 to 1.30 t ha−1 yr−1. NT demonstrated the highest individual effectiveness, reducing sediment export by 72.2% on average. The Comb approach (NT + CCs) achieved a superior performance with a 75.9% sediment export reduction and a 70.5% soil loss reduction. Spatial analysis revealed that high-retention zones were concentrated in forest and shrubland, while agricultural zones showed the greatest potential for NBS implementation. NBSs significantly enhance sediment retention services in Mediterranean agricultural watersheds. The InVEST SDR model proves to be effective for watershed-scale assessment. The results provide actionable guidance for sustainable land management and soil conservation policy in erosion-prone Mediterranean environments.

Climate change is impacting Pacific Island food systems, reducing household food security, resilience and economic stability. This study examines climate change impacts on taro food loss in Upolu, Samoa, and Tongatapu, Tonga, focusing on postharvest handling and strategies to improve food security. It compares taro farmer climate change perceptions, postharvest handling and losses to highlight similarities and differences in taro farming practices in Samoa and Tonga. Data for this study were collected through farmer interviews and taro shelf-life analysis. Seventy farmers were surveyed, and eight were shadowed from harvest to sale. The study findings reveal that climate change factors including shifting rainfall patterns and extreme weather events exacerbate postharvest losses. Non-climatic factors such as labor shortages, poor handling, limited transportation, and poor storage practices are primary factors also contributing to the affordability and availability of taro. Farmers have adopted strategies such as the use of early warning systems, prompt harvesting and soil protection practices to mitigate climate change induced losses. By linking climate change, food security, and food loss along the taro value chain, the study enhances understanding of the postharvest handling procedures of taro in Samoa and Tonga and identifies strategies for policies that can provide support for infrastructure development for fostering sustainable, climate-resilient taro farming systems in the Pacific Islands.