ABSTRACT Soil salinity is a persistent environmental constraint that threatens agricultural productivity, food security and ecosystem sustainability. Advances in remote sensing (RS) and artificial intelligence (AI) are increasingly expanding the capacity to detect, map, monitor, and manage salt‐affected soils across spatial and temporal scales. This review synthesizes 61 studies published between 2010 and 2025 and identified through Scopus and Web of Science, of which 18 (29.5%) were published during 2010–2020 and 43 (70.5%) during 2021–2025. The analysis examines the principal satellite and sensor datasets, spectral indices, machine‐learning (ML) and deep‐learning (DL) methods, multi‐sensor integration strategies, and management‐oriented applications reported in the selected literature. Landsat was the most frequently used individual platform (21.3%), followed by Sentinel‐2 (14.8%), multi‐satellite approaches (13.1%), while combined Landsat–Sentinel‐2 and Landsat–MODIS applications accounted for 9.8% and 8.2% of the studies, respectively. Overall, optical RS remained dominant because of its accessibility, long‐term data availability, spectral sensitivity to soil and vegetation responses associated with salinity, and compatibility with data‐driven modelling. ML methods were more frequently applied than DL approaches and were particularly common in studies relying on relatively limited field datasets. By contrast, DL, multi‐sensor fusion, hyperspectral observations, UAV imagery, and cloud‐computing environments emerged as promising directions for increasing spatial detail, strengthening predictive performance, and improving temporal monitoring. Cross‐study comparison nevertheless reveals persistent constraints related to limited and spatially uneven field observations, inconsistent validation strategies, weak geographic transferability, inadequate uncertainty assessment, and incomplete harmonization across sensors and acquisition conditions. A further limitation is the still limited translation of salinity maps and predictive outputs into operational land‐management decisions. The evidence therefore points to a need to move beyond site‐specific accuracy optimization toward transferable, uncertainty‐aware and management‐oriented frameworks. Future research should prioritize multi‐sensor data fusion, harmonized soil‐salinity datasets, robust and spatially explicit field validation, transparent uncertainty assessment, and operational frameworks linking RS‐ and AI‐derived information with practical salinity mitigation and land‐management strategies.
ABSTRACT Soil organic carbon (SOC) and soil total nitrogen (STN) are critical indicators of soil health and vital sinks in agroecosystems. However, knowledge gaps remain regarding how long‐term crop‐livestock integration impacts coupled SOC and STN stocks on commercial versus communal smallholder farms in Africa. This study quantified whole profile SOC and STN stocks under two land‐use types (croplands and grazing lands) and two livestock management facilities (congregation points and corrals), across two adjacent, distinct mixed crop‐livestock farming systems, while evaluating how these land uses influence δ 13 C and δ 15 N isotopic signatures. A commercial farm and an adjacent communal smallholder farm with similar soil types were selected. Soil samples were collected from six depth increments (0–5, 5–10, 10–20, 20–40, 40–70, 70–100 cm) to determine profile‐wide SOC and STN stocks alongside their δ 13 C and δ 15 N signatures. Land‐use and management type impacted SOC and STN stocks more significantly in the communal system ( p < 0.001) than on the commercial farm ( p < 0.05). On the commercial farm, whole‐profile SOC (Mg C ha −1 ± SE) and STN (Mg N ha −1 ± SE) stocks peaked in grazing lands (129.7 ± 12.8; 10.32 ± 1.14) and reached minimums at livestock congregation points (68.1 ± 2.7; 7.04 ± 1.16). Conversely, in the communal smallholder system, both stocks peaked sharply at livestock congregation points (150.0 ± 4.6; 12.26 ± 0.27) and were lowest in croplands (56.5 ± 1.5; 5.20 ± 0.20). Deep subsoil layers (40–100 cm) stored a greater relative proportion of total stocks on the commercial farm (36%–43% SOC; 39%–45% STN) than on the smallholder farm (28%–39% SOC; 29%–39% STN). Soil δ 15 N values increased with depth across all land‐use and management types. Lower δ 15 N values in commercial croplands compared with smallholder croplands likely reflect higher synthetic fertilizer use, while the highest δ 15 N values occurred in cattle corrals. Contrary to typical microbial fractionation—where preferential loss of 12 C during respiration enriches δ 13 C at depth—no consistent vertical increase in δ 13 C was observed; all land‐use types were enriched, averaging −17‰. Our results demonstrate that, within the narrow range of soil textures evaluated, land‐use and management serve as the primary determinants of SOC and STN stocks and their isotopic signatures. While livestock congregation points create intense, localized nutrient hotspots, their small spatial extent limits their landscape‐scale sequestration. Consequently, adopting conservation practices in commercial croplands as a proactive intervention, and optimizing the management of extensive communal grazing lands and croplands are collectively essential for enhancing regional biogeochemical storage and ecosystem resilience.
ABSTRACT Soil organic matter (SOM) level is a key factor determining soil resilience and condition; however, the optimal level of SOM in soils increases with clay content. Previous work has promoted the concept of Clay to Soil Organic Carbon (SOC) ratios to derive critical thresholds of soil degradation. Here, we analyse a dataset comprising 485 soil samples across England & Wales, where Visual Estimation of Soil Structure (VESS) scores was used alongside measurements of Clay and SOC contents (0–25 cm depth) to integrate soil structure into a new data‐driven Rating framework of soil condition and resilience. SOM (calculated as SOC/58%) increased with clay content. Both SOM and VESS scores were significantly better under grassland. For similar soil structure scores, SOM and clay content best fitted to diminishing power relationships (curves). Based on VESS scores, empirical bands are derived to rate the SOM as Maximum under grassland, Very Good, Good, Moderate, Deficient or Very Deficient at any given clay content. Most grassland sites were classified in the top three categories, whereas arable land spanned all six. On arable land, the existing Clay:SOC ratio framework classed 86% of heavy‐textured soils as ‘Degraded’ whereas the new Rating system categorises only 36% as Deficient. Conversely, on lighter land the Rating bands identified more Deficient soils than the Clay:SOC relationship. Across all soil types, Clay:SOC ratio classed 46% as Degraded whereas the Rating bands judged 11% as Deficient plus 12% as Very Deficient. 34% were rated Good or Very Good. VESS score was Good or Very Good in 37% of cases, while 26% were inadequate (VESS > 3.0), of which 14% were 4.0 or worse. Under grass leys, Clay:SOC ratio designated 12% as Degraded whereas the Rating only found 7% as Deficient; 3% had VESS 4 or 5. 59% of leys and 73% of permanent grassland had VESS 1 or 2 and only 2% had VESS 4 or 5. A substantial part of the variation in VESS scores is not captured by the Clay‐v‐SOM relationship which we attribute to management (cultivation and crop rotation). Structure was generally worse on sites with a history of minimal/shallow tillage. Conversely, cultivated soils could have good structure despite Moderate Rating. The new Rating categories are proposed as a robust and practical framework to describe soil structural condition and resilience for farmers and advisers to inform management guidance, including whether continuous reduced cultivation is appropriate or not, and where SOC should be raised, in contrast to soils already high in SOC that are unlikely to benefit from (nor retain) increased Carbon inputs. We estimate that an increase of 13 t/ha C in the topsoil is sufficient to restore a Very Deficient soil to Moderate Resilience. The data and new framework proposed in this study indicate that soil structure assessment needs to be integrated with SOC and clay for a more relevant metric of soil degradation and resilience. The methodology was developed on soils in England and Wales but has the potential to be used on soils worldwide.
Straw return is widely used to improve soil quality, but it remains unclear which return mode better supports persistent soil organic carbon stabilization under highly weathered conditions. Based on a field experiment with more than 15 years of continuous straw return in an acidic red soil, we compared no straw return (CK), straw mulching (SM) and straw crushing (SC) across the 0-40 cm soil profile. Aggregate fractionation was combined with measurements of soil organic carbon (SOC), reactive metal-bound organic carbon (BOC), and oxalate- and pyrophosphate-extractable iron, aluminium and manganese pools. Both straw return modes increased aggregate-associated carbon, with SOC increasing from 3.29-6.97 g kg-1 under CK to 6.70-15.06 g kg-1 under SM and 13.07-20.18 g kg-1 under SC, and BOC increasing from 1.07-3.26 g kg-1 to 2.33-9.17 and 6.74-11.85 g kg-1, respectively. SM mainly favoured SOC and BOC enrichment in microaggregates, suggesting a stronger role of aggregate turnover and physical occlusion. In contrast, SC increased SOC and BOC across a wider range of aggregate fractions, especially in 1-2 mm aggregates and microaggregates in the subsoil. Reactive metal pools also increased under straw return, with the largest responses under SC. Molar ratio patterns suggested that newly accumulated carbon was preferentially associated with oxalate-extractable reactive metal pools, while pyrophosphate-extractable pools retained further binding capacity. Overall, these results show that the choice of straw return mode matters for long-term carbon storage in acidic red soil, with straw crushing providing a stronger basis for enhancing mineral protection and improving carbon sequestration in red soil croplands.
Soil degradation threatens crop productivity and rural livelihoods worldwide. Efforts to reverse this trend are more likely to succeed when farmers and agricultural advisors share an understanding of what defines 'healthy' or 'degraded' soils, since both groups influence soil management decisions. In Nepal's Terai (lowland plains), the country's main grain-producing region, such knowledge remains poorly documented. This study investigates which indicators farmers and advisors use to assess soil health and how they perceive local soil fertility trends. Semi-structured interviews with 43 farmers and five advisors were analysed using a mental model approach and visualised as weighted semantic web diagrams. After applying frequency thresholds, 32 indicators were retained and grouped into five categories: sensory signs, agronomic outcomes, biological indicators, management activities and scientific testing. Despite broad agreement that local soils are degrading (79% of farmers; all advisors), each group relied on distinct measures. Farmers mainly used sensory, agronomic and biological indicators, whereas advisors emphasised quantifiable indicators and scientific testing. Only 11 of the 32 indicators overlapped, revealing gaps between advisory frameworks and farmers' soil assessment practices. Gender also subtly influenced farmers' soil assessments. These gaps reflect more than divergent vocabularies: each group assesses soil health through the lens of their own experience, shaped by how they manage and interact with the land. Any single-indicator framework, whether farmer-derived or scientific, is therefore inherently partial. Drawing on this diversity of perspectives is essential for understanding and responding to complex environmental challenges, and developing soil management guidance that integrates local experience, gendered knowledge and scientific approaches.
