ABSTRACT The application of organic amendments is a promising strategy to enhance soil organic carbon (SOC) accumulation in Mediterranean poorly developed soils. This study evaluated the short‐term effects of olive pomace compost application on SOC stock and soil physical and chemical properties in a traditional Mediterranean olive grove. Three compost types (A, B and C) were applied at three doses (10, 20 and 40 t ha −1 ) and evaluated 1 year after application. Results revealed a clear dose‐dependent increase in SOC content, with the 40 t ha −1 treatment yielding the most pronounced effect (SOC up to 12.37 t ha −1 relative to a control mean of 7.06 t ha −1 ). However, Carbon use efficiency (CUE = ΔSOC/C applied) decreased consistently with increasing dose across all compost types, ranging from 0.61–1.51 at 10 t ha −1 to 0.42–0.53 at 40 t ha −1 , indicating that lower application rates achieved proportionally greater SOC accumulation per unit of C input. Increases in electrical conductivity and cation exchange capacity indicated improvement in soil nutrient availability and buffering capacity, with the most pronounced effects observed under compost C (lowest C/N ratio) at the high dose. Principal component analysis explained 80% of total variance across three components: PC1 (40%) captured the dose‐driven gradient in SOC quantity and chemical fertility (pH, total N, P and K); PC2 (23%) reflected SOM quality as expressed by the C/N ratio, differentiating composts along a mineralization gradient; and PC3 (17%) was associated with aggregate stability as a secondary structural variable. SOC spatial distribution across the sloping experimental plot was homogenous. Overall, these findings demonstrate that olive pomace compost can perform as an effective management strategy to increase short‐term SOC storage and soil fertility in Mediterranean Leptosols, whilst providing quantitative evidence of the potential for circular valorization of agro‐industrial by‐products, promoting soil carbon storage and sustainability in olive‐based landscapes.
ABSTRACT Understanding the interactive effects of rainfall and vegetation on soil quality and functioning is critical for sustainable land management. This study investigated three typical vegetation types—arboreal forests, shrublands and grasslands—across different rainfall zones in the Loess Hilly Region in China. We measured 22 soil physical, chemical and biological indicators, and two mainstream soil quality assessment approaches, Principal Component Analysis (PCA) and Network Analysis (NA), were employed to construct a Minimum Data Set (MDS). A total of 12 Soil Quality Index (SQI) models (2 × 2 × 3) were developed by combining additive and weighted integration methods with three scoring functions: linear, non‐linear and membership function. The SQI results derived from each model were systematically compared. The key findings are: (1) Soil properties and SQI values differed markedly among vegetation types along the rainfall gradient. Lower rainfall areas generally exhibited poorer soil quality, though shrublands maintained relatively higher SQI values. With increasing rainfall, overall SQI improved, and arboreal forests gradually demonstrated superior soil quality. In high‐rainfall zones, a distinct soil quality hierarchy emerged: arboreal forests > shrublands > grasslands. (2) Compared to PCA, the MDS derived from NA contained fewer indicators while still capturing the core parameters essential for soil quality evaluation. (3) The soil sensitivity index calculated using NA (1.45–6.81) was higher than that obtained from PCA (1.34–4.03). Furthermore, the linear scoring function yielded higher sensitivity indices than the non‐linear and membership function approaches. In conclusion, as a straightforward and robust tool, NA can effectively identify key soil indicators under varying rainfall regimes when constructing the MDS. It also demonstrates higher sensitivity than PCA in computing SQI, thereby better discriminating spatiotemporal variations in precipitation patterns. The NA based models with linear scoring functions (SQINAla and SQINAlw) are recommended as a suitable framework for soil quality assessment in the loess hilly region, providing a scientific basis for regional land management and ecological restoration.
ABSTRACT For spring‐sown crops such as maize ( Zea mays L.), the extended growing season enables a substantial contribution of organically derived nitrogen (N), particularly from soil organic matter (SOM) mineralization. Consequently, the soil N mineralization rate (SMR) is a key determinant of maize N uptake and yield formation. Year‐to‐year and spatial variability in climate, soil properties, and management practices introduce considerable fluctuations in SMR, making it a major source of uncertainty in site‐specific N management. Near‐infrared reflectance spectroscopy (NIRS) offers a rapid, cost‐efficient and scalable tool with strong potential for improving SMR estimation and, consequently, site‐specific N management in maize production. In this study, 268 soil samples were collected across Germany with varying SOM content and incubated for 105 days to simulate the maize growing period, allowing laboratory quantification of SMR. Simultaneously, NIRS spectra (1100–2498 nm) were collected, preprocessed, and SMR was modelled using Partial Least Squares Regression (PLSR) and different machine learning (ML) algorithms. SMR was modelled more accurately by ML approaches, with non‐linear models such as the stacked ensemble model ( R 2 = 0.72, RMSE = 0.21 kg N ha −1 day −1 ) or random forest ( R 2 = 0.70, RMSE = 0.20 kg N ha −1 day −1 ) outperforming PLSR ( R 2 = 0.55, RMSE = 0.33 kg N ha −1 day −1 ). Incorporating ancillary soil and management data did not improve performance. NIRS‐ML modelling improved SMR prediction by up to 36%, with slight underestimation of laboratory‐measured SMR values due to indirect spectral relationship with NIRS. Nevertheless, the approach shows great potential as a rapid and scalable tool for SMR estimation, warranting further model refinement and validation across diverse environments.
ABSTRACT Atmosphere warming causes higher summer temperatures, droughts and more intense rain events. At the same time, land conversion towards more simplified crop sequences affects soil hydraulic properties, which determine water capture, transport and storage, threatening agricultural production. This study explores the soil hydraulic properties response to contrasting climatic scenarios and the consequent impact on soil water dynamics under two contrasting agricultural managements. The objective of this work was to evaluate the effects of alternative management with winter cover cropping during fallow (CF) on soil hydraulic properties (saturated hydraulic conductivity, K 0 ; pore size distribution; plant available water content, PAWC; soil sorptivity, S; and pore connectivity, Cw) and soil water content (0–20 cm and 20–40 cm depth) in a long‐term field experiment (2018–2024), including wet, normal and dry seasons, as compared to bare fallow management (BF). Results show that climate was one of the main factors affecting soil hydraulic properties, increasing water retention capacity during wet and normal periods, while dry periods caused increasing macroporosity. With the inclusion of cover crops, the soil plant available water content (PAWC) was up to 0.13 m 3 m −3 higher as compared to bare fallow, ameliorating the impact of drought periods. Under cover cropping management, increasing the soil capacity of capture and storage water, with a more interconnected pore system, improved the soil water status for the summer crop, especially in the 0–20 cm soil depth. The use of cover crops increased up to 60% the mean values of soil volumetric water content during the summer crop, even after dry winters. The results found in this work support the strategic use of winter cover crops as a resilience tool in agricultural systems facing climate risk.
High-Andean forage systems are characterized by extensive management of cultivated forages, primarily through rotation of forage oats (Avena sativa) and alfalfa (Medicago sativa), under marginal edaphoclimatic conditions. Within this context, soil sodicity severely constrains the productivity of these systems in the districts of Ayaviri and Macar & iacute;, Melgar Province, Peru. However, spatially explicit information on gypsum requirements (GR) for sodic-soil reclamation remains unavailable. To address this gap, this study aimed to: (i) develop a weighted Soil Quality Index (SQIw) for sodic soils, (ii) delineate sodicity-affected areas across districts, soil classes and environmental conditions and (iii) generate high-resolution maps of gypsum requirements using regression kriging (RK). A total of 1273 soil samples were analysed in high-altitude areas of Ayaviri and Macar & iacute;. Key sodicity-related variables included exchangeable sodium percentage (ESP), clay flocculating charge and Modified Soil-Adjusted Vegetation Index (MSAVI) selected through Pearson correlation and principal component analysis to construct the SQIw, while regression kriging was used to generate spatially explicit gypsum requirement maps. Results showed wide variability in pH and ESP, reflecting heterogeneous sodicity conditions across the study area, with Macar & iacute; presenting higher soil quality (SQIw = 0.61) than Ayaviri (SQIw = 0.44). Sandy loam soils and acid saline-sodic soils exhibited the greatest vulnerability. Regression kriging achieved high predictive accuracy for GR estimation (R-2 = 0.97 in Ayaviri; R-2 = 0.85 in Macar & iacute;), enabling precise delineation of priority intervention zones. The resulting GR maps provide a robust basis for site-specific gypsum application, improving amendment efficiency, reducing unnecessary costs and supporting sustainable sodicity management in vulnerable High-Andean agroecosystems.
ABSTRACT Subsoil acidification is an intractable agronomic challenge worldwide, and its amelioration is costly and often practically infeasible. Because most studies are confined to the topsoil, acidity development in subsoil commonly remains undetected until it becomes severe. This review presents a bibliometric analysis of Web of Science and Scopus records (1984–2025) to identify research trends and patterns, and integrates evidence on the extent, causes, consequences and remediation of acidity at depth. The highest research active country on subsoil acidity is United States, contributing approximately 30.9% of total publications over the study period, followed by Australia (23.9%) and Brazil (14.0%). Mechanisms for subsoil acidification differ from topsoil acidification and imbalance in plant cation‐anion uptake may dominates as a contributor for subsoil acidification. Surface liming often corrects topsoil pH however leaves acidic subsoil layers and Al toxicity unresolved due to slow alkalinity movement. Evidence indicates that rapid subsoil remediation requires strategic deep incorporation or placement of alkalinity, sometimes combined with complementary amendments to enhance base cation (i.e., Ca/Mg) supply and alkalinity movement, while avoiding over‐liming risks. However, many proposed amendments and technologies still need thorough field‐scale evaluation to support their implementation in the agriculture sector. Overall, key priorities are depth‐resolved diagnostics and integrated field‐scale interventions that address co‐occurring constraints to prevent further acidification and sustain long‐term yields.
Black soils in Northeast China are crucial for grain production and climate mitigation, but decades of intensive tillage and monoculture have degraded their physical, chemical and biological properties. Designing crop rotations that maintain farm income while restoring soil health has therefore become a central environmental management challenge. We develop a soil-health-based optimization framework for conservation tillage rotations that combines a composite soil health index (SHI), dynamic soil responses, and farm resource constraints to quantify trade-offs between net present value (NPV) and soil health. We aggregate a soil indicator system into a composite SHI and construct nine representative archetype plots spanning high-, medium- and low-health conditions. A state-transition model links annual changes in soil indicators to crop choices and conservation tillage, and is embedded in a multi-period goal-programming model over 3-, 5- and 10-year horizons. The optimized solutions reveal a consistent production-restoration zoning pattern. High- and medium-health plots mainly support production-oriented rotations, whereas low-health plots receive more fallow/green manure years and achieve the largest soil-health improvement. Across planning horizons, high-health plots remain close to their initial SHI, while medium- and low-health plots show clear recovery and remain above the ecological safety threshold. Robustness analyses further show that this pattern is stable under parameter uncertainty, alternative SHI weights, data-driven plot classification, economic changes, unequal plot areas and alternative soil-response functions. The framework links soil health assessment, dynamic soil evolution and representative-plot optimization, providing decision support for differentiated conservation tillage and targeted restoration in degraded black soil regions.
ABSTRACT The incorporation of crop residues and stoichiometrically balanced nutrients to achieve optimal decomposition is a novel management practice reported to increase C stability by preserving microbial necromass. There is still a lack of understanding on the biotic and abiotic effects on soil functionality. This study investigated the impact of this practice on aggregate stability in a dispersive subsoil. A 180‐day incubation experiment applied sorghum ( Sorghum bicolor ) residues with high and low carbon to nitrogen (C:N) ratio (Res‐1 and Res‐2, respectively) to a neutral, dispersive subsoil from a continuously cropped field. Exogenous nutrient treatments included control, theoretical Half and Full N rates, and a tertiary treatment that consisted of N applied alone, with additional sulfur (S), or additional S and phosphorus (P). Soil spontaneous and mechanical turbidity were measured to assess aggregate stability, and total organic carbon (TOC) and C fractions were measured. Application of both residues without additional nutrients improved soil spontaneous turbidity by 61% while only the lower C:N residue (Res‐2) improved mechanical turbidity (by 27%). Mechanical turbidity improved by 33% with the Half N rate and by 94% with the Full N rate, while supplementary S and P had limited additional effects on turbidity or soil C fractions. Spontaneous and mechanical turbidity did not correlate with TOC or C fractions despite the application of residue increasing TOC from 0.75% to 0.86%. Improvements in spontaneous and mechanical turbidity correlated with decreased electrical double layer thickness, increased EC, and decreased pH. A combination of electrolyte effects and pH‐altered charges from the residue and nutrient treatments contributed to improved aggregate stability under the incubation conditions, presumably as a result of effects on reducing electrical double layer thickness. The changes were driven by N rate and residue quality while additional S and P provided limited additional benefit. The study suggests IRNM can improve aggregate stability on soils that are prone to disaggregation. The relative role of biotic and abiotic mechanisms requires further investigation to refine strategies for the management of dispersive soils.
Accurate monitoring of soil salinity is crucial for crop productivity and agricultural management. Although remote sensing has been widely applied, the complementary value of features derived from single-date and seasonal composite imagery in soil salinity prediction has not been systematically evaluated. This study evaluated soil salinity prediction models based on single-date and seasonal-scale multi-temporal Sentinel-2 imagery using 214 soil electrical conductivity (EC) samples in Kenli District, China. Four Random Forest (RF) models were developed using (1) single-date features extracted from images acquired on the sampling dates; (2) seasonal composite features derived from mean-composited images within each sampling season; (3) integrated multi-temporal features combining both single-date and seasonal composite features; and (4) an optimal subset of integrated multi-temporal and environmental features identified via SHAP-based feature selection. Results showed that the single-date model performed poorly (R 2 = 0.34, RMSE = 726.24 mu S/cm), while the seasonal composite model offered only slight improvement (R 2 = 0.30, RMSE = 724.63 mu S/cm). Integrating both substantially enhanced prediction accuracy (R 2 = 0.44, RMSE = 646.19 mu S/cm), yielding an increase of 0.10-0.14 in R 2 and a reduction of 78-80 mu S/cm in RMSE. Further a refined model achieved the best performance (R 2 = 0.56, RMSE = 556.94 mu S/cm). Soil salinity exhibited a southwest-northeast gradient, decreasing in June due to irrigation and rainfall and increasing in October after harvest under higher evaporation. This study provides a framework for seasonal soil salinity monitoring and guidance for cropland salinisation management.
Agricultural management produces soil compaction depending on intensity of use, specific management and soil properties. We used an adapted, home-built dynamic penetrometer to evaluate 20 long-term sites of arable land, tree orchards, vineyards and grassland in Europe and China, each with different tillage and cover crop strategies. To ensure comparable results across all sites, we pre-tested different penetrometer settings in the laboratory to cover all local conditions and provided Standard Operating Procedures. The laboratory tests showed that different settings in terms of falling hammer height and cone angle produced replicable results, and that narrow plough pans (3 cm) could be detected, even though their density was underestimated by 50%. The pre-tests also demonstrated the dependence of the measurements on soil water content and texture, even under preset conditions close to field capacity. Consequently, the effects of different management practices were only compared directly for each site individually. The field study showed that tillage had a greater effect on penetration resistance than different cover crop systems and intensity. The majority of the more intensively tilled fields showed penetration resistance that was up to 3 MPa lower, at least partially, up to the ploughing depth compared to less intensively tilled fields. However, in only 27% of the fields, the no-till or reduced-till management led to an SPR greater than 2.5 MPa, indicating harmful compaction, compared to conventional management. The effects of cover crops on soil compaction were unclear with differences observed between different mixtures at only one site. Nevertheless, unlike bare soil, cover crops increased penetration resistance in the tillage horizon and reduced infiltration capacity in 75% of the fields investigated. Trends depending on management practices varied due to local soil properties. We therefore recommend farmers to include penetrometer measurements in their routine to recognize soil compaction in time and apply tailored mitigation strategies.
Plastic film mulching is a cornerstone of intensive agriculture, yet its long-term impact on the coupling of soil carbon-nitrogen (C-N) cycles and microbial assembly-particularly in humid subtropical regions-remains poorly understood. Here, we investigated the biogeochemical and microbiological trajectories of vegetable soils in South China following 10 and 20 years of continuous polyethylene mulching. We show that long-term mulching fundamentally reshapes the soil micro-environment, driving a deterministic shift toward a high-nitrification, carbon-enriched state. Mulching elevated soil organic carbon (SOC) and nitrate levels, creating a specific 'incubator effect' that tightened C-N coupling. This environmental selection favoured stress-tolerant taxa, significantly enriching the nitrite-oxidizing genus Nitrospira and the spore-forming Neobacillus within the 'plastisphere', while competitively suppressing plant-growth-promoting guilds such as Sphingomonas. This community succession underpins a self-reinforcing feedback loop: carbon accumulation fuels accelerated nitrification, which in turn exacerbates moderate soil acidification and nitrate accumulation, reducing functional redundancy and ecosystem stability. Our findings highlight the 'double-edged' nature of plastic mulching-enhancing agronomic fertility at the expense of micro-ecological resilience. We propose that sustainable intensification in subtropical systems must transition toward adaptive management strategies, integrating biodegradable alternatives and microbiome-targeted regulation to mitigate these latent ecological risks.
Understanding the spatiotemporal variability of soil properties is critical for designing effective land management strategies in heterogenous agroecosystems. This study assessed soil property variability across four land management systems-conventional arable, biochar-amended arable, fallow/grazing, and teak plantation-within the Guinea Savanna region of Nigeria, where landscapes are subject to recurrent seasonal bush burning. Composite topsoil samples (0-20 cm) were analysed for key physicochemical properties including pH, exchangeable acidity (EA), effective cation exchange capacity (ECEC), total nitrogen (TN), soil organic carbon (SOC), available phosphorus (AvP), exchangeable bases, and selected micronutrients. Analyses of variance and correlation analyses indicated significant differences among land management systems for TN, iron availability, ECEC, and pH, whereas SOC and AvP showed no significant variation. Across systems, soils were near neutral in reaction and exhibited moderate SOC levels (0.67%-0.72%). Available P ranged from 7.59 mg/kg in biochar-amended soils to 12.35 mg/kg in plantation soils. The fallow system recorded the highest TN (0.13%), exchangeable K+ (0.7 cmol(+)/kg), and ECEC (4.2 cmol (+)/kg), indicating relatively greater nutrient retention capacity. Given that all sites experience similar seasonal burning regimes, the observed differences primarily reflect land-use effects within a shared fire-affected landscape rather than direct impacts of burning. The lack of significant SOC differentiation across systems suggests convergence in soil carbon levels despite contrasting land uses, potentially indicating that recurrent disturbance constrains organic matter accumulation. These findings underscore the need for integrated land management strategies that reduce reliance on frequent burning while enhancing organic matter inputs-such as residue retention, agroforestry integration, and improved fallow systems-to rebuild soil carbon stocks and sustain soil fertility in savanna agroecosystems.
ABSTRACT Sustainable agricultural productivity relies on soil nutrient management (SNM), although traditional methods of periodic soil testing and applying uniform amounts of fertilizer usually do not account for spatial and temporal variability. The latest breakthroughs in the technologies of artificial intelligence (AI) and Internet of Things (IoT) enabled data‐driven solutions for soil nutrient monitoring and decision‐making. This paper summarizes the results of 82 peer‐reviewed papers on the topic to critically analyse the use of AI and IoT in SNM, focusing on the characteristics of datasets, modelling, sensing methods, as well as constraints related to practical implementation. The literature review demonstrates that AI‐IoT systems have the potential to enable better nutrient management by optimizing the timing and application of fertilizers due to changing soil and environmental factors. Nevertheless, one key conclusion is that the performance of the system is not limited by the sophistication of the algorithms, but more by the quality of data, sensing realism and the extent of its coverage. Machine‐learning (ML) models tend to have more reliable and portable behaviour compared to deep‐learning (DL) models with less well‐organized but heterogeneous and proxy‐based datasets, which are characteristic of modern applications. Although the technologies of IoT allow high‐frequency observations, the majority of systems are based on the indirect indicators of the nutrient condition and the low levels of sensor density, which reduces the implementation of the technology in the field. The environmental benefits are reported mainly on the basis of decreased losses of nutrients, and the effects of climate are not quantified adequately. In general, the synthesis points at the necessity to prioritize data‐focused design, scalable sensing policies and decision relevance to support AI‐IoT‐based SNM.
Land quality assessment plays a significant role in ensuring food security, maintaining ecological balance, and promoting sustainable agricultural development, especially in environmentally vulnerable areas. Taking the Qingshuihe Plain in Ningxia as the study area, this research constructed a land quality evaluation system adapted to the characteristics of the semi-arid region of the Loess Plateau. While soil quality specifically refers to the capacity of a soil to function within ecosystem boundaries, land quality is a broader concept that integrates soil properties with topography, climate, and vegetation status, and systematically analysed land quality conditions and their differences under various land use types. Through principal component analysis, Pearson correlation analysis, and common factor variance calculation, Minimum Data Sets (MDS) were screened and constructed for four land use types: grassland, dry farmland, forestland, and irrigated land. Results showed that: (1) Soil properties, topography, and ecological status varied significantly across land use types. Irrigated land had higher available and total nutrient content, grassland and forestland had higher organic matter and nitrogen content, while dry farmland fell between these two groups; (2) The MDS composition for each land use type showed distinct specificity. The grassland MDS included 8 indicators: SOM, Zn, NDBSI, Cr, Elev, AP, AK, and WET; dry farmland MDS included 5 indicators: Ni, SOM, TSC, NDVI, and Elev; forestland MDS included 4 indicators: Zn, AHN, TP, and NDBSI; irrigated land MDS included 5 indicators: Zn, TN, NDBSI, pH, and Slope; (3) The Land Quality Index based on MDS (LQI-MDS) showed a significant positive correlation with the Land Quality Index based on the total data set (LQI-TDS), validating the reliability of the MDS; (4) The average LQI-MDS values for the four land use types ranked as follows: irrigated land (0.539) > dry farmland (0.496) > forestland (0.340) > grassland (0.309), reflecting the close relationship between human management intensity and land quality. The research results indicate that a differentiated land quality evaluation system adapted to regional characteristics can effectively characterise land quality conditions under different land use types in the loess region, providing a scientific basis for sustainable management of regional land resources and ecological environmental protection.
Soil moisture and soil temperature are key drivers of crop productivity, yet their interactions under humid subtropical conditions remain insufficiently characterised. This study investigated soil hydrothermal dynamics in a cornfield at North Carolina A&T State University during the 2024 growing season. A capacitance-based CropX probe continuously monitored soil moisture at depths of 10, 30 and 56 cm, and soil temperature at 10 and 30 cm. Results showed substantial fluctuations in surface moisture (18.4%-41.8%) and temperature (20 degrees C-31 degrees C) at 10 cm, while deeper layers remained stable (47%-49%). Hysteresis analysis revealed asymmetric wetting and drying dynamics, with nearly twice as many significant drying events as wetting events. Moisture depletion at 30 cm was associated with surface moisture declining below 34.2%, providing a practical threshold for irrigation scheduling under the conditions studied. Soil temperature correlated strongly with air temperature (r approximate to 0.95) and inversely with surface moisture (r approximate to -0.58), reflecting the moderating effect of water on thermal stress. These findings highlight the value of depth-specific sensor monitoring for optimising irrigation scheduling and improving crop resilience in humid subtropical agricultural systems.
Land use and soil drainage influence soil organic carbon (SOC) dynamics by impacting organic matter accumulation, decomposition and redox conditions. A total of 158 A horizon samples were collected from Alfisols under forest, pasture, and crop-pasture rotation. The soil samples were scanned using a MIR spectrometer (4000-400 cm(-1)). Aerial imagery was used to determine cultivation periods and the age and extent of forest cover. Poorly drained soils had higher SOC stocks and concentrations, bulk density (BD), soil inorganic carbon (SIC) and A horizon thickness (AHT). Moderately well drained soils had lower SOC stock, SOC concentration, BD and AHT. Soils under short-term (12-year) pasture and crop-pasture rotation areas had higher SOC concentration than other land uses. SIC exhibited large variation across land use and drainage classes. After > 75 years, converting forest to cropland increased SOC stock from 34.6 to 62.6 Mg ha(-1) due to a thicker A horizon, but it decreased SOC concentration from 21.1 to 19.5 g kg(-1) compared to soils under forest. Over time, SOC stock and SOC concentration decreased in pasture areas. Soils under forest differed from other soils in some regions of MIR spectra because of specific clay mineral peaks (3695, 3620 and 916 cm(-1)), alkyl groups (2930 and 2850 cm(-1)), carbonates (2517 cm(-1)), amides (1640 cm(-1)), aromatics (1510 cm(-1)), carbohydrates (1050 cm(-1)), and quartz (811, 790 and 693 cm(-1)). The spectral points at 3695, 3620 and 1050 cm(-1) were associated with differences in soil drainage. It was concluded that SOC stock and SOC concentration are influenced by long-term land use and drainage conditions, and the MIR spectrometer can be a valuable tool for identifying their impacts on soil.
Tillage practices regulate soil functioning in semiarid agroecosystems, where moisture limits nutrient cycling and bacterial activity. This study evaluated soil physicochemical properties, bacterial community composition, and the abundance of functional genes linked to anaerobic metabolisms under long-term conventional (CT), minimum (MT), and no-tillage (NT) systems. Most soil physicochemical properties and diversity metrics remained stable across treatments, although multivariate analyses indicated moderate microbial and biogeochemical shifts. MT was associated with a higher relative abundance of phyla such as Actinobacteria, Chloroflexi, Planctomycetota, and Nitrospirota, suggesting increased microsite heterogeneity under moderate disturbance. NT favoured bacterial communities typical of stable, resource-limited conditions, including Verrucomicrobiota, while CT was associated with copiotrophic groups such as Proteobacteria and Actinobacteria under more disturbed and oxidative conditions. Functional gene quantification showed higher potential for nitrogen and sulphur cycling under NT and MT, including genes associated with denitrification, iron reduction, and sulphate reduction. In contrast, methanogenic potential remained unchanged across treatments. Correlation analyses revealed consistent associations between functional genes, soil chemical properties and different responses of oligotrophic and copiotrophic phyla. Multivariate analyses indicated that MT is associated with changes in soil chemical properties and microbial functional potential, reflecting an intermediate condition between NT and CT. However, these effects did not provide improvements in crop yield across seasons, with NT generally showing more stable yield. Overall, long-term MT influenced soil biochemical functioning, while NT may provide a more consistent balance between soil fertility and agronomic output.
Sustainable soil management in arid regions is constrained by the limited translation of subgroup-level soil taxonomy into site-specific agronomic recommendations. Hence, this study integrated field observations and laboratory analyses from 18 soil profiles with freely available Google Earth Engine (GEE) datasets. Long-term Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were processed in GEE and combined with land suitability index (SI) and multiple soil indices, including the Soil Quality Index (SQI), Nutrient Potential Index (NPI), Salinity-Sodicity Hazard Index (SSHI), Cation Ratio of Structural Stability (CROSS), Erodible Fraction (EF) and Soil Condition Factor (SCF). Results revealed two soil orders (Entisols and Aridisols) with considerable subgroup-level diversity, comprising Typic Torripsamments, Torriorthents, Torrifluvents, Haplocalcids, Haplogypsids, and Calcigypsids, all under thermic temperature and torric moisture regimes. Soils exhibited coarse-to-fine textures, low organic matter (mean 0.33%), variable CaCO3 (1.46%-35.04%), localized gypsum (up to 9.56%), and moderately alkaline pH (mean 8.12), under generally non-sodic and non-saline to slightly saline conditions. Integrating these indices revealed distinct patterns: SQI and NPI showed positive correlations with clay content and vegetation vigour (p < 0.05 to p < 0.001) and negative correlations with erosion risks (p < 0.001). While finer-textured Aridisols experienced greater chemical constraints from CaCO3 and ECe, they exhibited higher overall SI. Conversely, coarse-textured Entisols were primarily limited by low water- and nutrient-retention capacities, requiring organic amendments, precision drip irrigation, and erosion control. Calcareous and gypsiferous Aridisols necessitate salt-tolerant crops, irrigation management to prevent secondary salinization, and targeted amendments to mitigate nutrient fixation. This research demonstrates that coupling subgroup-level taxonomy with multi-source soil and remote sensing indices provides a scalable framework for site-specific land management in arid environments.
ABSTRACT The sustainable intensification of agriculture requires management practices that simultaneously enhance productivity, improve soil health and reduce environmental impacts. Although no‐tillage (NT) and residue retention (R) are widely encouraged, their individual effectiveness is often limited, and the potential to combine them to achieve enhanced outcomes remains poorly understood globally. Through a meta‐analysis of 68 published studies on dryland winter wheat, we show that NT + R produces synergistic effects that exceed the sum of the two practices alone. Compared to conventional tillage with residue removal (CT), NT + R significantly increased soil organic carbon (22.3%), microbial biomass carbon (18.4%) and water use efficiency (25.4%), whereas reducing nitrous oxide (N 2 O) emissions by 24.8% ( p < 0.05). Additionally, NT + R reduces the yield penalty often associated with no‐tillage alone, increasing grain yield by 23.5% ( p < 0.05) compared to CT. Environmental factors heavily influence the success of this system. The greatest multifunctional benefits, reflecting a ‘win‐win’ for climate mitigation and crop production, were consistently seen in semi‐arid regions (MAP 350–600 mm) with moderate temperatures (MAT 8°C–15°C) and fine‐textured soils. Conversely, neither NT nor R alone achieved these coordinated improvements. Our findings strongly suggest that integrated conservation practices are key to balancing productivity and environmental goals. We highlight the specific agro‐ecological conditions where NT + R offers the greatest benefits, providing an essential framework for targeted implementation to maximize its role in global food security and climate change mitigation.
The widespread use of pesticides in modern agriculture has led to the accumulation of persistent residues in soil, posing significant environmental and health risks. In this study, we evaluated the performance of solar-assisted Advanced Oxidation Processes (AOPs) for the remediation of boscalid, a broad-spectrum fungicide, in two agricultural soils with different organic matter (OM) contents and related physicochemical characteristics. Soil 1 (S1) had 0.03% OM, whereas Soil 2 (S2) contained 0.39% OM. Degradation experiments were conducted under natural sunlight using TiO2-photocatalysis, modified photo-Fenton-like treatment, and persulfate (PS)-photooxidation, along with their corresponding dark controls under controlled laboratory conditions. The results revealed that solar-assisted AOPs significantly enhanced boscalid degradation compared to dark treatments, with PS-photooxidation exhibiting the highest degradation percentages in both soils (98% in S1 and 87% in S2 after 24 h of irradiation). TiO2-photocatalysis and modified photo-Fenton-like treatment achieved moderate to high removal in S1 (91% and 79%, respectively) but were less effective in S2 (59% and 68%, respectively). Kinetic analysis confirmed the superiority of solar-assisted processes, with PS-photooxidation showing the highest rate constants (655 & times; 10-3 h-1 in S1 and 79 & times; 10-3 h-1 in S2). Soil OM content appeared to influence treatment efficiency, with S2 consistently exhibiting lower degradation rates owing to radical scavenging, contaminant sorption and its darker colour. Although the contribution of other soil properties cannot be excluded. Post-treatment soil analysis revealed moderate changes in pH, electrical conductivity, and OM content, which were more pronounced in the PS-based systems. These results, obtained under controlled laboratory conditions, indicate the potential of solar-assisted AOPs for the remediation of pesticide-contaminated soils and highlight the importance of considering soil properties when selecting appropriate treatment strategies.
