International Soil and Water Conservation Research最新文献

{"title":"Utilizing geodetectors to identify conditioning factors for gully erosion risk in the black soil region of northeast China","authors":"Donghao Huang ,&nbsp;Xinrui Zhao ,&nbsp;Zhe Yin ,&nbsp;Wei Qin","doi":"10.1016/j.iswcr.2024.07.004","DOIUrl":"10.1016/j.iswcr.2024.07.004","url":null,"abstract":"<div><div>In the black soil region of Northeast China, the issue of gully erosion persists as a significant threat, resulting in extensive damage to farmland, severe degradation of the black soil, and decreased productivity. It is therefore of utmost importance to accurately identify areas that are susceptible to gully erosion to effectively prevent and control its negative impact. This study tried to utilize geographical detectors (geodetectors) as a means to identify the factors that contribute to the distribution of gullies and assess the risk of gully erosion (GER) in five catchments within the region, with areas ranging from approximately 80 km²–200 km². By employing the geodetectors method, fourteen geo-environmental factors were analyzed, including topographic attributes (such as aspect, catchment area, convergence index, elevation, plan curvature, profile curvature, slope length, slope, stream power index, and topographic wetness index), channel network distance, vegetation index (NDVI and EVI), as well as land use/land cover (LULC). The modeling of GER was conducted using the random forest algorithm (RFA). Out of the fourteen examined geo-environmental factors, only a subset, comprising less than or equal to 50%, demonstrated a significant (<em>p</em> &lt; 0.05) influence on the spatial distribution of gullies. These selected factors were sufficient in assessing GER, with LULC (mean q-value = 0.270) and elevation (mean q-value = 0.113) identified as the two most important factors. Furthermore, the RFA exhibited satisfactory performance across all catchments, achieving AUC values ranging from 0.712 to 0.933 (mean = 0.863) in predicting GER. Overall, the catchment areas were classified into high, moderate, low, and very low-risk levels, representing 9.67%–15.95%, 19.28%–26.08%, 24.59%–30.55%, and 30.54%–39.08% of the total area, respectively. Importantly, a significant positive linear relationship (r² = 0.722, <em>p</em> &lt; 0.05) was observed between the proportion of cropland area and the occurrence of high-level GER. Although the primary risk levels were categorized as low and very low, the proportion of high-risk levels exceeded the existing gully coverage (0.34%–3.69%). These findings highlight the substantial potential for gully erosion and underscore the necessity for intensified efforts in the prevention and control of gully erosion within the black soil region of Northeast China.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 808-827"},"PeriodicalIF":7.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated quantification of contouring as support practice for improved soil erosion estimation considering ridges","authors":"Dominik Scholand,&nbsp;Britta Schmalz","doi":"10.1016/j.iswcr.2024.07.001","DOIUrl":"10.1016/j.iswcr.2024.07.001","url":null,"abstract":"<div><div>Using soil conservation practices such as contouring are able to reduce soil loss on arable land parcels. In the empirical model of the Universal Soil Loss Equation (USLE), these measures are taken into account by the P-factor for support practice management. In the context of application, there is usually a lack of sufficient data or suitable methodology to accurately determine the P-factor within a plot-specific analysis. In this study, we demonstrate the effort and benefit of deriving an individual P-factor for each land parcel within a typical application scale. For this purpose, we apply the Fast Line Detector algorithm to open remote sensing data of Google Earth from May 2016 in German low mountain range. The algorithm detects lines from tire tracks and seed rows, which allows to determine an individual main cultivation direction for each land parcel. The success rate was 94.9 % for 2495 land parcels with 26 different crops. The results show a major time advantage for the automated method when considering a large number of parcels. Subsequently, we used the detailed information obtained to calculate the P-factor under regional German conditions using the German standard DIN 19708 and, secondly, an approach based on revised USLE. It is apparent that the current German standard cannot be applied with the necessary level of detail for 78.1 % of all land parcels in this low mountain range study due to unsuitable equations and validity ranges for slope steepness and length and a non-consideration of ridges and off-grade contouring and therefore needs to be revised to avoid being restricted in its application.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 761-774"},"PeriodicalIF":7.3,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-dimensional spatiotemporal variation of soil organic carbon and its influencing factors at the basin scale","authors":"","doi":"10.1016/j.iswcr.2024.05.001","DOIUrl":"10.1016/j.iswcr.2024.05.001","url":null,"abstract":"<div><div>The variability of soil organic carbon (SOC) extends across three dimensions. However, quantitative analyses of the factors influencing spatiotemporal variations of SOC in various soil depth is scarce. This study leverages legacy data from two soil surveys conducted in the Dongting Lake Basin during the 1980s and the 2010s, employing Random Forest models to generate three-dimensional SOC maps. Through correlation analysis and permutation importance, we identified the primary factors driving temporal and spatial changes of SOC. The results showed that in the 2010s, SOC storage up to a depth of 1 m in the Dongting Lake Basin was approximately 2.95 Pg, increasing at an average rate of 0.0047 Pg C per year since the 1980s. Regions with higher average SOC contents were predominantly found in the western, southern, and eastern parts of the basin, despite significant losses over the 30-year period. In contrast, the central and northern areas, which initially had lower SOC contents in the 1980s, exhibited increases by the 2010s. Soil depth was the most influential predictor of SOC patterns in both the 1980s and 2010s. Meanwhile, relief and organism factors were primarily responsible for spatial variations in SOC, with the influence of organism factors diminishing by the 2010s. The temporal variations of SOC were chiefly attributed to changes in soil conservation practices, extreme precipitation events, and grain production. Consequently, it is imperative to prioritize ecological restoration and conservation tillage practices to mitigate the impacts of extreme weather conditions and safeguard food security.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 885-895"},"PeriodicalIF":7.3,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141045991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can hydraulic-energy-indices be effectively used to describe the saturated hydraulic conductivity?","authors":"","doi":"10.1016/j.iswcr.2024.05.002","DOIUrl":"10.1016/j.iswcr.2024.05.002","url":null,"abstract":"<div><div>The saturated hydraulic conductivity (<em>K</em>_<em>s</em>) and water retention curve (SWRC) parameters are important properties for simulating soil hydrological processes and characterizing soil conservation around the world. Therefore, <em>K</em>_<em>s</em> and SWRC are related with the soil physical quality (SPQ) and several SPQ indices can be derived from SWRC, such as the pore size distribution, relative field capacity, plant available water, drainable porosity, and soil hydraulic-energy indices (SHEI). It is well known that the soil structure can be assessed by using SHEI, but a possible physical relationship between <em>K</em>_<em>s</em> and SHEI was not examined yet. Therefore, the objective of this study was to investigate the behavior of <em>K</em>_<em>s</em> as function of SHEI for several soil textural classes. If this relationship be proved, then SHEI might be applied to improve the <em>K</em>_<em>s</em> prediction by PTF models. In this work, a data set of 395 measured SWRC's were fitted to the vG equation to obtain the SHEI to verify whether they are statistically correlated and physically dependent on <em>K</em>_<em>s</em>. The resulting parametric and non-parametric correlation results were split up according to six textural classes. The significant influence of <em>K</em>_<em>s</em> on at least one of the absolute SHEI (<em>A</em>_<em>a</em> or <em>WR</em>_<em>a</em>) was verified on the numerical scale when all textures were grouped and on numerical and <em>pF</em> scales for clayey and silty textures. <em>K</em>_<em>s</em> showed significant impact on <em>A</em>_<em>a</em> and <em>WR</em>_<em>a</em> indices in four textural classes. Furthermore, <em>K</em>_<em>s</em> had influence on the sum <em>A</em>_<em>a</em> + <em>WR</em>_<em>a</em> denoted in <em>pF</em> scale for five of the six textural classes, with a significant linear correlation in the clayey texture when log (<em>A</em>_<em>a</em> + <em>WR</em>_<em>a</em>) was applied. The significant and high correlation of <em>K</em>_<em>s</em> on the ratios <em>WR</em>_<em>a</em><em>/AWC</em> and <em>A</em>_<em>a</em><em>/φ</em>_<em>D</em> was also observed in four of the six classes, and therefore the use of these indices is recommended for the development of PTFs for <em>K</em>_<em>s</em> prediction.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 798-807"},"PeriodicalIF":7.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141032206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National variability in soil organic carbon stock predictions: Impact of bulk density pedotransfer functions","authors":"","doi":"10.1016/j.iswcr.2024.04.002","DOIUrl":"10.1016/j.iswcr.2024.04.002","url":null,"abstract":"<div><div>Accurate soil organic carbon storage (SOCS) estimation is crucial for sustaining ecosystem health and mitigating climate change impacts. This study investigated the accuracy and variability of SOCS predictions, focusing on the role of pedotransfer functions (PTFs) in estimating soil bulk density (BD). Utilizing a comprehensive dataset from the Korean Rural Development Administration (RDA database), which includes 516 soil horizons, we evaluated 36 widely-used BD PTFs, well-established formulas that estimate BD by considering soil properties, including soil organic carbon (SOC), soil organic matter (OM), sand, gravel, silt, and clay. These PTFs demonstrated varying levels of precision, with root mean squared errors (RMSE) ranging from 0.177 to 0.377 Mg m⁻³ and coefficients of determination (R²) from 0.176 to 0.658; hence, the PTFs have been classified into excellent, moderate, and poor-performing groups for predicting BD. Further, a novel PTF based on an exponential function of SOC was developed, showing superior predictive power (R² = 0.73) compared to existing PTFs, using an independent validation dataset. Our findings reveal significant differences in SOCS predictions and observations among the PTFs, with a p-value &lt;0.05. The highest concentrations of SOCS were noted in forest soils, considerably above the national average, highlighting the importance of tailored soil management practices to enhance carbon sequestration. These findings are crucial for refining PTF precision to improve the accuracy of national SOCS estimates, supporting effective land management and climate change mitigation strategies.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 868-884"},"PeriodicalIF":7.3,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141041823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Divergent behaviour of soil nutrients imprinted by different land management practices in the Three Gorges Reservoir Area, China","authors":"","doi":"10.1016/j.iswcr.2024.03.003","DOIUrl":"10.1016/j.iswcr.2024.03.003","url":null,"abstract":"<div><div>Soil nutrients are essentially regulated by land management practices via modulating biotic element input and metabolism. The Three Gorges Reservoir Area in China was dominated by a farming landscape, but land management has become diversified over recent decades. How these restorative management practices may have affected soil nutrients is not completely understood. In this study, a space-time substitution approach was applied to evaluate soil nutrients and their stoichiometric changes in response to post-farming land management practices. Soil samples (0–10 cm, 10–20 cm, and 20–40 cm) were collected from present-day croplands, cypress plantations, eucalyptus plantations, abandoned croplands, and citrus plantations. Soil organic matter, soil organic carbon, total nitrogen, alkaline hydrolyzed nitrogen, total phosphorus, and available phosphorus were determined. The results showed that soil organic matter and total nitrogen in abandoned croplands, cypress plantations, eucalyptus plantations and citrus plantations were increased by 186% and 190%, 184% and 107%, 45% and 33%, 45% and 54%, respectively, in comparison with those of present-day croplands. Soil nutrients except for total phosphorus decreased with soil depth by exclusion of tillage mixing. Comprehensive soil nutrient index showed that abandoned croplands (0.90) and cypress plantations (0.72) exhibited favorable nutrient recovery capacity. Soil C:P and N:P ratios increased in abandoned croplands, cypress plantations, and eucalyptus plantations. Phosphorus may become a limiting factor for plant growth with prolonged recovery in abandoned croplands, cypress plantations, and eucalyptus plantations, while soil organic matter and total nitrogen deficiencies were exacerbated in citrus plantations and present-day croplands. Therefore, cropland abandonment and reforestation (particularly cypress trees plantation) are recommended options for restoring soil nutrients in the Three Gorges Reservoir Area.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 896-907"},"PeriodicalIF":7.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling gully initiation by two codeless nonlinear methods: A case study in a small watershed on the Tibetan Plateau","authors":"","doi":"10.1016/j.iswcr.2024.03.002","DOIUrl":"10.1016/j.iswcr.2024.03.002","url":null,"abstract":"<div><div>Land and soil resources are scarce in the Tibetan Plateau, and the region is facing ecological pressure from climate warming and increasing human activities. As a major ecological problem, gully erosion is destroying land and soil resources on the Tibetan Plateau, but related research is limited, and susceptibility areas and influencing factors are unclear. Machine learning methods are often applied to study gully initiation susceptibility, but they require a programming foundation. Therefore, the Redui watershed on the southern Tibetan Plateau with severe gully erosion was selected to evaluate the susceptibility and influencing factors of gully initiation through 12 influencing factors including topography, human activity, and underlying surface conditions, and all 2310 gully headcut sites. Two non-code nonlinear modeling methods, the categorical Regression (CATREG) and geographical detector (Geodetector) methods, were first used in the spatial modeling of gully initiation susceptibility. The results showed that the gully initiation susceptibility of the hillslope around the alluvial fan was highest. The very high susceptibility areas of the CATREG model and Geodetector model account for 18.2% and 16% of the total, respectively. The main influencing factors of gully initiation were elevation, relief, and soil type recognized by CATREG, and elevation, human footprint, and soil type recognized by Geodetector. Elevation is the primary factor controlling downstream susceptibility in both models. The primary factors in the upper and middle reaches are soil type and relief identified by CATREG. Human footprint, soil type, and distance to road are primary factors in the upper and middle reaches identified by Geodetector. The explanatory power of elevation, elevation-relief interaction, Geodetector model and CATREG model were 39%, 54%, 46.4% and 73.8%, respectively, at extremely significant levels (P &lt; 0.001), which means that the influencing factors were well considered and that the methods have great application potential in the future.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 747-760"},"PeriodicalIF":7.3,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and constraints of conservation agriculture adoption in smallholder farms in sub-Saharan Africa: A review","authors":"","doi":"10.1016/j.iswcr.2024.03.001","DOIUrl":"10.1016/j.iswcr.2024.03.001","url":null,"abstract":"<div><div>Common farming practices in sub-Saharan Africa (SSA) such as intensive and repeated tillage, complete crop residue removal, and biomass burning create risks of soil degradation. To reduce these risks, conservation agriculture (CA) uses minimal soil disturbance, crop residue retention, and crop rotation in order to reduce soil erosion, improve soil quality and crop production, and facilitate climate change mitigation and adaptation. Nevertheless, CA adoption in SSA is extremely low. This paper aims to review current practices, challenges, and constraints to the adoption of CA in SSA. Our analyses show that CA is practiced in only about 1.25% of the total cultivated area in SSA, despite two decades of efforts to promote CA adoption among smallholder farmers. Specific difficulties in CA adoption by smallholder farmers in SSA may be attributed to i) lack of locally adaptable CA systems, particularly those integrating the needs of livestock production; ii) lack of adequate crop residues for surface mulch; iii) inconsistent and low crop yields; iv) lack of smallholder CA equipment for direct sowing; v) limited availability, high cost, and inadequate knowledge associated with the use of appropriate fertilizer and herbicides; and vi) lack of CA knowledge and training. Other problems relate to the management of specific soil orders, e.g., CA implementation on steeply sloping land and poorly drained soils such as Vertisols. CA adoption by smallholder farmers is also obstructed by socio-economic factors due to smallholder farmers’ focus on short term yield increases and their lack of access to markets, loans, and education. To facilitate wider adoption by smallholder farmers in SSA, CA approaches should be downscaled to fit the existing tillage tools and the specific agroecological and socio-economic farm settings.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 828-843"},"PeriodicalIF":7.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140273513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agroecology-based land use/land cover change detection, prediction and its implications for land degradation: A case study in the Upper Blue Nile Basin","authors":"","doi":"10.1016/j.iswcr.2024.02.002","DOIUrl":"10.1016/j.iswcr.2024.02.002","url":null,"abstract":"<div><div>This study examined land use/land cover (LULC) changes in Chemoga watershed of the Upper Blue Nile Basin, comprising four distinct agroecological regions: Wet Wurch, Moist Dega, Moist Weyna Dega, and Moist Kolla. We used multi-temporal Landsat images from 1985 to 2020, a hybrid classification method and the Cellular Automata-Markov model to analyze historical and predict future (2020–2060) LULC changes under business-<em>as</em>-usual (BAU) and land conservation (LC) scenarios. Magnitudes and patterns of spaciotemporal LULC changes were analyzed using intensity analysis. Cropland expanded across all agroecologies from 1985 to 2020, with Moist Kolla experiencing the highest increase at the expense of woodland, due the introduction of commercial farming to this hotter, less populated and inaccessible area. Moist Dega exhibited the highest allocation changes within cropland and forest, attributable to farmers’ adoption of rotational land use to rehabilitate extensively degraded cultivated lands. Under the BAU scenario, projections suggest further cropland expansion at expense of woodland in Moist Kolla and built-up areas at the expense of cropland and grassland in Moist Dega. Under the LC scenario, forest cover is expected to increase at the expense of cropland across all agroecologies. The historical and projected BAU LULC change scenario substantially increased soil erosion and reduced ecosystem services. These effects can be minimized if LC scenario is properly implemented. The agroecology-based LULC intensity analysis reveals local drivers of change and associated impacts, providing vital insights for targeted land use planning in this study watershed and other watersheds facing similar challenges.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 786-797"},"PeriodicalIF":7.3,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139966119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VIS-NIR spectroscopy and environmental factors coupled with PLSR models to predict soil organic carbon and nitrogen","authors":"","doi":"10.1016/j.iswcr.2024.02.001","DOIUrl":"10.1016/j.iswcr.2024.02.001","url":null,"abstract":"<div><div>Soil profile organic carbon (OC) and total nitrogen (TN) are influenced by topographic attributes, and land use. The visible and near-infrared (Vis-NIR) spectroscopy method can be used for the prediction of OC and TN because it is reliable, nondestructive, fast, and cost-effective. VIS-NIR soil spectral and environmental data were combined with the Partial least squares regression (PLSR) model to examine the effect of topography attributes and land use on topsoil and subsoil OC and TN stocks. After this, based on the soil depth, 114 soil samples were collected from 0 to 20 cm (topsoil) and 20–50 cm (subsoil) under three land uses, as well as OC and TN, along with several soil properties including soil particles (sand, silt, clay), pH, and bulk density in both topsoil and subsoil samples were measured. A DEM with a resolution of 30 m was used to derive the topography factors and remote sensing data was used to calculate the vegetation index. Soils (0–50 cm) under orchard land use had the highest stock of SOC (7.4 kg m⁻²) as well as TN (2.4 kg m⁻²). There was a significant increase in the organic matter stock of soils located on the south aspect (8.3 kg m⁻²) compared to soils located on other aspects, particularly on the north aspect (3.9% increase). Soils on the south aspect contain higher soil-water contents and lower temperatures, resulting in a decrease in the decomposition of soil organic matter. A strong positive correlation was demonstrated between topography wetness index (0.57–0.63) and topography TN stocks (0.54–0.66) as well as the highest loading score among terrain attributes, suggesting that topography is the primary factor controlling SOC stocks, particularly subsoil stocks. Additionally, we found that soils on the south-facing aspects (N aspects) had the highest spectra. Additionally, the PLSR, which showed an R² of 0.82, a RMSE of 0.15 %, and a RPD of 0.39 indicated excellent prediction capabilities for the OC content. We concluded that the PLSR model coupled with Vis-NIR spectroscopy is able to predict topsoil and subsoil OC and N content under different aspect slopes.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"12 4","pages":"Pages 844-854"},"PeriodicalIF":7.3,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139876070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}