The conversion of natural grassland to cropland is a widespread practice that threatens soil carbon stocks. However, its impact on the dynamics of dissolved organic matter (DOM), particularly the thermodynamic stability of subsoil DOM, remains poorly quantified. This study investigated the distribution and biochemical characterization of DOM and its transformation processes following 11 and 40 years of conversion from natural grassland to agricultural land on a Haplic Chernozems soil in northern China. The post-conversion agricultural management was no-tillage, focused on rain-fed cultivation of wheat and oats. The chemical characterization of DOM was conducted using UV–visible and fluorescence spectroscopy and Fourier-transform ion cyclotron resonance mass spectrometry combined with PARAFAC analysis and substrate-explicit modeling. The results showed a critical depth-dependent shift of DOM content and stability: while topsoil (0–20 cm) dissolved organic carbon (DOC) decreased by 88 %, its thermodynamic stability increased. Conversely, subsoil (80–100 cm) DOC increased by 1.8-fold, yet this accumulated pool was characterized by lower molecular weight, enrichment of a blue-shifted humic-like component (C2), and significantly higher thermodynamic degradability. Molecular-level evidence revealed that this destabilization was primarily driven by the transformation of complex subsoil organic matter into labile DOM, a process strongly linked to nutrient enrichment. Although derived from a single representative soil type, our findings provide a mechanistic framework for assessing carbon vulnerability in agroecosystems following land-use change.
{"title":"The conversion of natural grassland to cropland drives thermodynamic destabilization of subsoil DOM over decades","authors":"Yuxin Yan, Yumei Peng, Jia Shi, Chunpeng Huo, Zhongmin Fan, Xiang Wang","doi":"10.1016/j.still.2025.107051","DOIUrl":"10.1016/j.still.2025.107051","url":null,"abstract":"<div><div>The conversion of natural grassland to cropland is a widespread practice that threatens soil carbon stocks. However, its impact on the dynamics of dissolved organic matter (DOM), particularly the thermodynamic stability of subsoil DOM, remains poorly quantified. This study investigated the distribution and biochemical characterization of DOM and its transformation processes following 11 and 40 years of conversion from natural grassland to agricultural land on a Haplic Chernozems soil in northern China. The post-conversion agricultural management was no-tillage, focused on rain-fed cultivation of wheat and oats. The chemical characterization of DOM was conducted using UV–visible and fluorescence spectroscopy and Fourier-transform ion cyclotron resonance mass spectrometry combined with PARAFAC analysis and substrate-explicit modeling. The results showed a critical depth-dependent shift of DOM content and stability: while topsoil (0–20 cm) dissolved organic carbon (DOC) decreased by 88 %, its thermodynamic stability increased. Conversely, subsoil (80–100 cm) DOC increased by 1.8-fold, yet this accumulated pool was characterized by lower molecular weight, enrichment of a blue-shifted humic-like component (C2), and significantly higher thermodynamic degradability. Molecular-level evidence revealed that this destabilization was primarily driven by the transformation of complex subsoil organic matter into labile DOM, a process strongly linked to nutrient enrichment. Although derived from a single representative soil type, our findings provide a mechanistic framework for assessing carbon vulnerability in agroecosystems following land-use change.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107051"},"PeriodicalIF":6.8,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soil mechanical properties are essential for evaluating its strength and stability when subjected to stress from farm machinery. Conventional methods for measuring these properties are often slow, labour-intensive, and destructive. This study presents a novel approach that utilizes on-the-go proximal soil sensors (PSS), specifically electromagnetic induction (EMI) and gamma-ray spectroscopy (GRS), alongside terrain attributes, to evaluate their effectiveness in estimating three key soil mechanical properties: (1) precompression stress (σpc), (2) compression index (CC), and (3) strain at 100 kPa (Ɛ100kPa). For this, a geophysical survey and soil sampling were conducted across three arable fields, yielding 129 bulk soil samples and 516 intact soil cores (100 cm3) collected from 69 sampling points at two depths (0.15 and 0.40 m). Uniaxial confined compression tests (UCCT) were conducted to measure these mechanical properties, with multiple linear regression (MLR) applied for their estimation and cross-validation with the leave-one-out method (LOOCV). Both site-specific and unified datasets were analyzed, revealing higher prediction accuracy at 0.15 m compared to 0.40 m depth. Among the examined soil mechanical properties, CC was estimated most accurately, followed by σpc and Ɛ100kPa. Estimates of σpc derived from on-the-go PSS combined with terrain attributes substantially outperformed those obtained from the existing pedotransfer function. Furthermore, digital maps of these properties were successfully generated to visualize their spatial variability at the field scale. This study shows that on-the-go PSS provide a rapid, field-scale and non-destructive framework for estimating soil mechanical properties, supporting improved soil compaction assessment and monitoring.
{"title":"High-resolution mapping of soil mechanical properties by integrating geophysical sensors and terrain attributes","authors":"Ameesh Khatkar, Triven Koganti, Amélie Beucher, Alvaro Calleja-Huerta, Lars Juhl Munkholm, Mathieu Lamandé","doi":"10.1016/j.still.2025.107019","DOIUrl":"10.1016/j.still.2025.107019","url":null,"abstract":"<div><div>Soil mechanical properties are essential for evaluating its strength and stability when subjected to stress from farm machinery. Conventional methods for measuring these properties are often slow, labour-intensive, and destructive. This study presents a novel approach that utilizes on-the-go proximal soil sensors (PSS), specifically electromagnetic induction (EMI) and gamma-ray spectroscopy (GRS), alongside terrain attributes, to evaluate their effectiveness in estimating three key soil mechanical properties: (1) precompression stress (σ<sub>pc</sub>), (2) compression index (C<sub>C</sub>), and (3) strain at 100 kPa (Ɛ<sub>100kPa</sub>). For this, a geophysical survey and soil sampling were conducted across three arable fields, yielding 129 bulk soil samples and 516 intact soil cores (100 cm<sup>3</sup>) collected from 69 sampling points at two depths (0.15 and 0.40 m). Uniaxial confined compression tests (UCCT) were conducted to measure these mechanical properties, with multiple linear regression (MLR) applied for their estimation and cross-validation with the leave-one-out method (LOOCV). Both site-specific and unified datasets were analyzed, revealing higher prediction accuracy at 0.15 m compared to 0.40 m depth. Among the examined soil mechanical properties, C<sub>C</sub> was estimated most accurately, followed by σ<sub>pc</sub> and Ɛ<sub>100kPa</sub>. Estimates of σ<sub>pc</sub> derived from on-the-go PSS combined with terrain attributes substantially outperformed those obtained from the existing pedotransfer function. Furthermore, digital maps of these properties were successfully generated to visualize their spatial variability at the field scale. This study shows that on-the-go PSS provide a rapid, field-scale and non-destructive framework for estimating soil mechanical properties, supporting improved soil compaction assessment and monitoring.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107019"},"PeriodicalIF":6.8,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.still.2025.107055
Yujiao Wei , Yiyun Chen , Jiaxue Wang , Zheng Sun , Bo Wang , Chi Zhang , Chen Wu , Tianjie Zhao , Huanfeng Shen
Accurate farmland soil carbon mapping is essential for assessing soil health and monitoring carbon dynamics. Currently, most studies primarily rely on vegetation indicators (e.g., optical vegetation indices) or other surface features that are indirectly related to soil carbon. In contrast, surface soil properties beneath vegetation cover play a more direct and critical role in governing soil carbon dynamics and spatial distribution. Here, we developed an innovative soil carbon mapping framework based on machine learning, which integrates multi-source remote sensing data to leverage complementary vegetation and soil surface characteristics. Furthermore, the radar backscatter model was incorporated to enhance prediction accuracy by capturing additional soil structural properties. Specifically, Sentinel-2 (S2) optical data were used to derive comprehensive surface indicators including vegetation indices, brightness indices, and moisture indices. Sentinel-1 (S1) radar data provided complementary ground surface information beneath vegetation cover through its microwave penetration capability. To ensure that S1 backscatter accurately reflects soil physical properties, we applied the Water-Cloud Model (WCM) for vegetation correction, and compared correction results using NDVI and NDWI. A total of 154 topsoil samples were systematically collected from the central Songliao Plain, China, to validate the framework’s performance. By employing the XGBoost algorithm, we developed soil carbon prediction models under four distinct modeling strategies: (Ι) climatic conditions, topographical features, and S2-derived variables; (Ⅱ) adding uncorrected S1-derived variables; (Ⅲ) adding NDVI-corrected S1-derived variables; and (Ⅳ) adding NDWI-corrected S1-derived variables. The results indicated that compared with strategy Ι, strategy Ⅱ achieved an RMSE of 1.70 g/kg (9.09 % lower) and an R2 of 0.47 (30.56 % higher), demonstrating the added value of radar information. The NDWI-corrected model (strategy Ⅳ) performed best, with an RMSE of 1.66 g/kg (11.23 % lower than strategy Ι) and an R2 of 0.50 (38.89 % higher), highlighting the effectiveness of vegetation correction using NDWI. These findings emphasize how integrating optical and radar remote sensing can enrich the data dimensions used in soil carbon mapping. Proper vegetation correction is also crucial for improving mapping accuracy. Together, these approaches provide a scalable framework for precise farmland soil carbon prediction.
{"title":"Enhancing farmland soil carbon mapping: Integrating radar backscatter model and machine learning","authors":"Yujiao Wei , Yiyun Chen , Jiaxue Wang , Zheng Sun , Bo Wang , Chi Zhang , Chen Wu , Tianjie Zhao , Huanfeng Shen","doi":"10.1016/j.still.2025.107055","DOIUrl":"10.1016/j.still.2025.107055","url":null,"abstract":"<div><div>Accurate farmland soil carbon mapping is essential for assessing soil health and monitoring carbon dynamics. Currently, most studies primarily rely on vegetation indicators (e.g., optical vegetation indices) or other surface features that are indirectly related to soil carbon. In contrast, surface soil properties beneath vegetation cover play a more direct and critical role in governing soil carbon dynamics and spatial distribution. Here, we developed an innovative soil carbon mapping framework based on machine learning, which integrates multi-source remote sensing data to leverage complementary vegetation and soil surface characteristics. Furthermore, the radar backscatter model was incorporated to enhance prediction accuracy by capturing additional soil structural properties. Specifically, Sentinel-2 (S2) optical data were used to derive comprehensive surface indicators including vegetation indices, brightness indices, and moisture indices. Sentinel-1 (S1) radar data provided complementary ground surface information beneath vegetation cover through its microwave penetration capability. To ensure that S1 backscatter accurately reflects soil physical properties, we applied the Water-Cloud Model (WCM) for vegetation correction, and compared correction results using NDVI and NDWI. A total of 154 topsoil samples were systematically collected from the central Songliao Plain, China, to validate the framework’s performance. By employing the XGBoost algorithm, we developed soil carbon prediction models under four distinct modeling strategies: (Ι) climatic conditions, topographical features, and S2-derived variables; (Ⅱ) adding uncorrected S1-derived variables; (Ⅲ) adding NDVI-corrected S1-derived variables; and (Ⅳ) adding NDWI-corrected S1-derived variables. The results indicated that compared with strategy Ι, strategy Ⅱ achieved an RMSE of 1.70 g/kg (9.09 % lower) and an R<sup>2</sup> of 0.47 (30.56 % higher), demonstrating the added value of radar information. The NDWI-corrected model (strategy Ⅳ) performed best, with an RMSE of 1.66 g/kg (11.23 % lower than strategy Ι) and an R<sup>2</sup> of 0.50 (38.89 % higher), highlighting the effectiveness of vegetation correction using NDWI. These findings emphasize how integrating optical and radar remote sensing can enrich the data dimensions used in soil carbon mapping. Proper vegetation correction is also crucial for improving mapping accuracy. Together, these approaches provide a scalable framework for precise farmland soil carbon prediction.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107055"},"PeriodicalIF":6.8,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.still.2025.107049
Lifeng Zhou , Hanzhi Tao , Yang Qiliang , Hao Feng , Kadambot H.M. Siddique , Ting Jin
In clay soil regions, soil hypoxia frequently induces premature senescence of sweet corn under mulched drip irrigation (MDI), particularly in late-season crops within continuous multi-season planting systems. While biochar’s effect on soil moisture is well documented, its influence on soil oxygen dynamics remains unclear. In this study, unsorted biochar particles (UBP), large biochar particles (LBP), and small biochar particles (SBP) were applied, with no biochar as the control (CK). We evaluated soil pore distribution, gas transport indicators, moisture content, and oxygen partial pressure (pO2), and assessed their impact on root and leaf senescence and grain yield in early- and late-season sweet corn crops. LBP increased total soil porosity and reduced soil bulk density, whereas UBP and SBP had no significant effect. LBP enlarged macropores (30–100 μm) and micropores (3–10 μm), resulting in a bimodal pore distribution, in contrast to the single-peak distribution (10–30 μm) in CK and SBP. LBP also enhanced macropore connectivity and reduced tortuosity, leading to higher air-filled porosity, air permeability, and gas diffusivity. SBP improved soil water-holding capacity but impeded gas transport due to pore “fineness”. Consequently, LBP decreased residual water content and increased plant-available water, balancing the tradeoff between water and oxygen under MDI. Soil hypoxia occurred in SBP and CK, causing roots to float and extend horizontally, whereas LBP prevented these effects. LBP significantly increased soil pO2 and delayed senescence, ultimately enhancing sweet corn yield in both growing seasons. We recommend applying large biochar particles (2.0–4.0 mm) to improve aeration and pO2 in clay soils. Additionally, the influence of fine soil particles on biochar’s internal pore structure warrants further study, particularly in irrigated farmland.
{"title":"Biochar particle size shapes soil water–oxygen conditions and delays senescence in sweet corn under mulched drip irrigation","authors":"Lifeng Zhou , Hanzhi Tao , Yang Qiliang , Hao Feng , Kadambot H.M. Siddique , Ting Jin","doi":"10.1016/j.still.2025.107049","DOIUrl":"10.1016/j.still.2025.107049","url":null,"abstract":"<div><div>In clay soil regions, soil hypoxia frequently induces premature senescence of sweet corn under mulched drip irrigation (MDI), particularly in late-season crops within continuous multi-season planting systems. While biochar’s effect on soil moisture is well documented, its influence on soil oxygen dynamics remains unclear. In this study, unsorted biochar particles (UBP), large biochar particles (LBP), and small biochar particles (SBP) were applied, with no biochar as the control (CK). We evaluated soil pore distribution, gas transport indicators, moisture content, and oxygen partial pressure (pO<sub>2</sub>), and assessed their impact on root and leaf senescence and grain yield in early- and late-season sweet corn crops. LBP increased total soil porosity and reduced soil bulk density, whereas UBP and SBP had no significant effect. LBP enlarged macropores (30–100 μm) and micropores (3–10 μm), resulting in a bimodal pore distribution, in contrast to the single-peak distribution (10–30 μm) in CK and SBP. LBP also enhanced macropore connectivity and reduced tortuosity, leading to higher air-filled porosity, air permeability, and gas diffusivity. SBP improved soil water-holding capacity but impeded gas transport due to pore “fineness”. Consequently, LBP decreased residual water content and increased plant-available water, balancing the tradeoff between water and oxygen under MDI. Soil hypoxia occurred in SBP and CK, causing roots to float and extend horizontally, whereas LBP prevented these effects. LBP significantly increased soil pO<sub>2</sub> and delayed senescence, ultimately enhancing sweet corn yield in both growing seasons. We recommend applying large biochar particles (2.0–4.0 mm) to improve aeration and pO<sub>2</sub> in clay soils. Additionally, the influence of fine soil particles on biochar’s internal pore structure warrants further study, particularly in irrigated farmland.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107049"},"PeriodicalIF":6.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.still.2025.107048
Muhammad Riaz , Lei Yan , Xia Hao
Boron (B) is an essential micronutrient for plant physiological processes, yet excessive soil concentrations can severely impair plant health, particularly in sensitive crops such as rice. Although biochar is known to improve soil conditions and mitigate various environmental stressors, its capacity to alleviate B toxicity remains insufficiently studied. This research examined the effects of biochar application on rice seedling growth and soil microbial communities under boron toxicity (BT). The treatments were designated as CK (control), BC (biochar with normal boron), BT (B toxicity), and BC+BT (biochar with B toxicity). Boron stress significantly reduced shoot length, fresh and dry biomass, and leaf chlorophyll content. In contrast, BC+BT markedly improved these growth traits relative to BT alone. Biochar also altered the distribution of B fractions in soil by lowering easily soluble and residual B while increasing organically bound B. Changes in soil properties under BC included higher total nitrogen (TN), available potassium (AK), and soil organic matter (SOM). Furthermore, the study revealed clear differences in soil bacterial diversity, with the BC+BT treatment showing higher alpha-diversity metrics than the other treatments, while fungal diversity remained largely unchanged. Community composition analyses indicated that biochar application reshaped both bacterial and fungal community structures. These findings highlight the potential of biochar as an effective soil amendment for mitigating the adverse effects of B contamination on rice seedlings and improving overall soil health.
{"title":"Biochar application enhances tolerance to boron toxicity in rice (Oryza sativa) seedlings","authors":"Muhammad Riaz , Lei Yan , Xia Hao","doi":"10.1016/j.still.2025.107048","DOIUrl":"10.1016/j.still.2025.107048","url":null,"abstract":"<div><div>Boron (B) is an essential micronutrient for plant physiological processes, yet excessive soil concentrations can severely impair plant health, particularly in sensitive crops such as rice. Although biochar is known to improve soil conditions and mitigate various environmental stressors, its capacity to alleviate B toxicity remains insufficiently studied. This research examined the effects of biochar application on rice seedling growth and soil microbial communities under boron toxicity (BT). The treatments were designated as CK (control), BC (biochar with normal boron), BT (B toxicity), and BC+BT (biochar with B toxicity). Boron stress significantly reduced shoot length, fresh and dry biomass, and leaf chlorophyll content. In contrast, BC+BT markedly improved these growth traits relative to BT alone. Biochar also altered the distribution of B fractions in soil by lowering easily soluble and residual B while increasing organically bound B. Changes in soil properties under BC included higher total nitrogen (TN), available potassium (AK), and soil organic matter (SOM). Furthermore, the study revealed clear differences in soil bacterial diversity, with the BC+BT treatment showing higher alpha-diversity metrics than the other treatments, while fungal diversity remained largely unchanged. Community composition analyses indicated that biochar application reshaped both bacterial and fungal community structures. These findings highlight the potential of biochar as an effective soil amendment for mitigating the adverse effects of B contamination on rice seedlings and improving overall soil health.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107048"},"PeriodicalIF":6.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-31DOI: 10.1016/j.still.2025.107028
Shuo Li , Yuwei Zhou , Abdul Mounem Mouazen , Songchao Chen , Raphael A. Viscarra Rossel , Asim Biswas , Wenjun Ji , Zhou Shi , Shanqin Wang
To promote soil organic carbon (SOC) storage and to meet growing food demand with limited land, it is essential to understand the spatial characteristics of SOC across the entire profile of paddy soils. The establishment of soil spectral libraries (SSLs) at various geographical scales has made near-infrared (NIR: 700–1100 nm) and shortwave-infrared (SWIR: 1100–2500 nm) hyperspectral imaging (HSI) more feasible for the rapid and cost-effective estimation of SOC. This study aimed to integrate SSLs with HSI for fine-scale mapping of elemental concentrations with a high spatial resolution of 1 mm per pixel (image size: 980 × 160 pixels) with a spectral range of 900–1700 nm. We apply SOC-reflectance calibrations from Global Soil Spectral Library (GSSL) to an independent local field site for the entire profile in paddy soils to a depth of 1-m, combined with spectral similarity with continuum removal (SS-CR) analysis, three spectral matching methods (e.g., Euclidean distances [ED], Mahalanobis distances [MD], and Spectral angle mapper [SAM]) and two modeling algorithms (e.g., random forest [RF] and Cubist). Additionally, we compared the performance of different Global and Local models in characterizing the distribution of SOC across the entire profile. Results indicated that although the Cubist-Local model provided good prediction accuracy (R2 ≥ 0.77, RMSE ≤ 0.77 %, RPIQ ≥ 1.90), its ability to fine-scale mapping of the profile SOC was limited. In contrast, the RF-Local model based on the ED spectral matching method (ED-RF-Local) not only achieved the best performance (R2 = 0.80, RMSE = 0.78 %, RPIQ = 1.86), but also successfully mapped SOC across the entire soil profile. This model used two-fifths of the samples compared to the Global model. The findings of this study provide a valuable reference for SOC prediction and mapping at the field scale and across the entire soil profile using HSI techniques and GSSL, emphasizing the potential for predictions in paddy soils with high vertical resolution.
{"title":"Integrating soil spectral libraries with laboratory hyperspectral imaging for profile organic carbon prediction in paddy soils","authors":"Shuo Li , Yuwei Zhou , Abdul Mounem Mouazen , Songchao Chen , Raphael A. Viscarra Rossel , Asim Biswas , Wenjun Ji , Zhou Shi , Shanqin Wang","doi":"10.1016/j.still.2025.107028","DOIUrl":"10.1016/j.still.2025.107028","url":null,"abstract":"<div><div>To promote soil organic carbon (SOC) storage and to meet growing food demand with limited land, it is essential to understand the spatial characteristics of SOC across the entire profile of paddy soils. The establishment of soil spectral libraries (SSLs) at various geographical scales has made near-infrared (NIR: 700–1100 nm) and shortwave-infrared (SWIR: 1100–2500 nm) hyperspectral imaging (HSI) more feasible for the rapid and cost-effective estimation of SOC. This study aimed to integrate SSLs with HSI for fine-scale mapping of elemental concentrations with a high spatial resolution of 1 mm per pixel (image size: 980 × 160 pixels) with a spectral range of 900–1700 nm. We apply SOC-reflectance calibrations from Global Soil Spectral Library (GSSL) to an independent local field site for the entire profile in paddy soils to a depth of 1-m, combined with spectral similarity with continuum removal (SS-CR) analysis, three spectral matching methods (<em>e.g.</em>, Euclidean distances [ED], Mahalanobis distances [MD], and Spectral angle mapper [SAM]) and two modeling algorithms (<em>e.g.</em>, random forest [RF] and Cubist). Additionally, we compared the performance of different Global and Local models in characterizing the distribution of SOC across the entire profile. Results indicated that although the Cubist-Local model provided good prediction accuracy (<em>R</em><sup>2</sup> ≥ 0.77, RMSE ≤ 0.77 %, RPIQ ≥ 1.90), its ability to fine-scale mapping of the profile SOC was limited. In contrast, the RF-Local model based on the ED spectral matching method (ED-RF-Local) not only achieved the best performance (<em>R</em><sup>2</sup> = 0.80, RMSE = 0.78 %, RPIQ = 1.86), but also successfully mapped SOC across the entire soil profile. This model used two-fifths of the samples compared to the Global model. The findings of this study provide a valuable reference for SOC prediction and mapping at the field scale and across the entire soil profile using HSI techniques and GSSL, emphasizing the potential for predictions in paddy soils with high vertical resolution.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107028"},"PeriodicalIF":6.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.still.2025.107044
Yuling Shi , Zihao Wu , Pu Shi , Yuanli Zhu
Soil erosion in Northeast China’s black soil region poses serious challenges to agricultural productivity and ecosystem sustainability. This study proposes a novel framework for high-resolution (10 m) mapping of soil erodibility by integrating Sentinel-2 spectral data with a gradient boosting decision tree (GBDT) model. A comprehensive soil erodibility index (CSEI) was developed to represent the combined effects of soil texture, structure, and organic stability. The GBDT model was used to identify the dominant environmental drivers and their nonlinear relationships with CSEI. Results indicate that the normalized difference tillage index (NDTI), soil moisture, and mean annual precipitation are the key influencing factors, collectively explaining 69.3 % of the spatial variability in soil erodibility. Threshold effects were observed, including an inverse S-curve for soil moisture and an inverted-U response to precipitation, reflecting shifts in erosion mechanisms under varying surface conditions. These findings provide quantitative evidence for targeted soil conservation and land-use optimization, supporting management strategies such as conservation tillage, slope-specific terracing, and vegetation restoration to mitigate erosion risks in vulnerable landscapes.
{"title":"Spectra-based predictive mapping of soil erodibility and analysis of its influence mechanism: A typical case study for Northeast China","authors":"Yuling Shi , Zihao Wu , Pu Shi , Yuanli Zhu","doi":"10.1016/j.still.2025.107044","DOIUrl":"10.1016/j.still.2025.107044","url":null,"abstract":"<div><div>Soil erosion in Northeast China’s black soil region poses serious challenges to agricultural productivity and ecosystem sustainability. This study proposes a novel framework for high-resolution (10 m) mapping of soil erodibility by integrating Sentinel-2 spectral data with a gradient boosting decision tree (GBDT) model. A comprehensive soil erodibility index (CSEI) was developed to represent the combined effects of soil texture, structure, and organic stability. The GBDT model was used to identify the dominant environmental drivers and their nonlinear relationships with CSEI. Results indicate that the normalized difference tillage index (NDTI), soil moisture, and mean annual precipitation are the key influencing factors, collectively explaining 69.3 % of the spatial variability in soil erodibility. Threshold effects were observed, including an inverse S-curve for soil moisture and an inverted-U response to precipitation, reflecting shifts in erosion mechanisms under varying surface conditions. These findings provide quantitative evidence for targeted soil conservation and land-use optimization, supporting management strategies such as conservation tillage, slope-specific terracing, and vegetation restoration to mitigate erosion risks in vulnerable landscapes.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107044"},"PeriodicalIF":6.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-27DOI: 10.1016/j.still.2025.107037
Lei Sun , Shouhao Zhang , Wenqi Tang , Abdul Hakim Jamshidi , Luyue Xu , Yunpeng Wang , Zhaofei Fan , Xia Liu , Lei Gao
Soil erosion is a primary cause of soil degradation in the typical black soil region in Northeast China, yet the mechanisms and key driving factors are still not well-known. This study aimed to elucidate the mechanisms of erosion-induced degradation, quantify the contributions of contextual factors and anthropogenic interventions, and identify the key driving factors. Our models indicated that climate showed the strongest statistical association with regional-scale patterns of erosion indicators (A-horizon thickness and gully density) and chemical properties, with path coefficients of 0.81 and −0.67, respectively (p < 0.01). The underlying surface (slope gradient and length) was found to exert a significant indirect influence on erosion indicators and soil properties through anthropogenic factors (ridge-slope angle and total porosity) via mechanical ridging (creating wheel-compacted rutting strips and subsurface compaction zones) and its associated soil compaction. At the plot scale, slope gradient, total porosity, mean annual temperature, and ridge-slope angle made comparable contributions to explaining the variance in A-horizon thickness. Furthermore, the primary statistical influence of precipitation on gully density was contingent on slope gradient and ridge-slope angle. Given the intensified gully density observed where low-RSA ridging meets steep slopes, we recommend adopting precision contour farming on steep slopes to disrupt runoff concentration at its inception, alongside conservation tillage to eliminate compaction-induced porosity loss. By decoupling climate - erosion linkages through targeted terrain management, such practices offer a means to reconcile regional climatic constraints with local controllability.
{"title":"Mechanisms and key driving factors of erosion-induced degradation of sloping cropland in the typical black soil region in Northeast China","authors":"Lei Sun , Shouhao Zhang , Wenqi Tang , Abdul Hakim Jamshidi , Luyue Xu , Yunpeng Wang , Zhaofei Fan , Xia Liu , Lei Gao","doi":"10.1016/j.still.2025.107037","DOIUrl":"10.1016/j.still.2025.107037","url":null,"abstract":"<div><div>Soil erosion is a primary cause of soil degradation in the typical black soil region in Northeast China, yet the mechanisms and key driving factors are still not well-known. This study aimed to elucidate the mechanisms of erosion-induced degradation, quantify the contributions of contextual factors and anthropogenic interventions, and identify the key driving factors. Our models indicated that climate showed the strongest statistical association with regional-scale patterns of erosion indicators (A-horizon thickness and gully density) and chemical properties, with path coefficients of 0.81 and −0.67, respectively (p < 0.01). The underlying surface (slope gradient and length) was found to exert a significant indirect influence on erosion indicators and soil properties through anthropogenic factors (ridge-slope angle and total porosity) via mechanical ridging (creating wheel-compacted rutting strips and subsurface compaction zones) and its associated soil compaction. At the plot scale, slope gradient, total porosity, mean annual temperature, and ridge-slope angle made comparable contributions to explaining the variance in A-horizon thickness. Furthermore, the primary statistical influence of precipitation on gully density was contingent on slope gradient and ridge-slope angle. Given the intensified gully density observed where low-RSA ridging meets steep slopes, we recommend adopting precision contour farming on steep slopes to disrupt runoff concentration at its inception, alongside conservation tillage to eliminate compaction-induced porosity loss. By decoupling climate - erosion linkages through targeted terrain management, such practices offer a means to reconcile regional climatic constraints with local controllability.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107037"},"PeriodicalIF":6.8,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-27DOI: 10.1016/j.still.2025.107036
Cristiano Andre Pott , Leandro Taubinger , Vitor Hugo Outeiro , Leandro Rampim , Miguel David Fuentes-Guevara , Aline Marques Genú , Marcelo Marques Lopes Müller
Understanding the spatial variability of crop yields in no-tillage systems under precision agriculture is crucial for improving production efficiency. Yield maps may serve as effective tools for defining management zones and guiding soil sampling to identify factors that limit crop yield. This study aimed to determine yield classes using yield maps and assess how soil physical and chemical properties influence the yields of maize and common bean in farm field conditions, and identify the critical soil compaction limits in no-tillage system. The research was conducted in a commercial farm with spatial variability in crop yields, measured by monitoring onboard harvesters during the maize and common bean harvests. Soil samples were collected from four productivity classes (high, medium-high, medium-low, and low), as defined by the yield maps. Soil compaction degree was calculated as the ratio between soil bulk density and maximum bulk density obtained from the Proctor test. Results showed that high productivity zones had higher total porosity, lower bulk density, reduced soil compaction degree, higher soil organic matter and higher cation exchange capacity. Soil compaction was the main limiting factor, with critical limit more pronounced in shallower layers. The critical limiting of soil compaction degree in the 0.00–0.40 m profile was 85 % in farm field conditions. Soil compaction is a key limiting factor for productivity in clayey soils. Yield maps, along with soil chemical and physical properties analysis, are valuable tools for identifying limiting factors and improving agricultural management.
{"title":"Soil compaction limits maize and bean yields in precision agriculture zones under no-tillage system","authors":"Cristiano Andre Pott , Leandro Taubinger , Vitor Hugo Outeiro , Leandro Rampim , Miguel David Fuentes-Guevara , Aline Marques Genú , Marcelo Marques Lopes Müller","doi":"10.1016/j.still.2025.107036","DOIUrl":"10.1016/j.still.2025.107036","url":null,"abstract":"<div><div>Understanding the spatial variability of crop yields in no-tillage systems under precision agriculture is crucial for improving production efficiency. Yield maps may serve as effective tools for defining management zones and guiding soil sampling to identify factors that limit crop yield. This study aimed to determine yield classes using yield maps and assess how soil physical and chemical properties influence the yields of maize and common bean in farm field conditions, and identify the critical soil compaction limits in no-tillage system. The research was conducted in a commercial farm with spatial variability in crop yields, measured by monitoring onboard harvesters during the maize and common bean harvests. Soil samples were collected from four productivity classes (high, medium-high, medium-low, and low), as defined by the yield maps. Soil compaction degree was calculated as the ratio between soil bulk density and maximum bulk density obtained from the Proctor test. Results showed that high productivity zones had higher total porosity, lower bulk density, reduced soil compaction degree, higher soil organic matter and higher cation exchange capacity. Soil compaction was the main limiting factor, with critical limit more pronounced in shallower layers. The critical limiting of soil compaction degree in the 0.00–0.40 m profile was 85 % in farm field conditions. Soil compaction is a key limiting factor for productivity in clayey soils. Yield maps, along with soil chemical and physical properties analysis, are valuable tools for identifying limiting factors and improving agricultural management.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107036"},"PeriodicalIF":6.8,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1016/j.still.2025.107035
D. Autovino , V. Bagarello , C. Bondì , G. Russo , F. Zanna , K. Zhioua
Little is known about short-term effects of compost and zeolite addition on hydrodynamic properties of near-saturated coarse-textured soils. These effects were tested for a sandy-loam soil by a Mini-Disk Infiltrometer at three pressure heads (-6, −3 and −1 cm) and a wide range of amendment percentages, pa. Soil hydraulic conductivity was determined on two dates separated by nearly one month whereas soil sorptivity was determined at the end of the sampling period. Overall, the effect of the compost varied from null to appreciable since increasing pa from 0 % to 40 % did not affect the considered parameter or induced a decrease by up to eight times. Instead, the zeolite was largely ineffective since the tested parameters did not vary with pa. At the end of the experiment, the soil amended with zeolite was up to 70–90 % more sorptive and conductive than that amended with the compost. Perhaps the particles of compost represented a physical obstacle to water flow and probably also induced some soil water repellency. Instead, the particles of zeolite were wettable, and they did not appreciably alter the pore size distribution. Adding compost can determine a decrease in the ability of a near-saturated soil to draw and conduct water but this ability does not change with zeolite. Other investigations are required to confirm these results, test the suggested explanation and finally draw general conclusions. The applied methodology in this investigation is easy, cheap and suitable for prolonged monitoring without causing an appreciable alteration of the sampled soil.
{"title":"Hydrodynamic behavior of a near-saturated sandy-loam soil shortly after incorporating compost or zeolite","authors":"D. Autovino , V. Bagarello , C. Bondì , G. Russo , F. Zanna , K. Zhioua","doi":"10.1016/j.still.2025.107035","DOIUrl":"10.1016/j.still.2025.107035","url":null,"abstract":"<div><div>Little is known about short-term effects of compost and zeolite addition on hydrodynamic properties of near-saturated coarse-textured soils. These effects were tested for a sandy-loam soil by a Mini-Disk Infiltrometer at three pressure heads (-6, −3 and −1 cm) and a wide range of amendment percentages, <em>p</em><sub><em>a</em></sub>. Soil hydraulic conductivity was determined on two dates separated by nearly one month whereas soil sorptivity was determined at the end of the sampling period. Overall, the effect of the compost varied from null to appreciable since increasing <em>p</em><sub><em>a</em></sub> from 0 % to 40 % did not affect the considered parameter or induced a decrease by up to eight times. Instead, the zeolite was largely ineffective since the tested parameters did not vary with <em>p</em><sub><em>a</em></sub>. At the end of the experiment, the soil amended with zeolite was up to 70–90 % more sorptive and conductive than that amended with the compost. Perhaps the particles of compost represented a physical obstacle to water flow and probably also induced some soil water repellency. Instead, the particles of zeolite were wettable, and they did not appreciably alter the pore size distribution. Adding compost can determine a decrease in the ability of a near-saturated soil to draw and conduct water but this ability does not change with zeolite. Other investigations are required to confirm these results, test the suggested explanation and finally draw general conclusions. The applied methodology in this investigation is easy, cheap and suitable for prolonged monitoring without causing an appreciable alteration of the sampled soil.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"258 ","pages":"Article 107035"},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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