Pub Date : 2026-08-01Epub Date: 2026-02-07DOI: 10.1016/j.still.2026.107103
Miyanda Chilipamushi , Claudia von Brömssen , Tino Colombi , Thomas Kätterer , Mats Larsbo
Roots are a major pathway for carbon (C) input into agricultural soils, yet field-scale measurements of belowground C inputs and associated root traits remain limited. Consequently, many soil carbon models rely on fixed root-to-shoot ratios, and root trait variability is rarely considered. In this study, we quantified within-field variation in root-to-shoot ratios and root traits (root diameter, root length density and root tissue density) in spring barley (Hordeum vulgare L.) grown in southwestern Sweden in soil classified as Stagnic Eutric Cambisol, Eutric Stagnosol or Haplic Phaeozem according to the World Reference Base system. Roots (0–40 cm) and shoots were sampled during early to mid-reproductive stage, i.e. milking/early dough development stage, in a 50 × 50 cm grid at 11 sampling locations in the same field in two consecutive years. Shoot and root biomass were not correlated, resulting in variable root-to-shoot ratios (quartile coefficients of variation 7–18 %) and no consistent spatial pattern between years. Root traits displayed clear between year and depth variation, with coarser roots in the topsoil and root tissue densities and root length densities shifting across the profile, reflecting the highly plastic nature of root systems. The spatial variation in root properties in the field could not be explained by basic soil properties. Our findings call for a more mechanistic understanding of the drivers for root-to-shoot ratios and the plastic response of root traits to improve field-scale estimates of root-derived C inputs and SOC modelling accuracy.
{"title":"Within-field variation in root-to-shoot ratios and root traits in spring barley: Implications for estimating carbon inputs","authors":"Miyanda Chilipamushi , Claudia von Brömssen , Tino Colombi , Thomas Kätterer , Mats Larsbo","doi":"10.1016/j.still.2026.107103","DOIUrl":"10.1016/j.still.2026.107103","url":null,"abstract":"<div><div>Roots are a major pathway for carbon (C) input into agricultural soils, yet field-scale measurements of belowground C inputs and associated root traits remain limited. Consequently, many soil carbon models rely on fixed root-to-shoot ratios, and root trait variability is rarely considered. In this study, we quantified within-field variation in root-to-shoot ratios and root traits (root diameter, root length density and root tissue density) in spring barley (<em>Hordeum vulgare</em> L.) grown in southwestern Sweden in soil classified as Stagnic Eutric Cambisol, Eutric Stagnosol or Haplic Phaeozem according to the World Reference Base system. Roots (0–40 cm) and shoots were sampled during early to mid-reproductive stage, i.e. milking/early dough development stage, in a 50 × 50 cm grid at 11 sampling locations in the same field in two consecutive years. Shoot and root biomass were not correlated, resulting in variable root-to-shoot ratios (quartile coefficients of variation 7–18 %) and no consistent spatial pattern between years. Root traits displayed clear between year and depth variation, with coarser roots in the topsoil and root tissue densities and root length densities shifting across the profile, reflecting the highly plastic nature of root systems. The spatial variation in root properties in the field could not be explained by basic soil properties. Our findings call for a more mechanistic understanding of the drivers for root-to-shoot ratios and the plastic response of root traits to improve field-scale estimates of root-derived C inputs and SOC modelling accuracy.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107103"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134153","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 : 2026-08-01Epub Date: 2026-02-13DOI: 10.1016/j.still.2026.107125
Feng Gao , Guoyong Yan , Chao Liang , Guancheng Liu , Lijiang Xu , Yajuan Xing , Liming Yin , Qinggui Wang
Plant carbon inputs can influence soil organic carbon (SOC) decomposition rate, i.e., the priming effect, which can be regulated by nitrogen (N) addition. How long-term N addition affects the priming effect remains unclear yet, especially in boreal forests with severer N limitation. Here, soils were collected from a 12-year in-situ N addition field experiment in a boreal forest, and were incubated with 13C-labeled cellulose and glucose. Further, a global data synthesis was conducted to compare the effect of N addition on priming between simple and complex C types. The results showed that cellulose-induced priming effect was inhibited by on average 150 % by 12-yr N addition. Hydrolytic enzymes were decreased, while oxidases were increased, suggesting a shift in microbial C use strategy in response to C limitation. Subsequently, microbial C use efficiency (CUE) was increased by 12-yr N addition. 68.3 % of the variation in the priming effect among all the treatments was explained by microbial CUE, hydrolytic enzymes and oxidases, reflecting the important role in regulating the priming effect. Compared to glucose, the N inhibited effect on cellulose-induced priming was greater, while the N inhibited effect on substrate-derived CO2-C was lower, broadly supporting the microbial N mining and C utilization hypotheses. Further, the N-inhibited effect on priming caused by complex C was greater than that by simple C across the globe. We highlight that substrate C type should be considered for accurately assessing SOC decomposition via the priming effect in the context of N deposition in boreal forests.
{"title":"Priming effect is inhibited by 12-year field nitrogen addition in a boreal forest with the extent depending on substrate carbon type","authors":"Feng Gao , Guoyong Yan , Chao Liang , Guancheng Liu , Lijiang Xu , Yajuan Xing , Liming Yin , Qinggui Wang","doi":"10.1016/j.still.2026.107125","DOIUrl":"10.1016/j.still.2026.107125","url":null,"abstract":"<div><div>Plant carbon inputs can influence soil organic carbon (SOC) decomposition rate, <em>i.e.</em>, the priming effect, which can be regulated by nitrogen (N) addition. How long-term N addition affects the priming effect remains unclear yet, especially in boreal forests with severer N limitation. Here, soils were collected from a 12-year <em>in-situ</em> N addition field experiment in a boreal forest, and were incubated with <sup>13</sup>C-labeled cellulose and glucose. Further, a global data synthesis was conducted to compare the effect of N addition on priming between simple and complex C types. The results showed that cellulose-induced priming effect was inhibited by on average 150 % by 12-yr N addition. Hydrolytic enzymes were decreased, while oxidases were increased, suggesting a shift in microbial C use strategy in response to C limitation. Subsequently, microbial C use efficiency (CUE) was increased by 12-yr N addition. 68.3 % of the variation in the priming effect among all the treatments was explained by microbial CUE, hydrolytic enzymes and oxidases, reflecting the important role in regulating the priming effect. Compared to glucose, the N inhibited effect on cellulose-induced priming was greater, while the N inhibited effect on substrate-derived CO<sub>2</sub>-C was lower, broadly supporting the microbial N mining and C utilization hypotheses. Further, the N-inhibited effect on priming caused by complex C was greater than that by simple C across the globe. We highlight that substrate C type should be considered for accurately assessing SOC decomposition <em>via</em> the priming effect in the context of N deposition in boreal forests.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107125"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175019","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 : 2026-08-01Epub Date: 2026-02-09DOI: 10.1016/j.still.2026.107106
Yajun Geng , Hongmin Zhang , Xueting Zheng , Junming Liu , Tao Zhou , Dongxu Dai , Xiaoyan Liu , Tingting Liu , Angela Lausch , Bingcheng Si , Shengxiang Xu , Feng Liu
Accurate mapping of soil organic carbon (SOC) using optical remote sensing is often constrained by persistent cloud cover, which limits data availability in many regions. While recent studies have explored the feasibility of radar sensors for SOC mapping to overcome this limitation, they have predominantly relied on backscatter features, largely overlooking the potential of interferometric coherence. To address this gap, this study assessed the potential of synergistically using backscatter/coherence observations from Sentinel-1 and optical data from Sentinel-2 for mapping SOC across the Iberian Peninsula. Backscatter, coherence, optical, and traditional auxiliary data (terrain and climate) were utilized as input features, and their various combinations were integrated with the LUCAS 2018 soil database to develop machine learning-based SOC prediction models. We evaluated how the temporal interval of backscatter composites and the temporal baseline of coherence data affected model performance. Both radar metrics showed strong predictive power for SOC, and their temporal configurations substantially affected modeling performance. Backscatter images with a monthly interval achieved the best performance, whereas longer intervals progressively decreased predictive accuracy. Models trained on coherence with shorter temporal baselines outperformed those with longer temporal baselines. The joint use of these two radar metrics improved predictive accuracy (R2 = 0.42), surpassing models that only used Sentinel-2 optical data (R2 = 0.38). Our results demonstrate promising prospects of coherence/backscatter data as substitutes or complements to optical data for SOC mapping. Integrating these three complementary and relatively independent remote sensing sources notably improved model performance, achieving accuracy no lower than models based on traditional auxiliary data. Variable importance analysis indicated that radar-derived backscatter and coherence were crucial input features for SOC mapping. The contribution of backscatter to SOC prediction was influenced by polarization modes and orbital directions, with cross-polarization and ascending-orbit backscatter showing greater importance than co-polarization and descending-orbit backscatter, respectively. The mapping results derived solely from coherence and backscatter data exhibited spatial patterns broadly consistent with those obtained from optical and traditional auxiliary data. The proposed cloud computing-based workflow utilizing freely available Sentinel optical and radar imagery provides a cost-effective and reproducible approach for large-scale SOC mapping.
{"title":"Contribution of Sentinel-1 radar backscatter/coherence and Sentinel-2 optical data to digital mapping of soil organic carbon in the Iberian Peninsula","authors":"Yajun Geng , Hongmin Zhang , Xueting Zheng , Junming Liu , Tao Zhou , Dongxu Dai , Xiaoyan Liu , Tingting Liu , Angela Lausch , Bingcheng Si , Shengxiang Xu , Feng Liu","doi":"10.1016/j.still.2026.107106","DOIUrl":"10.1016/j.still.2026.107106","url":null,"abstract":"<div><div>Accurate mapping of soil organic carbon (SOC) using optical remote sensing is often constrained by persistent cloud cover, which limits data availability in many regions. While recent studies have explored the feasibility of radar sensors for SOC mapping to overcome this limitation, they have predominantly relied on backscatter features, largely overlooking the potential of interferometric coherence. To address this gap, this study assessed the potential of synergistically using backscatter/coherence observations from Sentinel-1 and optical data from Sentinel-2 for mapping SOC across the Iberian Peninsula. Backscatter, coherence, optical, and traditional auxiliary data (terrain and climate) were utilized as input features, and their various combinations were integrated with the LUCAS 2018 soil database to develop machine learning-based SOC prediction models. We evaluated how the temporal interval of backscatter composites and the temporal baseline of coherence data affected model performance. Both radar metrics showed strong predictive power for SOC, and their temporal configurations substantially affected modeling performance. Backscatter images with a monthly interval achieved the best performance, whereas longer intervals progressively decreased predictive accuracy. Models trained on coherence with shorter temporal baselines outperformed those with longer temporal baselines. The joint use of these two radar metrics improved predictive accuracy (R<sup>2</sup> = 0.42), surpassing models that only used Sentinel-2 optical data (R<sup>2</sup> = 0.38). Our results demonstrate promising prospects of coherence/backscatter data as substitutes or complements to optical data for SOC mapping. Integrating these three complementary and relatively independent remote sensing sources notably improved model performance, achieving accuracy no lower than models based on traditional auxiliary data. Variable importance analysis indicated that radar-derived backscatter and coherence were crucial input features for SOC mapping. The contribution of backscatter to SOC prediction was influenced by polarization modes and orbital directions, with cross-polarization and ascending-orbit backscatter showing greater importance than co-polarization and descending-orbit backscatter, respectively. The mapping results derived solely from coherence and backscatter data exhibited spatial patterns broadly consistent with those obtained from optical and traditional auxiliary data. The proposed cloud computing-based workflow utilizing freely available Sentinel optical and radar imagery provides a cost-effective and reproducible approach for large-scale SOC mapping.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107106"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146675","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 : 2026-08-01Epub Date: 2026-02-10DOI: 10.1016/j.still.2026.107105
Miri Choi , Sora Lee , Chaelin Jo , Jihyeon Lee , Nayoung Choi , Jeong-Gu Lee , Chaein Na
Cover crop residue (shoot or root) decomposition regulates nutrient cycling and soil organic matter in crop rotation. This study examined the decomposition and nutrient release of Italian ryegrass (Lolium multiflorum, IRG) shoot and root residues incorporated into soil under an IRG–soybean (Glycine max L.) rotation for three years. IRG shoot and root litterbags were incorporated into the soil, and retrieved over 140 days. Dry matter, C, and N fitted asymptotic single-exponential models to residue remaining, and rise-to-maximum models to cumulative C and N release. Initial residue quality and weather covariates supported interpretation. Shoot biomass ranged from 4.43 to 6.92 t ha⁻¹ , which was approximately 2.7–5.3 times greater than root. Moreover, shoot had higher nitrogen concentrations (12.8–20.1 g kg⁻¹) and consistently exhibited lower C/N ratio and lignin/N ratios. The IRG shoot residues decomposed 2.2–2.5 times faster and released up to average 60 kg N ha⁻¹ within 40 days, matching the initial N demand of the subsequent crop. In contrast, root residues decomposed dry matter and C slowly, released negligible N, but contributed to sustained C (129 kg ha⁻¹), indicating potential for soil organic matter stabilization. These results suggest the shoot residue serves as an immediate N source for the subsequent crop and the roots contribute to long-term soil C sequestration in the field. In tilled IRG–soybean systems, incorporating both plant parts have impacts: shoot residues supply starter N and root residues add a slower, more persistent C input that supports soil organic matter accumulation.
作物轮作中覆盖残茬（茎或根）分解调节养分循环和土壤有机质。研究了3年轮作条件下意大利黑麦草（Lolium multiflorum， IRG）茎部和根部残体在土壤中的分解和养分释放情况。将IRG的茎和根垃圾袋放入土壤中，并在140天内回收。干物质、碳和氮对残馀量拟合渐近单指数模型，对累积碳和氮释放拟合上升至最大值模型。初始残留质量和天气协变量支持解释。茎部生物量从4.43到6.92 t ha⁻¹ ，大约是根的2.7-5.3 倍。此外，茎部的氮浓度较高（12.8-20.1 g kg⁻¹），C/N比和木质素/N比始终较低。IRG苗残体分解速度快2.2-2.5 倍，在40天内平均释放出60 kg N ha⁻¹ ，与后续作物的初始N需求相匹配。相比之下，根残对干物质和碳的分解较慢，释放的氮可以忽略不计，但对持续的碳有贡献（129 kg ha⁻¹），表明土壤有机质稳定的潜力。这些结果表明，地上部残茬为后续作物提供了直接的氮源，而根系有助于田间长期的土壤碳封存。在耕作的irg -大豆系统中，将植物的两个部分结合在一起会产生影响：茎部残留物提供启动氮，根系残留物增加更慢、更持久的碳输入，支持土壤有机质积累。
{"title":"Multi-year comparisons of shoot and root decomposition dynamics of Italian ryegrass (Lolium multiflorum) under soybean cropping","authors":"Miri Choi , Sora Lee , Chaelin Jo , Jihyeon Lee , Nayoung Choi , Jeong-Gu Lee , Chaein Na","doi":"10.1016/j.still.2026.107105","DOIUrl":"10.1016/j.still.2026.107105","url":null,"abstract":"<div><div>Cover crop residue (shoot or root) decomposition regulates nutrient cycling and soil organic matter in crop rotation. This study examined the decomposition and nutrient release of Italian ryegrass (<em>Lolium multiflorum</em>, IRG) shoot and root residues incorporated into soil under an IRG–soybean (<em>Glycine max</em> L.) rotation for three years. IRG shoot and root litterbags were incorporated into the soil, and retrieved over 140 days. Dry matter, C, and N fitted asymptotic single-exponential models to residue remaining, and rise-to-maximum models to cumulative C and N release. Initial residue quality and weather covariates supported interpretation. Shoot biomass ranged from 4.43 to 6.92 t ha⁻¹ , which was approximately 2.7–5.3 times greater than root. Moreover, shoot had higher nitrogen concentrations (12.8–20.1 g kg⁻¹) and consistently exhibited lower C/N ratio and lignin/N ratios. The IRG shoot residues decomposed 2.2–2.5 times faster and released up to average 60 kg N ha⁻¹ within 40 days, matching the initial N demand of the subsequent crop. In contrast, root residues decomposed dry matter and C slowly, released negligible N, but contributed to sustained C (129 kg ha⁻¹), indicating potential for soil organic matter stabilization. These results suggest the shoot residue serves as an immediate N source for the subsequent crop and the roots contribute to long-term soil C sequestration in the field. In tilled IRG–soybean systems, incorporating both plant parts have impacts: shoot residues supply starter N and root residues add a slower, more persistent C input that supports soil organic matter accumulation.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107105"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152897","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 : 2026-08-01Epub Date: 2026-02-12DOI: 10.1016/j.still.2026.107101
Sun Xiaoqin , She Dongli , Pan Yongchun , Wang Hongde , Cao Taohong , Ge Jiamin , Ju Xinni
The plain river network (PRN) areas are key high-yield agricultural zones in China, but the excessive use of fertilizers and pesticides has caused severe non-point source (NPS) pollution. Soil pore structure plays a crucial role in regulating NPS generation and transport by controlling water flow and solute transport. However, conventional static fractal parameters are insufficient to capture the actual pathways of solute transport. To overcome this limitation, we introduced the spectral fractal dimension (), a dynamic measure of pore structure derived from random walk theory. In this study, we investigated paddy fields, orchards and vegetable fields in the Jiangsu PRN region, analyzing both pore morphology, the static fractal dimension (mass fractal dimension () and surface fractal dimension ()) and the dynamic pore characteristic () across soil depths. Solute transport parameters were determined using breakthrough curves and the continuous time random walk (CTRW) approach to assess the governing role of pore structure. Our results revealed that surface soils exhibited higher pore abundance and more complex pore structures, resulting in increased solute transport velocity () and dispersion coefficient () compared to deeper layers. Pore morphology (e.g. Volume, Surface, Mean Breadth) and heterogeneity ( and ) controlled and by providing fast flow paths and extensive surface interactions, whereas connectivity () governed anomalous transport () and timing (, ) via tortuous, retention-enhancing pathways. Pore shape further affected solute transport indirectly through connectivity. Among land use types, paddy soils showed a less complex and poorly connected pore structure compared to orchard soils and vegetable soils. To improve the pore structure of paddy soils and mitigate the risk of agricultural non-point source (NPS) pollution in PRN areas, the adoption of water-saving irrigation combined with straw return is recommended.
{"title":"Governing role of pore structure in solute transport processes in plain river network areas: Insights from CT imaging","authors":"Sun Xiaoqin , She Dongli , Pan Yongchun , Wang Hongde , Cao Taohong , Ge Jiamin , Ju Xinni","doi":"10.1016/j.still.2026.107101","DOIUrl":"10.1016/j.still.2026.107101","url":null,"abstract":"<div><div>The plain river network (PRN) areas are key high-yield agricultural zones in China, but the excessive use of fertilizers and pesticides has caused severe non-point source (NPS) pollution. Soil pore structure plays a crucial role in regulating NPS generation and transport by controlling water flow and solute transport. However, conventional static fractal parameters are insufficient to capture the actual pathways of solute transport. To overcome this limitation, we introduced the spectral fractal dimension (<span><math><mi>d</mi></math></span>), a dynamic measure of pore structure derived from random walk theory. In this study, we investigated paddy fields, orchards and vegetable fields in the Jiangsu PRN region, analyzing both pore morphology, the static fractal dimension (mass fractal dimension (<span><math><mrow><mi>D</mi><mi>ₘ</mi></mrow></math></span>) and surface fractal dimension (<span><math><mrow><mi>D</mi><mi>ₛ</mi></mrow></math></span>)) and the dynamic pore characteristic (<span><math><mi>d</mi></math></span>) across soil depths. Solute transport parameters were determined using breakthrough curves and the continuous time random walk (CTRW) approach to assess the governing role of pore structure. Our results revealed that surface soils exhibited higher pore abundance and more complex pore structures, resulting in increased solute transport velocity (<span><math><mi>v</mi></math></span>) and dispersion coefficient (<span><math><mi>E</mi></math></span>) compared to deeper layers. Pore morphology (e.g. Volume, Surface, Mean Breadth) and heterogeneity (<span><math><mrow><mi>D</mi><mi>ₘ</mi></mrow></math></span> and <span><math><mrow><mi>D</mi><mi>ₛ</mi></mrow></math></span>) controlled <span><math><mi>v</mi></math></span> and <span><math><mi>E</mi></math></span> by providing fast flow paths and extensive surface interactions, whereas connectivity (<span><math><mi>d</mi></math></span>) governed anomalous transport (<span><math><mi>β</mi></math></span>) and timing (<span><math><msub><mrow><mi>t</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>t</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>) via tortuous, retention-enhancing pathways. Pore shape further affected solute transport indirectly through connectivity. Among land use types, paddy soils showed a less complex and poorly connected pore structure compared to orchard soils and vegetable soils. To improve the pore structure of paddy soils and mitigate the risk of agricultural non-point source (NPS) pollution in PRN areas, the adoption of water-saving irrigation combined with straw return is recommended.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107101"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161052","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 : 2026-08-01Epub Date: 2026-02-09DOI: 10.1016/j.still.2026.107110
Meixue Zhang , Qinglin Li , Xuanbing Luo , Wenjuan Chen , Rui Wang , Shuailong Yu , Guang Yang
This study focuses on cold arid regions in Xinjiang, China, and investigates the reinforcement effect of Alhagi sparsifolia roots on sandy soil under freeze-thaw conditions. Freeze-thaw cycle and direct shear tests, combined with environmental scanning electron microscopy (ESEM), were conducted to analyze the effects of root reinforcement on the deformation and strength characteristics of sandy soil under varying soil water contents (8–14 %) and freezing temperatures (−5 to −20 ℃). The results revealed that soil deformation during freeze-thaw cycle underwent five distinct stages and was strongly controlled by soil water content and temperature. Root incorporation reduced the maximum soil deformation by more than 30 %, and the suppressive effect exceeded 51 % at high soil water content (14 %). In low-water-content soils (8 %), excessive root content (>0.35 %) induced deformation rebound, which was attributed to root clustering and the development of interfacial voids. At the optimal root content (0.28–0.35 %), the maximum shear stress of the root-soil composite increased by 5–45 %, with the specific magnitude depending on soil water content and freezing temperature. Moreover, the optimal root content (η) decreased with increasing soil water content. The results demonstrate the effectiveness of A. sparsifolia in enhancing soil stability under freeze-thaw conditions and highlight the nonlinear and moisture-sensitive characteristics of root reinforcement. This study provides a theoretical basis for optimizing vegetation-based slope stabilization strategies in cold arid environments.
{"title":"Effects of Alhagi sparsifolia root content and soil moisture content on soil deformation and strength under different freeze-thaw temperature conditions","authors":"Meixue Zhang , Qinglin Li , Xuanbing Luo , Wenjuan Chen , Rui Wang , Shuailong Yu , Guang Yang","doi":"10.1016/j.still.2026.107110","DOIUrl":"10.1016/j.still.2026.107110","url":null,"abstract":"<div><div>This study focuses on cold arid regions in Xinjiang, China, and investigates the reinforcement effect of <em>Alhagi sparsifolia</em> roots on sandy soil under freeze-thaw conditions. Freeze-thaw cycle and direct shear tests, combined with environmental scanning electron microscopy (ESEM), were conducted to analyze the effects of root reinforcement on the deformation and strength characteristics of sandy soil under varying soil water contents (8–14 %) and freezing temperatures (−5 to −20 ℃). The results revealed that soil deformation during freeze-thaw cycle underwent five distinct stages and was strongly controlled by soil water content and temperature. Root incorporation reduced the maximum soil deformation by more than 30 %, and the suppressive effect exceeded 51 % at high soil water content (14 %). In low-water-content soils (8 %), excessive root content (>0.35 %) induced deformation rebound, which was attributed to root clustering and the development of interfacial voids. At the optimal root content (0.28–0.35 %), the maximum shear stress of the root-soil composite increased by 5–45 %, with the specific magnitude depending on soil water content and freezing temperature. Moreover, the optimal root content (η) decreased with increasing soil water content. The results demonstrate the effectiveness of <em>A. sparsifolia</em> in enhancing soil stability under freeze-thaw conditions and highlight the nonlinear and moisture-sensitive characteristics of root reinforcement. This study provides a theoretical basis for optimizing vegetation-based slope stabilization strategies in cold arid environments.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107110"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146672","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 : 2026-08-01Epub Date: 2026-02-09DOI: 10.1016/j.still.2026.107111
Guocui Ren , Xiuli Xin , Haowei Ni , Xianfeng Zhang , Lan Mu , Wenliang Yang , Shuchun Ge , Shaopu Pang , Anning Zhu
Nitrogen (N) fertilization is a critical management practice for enhancing soil organic matter (SOM) sequestration, yet its efficacy often varies unpredictably within intensive annual rotation systems. To unravel the phase-specific mechanisms regulating stabilization outcomes of straw-derived C and N, we conducted two in situ dual-labeled (13C and 15N) wheat and maize straw tracing studies nested within a 5-year field experiment under contrasting N rates (0, 150, and 250 kg N ha−1). We found that SOM stabilization outcomes were strictly regulated by the distinct biochemical environments inherent to each rotation phase. In the wheat straw phase, high N input (N250) primed an oxidative enzyme-bacterial pathway. Although this pathway generated substantial bacterial necromass (contributing up to 56.9 % of 13C-SOC), it was characterized by rapid turnover. Path analysis revealed that this intense bacterial cycling negatively impacted stable C retention (r = -0.83, P < 0.001), ultimately leading to a 17.6 % reduction in straw-derived mineral-associated organic carbon (13C-MAOC) content compared to N0. In contrast, the maize straw phase exhibited a distinct C and N decoupling, with 13C preferentially retained in particulate organic matter (POM) and 15N in MAOM. High N input activated a hydrolytic enzyme-fungal pathway, boosting fungal PLFAs by 70.8 % and necromass contribution to 31.7 %. Crucially, unlike the bacterial pathway in wheat, this fungal-mediated process acted as a strong positive driver of MAOM formation (r = 0.84, P < 0.001), facilitating the persistence of straw-derived C and N via physical and chemical protection. These findings demonstrate that N fertilization primes a leaky “bacterial turnover” pump in the wheat straw phase but a conservative “fungal persistence” pathway in the maize straw phase. Consequently, we propose a phase-specific N management strategy that combines moderate N inputs for wheat straw to minimize turnover losses with higher N inputs for maize to leverage fungal stabilization, thereby optimizing system-level C storage.
氮(N)施肥是提高土壤有机质（SOM）固存的关键管理措施，但在集约轮作系统中，其效果往往发生不可预测的变化。为了揭示调节秸秆碳氮稳定结果的阶段性机制，我们在5年的田间试验中进行了两项原位双标记（13C和15N）小麦和玉米秸秆追踪研究，分别在不同的施氮量（0、150和250 kg N ha−1）下进行。我们发现SOM稳定结果受到每个旋转阶段固有的不同生化环境的严格调节。在麦秸期，高N输入（N250）启动了一个氧化酶-细菌途径。尽管这一途径产生了大量的细菌坏死块（占13C-SOC的56.9% %），但其特点是快速转换。通径分析显示，这种强烈的细菌循环对稳定的碳保留产生了负面影响（r = -0.83,P <； 0.001），最终导致秸秆衍生矿物相关有机碳（13C-MAOC）含量与N0相比降低了17.6 %。相反，玉米秸秆阶段表现出明显的碳氮解耦，13C优先保留在颗粒有机质（POM）中，15N优先保留在MAOM中。高氮输入激活了水解酶-真菌途径，使真菌PLFAs提高了70.8% %，坏死团贡献提高了31.7% %。关键的是，与小麦中的细菌途径不同，真菌介导的这一过程是MAOM形成的一个强大的正驱动因素（r = 0.84,P <； 0.001），通过物理和化学保护促进秸秆来源的C和N的持续存在。这些发现表明，氮肥在小麦秸秆期启动了一个渗漏的“细菌周转”泵，而在玉米秸秆期启动了一个保守的“真菌持续”途径。因此，我们提出了一种阶段性氮素管理策略，将小麦秸秆的适度氮素投入与玉米的高氮素投入相结合，以最大限度地减少周转损失，从而利用真菌稳定，从而优化系统级碳储存。
{"title":"Nitrogen fertilization drives bacterial turnover versus fungal persistence for straw-derived C and N stabilization in a wheat-maize rotation","authors":"Guocui Ren , Xiuli Xin , Haowei Ni , Xianfeng Zhang , Lan Mu , Wenliang Yang , Shuchun Ge , Shaopu Pang , Anning Zhu","doi":"10.1016/j.still.2026.107111","DOIUrl":"10.1016/j.still.2026.107111","url":null,"abstract":"<div><div>Nitrogen (N) fertilization is a critical management practice for enhancing soil organic matter (SOM) sequestration, yet its efficacy often varies unpredictably within intensive annual rotation systems. To unravel the phase-specific mechanisms regulating stabilization outcomes of straw-derived C and N, we conducted two <em>in situ</em> dual-labeled (<sup>13</sup>C and <sup>15</sup>N) wheat and maize straw tracing studies nested within a 5-year field experiment under contrasting N rates (0, 150, and 250 kg N ha<sup>−1</sup>). We found that SOM stabilization outcomes were strictly regulated by the distinct biochemical environments inherent to each rotation phase. In the wheat straw phase, high N input (N250) primed an oxidative enzyme-bacterial pathway. Although this pathway generated substantial bacterial necromass (contributing up to 56.9 % of <sup>13</sup>C-SOC), it was characterized by rapid turnover. Path analysis revealed that this intense bacterial cycling negatively impacted stable C retention (<em>r</em> = -0.83, <em>P</em> < 0.001), ultimately leading to a 17.6 % reduction in straw-derived mineral-associated organic carbon (<sup>13</sup>C-MAOC) content compared to N0. In contrast, the maize straw phase exhibited a distinct C and N decoupling, with <sup>13</sup>C preferentially retained in particulate organic matter (POM) and <sup>15</sup>N in MAOM. High N input activated a hydrolytic enzyme-fungal pathway, boosting fungal PLFAs by 70.8 % and necromass contribution to 31.7 %. Crucially, unlike the bacterial pathway in wheat, this fungal-mediated process acted as a strong positive driver of MAOM formation (<em>r</em> = 0.84, <em>P</em> < 0.001), facilitating the persistence of straw-derived C and N via physical and chemical protection. These findings demonstrate that N fertilization primes a leaky “bacterial turnover” pump in the wheat straw phase but a conservative “fungal persistence” pathway in the maize straw phase. Consequently, we propose a phase-specific N management strategy that combines moderate N inputs for wheat straw to minimize turnover losses with higher N inputs for maize to leverage fungal stabilization, thereby optimizing system-level C storage.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107111"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146697","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 : 2026-08-01Epub Date: 2026-02-14DOI: 10.1016/j.still.2026.107109
Xue Zhang , Shuren Wang , Meng Li , Wei Hu
Soil quality degradation threatens food and ecological security on 33 % of global terrestrial land, and there is an urgent need for effective evaluation systems. The objective was to clarify the multifactorial interactions driving soil-quality degradation and develop an accurate evaluation framework. In a typical wind-eroded region in northeastern China, 316 surface soil samples (0–20 cm) were collected using the grid sampling method, with 20 physicochemical and biological indicators tested. Core indicators for the minimum data set were identified via network analysis, and four machine learning algorithms—support vector machine, random forest, light gradient boosting machine (LightGBM), and extreme gradient boosting—were combined with 14 high-resolution environmental factors to predict the soil quality index (SQI). Results showed that soil water content, soil organic carbon, and field water holding capacity were core indicators for constructing SQI. LightGBM had the highest prediction accuracy (mean absolute error = 0.10, root mean square error = 0.12, coefficient of determination = 0.72, concordance correlation coefficient = 0.81) and was optimal for removing redundant environmental factors. Latitude (19.5 %–40.6 %), mean annual temperature (12.8 %–19.1 %), and near-surface wind speed (10.1 %–12.5 %) were dominant drivers of SQI spatial variability. Soil quality exhibited a decreasing trend from northeast to southwest, with low SQI values (<0.4, 27.2 %) prevalent in low-latitude, high-temperature, and strong-wind-speed areas. The integration of network analysis, high-resolution environmental factors, and machine learning provides an effective framework for evaluating soil quality. Notably, soil quality in low-latitude, high-temperature, and strong-wind-speed regions should be emphasized in the context of future global warming.
{"title":"Network analysis and machine learning-aided soil quality index prediction: Insights from a wind-eroded region in northeastern China","authors":"Xue Zhang , Shuren Wang , Meng Li , Wei Hu","doi":"10.1016/j.still.2026.107109","DOIUrl":"10.1016/j.still.2026.107109","url":null,"abstract":"<div><div>Soil quality degradation threatens food and ecological security on 33 % of global terrestrial land, and there is an urgent need for effective evaluation systems. The objective was to clarify the multifactorial interactions driving soil-quality degradation and develop an accurate evaluation framework. In a typical wind-eroded region in northeastern China, 316 surface soil samples (0–20 cm) were collected using the grid sampling method, with 20 physicochemical and biological indicators tested. Core indicators for the minimum data set were identified via network analysis, and four machine learning algorithms—support vector machine, random forest, light gradient boosting machine (LightGBM), and extreme gradient boosting—were combined with 14 high-resolution environmental factors to predict the soil quality index (SQI). Results showed that soil water content, soil organic carbon, and field water holding capacity were core indicators for constructing SQI. LightGBM had the highest prediction accuracy (mean absolute error = 0.10, root mean square error = 0.12, coefficient of determination = 0.72, concordance correlation coefficient = 0.81) and was optimal for removing redundant environmental factors. Latitude (19.5 %–40.6 %), mean annual temperature (12.8 %–19.1 %), and near-surface wind speed (10.1 %–12.5 %) were dominant drivers of SQI spatial variability. Soil quality exhibited a decreasing trend from northeast to southwest, with low SQI values (<0.4, 27.2 %) prevalent in low-latitude, high-temperature, and strong-wind-speed areas. The integration of network analysis, high-resolution environmental factors, and machine learning provides an effective framework for evaluating soil quality. Notably, soil quality in low-latitude, high-temperature, and strong-wind-speed regions should be emphasized in the context of future global warming.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107109"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175066","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 : 2026-08-01Epub Date: 2026-02-10DOI: 10.1016/j.still.2026.107120
Yang Xu, Xiaobo Gu, Penglin Li, Bowen Sun, Zhikai Cheng, Tongtong Zhao, Zhengtao Zhang, Chunyu Wei, Yadan Du
Timely and precise quantification of aboveground biomass (AGB) is essential for evaluating crop development, optimizing agronomic practices, and ensuring food security. However, traditional AGB estimation approaches are destructive, time-consuming, and impractical for extensive-scale applications. Remote sensing methods, although non-destructive, often encounter obstacles including spectral saturation and interference from soil or plastic mulch, especially under dense canopy cover or within ridge-furrow mulching systems. To overcome these limitations, this study focused on winter wheat cultivated under a ridge-furrow film mulching system, utilizing high-resolution UAV imagery combined with machine learning techniques to reconstruct spectral information by integrating vegetation indices, textural metrics, and color space-converted variables. Feature selection was performed using Recursive Feature Elimination (RFE), and three modeling strategies—vegetation indices and texture features (VT), VT with color space transformation (VTSC), and VTSC with spectral reconstruction (REVTSC)—were developed and evaluated using Ridge Regression (RR), Support Vector Machine (SVM), and Random Forest (RF) algorithms across the turning green, jointing, heading, and filling growth stages. The results showed that integrating color space-transformed features significantly enhanced AGB prediction accuracy, especially during early growth stages (turning green and jointing stages). Spectral feature reconstruction further improved model performance by mitigating background interference from soil and plastic mulch. The highest estimation accuracy was achieved during the filling stage, with R2 of 0.81 and 0.76, and RMSE of 1581.16 kg ha–1 and 1737.47 kg ha–1 for the training and test sets, respectively. An improvement in R2 of up to 33.10 % was observed when using the REVTSC model over the VT model, underscoring the benefit of spectral reconstruction. RR performed better in early growth stages (turning green and jointing stages), while SVM showed the weakest performance throughout the whole growth stages of winter wheat. The results of present study would provide a novel approach to AGB estimation under mixed background conditions by integrating color space transformation and spectral reconstruction, offering a scalable, accurate solution for AGB monitoring in plastic-mulched cropping systems.
及时、准确地量化地上生物量（AGB）对于评估作物发展、优化农艺做法和确保粮食安全至关重要。然而，传统的AGB估计方法是破坏性的，耗时的，并且不适合大规模的应用。遥感方法虽然是非破坏性的，但经常遇到障碍，包括光谱饱和和土壤或塑料覆盖的干扰，特别是在茂密的树冠覆盖下或垄沟覆盖系统内。为了克服这些局限性，本研究以垄沟膜覆盖系统下栽培的冬小麦为研究对象，利用高分辨率无人机图像结合机器学习技术，通过整合植被指数、纹理指标和色彩空间转换变量，重构光谱信息。利用递归特征消除（RFE）进行特征选择，并利用岭回归（RR）、支持向量机（SVM）和随机森林（RF）算法开发了植被指数和纹理特征（VT）、VT结合色彩空间变换（VTSC）和VTSC结合光谱重建（REVTSC）三种建模策略，并对其在绿化、拔节、抽头和填充生长阶段进行了评估。结果表明，整合颜色空间变换特征显著提高了AGB预测精度，特别是在生长早期（变绿期和拔节期）。光谱特征重建通过减轻土壤和地膜的背景干扰进一步提高了模型的性能。在填充阶段估计精度最高，R2分别为0.81和0.76，训练集和测试集的RMSE分别为1581.16 kg ha-1和1737.47 kg ha-1。与VT模型相比，REVTSC模型的R2提高高达33.10 %，这表明了光谱重建的优势。在冬小麦生育早期（转绿期和拔节期），RR表现较好，而SVM在整个生育期表现最弱。本文的研究结果将为混合背景下的AGB估计提供一种新的方法，该方法将结合色彩空间变换和光谱重建，为地膜作物系统AGB监测提供一种可扩展、精确的解决方案。
{"title":"Enhancing aboveground biomass estimation for winter wheat using UAV-based color space transformation and spectral feature reconstruction","authors":"Yang Xu, Xiaobo Gu, Penglin Li, Bowen Sun, Zhikai Cheng, Tongtong Zhao, Zhengtao Zhang, Chunyu Wei, Yadan Du","doi":"10.1016/j.still.2026.107120","DOIUrl":"10.1016/j.still.2026.107120","url":null,"abstract":"<div><div>Timely and precise quantification of aboveground biomass (AGB) is essential for evaluating crop development, optimizing agronomic practices, and ensuring food security. However, traditional AGB estimation approaches are destructive, time-consuming, and impractical for extensive-scale applications. Remote sensing methods, although non-destructive, often encounter obstacles including spectral saturation and interference from soil or plastic mulch, especially under dense canopy cover or within ridge-furrow mulching systems. To overcome these limitations, this study focused on winter wheat cultivated under a ridge-furrow film mulching system, utilizing high-resolution UAV imagery combined with machine learning techniques to reconstruct spectral information by integrating vegetation indices, textural metrics, and color space-converted variables. Feature selection was performed using Recursive Feature Elimination (RFE), and three modeling strategies—vegetation indices and texture features (VT), VT with color space transformation (VTSC), and VTSC with spectral reconstruction (REVTSC)—were developed and evaluated using Ridge Regression (RR), Support Vector Machine (SVM), and Random Forest (RF) algorithms across the turning green, jointing, heading, and filling growth stages. The results showed that integrating color space-transformed features significantly enhanced AGB prediction accuracy, especially during early growth stages (turning green and jointing stages). Spectral feature reconstruction further improved model performance by mitigating background interference from soil and plastic mulch. The highest estimation accuracy was achieved during the filling stage, with R<sup>2</sup> of 0.81 and 0.76, and RMSE of 1581.16 kg ha<sup>–1</sup> and 1737.47 kg ha<sup>–1</sup> for the training and test sets, respectively. An improvement in R<sup>2</sup> of up to 33.10 % was observed when using the REVTSC model over the VT model, underscoring the benefit of spectral reconstruction. RR performed better in early growth stages (turning green and jointing stages), while SVM showed the weakest performance throughout the whole growth stages of winter wheat. The results of present study would provide a novel approach to AGB estimation under mixed background conditions by integrating color space transformation and spectral reconstruction, offering a scalable, accurate solution for AGB monitoring in plastic-mulched cropping systems.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107120"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152896","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}
Root exudates mobilize soil nutrients and create an important pathway for plants to obtain resources. Understanding nutrient-acquisition strategies based on root exudation by coexisting grassland species is crucial for vegetation regrowth and productivity after grazing. We analyzed the nutrient-acquisition strategies and productivity maintenance mechanisms of Leymus chinensis, Stipa grandis and Cleistogenes squarrosa over two consecutive years in a long-term grazing experimental plot in a typical grassland in Inner Mongolia. Grazing significantly promoted the root exudation rates of carbon (C), nitrogen (N), and organic acids. Grazing increased the maximum quantum efficiency of photosystem II, root salicylic acid, and total soluble sugars (TSS), which increased root exudation by improving competitive traits such as root nitrogen (RN) and specific root area (SRA), while reducing tissue-construction traits such as root tissue density (RTD). This shift led L. chinensis to adopt a competitive strategy. Stipa grandis exhibited a higher net photosynthetic rate and non-photochemical quenching, which promoted C and organic acid exudation, thereby increasing specific root length (SRL). Nitrogen exudation further increased RTD, resulting in a conservative strategy. Cleistogenes squarrosa demonstrated a higher carboxylation efficiency, electron transport rate, and TSS, which promoted N and organic acid exudation, and increased SRA and RTD, whereas C exudation increased RN, forming a facultative nutrient-acquisition strategy. These processes mobilized rhizosphere soil nutrients, especially ammonium nitrogen (NH4+-N), and thereby improved aboveground productivity. Our results highlight the importance of plant metabolite in regulating changes in root exudation rates. Furthermore, the trade-offs between plant root exudation and root morphology determined the strategy of belowground resource acquisition, and the mobilization of soil nitrogen and other nutrients. Our results have important theoretical and practical implications for understanding the coexistence of grassland species under grazing pressure and for developing restoration strategies for degraded grasslands.
{"title":"Trade-offs between root exudation and root traits induced by coexisting species under a grazing gradient can mobilize available nitrogen to promote grassland productivity","authors":"Guisen Yang, Jirui Gong, Shangpeng Zhang, Ruijing Wang, Tong Wang, Yaohong Yu, Qin Xie","doi":"10.1016/j.still.2026.107108","DOIUrl":"10.1016/j.still.2026.107108","url":null,"abstract":"<div><div>Root exudates mobilize soil nutrients and create an important pathway for plants to obtain resources. Understanding nutrient-acquisition strategies based on root exudation by coexisting grassland species is crucial for vegetation regrowth and productivity after grazing. We analyzed the nutrient-acquisition strategies and productivity maintenance mechanisms of <em>Leymus chinensis</em>, <em>Stipa grandis</em> and <em>Cleistogenes squarrosa</em> over two consecutive years in a long-term grazing experimental plot in a typical grassland in Inner Mongolia. Grazing significantly promoted the root exudation rates of carbon (C), nitrogen (N), and organic acids. Grazing increased the maximum quantum efficiency of photosystem II, root salicylic acid, and total soluble sugars (<em>TSS</em>), which increased root exudation by improving competitive traits such as root nitrogen (<em>RN</em>) and specific root area (<em>SRA</em>), while reducing tissue-construction traits such as root tissue density (<em>RTD</em>). This shift led <em>L. chinensis</em> to adopt a competitive strategy. <em>Stipa grandis</em> exhibited a higher net photosynthetic rate and non-photochemical quenching, which promoted C and organic acid exudation, thereby increasing specific root length (<em>SRL</em>). Nitrogen exudation further increased <em>RTD</em>, resulting in a conservative strategy. <em>Cleistogenes squarrosa</em> demonstrated a higher carboxylation efficiency, electron transport rate, and <em>TSS</em>, which promoted N and organic acid exudation, and increased <em>SRA</em> and <em>RTD</em>, whereas C exudation increased <em>RN</em>, forming a facultative nutrient-acquisition strategy. These processes mobilized rhizosphere soil nutrients, especially ammonium nitrogen (NH<sub>4</sub><sup>+</sup>-N), and thereby improved aboveground productivity. Our results highlight the importance of plant metabolite in regulating changes in root exudation rates. Furthermore, the trade-offs between plant root exudation and root morphology determined the strategy of belowground resource acquisition, and the mobilization of soil nitrogen and other nutrients. Our results have important theoretical and practical implications for understanding the coexistence of grassland species under grazing pressure and for developing restoration strategies for degraded grasslands.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"260 ","pages":"Article 107108"},"PeriodicalIF":6.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134154","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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