Soil最新文献

{"title":"Closing the phenotyping gap with non-invasive belowground field phenotyping","authors":"Guillaume Blanchy, Waldo Deroo, Tom De Swaef, Peter Lootens, Paul Quataert, Isabel Roldán-Ruíz, Roelof Versteeg, Sarah Garré","doi":"10.5194/soil-11-67-2025","DOIUrl":"https://doi.org/10.5194/soil-11-67-2025","url":null,"abstract":"Abstract. Breeding climate-robust crops is one of the needed pathways for adaptation to the changing climate. To speed up the breeding process, it is important to understand how plants react to extreme weather events such as drought or waterlogging in their production environment, i.e. under field conditions in real soils. Whereas a number of techniques exist for aboveground field phenotyping, simultaneous non-invasive belowground phenotyping remains difficult. In this paper, we present the first data set of the new HYDRAS (HYdrology, Drones and RAinout Shelters) open-access field-phenotyping infrastructure, bringing electrical resistivity tomography, alongside drone imagery and environmental monitoring, to a technological readiness level closer to what breeders and researchers need. This paper investigates whether electrical resistivity tomography (ERT) provides sufficient precision and accuracy to distinguish between belowground plant traits of different genotypes of the same crop species. The proof-of-concept experiment was conducted in 2023, with three distinct soybean genotypes known for their contrasting reactions to drought stress. We illustrate how this new infrastructure addresses the issues of depth resolution, automated data processing, and phenotyping indicator extraction. The work shows that electrical resistivity tomography is ready to complement drone-based field-phenotyping techniques to accomplish whole-plant high-throughput field phenotyping.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"110 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical Protection of Soil Carbon Stocks Under Regenerative Agriculture","authors":"Sam G. Keenor, Rebekah Lee, Brian J. Reid","doi":"10.5194/egusphere-2024-4029","DOIUrl":"https://doi.org/10.5194/egusphere-2024-4029","url":null,"abstract":"<strong>Abstract.</strong> Regenerative agriculture is emerging as a strategy for carbon sequestration and climate change mitigation. However, for sequestration efforts to be successful, long-term stabilisation of Soil Organic Carbon (SOC) is needed. This can be achieved either through the uplift in recalcitrant carbon stocks, and/or through physical protection and occlusion of carbon within stable soil aggregates. In this research, soils from blackcurrant fields under regenerative management (0 to 7 years) were analysed with respect to: soil bulk density (SBD), aggregate fractionation (water stable aggregates vs. non-water stable aggregates (WSA and NWSA respectively)), soil carbon content, and carbon stability (recalcitrant vs. labile carbon). From this, long term carbon sequestration potential was calculated from both recalcitrant and physically occluded carbon stocks (stabilised carbon). Results indicated favourable shifts in the proportion of NWSA:WSA with time. This ratio increasing from 27.6 % : 5.8 % (control soil) to 12.6 % : 16.0 % (alley soil), and 16.1 % : 14.4 % (bush soil) after 7 years. While no significant (p ≥ 0.05)) changes in recalcitrant carbon stocks were observed after 7 years, labile carbon stocks increased significantly (p ≤ 0.05) from 10.44 t C ha^-1 to 13.87 t C ha^-1. As a result, total sequesterable carbon (<em>stabilised carbon</em>) increased by 1.7 t C ha^-1 over the 7 year period, due to the occlusion and protection of this labile carbon stock within WSA fraction. This research provides valuable insights into the mechanisms of soil carbon stabilisation under regenerative agriculture practices and highlights the importance of soil aggregates in physically protecting carbon net-gains.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"38 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On soil health and the pivotal role of proximal sensing","authors":"Yang Hu, Adam Cross, Zefang Shen, Johan Bouma, Raphael A. Viscarra Rossel","doi":"10.5194/egusphere-2024-3939","DOIUrl":"https://doi.org/10.5194/egusphere-2024-3939","url":null,"abstract":"<strong>Abstract.</strong> Soil underpins the functioning of all terrestrial ecosystems. Sustainable soil management is crucial to preventing further degradation of the non-renewable soil resources and achieving sustainability. The soil health concept has gained popularity as a means to this end and has been integrated into the policies of many countries and supranational organisations. We need an accurate definition and scientifically robust assessment framework for effectively measuring, monitoring and managing soil health, a framework that can effectively be communicated to the policy arena and to stakeholders. Linking soil health to the provision of ecosystem services in line with selected UN Sustainable Development Goals (SDGs) provides an effective link with the policy arena focusing on sustainable development. This is needed because lack of operational procedures to measure soil health leads to policies that ignore soils and focus on management measures. We review the literature on soil health, its conceptualisation, the current criteria for selecting indicators and thresholds, as well as the implementation of different soil health assessment frameworks. Most published studies on soil health focus on agriculture; however, a broader perspective that includes various terrestrial ecosystems is needed. Soil health assessments should not be limited to agricultural contexts. We highlight the significant potential of advanced sensing technologies to improve current soil health evaluations, which often rely on traditional methods that are time-consuming and costly. We propose a soil health assessment framework that prioritises ecological considerations and is free from anthropogenic bias. The proposed approach leverages modern technological advancements, including proximal sensing, remote sensing, machine learning, and sensor data fusion. This combined use of technologies enables objective, quantitative, reliable, rapid, cost-effective, scalable, and integrative soil health assessments.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"48 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive increase in CO2 release by drying–rewetting cycles among Japanese forests and pastureland soils and exploring predictors of increasing magnitude","authors":"Yuri Suzuki, Syuntaro Hiradate, Jun Koarashi, Mariko Atarashi-Andoh, Takumi Yomogida, Yuki Kanda, Hirohiko Nagano","doi":"10.5194/soil-11-35-2025","DOIUrl":"https://doi.org/10.5194/soil-11-35-2025","url":null,"abstract":"Abstract. It is still difficult to precisely quantify and predict the effects of drying–rewetting cycles (DWCs) on soil carbon dioxide (CO2) release due to the paucity of studies using constant moisture conditions equivalent to the mean water content during DWC incubation. The present study was performed to evaluate overall trends in the effects of DWCs on CO2 release and to explore environmental and soil predictors for variations in the effect size in 10 Japanese forests and pastureland soils variously affected by volcanic ash during their pedogenesis. Over an 84 d incubation period including three DWCs, CO2 release was 1.3- to 3.7-fold greater than under continuous constant moisture conditions (p<0.05) with the same mean water content as in the DWC incubations. Analysis of the relations between this increasing magnitude of CO2 release by DWCs (IFCO2) and various environmental and soil properties revealed significant positive correlations between IFCO2 and soil organo-metal complex contents (p<0.05), especially pyrophosphate-extractable aluminum (Alp) content (r=0.74). Molar ratios of soil total carbon (C) and pyrophosphate-extractable C (Cp) to Alp contents and soil-carbon-content-specific CO2 release rate under continuous constant moisture conditions (qCO2_soc) were also correlated with IFCO2 (p<0.05). The covariations among Alp, total Cp/Alp, and Cp/Alp molar ratios and qCO2_soc suggested Alp to be the primary predictor of IFCO2. Additionally, soil microbial biomass C and nitrogen (N) levels were significantly lower in DWCs than under continuous constant moisture conditions, whereas there was no significant relation between the microbial biomass decrease and IFCO2. The present study showed a comprehensive increase in soil CO2 release by DWC in Japanese forests and pastureland soils, suggesting that Alp is a predictor of the effect size, likely due to vulnerability of organo-Al complexes to DWC.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"28 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixed Signals: interpreting mixing patterns of different soil bioturbation processes through luminescence and numerical modelling","authors":"W. Marijn van der Meij, Svenja Riedesel, Tony Reimann","doi":"10.5194/soil-11-51-2025","DOIUrl":"https://doi.org/10.5194/soil-11-51-2025","url":null,"abstract":"Abstract. Soil bioturbation plays a key role in soil functions such as carbon and nutrient cycling. Despite its importance, fundamental knowledge on how different organisms and processes impact the rates and patterns of soil mixing during bioturbation is lacking. However, this information is essential for understanding the effects of bioturbation in present-day soil functions and on long-term soil evolution. Luminescence, a light-sensitive mineral property, serves as a valuable tracer for long-term soil bioturbation over decadal to millennial timescales. The luminescence signal resets (bleaches) when a soil particle is exposed to daylight at the soil surface and accumulates when the particle is buried in the soil, acting as a proxy for subsurface residence times. In this study, we compiled three luminescence datasets of soil mixing by different biota and compared them to numerical simulations of bioturbation using the ChronoLorica soil-landscape evolution model. The goal was to understand how different mixing processes affect depth profiles of luminescence-based metrics, such as the modal age, width of the age distributions and fraction of the bleached particles. We focus on two main bioturbation processes: mounding (advective transport of soil material to the surface) and subsurface mixing (diffusive subsurface transport). Each process has a distinct effect on the luminescence metrics, which we summarized in a conceptual diagram to help with qualitative interpretation of luminescence-based depth profiles. A first attempt to derive quantitative information from luminescence datasets through model calibration showed promising results but also highlighted gaps in the data that must be addressed before accurate, quantitative estimates of bioturbation rates and processes are possible. The new numerical formulations of bioturbation, which are provided in an accompanying modelling tool, provide new possibilities for calibration and more accurate simulation of the processes in soil function and soil evolution models.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"20 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large errors in soil carbon measurements attributed to inconsistent sample processing","authors":"Rebecca J. Even, Megan B. Machmuller, Jocelyn M. Lavallee, Tamara J. Zelikova, M. Francesca Cotrufo","doi":"10.5194/soil-11-17-2025","DOIUrl":"https://doi.org/10.5194/soil-11-17-2025","url":null,"abstract":"Abstract. To build confidence in the efficacy of soil carbon (C) crediting programs, precise quantification of soil organic carbon (SOC) is critical. Detecting a true change in SOC after a management shift has occurred, specifically in agricultural lands, is difficult as it requires robust soil sampling and soil processing procedures. Informative and meaningful comparisons across spatial and temporal timescales can only be made with reliable soil C measurements and estimates, which begin on the ground and in soil testing facilities. To gauge soil C measurement inter-variability, we conducted a blind external service laboratory comparison across eight laboratories selected based on status and involvement in SOC data curation used to inform C market exchanges, which could include demonstration projects, model validation, and project verification activities. Further, to better understand how soil processing procedures and quantification methods commonly used in soil testing laboratories affect soil C concentration measurements, we designed an internal experiment assessing the individual effect of several alternative procedures (i.e., sieving, fine grinding, and drying) and quantification methods on total (TC), inorganic (SIC), and organic (SOC) soil C concentration estimates. We analyzed 12 different agricultural soils using 11 procedures that varied in either the sieving, fine-grinding, drying, or quantification step. We found that a mechanical grinder, the most commonly used method for sieving in service laboratories, did not effectively remove coarse materials (i.e., roots and rocks) and thus resulted in higher variability and significantly different C concentration measurements from the other sieving procedures (i.e., 8 + 2, 4, and 2 mm with a rolling pin). A finer grind generally resulted in a lower coefficient of variance, where the finest grind to &lt; 125 µm had the lowest coefficient of variance, followed by the &lt; 250 µm grind and, lastly, the &lt; 2000 µm grind. Not drying soils in an oven prior to elemental analysis on average resulted in a 3.5 % lower TC and 5 % lower SOC relative to samples dried at 105 °C due to inadequate removal of moisture. Compared to the reference method used in our study where % TC was quantified by dry combustion on an elemental analyzer, % SIC was measured using a pressure transducer, and % SOC was calculated by the difference in % TC and % SIC, predictions of all three soil properties (% TC, % SIC, and % SOC) using Fourier-transformed infrared spectroscopy (FTIR) were in high agreement (R2 = 0.97, 0.99, and 0.90, respectively). For % SOC, quantification by loss on ignition had a relatively low coefficient of variance (5.42 ± 3.06 %) but the least agreement (R2 = 0.83) with the reference method. We conclude that sieving to &lt; 2 mm with a mortar and pestle or rolling pin to remove coarse materials, drying soils at 105 °C, and fine-grinding soils prior to elemental analysis are required to improve accuracy and precision","PeriodicalId":48610,"journal":{"name":"Soil","volume":"21 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Monte Carlo conformal prediction to evaluate the uncertainty of deep learning soil spectral models","authors":"Yin-Chung Huang, José Padarian, Budiman Minasny, Alex B. McBratney","doi":"10.5194/egusphere-2024-3703","DOIUrl":"https://doi.org/10.5194/egusphere-2024-3703","url":null,"abstract":"<strong>Abstract.</strong> Uncertainty quantification is a crucial step for the practical application of soil spectral models, particularly in supporting real-world decision making and risk assessment. While machine learning has made remarkable strides in predicting various physiochemical properties of soils using spectroscopy, predictions devoid of quantified uncertainty offer limited utility in guiding critical decisions. However, uncertainty quantification remains underutilised in the reporting of soil spectral models, with existing methods facing significant limitations. These approaches are either computationally demanding, fail to achieve the desired coverage of observed data, or struggle to handle out-of-domain uncertainty effectively. This study introduces the innovative use of Monte Carlo conformal prediction (MC-CP) as a novel approach to quantify uncertainty in the prediction of clay content from mid-infrared spectroscopy. We compared MC-CP with two established methods: (1) Monte Carlo dropout and (2) conformal prediction. Monte Carlo dropout generates prediction intervals for each sample and is effective at addressing larger uncertainties associated with out-of-domain data. However, it falls short in achieving the desired coverage – its 90 % prediction intervals only covered the observed values in 74 % of cases, well below the expected 90 % coverage. Conformal prediction, on the other hand, guarantees ideal coverage of true values but generates unnecessarily wide prediction intervals, making it overly conservative for many practical applications. In contrast, MC-CP successfully combines the strengths of both methods. It achieved a prediction interval coverage probability of 91 %, closely matching the expected 90 % coverage, and far surpassing the performance of Monte Carlo dropout. Additionally, the mean prediction interval width for MC-CP was 9.05 %, narrower than conformal prediction’s 11.11 %, while still effectively addressing the higher uncertainty in out-of-domain samples. By generating accurate prediction intervals alongside point predictions, MC-CP demonstrated its ability to deliver practical and reliable uncertainty quantification. This breakthrough enhances the real-world applicability of soil spectral models and represents a significant advancement in the field of soil science. The success of MC-CP paves the way for its integration into large-scale machine-learning models, such as soil inference systems, further revolutionising decision-making and risk assessment in soil science.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"13 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactions of fertilisation and crop productivity in soil nitrogen cycle microbiome and gas emissions","authors":"Laura Kuusemets, Ülo Mander, Jordi Escuer-Gatius, Alar Astover, Karin Kauer, Kaido Soosaar, Mikk Espenberg","doi":"10.5194/soil-11-1-2025","DOIUrl":"https://doi.org/10.5194/soil-11-1-2025","url":null,"abstract":"Abstract. Fertilised soils are a significant source of nitrous oxide (N2O), a highly active greenhouse gas and a stratospheric ozone depleter. Nitrogen (N) fertilisers, while boosting crop yield, also lead to N2O emissions into the atmosphere, impacting global warming. We investigated relationships between mineral N fertilisation rates and additional manure amendment with different crop types through the analysis of abundances of N cycle functional genes, soil N2O and N2 emissions, nitrogen use efficiency (NUE), soil physicochemical analysis and biomass production. Our study indicates that N2O emissions are predominantly dependent on the mineral N fertilisation rate and enhance with an increased mineral N fertilisation rate. Crop type also has a significant impact on soil N2O emissions. Higher N2O emissions were attained with the application of manure in comparison to mineral fertilisation. Manure amendment also increased the number of N cycle genes that are significant in the variations of N2O. The study indicates that N2O emissions were mainly related to nitrification in the soil. Quantification of nitrogen cycle functional genes also showed the potential role of denitrification, comammox (complete ammonia oxidation) and dissimilatory nitrate reduction to ammonium (DNRA) processes as a source of N2O. Our study did not find soil moisture to be significantly linked to N2O emissions. The results of the study provide evidence that, for wheat, a fertilisation rate of 80 kg N ha−1 is closest to the optimal rate for balancing biomass yield and N2O emissions and achieving a high NUE. Sorghum showed good potential for cultivation in temperate climates, as it showed a similar biomass yield compared to the other crop types and fertilisation rates but maintained low N2O emissions and N losses in a mineral N fertilisation rate of 80 kg N ha−1.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"34 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simple model of the turnover of organic carbon in a soil profile: model test, parameter identification and sensitivity","authors":"Elsa Coucheney, Anke Marianne Herrmann, Nicholas Jarvis","doi":"10.5194/egusphere-2024-3883","DOIUrl":"https://doi.org/10.5194/egusphere-2024-3883","url":null,"abstract":"<strong>Abstract.</strong> Simulation models are potentially useful tools to test our understanding of the processes involved in the turnover of soil organic carbon (SOC) and to evaluate the role of management practices in maintaining stocks of SOC. We describe here a simple model of SOC turnover at the soil profile scale that accounts for two key processes determining SOC persistence (i.e. microbial energy limitation and physical protection due to soil aggregation). We tested the model and evaluated the identifiability of key parameters using topsoil SOC contents measured in three treatments with contrasting organic matter inputs (i.e. fallow, mineral fertilized and cropped, with and without straw addition) in a long-term field trial. The estimated total input of organic matter (OM) in the treatment with straw added was roughly three times that of the treatment without straw addition, but only 12 % of the additional OM input remained in the soil after 54 years. By taking microbial energy limitation and enhanced physical protection of root residues into account, the model could explain the differences in C persistence among the three treatments, whilst also accurately matching the time-courses of SOC contents using the same set of model parameters. Models that do not explicitly consider microbial energy limitation and physical protection would need to adjust their parameter values (either decomposition rate constants or the retention coefficient) to match this data. We also performed a sensitivity analysis to identify the most influential parameters in the model determining soil profile stocks of OM at steady-state. Input distributions for soil and crop parameters in the model were defined for the agricultural production area of PO4 (east-central Sweden), which includes Uppsala. The resulting model predictions compared well with aggregated soil survey data for the PO4 region. This analysis showed that model parameters affecting SOC decomposition rates, including the rate constant for microbial-processed SOC and the parameters regulating physical protection and microbial energy limitation, are more sensitive than parameters determining OM inputs. Thus, the development of pedotransfer approaches to estimate SOC decomposition rates from soil properties would help to support predictive applications of the model at larger spatial scales.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"23 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rubber plant root properties induce contrasting soil aggregate stability through cohesive force and reduced land degradation risk in southern China","authors":"Waqar Ali, Amani Milinga, Tao Luo, Mohammad Nauman Khan, Asad Shah, Khurram Shehzad, Qiu Yang, Huai Yang, Wenxing Long, Wenjie Liu","doi":"10.5194/egusphere-2024-3602","DOIUrl":"https://doi.org/10.5194/egusphere-2024-3602","url":null,"abstract":"<strong>Abstract.</strong> In southern China, Hainan Island faces land degradation risks due to poor soil physical properties, such as a high proportion of microaggregates (&lt; 0.25 mm), low soil organic matter (SOM) content, and frequent uneven rainfall. The cohesive force between soil particles, which is influenced by plant root properties and root-derived SOM, is essential for improving soil aggregate stability and mitigating land degradation. However, the mechanisms by which rubber root properties and root-derived SOM affect soil aggregate stability through cohesive forces in tropical regions remain unclear. This study compared rubber plants of varying ages to assess the effects of root properties and root-derived SOM on soil aggregate stability and cohesive forces. Older rubber plants (&gt; 11-years-old) showed greater root diameters (RD) (0.81–0.91 mm), higher root length (RL) densities (1.83–2.70 cm/cm³), and increased proportions of fine (0.2–0.5 mm) and medium (0.5–1 mm) roots, leading to higher SOM due to lower lignin and higher cellulose contents. Older plants exhibited higher soil cohesion, with significant correlations among root characteristics, SOM, and cohesive force, whereas the random forest (RF) model identified aggregates (&gt; 0.25 mm), root properties, SOM, and cohesive force as the key factors influencing mean weight diameter (MWD) and geometric mean diameter (GMD). Furthermore, partial least squares-path models (PLS-PM) showed that the RL density (RLD) directly influenced SOM (path coefficient 0.70) and root-free cohesive force (RFCF) (path coefficient 0.30), which in turn affected the MWD, with additional direct RLD effects on the SOM (path coefficient 0.45) and MWD (path coefficient 0.64) in the surface soil. Cohesive force in rubber plants of different ages increased macroaggregates (&gt; 0.25 mm) and decreased microaggregates (&lt; 0.25 mm), with topsoil average MWD following the order: CK (0.98 mm) &lt; 5Y_RF (1.26 mm) &lt; MF (1.31 mm) &lt; 11Y_RF (1.36 mm) &lt; 27Y_RF (1.48 mm) &lt; 20Y_RF (1.51 mm). Rubber plant root properties enhance soil aggregate stability and reduce the land degradation risk in tropical regions.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"46 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}