Soil and Tillage Research最新文献

{"title":"Straw return enhances soil multifunctionality by promoting protist-dominated microbial multitrophic interactions","authors":"Likun Wu, Yansong Bai, Yu Chen, Xiao Wei, Na Wen, Zhiwen Xu, Xining Zhao, Gehong Wei, Duntao Shu","doi":"10.1016/j.still.2025.106903","DOIUrl":"https://doi.org/10.1016/j.still.2025.106903","url":null,"abstract":"Straw return is a key cropland anthropogenic management that influences soil carbon sequestration, individual microbial biodiversity, and microbial functional profiles. Despite its importance, how microbe interactions across multiple trophic levels influence soil multifunctionality in response to straw return remains poorly understood, particularly in dryland agricultural ecosystems. Here, we investigated the influences of straw return on microbial hierarchical groups (bacteria, fungi, and protists) and the cascading effects on soil multifunctionality across different maize growth stages in an arid region. Our results showed that straw inputs increased Shannon diversity of bacteria and fungi communities during the filling and maturity stages, and that of the protist community at the maturity stage. Importantly, compared to non-straw treatment, straw return significantly increased soil multifunctionality by 16–22 % at filling and maturity stages. Moreover, we found that straw inputs shifted the soil multiple functions and multitrophic interaction networks from bacteria and fungi dominated to protist dominated. Straw return enhanced network connectivity by 50 %, as evidenced by a 50 % increase in linkages and a 43 % increase in node average edges. Furthermore, protist-dominated cross-trophic interactions primarily explained 51 % of variation in soil multifunctionality under straw return. Additionally, phagotrophic protists suppressed the relative abundance of Fusarium, while straw return enhanced the associations between phagotrophic protists and potential beneficial bacterial genera. These findings collectively indicate that crop straw return enhances soil multifunctionality by promoting higher trophic level microbial diversity and facilitating potential top-down regulations. Our study underscores the critical roles of protists in maintaining soil functions and provides novel insights into the ecological consequences of straw return on microbial hierarchical interactions and multiple microbially mediated ecosystem functions.","PeriodicalId":501007,"journal":{"name":"Soil and Tillage Research","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulating field soil temperature variations with physics-informed neural networks","authors":"Xiaoting Xie, Hengnian Yan, Yili Lu, Lingzao Zeng","doi":"10.1016/j.still.2024.106236","DOIUrl":"https://doi.org/10.1016/j.still.2024.106236","url":null,"abstract":"Information on soil temperature is crucial for modeling hydrological and climatic processes. Nevertheless, direct measurements of soil temperature are usually rather limited in space, leading to an urgent need for improved spatial resolution. To address this issue, a Physics-Informed Neural Networks (PINN) method for estimating soil temperature () profile variations was proposed in this study. This method combines the advantages of Deep Neural Networks (DNN) in modeling complex non-linear relationships and physical laws for more robust predictions. The performance was evaluated using in-situ annual soil at depths of 5 cm, 10 cm and 20 cm on a maize field in Northeast China. Cross-validation was used, a PINN was used to derive the new data at unobserved depth from observations at the other two depths. The results demonstrated that the performance of the PINN was superior to the commonly used process-based method and a DNN for all situations. Compared to the traditional method, the PINN achieved a 0.69°C and 0.39°C reduction in root-mean-square error (RMSE) for estimates at 10 cm and 20 cm depths, respectively, under plowed tillage condition, while it could also accurately estimate at 5 cm depth with RMSE of 0.56 °C. In addition, the PINN does not require inputs of soil thermal properties e.g., apparent thermal diffusivity (κ), as the space and time-dependent κ values could also be learned during the training process. The results presented here demonstrated that a PINN could successfully utilize limited observation data to estimate unknown soil profiles, and solve some challenging problems beyond the reach of existing methods in simulating soil thermal dynamics.","PeriodicalId":501007,"journal":{"name":"Soil and Tillage Research","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141768699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}