Xuebin Xu , Xianting Wang , Ping Zhou , Zhenke Zhu , Liang Wei , Shuang Wang , Periyasamy Rathinapriya , Qicheng Bei , Jinfei Feng , Fuping Fang , Jianping Chen , Tida Ge
{"title":"Coupling of microbial-explicit model and machine learning improves the prediction and turnover process simulation of soil organic carbon","authors":"Xuebin Xu , Xianting Wang , Ping Zhou , Zhenke Zhu , Liang Wei , Shuang Wang , Periyasamy Rathinapriya , Qicheng Bei , Jinfei Feng , Fuping Fang , Jianping Chen , Tida Ge","doi":"10.1016/j.csag.2024.100001","DOIUrl":"10.1016/j.csag.2024.100001","url":null,"abstract":"<div><p>Modeling soil organic carbon (SOC) is helpful for understanding its distribution and turnover processes, which can guide the implementation of effective measures for carbon (C) sequestration and enhance land productivity. Process-based simulation with high interpretability and extrapolation, and machine learning modeling with high flexibility are two common methods for investigating SOC distribution and turnover. To take advantage of both methods, we developed a hybrid model by coupling of a two-carbon pool microbial model and machine learning for SOC modeling. Here, we assessed the SOC model's predictive, mapping, and interpretability capabilities for the SOC turnover process on Ningbo region. The results indicate that the microbial model with density-dependence (β = 2) and microbial biomass carbon simulation performed better in modeling the parameters of the microbial-based C cycle, such as microbial carbon use efficiency (CUE), microbial mortality rate, and assimilation rate. By integrating this optimal microbial model and random forest (RF) model, the hybrid model improved the prediction accuracy of SOC, with an increased R<sup>2</sup> from 0.74 to 0.84, residual prediction deviation increased from 1.97 to 2.50, and reduced the root-mean-square error from 4.65 to 3.67 g kg<sup>−1</sup> compared to the conventional RF model. As a result, the predicted SOC distribution exhibited high spatial variation and provided abundant details. Microbial CUE and potential C input, represented by net primary productivity, emerged as the primary factors driving SOC distribution in Ningbo region. Projections of SOC under the CMIP6 SSP2-4.5 scenario revealed that regional C loss in high SOC areas was mainly caused by decreased microbial CUE and C input, induced by climate change. Our findings highlight the potential of combining the microbial-explicit model and machine learning to improve SOC prediction accuracy and understand SOC feedback in a changing climate.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950409024000017/pdfft?md5=51776cd89ac145dabbf44e66f0e6d8b5&pid=1-s2.0-S2950409024000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140793462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fish Farms Effluents for Irrigation and Fertilizer: Field and Modeling Studies","authors":"A. Zohry, S. Ouda","doi":"10.1007/978-3-030-93111-7_3","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_3","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80250760","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":"Integration Between Crop-Smart, Water-Smart and Soil-Smart Practices","authors":"A. Zohry, S. Ouda","doi":"10.1007/978-3-030-93111-7_4","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_4","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"264 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91445131","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":"Practices Contribute in Reducing the Emission of Greenhouse Gases","authors":"S. Ouda, A. Zohry","doi":"10.1007/978-3-030-93111-7_8","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_8","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91321522","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":"Water-Smart Practices to Manage Water Scarcity","authors":"S. Ouda, A. Zohry","doi":"10.1007/978-3-030-93111-7_1","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_1","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73960792","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}
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