{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:16:27Z","timestamp":1777706187203,"version":"3.51.4"},"reference-count":51,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>\n                    Sustainable agriculture revolves around soil organic carbon (SOC), which is essential for numerous soil functions and ecological attributes. Farmers are interested in conserving and adding additional soil organic carbon to certain fields in order to improve soil health and productivity. The relationship between soil and environment that has been discovered and standardized throughout time has enhanced the progress of digital soil-mapping techniques; therefore, a variety of machine learning techniques are used to predict soil properties. Studies are thriving at how effectively each machine learning method maps and predicts SOC, especially at high spatial resolutions. To predict SOC of soil at a 30 m resolution, four machine learning models\u2014Random Forest, Support Vector Machine, Adaptive Boosting, and k-Nearest Neighbour were used. For model evaluation, two error metrics, namely R\n                    <jats:sup>2<\/jats:sup>\n                    and RMSE have been used. The findings demonstrated that the calibration and validation sets\u2019 descriptive statistics sufficiently resembled the entire set of data. The range of the calculated SOC content was 0.06 to 1.76%. According to the findings of the study, Random Forest showed good results for both cases, i.e., evaluation using cross validation and without cross validation. Using cross validation, RF confirmed highest R\n                    <jats:sup>2<\/jats:sup>\n                    as 0.5278 and lowest RMSE as 0.1683 for calibration dataset while without cross validation it showed R\n                    <jats:sup>2<\/jats:sup>\n                    as 0.8612 and lowest RMSE as 0.0912 for calibration dataset. The generated soil maps will help farmers adopt precise knowledge for decisions that will increase farm productivity and provide food security through the sustainable use of nutrients and the agricultural environment.\n                  <\/jats:p>","DOI":"10.1177\/18758967251353377","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:05:03Z","timestamp":1751947503000},"page":"789-804","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Soil Organic Carbon using Machine Learning Techniques and Geospatial Data for Sustainable Agriculture"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5752-9162","authenticated-orcid":false,"given":"Shyamal","family":"Mundada","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, 441108, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4216-7787","authenticated-orcid":false,"given":"Pooja","family":"Jain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, 441108, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9316-8010","authenticated-orcid":false,"given":"Nirmal","family":"Kumar","sequence":"additional","affiliation":[{"name":"Division of Remote Sensing Applications, National Bureau of Soil Survey and Land Use Planning, Amravati Road, Shankar Nagar Post, Nagpur, 440033, Maharashtra, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"e_1_3_3_2_1","first-page":"296","article-title":"Assessment the effects of land use changes on soil physicochemical properties in jafarabad of golestan province, Iran","volume":"3","author":"Abad J. 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