S. K. Reza, S. Chattaraj, S. Mukhopadhyay, A. Daripa, S. Saha, S. K. Ray
{"title":"利用数字土壤制图划定高分辨率土壤碳管理区:印度喜马拉雅东北部缓解气候变化的一步","authors":"S. K. Reza, S. Chattaraj, S. Mukhopadhyay, A. Daripa, S. Saha, S. K. Ray","doi":"10.1111/sum.12995","DOIUrl":null,"url":null,"abstract":"Delineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM), and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using quadratic spline method (Equal-area) were carried out prior to quantile regression forest (QRF) algorithm based modeling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy <i>k</i>-means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha<sup>-1</sup> in the top layer (0-5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha<sup>-1</sup> in the depths of 5-15, 15-30, 30-60, 60-100 and 100-200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R<sup>2</sup>, which ranged from 0.67 to 0.83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site-specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.","PeriodicalId":21759,"journal":{"name":"Soil Use and Management","volume":"190 ","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Delineation of high-resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India\",\"authors\":\"S. K. Reza, S. Chattaraj, S. Mukhopadhyay, A. Daripa, S. Saha, S. K. Ray\",\"doi\":\"10.1111/sum.12995\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Delineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM), and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using quadratic spline method (Equal-area) were carried out prior to quantile regression forest (QRF) algorithm based modeling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy <i>k</i>-means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha<sup>-1</sup> in the top layer (0-5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha<sup>-1</sup> in the depths of 5-15, 15-30, 30-60, 60-100 and 100-200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R<sup>2</sup>, which ranged from 0.67 to 0.83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site-specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.\",\"PeriodicalId\":21759,\"journal\":{\"name\":\"Soil Use and Management\",\"volume\":\"190 \",\"pages\":\"\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2023-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soil Use and Management\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1111/sum.12995\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOIL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soil Use and Management","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1111/sum.12995","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOIL SCIENCE","Score":null,"Total":0}
Delineation of high-resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India
Delineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM), and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using quadratic spline method (Equal-area) were carried out prior to quantile regression forest (QRF) algorithm based modeling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy k-means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha-1 in the top layer (0-5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha-1 in the depths of 5-15, 15-30, 30-60, 60-100 and 100-200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R2, which ranged from 0.67 to 0.83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site-specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.
期刊介绍:
Soil Use and Management publishes in soil science, earth and environmental science, agricultural science, and engineering fields. The submitted papers should consider the underlying mechanisms governing the natural and anthropogenic processes which affect soil systems, and should inform policy makers and/or practitioners on the sustainable use and management of soil resources. Interdisciplinary studies, e.g. linking soil with climate change, biodiversity, global health, and the UN’s sustainable development goals, with strong novelty, wide implications, and unexpected outcomes are welcomed.