Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods
Understanding the spatial variability of soil organic matter (SOM) is crucial for implementing precise land degradation control and fertilization to improve crop productivity. Studying spatial variability provides a scientific basis for precision fertilization and land degradation control. In this s...
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MDPI AG
2021-05-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/6/366 |
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| author | Ping Yan Kairong Lin Yiren Wang Xinjun Tu Chunmei Bai Luobin Yan |
| author_facet | Ping Yan Kairong Lin Yiren Wang Xinjun Tu Chunmei Bai Luobin Yan |
| author_sort | Ping Yan |
| collection | DOAJ |
| description | Understanding the spatial variability of soil organic matter (SOM) is crucial for implementing precise land degradation control and fertilization to improve crop productivity. Studying spatial variability provides a scientific basis for precision fertilization and land degradation control. In this study, geostatistics and classical statistical methods were used to analyze the spatial variability of SOM and its influencing factors under various degrees of land degradation in the red bed area of southern China. The results demonstrate a declining trend for SOM content with increasing land degradation. The SOM content differs profoundly under different land degradation degrees. The coefficient of variation ranges from 13.61% for extreme land degradation to 8.98% for mild land degradation, 7.96% for moderate land degradation, and 5.64% for severe land degradation. A significant positive correlation is displayed between the altitude and the SOM (<i>p</i> < 0.01) under mild and moderate land degradation conditions. Bulk density and pH value have a significant negative correlation with SOM (<i>p</i> < 0.01). It can be observed that terrain factors, as well as physical and chemical soil parameters, have a great influence on SOM. |
| first_indexed | 2024-03-10T10:54:28Z |
| format | Article |
| id | doaj.art-3be6efd2230a4033a74922bfc9a91a3b |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T10:54:28Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-3be6efd2230a4033a74922bfc9a91a3b2023-11-21T21:59:09ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-05-0110636610.3390/ijgi10060366Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical MethodsPing Yan0Kairong Lin1Yiren Wang2Xinjun Tu3Chunmei Bai4Luobin Yan5School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geographical Sciences, Southwest University, Chongqing 400715, ChinaUnderstanding the spatial variability of soil organic matter (SOM) is crucial for implementing precise land degradation control and fertilization to improve crop productivity. Studying spatial variability provides a scientific basis for precision fertilization and land degradation control. In this study, geostatistics and classical statistical methods were used to analyze the spatial variability of SOM and its influencing factors under various degrees of land degradation in the red bed area of southern China. The results demonstrate a declining trend for SOM content with increasing land degradation. The SOM content differs profoundly under different land degradation degrees. The coefficient of variation ranges from 13.61% for extreme land degradation to 8.98% for mild land degradation, 7.96% for moderate land degradation, and 5.64% for severe land degradation. A significant positive correlation is displayed between the altitude and the SOM (<i>p</i> < 0.01) under mild and moderate land degradation conditions. Bulk density and pH value have a significant negative correlation with SOM (<i>p</i> < 0.01). It can be observed that terrain factors, as well as physical and chemical soil parameters, have a great influence on SOM.https://www.mdpi.com/2220-9964/10/6/366soil organic matterland degradationsemivariogramspatial distributionclassical statistics |
| spellingShingle | Ping Yan Kairong Lin Yiren Wang Xinjun Tu Chunmei Bai Luobin Yan Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods ISPRS International Journal of Geo-Information soil organic matter land degradation semivariogram spatial distribution classical statistics |
| title | Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods |
| title_full | Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods |
| title_fullStr | Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods |
| title_full_unstemmed | Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods |
| title_short | Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods |
| title_sort | assessment of influencing factors on the spatial variability of som in the red beds of the nanxiong basin of china using gis and geo statistical methods |
| topic | soil organic matter land degradation semivariogram spatial distribution classical statistics |
| url | https://www.mdpi.com/2220-9964/10/6/366 |
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