Spatial distribution of soil organic matter in tillage layers in a southern China basin using classifications and spatial interpolation algorithms

文献类型： 外文期刊

第一作者： Deng, X. F.

作者： Deng, X. F.;Lv, X. N.;Zhang, M. H.;Li, S. Q.

作者机构：

关键词： Classification;spatial interpolation;soil organic matter;distribution

期刊名称：2013 SECOND INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS)

ISSN： 2334-3168

年卷期： 2013 年

页码：

收录情况： SCI

摘要： Soil organic matter (SOM) in tillage layers is a very important component of soil nutrients. A critical issue for effective management of agricultural fields is proper analysis and interpolating SOM point sampling data onto a surface. An optimal approach needs to be developed to investigate the concentration of SOM in tillage layers. In this study, a total of 2341 soil samples were collected. The landforms (Valley plains, Low hilly areas and High hilly areas), land-use patterns (Paddy field and Citrus orchard) and soil types (including Paddy soils and Red earths), which could affect the concentration of SOM in tillage layers, were taken into account as impact factors for the data classification. Five different spatial interpolation algorithms-inverse distance weighting (IDW), radial basis function (RBF), and kriging (including simple kriging (SK), ordinary kriging (OK), and universal kriging (UK)) - were used to analyze the spatial variations of SOM. Cross-validation was applied to evaluate the accuracy of these methods, and subsequent, mean error (ME) and root mean square error (RMSE) were used to compare and assess the performance of these interpolation methods. The results showed that in each classification of soil samples, the spatial variation of SOM in tillage layers was moderate to strong and the ratio of sill to nugget falls varied by 10%-50%. The study also revealed that RBF had the lowest RMSE value in most classification groups. With overall evaluation, the RBF was the optimal algorithm for interpolating SOM value in the study region, followed by SK, OK, UK and IDW. Finally, one classification group using RBF method was found to be the optimal method for predicting SOM in tillage layers in our study area, and a prediction map for the concentration of SOM was produced using this method.

分类号：

  • 相关文献

[1]Collecting and Conserving Wild Musa Germplasm in Guangxi, China. Quan, X. Q.,Long, X.,Peng, H. X.. 2011

[2]Collection and evaluation of wild tulip (Tulipa spp.) resources in China. Xing, Guimei,Qu, Lianwei,Xue, Li,Lei, Jiajun,Xing, Guimei,Qu, Lianwei,Zhang, Yanqiu,Su, Junwei.

[3]Evaluating and Classifying Field-Scale Soil Nutrient Status in Beijing using 3S Technology. Xue, Yong-An,Huang, Lin-Sheng,Zhang, Dong-Yan,Zhao, Jin-Ling,Yang, Hao. 2012

[4]Estimating Filed-Scale Soil Nutrient Levels in Shun Yi District, Beijing. Zhao, J. L.,Huang, W. J.,Zhang, D. Y.,Huang, L. S.,Luo, J. H.. 2012

[5]Labile soil organic matter fractions as influenced by non-flooded mulching cultivation and cropping season in rice-wheat rotation. Fan, Mingsheng,Li, Xiaolin,Lu, Shihua,Tian, Jing,Kuzyakov, Yakov. 2013

[6]Effect of Spectral Resolution on Black Soil Organic Matter Content Predicting Model Based on Laboratory Reflectance. Liu Huan-jun,Wu Bing-fang,Liu Huan-jun,Zhao Chun-jiang,Zhao Yun-Sheng. 2012

[7]The inversion model of soil organic matter of cultivated land based on hyperspectral technology. Gu, Xiaohe,Wang, Yancang,Song, Xiaoyu,Xu, Xingang. 2015

[8]Spatial scaling effects on variability of soil organic matter and total nitrogen in suburban Beijing. Hu, Kelin,Huang, Feng,Li, Baoguo,Wang, Shuying,Li, Hong,Li, Hong. 2014

[9]Greenhouse gas emissions and stocks of soil carbon and nitrogen from a 20-year fertilised wheat -maize intercropping system: A model approach. Zhang, Xubo,Xu, Minggang,Sun, Nan,Wang, Boren,Zhang, Xubo,Wu, Lianhai,Liu, Jian.

[10]Soil organic carbon sequestration under different fertilizer regimes in north and northeast China: RothC simulation. Wang, J.,Lu, C.,Xu, M.,Zhang, W.,Zhu, P.,Peng, C.,Huang, S.,Chen, X.,Wu, L.. 2013

[11]Remote sensing of soil organic matter of farmland with hyperspectral image. Gu, Xiaohe,Wang, Lei,Yang, Guijun,Zhang, Liyan. 2017

[12]A New Method to Decline the SWC Effect on the Accuracy for Monitoring SOM with Hyperspectral Technology. Wang Chao,Feng Mei-chen,Yang Wu-de,Xiao Lu-jie,Li Guang-xin,Zhao Jia-jia,Ren Peng,Li Guang-xin. 2015

[13]Long-term effects of returning wheat straw to croplands on soil compaction and nutrient availability under conventional tillage. Guo, Z.,Wang, D. Z.,Guo, Z.,Wang, D. Z.. 2013

[14]Effect of conservation farming practices on soil organic matter and stratification in a mono-cropping system of Northern China. Zhang, Zhiqiang,Qiang, Hongjun,He, Jin,Li, Hongwen,Wang, Qingjie,McHugh, Allen D.,Lu, Zhanyuan.

[15]Manure substitution of mineral fertilizers increased functional stability through changing structure and physiology of microbial communities. Yue, Xianlu,Shi, Andong,Yao, Shuihong,Zhang, Bin,Zhang, Jiguang.

[16]Hyperspectral extraction of soil organic matter content based on principal component regression. Yanli, Lu,Youlu, Bai,Lipng, Yang,Hongjuan, Wang.

[17]Soil aggregation regulates distributions of carbon, microbial community and enzyme activities after 23-year manure amendment. Wang, Yidong,Wang, Zhong-Liang,Hu, Ning,Lou, Yilai,Ge, Tida,Kuzyakov, Yakov,Li, Zhongfang,Tang, Zheng,Chen, Yi,Wu, Chunyan.

[18]Modeling Soil Organic Matter Dynamics Under Intensive Cropping Systems on the Huang-Huai-Hai Plain of China. Lei Hong-Jun,Li Bao-Guo,Bai You-Lu,Huang Yuan-Fang,Lu Yi-Zhong,Li Gui-Tong.

[19]Integrated management systems and N fertilization: effect on soil organic matter in rice-rapeseed rotation. Fan, Mingsheng,Li, Xiaolin,Tian, Jing,Fan, Mingsheng,Li, Xiaolin,Lu, Shihua,Tian, Jing,Kuzyakov, Yakov.

[20]Fungi contribute more than bacteria to soil organic matter through necromass accumulation under different agricultural practices during the early pedogenesis of a Mollisol. Li, Na,Xu, Yu-Zhi,Han, Xiao-Zeng,Zhang, Bin,He, Hong-Bo,Zhang, Xu-dong,Zhang, Bin.

作者其他论文 更多>>

微信小程序