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Screening for factors affecting rice uptake of cadmium based on spatial clustering and random forests
Received:January 23, 2019  
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KeyWord:rice;cadmium;spatial cluster;random forest;correlation analysis
Author NameAffiliationE-mail
GUO Xin-lei Key Laboratory for Environmental Factors Control of Agro-Products Quality Safety, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China 
 
ZHAO Yu-jie Key Laboratory for Environmental Factors Control of Agro-Products Quality Safety, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China yujiezhao@126.com 
LIU Xiao-wei Key Laboratory for Environmental Factors Control of Agro-Products Quality Safety, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China xwliu2006@163.com 
ZHOU Qi-wen Key Laboratory for Environmental Factors Control of Agro-Products Quality Safety, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China  
WANG Xia-hui Chinese Academy of Environmental Planning, Beijing 100012, China  
LI Zhi-tao Chinese Academy of Environmental Planning, Beijing 100012, China  
ZHU Zhi-wei China National Rice Research Institute, Hangzhou 310006, China  
ZHANG Tie-liang Key Laboratory for Environmental Factors Control of Agro-Products Quality Safety, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China  
WANG Zu-guang Key Laboratory for Environmental Factors Control of Agro-Products Quality Safety, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China  
ZHANG Fan Key Laboratory for Environmental Factors Control of Agro-Products Quality Safety, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China  
SUN Yang Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China  
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Abstract:
      Understanding the main soil-related factors affecting the accumulation of Cd in rice samples is key to regulating rice Cd concentrations. Regression predictions based on point data often do not reflect regional differences, and most of the main controlling factors show clustering and spatial differences. Based on this principle, a county in Hunan Province was selected as an example and spatial clustering was used to screen hotspots for various soil factors. Soil samples screened in the hotspots were analyzed using multi-parameter detection and the main controlling factors affecting the accumulation of Cd in rice samples were determined through random forest regression. There was up to a 10 fold difference between the Cd enrichment coefficients of rice samples in the high-high and high-low clustering areas, and up to a 17 fold difference between the low-low and low-high clustering areas. In the study area, the critical factors affecting Cd accumulation in rice samples were Ca, pH, and Mn, followed by Fe and Si, and then Zn, DOC, Cl, K, P, Mg, S, Cd, Na, and Cu with a gradually decreasing degree of influence; the influence of SOM was lowest. Regulating the Ca concentration in the soil had a significant impact on soil pH values and subsequently on the uptake of Cd by rice. With an increase in Fe and Mn concentrations in the soil, the rice Cd concentrations decreased exponentially. The Si concentration in the soil was negatively correlated with soil pH values and Fe concentration, and positively correlated with Cd concentration in rice. Organic matter had no significant effect on Cd accumulation in rice. This study confirmed that the combination of bivariate spatial clustering and random forest regression can effectively determine the main factors of soils controlling Cd accumulation in rice samples. Such studies can provide fundamental support for the remediation and control of Cd accumulation in rice and hotspot screening of pollution sources.