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    MA Xudong, YU Tao, YANG Zhongfang, ZHANG Husheng, WU Zhiliang, WANG Jue, LI Minghui, LEI Fenghua. Geochemical characteristics of zinc in soil and prediction of zinc content in maize and rice grains in Linshui County, Sichuan Province[J]. GEOLOGY IN CHINA, 2022, 49(1): 324-335. DOI: 10.12029/gc20220121
    Citation: MA Xudong, YU Tao, YANG Zhongfang, ZHANG Husheng, WU Zhiliang, WANG Jue, LI Minghui, LEI Fenghua. Geochemical characteristics of zinc in soil and prediction of zinc content in maize and rice grains in Linshui County, Sichuan Province[J]. GEOLOGY IN CHINA, 2022, 49(1): 324-335. DOI: 10.12029/gc20220121

    Geochemical characteristics of zinc in soil and prediction of zinc content in maize and rice grains in Linshui County, Sichuan Province

    • This paper is the result of the soil geochemical survey engineering.
      Objective Zinc (Zn) is an essential trace element for human body. Using regional geochemical survey data to accurately predict the Zn content in crops and carry out development planning for Zn-rich agricultural products is still a problem.
      Methods In this paper, we chose Linshui County of Sichuan Province as the study area. Basing on the investigation results obtained from the geochemical survey of land quality, content and spatial distribution characteristics of Zn in the soil and crop were studied and the factors affecting Zn element uptake by maize and rice grain were analyzed.
      Results The Zn content of topsoil in the study area ranged from 25.00-142.00mg/kg with a mean value of 81.93 mg/kg. The higher content of Zn in soil were mainly distributed in exposure of carbonate rocks and Emei Shan basalt in Huaying mountain. The average content of Zn in maize and rice were 17.18 mg/kg and 11.20 mg/kg, respectively. The Zn enrichment rates were 44.0% and 8.2%, respectively. The prediction of the planting areas of Zn rich maize and Zn rich rice in Linshui County reached 235.34 km2 and 30.99 km2 respectively by using back-propagation neural network models.
      Conclusions The main factors affecting the Zn accumulation of maize and rice in the study area were Fe2O3, Mn, pH, SiO2/Al2O3, Cao, organic matter and nutrient element P in soil. The back-propagation neural network models could better simulate the relationship between Zn in crop grains and physicochemical properties of soil, which could be used for region specific calculation of crop Zn content.
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