四川省邻水县土壤锌地球化学特征及玉米水稻籽实锌含量预测

马旭东; 余涛; 杨忠芳; 张虎生; 武芝亮; 王珏; 李明辉; 雷风华

doi:10.12029/gc20220121

四川省邻水县土壤锌地球化学特征及玉米水稻籽实锌含量预测

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

摘要

摘要:
研究目的 锌(Zn)是一种人体所必需的微量元素。利用区域地球化学调查数据，准确预测农作物中Zn含量，从而开展富Zn农产品开发规划仍存在较大难度。
研究方法 本文选择四川省邻水县为研究区，依据土地质量地球化学调查所获得的表层土壤、农作物及根系土中地球化学指标数据，系统研究了土壤与农作物中Zn含量和空间分布特征，分析了玉米、水稻吸收Zn的影响因素。
研究结果 邻水县表层土壤Zn含量范围为25.00~142.00 mg/kg，平均值为81.93 mg/kg，土壤Zn高值区主要分布在邻水县华蓥山碳酸盐岩和峨眉山玄武岩出露区。研究区玉米、水稻籽实平均Zn含量分别为17.18 mg/kg和11.20 mg/kg，富锌率分别为44.0%和8.2%。利用反向传播神经网络模型分别预测出邻水县富Zn玉米、富Zn水稻种植面积为235.34 km²、30.99 km²。
结论影响研究区玉米、水稻籽实Zn生物富集主要因素有土壤Fe₂O₃、Mn、pH、SiO₂/Al₂O₃、CaO、有机质以及营养元素P等；反向传播神经网络模型能较好地模拟籽实Zn元素与土壤理化性质的关系，可以应用于区域农作物Zn含量的计算。

Abstract:
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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