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Influencing factors of selenium transformation in a soil–rice system and prediction of selenium content in rice seeds: a case study in Ninghua County, Fujian Province

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Abstract

Selenium (Se) is an essential element for human and animal health and has antioxidant, anticancer, and antiviral effects. However, more than 100 million people in China do not have enough Se in their diets, resulting in a state of low Se in the human body. Since the absorption of Se by crop seeds depends not only on the Se content in soil, there are many omissions and misjudgments in the division of Se-rich producing areas. Soil pH, total iron oxide content (TFe2O3), soil organic matter (SOM), and P and S contents were the main factors affecting Se migration and transformation in the soil–rice system. In this study, we compared the performance of the back propagation neural network (BP network) and multiple linear regression (MLR) using 177 pairs of soil–rice samples. Our results showed that the BP network had higher accuracy than MLR. The accuracy and precision of the prediction data met the requirements, and the prediction data were reliable. Based on the Se data of surface paddy fields, 26,900 ha of Se-rich rice planting area was planned using this model, accounting for 77% of the paddy field area. In the planned Se-rich area for rice, the proportion of soil Se content greater than 0.4 mg·kg−1 was only 5.29%. Our research is of great significance for the development of Se-rich lands.

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Funding

This study was supported by the Project of comprehensive investigation and evaluation of resource and environment carrying capacity in Fujian Province by China Geological Survey (Granted No. DD20190301). The authors are grateful for these assistants of the editors and anonymous reviewers for their critical reviews and constructive comments.

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Authors and Affiliations

  1. School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China

    Ying Wang, Zhongfang Yang & Weikang Sheng

  2. China Chemical Mingda Holding Group, Beijing, 100013, China

    Ying Wang

  3. Key Laboratory of Ecological Geochemistry, Ministry of Natural Resources, Beijing, 100037, China

    Zhongfang Yang

  4. Nanjing Center, China Geological Survey, Nanjing, 210016, Jiangsu, China

    Guoguang Chen, Long Zhan, Ming Zhang & Mo Zhou

Authors
  1. Ying Wang
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  2. Zhongfang Yang
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  3. Guoguang Chen
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  5. Ming Zhang
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  6. Mo Zhou
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  7. Weikang Sheng
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Contributions

Ying Wang: methodology, conceptualization, formal analysis, investigation, writing — original draft, writing — review and editing, visualization. Zhongfang Yang: conceptualization, writing — review and editing, supervision. Guoguang Chen: project administration. Long Zhan: project administration. Ming Zhang: methodology, data curation. Mo Zhou: software, investigation. Weikang Sheng: investigation. All authors read and approved the final manuscript.

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Correspondence to Zhongfang Yang.

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Highlights

• Many omissions can be made in the division of Se-rich producing areas only by Se content in soil.

• BP neural network model had better performance than MLR.

• The proportion of Se-rich rice was greatly increased by using BP model.

• Distribution map of predictive rice Se was proposed by BP model.

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Wang, Y., Yang, Z., Chen, G. et al. Influencing factors of selenium transformation in a soil–rice system and prediction of selenium content in rice seeds: a case study in Ninghua County, Fujian Province. Environ Sci Pollut Res 31, 995–1006 (2024). https://doi.org/10.1007/s11356-023-31193-1

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