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Prediction of arsenic accumulation in a calcareous soil-wheat/maize rotation system with continuous amendment of sewage sludge

Author

Listed:
  • Huiqing Chang
  • Linlin Huang

    (Henan University of Science and Technology, Luoyang, P.R. China)

  • Panpan Song

    (Henan University of Science and Technology, Luoyang, P.R. China)

  • Liyang Ru

    (Henan University of Science and Technology, Luoyang, P.R. China)

Abstract

A potted experiment was conducted to explore the accumulation of arsenic (As) and predict the uptake of As by a wheat-maize rotation system in calcareous soil with different rates of sewage sludge (SS) amendment over two consecutive years. The SS amendment decreased the pH value of calcareous soil but increased the cation exchange capacity (CEC), calcium carbonate (CC), organic carbon (OC) and As accumulation in soil and crops with increasing SS addition. The As bioconcentration factor (BCF) of wheat and maize had a significant negative correlation with pH, CC and a significant positive correlation with OC. Soil CEC had a significant positive correlation only with the As BCF of wheat. Regression analysis showed that soil As, pH, OC, CC and CEC were good predictors of the As concentration in wheat/maize. The regression model for each part of the wheat/maize plants had a high model efficiency value and explained 67~88% of the variability. The R2 values of the wheat and maize grain prediction models were 79% and 76%, respectively. Thus, these models contribute to the study of As risk assessment for sewage sludge utilisation in calcareous soil-wheat/maize rotation systems.

Suggested Citation

  • Huiqing Chang & Linlin Huang & Panpan Song & Liyang Ru, 2022. "Prediction of arsenic accumulation in a calcareous soil-wheat/maize rotation system with continuous amendment of sewage sludge," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 68(11), pages 516-524.
    • Handle: RePEc:caa:jnlpse:v:68:y:2022:i:11:id:207-2022-pse
    DOI: 10.17221/207/2022-PSE
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    References listed on IDEAS

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    1. Bi, Yingtao & Jeske, Daniel R., 2010. "The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1622-1637, August.
    2. Ke Liu & Jialong Lv & Yunchao Dai & Hong Zhang & Yingfei Cao, 2016. "Cross-Species Extrapolation of Models for Predicting Lead Transfer from Soil to Wheat Grain," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
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