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<p>In southern Xinjiang, nitrogen nutrition diagnostic methods for drip-irrigated relay-cropped maize are relatively scarce, and the differences in the Critical Nitrogen Dilution Curve (CNDC) under different drip irrigation patterns remain unclear. A two-year (2022–2023) pit experiment was conducted. Herein, a nitrogen nutrition management model for southern Xinjiang’s relay-cropped maize was established, and its nitrogen status under mulched drip (MD) and bare land (LD) irrigation was diagnosed. With five nitrogen gradients (0, 224.6, 278.4, 368.9, 464&#xa0;kg·hm<sup>−2</sup>), CNDCs were constructed via classical and Bayesian methods. The Bayesian method’s higher stability and convenience in CNDC construction. Through 95% confidence interval and covariance analysis, model parameter uncertainty rose with biomass, and LD’s nitrogen dilution rate was higher than MD’s at low biomass. Integrated data led to CNDC models for MD (Nc = 3.924W<sup>−0.462</sup>) and LD (Nc = 3.326W<sup>−0.436</sup>). A novel NNI estimation method based on Leaf Area Index (LAI) and SPAD values was proposed with high accuracy (R<sup>2</sup> = 0.87—0.99). Notably, 278.4&#xa0;kg·hm<sup>−2</sup> nitrogen under MD achieved high yield for relay-cropped maize, offering a basis for optimizing nitrogen nutrition management in arid areas.</p> Graphical Abstract <p></p>

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Nitrogen Nutrition Diagnosis for Relay-cropped Maize with Drip Irrigation: Embracing Bayesian Methods for Precision Agriculture

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • LI Feng-xiu,
  • ZUO Shun-li,
  • MA Ying-jie,
  • LIU Ze-long,
  • XU Yuan

摘要

In southern Xinjiang, nitrogen nutrition diagnostic methods for drip-irrigated relay-cropped maize are relatively scarce, and the differences in the Critical Nitrogen Dilution Curve (CNDC) under different drip irrigation patterns remain unclear. A two-year (2022–2023) pit experiment was conducted. Herein, a nitrogen nutrition management model for southern Xinjiang’s relay-cropped maize was established, and its nitrogen status under mulched drip (MD) and bare land (LD) irrigation was diagnosed. With five nitrogen gradients (0, 224.6, 278.4, 368.9, 464 kg·hm−2), CNDCs were constructed via classical and Bayesian methods. The Bayesian method’s higher stability and convenience in CNDC construction. Through 95% confidence interval and covariance analysis, model parameter uncertainty rose with biomass, and LD’s nitrogen dilution rate was higher than MD’s at low biomass. Integrated data led to CNDC models for MD (Nc = 3.924W−0.462) and LD (Nc = 3.326W−0.436). A novel NNI estimation method based on Leaf Area Index (LAI) and SPAD values was proposed with high accuracy (R2 = 0.87—0.99). Notably, 278.4 kg·hm−2 nitrogen under MD achieved high yield for relay-cropped maize, offering a basis for optimizing nitrogen nutrition management in arid areas.

Graphical Abstract