Dynamic Quantification and Prediction of Salt Tolerance Threshold in Summer Maize Under Different Regimes of Brackish Water Irrigation

Abstract

To investigate how different training modes of salt stress priming affect the dynamic variation of the salt tolerance threshold (STT) in summer maize, and to enable the accurate quantification and prediction of STT, a micro-plot experiment utilizing diverse regimes of brackish water irrigation was conducted. Utilizing physiological, shoot, and root indicators, a comprehensive evaluation framework was developed to define a dynamic salt tolerance coefficient (αSTT), enabling the precise quantification of STT across growth stages. Building on this, the study established a unified predictive framework to systematically evaluate the performance of diverse modeling pathways and machine learning algorithms. The results revealed a distinct two-stage stress response pattern of summer maize to salt stress, characterized by an initial physiological adaptation phase dominated by regulatory adjustments, followed by a phenotypic adaptation phase associated primarily with improvements in growth performance. Different training modes led to distinct salt stress memory effects by modulating the coordination between these two adaptive stages. Among all modes, the S1-2-3 training regime exhibited the most favorable adaptive outcome, with the αSTT gradually recovering to 1.0 during later growth stages, indicating full adaptation to saline stress, and concomitantly exhibiting a relatively high STT. Regarding predictive performance, the PCR-STP modeling pathway incorporating process constraints outperformed purely data-driven pathways, and its combination with CatBoost achieved the highest accuracy (R² = 0.910, RMSE = 0.241). Overall, our study elucidates the dynamic nature of salt tolerance in summer maize, while the proposed STT quantification and prediction method provides a scientific basis for refining salt stress modules in crop models, optimizing brackish water irrigation regimes, and improving precision water resource management in arid and semi-arid regions.

Plant phenotyping relevance

塩耐性閾値（STT）の動的定量・予測フレームワークと機械学習モデルの構築が研究の中心であり、植物の生理・生育状態を抽出する方法論的貢献が明確である。

Code and data availability

The supplied blocks describe a maize salt-tolerance phenotyping experiment and machine-learning prediction framework, but contain no public phenotype dataset, images, code repository, or trained model deposit. Data analysis used Excel, SPSS, and Origin with no availability statements or author URLs; allowed_urls is non

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