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February 16, 2026Plants2 citationsOpen Access

Deconfounding Phenology in SPAD-Based Rice Nitrogen Diagnosis Using Physiological Time and Canopy-Stratified Measurements

CQChengyingying QinHXHaitao XiangQHQiaoyi Huang

Key Points

  • This research aims to evaluate how physiological time can improve SPAD-based nitrogen diagnosis in rice.
  • Analyzed 1141 observations from 20 field experiments across five sites.
  • Measured SPAD readings on multiple leaf positions (LFT1–LFT5).
  • Assessed leaf nitrogen concentration, plant nitrogen concentration, and nitrogen nutrition index.
  • Used cross-validation to evaluate the effectiveness of incorporating growing degree days with SPAD measurements.
  • Using growing degree days improved accuracy of leaf nitrogen concentration predictions (mean R2 up to 0.75).
  • Plant nitrogen concentration predictions also increased in accuracy (mean R2 up to 0.79).
  • Residual trends along growing degree days were significantly reduced.
  • Combining GDD with a two-leaf SPAD protocol maintained most accuracy for nitrogen concentration targets.

Abstract

Phenology can confound rice nitrogen diagnosis based on SPAD readings because leaf greenness and nitrogen concentration change nonlinearly with development. We tested whether physiological time, expressed as growing degree days (GDD), can reduce this developmental bias and improve the portability of SPAD-based diagnosis. We analyzed 1141 observations from 20 independent field experiments across five sites, spanning japonica, indica, and hybrid cultivars and nitrogen fertilizer treatments (0–300 kg N ha−1). SPAD was measured on up to five leaf-from-top positions (LFT1–LFT5) and used to predict leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), and nitrogen nutrition index (NNI). Across group-wise cross-validation by experiment, adding GDD to SPAD consistently improved cross-environment accuracy (mean R2 up to 0.75 for LNC and 0.79 for PNC) and markedly weakened residual trends along GDD. Multiplicative SPAD×GDD degraded performance, while explicit interaction terms provided little gain over a simple additive SPAD + GDD form. Interpretable analyses further showed that diagnostic information is concentrated in mid-canopy leaves and shifts with physiological time. Combining GDD with a two-leaf SPAD protocol retained most accuracy for concentration targets, supporting a time-aligned and field-practical approach for robust nitrogen diagnosis.

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Cite This Study

Qin et al. (2026) studied this question.

synapsesocial.com/papers/6992b3b19b75e639e9b087b7https://doi.org/10.3390/plants15040591
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Effect of various nitrogen rates on MR303 rice yield performance and NDVI spectral reflectance2025
  2. 2Integrating Plant Height into Hyperspectral Inversion Models for Estimating Chlorophyll and Total Nitrogen in Rice Canopies2026
  3. 3Machine Learning–Based Estimation of Leaf Nitrogen Content in Greenhouse Cucumber Using Spectral Data and SPAD Measurements2026
  4. 4Development of a Hyperspectral-Based Inversion Model for Cotton Leaf SPAD Measurement2026
  5. 5Enhancing nitrogen use efficiency of wheat through real-time SPAD-based N fertilization2026