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

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<sup>-1</sup>). 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 R<sup>2</sup> 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.

Authors

Institutions

Publication Details

Journal
Plants
Published
2026-02-13
DOI
https://doi.org/10.3390/plants15040591
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
31.91

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Haitao Xiang, Qiaoyi Huang, Chengyingying Qin, Yuan Wang
1 citations
Plants
Remote Sensing in Agriculture
31.91
article

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

Haitao Xiang, Qiaoyi Huang, Chengyingying Qin, Yuan Wang
article en
1 citations

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<sup>-1</sup>). 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 R<sup>2</sup> 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.

PlantsVol. 15(4)
Chinese Academy of Sciences (CN), Guangdong Academy of Agricultural Sciences (CN), University of Chinese Academy of Sciences (CN), Institute of Soil Science (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Institute of Soil Science, Chinese Academy of Sciences, Guangdong Academy of Agricultural Sciences, National Key Research and Development Program of China
Zero hunger
Openalex Percentile: Top 1%
Remote Sensing in Agriculture
31.91
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.