Turning canopy temperature into a multidimensional tool: phenotyping diurnal canopy temperature dynamics in spring wheat breeding programs
- 1Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, Universidad de Talca
- 2CRI-Quilamapu, Instituto de Investigaciones Agropecuarias
Abstract
Introduction: Canopy temperature (Tc) measured at 14:00 h is a standard procedure in wheat breeding programs for assessing crop water status and heat stress tolerance. However, a single reading captures only a fraction of the thermal variation that unfolds over a full day.
Methods: This study phenotyped 196 spring wheat genotypes at two contrasting Mediterranean sites in Chile, a dryland rainfed site (WS) and a fully irrigated site (FI), during the 2024 growing season. Hourly Tc and canopy–air temperature differential (CTD) were recorded from 09:00 to 18:00 h across four phenological stages, including anthesis, milk stage, early grain filling, and late grain filling. Spatially corrected best linear unbiased estimates (BLUEs) were derived using Spatial Analysis of Field Trials with Splines (SpATS), and the best-fitting diurnal curve per genotype per stage was selected from ten candidate model families by minimizing the Bayesian Information Criterion (BIC). Curve parameters and pairwise rates of change (ROC) extracted from the fitted Tc and CTD profiles were evaluated as predictors of seven agronomic traits, including grain yield, harvest index, thousand-kernel weight, kernels per spike, kernels per square meter, shoot dry weight, and spikelets per square meter. Models were evaluated using in-sample partial least squares (PLS) regression R², nested cross-validated Q², a permutation-based null model, and hold-out-genotype testing.
Results: The most predictive single measurement hour shifted across traits, stages, and sites with no consistent pattern, and the conventional 14:00 h reading was the best single hour in about 9% of stage-trait combinations tested. When all features within each predictor category were used jointly in a PLS regression, in-sample R² exceeded the 14:00 h control across most site–stage combinations, and k-means clustering further increased within-cluster R², though these apparent gains may be inflated by predictor number and cluster subsetting. Under stricter, cross-validated evaluation, whole-panel Q² was close to zero for all predictor categories (median -0.023 to -0.009), no cluster consistently outperformed random genotype partitions on held-out genotypes, and representative cluster-level Q² values ranged from -0.31 to 0.76.
Discussion: These results indicate that in-sample R² substantially overestimated the predictive value of diurnal clustering, and that cross-validated Q², not in-sample R², should be the primary metric for such claims.
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BibTeX
@article{khan2026turning,
title = {{Turning canopy temperature into a multidimensional tool: phenotyping diurnal canopy temperature dynamics in spring wheat breeding programs}},
author = {Hamza-Ali Khan and Iván Matus and Dalma Castillo and Josefa Pacheco and Alejandro del Pozo and Gustavo A. Lobos},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1936928},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1936928/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Turning canopy temperature into a multidimensional tool: phenotyping diurnal canopy temperature dynamics in spring wheat breeding programs
AU - Hamza-Ali Khan
AU - Iván Matus
AU - Dalma Castillo
AU - Josefa Pacheco
AU - Alejandro del Pozo
AU - Gustavo A. Lobos
PY - 2026
DA - 2026/10/05
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1936928
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1936928/full
AB - Introduction: Canopy temperature (Tc) measured at 14:00 h is a standard procedure in wheat breeding programs for assessing crop water status and heat stress tolerance. However, a single reading captures only a fraction of the thermal variation that unfolds over a full day. Methods: This study phenotyped 196 spring wheat genotypes at two contrasting Mediterranean sites in Chile, a dryland rainfed site (WS) and a fully irrigated site (FI), during the 2024 growing season. Hourly Tc and canopy–air temperature differential (CTD) were recorded from 09:00 to 18:00 h across four phenological stages, including anthesis, milk stage, early grain filling, and late grain filling. Spatially corrected best linear unbiased estimates (BLUEs) were derived using Spatial Analysis of Field Trials with Splines (SpATS), and the best-fitting diurnal curve per genotype per stage was selected from ten candidate model families by minimizing the Bayesian Information Criterion (BIC). Curve parameters and pairwise rates of change (ROC) extracted from the fitted Tc and CTD profiles were evaluated as predictors of seven agronomic traits, including grain yield, harvest index, thousand-kernel weight, kernels per spike, kernels per square meter, shoot dry weight, and spikelets per square meter. Models were evaluated using in-sample partial least squares (PLS) regression R², nested cross-validated Q², a permutation-based null model, and hold-out-genotype testing. Results: The most predictive single measurement hour shifted across traits, stages, and sites with no consistent pattern, and the conventional 14:00 h reading was the best single hour in about 9% of stage-trait combinations tested. When all features within each predictor category were used jointly in a PLS regression, in-sample R² exceeded the 14:00 h control across most site–stage combinations, and k-means clustering further increased within-cluster R², though these apparent gains may be inflated by predictor number and cluster subsetting. Under stricter, cross-validated evaluation, whole-panel Q² was close to zero for all predictor categories (median -0.023 to -0.009), no cluster consistently outperformed random genotype partitions on held-out genotypes, and representative cluster-level Q² values ranged from -0.31 to 0.76. Discussion: These results indicate that in-sample R² substantially overestimated the predictive value of diurnal clustering, and that cross-validated Q², not in-sample R², should be the primary metric for such claims.
SN - 1664-462X
ER -