Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation
Abstract
Grain filling is the decisive period for rice grain weight formation. However, traditional static traits fail to capture its complex, nonlinear dynamics, while direct panicle weighing is hindered by canopy occlusion. Given the intrinsic synchronization between grain filling and dehydration from anthesis to physiological maturity, monitoring grain moisture content (GMC) dynamics serves as a robust proxy for characterizing the filling process. Here, we propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration. Leveraging a 4-year dataset across 135 cultivar-environment combinations, we demonstrate that the GMC threshold for physiological maturity is relatively stable (≈25%). Concurrently, we developed 2 image-based models for GMC estimation, achieving high accuracies (R2 = 0.82 and 0.86). Integrating this physiological threshold with GMC estimation models enabled the successful reconstruction of the dehydration process. Validation on 26 independent cultivars across 2 sowing dates predicted physiological maturity with a root mean square error of 2.4 to 3.3 d. Traits extracted from these dehydration profiles accounted for 42% of the variance in grain weight, doubling the explanatory power of traditional traits. These gains are largely attributed to a new integrated trait, the moisture maintenance index, which showed a higher and more stable correlation with thousand-grain weight (r = 0.6). This framework offers a scalable approach for monitoring large-scale dehydration dynamics to deepen our understanding of grain weight formation, facilitating the genetic improvement of the filling process to enhance crop yield.
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BibTeX
@article{wang2026physiologyinformed,
title = {{Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation}},
author = {Xu Wang and Feng Xiao and Zejun Li and Zhenghui Liu and Frédéric Baret and Yanfeng Ding and Chen Zhu and Shouyang Liu},
journal = {Plant Physiology},
year = {2026},
month = jul,
doi = {10.1093/plphys/kiag526},
url = {https://hal.inrae.fr/hal-05781915v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation
AU - Xu Wang
AU - Feng Xiao
AU - Zejun Li
AU - Zhenghui Liu
AU - Frédéric Baret
AU - Yanfeng Ding
AU - Chen Zhu
AU - Shouyang Liu
PY - 2026
DA - 2026/07/02
JO - Plant Physiology
DO - 10.1093/plphys/kiag526
UR - https://hal.inrae.fr/hal-05781915v1
AB - Grain filling is the decisive period for rice grain weight formation. However, traditional static traits fail to capture its complex, nonlinear dynamics, while direct panicle weighing is hindered by canopy occlusion. Given the intrinsic synchronization between grain filling and dehydration from anthesis to physiological maturity, monitoring grain moisture content (GMC) dynamics serves as a robust proxy for characterizing the filling process. Here, we propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration. Leveraging a 4-year dataset across 135 cultivar-environment combinations, we demonstrate that the GMC threshold for physiological maturity is relatively stable (≈25%). Concurrently, we developed 2 image-based models for GMC estimation, achieving high accuracies (R2 = 0.82 and 0.86). Integrating this physiological threshold with GMC estimation models enabled the successful reconstruction of the dehydration process. Validation on 26 independent cultivars across 2 sowing dates predicted physiological maturity with a root mean square error of 2.4 to 3.3 d. Traits extracted from these dehydration profiles accounted for 42% of the variance in grain weight, doubling the explanatory power of traditional traits. These gains are largely attributed to a new integrated trait, the moisture maintenance index, which showed a higher and more stable correlation with thousand-grain weight (r = 0.6). This framework offers a scalable approach for monitoring large-scale dehydration dynamics to deepen our understanding of grain weight formation, facilitating the genetic improvement of the filling process to enhance crop yield.
SN - 0032-0889
SN - 1532-2548
ER -