Quantitative estimation of water status in field-grown wheat using beta mixed regression modelling based on fast chlorophyll fluorescence transients: A method for drought tolerance estimation

Warning

This publication doesn't include Faculty of Arts. It includes Central European Institute of Technology. Official publication website can be found on muni.cz.

Authors	SPYROGLOU Ioannis RYBKA K. RODRIGUEZ R.M. STEFANSKI P. MADZIA VALASEVICH Natallia
Year of publication	2021
Type	Article in Periodical
Magazine / Source	JOURNAL OF AGRONOMY AND CROP SCIENCE
MU Faculty or unit	Central European Institute of Technology
Citation
Web	https://onlinelibrary.wiley.com/doi/10.1111/jac.12473
Doi	http://dx.doi.org/10.1111/jac.12473
Keywords	beta regression; mixed model; multilevel principal component analysis; OJIP; Triticum aestivum
Description	Maintaining a steady increase of yields requires knowledge of plant stress physiology and modern techniques of quantitative data collection and analysis. Here, the chlorophyll fluorescence parameters are used for modelling of relative water content (RWC) in field-grown wheat cultivars. RWC is commonly used for the detection of plant tolerance to temporary droughts, but its determination is laborious and does not meet the requirements of a mass test like fluorescence detection. The paper presents a beta generalized linear mixed model (GLMM) fitted for RWC prediction based on chlorophyll fluorescence data repeatedly measured over time. The nature of fluorescence parameters with the strong correlations between them leads to the use of a multilevel principal component analysis to overcome this issue prior to the fitting of the model. Furthermore, a beta generalized estimating equation (GEE) model is fitted for identifying population-average effects of the parameters used. Finally, highly significant results in terms of prediction with the use of 10-fold cross-validation (RPearson-CV = 0.86, MAE(CV) = 0.0365, RMSECV = 0.048) were obtained. Moreover, the population-average effects provide important information for the parameters used in RWC prediction. The beta GLMM can provide good predictions combined with important cultivar-specific information. Conclusively, these implementations can be a useful tool for drought tolerance improvement.
Related projects:	Signal integration and epigenetic reprograming for plant productivity