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

J 2021

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

SPYROGLOU, Ioannis; K. RYBKA; R.M. RODRIGUEZ; P. STEFANSKI; Natallia MADZIA VALASEVICH et. al.

Basic information

Original name

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

Authors

SPYROGLOU, Ioannis (300 Greece, guarantor, belonging to the institution); K. RYBKA; R.M. RODRIGUEZ; P. STEFANSKI and Natallia MADZIA VALASEVICH (112 Belarus, belonging to the institution)

Edition

JOURNAL OF AGRONOMY AND CROP SCIENCE, HOBOKEN, WILEY, 2021, 0931-2250

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

40106 Agronomy, plant breeding and plant protection

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

References:

URL

Impact factor

Impact factor: 4.153

RIV identification code

RIV/00216224:14740/21:00124259

Organization unit

Central European Institute of Technology

DOI

https://doi.org/10.1111/jac.12473

UT WoS

000606615000001

EID Scopus

2-s2.0-85099223186

Keywords in English

beta regression; mixed model; multilevel principal component analysis; OJIP; Triticum aestivum

Abstract

In the original language

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.

Links

EF16_026/0008446, research and development project

Name: Integrace signálu a epigenetické reprogramování pro produktivitu rostlin

Cite

SPYROGLOU, Ioannis; K. RYBKA; R.M. RODRIGUEZ; P. STEFANSKI and Natallia MADZIA VALASEVICH. 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. JOURNAL OF AGRONOMY AND CROP SCIENCE. HOBOKEN: WILEY, 2021, vol. 207, No 4, p. 589-605. ISSN 0931-2250. Available from: https://doi.org/10.1111/jac.12473.

@article{1836442,
   author = {Spyroglou, Ioannis and Rybka, K. and Rodriguez, R.M. and Stefanski, P. and Madzia Valasevich, Natallia},
   article_location = {HOBOKEN},
   article_number = {4},
   doi = {https://doi.org/10.1111/jac.12473},
   keywords = {beta regression; mixed model; multilevel principal component analysis; OJIP; Triticum aestivum},
   language = {eng},
   issn = {0931-2250},
   journal = {JOURNAL OF AGRONOMY AND CROP SCIENCE},
   title = {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},
   url = {https://onlinelibrary.wiley.com/doi/10.1111/jac.12473},
   volume = {207},
   year = {2021}
}

TY  - JOUR
ID  - 1836442
AU  - Spyroglou, Ioannis - Rybka, K. - Rodriguez, R.M. - Stefanski, P. - Madzia Valasevich, Natallia
PY  - 2021
TI  - 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
JF  - JOURNAL OF AGRONOMY AND CROP SCIENCE
VL  - 207
IS  - 4
SP  - 589-605
EP  - 589-605
PB  - WILEY
SN  - 09312250
KW  - beta regression
KW  - mixed model
KW  - multilevel principal component analysis
KW  - OJIP
KW  - Triticum aestivum
UR  - https://onlinelibrary.wiley.com/doi/10.1111/jac.12473
N2  - 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.
ER  -

SPYROGLOU, Ioannis; K. RYBKA; R.M. RODRIGUEZ; P. STEFANSKI and Natallia MADZIA VALASEVICH. 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. \textit{JOURNAL OF AGRONOMY AND CROP SCIENCE}. HOBOKEN: WILEY, 2021, vol.~207, No~4, p.~589-605. ISSN~0931-2250. Available from: https://doi.org/10.1111/jac.12473.

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