Evaluation of winter bread wheat genotypes based on remote sensing data and agronomic traits related to yield
DOI:
https://doi.org/10.31548/dopovidi5(105).2023.012Keywords:
Triticum aestivum L., genotype, NDVI index, GGE-biplot, REML/BLUP analysis, genetic gain, selection indexAbstract
Genetic improvement of wheat requires enhancement and application of more effective methods of phenotyping and assessment of genetic gain of breeding lines. Purpose. To evaluate the possibility of using spectral vegetation indices with the involvement of determined genotypic values, to compare the genetic increase in grain yield and other traits, to select the best wheat genotypes using a multi-trait indices and multivariate statistical methods. Methods: field, determination of vegetation indices using UAV, multiple regression, AMMI, GGE-biplot and REML/BLUP methods. Selection indices were calculated based on a set of traits. Results. There were evaluated 12 varieties and lines of bread winter wheat by grain yield, NDVI index and other characteristics. When using GGE-biplot and AMMI analysis, a comprehensive evaluation of genotypes for productivity and stability was carried out. With application of REML/BLUP analysis, genetic parameters and genotypic values were determined for a number of investigated traits.
On the basis of the obtained data, selection indices were calculated based on a set of traits. The possibility of using spectral vegetation indices obtained from UAVs in breeding process has been established. More accurate identification of genotypes by a set of features is provided by the combined use of multivariate statistical methods, selection indices and NDVI index. The REML/BLUP method in combination with the multivariate AMMI and GGE-biplot methods with the graphical identification of genotypes by the Z index allows to determine the promising set of traits. The Lines LUT 55198 LUT 37519, LUT 60049, LUT 60107 and the cultivars MIP Lada, MIP Dnipryanka were selected for further use in breeding programs. The prospect of further research is to increase the accuracy of assessment and selection of potentially high-yielding and stable wheat lines using remote sensing.
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