Variability of the VRN1 promoter region of the unique collection of spring polyploid and wild diploid wheat species together with diploid goatgrasses (donor of B and D genomes of polyploid wheats) were investigated. Accessions of wild diploid (T. boeoticum, T. urartu) and tetraploid (T. araraticum, T. timopheevii) species were studied for the first time.

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.

This issue of the Vavilov Journal of Genetics and Breeding is composed of reports of top Russian breeders delivered at the scientific session of the RAS Department of Agricultural Sciences “Scientific support of the efficient development of crop breeding and seed production in the Russian Federation” held in Moscow on December 7, 2020.