Earliness and morphotypes of common wheat cultivars of Western and Eastern Siberia

Abstract

The global and local climate changes determine the producing of highly-adaptive common (bread) wheat commercial cultivars of a new generation whose optimal earliness matches the climatic features of the territory where the cultivars are farmed. Principal component analysis involving our own and published data has been applied to investigate 98 commercial common wheat cultivars from Western and Eastern Siberia comparing their morphotypes; cultivar zoning time; length of the vegetation period; 1000-grain weight, and inheritance of spring growth habit. It demonstrated that the dominant Vrn gene polymorphism determining the spring growth habit of the Siberian cultivars was minimally polymorphic. In 75 % of the tested cultivars, the spring growth habit was controlled by digenic, namely dominant Vrn-A1 and Vrn-B1 genes. In 25 % of them (24 cultivars), spring growth habit is controlled by a single gene. In 19 and 5 of these cultivars spring growth habit is controlled by only one dominant gene, Vrn-B1 or Vrn-A1, respectively. In cv. Tulun 15, a trigenic control was identified. A conclusion about the optimality of the digenic control for the climatic conditions of both Western and Eastern Siberia has been confirmed. However, since none of the tested cultivars had the dominant Vrn-D1 gene typical of the regions of China and Central Asia bordering Siberia, it can be considered as an additional argument in favor of the European origin of Siberian common wheat cultivars. The revealed high frequency of the Vrn-B1c allele in the Western Siberian cultivars and the Vrn-B1a allele in the Eastern Siberian cultivars suggests their selectivity. The analysis also confirmed the dominance of red glume (ferrugineum, milturum) and awned spike (ferrugineum, erythrospermum) varieties in the Eastern Siberian cultivars, and white glume and awnedless spike (lutescens and albidum) ones in the Western Siberian cultivars. Small grain size cultivars are more typical of Eastern than Western Siberia. The retrospective analysis based on the cultivars' zoning time included in the "State Register for Selection Achievements Admitted for Usage" brought us to the conclusion that the earliness/lateness of modern Siberian commercial cultivars was not regionally but rather zonally-associated (taiga, subtaiga, forest-steppe and steppe zones).

Analysis of the Effects of the Vrn-1 and Ppd-1 Alleles on Adaptive and Agronomic Traits in Common Wheat (Triticum aestivum L.)

“…PCA is an important tool frequently used in plant genetics to compare a large number of different cultivars based on their genetics and agronomic traits, countries of origin, etc. in order to find a specific pattern via data dimensionality reduction [ 15 , 65 , 66 ]. The PCA approach revealed three main principal components (PC) together explaining more than 70% of the data variation, where PC1 can be interpreted as a measure of plant habitus and habitus-related agronomic traits, PC2 as an earliness and spike fertility, and PC3 as a measure of wheat heading time and awnlessness.…”

“…Thus, wheat’s late heading is advantageous for regions with longer growing seasons, like central or northern parts of Europe, the United Kingdom, the Middle Volga Region of the Russian Federation, etc. On the other hand, for regions with short growing seasons, like the north of Canada or Western Siberia, early heading helps to avoid late spring and early autumn frost damage and therefore secures a high yield [ 10 , 13 , 14 , 15 ]. With global warming acceleration, wheat heading time adjustment towards earliness might become an important breeding strategy also for agro-climatic zones characterized by longer growing seasons, presuming it will facilitate the avoidance of mid-summer heat waves, infection manifestations, excessive rainfall, or freezing events in early autumn [ 9 , 16 , 17 , 18 , 19 ].…”

“…Principal component analysis (PCA) is a statistical tool frequently used to address this issue [ 63 , 64 ]. The PCA approach helps to reduce dataset complexity and to transform multiple genetic, phenotypical, and agronomic traits into a few principal components (PCs), followed by finding the most important correlations between them and dissecting the noise produced by the large volume of data [ 15 , 63 , 64 , 65 , 66 ].…”

Multivariate analysis of long-term climate data in connection with yield, earliness and the problem of global warming

“…The paradigm shift determined by the warming predic tion is most often discussed in the context of probable aridization of huge areas (Trifonov, Karakhanyan, 2004), the resulting necessity of raising drought resistance of various crops (Zotova et al, 2020;Cooper, Messina, 2023), and search for new droughtresistant plant species applicable for cultivation (Baltzoi et al, 2015). Strategies of adaptation to climate changes may include better fitness of plant phenology to moisture availability (Ceccarelli et al, 2010), broader access to varieties with different duration of vegetative period (earliness) (Smolenskaya, Goncharov, 2023) in order to avoid stress at critical stages of their life cycles, water use improvement, and switch to breeding newgeneration varie ties for mitigating the rising unpredictability (Ceccarelli et al, 2010). Anyhow, breeders should take into consideration the high probability of cli mate changes in the decades to come, even if the formerly recorded extreme levels, which may aridize broad areas and shift agricultural zones from south to north, are not reached.…”

“…It is predicted that climate changes will affect not only the production but also the quality of food (Atkinson et al, 2008), thereby jeopardizing food security. The broadening of polymorphism for many cha racters (Trifonova et al, 2021) with ensuing breeding for optimal duration of vegetative period (earliness) of crops are becoming more and more relevant (Kamran et al, 2014;Smolenskaya et al, 2022).…”