Spring durum wheat occupies over 0.5 M ha in Kazakhstan and represents an important domestic and export commodity. This study aimed to characterize 151 durum wheat cultivars and advanced lines originating from eight breeding programs of the Kazakhstan–Siberia Spring Wheat Improvement Network (KASIB) between 2003 and 2018. The phenotypic characterization was performed in two contracting evaluation sites more than 1000 km apart (Almaty in the Southeast and Shortandy in the North) for two years and a total of 11 agronomic traits were recorded. Field trials at both locations followed regional agronomy practices, including sowing, harvesting, and genotype evaluation using a randomized complete block design (RCBD). The growing season was longer in Almaty, resulting in a higher number of grains per spike. Though grains are smaller in size with an overall higher yield, 243 g/m² versus 170 g/m², there was no correlation between germplasm performance at the two sites. Molecular characterization was performed with 10 iPBS-retrotransposons primers that resulted in a total of 345 bands and showed a mean polymorphism of 91.9%. Mean values of gene diversity (0.251), Shannon’s information index (0.388), and expected heterozygosity (0.233) revealed a relatively high level of genetic diversity in the KASIB set. AMOVA revealed higher genetic variations due to differences within the populations. Marker-based cluster analysis, including STRUCTURE and neighbor-joining algorithms, divided the material into two populations with clear differences in geographic origin. Superiors and diverse germplasm identified in the study are recommended for marker assisted selection and breeding.

Kazakhstan cultivates over 12 million hectares of wheat, primarily spring wheat in the northern region. Spring wheat yields are low, ranging from 1.2 to 1.7 t/ha depending on weather conditions. Northern Kazakhstan is served by five spring wheat breeding programs: A.I. Barayev Research and Production Centre for Grain Farming and Agricultural Experimental Stations located in the Aktobe, Karagandy, Kostanay, and North Kazakhstan regions. In 2022, a germplasm set was assembled, including cultivars and breeding lines from the five breeding programs, totaling 84 genotypes. This set was evaluated in field trials during 2022 and 2023 at the breeding programs that contributed to the germplasm (except Aktobe). The material was also screened for molecular markers associated with genes for agronomic traits. The study objective was to compare the diversity and performance of germplasm originating from different breeding programs and identify potential underlying drivers. Breeding sites grouped based on variations in air temperature, precipitation, and grain yield demonstrated both similarities and differences among sites. However, these similarities were not reflected in the agronomic performance of materials originating from different locations. The expectation that germplasm would perform best for grain yield at its “home” location was not always confirmed. Grouping of germplasm based on genetic diversity of 20 molecular markers was not related to similarities in environmental conditions at the places of origin. The performance and diversity of germplasm from each of the five breeding programs is apparently driven by factors beyond environment, including breeding strategy and methodology, parental pool, and, in the absence of modern tools, breeders’ intuition and selection robustness. Kazakh spring wheat breeding programs require improvement to remain competitive in the face of increasing pressure from introduced foreign cultivars.

The Ural Mountains in the Western Siberia region cultivate over 3.5 M ha of short season spring wheat, with an average grain yield of 1.6–2.0 t/ha. The study focus was the analysis of climate change and weather effects on spring wheat yields from 2001 to 2024 and on genotype–environment interactions in the Kazakhstan–Siberia Spring Wheat Improvement network (KASIB) trials from 2019 to 2024. Climate change has the tendency to gradually reduce precipitation and increase air temperatures, which negatively affect spring wheat yields. Based on regional yield and weather, the region was divided into subregions: Tyumen in the North with a high yield; Chelyabinsk with lower precipitation and a lower grain yield; and Omsk and Kurgan were similar in most years. Environments at the four breeding programs (Chelyabinsk Agricultural Research Institute, Kurgan Seeds, and Omsk and Tyumen State Agrarian Universities) did not fully reflect the target production areas due to a very high yield gap and lack of association between the research and production yields. Genotype–environment interaction analysis showed that the Tyumen site had the highest yield and best discriminating ability, while Chelyabinsk best represented the whole target region. Most of the highest yielding material in KASIB trials originated from outside of the region. Spring wheat breeding programs in the region ought to improve to maintain a competitive edge.

Grain yield stability is a key requirement for spring bread wheat (Triticum aestivum L.) in the Non-Chernozem Zone of Russia, where interannual variation in moisture and heat supply generates substantial genotype × environment interaction (G × E). Six genotypes were evaluated across six growing seasons (2019–2024) at a single site in the Moscow region. Stability analyses (AMMI, WAAS, YSI and HMRP) were applied to grain yield only; phenology, plant height and 1000-kernel weight were characterised by means, coefficients of variation and LSD values. Seasons differed markedly: vegetation-period precipitation ranged from 182.8 to 547.0 mm and the seasonal hydrothermal coefficient from 1.1 to 3.8. Mean yield ranged from 3.21 t ha⁻¹ (Line No. 153) to 3.87 t ha⁻¹ (Zlata), with a grand mean of 3.58 t ha⁻¹. IPCA1 and IPCA2 explained 73.5% and 20.4% of the G × E sum of squares. Zlata had the highest mean yield, within the LSD of Line No. 178, together with the lowest YSI (3), whereas Line No. 178 showed the highest single-year yield (7.03 t ha⁻¹) and the greatest variability (CV = 45.6%). Precipitation during emergence–heading was most closely related to the interaction gradient (r = −0.875). Exploratory marker–IPCA1 associations were partly confounded with germplasm origin and require validation in larger, multi-location datasets.

В данной статье рассматриваются результаты исследования яровой твердой пшеницы, проведенного с использованием методов RGB фенотипирования в условиях Акмолинской области Казахстана в 2021 и 2022 годах. Цель исследования заключалась в выявлении перспективных образцов яровой твердой пшеницы, устойчивых к неблагоприятным условиям и обладающих высокой урожайностью, для оптимизации селекционной работы. В условиях изменчивого климата и дефицита водных ресурсов современные технологии дистанционного зондирования играют ключевую роль в анализе состояния растений и оптимизации сельскохозяйственных процессов. Исследование охватывает более 150 образцов яровой твердой пшеницы и выделено высокоурожайных 20 образцов описанных в данной статье, с акцентом на показатели урожайности и фенологии. Основное внимание уделяется выявлению сортов, устойчивых к неблагоприятным условиям. В результате анализа, лучшие образцы, такие как Gordeiforme_00-96-8 и Gordeiforme_924, демонстрируют высокую продуктивность и устойчивость к стрессовым условиям, что подчеркивает их потенциал для использования в селекции. Использование линейной регрессии и корреляционного анализа позволяет установить значимое влияние площади под кривой зеленой площади и индекса увядания на урожай. Дисперсионный анализ подтверждает наличие статистически значимых взаимосвязей между цифровыми показателями и урожай. Полученные данные свидетельствуют о значимости применения методов дистанционного зондирования для улучшения селекционных программ и повышения продовольственной безопасности в Казахстане

Spring bread wheat (Triticum aestivum L.) is the principal crop of the vast rainfed region spanning Northern Kazakhstan, the Urals, and Western Siberia, where productivity is limited by moisture stress, diseases, and the slow adoption of genomic tools in traditional breeding. We performed a genome-wide association study (GWAS) of 142 spring wheat genotypes from the Kazakhstan–Siberia Network for Spring Wheat Improvement (KASIB), evaluated at four sites in Russia and Kazakhstan during 2020–2022. The analysis identified 23 significant marker–trait associations (SNPs), of which 21 were stable across at least two environments, and 11 showed pleiotropic effects on two or more traits. Grain yield was associated with two SNPs (1A_12366171_G_A and 1B_15284504_C_T), each also affecting heading and maturity. Heading and maturity were controlled by a cluster of SNPs on chromosomes 1B, 2B, 5A, and 7B, with effects of up to 5.1 days. Plant height was associated with SNPs on 1A, 5A, and 6B, including one marker (5A_585412831_A_G) that was pleiotropic with 1000-kernel weight. Spike productivity was associated with loci on 1A, 3D, 4A, 4D, 5B, and 7B (grain weight and grains per spike, spikelets per spike, and spike harvest index). Grain size and 1000-kernel weight were linked to SNPs on 1B, 5A, and 6B, and protein and gluten content to two loci on 3A and 4B, which raised protein by up to 0.62% and gluten by up to 1.39%.