Trait-based clustering and index ranking to identify high-yielding spring wheat genotypes under the conditions of northern KazakhstanAbstract
This article presents the results of a multi-year evaluation of spring bread wheat based on key morphological and yield-related traits under the conditions of Northern Kazakhstan. Descriptive statistics, correlation and cluster analysis, and selection index methods were applied. Strong positive correlations were identified between the traits of number of grains per spike, productive tillering, and grain yield. Cluster analysis distinguished three groups of genotypes differing in productivity and qualitative characteristics. The use of selection indices allowed to identify the most promising genotypes combining desirable traits. The results highlight the effectiveness of an integrated approach to the selection of breeding material and its relevance for the development of adapted wheat varieties under changing climatic conditions.
Keywords:
spring wheat; yield components; selection index; cluster analysis; genotype evaluation
Resumo
Este artigo apresenta os resultados de uma avaliação plurianual do trigo de primavera com base em características morfológicas e de produtividade sob as condições do norte do Cazaquistão. Foram aplicados métodos de estatística descritiva, análise de correlação e de cluster, bem como índices de seleção. Foram identificadas correlações positivas fortes entre o número de grãos por espiga, o perfilhamento produtivo e o rendimento de grãos. A análise de cluster distinguiu três grupos de genótipos com diferentes níveis de produtividade e características qualitativas. O uso de índices de seleção permitiu identificar os genótipos mais promissores, combinando características desejáveis. Os resultados destacam a eficácia de uma abordagem integrada na seleção de material genético e a sua relevância para o desenvolvimento de variedades de trigo adaptadas às condições climáticas em mudança.
Palavras-chave:
trigo de primavera; componentes de rendimento; índice de seleção; análise de agrupamento; avaliação de genótipos
1.Introduction
In the 21st century there is unprecedented pressure on agricultural production. According to the United Nations (UN) and the World Health Organization (WHO), the global population is projected to exceed 10 billion by 2050. Currently, approximately 720–811 million people suffer from undernourishment, and climate change could expose an additional 8–80 million people to food insecurity by mid-century (FAO, 2023a).
Wheat (Triticum aestivum L.) is the world's most widely cultivated cereal and a major source of calories and protein. In 2023, global wheat production reached approximately 799 million tons (FAO, 2023b), but ensuring its stability has become increasingly challenging due to environmental constraints, genetic erosion, and increasing global food demand. Climate change has emerged as one of the most significant threats to sustainable crop production. Rising temperatures, droughts, and irregular precipitation patterns have led to yield instability, especially in arid and semi-arid regions (IPCC, 2022). It is estimated that an increase of 1 °C in global mean temperature is able to cause a 5–10% yield loss in wheat, with projected reductions up to 45–50% in drought-prone areas (Lobell et al., 2011).
In Central Asia and Northern Kazakhstan, where the study was conducted, climatic extremes such as high heat stress, reduced precipitation, and variable temperatures in growing seasons have been increasingly challenging for the traditional breeding strategies and demand the development of climate-resilient genotypes (Trnka et al., 2014). To address these challenges, the genetic improvement of exisiting wheat cultivars and/or releasing new high-yielding varieties play a critical role in achieving sustainable food production. Institutions such as the International Maize and Wheat Improvement Center (CIMMYT) and the Consultative Group on International Agricultural Research (CGIAR) implement plant breeding programs to develop drought-tolerant, heat-resistant, and high-yielding wheat varieties (Reynolds et al., 2022). Recent studies highlighted the importance of genetic diversity in breeding programs. Unique germplasm collections, such as Vovilov and Watkins wheat collection from the early 20th century, have become a “goldmine” for reintroducing lost adaptive traits into modern germplasm (Riaz et al., 2024). This underlines the need to integrate phenotypic, genotypic, and environmental data for improving selection efficiency.
Spring wheat (Triticum aestivum L.) remains a key cereal crop for food security in Kazakhstan, especially in the northern regions, which are characterized by unstable precipitation and high climatic risks (Amantayev et al., 2025). In the face of climate change and frequent abiotic stresses, the selection of genotypes with high productivity and adaptive potential becomes increasingly important. Phenotypic evaluation in field conditions remains a fundamental step in identifying valuable genetic resources, particularly when access to molecular data is limited (Agho et al., 2025).
Multi-year field experiments are crucial for identifying stable and high-performing genotypes under varying environmental conditions. The analysis of key agronomic traits such as grain yield, plant height, productive tillering, thousand grain weight, protein content, and gluten quality allows researchers to screen for breeding material best adapted to regional challenges (Reynolds et al., 2020; Lopes et al., 2015; Sadras and Denison, 2009). These traits are closely linked to both genotype and environmental conditions, including temperature dynamics and precipitation levels during the vegetation period.
Field-based phenotyping is particularly valuable in areas such as Northern Kazakhstan, where the growing season is relatively short, and accumulation of effective temperatures (above 5 °C) plays a crucial role in crop development. Analysis of the growing seasons from 2021 to 2023 revealed interannual variability in weather conditions, highlighting the importance of identifying genotypes with stable performance under environmental stress.
The objective of this study was to assess the collection of spring wheat genotypes under field conditions based on morphological and agronomic traits over three growing seasons (2021–2023) in Northern Kazakhstan, with the aim of selecting promising breeding material for future genetic improvement programs.
2. Materials and Methods
2.1. Climate conditions
The agroclimatic conditions and weather conditions during the growing seasons (May–September) of 2021–2023 in Northern Kazakhstan were characterized by significant year-to-year variability in both temperature and precipitation levels.
The growing season of 2021 was characterized by moderate temperatures and relatively high rainfall, especially in August and September. These favorable moisture conditions seem to improve the grain filling and prolonged vegetation. In comparison, climatic conditions of the year 2022 exhibited slightly warmer temperatures, with a noticeable peak in July. A significant rainfall occurred in July, while May and September were drier, which affected the early and late growth stages. The growing year of 2023 was the warmest and driest season compared to 2021 and 2022. The highest temperatures (over 25 °C) was observed in July, while precipitation was critically low in July and August. These conditions accelerated phenological development but may have reduced yield potential due to drought stress (Figure 1).
Average Monthly Temperature and Precipitation During the Growing Season (May–September) in 2021–2023.
The corrected sum of effective temperatures ≥5 °C indicated that 2023 was the warmest year in terms of accumulated thermal resources, reaching 2105 °C by September. The years of 2022 and 2021 were similar accounting for 2034 °C and 2027 °C, respectively.
All three years showed the steepest temperature accumulation during July and August. The most pronounced heat build-up in 2023 accelerated phenological development, but under water-limited conditions in early growth stages, such thermal advantage may not have been translated into higher yields (Figure 2).
Accumulated Effective Temperatures ≥5 °C During the Growing Season (May–September) in 2021–2023.
2.2. Plant material and Field experiment details
For the evaluation of plant characteristics, a diversity panel consisting of 101 local and international spring wheat genotypes was tested in the field trials of the A. I. Baraev Research and Production Centre for Grain Farming (Akmola region, Kazakhstan) over a three-year period (2021–2023) (Supplementary Material, Supplementary Table 1)
The field experiment was conducted according to a randomized complete block design (RCBD) with three replications to minimize environmental variation. Each genotype was sown in a 2 m2 plot (1.6 m × 1.25 m) with 15 cm row spacing and 1 m inter-block alleys to reduce border effects. The layout of genotypes within each block was randomized to ensure unbiased distribution across the field.
Standard check varieties (Astana, Akmola 2 and Tselinnaya Yubileynaya) were included in each replication for comparison and environmental control. The sowing was carried out on May 22–23 using an SSFK-7 drilling machine.
All standard agronomic practices for wheat cultivation, including fertilization, weed, and pest control, were applied uniformly across the experimental field.To identify the promosing genotypes, developmemtal phases: tillering, heading, flowering, milk and full ripeness) and morphological traits (plant height (PH), spike length (SL), 1000 seed weight (TSW), number of seeds per spike (NSPS), seed weight per spike (SWPS, g), productive tillering (PT), sprouting (SP), grain yield (GY) and grain quality (GQ)) were scored. Evaluation of the germplasm followed the protocol described in “Methodological guidelines for replenishment, maintenance and study of the world wheat, Aegilops and triticale collection” (VIR, 2004).
2.3. Grain quality analysis
The protein content in wheat grain was determined using InfraLUM infrared analyzer (InfraLUM, Russia). The device operates on the principle of near-infrared spectroscopy (NIR), measuring the reflectance spectrum of the grain within the range of 850–1050 nm. Prior to analysis, grain samples were cleaned from impurities and soil to have homogeneous consistency. For each sample, at least two replicate measurements were taken, and the mean value was calculated. Calibration of the instrument was carried out using the state-certified reference materials for wheat plants. The results were expressed as a percentage of dry matter (ST RK 1564-2006) (Committee for Technical Regulation and Metrology of the Republic of Kazakhstan, 2006).
2.4. Statistical data processing
Statistical analysis was performed using several statistical software packages. The primary tools included SNEDECOR (designed for agricultural trials), Microsoft Excel for preliminary calculations and data structuring, STATISTICA 10 for advanced descriptive statistics and ANOVA, as well as R (version 4.x) for multivariate analysis, including correlation matrices and hierarchical clustering.
The analysis included calculation of arithmetic means, standard deviations, coefficients of variation (CV), and pairwise Pearson correlation coefficients between the studied traits. For visualization of statistical relationships, heatmaps and boxplots were generated using R and Python (matplotlib, seaborn libraries).
3. Results
3.1. Growing period
One of the key agronomic traits in the breeding of spring bread wheat is duration of the growing period, which reflects the plant's ability to adapt to specific regional conditions. The length of the growing season directly influences yield formation, especially in areas of risk-prone agriculture where summer droughts or early autumn frosts are common (reference). Breeding selection based on this trait facilitates the identification of early-maturing lines capable of escaping end-season stress and mid- to late-maturing ones with enhanced yield potential in optimal environments. During the three-year study (2021–2023), the ripening periods of 101 spring wheat genotypes were assessed. The analysis included check varieties with different maturity times (Astana, Akmola 2, Tselinnaya Yubileynaya), which made it possible to perform clustering and classify the studied material according to earliness. The results enabled us to identify stability and environmental responsiveness of the genotypes across the different growing seasons (Figure 3).
Boxplot of vegetation period by year (2021-2023 year). Note: PH – plant height, SL – spike length, PT – productive tillering, NSPS – number of seeds per spike, TSW – 1000 seed weight, Y – yield, VP – vegetation period, PC – protein content.
In 2021, the variability of the vegetation period was lower compared to other years. The mean value was around 83 days, with most varieties falling within the range of 80–86 days. This indicates relatively uniform vegetation conditions during that year, likely due to moderately favorable weather conditions.
In 2022, the range of values was broader, with some varieties exhibiting a sharply shortened or extended vegetation period, which was reflected in the form of the outliers on the boxplot. The mean increased to about 85 days, which may be associated with drought conditions during the first half of the growing season.
In contrast, 2023 demonstrated moderate variability, with a median of approximately 84 days. Both early- and late-maturing genotypes were present, but extreme values were less pronounced compared to 2022.
Thus, the boxplot revealed annual differences in plant development rates and allowed for the assessment of varietal stability in response to the environmental conditions. It also highlight he importance of multi-year evaluations of this trait to ensure more objective selection of the genotypes with the desired earliness (Table 1).
Mean values and standard deviation of trait expression in spring bread wheat varieties (2021–2023, Akmola region).
To identify varieties with the most stable response to environmental conditions in terms of growing period (i.e., minimal variation between 2021 and 2023), the range of values for each variety was calculated. The varieties with a range not exceeding 5 days are listed in the Table 1 These genotypes demonstrate consistency in the trait expression under varying weather conditions, making them valuable for further breeding schems.
The analysis of the phenotypic traits related to the grain yield of spring wheat genotypes over the 2021–2023 period revealed a wide range of variation, reflecting genetic diversity of the studied material (Table 2).
Mean values and standard deviation of trait expression in spring bread wheat varieties (2021–2023, Akmola region).
The plant height ranged from 39.6 to 69.6 cm, with a mean value of 58.9 cm. The variation among years was statistically significant (P < 0.001), indicating a strong influence of environmental factors on plant growth. This reflects the predominance of medium-height genotypes, although certain accessions exhibited either dwarfism or tall growth, which can be associated with specific adaptive or agronomic traits.
The spike length was relatively uniform, with an average of 7.4 cm and a narrow standard deviation (0.76 cm). Nevertheless, year-to-year differences were also significant (P = 0.00038), suggesting that even a relatively stable trait may be affected by environmental fluctuations. Accessions with spike lengths exceeding 9.2 cm could be of particular interest for breeding programs aimed at improving sink capacity and enhancing yield potential.
Productive tillering showed limited variability, with values ranging from 0.9 to 2.1 shoots per plant and a mean of 1.4. Differences among years were not statistically significant (P = 0.2843), confirming the relative stability of this trait. However, genotypes producing more than two productive shoots per plant may contribute to yield enhancement under favorable conditions.
A broader variation was observed in the number of grains per spike, which ranged from 17.6 to 35.3, with an average of 25.9 grains. The interannual variation was statistically significant (P = 0.00305), indicating that this trait was sensitive to environmental conditions. The presence of both highly productive and less fertile genotypes provides valuable material for selection in both directions.
Thousand seed weight (TSW) varied between 29.6 and 42.2 g, with an average of 36.0 g, showing significant differences among years (P < 0.001). Accessions with TSW > 40 g are particularly valuable for breeding programs focused on grain quality and market preferences.
Grain yield per square meter exhibited the highest variability among all traits, ranging from 79.2 to 314.3 g/m2, with a mean of 190.3 g/m2 and a standard deviation exceeding 40.5 g/m2. The variation among years was statistically significant (P < 0.001), confirming the importance of multi-year evaluation and emphasizing the need to identify stable, high-yielding genotypes across diverse environmental conditions. Yield is determined by a combination of interrelated environmental factors. Correlation analysis made it possible to identify positive and negative influences and focus efforts on eliminating the most unfavorable of them (Figure 4).
Correlation matrix of key agronomic traits (average data). The hierarchical clustering results are shown in Figure 5.
The correlation analysis of the mean values of the main agronomic traits of spring wheat over 2021–2023 revealed that most relationships among yield-related traits were statistically significant (P < 0.05). Moderate positive correlations were observed between grain yield and plant height (r = 0.43; P < 0.001), number of grains per spike (r = 0.36; P < 0.001), spike length (r = 0.22; P = 0.0006), and thousand-kernel weight (r = 0.23; P = 0.0005). Thus, the greatest influence on yield was exerted by traits associated with spike architecture and grain weight, while vegetative parameters such as tillering contributed to a lesser extent.
Thus, the results emphasize the importance of a comprehensive assessment of yield, including spike structure and tillering traits.
The identified relationships can be used for more effective selection of breeding material with high productivity potential.
The cluster analysis of accessions based on key agronomic traits allowed the collection to be classified into three clusters (Figure 5): cluster 1 – High-yielding accessions. These genotypes demonstrated above-average productivity indicators: high thousand seed weight (+0.43), number of grains per spike (+0.47), yield (+0.41), plant height (+0.51), and spike length (+0.51). Growing season was slightly extended (+0.39). Protein content was slightly reduced (–0.30). These genotypes are the most promising in terms of overall agronomic performance. They achieve high productivity through well developed morphological characteristics and a moderately extended growing period, cluster 2 – Accessions with high productive tillering. This group is characterized by high productive tillering (+1.00), while other traits vary near the average (from –0.06 to +0.02). Protein content is slightly reduced (–0.19). These genotypes show potential as sources of high tillering capacity for breeding purposes, although they have moderate values for other traits, cluster 3 – High-protein but low-yielding accessions. These accessions had the highest protein content (+1.00), but low values for other traits: yield (–0.17), thousand seed weight (–0.49), number of grains per spike (–0.38), plant height (–0.35), and spike length (–0.35). Growing season was close to average (–0.04). These genotypes are valuable as donors of high grain protein content, despite their lower productivity.
In the breeding of spring wheat, one of the key steps is the evaluation and selection of genotypes with the most favorable combination of valuable agronomic traits. To achieve this, we applied the Selection Index method, which integrates multiple traits into a single composite score. This approach simplifies the ranking of accessions by their breeding value and facilitates the identification of genotypes with the highest potential for inclusion into plant breeding programs (Figure 6) Cluster 1: Includes the most productive genotypes, combining high yield with low index values, indicating a balanced expression of both morphological and yield-related traits. This is the priority group for breeding selection.
Cluster 2: Characterized by moderate yield and intermediate index values, this cluster includes genotypes with high productive tillering, which can be used as donors of the specific traits in breeding programs.
Cluster 3: Comprises low-yielding genotypes with high index values, which, despite their lower productivity, are potentially valuable for other agronomic traits (e.g., higher protein content).
Based on the three-year yield data (2021–2023), the most productive genotypes included Maks, Niva Priirtyshya, L. 654, Stepnodar 90, and Lutescens 1135, with average yields exceeding 260 g/m2. These genotypes demonstrated high yield potential and are considered valuable sources for developing high-performing varieties under favorable growing conditions.
In contrast, genotypes such as Sibakovskaya Yubileynaya, Ilimenskaya 2, Tyumenskaya 31, and Stepnaya 62 showed lower productivity (below 140 g/m2) but may possess adaptive traits such as early maturity or stress tolerance. Therefore, they can serve as donors in breeding programs aimed at improving stability and adaptation under drought-prone environments.
The analysis revealed considerable variability among accessions in their index scores, allowing us to classify the collection into the groups based on their breeding potential and to identify the top-performing genotypes that can be recommended for future spring wheat breeding programs (Figure 6).
4. Discussion
This research work presents the results of analyzing the spring bread wheat germplasm to identify the most promising accessions suitable for inclusion in breeding process. The selection of genotypes that are well adapted to diverse growing conditions and possess valuable productive traits is of great importance for future breeding programs aimed at developing high-yielding varieties (Dyussibayeva et al., 2024).
The analysis of phenotypic data on the spring wheat collection collected in Northern Kazakhstan for three years showed significant diversity in the main valuable agronomic characteristics, which confirms the high variability of the studied material and the apparent influence of weather conditions. Similar results were previously obtained by Ingver et al. (2024), who demonstrated a significant “genotype × environment” interaction when studying spring wheat varieties in the Baltic region.
There was a high variability of the growing season between the years. Thus, 2021 was characterized by moderately favorable conditions, which ensured minimal variability in the growing season. In 2022, on the contrary, dry weather led to an expansion of the range of maturation periods, which coincides with the conclusions of Kheiri et al. (2021), who showed the sensitivity of the phenological phases of wheat to moisture deficiency. In 2023, there was moderate variability, which is associated with a more balanced precipitation distribution.
Correlation analysis showed that the grain weight per spike had a positive relationship with number of grains per spike (r = 0.87) and yield (r = 0.48). Similar results were obtained by Yousaf et al. (2017), where it was noted that the number of grains per spike and the mass of 1000 seed are the key indicators of productivity formation.
Yield was positively correlated with 1000 seed weight (r = 0.36), plant height (r = 0.43), Similar patterns were noted by Bigyan et al. (2025)
Interestingly, protein content showed a moderate negative correlation with yield (r = −0.17) and number of grains per spike (r = −0.38), which may be due to the dilution effect: with increasing crop weight, the protein concentration in the grain decreases (Iqbal et al., 2022).
Cluster analysis divided the collection into three groups: Cluster 1 — highly productive samples. They were distinguished by high indicators of 1000 seed weight (+0.43), number of grains per spike (+0.47), yield (+0.41), plant height (+0.51) and spike length (+0.51). Growing season was slightly extended (+0.39), and protein content was slightly reduced (−0.30). Cluster 2 — samples with high productive tillering (+1.00). The remaining traits were near the average values, which makes this cluster promising as a source of high tillering. Cluster 3 — high protein, but unproductive samples. The protein content reached maximum values (+1.00), but the yield and 1000 seed weight were low. These samples are valuable for improving grain quality.
The comprehensive assessment of valuable agronomical traits in wheat has become an essential component of modern breeding programs aimed at improving yield potential, grain quality, and resilience to changing climatic conditions. The integration of multi-year phenotypic data with statistical analysis and machine learning methods allows for a more precise identification of the genotypes with high adaptive potential.
One of the most effective approaches is the integration of morphological, productivity, and quality traits into a unified evaluation system, such as the use of selection indexes, multivariate statistics (PCA, cluster analysis), and correlation analysis (Dawson et al., 2015; Mohammadi and Prasanna, 2003). These methods not only help quantify the contribution of each trait to overall productivity but also enable the identification of genotypes with a desirable combination of characteristics.
Special attention is paid to the resilience of the genotypes under stress conditions—such as drought, heat, and soil degradation. As highlighted by Reynolds et al. (2022), integrative approaches that combine agronomic, physiological, and biochemical evaluations are the key to achieving breakthroughs in the breeding of stress-tolerant varieties. In Central Asia, where drought tolerance is of particular importance, the key traits include productive tillering, 1000 seed weight, and number of grains per spike (Kahrizi et al., 2010; Ali et al., 2008).
Thus, a comprehensive approach to evaluating valuable agronomic traits—incorporating multifactorial statistical analysis and the use of integrative selection indices—is a promising direction in wheat breeding. It not only increases the efficiency of selection but also facilitates the development of the varieties adapted to specific agroecological conditions.
5. Conclusions
The field evaluation of 101 spring wheat genotypes over a three-year period (2021–2023) under the conditions of Northern Kazakhstan revealed significant genetic diversity in agronomic and morphological traits. Grain yield ranged from 197 to 524 g/m2, thousand seed weight varied from 31.3 to 45.5 g, and number of grains per spike ranged from 25.1 to 47.6. Productive tillering coefficient ranged from 1.10 to 2.33, and protein content varied between 10.9% and 16.4%.
The present study demonstrated that most of the analyzed agronomic traits of spring wheat were significantly interrelated, reflecting the complex nature of yield formation. The results confirmed that yield is primarily influenced by spike-related traits such as the number of grains per spike, spike length, and thousand-kernel weight, which showed statistically significant positive correlations with grain yield (P < 0.05).
Plant height also exhibited a moderate positive association with yield, suggesting that vigorous growth contributes to productivity under the environmental conditions of Northern Kazakhstan. However, excessive vegetative growth should be balanced to avoid lodging and ensure efficient assimilate distribution.
In contrast, productive tillering had a weak and non-significant correlation with yield, indicating that under dry-steppe conditions, yield formation depends more on the efficiency of the main stem and primary spikes than on the total number of tillers.
Overall, the study highlights the importance of integrating morphological and yield-related traits in breeding programs aimed at improving spring wheat productivity and adaptability to arid and semi-arid environments.
PCA and clustering analyses revealed three main genotype groups, including a cluster of high-yielding, morphologically balanced genotypes with low selection index scores. These genotypes are considered the most promising for breeding programs. The application of the selection index and multivariate statistics provided an effective tool to identify genotypes combining high productivity, balanced trait expression, and breeding value.
The results emphasize the importance of integrating multi-trait evaluation methods—including correlation, PCA, clustering, and selection index analysis—for more efficient pre-breeding and selection strategies in spring wheat. This approach enables the identification of ideotypes tailored to local agroecological conditions and contributes to the development of high-yielding, stress-resilient wheat cultivars adapted to the dryland zones of Northern Kazakhstan.
Supplementary Material
Supplementary material accompanies this paper.
Supplementary Table 1
This material is available as part of the online article from https://doi.org/10.1590/1519-6984.300898
Acknowledgments
This work was carried out within the framework of the 0124RK00959 scientific technical program — Breeding and genetic agrobiodiversity development technology (long-term storage, restoration, monitoring, rational use) as basic for improving breeding programs of the Republic of Kazakhstan (BR22885305) (2024-2026).
Data Availability Statement
The entire data set that supports the results of this study was published in the article itself.
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