Morpho-Biological and Yield Characteristics of Wheat Varieties Originating from Central Asian Breeding

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F. Sultonov, Yulduz Matyakubova, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8853561/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In this study, the morpho-biological traits and yield performance of 13 wheat varieties originating from breeding programs of four Central Asian countries (Uzbekistan, Tajikistan, Kyrgyzstan, and Kazakhstan) were evaluated under saline soil conditions of the Khorezm region. Statistical analysis revealed a positive correlation between spike length and grain yield (r = 0.502), whereas a negative relationship was observed between leaf area and yield (r = − 0.446). The highest yield levels were recorded in the SILA (121 c/ha) and KAMOL (108.8 c/ha) varieties. The results demonstrated that, under the hot climatic conditions of the Khorezm oasis, the selection of genotypes with a leaf area of 50–60 cm² and longer spikes ensures higher productivity. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Genetics Biological sciences/Plant sciences Wheat yield leaf area spike length correlation analysis Central Asia Figures Figure 1 Figure 2 Figure 3 Introduction Wheat is considered one of the most strategically important crops in ensuring global food security. Particularly under conditions of climate change and increasing water scarcity, the selection of high-yielding varieties with tolerance to various abiotic stress factors has become one of the most urgent priorities in modern wheat breeding programs (Meliev et al., 2021 ). According to global climate models, the average ambient temperature is projected to increase by 1.5°C over the next two decades (Masson-Delmotte et al., 2021 ), exposing agricultural crops to increasing heat stress. Wheat is one of the most important cereal crops worldwide in terms of cultivated area, contributing approximately 28% to global grain production and 41.5% to international grain trade (FAO, 2020 ). Prolonged heat stress (average daily temperatures exceeding 17.5°C) affects approximately 7 million hectares of wheat-growing areas in developing countries, while terminal (late-season) heat stress represents a major constraint for about 40% of temperate regions, covering nearly 36 million hectares (Reynolds et al., 2010 ). Understanding morpho-physiological and biochemical traits associated with heat stress tolerance is of great practical importance, as it enables the identification of different tolerance mechanisms and the application of mitigating strategies in wheat production systems (Langridge et al., 2021; Rehman et al., 2021 ). Considering the dynamic nature of abiotic stress factors, the implementation of experimental approaches under both optimal and stress conditions is regarded as essential in breeding programs, particularly for maintaining yield stability across environments (He et al., 2018 ). Among the key morphological traits influencing yield formation in wheat, leaf organs play a central role. The three uppermost leaves (from top to bottom: flag leaf, second leaf, and third leaf) contribute significantly and positively to grain yield formation (Lou et al., 2021; Sanchez-Bragado et al., 2020; Tu et al., 2020 ). Among these, the flag leaf (including its sheath) has a particularly important role compared to other leaves, as it intercepts up to 30% of incoming light radiation (Liu et al., 2018 ; Sanchez-Bragado et al., 2014). Several studies have reported that the flag leaf can contribute up to 50% of the total assimilates required for grain yield, making it a major photosynthetic organ during grain filling (Ba et al., 2020 ; Liu et al., 2018 ). The positive effect of the flag leaf on yield is closely associated with chloroplast activity. Chloroplasts (chlorophyll pigments) play a crucial role in carbon fixation, the formation of vegetative organs during growth through photosynthesis, and the response to environmental stresses, including heat stress (Ali et al., 1999). Studies conducted by Meliev et al. demonstrated significant positive relationships between leaf physiological traits, leaf area, and quantitative yield indicators of wheat genotypes under unfavorable environmental conditions. Based on these findings, promising wheat genotypes were selected as initial breeding materials to develop environmentally adaptable and high-yielding wheat varieties for Uzbekistan (Meliev et al., 2023; 2025 ). Genetically related genotypes may also differ in chlorophyll content depending on environmental conditions and cultivation practices, resulting in diverse physiological responses to external stress factors (Dadoboeva et al., 2018 ). High chlorophyll content has been widely reported in the literature as a potential criterion for assessing heat tolerance in wheat (Ramya et al., 2015; Munjal et al., 2016). Under high-temperature conditions, chlorophyll acts as a low-level photoinhibitor, thereby enhancing heat tolerance (Choudhary et al., 2020). Experimental studies have also scientifically confirmed the critical role of chloroplasts in activating cellular signaling pathways under heat stress conditions (Yuan et al., 2015). Chlorophyll pigments are responsible for harvesting light energy and regulating electron transport during the initial and essential stages of photosynthesis. The Central Asian region, particularly the Khorezm oasis of Uzbekistan, is characterized by unique soil and climatic conditions, including highly saline soils and extreme summer air temperatures. Under such conditions, wheat yield depends not only on genetic potential but also on the balanced development of morpho-biological traits that ensure adaptation to environmental stress factors. Materials and methods Study Area and Experimental Design The research was conducted during the 2023–2025 growing seasons in the Khorezm region of Uzbekistan (41°19′60.00″ N, 61°00′0.00″ E). Field experiments were carried out in Urgench city at the Khorezm Scientific Experimental Station of the Research Institute of Selection, Seed Production, and Agrotechnology of Cotton Growing. The study aimed to evaluate the relationships between physiological, morphological, and yield-related traits of wheat and environmental factors under the soil and climatic conditions of the Khorezm region, as well as to assess the adaptability of wheat varieties to local agro-climatic conditions. Experimental plots of 2 m² were arranged in a randomized design with three replications. For each trait evaluation, 30 plants were randomly selected from each replication. Leaf area was determined using the Petiole mobile application (Petiole APK – APKPure.com) by digital image analysis. Yield Components and Statistical Analysis Yield-related traits, including spike weight, spike length, number of spikelets per spike, number of grains per spike, grain weight per spike, and thousand-grain weight, were measured at maturity. Data processing and statistical analyses were performed using Microsoft Excel and Statgraphics Centurion 34 software to compare the performance of different wheat varieties. Plant Material A total of 138 soft wheat varieties of diverse genetic origin from Central Asia were initially evaluated under the environmental conditions of the Khorezm region. Among them, 138 varieties were identified as cold-sensitive, while 13 cold-tolerant genotypes were selected for further investigation. These selected varieties originated from Kazakhstan (Steklovidnaya 24, Egemen, and Naz), Kyrgyzstan (Vlada, Kiyal, and Kaset), Tajikistan (SILA, KAMOL, and AYVINA), and Uzbekistan (Andijan 4, Dostlik, ASR, and Durdona). Data Recording and Analysis Quantitative traits determining yield performance were recorded and averaged based on observations from 30 plants per variety. Yield components were calculated using the Ken Saera formula developed by CIMMYT scientists (Ken et al., 1997). The resulting data were used to assess varietal differences and to identify key morpho-biological traits associated with yield performance under saline soil and high-temperature conditions. Results and Discussion Spike Length Spike length is one of the key morphological traits determining yield components in wheat, as it directly influences both the number and weight of grains per spike. In the present study, the spike length (cm) of wheat varieties originating from breeding programs of four Central Asian countries—Uzbekistan, Tajikistan, Kyrgyzstan, and Kazakhstan—was comparatively evaluated. The statistical analysis, illustrated using box plot diagrams, revealed not only differences in mean spike length among the varieties but also the variability of this trait under the influence of environmental factors. This indicates that spike length is a highly responsive morphological characteristic, reflecting both genetic potential and environmental adaptability. Graphical analysis showed that the highest average spike length values were observed in varieties originating from Kyrgyzstan (KGZ) and Tajikistan (TAJ) (Fig. 1 ), highlighting their superior morphological performance under the environmental conditions of the Khorezm region. Country-wise results showed that wheat varieties from Kyrgyzstan (KGZ) recorded the highest spike length values. In particular, the Kiyal variety exhibited an average spike length of 11.6 cm, representing the highest value observed in this study. Another Kyrgyz variety, Kasiet, also demonstrated high yield potential with a spike length of 11.4 cm. Within the group of Tajik varieties (TAJ), SILA stood out with an average spike length of 11.4 cm, reflecting strong genetic stability. The AYVINA variety had a slightly lower average spike length of 10.8 cm, but displayed a wide range of variation, indicating a higher environmental responsiveness. Among the local Uzbek varieties (UZ), ASR recorded the best performance with an average spike length of 10.4 cm. In the Kazakh group (KAZ), Steklovidnaya-24 exhibited a high spike length of 11.0 cm, while the Egemen variety had an average of 10.9 cm but showed the greatest variability, reflecting its sensitivity to environmental factors. Based on these results, under the saline and arid climatic conditions of the Khorezm region, the Kyrgyz Kiyal, Tajik SILA, and Uzbek ASR varieties are recommended as donor genotypes in breeding programs aimed at improving spike traits. These varieties play a crucial role in enhancing yield by maximizing spike length. Leaf Area of Wheat Varieties Among the biometric traits of wheat, leaf area plays a particularly important role, as it is a key factor determining photosynthetic efficiency and, consequently, grain yield. In this study, statistical analysis of leaf area (cm²) among varieties from four Central Asian countries revealed significant differences between the genotypes. Graphical analysis (Fig. 2 ) indicated that the highest leaf area values were observed in varieties from Kazakhstan (KAZ) and Uzbekistan (UZ). Within the Kazakh varieties, Egemen exhibited the highest average leaf area of 74.4 cm², followed by Naz with an average of 65.5 cm². Among the Uzbek local varieties, Andijan 4 had the largest assimilation surface area at 68.1 cm²; however, the high variability observed in this variety indicated a strong responsiveness to environmental factors. The ASR variety demonstrated a relatively stable leaf area of 60.7 cm². Within the Tajik varieties (TAJ), AYVINA and SILA exhibited moderate leaf area values of 53.4 cm² and 50.3 cm², respectively, while KAMOL was characterized by a smaller leaf area of 37.2 cm². These results highlight the considerable genetic and environmental influence on leaf area, which is critical for optimizing photosynthesis and enhancing wheat yield under local conditions. Among Kyrgyz varieties (KGZ), Kasiet showed relatively good performance with a leaf area of 54.1 cm², while Kiyal exhibited the lowest value in the study at 34.8 cm². The results indicate that grain yield formation in wheat varieties (average 100.8 c/ha) is determined by the balanced interaction between leaf area and reproductive organs. Although the highest leaf area values were observed in Egemen (74.4 ± 2.06 cm²) and Andijan4 (68.1 ± 8.03 cm²), this did not proportionally translate into increased grain yield. In the Andijan 4 variety, despite the large leaf area, grain yield was limited to 90.8 ± 0.92 c/ha due to shorter spike length (8.6 ± 0.38 cm) and restricted grain formation. This is scientifically explained by the fact that the assimilates provided by the leaf area were insufficient for effective grain development. In contrast, the maximum grain yield recorded in the SILA variety (121 ± 1.15 c/ha) was attributed to its moderately optimal leaf area (53 ± 0.69 cm²) and highly developed spike length (11.4 ± 0.72 cm). In this variety, the translocation of photosynthetic products to reproductive organs was highly efficient, resulting in the highest grain weight per unit leaf area. Similarly, in the Kasiet (103.8 ± 1.04 c/ha) and ASR (98.6 ± 0.92 c/ha) varieties, the positive balance between leaf area and spike length served as a key factor ensuring stable grain yield. Yield Performance Grain yield is one of the most important and complex traits in wheat breeding, as it represents an integrated expression of a plant’s genetic potential and its adaptability to environmental factors such as climate, soil conditions, and agronomic practices. In this study, the grain yield (c/ha) of wheat varieties originating from breeding programs of four Central Asian countries—Uzbekistan, Tajikistan, Kyrgyzstan, and Kazakhstan—was comparatively evaluated. The obtained results demonstrated considerable variation in grain yield among the studied varieties, reflecting differences in their genetic potential and adaptability to local environmental conditions. Yield performance differed significantly depending on the region of origin and genetic background of the varieties. Graphical analysis indicated that the highest grain yield values were observed in varieties originating from Tajikistan (TAJ) (see Fig. 3 ). All varieties originating from this country recorded grain yield values exceeding 100 c/ha. The next highest yield levels were observed in varieties from Uzbekistan (UZ) and Kyrgyzstan (KGZ), which demonstrated comparable performance. In contrast, the lowest average yield values were recorded in varieties from Kazakhstan (KAZ). Among all evaluated genotypes, the highest grain yield was obtained from the Tajik variety SILA, reaching 121 c/ha. Similarly, another Tajik variety, KAMOL, also exhibited high productivity with a yield of 109 c/ha. The lowest grain yield was observed in the Uzbek variety Andijan 4 (90.8 c/ha) and the Kazakh variety Naz (91.1 c/ha). A country-wise comparison revealed that the most adaptable and high-yielding varieties included Durdona (102 c/ha) from Uzbekistan (UZ), SILA (121 c/ha) from Tajikistan (TAJ), Kasiet (104 c/ha) from Kyrgyzstan (KGZ), and Steklovidnaya-24 (99.4 c/ha) from Kazakhstan (KAZ), all of which outperformed the remaining varieties within their respective groups. Based on the results obtained under the environmental conditions of the Khorezm region, the use of the Tajik varieties SILA and KAMOL, as well as the local variety Durdona, is recommended for wheat breeding programs aimed at achieving high grain yield. These varieties are distinguished by their high productivity and strong tolerance to the region’s adverse environmental factors. The interrelationship of the studied traits In this study, a comparative analysis of key morphological and yield-related traits of wheat varieties from four Central Asian countries was conducted, focusing on spike length, leaf area, and grain yield. These traits are important indicators of both genetic potential and environmental adaptability. The analysis specifically evaluated how these traits varied across the countries, providing insight into the performance and stability of the studied genotypes under different environmental conditions (Table 1 ). This comparative assessment is essential for identifying promising varieties that can be used in breeding programs aimed at improving wheat productivity in arid and saline regions. Table 1 Interrelationship of traits Varieties Leaf Area, cm² Spike Length, cm Grain Yield, c/ha Andijan 4 68,1 ± 8,03 8,6 ± 0,38 90,8 ± 0,92 ASR 60,7 ± 1,88 10,4 ± 0,31 98,6 ± 0,92 Dostlik 38,2 ± 0,99 8,6 ± 0,38 97,6 ± 0,87 Durdona 49,9 ± 2,85 9,5 ± 0,35 102 ± 0,87 AYVINA 53,4 ± 1,23 10,8 ± 0,72 101,6 ± 0,92 KAMOL 37,2 ± 0,51 9 ± 0,35 108,8 ± 0,92 SILA 50,3 ± 0,69 11,4 ± 0,72 121 ± 1,15 Vlada 48,7 ± 2,66 9,1 ± 0,55 98,8 ± 0,75 Kasiet 54,1 ± 1,67 11,4 ± 0,38 103,8 ± 1,04 Kiyal 34,8 ± 0,53 10,9 ± 1,01 98,3 ± 0,87 Egemen 74,4 ± 2,06 9,4 ± 1,09 94,3 ± 0,75 Naz 65,5 ± 1,46 9,4 ± 0,65 91,1 ± 0,92 Steklovidnaya 24 53 ± 2,26 11 ± 0,26 99,4 ± 0,81 Mean 53,3 10,1 100,8 Standard Deviation (SD) 11,8 0,98 8,4 Coefficient of Variation (CV, %) 22,10% 9,70% 8,30% Least Significant Difference (LSD, 0.05) 3,42 0,76 2,15 The unusual case observed in the Kiyal variety can be explained by the fact that, despite its high genetic potential for spike length (10.9 ± 1.01 cm), the relatively small leaf area (34.8 ± 0.53 cm²) acted as a limiting factor in yield formation. The limited leaf area was unable to supply sufficient organic matter during the grain-filling period, resulting in a grain yield (98.3 c/ha) below the variety's theoretical potential. Excessively large leaf area (as observed in the Andijan 4 and Egemen varieties) can increase transpiration intensity, leading to a negative response of the plant under water-deficit conditions, though it does not always play a decisive role in increasing yield. For the saline and hot climatic conditions of the Khorezm region, selecting varieties with a leaf area of 50–60 cm² and spike length above 11 cm, such as SILA, Kasiet, and Steklovidnaya 24, provides the highest economic efficiency. Correlation analysis of our study results revealed varying degrees of association between the morphological traits of wheat varieties and grain yield. Analysis of the relationship between spike length and grain yield indicated a moderate positive correlation (r = 0.502). Statistically, the significance level of this correlation was p = 0.080, which is considered significant at the 10% error threshold. This confirms that increased spike length is one of the main factors contributing to higher yield. In particular, the varieties with the longest spikes, SILA (11.4 cm) and Kasiet (11.4 cm), exhibited the highest yields, practically demonstrating this correlation. The relationship between leaf area and grain yield showed a moderate negative correlation (r = -0.446). The significance level of this correlation was p = 0.126. The presence of a negative correlation indicates that excessively large leaf area tends to reduce yield. This is explained by increased transpiration and plant water stress in varieties with very large leaf areas under the extreme hot and dry climate of the Khorezm region (Egemen – 74.4 cm², Andijan 4–68.1 cm²), which limits yield formation (Tabel 2). Tabel 2. Correlation Analysis of Traits Parameters Statistics Leaf Area Spike Length Grain Yield Leaf Area Pearson Corr. -0,09758 -0,44641 p-value - 0,75113 0,12623 N 13 13 13 Spike Length Pearson Corr. -0,09758 0,50212 p-value 0,75113 - 0,08037 N 13 13 13 Grain Yield Pearson Corr. -0,44641 0,50212 p-value 0,12623 0,08037 - N 13 13 13 In the interrelationships among morphological traits, no significant correlation was observed between leaf area and spike length (r = -0.097; p = 0.751). This indicates that these two traits are independent of each other, allowing for the possibility of selecting them separately in wheat breeding programs. Discussion The adaptability of soft wheat to adverse environmental conditions largely depends on the optimization of its morphological and economically valuable traits, which plays a critical role in enhancing grain yield (Shachai et al., 2024 ). In our study, the observed relationships between yield components and morpho-biological traits were directly influenced by the impact of abiotic stress factors on plant physiology. The results indicate that the factors determining wheat productivity in the saline soils and hot climate of the Khorezm region differ from those in conventional moderate climates. A positive correlation between spike length and grain yield (r = 0.502) was detected in this study, consistent with findings reported by Bhutto (2016) and Meliev et al. ( 2025 ) (Bhutto et al., 2016 ; Meliev et al., 2025 ). These studies emphasized that under heat stress conditions, the potential of reproductive organs serves as a key driver of yield. Specifically, Plisko et al. (2017) reported that longer spikes (11.4 cm) increased the number of grains per spike, resulting in yields up to 121 c/ha. This underscores the importance of selecting genotypes with high grain-filling potential under abiotic stress conditions. Qaseem et al. (2020) and Sattar et al. ( 2020 ) highlighted that the combined effects of heat and drought lead to degradation of chlorophyll and protein molecules and slow the mobilization of reserves to grains. Consequently, excessively large leaf area under high temperatures may not confer a photosynthetic advantage but rather acts as a factor negatively affecting plant water status (Qaseem et al., 2020; Sattar et al., 2020 ), which is consistent with the negative correlation observed in our study (r = -0.446). In contrast, under moderate conditions, larger leaf area increases the photosynthetic surface and positively impacts yield (Wang et al., 2021). However, in the extreme hot conditions of Khorezm, excessive leaf area (as in Egemen and Andijan 4 varieties) intensified transpiration and contributed to yield limitations. Zulkiffal et al. ( 2021 ) reported that under terminal heat stress, large leaf area rapidly depletes plant water resources, leading to drought stress during the grain-filling period. In our study, varieties with a leaf area of 50–60 cm² showed high yields, demonstrating that under climate change, a "moderate leaf area – reduced transpiration" strategy is effective. This supports the concept of "adaptable morphotypes" proposed by Meliev et al. (2023). The superior performance of Tajikistan varieties (SILA, KAMOL) can be attributed to the genetic integration of heat tolerance and yield-related traits in these genotypes. Khayatnezhad et al. ( 2011 ) and Abilfazova ( 2016 ) reported that drought-tolerant genotypes maintain higher chlorophyll content in leaves, which helps sustain photosynthetic activity and ensures yield formation even under stress conditions (Khayatnezhad et al., 2011 ; Abilfazova, 2016 ). Baboeva et al. ( 2023 ) also highlighted that stable chlorophyll content in leaves serves as a key indicator of wheat genotypes’ tolerance to abiotic stresses, particularly drought and heat. In tolerant genotypes, chlorophyll levels showed minimal changes under adverse conditions, whereas in sensitive varieties, they decreased sharply (Baboeva et al., 2023 ). In Kazakhstan varieties (Naz, Egemen), despite large leaf areas, yields were low under Khorezm conditions, indicating that these genotypes are adapted to northern latitudes and could not withstand transpiration stress in the hot southern climate. Similar findings were reported by Tajibayev et al. ( 2021 ), who concluded that it is difficult to develop a single variety suitable for all regions, and breeding varieties adapted to two neighboring regions is more practical (Tajibayev et al., 2021 ). The results of this study confirm previous research and emphasize the importance of selecting wheat varieties with appropriate morphological traits for breeding programs, particularly when comparing varieties across Central Asian countries and targeting drought-prone and saline regions. Conclusion Research conducted under the saline soils and hot climatic conditions of the Khorezm region demonstrated that the highest grain yields were recorded in the SILA (121 q/ha), KAMOL (108.8 q/ha), and Durdona (102 q/ha) wheat varieties. Statistical analyses revealed a positive correlation between spike length and grain yield (r = 0.502), indicating that the Kiyal and SILA varieties are the most promising donors for this trait. Conversely, a negative correlation was observed between leaf area and grain yield (r = -0.446), suggesting that leaf areas exceeding 70 cm² increase transpiration and limit yield, while the optimal range is approximately 50–60 cm². Therefore, to achieve high productivity, it is recommended that breeding programs select genotypes with moderate leaf area and long spikes. Declarations Author Disclosure Statement The authors declare no conflict of interest. Funding: The authors declare that this research received no external funding. Author Contribution Z.B. Alloberganova and A.A. Dolimov contributed to the conceptualization, supervision, and overall coordination of this study. S.K. Baboev, M.F. Sultonov and Yu.A. matyakubova were involved in experimental design, data acquisition, and methodology development. I.Ismailova and D.R. Annamuratova participated in sample preparation, laboratory analyses, and data validation. N.S. Yadgarova contributed to data curation, statistical analysis, and figure preparation. All authors reviewed and approved the final version of the manuscript. Acknowledgments Z.B. Alloberganova and A.A. Dolimov contributed to the conceptualization, supervision, and overall coordination of this study. S.K. Baboev, M.F. Sultonov and Yu.A. matyakubova were involved in experimental design, data acquisition, and methodology development. I.Ismailova and D.R. Annamuratova participated in sample preparation, laboratory analyses, and data validation. N.S. Yadgarova contributed to data curation, statistical analysis, and figure preparation. All authors reviewed and approved the final version of the manuscript. Data Availability The data that support the findings of this study are available from *Institute of Genetics and Plant Experimental Biology* but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of *Institute of Genetics and Plant Experimental Biology* . References Abilfazova, Y. S. 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Ethyl-ene inhibiting compound 1-MCP delays leaf senescence in cotton plants under abiotic stress conditions. //Journal of Integrative Agriculture. 2015. V. 14 № 7, – P. 1321–1331. Zulkiffal, M. et al. Heat and drought stresses in wheat (Triticum aestivum L.): Substantial yield losses, practical achievements, improvement approaches, and adaptive mechanisms. InPlant stress physiology. Jan 20. IntechOpen. (2021). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8853561","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625727311,"identity":"eb4ddccf-fe6c-4db6-b4c2-5fcf80aa0f73","order_by":0,"name":"Zebo Alloberganova","email":"","orcid":"","institution":"Urgench State University the named Abu Rayhon Beruniy","correspondingAuthor":false,"prefix":"","firstName":"Zebo","middleName":"","lastName":"Alloberganova","suffix":""},{"id":625727312,"identity":"e776b73f-fd7b-4c4a-889c-2a8a55453086","order_by":1,"name":"Saidmurod Baboev","email":"","orcid":"","institution":"Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan","correspondingAuthor":false,"prefix":"","firstName":"Saidmurod","middleName":"","lastName":"Baboev","suffix":""},{"id":625727313,"identity":"38651a7d-6af2-461f-9292-1b58664171fa","order_by":2,"name":"M. F. Sultonov","email":"","orcid":"","institution":"Khorezm Mamun Academy","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"F.","lastName":"Sultonov","suffix":""},{"id":625727314,"identity":"9511010b-ea50-44b4-a550-9bc13fb6f438","order_by":3,"name":"Yulduz Matyakubova","email":"","orcid":"","institution":"Urgench State University the named Abu Rayhon Beruniy","correspondingAuthor":false,"prefix":"","firstName":"Yulduz","middleName":"","lastName":"Matyakubova","suffix":""},{"id":625727315,"identity":"e26a8f50-6287-4051-b1bb-bcc9646bbd9f","order_by":4,"name":"Intizor Ismailova","email":"","orcid":"","institution":"Urgench State University the named Abu Rayhon Beruniy","correspondingAuthor":false,"prefix":"","firstName":"Intizor","middleName":"","lastName":"Ismailova","suffix":""},{"id":625727316,"identity":"a271c6b8-7138-48e5-88d6-3f180f016ba6","order_by":5,"name":"D.R Annamuratova","email":"","orcid":"","institution":"Urgench State University the named Abu Rayhon Beruniy","correspondingAuthor":false,"prefix":"","firstName":"D.R","middleName":"","lastName":"Annamuratova","suffix":""},{"id":625727317,"identity":"163ede80-1195-4dfb-8f4f-826f690276c9","order_by":6,"name":"Nazokat Yadgarova","email":"","orcid":"","institution":"Urgench State University the named Abu Rayhon Beruniy","correspondingAuthor":false,"prefix":"","firstName":"Nazokat","middleName":"","lastName":"Yadgarova","suffix":""},{"id":625727318,"identity":"67bdcaea-8585-4991-9276-337ada4e220a","order_by":7,"name":"Abdurauf A. Dolimov","email":"data:image/png;base64,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","orcid":"","institution":"Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan","correspondingAuthor":true,"prefix":"","firstName":"Abdurauf","middleName":"A.","lastName":"Dolimov","suffix":""}],"badges":[],"createdAt":"2026-02-11 15:40:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8853561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8853561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107360109,"identity":"ee8fadd9-79f0-44fc-9987-de35da7929a4","added_by":"auto","created_at":"2026-04-20 18:02:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63286,"visible":true,"origin":"","legend":"\u003cp\u003eSpike length values of wheat varieties (cm)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8853561/v1/fd06f80867daeb1594a1423c.png"},{"id":107486169,"identity":"92fbb219-8ba7-4a5d-b49e-aaa7ef847ff3","added_by":"auto","created_at":"2026-04-22 02:37:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76219,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis of Leaf Area (cm²) of Wheat Varieties from Four Central Asian Countries\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8853561/v1/9c23bc9fbe5db190b2e15c46.png"},{"id":107360111,"identity":"98b1537c-96bb-4de1-9d31-fc2b6e07aa60","added_by":"auto","created_at":"2026-04-20 18:02:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75410,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of yield performance of wheat varieties (c/ha)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8853561/v1/1f10a58fcf861ab71a808fd5.png"},{"id":107487828,"identity":"d2b53dc1-2f67-49bd-8813-382e02b95f81","added_by":"auto","created_at":"2026-04-22 02:42:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":541592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8853561/v1/6d71f0dd-5889-4ec0-9573-91ac22cf9739.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Morpho-Biological and Yield Characteristics of Wheat Varieties Originating from Central Asian Breeding","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat is considered one of the most strategically important crops in ensuring global food security. Particularly under conditions of climate change and increasing water scarcity, the selection of high-yielding varieties with tolerance to various abiotic stress factors has become one of the most urgent priorities in modern wheat breeding programs (Meliev et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to global climate models, the average ambient temperature is projected to increase by 1.5\u0026deg;C over the next two decades (Masson-Delmotte et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), exposing agricultural crops to increasing heat stress. Wheat is one of the most important cereal crops worldwide in terms of cultivated area, contributing approximately 28% to global grain production and 41.5% to international grain trade (FAO, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProlonged heat stress (average daily temperatures exceeding 17.5\u0026deg;C) affects approximately 7\u0026nbsp;million hectares of wheat-growing areas in developing countries, while terminal (late-season) heat stress represents a major constraint for about 40% of temperate regions, covering nearly 36\u0026nbsp;million hectares (Reynolds et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Understanding morpho-physiological and biochemical traits associated with heat stress tolerance is of great practical importance, as it enables the identification of different tolerance mechanisms and the application of mitigating strategies in wheat production systems (Langridge et al., 2021; Rehman et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the dynamic nature of abiotic stress factors, the implementation of experimental approaches under both optimal and stress conditions is regarded as essential in breeding programs, particularly for maintaining yield stability across environments (He et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Among the key morphological traits influencing yield formation in wheat, leaf organs play a central role. The three uppermost leaves (from top to bottom: flag leaf, second leaf, and third leaf) contribute significantly and positively to grain yield formation (Lou et al., 2021; Sanchez-Bragado et al., 2020; Tu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among these, the flag leaf (including its sheath) has a particularly important role compared to other leaves, as it intercepts up to 30% of incoming light radiation (Liu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sanchez-Bragado et al., 2014).\u003c/p\u003e \u003cp\u003eSeveral studies have reported that the flag leaf can contribute up to 50% of the total assimilates required for grain yield, making it a major photosynthetic organ during grain filling (Ba et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The positive effect of the flag leaf on yield is closely associated with chloroplast activity. Chloroplasts (chlorophyll pigments) play a crucial role in carbon fixation, the formation of vegetative organs during growth through photosynthesis, and the response to environmental stresses, including heat stress (Ali et al., 1999). Studies conducted by Meliev et al. demonstrated significant positive relationships between leaf physiological traits, leaf area, and quantitative yield indicators of wheat genotypes under unfavorable environmental conditions. Based on these findings, promising wheat genotypes were selected as initial breeding materials to develop environmentally adaptable and high-yielding wheat varieties for Uzbekistan (Meliev et al., 2023; \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetically related genotypes may also differ in chlorophyll content depending on environmental conditions and cultivation practices, resulting in diverse physiological responses to external stress factors (Dadoboeva et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). High chlorophyll content has been widely reported in the literature as a potential criterion for assessing heat tolerance in wheat (Ramya et al., 2015; Munjal et al., 2016). Under high-temperature conditions, chlorophyll acts as a low-level photoinhibitor, thereby enhancing heat tolerance (Choudhary et al., 2020). Experimental studies have also scientifically confirmed the critical role of chloroplasts in activating cellular signaling pathways under heat stress conditions (Yuan et al., 2015). Chlorophyll pigments are responsible for harvesting light energy and regulating electron transport during the initial and essential stages of photosynthesis.\u003c/p\u003e \u003cp\u003eThe Central Asian region, particularly the Khorezm oasis of Uzbekistan, is characterized by unique soil and climatic conditions, including highly saline soils and extreme summer air temperatures. Under such conditions, wheat yield depends not only on genetic potential but also on the balanced development of morpho-biological traits that ensure adaptation to environmental stress factors.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area and Experimental Design\u003c/h2\u003e \u003cp\u003eThe research was conducted during the 2023\u0026ndash;2025 growing seasons in the Khorezm region of Uzbekistan (41\u0026deg;19\u0026prime;60.00\u0026Prime; N, 61\u0026deg;00\u0026prime;0.00\u0026Prime; E). Field experiments were carried out in Urgench city at the Khorezm Scientific Experimental Station of the Research Institute of Selection, Seed Production, and Agrotechnology of Cotton Growing. The study aimed to evaluate the relationships between physiological, morphological, and yield-related traits of wheat and environmental factors under the soil and climatic conditions of the Khorezm region, as well as to assess the adaptability of wheat varieties to local agro-climatic conditions.\u003c/p\u003e \u003cp\u003eExperimental plots of 2 m\u0026sup2; were arranged in a randomized design with three replications. For each trait evaluation, 30 plants were randomly selected from each replication. Leaf area was determined using the Petiole mobile application (Petiole APK \u0026ndash; APKPure.com) by digital image analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eYield Components and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eYield-related traits, including spike weight, spike length, number of spikelets per spike, number of grains per spike, grain weight per spike, and thousand-grain weight, were measured at maturity. Data processing and statistical analyses were performed using Microsoft Excel and Statgraphics Centurion 34 software to compare the performance of different wheat varieties.\u003c/p\u003e\n\u003ch3\u003ePlant Material\u003c/h3\u003e\n\u003cp\u003eA total of 138 soft wheat varieties of diverse genetic origin from Central Asia were initially evaluated under the environmental conditions of the Khorezm region. Among them, 138 varieties were identified as cold-sensitive, while 13 cold-tolerant genotypes were selected for further investigation. These selected varieties originated from Kazakhstan (Steklovidnaya 24, Egemen, and Naz), Kyrgyzstan (Vlada, Kiyal, and Kaset), Tajikistan (SILA, KAMOL, and AYVINA), and Uzbekistan (Andijan 4, Dostlik, ASR, and Durdona).\u003c/p\u003e\n\u003ch3\u003eData Recording and Analysis\u003c/h3\u003e\n\u003cp\u003eQuantitative traits determining yield performance were recorded and averaged based on observations from 30 plants per variety. Yield components were calculated using the Ken Saera formula developed by CIMMYT scientists (Ken et al., 1997). The resulting data were used to assess varietal differences and to identify key morpho-biological traits associated with yield performance under saline soil and high-temperature conditions.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSpike Length\u003c/h2\u003e \u003cp\u003eSpike length is one of the key morphological traits determining yield components in wheat, as it directly influences both the number and weight of grains per spike. In the present study, the spike length (cm) of wheat varieties originating from breeding programs of four Central Asian countries\u0026mdash;Uzbekistan, Tajikistan, Kyrgyzstan, and Kazakhstan\u0026mdash;was comparatively evaluated.\u003c/p\u003e \u003cp\u003eThe statistical analysis, illustrated using box plot diagrams, revealed not only differences in mean spike length among the varieties but also the variability of this trait under the influence of environmental factors. This indicates that spike length is a highly responsive morphological characteristic, reflecting both genetic potential and environmental adaptability.\u003c/p\u003e \u003cp\u003eGraphical analysis showed that the highest average spike length values were observed in varieties originating from Kyrgyzstan (KGZ) and Tajikistan (TAJ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), highlighting their superior morphological performance under the environmental conditions of the Khorezm region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCountry-wise results showed that wheat varieties from Kyrgyzstan (KGZ) recorded the highest spike length values. In particular, the Kiyal variety exhibited an average spike length of 11.6 cm, representing the highest value observed in this study. Another Kyrgyz variety, Kasiet, also demonstrated high yield potential with a spike length of 11.4 cm.\u003c/p\u003e \u003cp\u003eWithin the group of Tajik varieties (TAJ), SILA stood out with an average spike length of 11.4 cm, reflecting strong genetic stability. The AYVINA variety had a slightly lower average spike length of 10.8 cm, but displayed a wide range of variation, indicating a higher environmental responsiveness.\u003c/p\u003e \u003cp\u003eAmong the local Uzbek varieties (UZ), ASR recorded the best performance with an average spike length of 10.4 cm. In the Kazakh group (KAZ), Steklovidnaya-24 exhibited a high spike length of 11.0 cm, while the Egemen variety had an average of 10.9 cm but showed the greatest variability, reflecting its sensitivity to environmental factors.\u003c/p\u003e \u003cp\u003eBased on these results, under the saline and arid climatic conditions of the Khorezm region, the Kyrgyz Kiyal, Tajik SILA, and Uzbek ASR varieties are recommended as donor genotypes in breeding programs aimed at improving spike traits. These varieties play a crucial role in enhancing yield by maximizing spike length.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLeaf Area of Wheat Varieties\u003c/h3\u003e\n\u003cp\u003eAmong the biometric traits of wheat, leaf area plays a particularly important role, as it is a key factor determining photosynthetic efficiency and, consequently, grain yield. In this study, statistical analysis of leaf area (cm\u0026sup2;) among varieties from four Central Asian countries revealed significant differences between the genotypes.\u003c/p\u003e \u003cp\u003eGraphical analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicated that the highest leaf area values were observed in varieties from Kazakhstan (KAZ) and Uzbekistan (UZ). Within the Kazakh varieties, Egemen exhibited the highest average leaf area of 74.4 cm\u0026sup2;, followed by Naz with an average of 65.5 cm\u0026sup2;. Among the Uzbek local varieties, Andijan 4 had the largest assimilation surface area at 68.1 cm\u0026sup2;; however, the high variability observed in this variety indicated a strong responsiveness to environmental factors. The ASR variety demonstrated a relatively stable leaf area of 60.7 cm\u0026sup2;.\u003c/p\u003e \u003cp\u003eWithin the Tajik varieties (TAJ), AYVINA and SILA exhibited moderate leaf area values of 53.4 cm\u0026sup2; and 50.3 cm\u0026sup2;, respectively, while KAMOL was characterized by a smaller leaf area of 37.2 cm\u0026sup2;. These results highlight the considerable genetic and environmental influence on leaf area, which is critical for optimizing photosynthesis and enhancing wheat yield under local conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong Kyrgyz varieties (KGZ), Kasiet showed relatively good performance with a leaf area of 54.1 cm\u0026sup2;, while Kiyal exhibited the lowest value in the study at 34.8 cm\u0026sup2;.\u003c/p\u003e \u003cp\u003eThe results indicate that grain yield formation in wheat varieties (average 100.8 c/ha) is determined by the balanced interaction between leaf area and reproductive organs. Although the highest leaf area values were observed in Egemen (74.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06 cm\u0026sup2;) and Andijan4 (68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03 cm\u0026sup2;), this did not proportionally translate into increased grain yield. In the Andijan 4 variety, despite the large leaf area, grain yield was limited to 90.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 c/ha due to shorter spike length (8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 cm) and restricted grain formation. This is scientifically explained by the fact that the assimilates provided by the leaf area were insufficient for effective grain development.\u003c/p\u003e \u003cp\u003eIn contrast, the maximum grain yield recorded in the SILA variety (121\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15 c/ha) was attributed to its moderately optimal leaf area (53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69 cm\u0026sup2;) and highly developed spike length (11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 cm). In this variety, the translocation of photosynthetic products to reproductive organs was highly efficient, resulting in the highest grain weight per unit leaf area. Similarly, in the Kasiet (103.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04 c/ha) and ASR (98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 c/ha) varieties, the positive balance between leaf area and spike length served as a key factor ensuring stable grain yield.\u003c/p\u003e\n\u003ch3\u003eYield Performance\u003c/h3\u003e\n\u003cp\u003eGrain yield is one of the most important and complex traits in wheat breeding, as it represents an integrated expression of a plant\u0026rsquo;s genetic potential and its adaptability to environmental factors such as climate, soil conditions, and agronomic practices. In this study, the grain yield (c/ha) of wheat varieties originating from breeding programs of four Central Asian countries\u0026mdash;Uzbekistan, Tajikistan, Kyrgyzstan, and Kazakhstan\u0026mdash;was comparatively evaluated.\u003c/p\u003e \u003cp\u003eThe obtained results demonstrated considerable variation in grain yield among the studied varieties, reflecting differences in their genetic potential and adaptability to local environmental conditions. Yield performance differed significantly depending on the region of origin and genetic background of the varieties. Graphical analysis indicated that the highest grain yield values were observed in varieties originating from Tajikistan (TAJ) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll varieties originating from this country recorded grain yield values exceeding 100 c/ha. The next highest yield levels were observed in varieties from Uzbekistan (UZ) and Kyrgyzstan (KGZ), which demonstrated comparable performance. In contrast, the lowest average yield values were recorded in varieties from Kazakhstan (KAZ). Among all evaluated genotypes, the highest grain yield was obtained from the Tajik variety SILA, reaching 121 c/ha. Similarly, another Tajik variety, KAMOL, also exhibited high productivity with a yield of 109 c/ha. The lowest grain yield was observed in the Uzbek variety Andijan 4 (90.8 c/ha) and the Kazakh variety Naz (91.1 c/ha).\u003c/p\u003e \u003cp\u003eA country-wise comparison revealed that the most adaptable and high-yielding varieties included Durdona (102 c/ha) from Uzbekistan (UZ), SILA (121 c/ha) from Tajikistan (TAJ), Kasiet (104 c/ha) from Kyrgyzstan (KGZ), and Steklovidnaya-24 (99.4 c/ha) from Kazakhstan (KAZ), all of which outperformed the remaining varieties within their respective groups. Based on the results obtained under the environmental conditions of the Khorezm region, the use of the Tajik varieties SILA and KAMOL, as well as the local variety Durdona, is recommended for wheat breeding programs aimed at achieving high grain yield. These varieties are distinguished by their high productivity and strong tolerance to the region\u0026rsquo;s adverse environmental factors.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe interrelationship of the studied traits\u003c/h2\u003e \u003cp\u003eIn this study, a comparative analysis of key morphological and yield-related traits of wheat varieties from four Central Asian countries was conducted, focusing on spike length, leaf area, and grain yield. These traits are important indicators of both genetic potential and environmental adaptability. The analysis specifically evaluated how these traits varied across the countries, providing insight into the performance and stability of the studied genotypes under different environmental conditions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This comparative assessment is essential for identifying promising varieties that can be used in breeding programs aimed at improving wheat productivity in arid and saline regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterrelationship of traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf Area, cm\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpike Length, cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrain Yield, c/ha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndijan 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68,1\u0026thinsp;\u0026plusmn;\u0026thinsp;8,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,6\u0026thinsp;\u0026plusmn;\u0026thinsp;0,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90,8\u0026thinsp;\u0026plusmn;\u0026thinsp;0,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,4\u0026thinsp;\u0026plusmn;\u0026thinsp;0,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98,6\u0026thinsp;\u0026plusmn;\u0026thinsp;0,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDostlik\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38,2\u0026thinsp;\u0026plusmn;\u0026thinsp;0,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,6\u0026thinsp;\u0026plusmn;\u0026thinsp;0,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97,6\u0026thinsp;\u0026plusmn;\u0026thinsp;0,87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurdona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49,9\u0026thinsp;\u0026plusmn;\u0026thinsp;2,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,5\u0026thinsp;\u0026plusmn;\u0026thinsp;0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;0,87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAYVINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53,4\u0026thinsp;\u0026plusmn;\u0026thinsp;1,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,8\u0026thinsp;\u0026plusmn;\u0026thinsp;0,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101,6\u0026thinsp;\u0026plusmn;\u0026thinsp;0,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKAMOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37,2\u0026thinsp;\u0026plusmn;\u0026thinsp;0,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;0,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108,8\u0026thinsp;\u0026plusmn;\u0026thinsp;0,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSILA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50,3\u0026thinsp;\u0026plusmn;\u0026thinsp;0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,4\u0026thinsp;\u0026plusmn;\u0026thinsp;0,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121\u0026thinsp;\u0026plusmn;\u0026thinsp;1,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVlada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48,7\u0026thinsp;\u0026plusmn;\u0026thinsp;2,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,1\u0026thinsp;\u0026plusmn;\u0026thinsp;0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98,8\u0026thinsp;\u0026plusmn;\u0026thinsp;0,75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKasiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54,1\u0026thinsp;\u0026plusmn;\u0026thinsp;1,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,4\u0026thinsp;\u0026plusmn;\u0026thinsp;0,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103,8\u0026thinsp;\u0026plusmn;\u0026thinsp;1,04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKiyal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34,8\u0026thinsp;\u0026plusmn;\u0026thinsp;0,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,9\u0026thinsp;\u0026plusmn;\u0026thinsp;1,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98,3\u0026thinsp;\u0026plusmn;\u0026thinsp;0,87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgemen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74,4\u0026thinsp;\u0026plusmn;\u0026thinsp;2,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,4\u0026thinsp;\u0026plusmn;\u0026thinsp;1,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94,3\u0026thinsp;\u0026plusmn;\u0026thinsp;0,75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65,5\u0026thinsp;\u0026plusmn;\u0026thinsp;1,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,4\u0026thinsp;\u0026plusmn;\u0026thinsp;0,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91,1\u0026thinsp;\u0026plusmn;\u0026thinsp;0,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteklovidnaya 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u0026thinsp;\u0026plusmn;\u0026thinsp;2,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;0,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99,4\u0026thinsp;\u0026plusmn;\u0026thinsp;0,81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard Deviation (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoefficient of Variation (CV, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeast Significant Difference (LSD, 0.05)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe unusual case observed in the Kiyal variety can be explained by the fact that, despite its high genetic potential for spike length (10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01 cm), the relatively small leaf area (34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 cm\u0026sup2;) acted as a limiting factor in yield formation. The limited leaf area was unable to supply sufficient organic matter during the grain-filling period, resulting in a grain yield (98.3 c/ha) below the variety's theoretical potential.\u003c/p\u003e \u003cp\u003eExcessively large leaf area (as observed in the Andijan 4 and Egemen varieties) can increase transpiration intensity, leading to a negative response of the plant under water-deficit conditions, though it does not always play a decisive role in increasing yield. For the saline and hot climatic conditions of the Khorezm region, selecting varieties with a leaf area of 50\u0026ndash;60 cm\u0026sup2; and spike length above 11 cm, such as SILA, Kasiet, and Steklovidnaya 24, provides the highest economic efficiency.\u003c/p\u003e \u003cp\u003eCorrelation analysis of our study results revealed varying degrees of association between the morphological traits of wheat varieties and grain yield. Analysis of the relationship between spike length and grain yield indicated a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.502). Statistically, the significance level of this correlation was p\u0026thinsp;=\u0026thinsp;0.080, which is considered significant at the 10% error threshold. This confirms that increased spike length is one of the main factors contributing to higher yield. In particular, the varieties with the longest spikes, SILA (11.4 cm) and Kasiet (11.4 cm), exhibited the highest yields, practically demonstrating this correlation.\u003c/p\u003e \u003cp\u003eThe relationship between leaf area and grain yield showed a moderate negative correlation (r = -0.446). The significance level of this correlation was p\u0026thinsp;=\u0026thinsp;0.126. The presence of a negative correlation indicates that excessively large leaf area tends to reduce yield. This is explained by increased transpiration and plant water stress in varieties with very large leaf areas under the extreme hot and dry climate of the Khorezm region (Egemen \u0026ndash; 74.4 cm\u0026sup2;, Andijan 4\u0026ndash;68.1 cm\u0026sup2;), which limits yield formation (Tabel 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTabel 2. Correlation Analysis of Traits\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeaf Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpike Length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrain Yield\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLeaf Area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Corr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,09758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,44641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,75113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,12623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSpike Length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Corr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,09758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,50212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,75113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,08037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eGrain Yield\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Corr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,44641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,50212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,12623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,08037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the interrelationships among morphological traits, no significant correlation was observed between leaf area and spike length (r = -0.097; p\u0026thinsp;=\u0026thinsp;0.751). This indicates that these two traits are independent of each other, allowing for the possibility of selecting them separately in wheat breeding programs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe adaptability of soft wheat to adverse environmental conditions largely depends on the optimization of its morphological and economically valuable traits, which plays a critical role in enhancing grain yield (Shachai et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our study, the observed relationships between yield components and morpho-biological traits were directly influenced by the impact of abiotic stress factors on plant physiology. The results indicate that the factors determining wheat productivity in the saline soils and hot climate of the Khorezm region differ from those in conventional moderate climates.\u003c/p\u003e \u003cp\u003eA positive correlation between spike length and grain yield (r\u0026thinsp;=\u0026thinsp;0.502) was detected in this study, consistent with findings reported by Bhutto (2016) and Meliev et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (Bhutto et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meliev et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These studies emphasized that under heat stress conditions, the potential of reproductive organs serves as a key driver of yield. Specifically, Plisko et al. (2017) reported that longer spikes (11.4 cm) increased the number of grains per spike, resulting in yields up to 121 c/ha. This underscores the importance of selecting genotypes with high grain-filling potential under abiotic stress conditions.\u003c/p\u003e \u003cp\u003eQaseem et al. (2020) and Sattar et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlighted that the combined effects of heat and drought lead to degradation of chlorophyll and protein molecules and slow the mobilization of reserves to grains. Consequently, excessively large leaf area under high temperatures may not confer a photosynthetic advantage but rather acts as a factor negatively affecting plant water status (Qaseem et al., 2020; Sattar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is consistent with the negative correlation observed in our study (r = -0.446). In contrast, under moderate conditions, larger leaf area increases the photosynthetic surface and positively impacts yield (Wang et al., 2021). However, in the extreme hot conditions of Khorezm, excessive leaf area (as in Egemen and Andijan 4 varieties) intensified transpiration and contributed to yield limitations.\u003c/p\u003e \u003cp\u003eZulkiffal et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that under terminal heat stress, large leaf area rapidly depletes plant water resources, leading to drought stress during the grain-filling period. In our study, varieties with a leaf area of 50\u0026ndash;60 cm\u0026sup2; showed high yields, demonstrating that under climate change, a \"moderate leaf area \u0026ndash; reduced transpiration\" strategy is effective. This supports the concept of \"adaptable morphotypes\" proposed by Meliev et al. (2023).\u003c/p\u003e \u003cp\u003eThe superior performance of Tajikistan varieties (SILA, KAMOL) can be attributed to the genetic integration of heat tolerance and yield-related traits in these genotypes. Khayatnezhad et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Abilfazova (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that drought-tolerant genotypes maintain higher chlorophyll content in leaves, which helps sustain photosynthetic activity and ensures yield formation even under stress conditions (Khayatnezhad et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Abilfazova, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Baboeva et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also highlighted that stable chlorophyll content in leaves serves as a key indicator of wheat genotypes\u0026rsquo; tolerance to abiotic stresses, particularly drought and heat. In tolerant genotypes, chlorophyll levels showed minimal changes under adverse conditions, whereas in sensitive varieties, they decreased sharply (Baboeva et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Kazakhstan varieties (Naz, Egemen), despite large leaf areas, yields were low under Khorezm conditions, indicating that these genotypes are adapted to northern latitudes and could not withstand transpiration stress in the hot southern climate. Similar findings were reported by Tajibayev et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who concluded that it is difficult to develop a single variety suitable for all regions, and breeding varieties adapted to two neighboring regions is more practical (Tajibayev et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The results of this study confirm previous research and emphasize the importance of selecting wheat varieties with appropriate morphological traits for breeding programs, particularly when comparing varieties across Central Asian countries and targeting drought-prone and saline regions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResearch conducted under the saline soils and hot climatic conditions of the Khorezm region demonstrated that the highest grain yields were recorded in the SILA (121 q/ha), KAMOL (108.8 q/ha), and Durdona (102 q/ha) wheat varieties. Statistical analyses revealed a positive correlation between spike length and grain yield (r\u0026thinsp;=\u0026thinsp;0.502), indicating that the Kiyal and SILA varieties are the most promising donors for this trait. Conversely, a negative correlation was observed between leaf area and grain yield (r = -0.446), suggesting that leaf areas exceeding 70 cm\u0026sup2; increase transpiration and limit yield, while the optimal range is approximately 50\u0026ndash;60 cm\u0026sup2;. Therefore, to achieve high productivity, it is recommended that breeding programs select genotypes with moderate leaf area and long spikes.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003eAuthor Disclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that this research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.B. Alloberganova and A.A. Dolimov contributed to the conceptualization, supervision, and overall coordination of this study. S.K. Baboev, M.F. Sultonov and Yu.A. matyakubova were involved in experimental design, data acquisition, and methodology development. I.Ismailova and D.R. Annamuratova participated in sample preparation, laboratory analyses, and data validation. N.S. Yadgarova contributed to data curation, statistical analysis, and figure preparation. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eZ.B. Alloberganova and A.A. Dolimov contributed to the conceptualization, supervision, and overall coordination of this study. S.K. Baboev, M.F. Sultonov and Yu.A. matyakubova were involved in experimental design, data acquisition, and methodology development. I.Ismailova and D.R. Annamuratova participated in sample preparation, laboratory analyses, and data validation. N.S. Yadgarova contributed to data curation, statistical analysis, and figure preparation. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from *Institute of Genetics and Plant Experimental Biology* but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of *Institute of Genetics and Plant Experimental Biology* .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbilfazova, Y. S. The pigment composition of peach leaves in the conditions of the Black Sea coast of the Krasnodar Territory. Matscientific. Conf. Factors of resistance of plants and microorganisms in extreme natural conditions and industrial environment, Irkutsk, pp. 40\u0026ndash;41. 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(2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wheat, yield, leaf area, spike length, correlation analysis, Central Asia","lastPublishedDoi":"10.21203/rs.3.rs-8853561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8853561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, the morpho-biological traits and yield performance of 13 wheat varieties originating from breeding programs of four Central Asian countries (Uzbekistan, Tajikistan, Kyrgyzstan, and Kazakhstan) were evaluated under saline soil conditions of the Khorezm region. Statistical analysis revealed a positive correlation between spike length and grain yield (r\u0026thinsp;=\u0026thinsp;0.502), whereas a negative relationship was observed between leaf area and yield (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.446). The highest yield levels were recorded in the SILA (121 c/ha) and KAMOL (108.8 c/ha) varieties. The results demonstrated that, under the hot climatic conditions of the Khorezm oasis, the selection of genotypes with a leaf area of 50\u0026ndash;60 cm\u0026sup2; and longer spikes ensures higher productivity.\u003c/p\u003e","manuscriptTitle":"Morpho-Biological and Yield Characteristics of Wheat Varieties Originating from Central Asian Breeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 18:02:11","doi":"10.21203/rs.3.rs-8853561/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-21T08:30:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T13:50:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322143411233236455222878298571269405461","date":"2026-04-15T18:59:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121470665124043023960125181431542927115","date":"2026-04-14T12:05:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143167204388485153598377250939808564066","date":"2026-04-13T09:08:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T08:29:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T08:28:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T23:22:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T06:22:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-09T06:06:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01e12d2d-aa48-4c5b-bc76-08a85bcab076","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66598593,"name":"Biological sciences/Ecology"},{"id":66598594,"name":"Earth and environmental sciences/Ecology"},{"id":66598595,"name":"Biological sciences/Genetics"},{"id":66598596,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-20T18:02:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 18:02:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8853561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8853561","identity":"rs-8853561","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}