نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی تولید و ژنتیک گیاهی، دانشگاه محقق اردبیلی، اردبیل، ایران
2 گروه به‌نژادی و بیوتکنولوژی گیاهی دانشگاه تبریز، تبریز، ایران
3 استاد گروه مهندسی تولید و ژنتیک گیاهی، دانشگاه محقق اردبیلی، اردبیل، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Extended Abstract
Introduction
Wheat (Triticum aestivum L.) is a cornerstone of global food security, contributing approximately 20% of dietary calories and protein worldwide (Arzani & Ashraf, 2017). In semi-arid regions like Iran, water scarcity, intensified by climate change, poses a significant threat to wheat production, reducing grain yield (GY) and compromising baking quality essential for bread-making (IPCC, 2021; Mwadzingeni et al., 2016). Drought stress affects physiological processes such as osmotic adjustment, stomatal regulation, and gluten protein stability, which are critical for maintaining yield and quality traits like seed protein content (SP), Zeleny sedimentation (ZS), water absorption (WA), and dough strength (RM) (Sallam et al., 2019; Rakszegi et al., 2019). The CIMMYT Mexico Core Germplasm (CIMCOG) collection provides a diverse genetic pool for breeding abiotic stress-tolerant wheat, yet its performance in Iran’s semi-arid environments, characterized by low annual precipitation (approximately 270 mm) and specific soil conditions, is underexplored (Ahmed et al., 2020; Mohammadi et al., 2013). Breeding for drought tolerance is challenging due to its polygenic nature and environmental interactions, necessitating robust field evaluations to identify genotypes that balance agronomic performance with end-use quality (Abid et al., 2018; Nazari et al., 2024). This study evaluated 60 CIMCOG genotypes under well-watered (WW) and water-deficient (WD) conditions in East Azerbaijan, Iran, during the 2022-2023 season, focusing on agronomic traits (e.g., GY, thousand-kernel weight [TW]) and quality parameters (e.g., SP, ZS, WA, RM) to identify drought-resilient genotypes suitable for sustainable wheat production in semi-arid regions.
Materials and Methods
The experiment was conducted at two semi-arid sites in East Azerbaijan, Iran (Site 1: 38°02’N, 47°28’E, 1567 m; Site 2: 38°14’N, 46°09’E, 1493 m), with sandy loam soils (pH 7.2–7.5), annual precipitation of approximately 270 mm, and mean anthesis temperatures of 15–18°C. Sixty CIMMYT CIMCOG genotypes from CIMMYT’s global collection were tested during the 2022-2023 growing season using a randomized complete block design (RCBD) with three replicates, each plot comprising three 2-meter rows per genotype. Irrigation treatments included well-watered (WW, 70 mm evaporation) and water-deficient (WD, 120 mm evaporation) conditions, measured via a Class A evaporation pan. Drought stress was induced at anthesis (Zadoks stage 65) for 14 days, reducing soil volumetric water content to 12% (WD) versus 25% (WW), monitored using time-domain reflectometry (TDR) probes calibrated to ±2% accuracy (Wu et al., 2019).
Traits assessed included grain yield (GY, grams per plot, adjusted to 12% moisture), thousand-kernel weight (TW), seed protein content (SP), seed moisture content (SM), Zeleny sedimentation value (ZS), water absorption (WA), seed hardness (SH), rapid mix test (RM), and kernel ash (KA). Quality traits were measured using a Perten IM8620 NIR Grain Analyzer, calibrated per manufacturer standards (Hrušková & Švec, 2009). TW was quantified with a Contador seed counter, and RM followed ICC Standard No. 115/1 for dough strength assessment (Sedláček & Horčička, 2011). Data normality and variance homogeneity were verified using Shapiro-Wilk and Levene’s tests, respectively. Combined analysis of variance (ANOVA) was performed in SAS (v9.4) to evaluate the effects of location (L), stress (S), genotype (G), and their interactions (Sattar et al., 2020). Membership Function Value (MFV) analysis was used to rank genotype performance, calculated as:
Xi = (X-Xmin) / (Xmax-Xmin)
where Xi is the MFV, X is the measured value, and Xmax and Xmin are the highest and lowest values across genotypes. Higher MFVs indicate better performance (Wu et al., 2019). Correlation analysis explored trait associations, visualized as a heatmap with color intensity indicating correlation strength (Zheng & Cao, 2022).
Results and Discussion
Combined ANOVA revealed significant effects of location, stress, and genotype on most traits (Table 1 in the original document). Stress significantly reduced GY (F = 2,562,975, P < 0.001) and impacted all traits except SM (P > 0.05), consistent with findings by Mohammadi et al. (2013). Genotype effects were highly significant (P < 0.001) for all traits, indicating substantial heritable variation (Rabieyan et al., 2023). Significant genotype × stress (G×S) interactions were observed for GY, SP, ZS, WA, SH, TW, and RM, highlighting differential drought responses among genotypes (Ahmed et al., 2019). Location × stress (L×S) interactions affected TW, GY, KA, SP, SM, WA, and SH, reflecting site-specific stress responses (Nazari et al., 2024). The absence of three-way (L×G×S) interactions simplified selection, as genotype performance was stable across locations under stress (Sallam et al., 2019).
Correlation analysis (Fig. 1 in the original document) showed strong positive correlations between ZS and SP, WA, and RM under both WW and WD conditions, aligning with Hrušková and Faměra (2003), who noted that higher gluten content enhances ZS. RM correlated positively with ZS, WA, and SP, supporting Sedláček and Horčička (2011). A negative correlation between SM and SP, ZS, WA, and SH was observed, consistent with Khalid et al. (2022) and Fra̧czek et al. (2005), indicating that lower moisture content enhances quality traits under stress. Seed hardness, measured via NIR spectroscopy, ranged from soft to medium-hard, with a negative association with SM (Hrušková & Švec, 2009). WA positively correlated with SP and gluten content, as higher protein levels enhance water retention, critical for bread-making (Kornarzyński et al., 2002).
MFV analysis identified genotypes 57, 29, and 25 as top performers, maintaining high SP (18% increase), ZS (22% increase), and WA (15% increase) under WD conditions, ensuring bakery quality (Fig. 4 in the original document). These genotypes also exhibited robust GY stability, comparable to WW conditions, aligning with findings by Ahmed et al. (2020). Other high-performing genotypes (40, 59, 13, 58, 20, 23) ranked in the top MFV quartile, indicating their potential for semi-arid environments (Abid et al., 2018). The significant G×S interactions for GY and quality traits provide a genetic basis for stress-adapted breeding, while the absence of complex interactions facilitates broad adaptation (Sattar et al., 2020). These results highlight the potential to breed wheat varieties that combine drought tolerance with superior bakery quality, addressing both food security and processing needs (Rakszegi et al., 2019).
Conclusion
This study identifies genotypes 57, 29, 25, 40, 59, 13, 58, 20 and 23 as promising candidates for semi-arid wheat production, offering resilience to drought while preserving bakery quality. These genotypes maintained high GY, SP, ZS, and WA under WD conditions, ensuring nutritional value and bread-making efficiency (Ahmed et al., 2019). Strong correlations among quality traits (ZS, WA, SP, RM) and their stability under stress underscore their breeding potential (Hrušková & Faměra, 2003; Sedláček & Horčička, 2011). The absence of L×G×S interactions enables straightforward selection across semi-arid regions, while significant G×S interactions provide a genetic foundation for targeted breeding (Rabieyan et al., 2023). Future efforts should integrate genomic tools, such as genome-wide association studies (GWAS), to elucidate the genetic mechanisms driving drought tolerance and quality traits, accelerating the development of climate-smart wheat varieties (Sallam et al., 2019; Nazari et al., 2024). These findings offer a roadmap for enhancing wheat production in water-scarce environments, ensuring food security and end-use quality under climate change challenges (Mwadzingeni et al., 2016; IPCC, 2021).
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