با همکاری انجمن علمی منظر ایران
نوع مقاله : مقالات پژوهشی
نویسندگان
دانشگاه فردوسی مشهد- دانشکده کشاورزی-گروه علوم باغبانی و مهندسی فضای سبز
کلیدواژهها
عنوان مقاله English
نویسندگان English
Title
Performance Pattern and Multivariate Analysis of Quantitative Traits in Sweet Pepper Genotypes
Introduction:
Sweet pepper (Capsicum annuum L.) is one of the most widely grown vegetable crops in the world and is highly valued for both its nutritional and economic importance. Its fruits are rich in vitamins, antioxidants, and other beneficial compounds, making them an important part of the human diet. In recent years, the demand for sweet pepper has increased considerably due to changing consumer preferences and the expansion of greenhouse production systems. As a result, improving yield and fruit quality has become a major goal in sweet pepper breeding programs. However, yield is a complex trait influenced by many genetic and environmental factors, which makes direct selection based only on yield less effective. For this reason, plant breeders often focus on traits that are closely associated with yield, such as fruit number, fruit weight, fruit size, plant growth, and earliness. Understanding the relationships among these traits can help breeders select superior genotypes more efficiently and improve breeding outcomes. In addition, evaluating the genetic diversity within available germplasm is an important step in identifying promising materials for future breeding programs. Therefore, the present study was conducted to evaluate genetic variation among sweet pepper genotypes, examine the relationships among important morphological and yield-related traits, and develop simple predictive models for yield and its major components under greenhouse conditions.
Materials and Methods:
The experiment was conducted during the spring of 2023 in a research greenhouse located in Mashhad, Iran. A total of 72 sweet pepper genotypes were evaluated using a completely randomized design (CRD) with three replications. Greenhouse conditions were maintained at approximately 25°C with relative humidity ranging from 40% to 50%, and standard ventilation, cooling, and heating systems were used throughout the experiment. Seeds were sown in sterilized 128-cell trays containing a substrate mixture of cocopeat, peat moss, and perlite. After reaching the four-leaf stage, healthy and uniform seedlings were transplanted into prepared greenhouse soil with 60 cm spacing between plants and 80 cm between rows. All plants received the same management practices during the growing season, including drip irrigation, fertilization, pruning, weed control, and pest management. Several morphological and yield-related traits were evaluated, including plant height, fruit yield per plant, fruit number, average fruit weight, fruit length, fruit width, cortex thickness, pedicel length, and pedicel thickness. Fruit dimensions were measured using a digital caliper, while fruit weight was determined using a precision digital balance. Plant yield was calculated based on the average of three harvests per plant. Statistical analyses included analysis of variance (ANOVA), correlation analysis, cluster analysis using Ward’s method, and multiple regression analysis to determine relationships among traits and predict yield components.
Results and Discussion:
The results showed significant variation among the studied genotypes for most of the measured traits, indicating the presence of considerable genetic diversity within the population. Such diversity is highly valuable in breeding programs because it increases the chances of identifying superior genotypes with desirable agronomic characteristics. Among the evaluated genotypes, G71 produced the highest yield (3138.06 g per plant), while G27 recorded the highest average fruit weight (301.30 g). Yield showed strong positive relationships with fruit number, fruit weight, fruit width, and plant height. The strongest correlation was observed between yield and fruit number, suggesting that increasing fruit set plays a key role in improving productivity in sweet pepper. Fruit weight was also positively associated with fruit width, fruit length, pedicel length, and pedicel thickness. Similarly, cortex thickness showed positive relationships with both yield and fruit quality traits, highlighting its importance in fruit development and market value. These findings suggest that several of these traits can be effectively used for indirect selection in breeding programs. Cluster analysis grouped the genotypes into three distinct clusters, reflecting clear phenotypic differences among the studied materials. Cluster 3 showed the highest yield and the greatest level of variation, indicating strong potential for selection and future breeding work. Cluster 1 was mainly characterized by better fruit quality traits, including larger fruit size and greater cortex thickness, whereas Cluster 2 generally showed weaker performance across most traits. The box plot analysis further confirmed the differences among clusters. Cluster 3 combined high yield with broad variability, suggesting the presence of promising genotypes for yield improvement. In contrast, Cluster 1 produced heavier fruits with better quality characteristics, making it more suitable for fruit quality improvement. These results indicate that different genotype groups may serve different breeding objectives depending on whether the focus is on yield or fruit quality. Multiple regression analysis also demonstrated that yield and its components could be predicted using simple morphological and phenological traits. Plant height, pedicel thickness, and earliness index were identified as important predictors of fruit number, while fruit width, fruit length, and cortex thickness had the greatest influence on fruit weight. The ability to predict yield using easily measurable traits may help breeders identify superior genotypes at earlier growth stages and improve the efficiency of selection programs.
Conclusion:
This study demonstrated the presence of substantial genetic and phenotypic diversity among sweet pepper genotypes grown under greenhouse conditions. Yield was mainly influenced by fruit number, fruit weight, and vegetative growth characteristics, while several fruit quality traits also showed strong positive relationships with productivity. Cluster analysis successfully identified genotype groups with different breeding potentials, where Cluster 3 appeared more suitable for yield improvement and Cluster 1 showed greater potential for enhancing fruit quality traits. In addition, the regression models highlighted the importance of easily measurable traits such as plant height, earliness, fruit dimensions, and cortex thickness in predicting yield and its components. Overall, the findings suggest that combining correlation analysis, cluster analysis, and regression models can improve the efficiency of sweet pepper breeding programs and support the development of high-yielding and high-quality cultivars.
Acknowledgement:
The authors sincerely appreciate the greenhouse staff in Mashhad, Iran, for their valuable technical assistance and support throughout the experiment.
Keywords:
Sweet pepper, Genetic diversity, Modeling, Cluster analysis, Production per plant
کلیدواژهها English
