Hamid Reza Fanaei *1 
, Behzad Sorkhilalehloo1, Seid Mohammad Alavi-siney2, Majid Reza Kiani Freez3, Ali Eftekhari4, Abolghasem Moradgholi5, Manni Marefatzadeh-Khameneh1, Hossein Akbari Moghadam6
, Behzad Sorkhilalehloo1, Seid Mohammad Alavi-siney2, Majid Reza Kiani Freez3, Ali Eftekhari4, Abolghasem Moradgholi5, Manni Marefatzadeh-Khameneh1, Hossein Akbari Moghadam6
1- Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
2- Crop and Horticultural Science Research Department, Southern Kerman Agricultural and Natural Resources Research and Education Center, AREEO, Jiroft, Iran
3- Crop and Horticultural Science Research Department, Razavi Khorasan Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
4- Crop and Horticultural Science Research Department, Southern Khorasan Agricultural and Natural Resources Research and Education Center, AREEO, Birjand, Iran
5- Crop and Horticultural Science Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran
6- Knowledge-Based Research and Development Company of Agricultural and Natural Resources Idea Developers of Hirmand Region, Zabol, Iran
2- Crop and Horticultural Science Research Department, Southern Kerman Agricultural and Natural Resources Research and Education Center, AREEO, Jiroft, Iran
3- Crop and Horticultural Science Research Department, Razavi Khorasan Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
4- Crop and Horticultural Science Research Department, Southern Khorasan Agricultural and Natural Resources Research and Education Center, AREEO, Birjand, Iran
5- Crop and Horticultural Science Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran
6- Knowledge-Based Research and Development Company of Agricultural and Natural Resources Idea Developers of Hirmand Region, Zabol, Iran
Abstract: (231 Views)
Extended Abstract
Background: Given that some medicinal plants show high drought resistance, utilizing these species can be considered a strategy to increase productivity and manage water consumption in agricultural farming systems. Cumin is a suitable option for production in limited lands due to its nativeness, the presence of indigenous and diverse masses adapted to the diverse weather conditions of Iran, and having genes for resistance to drought, salinity, pests, and diseases. Despite the valuable features of green cumin, no officially improved varieties of this plant have been introduced so far; therefore, identifying and studying the genetic and functional diversity in the germplasm available in plant gene banks are significant for breeding purposes. The main breeding objectives in green cumin include increasing seed yield, improving essential oil quality with an emphasis on effective medicinal compounds, and enhancing resistance to biotic and abiotic stresses, such as diseases and drought. This experiment aimed to evaluate genotypes, environments, and relationships between genotypes and environments, and finally to identify stable genotypes with high performance and general and specific compatibility of cumin using the GGE-Biplot graphic method.
Methods: To evaluate the seed yield stability of 36 cumin genotypes, an experiment was conducted in a complete randomized block design (RCBD) with three replications in five research stations (Jiroft, Mashhad, Birjand, Zabul, and Karaj) in 2022-2023. The seeds of each genotype were planted in four rows with a row distance of 20 cm. To achieve a density of 100-120 plants per square meter, the field was thinned with a distance of 5-7 cm between two plants on the line in the 5-leaf stage. The plants were harvested at the ripening stage (80% yellowing of the plant). The plants were pounded after completely drying, and the seeds were separated from the straw, followed by calculating the seed yield per unit area. Data were analyzed using SAS and SPSS software. The GGE-Biplot graphical method was used to analyze the stability of the studied genotypes, interpret the genotype × environment interaction, and determine the super environment.
Results: The results of the combined variance analysis based on the data of five environments for grain yield showed that the effects of the genotype, environment, and the genotype ×environment interaction were significant. Analysis of stability was done for genotypes in different environments due to the significance of both the environment and the genotype × environment interaction. The results of GGE-Biplot analysis showed that the two main components explained 67% of the total variation of the genotype-environment interaction variance. Two macro environments and the compatible genotypes of each environment were determined based on the polygon diagram. Genotypes G30 and G6 showed the best reaction as specific compatibility in the super environment 1, including Mashhad, Zabul, Birjand, and Karaj. This super environment allocated almost four of the investigated environments located in the warm climate of the south and the temperate climate of the country, with the exception of Jiroft, which indicated the importance of determining this super environment. The second super environment included Jiroft, and the G35 genotype was identified as the superior genotype of this environment with specific compatibility. Based on the biplot, genotype G30 was the ideal genotype, and then the genotypes G24, G31, G5, G26, G6, G33, and G32 showed the smallest distance from the ideal genotype. In terms of both average performance and stability, therefore, it is better than the other genotypes, and they presented high general compatibility in the studied environments. Based on the results of the biplot of the ideal environment, the Mashhad environment showed the most differentiation and was found to be the most suitable environment to compare cumin genotypes.
Conclusions: The necessity of using stable cultivars with high yield potential has always been considered due to the current climatic conditions of the country and the persistence of drought and water shortage in most parts of the country, especially in hot and dry regions. In this research, stable and superior genotypes were clearly identified graphically, which shows the appropriate efficiency of the GGE biplot method for selecting high-yielding and stable cultivars. The Mashhad environment can be introduced as a favorable environment for selecting the best cumin genotypes. Based on the total results, G30, G6, G5, G26, and G35 genotypes were identified as suitable and ideal choices for cumin in the regions due to their higher seed yield, stability in performance, and general and specific compatibility. These genotypes can be evaluated in research and extension trials under farmers' conditions, and the superior genotype(s), compared to the local check, can enter the process of introducing new cumin cultivars.
Background: Given that some medicinal plants show high drought resistance, utilizing these species can be considered a strategy to increase productivity and manage water consumption in agricultural farming systems. Cumin is a suitable option for production in limited lands due to its nativeness, the presence of indigenous and diverse masses adapted to the diverse weather conditions of Iran, and having genes for resistance to drought, salinity, pests, and diseases. Despite the valuable features of green cumin, no officially improved varieties of this plant have been introduced so far; therefore, identifying and studying the genetic and functional diversity in the germplasm available in plant gene banks are significant for breeding purposes. The main breeding objectives in green cumin include increasing seed yield, improving essential oil quality with an emphasis on effective medicinal compounds, and enhancing resistance to biotic and abiotic stresses, such as diseases and drought. This experiment aimed to evaluate genotypes, environments, and relationships between genotypes and environments, and finally to identify stable genotypes with high performance and general and specific compatibility of cumin using the GGE-Biplot graphic method.
Methods: To evaluate the seed yield stability of 36 cumin genotypes, an experiment was conducted in a complete randomized block design (RCBD) with three replications in five research stations (Jiroft, Mashhad, Birjand, Zabul, and Karaj) in 2022-2023. The seeds of each genotype were planted in four rows with a row distance of 20 cm. To achieve a density of 100-120 plants per square meter, the field was thinned with a distance of 5-7 cm between two plants on the line in the 5-leaf stage. The plants were harvested at the ripening stage (80% yellowing of the plant). The plants were pounded after completely drying, and the seeds were separated from the straw, followed by calculating the seed yield per unit area. Data were analyzed using SAS and SPSS software. The GGE-Biplot graphical method was used to analyze the stability of the studied genotypes, interpret the genotype × environment interaction, and determine the super environment.
Results: The results of the combined variance analysis based on the data of five environments for grain yield showed that the effects of the genotype, environment, and the genotype ×environment interaction were significant. Analysis of stability was done for genotypes in different environments due to the significance of both the environment and the genotype × environment interaction. The results of GGE-Biplot analysis showed that the two main components explained 67% of the total variation of the genotype-environment interaction variance. Two macro environments and the compatible genotypes of each environment were determined based on the polygon diagram. Genotypes G30 and G6 showed the best reaction as specific compatibility in the super environment 1, including Mashhad, Zabul, Birjand, and Karaj. This super environment allocated almost four of the investigated environments located in the warm climate of the south and the temperate climate of the country, with the exception of Jiroft, which indicated the importance of determining this super environment. The second super environment included Jiroft, and the G35 genotype was identified as the superior genotype of this environment with specific compatibility. Based on the biplot, genotype G30 was the ideal genotype, and then the genotypes G24, G31, G5, G26, G6, G33, and G32 showed the smallest distance from the ideal genotype. In terms of both average performance and stability, therefore, it is better than the other genotypes, and they presented high general compatibility in the studied environments. Based on the results of the biplot of the ideal environment, the Mashhad environment showed the most differentiation and was found to be the most suitable environment to compare cumin genotypes.
Conclusions: The necessity of using stable cultivars with high yield potential has always been considered due to the current climatic conditions of the country and the persistence of drought and water shortage in most parts of the country, especially in hot and dry regions. In this research, stable and superior genotypes were clearly identified graphically, which shows the appropriate efficiency of the GGE biplot method for selecting high-yielding and stable cultivars. The Mashhad environment can be introduced as a favorable environment for selecting the best cumin genotypes. Based on the total results, G30, G6, G5, G26, and G35 genotypes were identified as suitable and ideal choices for cumin in the regions due to their higher seed yield, stability in performance, and general and specific compatibility. These genotypes can be evaluated in research and extension trials under farmers' conditions, and the superior genotype(s), compared to the local check, can enter the process of introducing new cumin cultivars.
Type of Study: Applicable |
Subject:
اصلاح نباتات، بیومتری
Received: 2025/01/18 | Accepted: 2025/09/22
Received: 2025/01/18 | Accepted: 2025/09/22
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