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Showing 8 results for Compatibility
Saeed Omrani, Amir Mohammad Naji, Mohsen Esmaeil Zadeh Moghadam,
Volume 10, Issue 25 (6-2018)
Volume 10, Issue 25 (6-2018)
Abstract
To evaluate the genotype × environment interaction and determine the stable genotypes of wheat, 30 genotypes of bread wheat along with two controls namely Chamran and Chamran 2 were studied in 6 locations (Ahwaz, Darab, Dezful, Iranshahr, Khorramabad, Zabul) and two years (from 2013 to 2015), in each using an alpha lattice design with 4 replications. The results obtained from AMMI analysis demonstrated that the main effects of genotype, environment, genotype × environment interaction and the first four principal components were highly significant. The first four principal components justified around 90.9% of the sum of squares of the interactions. By using the stability of the figures of the statistics lasting value AMMI (ASV), genotypes 2, 6, 14, 28 had the lowest (ASV) values. Genotypes 2, 6 and 14 with higher yields than the overall mean were identified as high yielding genotypes with stable performance. Drawing the biplot of the first principal component and the average yield for genotypes and environments suggested that genotypes 9, 28, 25, 12, 14, 10, 2 and 6 had low interactions, but genotypes 14, 10, 2 and 6 with higher than average yields and desired stability were selected. Biplot of the first two principal components showed that the interaction between genotypes 2, 6, 7 and 14 due to higher grain yield than the average of the total, were identified genotypes with good compatibility. Genotype grouping them into three groups based on the model SHMM placed in the first group of 22 genotypes, genotypes 9 in the second group and the third group was the only genotype 20.
Tohid Najafi Mirak, Manoochehr Dastfal, Bahram Andarzian, Mr Hossien Farzadi, Mohammad Bahari, Hassan Zali,
Volume 10, Issue 28 (12-2018)
Volume 10, Issue 28 (12-2018)
Abstract
To obtain high yield and stable genotypes, 18 durum wheat cultivars and promising lines along with two commercial durum (Behrang) and bread wheat (Chamran) as check cultivars, were evaluated in four warm and dry regions in Iran including Darab, Ahvaz, Khoramabad and Dezful during two cropping seasons (2013-2015). The experiments were conducted as a RCBD with three replications. Seed yield and some agronomic characteristics were recorded in each location. The results that first two bilinear AMMI model terms were significant and of which the first two terms explained 85.17% of the genotype × environment interaction. Also the results of AMMI model (AMMI1 and AMMI2) indicated that lines no. G5 (DW-93-5), G10 (DW-93-10) and G12 (DW-93-12) were the most stable lines with high mean yield performance. The polygon-view of GGE biplot recognized five superior lines (lines G5 (DW-93-5), G9 (DW-93-9), G13 (DW-93-13), G16 (DW-93-16) and G17 (DW-93-17)) and two mega-environments so that the best genotypes within each environment were determined. Simultaneous evaluation of yield and stability through average environment coordinate (AEC) biplot showed that line no. G5 (DW-93-5) with the highest seed yield and stability was the most stable line. Biplot analysis of correlation among environments revealed that environmental vectors of Khoramabad with three locations including Darab, dezful and Ahvaz were near to 90◦ so; these three locations were different from Khoramabad. Totally, Khoramabad, Darab and dezful locations had high discriminating ability so that were be able to show differences between lines and cultivars at ideal environment, so they had the highest discriminating ability and representativeness. Finally, lines no. G5 (DW-93-5) and G10 (DW-93-10) with high yield, broad adaptability, relative resistance to foliar diseases and seed quality were selected as best line for further investigation and to be candidate as commercial durum wheat cultivars.
Azim Khazaei, Farid Golzardi, Mohammad Shahverdi, Leyla Nazari, Ahmad Ghasemi, Seyed Ali Tabatabaei, Abdollah Shariati, Hasan Mokhtarpour,
Volume 14, Issue 42 (8-2022)
Volume 14, Issue 42 (8-2022)
Abstract
Extended Abstract
Introduction and Objective: Due to the spread of droughts and fodder shortages in the country, it is necessary to introduce new cultivars of forage sorghum. Investigating the genotype-environment interaction to select the superior genotype is one of the most important steps of breeding programs. In these programs, evaluating the compatibility of different genotypes to various environmental conditions is important.
Materials and Methods: Ten promising lines of forage sorghum were studied in a randomized complete block design with three replications at seven regions of Iran (Boroujerd, Zabol, Sanandaj, Shiraz, Karaj, Gorgan, and Yazd) for two years (2018-2019). The AMMI method was used to evaluate the yield stability and compatibility of genotypes.
Results: The results of combined analysis of variance showed that the effects of year, place, year × place, genotype, year × genotype, place × genotype, and year × place × genotype on the fresh and dry forage yield were significant (p≤0.01). The significant interaction of environment × genotype indicates different reactions of genotypes in different environments. The results of AMMI analysis showed that the six main components of the interaction of environment × genotype were significant for fresh and dry forage yields (p≤0.01). For fresh forage yield, the first main component of interaction (IPCA1) had the largest contribution (26.6%) in the expression of genotype-environment interaction and the second to seventh main components were in the next ranks of importance with 18.4, 17, 15.7, 10.5, 7.5 and 4.3%, respectively. For dry forage yield, the first main component of interaction (IPCA1) had the largest contribution (21.8%) in the expression of genotype-environment interaction and the second to seventh main components were in the next ranks of importance with 19.8, 14.7, 14, 11.1, 10.1 and 8.5%, respectively. In total, the cumulative contribution of the seven main components was 98% for fresh forage yield and 97% for dry forage yield. In terms of fresh forage yield, the KFS10, KFS12, and KFS17 lines had the lowest IPCA1 values, which are introduced as stable lines with high general compatibility. In terms of AMMI stability value (ASV) for fresh forage yield, the KFS10 line was determined as the most stable line. In terms of dry forage yield, the KFS2, KFS3, KFS9, and KFS17 lines had the lowest IPCA1 values, which are introduced as stable lines with high general compatibility. The KFS2 line had the lowest amount of ASV in terms of dry forage yield.
Conclusion: Overall, the KFS18 line with high fresh and dry forage yields (121.1 and 32.04 t.ha-1, respectively), low IPCA1, and also optimal forage production in most environments (according to the Biplot model of AMMI) is recognized as a superior genotype.
Introduction and Objective: Due to the spread of droughts and fodder shortages in the country, it is necessary to introduce new cultivars of forage sorghum. Investigating the genotype-environment interaction to select the superior genotype is one of the most important steps of breeding programs. In these programs, evaluating the compatibility of different genotypes to various environmental conditions is important.
Materials and Methods: Ten promising lines of forage sorghum were studied in a randomized complete block design with three replications at seven regions of Iran (Boroujerd, Zabol, Sanandaj, Shiraz, Karaj, Gorgan, and Yazd) for two years (2018-2019). The AMMI method was used to evaluate the yield stability and compatibility of genotypes.
Results: The results of combined analysis of variance showed that the effects of year, place, year × place, genotype, year × genotype, place × genotype, and year × place × genotype on the fresh and dry forage yield were significant (p≤0.01). The significant interaction of environment × genotype indicates different reactions of genotypes in different environments. The results of AMMI analysis showed that the six main components of the interaction of environment × genotype were significant for fresh and dry forage yields (p≤0.01). For fresh forage yield, the first main component of interaction (IPCA1) had the largest contribution (26.6%) in the expression of genotype-environment interaction and the second to seventh main components were in the next ranks of importance with 18.4, 17, 15.7, 10.5, 7.5 and 4.3%, respectively. For dry forage yield, the first main component of interaction (IPCA1) had the largest contribution (21.8%) in the expression of genotype-environment interaction and the second to seventh main components were in the next ranks of importance with 19.8, 14.7, 14, 11.1, 10.1 and 8.5%, respectively. In total, the cumulative contribution of the seven main components was 98% for fresh forage yield and 97% for dry forage yield. In terms of fresh forage yield, the KFS10, KFS12, and KFS17 lines had the lowest IPCA1 values, which are introduced as stable lines with high general compatibility. In terms of AMMI stability value (ASV) for fresh forage yield, the KFS10 line was determined as the most stable line. In terms of dry forage yield, the KFS2, KFS3, KFS9, and KFS17 lines had the lowest IPCA1 values, which are introduced as stable lines with high general compatibility. The KFS2 line had the lowest amount of ASV in terms of dry forage yield.
Conclusion: Overall, the KFS18 line with high fresh and dry forage yields (121.1 and 32.04 t.ha-1, respectively), low IPCA1, and also optimal forage production in most environments (according to the Biplot model of AMMI) is recognized as a superior genotype.
Solmaz Amiri, Ali Arminian, Hamid Hosseinian Khoshrou,
Volume 15, Issue 45 (5-2023)
Volume 15, Issue 45 (5-2023)
Abstract
Extended Abstract
Introduction and Objective: Due to the increase in population and unfavorable environmental conditions, especially various stresses and the problem of water scarcity, evaluation of plant genotypes in various environments is inevitable. The interaction effect of the genotype and environment can be used as a mean by plant breeders to select the best genotypes/cultivars in plant growth environments (in one or more years).
Material and Methods: In order to evaluate the stability of grain yield, this experiment was performed on 108 chickpea genotypes in the cropping years 2017-2018, 2018-2019 and 2019-2020 in the form of a randomized complete block design. By carefully planting, holding and harvesting operations, during the growing season and postharvest, seed yield, 100-seed weight, plant height, number of days to 50% flowering, number of days to maturity, number of pods per plant, number of main branches was recorded.
Results: The results of combined analysis of variance showed that there was a significant difference between the genotypes in terms of plant height, grain yield, number of pods per plant, number of main branches. Comparison of mean grain yield showed that genotype number 10 had the highest yield (54.038 gram).significancy of GE interaction effect showed the response of genotypes within various environments so then it is possible to perform the stability analysis of genotypes and for this reason, the GGE biplot was used in parallel with some findings of AMMI model and mixed AMMI model (AMMI in conjunction with BLUP) which GGE biplot resulted that first 2 components accounted for 76.3 percent of total variation of grain yield and in this context varieties 1, 26, 60 and 101 acconted as ideal ones and so genotypes 32, 69, 76, 89, 90, 94, 98 and 108 mentioned as suitable genotypes compared to other genotypes and that 4th enfironment (year 3-Kermanshah) mentioned as ideal environment.
Conclusion: Considering the superiority of some chickpea cultivars in this study in terms of both grain yield and yield stability, in 2 temperate and cold regions in terms of stability and high grain yield, these cultivars can be candidates to introduce the cultivar or be used as parents of crosses in future breeding programs of this plant.
Introduction and Objective: Due to the increase in population and unfavorable environmental conditions, especially various stresses and the problem of water scarcity, evaluation of plant genotypes in various environments is inevitable. The interaction effect of the genotype and environment can be used as a mean by plant breeders to select the best genotypes/cultivars in plant growth environments (in one or more years).
Material and Methods: In order to evaluate the stability of grain yield, this experiment was performed on 108 chickpea genotypes in the cropping years 2017-2018, 2018-2019 and 2019-2020 in the form of a randomized complete block design. By carefully planting, holding and harvesting operations, during the growing season and postharvest, seed yield, 100-seed weight, plant height, number of days to 50% flowering, number of days to maturity, number of pods per plant, number of main branches was recorded.
Results: The results of combined analysis of variance showed that there was a significant difference between the genotypes in terms of plant height, grain yield, number of pods per plant, number of main branches. Comparison of mean grain yield showed that genotype number 10 had the highest yield (54.038 gram).significancy of GE interaction effect showed the response of genotypes within various environments so then it is possible to perform the stability analysis of genotypes and for this reason, the GGE biplot was used in parallel with some findings of AMMI model and mixed AMMI model (AMMI in conjunction with BLUP) which GGE biplot resulted that first 2 components accounted for 76.3 percent of total variation of grain yield and in this context varieties 1, 26, 60 and 101 acconted as ideal ones and so genotypes 32, 69, 76, 89, 90, 94, 98 and 108 mentioned as suitable genotypes compared to other genotypes and that 4th enfironment (year 3-Kermanshah) mentioned as ideal environment.
Conclusion: Considering the superiority of some chickpea cultivars in this study in terms of both grain yield and yield stability, in 2 temperate and cold regions in terms of stability and high grain yield, these cultivars can be candidates to introduce the cultivar or be used as parents of crosses in future breeding programs of this plant.
Adel Ghadiri, Seiedeh Soodabeh Shobeiri, Ali Akbar Asadi1,
Volume 15, Issue 45 (5-2023)
Volume 15, Issue 45 (5-2023)
Abstract
Extended Abstract
Introduction and Objective: Existence of genotype × environment interaction for quantitative traits such as grain yield can limit the selection of superior genotypes for the development of improved cultivars. In order to calculate the interaction of genotype × environment, breeders evaluate genotypes in several environments to identify genotypes with high yield and stability. This experiment was performed to investigate the interaction of genotype in the environment on 11 bean genotypes using parametric and nonparametric methods and GGE bioplot model to evaluate genotypes and environments, determine the relationship between genotypes and environments and identify the ideal genotype.
Material and Methods: In this experiment, 9 lines of pinto beans along with Ghaffar cultivars and Cos16 lines (11 lines in total) were performed in a randomized complete block design with three replications in order to achieve high yield and marketable bean cultivars. Parametric and non-parametric methods were used to select stable genotypes with high yield and GGE bioplot analysis was used to select superior genotypes that are compatible with regional environments.
Results: There was a significant interaction between genotype and environment, indicating significant differences in the response of genotypes to different environments of the experiment. In parametric methods G4, G8, G9 and to some extent G2 genotypes and in nonparametric methods G2, G8, G3, G4 and to some extent G9 genotypes were introduced as stable cultivars. Biplot analysis showed that G1 genotype in Zanjan in first and Second years and G2 genotype in Khomein n first and second environments showed the highest yield. G11 and G7 genotypes with the longest distance to the ATC line had low yield and low yield stability. None of the genotypes were found to be desirable genotypes with average yield and high yield stability, but G2 genotype and later, G4 genotype was a short distance from the ideal genotype. None of the studied environments is close to the ideal environment and therefore none of them can be considered as representative of environments for genotype segregation.
Results: Zanjan in first and Second years and G2 genotype in Khomein n first and second environments have the highest yield. No ideal genotypes were observed, but two genotypes, G1 and G4, can be introduced as superior genotypes.
Introduction and Objective: Existence of genotype × environment interaction for quantitative traits such as grain yield can limit the selection of superior genotypes for the development of improved cultivars. In order to calculate the interaction of genotype × environment, breeders evaluate genotypes in several environments to identify genotypes with high yield and stability. This experiment was performed to investigate the interaction of genotype in the environment on 11 bean genotypes using parametric and nonparametric methods and GGE bioplot model to evaluate genotypes and environments, determine the relationship between genotypes and environments and identify the ideal genotype.
Material and Methods: In this experiment, 9 lines of pinto beans along with Ghaffar cultivars and Cos16 lines (11 lines in total) were performed in a randomized complete block design with three replications in order to achieve high yield and marketable bean cultivars. Parametric and non-parametric methods were used to select stable genotypes with high yield and GGE bioplot analysis was used to select superior genotypes that are compatible with regional environments.
Results: There was a significant interaction between genotype and environment, indicating significant differences in the response of genotypes to different environments of the experiment. In parametric methods G4, G8, G9 and to some extent G2 genotypes and in nonparametric methods G2, G8, G3, G4 and to some extent G9 genotypes were introduced as stable cultivars. Biplot analysis showed that G1 genotype in Zanjan in first and Second years and G2 genotype in Khomein n first and second environments showed the highest yield. G11 and G7 genotypes with the longest distance to the ATC line had low yield and low yield stability. None of the genotypes were found to be desirable genotypes with average yield and high yield stability, but G2 genotype and later, G4 genotype was a short distance from the ideal genotype. None of the studied environments is close to the ideal environment and therefore none of them can be considered as representative of environments for genotype segregation.
Results: Zanjan in first and Second years and G2 genotype in Khomein n first and second environments have the highest yield. No ideal genotypes were observed, but two genotypes, G1 and G4, can be introduced as superior genotypes.
Behrooz Vaezi, Raham Mohtashami, Asgar Jozeyan, Amir Mirzaei,
Volume 15, Issue 45 (5-2023)
Volume 15, Issue 45 (5-2023)
Abstract
Extended Abstract
Introduction and Objective: Considering the requirement of the country to meet the nutritional needs of the society and the nutritional needs of the existing livestock in order to meet the nutritional needs, it is necessary to introduce cultivars that can be superior to the existing cultivars in terms of adaptability and stability in arid and semi-arid regions. The use of forage plants such as Grass pea can play an important role in crop rotation, soil protection, reducing weeds and diseases due to high adaptability to dry and semi-arid climates and high yield potential, nitrogen fixation, tolerance to drought and salinity. This research was carried out with the aim of evaluating the interaction effect of genotype × environment and the stability of forage and grain yield of Grass pea genotypes in different regions of the country.
Material and Methods: This study was carried out with 16 advanced Grass pea lines in Gachsaran, Mehran, Shirvan Cherdavel and Kohdasht stations for three crop years in the form of a randomized complete block design with three replications. Each genotype was cultivated in 6 lines with a length of 7.03 meters and a distance of 0.25 cm from each other. After analyzing the composite variance for different years in different regions, mean comparison was done using the minimum significant difference method at 5% and 1% probability levels. To analys the stability and compatibility of lines, were used the stability methods of Francis and Kanenberg, Eberhut and Russell, Rick's equivalence, Shokla's stability variance, Plasted and Patterson's stability parameter, Finley and Wilkinson, Perkins and Jinks, Gang's total ranking, and the single-variable, non-parametric method, Nassar- Han and Tanazaro.
Results: The results of composite variance analysis of forage yield and grain yield showed that the simple effect of year, genotype, location and the genotype × location interaction effect for both traits were statistically non-significant, and the interaction effect of year × genotype was significant for forage yield and non-significant for grain yield. It was meaningful. The three-way effect of year × location × genotype was not significant at statistical probability levels for forage; but it was significant for grain yield. The forage yield for kholer lines from 12839 kg ha-1 for line number 5 to 16680 kg ha-1 for genotype number 10 with 11.5% drop and 15% superiority compared to Naghadeh, respectively. Also, in terms of grain yield, Grass pea lines fluctuated from 1239 kg ha-1 for line No. 7 to 1723 kg ha-1 for Line No. 10 with a 2% drop and 36.3% superiority compared to Naghadeh. The results of the stability analysis of forage yield showed that in terms of the Coefficient of variation parameter, genotypes 16, 5 and 10, in terms of Phenyl Wilkinson genotypes 15, 2, 13, in terms of Shokla genotypes 14, 9, 6, and in terms of Rick's equivalence, genotypes 14, 9, 6, and 13 were the most stable genotypes. The analysis of grain yield stability by univariate method showed that in terms of Coefficient of variation, genotypes 11, 12, 3, in terms of Phenyl Wilkinson, genotypes 13, 12, 4, 5, 6, in terms of Shukla's, genotypes 7, 12, 5, 6, and in terms of Rick's equivalence, genotypes 7, 12, 5, 6, and 8 were determined to be the most stable Grass pea genotypes.
Conclusion: Based on the obtained results, in terms of forage yield lines No. 1, 10, 3, 9 and 15 and in terms of grain yield genotypes No. 12, 15, 8, 10 and 7 had high stability and yield compared to other genotypes. In general, considering all parameters of stability and compatibility, lines number 1, 10, 12, 15, 8, 7 and 9 were selected as the most stable lines, and genotypes No. 10 and 15 were suitable for both forage and seed.
Introduction and Objective: Considering the requirement of the country to meet the nutritional needs of the society and the nutritional needs of the existing livestock in order to meet the nutritional needs, it is necessary to introduce cultivars that can be superior to the existing cultivars in terms of adaptability and stability in arid and semi-arid regions. The use of forage plants such as Grass pea can play an important role in crop rotation, soil protection, reducing weeds and diseases due to high adaptability to dry and semi-arid climates and high yield potential, nitrogen fixation, tolerance to drought and salinity. This research was carried out with the aim of evaluating the interaction effect of genotype × environment and the stability of forage and grain yield of Grass pea genotypes in different regions of the country.
Material and Methods: This study was carried out with 16 advanced Grass pea lines in Gachsaran, Mehran, Shirvan Cherdavel and Kohdasht stations for three crop years in the form of a randomized complete block design with three replications. Each genotype was cultivated in 6 lines with a length of 7.03 meters and a distance of 0.25 cm from each other. After analyzing the composite variance for different years in different regions, mean comparison was done using the minimum significant difference method at 5% and 1% probability levels. To analys the stability and compatibility of lines, were used the stability methods of Francis and Kanenberg, Eberhut and Russell, Rick's equivalence, Shokla's stability variance, Plasted and Patterson's stability parameter, Finley and Wilkinson, Perkins and Jinks, Gang's total ranking, and the single-variable, non-parametric method, Nassar- Han and Tanazaro.
Results: The results of composite variance analysis of forage yield and grain yield showed that the simple effect of year, genotype, location and the genotype × location interaction effect for both traits were statistically non-significant, and the interaction effect of year × genotype was significant for forage yield and non-significant for grain yield. It was meaningful. The three-way effect of year × location × genotype was not significant at statistical probability levels for forage; but it was significant for grain yield. The forage yield for kholer lines from 12839 kg ha-1 for line number 5 to 16680 kg ha-1 for genotype number 10 with 11.5% drop and 15% superiority compared to Naghadeh, respectively. Also, in terms of grain yield, Grass pea lines fluctuated from 1239 kg ha-1 for line No. 7 to 1723 kg ha-1 for Line No. 10 with a 2% drop and 36.3% superiority compared to Naghadeh. The results of the stability analysis of forage yield showed that in terms of the Coefficient of variation parameter, genotypes 16, 5 and 10, in terms of Phenyl Wilkinson genotypes 15, 2, 13, in terms of Shokla genotypes 14, 9, 6, and in terms of Rick's equivalence, genotypes 14, 9, 6, and 13 were the most stable genotypes. The analysis of grain yield stability by univariate method showed that in terms of Coefficient of variation, genotypes 11, 12, 3, in terms of Phenyl Wilkinson, genotypes 13, 12, 4, 5, 6, in terms of Shukla's, genotypes 7, 12, 5, 6, and in terms of Rick's equivalence, genotypes 7, 12, 5, 6, and 8 were determined to be the most stable Grass pea genotypes.
Conclusion: Based on the obtained results, in terms of forage yield lines No. 1, 10, 3, 9 and 15 and in terms of grain yield genotypes No. 12, 15, 8, 10 and 7 had high stability and yield compared to other genotypes. In general, considering all parameters of stability and compatibility, lines number 1, 10, 12, 15, 8, 7 and 9 were selected as the most stable lines, and genotypes No. 10 and 15 were suitable for both forage and seed.
Raham Mohtashami,
Volume 15, Issue 47 (10-2023)
Volume 15, Issue 47 (10-2023)
Abstract
Extended Abstract
Introduction and Objective: Considering the increase in per capita consumption of rice in the country and the need to increase rice production per unit area, it is very important to introduce new high-quality varieties with high yield and stable grain yield. The grain yield depends on the genotype and its response to environmental conditions. To increase the quantity and quality of rice, this research was conducted to evaluate the interaction between genotype × environment and to determine the stability of the grain yield of rice genotypes.
Materials and Methods: In this experiment, 8 quality rice lines were carried out including Kadus, Ali Kazemi, and Champa local cultivars in the form of Randomized Complet Block Design with three replications in Cheram and Basht regions during 2017 and 2018. In each year, the performance of tested genotypes was tested separately using simple variance analysis and using Duncan's method, and at the end of the second year, combined analysis was performed to determine the compatibility. To analyze the stability and compatibility of lines, Shukla's stability variance, Francis and Kanenberg's coefficient of environmental changes, Wrickes ecovalence, deviation from Eberhart and Russell's regression line, Finley and Wilkinson's regression coefficient and Pintos' coefficient of identification were used.
Results: The results showed a great diversity between the investigated genotypes in terms of grain yield and other agricultural traits. Composite variance analysis showed that there is a significant difference between years at the 5% probability level. The stability analysis of the genotypes by calculating the stability parameter shows that the highest stability was related to the local Champa variety and lines 7, 8, 5, and 6. Based on the calculated Eberhart & Russell Method, genotypes 7, 6, and 8 and the local Champa variety were favorable in both test environments. In terms of Wrickes ecovalence, and stability parameter, the local Champa cultivar and genotypes 6 and 5 were the best. Based on the results of the analysis and comparison of the average of the treatments, the superiority of the grain yield was related to lines 7 and 5 with an average yield of 9.60 and 8.85 tons per hectare. The mentioned lines were recognized as superior genotypes due to their average yield, conversion efficiency, high percentage of whole rice, and average amylose content.
Conclusion: Based on the results obtained from the stability methods of lines number 7 and 5, respectively, with an average yield of 9.60 and 8.85 tons per hectare and having stability variance, environmental change coefficient, and intra-location variance less than one, as well as the coefficient of the regression line equal to one they are recommended as stable genotypes for both regions and other similar regions.
Introduction and Objective: Considering the increase in per capita consumption of rice in the country and the need to increase rice production per unit area, it is very important to introduce new high-quality varieties with high yield and stable grain yield. The grain yield depends on the genotype and its response to environmental conditions. To increase the quantity and quality of rice, this research was conducted to evaluate the interaction between genotype × environment and to determine the stability of the grain yield of rice genotypes.
Materials and Methods: In this experiment, 8 quality rice lines were carried out including Kadus, Ali Kazemi, and Champa local cultivars in the form of Randomized Complet Block Design with three replications in Cheram and Basht regions during 2017 and 2018. In each year, the performance of tested genotypes was tested separately using simple variance analysis and using Duncan's method, and at the end of the second year, combined analysis was performed to determine the compatibility. To analyze the stability and compatibility of lines, Shukla's stability variance, Francis and Kanenberg's coefficient of environmental changes, Wrickes ecovalence, deviation from Eberhart and Russell's regression line, Finley and Wilkinson's regression coefficient and Pintos' coefficient of identification were used.
Results: The results showed a great diversity between the investigated genotypes in terms of grain yield and other agricultural traits. Composite variance analysis showed that there is a significant difference between years at the 5% probability level. The stability analysis of the genotypes by calculating the stability parameter shows that the highest stability was related to the local Champa variety and lines 7, 8, 5, and 6. Based on the calculated Eberhart & Russell Method, genotypes 7, 6, and 8 and the local Champa variety were favorable in both test environments. In terms of Wrickes ecovalence, and stability parameter, the local Champa cultivar and genotypes 6 and 5 were the best. Based on the results of the analysis and comparison of the average of the treatments, the superiority of the grain yield was related to lines 7 and 5 with an average yield of 9.60 and 8.85 tons per hectare. The mentioned lines were recognized as superior genotypes due to their average yield, conversion efficiency, high percentage of whole rice, and average amylose content.
Conclusion: Based on the results obtained from the stability methods of lines number 7 and 5, respectively, with an average yield of 9.60 and 8.85 tons per hectare and having stability variance, environmental change coefficient, and intra-location variance less than one, as well as the coefficient of the regression line equal to one they are recommended as stable genotypes for both regions and other similar regions.
Nasrin Akbari, Reza Darvishzadeh,
Volume 16, Issue 4 (11-2024)
Volume 16, Issue 4 (11-2024)
Abstract
Extended Abstract
Background: The process of selecting and introducing compatible genotypes with high yield potential requires evaluation in different years and places. Due to the severe and rapid changes in climate, which confront crops with all kinds of stress, especially drought stress, it is expected that the cultivated area of crops, such as sunflower, which is highly desirable for planting due to its special characteristics, will decrease. Undoubtedly, improving drought tolerance and developing high-yielding cultivars is one of the most important goals in breeding programs. On the other hand, in the selection and introduction of varieties, performance, the most important feature of the breeding program, due to its polygenic nature, is strongly affected by biotic and abiotic stresses. Therefore, taking into account the quantitative control, and the effects of the environment and the interaction of the genotype × environment, selection for this trait is complex, costly, and time-consuming. Therefore, understanding the genotype × environment interaction is essential for the development of high-yielding and stable genotypes. Crop stability in the agricultural concept means no deviation from the expected product response. Based on this, several methods have been introduced for selection with optimal efficiency and high accuracy. Stability indices are divided into two main groups: parametric and non-parametric stability indices. Each of these two groups has advantages and disadvantages. Thus, if parametric methods are more capable of evaluating the interaction effects of the genotype × environment and analysis of stability, non-parametric methods have a higher ability to analyze non-crossed interactions. However, it seems that the purpose of the plant breeder and the size of the studied sample are decisive in the superiority of these statistics. If the purpose of the plant breeder is only to rank genotypes among environments, non-parametric statistics are more suitable. If the sample size is small, the use of parametric statistics will be more appropriate than non-parametric ones, but if the sample size is large, the efficiency of both types of stability indices will be equal. It seems that using both stability indices helps in selecting genotypes with stable performance. In this research, it was tried to obtain comprehensive information about the studied genotypes using both groups of stability indices.
Methods: One hundred oilseed sunflower genotypes were evaluated in a 10 × 10 simple lattice design under two normal and drought stress (irrigation limitation) conditions during 2012 and 2013 (four environments) in terms of seed yield in Qezeljeh village in West Azerbaijan, Iran. For this purpose, cultivation was done in lines with 5-meter long. The distance between the lines was 60 cm, and the distance between the plants on the lines was 50 cm. The criterion for applying the treatment was the rate of evaporation from the class A evaporation pan. In both years, the field was irrigated up to the 8-leaf stage in both normal and limited irrigation experiments after 90 mm of evaporation from the Class A evaporation pan. From the 8-leaf stage onwards in the normal irrigation experiment, irrigation continued in the same way until the end of the growing season. In limited irrigation, irrigation was done after 180 mm of evaporation from the Class A evaporation pan. Parametric and non-parametric stability indices were used to select genotypes with high and stable performance. In this regard, the analysis of variance (ANOVA) was done with a mixed linear model, considering the environment and genotype as fixed effects and the year and replication as random effects. SAS software version 4.9 was used for ANOVA, and the STABILITYSOFT program under the R environment was used for stability analysis. Stability analysis was done with seven different parametric methods (based on ANOVA and regression analysis) and 11 non-parametric methods. Moreover, the STABILITYSOFT program was used to show the relationship between different stability indices in the form of a heat map plot.
Results: Based on the results of combined ANOVA, the effects of the genotype and genotype × environment were significant. Considering the variability observed among genotypes and the different reactions of genotypes from one environment to another, stability analysis was done with different parametric and non-parametric methods to select stable genotypes. Based on the correlation results, the average yield (MY) with S(3) statistics at the 5% probability level and with S(6), NP(2), NP(3), NP(4), GE (θ(i)), and Kang statistics at the 1% probability level showed a negative correlation and with NP(1), Wi2, σi2, Reg, MV (θi) and Sdi2 statistics at the 1% level showed positive and significant correlations. In particular, the three Shukla's statistics (σi2), Wick's equivalence (Wi2), and MV (θi) parameters showed a positive correlation with yield. Based on all parametric and non-parametric stability parameters, the AS613 genotype was introduced as a genotype with high yield and stability.
Conclusion: The stability indices, which evaluate the stability of genetic materials, can be beneficial to a large extent in the optimal and efficient selection of parental genotypes for developing high-yielding and stress-tolerant cultivars.
Background: The process of selecting and introducing compatible genotypes with high yield potential requires evaluation in different years and places. Due to the severe and rapid changes in climate, which confront crops with all kinds of stress, especially drought stress, it is expected that the cultivated area of crops, such as sunflower, which is highly desirable for planting due to its special characteristics, will decrease. Undoubtedly, improving drought tolerance and developing high-yielding cultivars is one of the most important goals in breeding programs. On the other hand, in the selection and introduction of varieties, performance, the most important feature of the breeding program, due to its polygenic nature, is strongly affected by biotic and abiotic stresses. Therefore, taking into account the quantitative control, and the effects of the environment and the interaction of the genotype × environment, selection for this trait is complex, costly, and time-consuming. Therefore, understanding the genotype × environment interaction is essential for the development of high-yielding and stable genotypes. Crop stability in the agricultural concept means no deviation from the expected product response. Based on this, several methods have been introduced for selection with optimal efficiency and high accuracy. Stability indices are divided into two main groups: parametric and non-parametric stability indices. Each of these two groups has advantages and disadvantages. Thus, if parametric methods are more capable of evaluating the interaction effects of the genotype × environment and analysis of stability, non-parametric methods have a higher ability to analyze non-crossed interactions. However, it seems that the purpose of the plant breeder and the size of the studied sample are decisive in the superiority of these statistics. If the purpose of the plant breeder is only to rank genotypes among environments, non-parametric statistics are more suitable. If the sample size is small, the use of parametric statistics will be more appropriate than non-parametric ones, but if the sample size is large, the efficiency of both types of stability indices will be equal. It seems that using both stability indices helps in selecting genotypes with stable performance. In this research, it was tried to obtain comprehensive information about the studied genotypes using both groups of stability indices.
Methods: One hundred oilseed sunflower genotypes were evaluated in a 10 × 10 simple lattice design under two normal and drought stress (irrigation limitation) conditions during 2012 and 2013 (four environments) in terms of seed yield in Qezeljeh village in West Azerbaijan, Iran. For this purpose, cultivation was done in lines with 5-meter long. The distance between the lines was 60 cm, and the distance between the plants on the lines was 50 cm. The criterion for applying the treatment was the rate of evaporation from the class A evaporation pan. In both years, the field was irrigated up to the 8-leaf stage in both normal and limited irrigation experiments after 90 mm of evaporation from the Class A evaporation pan. From the 8-leaf stage onwards in the normal irrigation experiment, irrigation continued in the same way until the end of the growing season. In limited irrigation, irrigation was done after 180 mm of evaporation from the Class A evaporation pan. Parametric and non-parametric stability indices were used to select genotypes with high and stable performance. In this regard, the analysis of variance (ANOVA) was done with a mixed linear model, considering the environment and genotype as fixed effects and the year and replication as random effects. SAS software version 4.9 was used for ANOVA, and the STABILITYSOFT program under the R environment was used for stability analysis. Stability analysis was done with seven different parametric methods (based on ANOVA and regression analysis) and 11 non-parametric methods. Moreover, the STABILITYSOFT program was used to show the relationship between different stability indices in the form of a heat map plot.
Results: Based on the results of combined ANOVA, the effects of the genotype and genotype × environment were significant. Considering the variability observed among genotypes and the different reactions of genotypes from one environment to another, stability analysis was done with different parametric and non-parametric methods to select stable genotypes. Based on the correlation results, the average yield (MY) with S(3) statistics at the 5% probability level and with S(6), NP(2), NP(3), NP(4), GE (θ(i)), and Kang statistics at the 1% probability level showed a negative correlation and with NP(1), Wi2, σi2, Reg, MV (θi) and Sdi2 statistics at the 1% level showed positive and significant correlations. In particular, the three Shukla's statistics (σi2), Wick's equivalence (Wi2), and MV (θi) parameters showed a positive correlation with yield. Based on all parametric and non-parametric stability parameters, the AS613 genotype was introduced as a genotype with high yield and stability.
Conclusion: The stability indices, which evaluate the stability of genetic materials, can be beneficial to a large extent in the optimal and efficient selection of parental genotypes for developing high-yielding and stress-tolerant cultivars.
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