Sustainability on Different Canola (Brassica napus L.) Cultivars by GGE Biplot Graphical Technique in Multi-Environment
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Published:2023-06-02
Issue:11
Volume:15
Page:8945
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ISSN:2071-1050
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Container-title:Sustainability
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language:en
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Short-container-title:Sustainability
Author:
Shojaei Seyed Habib1, Mostafavi Khodadad2, Ghasemi Seyed Hamed1, Bihamta Mohammad Reza3, Illés Árpád4ORCID, Bojtor Csaba4ORCID, Nagy János4, Harsányi Endre4, Vad Attila5, Széles Adrienn4ORCID, Mousavi Seyed Mohammad Nasir4ORCID
Affiliation:
1. Department of Biotechnology and Plant Breeding, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran 2. Department of Agronomy and Plant Breeding, Karaj Branch, Islamic Azad University, Karaj 31499-68111, Iran 3. College of Agriculture & Natural Resources (UCAN), University of Tehran, Karaj 31499-68111, Iran 4. Institute of Land Use, Engineering and Precision Farming Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 138 Böszörményi St., 4032 Debrecen, Hungary 5. Institutes for Agricultural Research and Educational Farm (IAREF), Farm and Regional Research Institutes of Debrecen (RID), Experimental Station of Látókép, 4032 Debrecen, Hungary
Abstract
Knowledge about the extent of genotype in environment interaction is helpful for farmers and plant breeders. This is because it helps them choose the proper strategies for agricultural management and breeding new cultivars. The main contribution of this paper is to investigate genotype on environmental interaction using the GGE biplot method (Genotype and the Genotype-by-Environment) in ten canola cultivars. The experimental design was a randomized complete block design (RCBD) with three replications to assess the stability of grain yield of ten canola cultivars in five regions of Iran, including Birjand, Karaj, Kashmar, Sanandaj, and Shiraz, within two agricultural years of 2016 and 2017. The results of combined ANOVA illustrated that the effects of the environment, genotype × environment, and genotype were highly significant at 1%. Variance Analysis showed that three environmental impacts, genotype, and interaction of genotype in the environment effect, produced 68.44%, 18.63%, and 12.9% of the total variance. The GGE biplot graphs were constructed using PCA. The first principle component (PC1) explained 65.3%, and the second (PC2) explained 18.8% of the total variation. The research examined polygon diagrams to identify two top genotypes and four mega-environments. Also, the appropriate genotypes for each environment were diagnosed. Using the GGE biplot, it was possible to make visual comparisons and identify superior genotypes in canola. Accordingly,. The results obtained from graphical analysis indicated that Licord, Hyola 401 and Okapi genotypes showed the highest yield and were selected as the most stable genotypes. Also, Karaj region was chosen as a experimental region where the screening of genotypes was very suitable. Based on the ranking of the genotypes in the most suitable region (Karaj), Okapi genotype was selected as the desired genotype. In examining the heatmap drawn between the genotypes and the investigated environments, a lot of similarity between the genotypes of Sarigal, Hyola 401 and Okapi was observed in the investigated environments. The GGE biplot graphs enabled the detection of stable and superior environments and the grouping of cultivars and environments based on grain yield. The results of this research can be used both for extension and for future breeding programs. Our results provide helpful information about the canola genotypes and environments for future breeding programs.
Funder
Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
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