Evaluation of Greenmass Yield of Some Soybean Varieties by Ammi Analysis Method
Abstract views: 110 / PDF downloads: 72
Keywords:Soybean, bi-plot, greenmass, ammi
This study was carried out with 4 soybean varieties in four different locations (Adana, Antalya, Manisa, Samsun) according to the Randomized Complete Block Design with six replications. In the study, the variation of greenmass yield of genotypes according to locations was evaluated with AMMI (Additive Main Effects and Multiplicative Interactions) analysis model. According to the analysis of variance genotype, environment, genotype×interaction and PC1 were found to be statistically significant. The greenmass yield for locations varied between 3064-5482 kg da-1, the highest yield was obtained from Adana location, and the lowest yield was obtained from Samsun location. The greenmass yield of the varieties varied between 3918-4520 kg da-1, the highest yield was obtained from 1530 (Yemsoy) variety and the lowest yield was obtained from Türksoy variety. In the AMMI analysis, PC1 accounted for 77.11% of the variation. According to the results obtained with the AMMI analysis, it was determined that the variety 1530 (Yemsoy) had the highest yield in the average of all four locations, while the Nazlıcan variety was above the average (vertical) curve and had high values. The variety 1530 (Yemsoy) was the most stable and the varieties 517 (Yeşilsoy) and Türksoy were far from the stability (horizontal) curve. Three locations, except Manisa location, are located in the same Mega-environment.
Altinok, S., Erdoğdu, İ., Rajcan, I., 2004. Morphology, forage and seed yield of soybean cultivars of different maturity grown as a forage crops in Turkey. Canadian Journal of Plant Science, 84: 181-186.
Asfaw, A., Alemayehu, F., Gurum, F., Atnaf, M., 2009. AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia. Scientific Research and Essay, 4(11): 1322-1330.
Ayaşan, T., 2011. Soya silajı ve hayvan beslemede kullanımı. Erciyes Üniversitesi Veterinerlik Fakültesi Dergisi, 8(3): 193-200.
Dalló, S.C., Zdziarski, A.D., Woyann, L.G., Milioli, A.S., Zanella, R., Conte, J., Benin, G., 2019. Across year and year-by-year GGE biplot analysis to evaluate soybean performance and stability in multi-environment trials. Euphytica, 215: 1-12.
Kendal E., Tekdal S., 2016. Application of AMMI model for evolution spring barley genotypes in Multi-Environment trials. Bangladesh Journal of Botany. 45(3): 613-620.
Kendal, E., 2016. GGE biplot analysis of multi-environment yield trials in barley (Hordeum vulgare L.) cultivars. Ekin Journal of Crop Breeding and Genetics, 2(1): 90-99.
Kendal, E., 2020. AMMI ve Biplot Teknikleri kullanılarak Diyarbakır şartlarına uygun arpa genotiplerinin belirlenmesi. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 9(1): 27-42.
Kılıç, H., Kendal, E., Aktaş, H., 2018. Evaluatıon of yield and some quality characters of wınter barley (Hordeum vulgare L.) genotypes usıng biplot analysıs. Agriculture & Forestry, 3: 101-111.
Kökten, K., Boydak, E., Kaplan, M., Seydoşoğlu, S., Kavurmacı, Z., 2013. Bazı soya fasulyesi (Glycine max L.) çeşitlerinden yapılan silajların besin değerlerinin belirlenmesi. Türk Doğa ve Fen Dergisi, 2(2): 7-10.
Kökten, K., Seydoşoğlu, S., Kaplan, M., Boydak, E., 2014. Forage nutritive value of soybean varieties. Legume Research, 37(2): 201 -206.
Mirosavljević, M., Pržulj, N., Boćanski, J., Stanisavljević, D., Mitrović, B., 2014. The application of AMMI model for barley cultivars evaluation in multi-year trials. Genetika, 46(2): 445-454.
Oral, E., Kendal, E., Dogan, Y., 2018. Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin, 27(7): 5179-5187.
Özer, N., 2021. Farklı fenolojik dönemlerde hasat edilen soya fasulyesinin (Glycine max L.) ot verimi ve bazı bitkisel özelliklerinin belirlenmesi. Yüksek Lisans Tezi, Tekirdağ Namık Kemal Üniversitesi Fen Bilimleri Enstitüsü, Tekirdağ.
Şenbek, G., Açıkgöz, E., 2019. Derry x Yemsoy soya (Glycine max (L.) Merr.) melezlerinin bazı tarımsal özellikleri üzerinde araştırmalar. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi, 33(1): 93-100.
Sousa, L.B., Hamawaki, O.T., Nogueira, A.P.O., Batista, R.O., Oliveira, V.M., Hamawaki, R.L., 2015. Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods. Genetics and Molecular Research, 14(4): 12660-12674.
Tayyar, Ş., Gül, M.K., 2007. Bazı soya fasulyesi (Glycine max (L.) merr.) genotiplerinin ana ürün olarak Biga şartlarındaki performansları. Yuzuncu Yıl University Journal of Agricultural Sciences, 17(2): 55-59.
TÜİK., 2021. Türkiye İstatistik Kurumu verileri, (https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=1), (Erişim Tarihi: 13.03.2023).
TÜİK., 2022 Soya İstatistikleri, Türkiye İstatistik Kurumu, (https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr), (Erişim Tarihi: 13.03.2023).
Yan, W., Hunt, L.A., 2001. Interpretation of genotype x environment interaction for winter wheat yield in Ontario, Crop Science, 41: 19-25.
Yan, W., Rajcan, I., 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop science, 42(1): 11-20.
Yaşar, M., Sezgin, M., 2022a. Investigation of yield and yield components in main crop soybean genotypes in Adana conditions. International Journal of Agriculture Environment and Food Sciences, 6(4): 667-675.
Yaşar, M., Sezgin, M., 2022b. İkinci ürün soya yetiştiriciliğinde genotip x çevre etkileşiminin araştırılması. Akademik Ziraat Dergisi, 11(2): 303-310.
Yasar, M., Sezgin, M., 2022c. Investigation of Genotype x Environment Interactions by AMMI Analysis of Oilseed Sunflower Genotypes Grown in Different Environmental Conditions. Journal of the Institute of Science and Technology, 12(4): 2532 - 2542.
Yaşar, M., Çil, A.N., Çil, A., 2023. Investigation of Genotype × Environment Interaction in Some Sunflower (Helianthus annuus L.) Genotypes in Different Environmental Conditions. MAS Journal of Applied Sciences, 8(1): 41–55.
How to Cite
Copyright (c) 2023 ISPEC Journal of Agricultural Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.