Grain yield stability analysis of lentil genotypes by additive main effects and multiplicative interactions model

Bu makalede İran'ın çok farklı çevre koşullarında test edilmiş on adet geliştirilmiş mercimek genotiplerinde eklemeli ana etkiler ve çarpımsal etkileşim (AMMI) modelinin verim ve bazı stabilite parametreleri sunulmaktadır. RMSPD (ortalama karekök tahmin farkı) değerleri vasıtasıyla çapraz doğrulama prosedürünün sadece AMMI modelinin ilk ICA ekseninin GE etkileşimi yorumlanması için yeterli olduğunu gösterirken, F-Gollob, AMMI modelinin ilk üç etkileşimi temel bileşen analizi (IPCA) ekseninin önemli olduğunu göstermiştir. EV1, D1, AMGE1, SIPC1 ve ASV parametrelerine göre, ILL 6037, ILL6199 ve Gachsaran çeşidi en stabil genotipler olarak bulunmuştur. EVF parametresine bağlı olarak FLIP 96-9L, FLIP 96-4L ve ILL 7946 genotipleri; DF parametresine bağlı olarak FLIP 96-9L, ILL 7946 ve ILL 6199 genotipleri en istikrarlı genotipler olarak belirlenmiştir. FLIP 92-12L, ILL 7946 ve ILL 6199 genotipleri SIPCF parametresine göre; AMGEF parametresi temelinde ve FLIP 82-1L, FLIP 96-9L ve ILL6199 genotipleri ise AMGEF parametresine göre en istikrarlı genotipler olarak bulunmuştur. Sıra korelasyon katsayılarına göre, ortalama verim AMMI model parametreleri ile herhangi bir pozitif anlamlı bir korelasyon göstermemiş, fakat EV1, D1, AMGE1, SIPC1 ve ASV parametreleri ile negatif anlamlı korelasyon göstermiştir. AMMI stabilite parametreleri ve ortalama verimin faktör analizi sonuçları sadece EVF’yi izleyen SIPCF parametrelerinin yüksek verim ve stabilitenin aynı zamanda seçilmesi için faydalı olabileceğini göstermiştir. İlk iki faktörün döndürülmüş puanlarının bir serpilme diyagramı, AMMI stabilite parametrelerinin verim istikrarının farklı statik ve dinamik kavramlarına karşılık gelen iki ayrı sınıf olarak sınıflandırıldığını göstermiştir.

Eklemeli ana etkiler ve çarpımsal etkileşim modeli ile mercimek genotiplerinin tane verimi stabilite analizi

This paper presents the yields and several stability parameters of additive main effects and multiplicative interactions (AMMI) model in ten improved lentil genotypes which tested in very diverse environmental conditions in Iran. The F-Gollob indicated that first three interaction principle component analysis (IPCA) axis of AMMI model was significant while cross validation procedure through RMSPD (root mean square prediction difference) values indicated only first an IPCA axis of AMMI model was adequate for GE interaction interpretation. According to EV1, D1, AMGE1, SIPC1 and ASV parameters, genotypes ILL 6037, ILL6199 and cultivar Gachsaran were the most stable genotypes. Based on EVF parameter, genotypes FLIP 96-9L, FLIP 96-4L and ILL 7946 and according to DF parameter genotypes FLIP 96-9L, ILL 7946 and ILL 6199 were the most stable genotypes. Genotypes FLIP 92-12L, ILL 7946 and ILL 6199 based on SIPCF parameter and genotypes FLIP 82-1L, FLIP 96-9L and ILL6199 based on AMGEF parameter were the most stable genotypes. According to the rank correlation coefficients, mean yield did not has any positive significant correlation with AMMI model parameters but showed negative significant correlation with EV1, D1, AMGE1, SIPC1 and ASV parameters. The results of factor analysis of AMMI stability parameters and mean yield indicated that only SIPCF following to EVF parameters would be useful for simultaneously selecting for high yield and stability. A scatter plot of the rotated scores of the first two factors indicated the AMMI stability parameters classified as two distinct classes that corresponded to different static and dynamic concepts of yield stability.

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  • Annicchiarico P (1997). Joint Regression vs AMMI Analysis of Genotype × Environment Interactions for Cereals in Italy, Euphytica, vol. 94, pp. 53–62.
  • Annicchiarico P (2002). Genotype × Environment Interaction: Challenges and Opportunities for Plant Breeding and Cultivar Recommendations, Food and Agriculture Organization of the United Nations.
  • Becker HC (1981). Correlations among Some Statistical Measures of Phenotypic Stability, Euphytica, vol. 30, pp. 835–840.
  • Becker HC. and Leon, J (1988). Stability Analysis in Plant Breeding, Plant Breed., vol. 101, pp. 1–23.
  • Burgueno J, Crossa J, and Vargas M (2001). SAS Programs for Graphing GE and GGE Biplots, Biometrics and Statistics Unit, CIMMYT.
  • Crossa J (1990). Statistical Analysis of Multilocation Trials, Advan. Agron., vol. 44, pp. 55–86.
  • Dehghani H, Sabaghpour SH, and Ebadi A (2010). Study of Genotype × Environment Interaction for Chickpea Yield in Iran, Agron. J., vol. 102, pp. 1–8.
  • Finlay KW, and Wilkinson GN (1963). The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res. 14: 742–754.
  • Flores F, Moreno MT, and Cubero, JI (1998). A Comparison of Univariate and Multivariate Methods to Analyze Environments, Field Crops Res., vol. 56, pp. 271–286.
  • Gauch HG, and Zobel RW (1988). Predictive and Postdicctive Success of Statistical Analyses of Yield Trial, Theor. Appl. Genet., vol. 76, pp. 1–10.
  • Gauch HG (2006). Statistical Analysis of Yield Trials by AMMI and GGE, Crop Sci., vol. 46, pp. 1488–1500.
  • Gauch HG (2007) MATMODEL version 3.0: Open source software for AMMI and related analyses, Available at http://www.css.cornell.edu/staff/gauch (verified 9 July. 2012). Crop and Soil Sciences, Cornell Univ., Ithaca, NY
  • Gauch HG (1992). Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs, Elsevier, Amsterdam. The Netherlands.
  • Gauch HG, Piepho, H.P., and Annicchiaricoc, P (2008). Statistical Analysis of Yield Trials by AMMI and GGE. Further considerations, Crop Sci., vol. 48, pp. 866–889.
  • Gauch HG, and Zobel RW (1996). AMMI Analysis of Yield Trials, In Kang, M.S., and Gauch, H.G., (ed.) Genotype by environment interaction. CRC Press, Boca Raton, FL.
  • Gollob HF (1968). A Statistical Model which Combines Features of Factor Analytic and Analysis of Variance Techniques, Psychometrika, vol. 33, pp. 73–115.
  • Kang MS (1998). Using Genotype-by-Environment Interaction for Crop Cultivar Development, Advan. Agron., vol. 62, 199–252.
  • Lin CS, Binns MR, and Lefkovitch LP (1986). Stability Analysis: Where Do We Stand?, 1998, Crop Sci., vol. 26, pp. 894–900.
  • Piepho HP, Denis JB, and van Eeuwijk, FA (1998). Predicting Cultivar Differences Using Covariates, J. Agric. Biolo. Environ. Statis., vol. 3, pp. 151–162.
  • Purchase JL (1997). Parametric Analysis to Describe G × E Interaction and Yield Stability in Winter Wheat, Ph.D. Thesis. Dep. of Agronomy, Faculty of Agriculture, Univ. of the Orange Free State, Bloemfontein, South Africa.
  • Sabaghnia N, Dehghani H and Sabaghpour SH (2006). Nonparametric Methods for Interpreting Genotype × Environment Interaction of Lentil Genotypes, Crop Sci., vol. 46, pp. 1100–1106.
  • Sabaghnia N, Dehghani H and Sabaghpour SH (2008). Graphic Analysis of Genotype by Environment Interaction for Lentil Yield in Iran, 2008a, Agron. J., vol. 100, pp. 760–764.
  • Sabaghnia N, Mohammadi M, and Karimizadeh R (2012). The Evaluation of Genotype × Environment Interactions of Durum Wheat’s Yield Using of the AMMI Model. Agric. Fores., vol. 55, pp. 5–21.
  • Sabaghnia N, Sabaghpour SH, and Dehghani H (2008). The Use of an AMMI Model and its Parameters to Analyze Yield Stability in Multi-Environment Trials. 2008b, J. Agric. Sci., vol. 146, pp. 571–581.
  • Sneller CH, Cilgore-Norquest L, and Dombek D (1997). Repeatability of Yield Stability in Soybean, Crop Sci., vol. 37, pp. 383–390.
  • Zobel RW (1994). Stress Resistance and Root Systems, pp. 80–99. In Proc. Of the Workshop on Adaptation of Plants to Soil Stress. 1–4 Aug. 1993. INTSORMIL Publ. 94–2. Inst. of Agriculture and Natural Resources, Univ. of Nebraska, Lincoln.
  • Zobel RW, Wright MJ, and Gauch HG (1988). Statistical Analysis of a Yield Trial, Agron. J., vol. 80, pp. 388-393.