EVALUATION OF MARKETABLE LEAF YIELD OF FLUTED PUMPKIN IN DIFFERENT ENVIRONMENTS USING ADDITIVE MAIN EFFECTS AND MULTIPLICATIVE INTERACTION (AMMI) MODEL

Fayeun L. Stephen

Abstract


This study was conducted to determine the yield stability and to analyse the Genotype by Environment Interaction (GEI) of twenty five genotypes of fluted pumpkin genotypes. The experiment was laid out in a randomized complete block design (RCBD) with three replications under four environments using Additive Main effects and Multiplicative Interaction (AMMI) analysis. The mean squares of the analysis of variance revealed significant genotype, environment and GEI on marketable leaf yield per plant. AMMI analysis revealed that the major contributions to treatment sum of squares were environments (3.24%), GEI (46.90%) and genotypes (49.70%), respectively, suggesting that the marketable leaf yield of the genotypes were under the major genotypic effects of GEI. The first two principal component axes (PCA 1 and 2) cumulatively contributed 93.50% of the total GEI and were significant (p ≤ 0.01). The biplot accounted for 85.82% of the total variation. The AMMI model identified genotypes Ftn44, Ftk20, and Fts34 as most stable, while Fta39 with highest yield (398.80g/plant) had the largest negative interaction. The best genotype with respect to Abeokuta location was Ftw21 while Fta39 was the best for Akure area. Therefore, these genotypes can be recommended according to their specific adaptation areas. Abeokuta in the 2012 and 2013 had positive interaction values of 14.38 and 9.46 respectively whereas Akure in 2012 and 2013 recorded negative interaction values of -5.03 and -18.81 respectively. Akure 2013 was the most discriminating environment and had the highest mean yield thus it is considered as a very good environment for cultivation of fluted pumpkin for marketable leaf yield.


Keywords


Fluted pumpkin, AMMI model, GEI, Marketable leaf yield

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References


Adomou, M., Ntare, B. R. and Williams, J. H. 1997. Stability of pod yields and parameters of a simple physiological model for yield among peanut lines in Northern Benin. Peanut Science. 24(2): 107-112.

Akoroda, M. O. 1990. Ethnobotany of Telfairia occidentalis (Cucurbitaceae) among Igbos of Nigeria. Econ. Bot. 44(1): 29-39.

Akoroda, M. O. and Adejoro, M. A. 1990. Pattern of vegetative and sexual development of Telfairia occidentalis Hook. F. Trop. Agric. (Trinidad) 67(3): 243-247.

Alake, C. O and Ariyo, O. J. 2012. Comparative Analysis of Genotype x Environment Interaction Techniques in West African Okra, (Abelmoschus caillei, A. Chev Stevels). Journal of Agricultural Science Vol. 4, No. 4; 2012

Ariyo, O. J. 1998. Use of additive main effect and multiplicative interaction model to analyse multilocation soybean varietal trials. J. Genetics and Breeding, 53: 129-134.

Ariyo, O. J. and Ayo-Vaughan, M. A. 2000. Analysis of genotype environment interaction of okra (Abelmoschus esculentus (L) Moench). Journal of Genetics and Breeding, 54: 35–40.

Baker, R. J., 1988. Test for crossover genotype-environmental interactions. Canadian Journal of Plant Sciences 68: 405-410.

Bradu, D. and Gabriel, K. R. 1978. The biplot as a diagnostic tool for models of two-way tables. Technometrics, 20: 47 – 68.

Crossa, J. 1990. Statistical analysis of multi-location trials. Advances in Agronomy 44: 55-85.

Crossa, J., Fox, P. V. Pfeiffer, N. H., Rajaram, S. and Gauch, H. G. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theor. Appl. Genet. 81,27 – 37.

Crossa, J., Gauch, H. G. and Zobel, R. W. 1989. Additive Main Effective and Multiplicative Interaction analyses of two international maize cultival trials. Crop Sci., 30: 493-500.

Ebdon, J. S., and Gauch, H. G. Jr. 2002. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype 3 environment interaction. Crop Sci. 42: 489-496.

Eberhart, S. A. and Russell, W. A. 1966. Stability parameters for comparing varieties. Crop Science 6:36-40.

El-Nasr, T. H. S., Ibrahim, M. M. and Aboud, K. A. 2006. Stability parameters in yield of white mustard (Brassica alba L.) in different environments. World Journal of Agricultural Sciences, 2(1): 47-55.

Fayeun, L S. 2011. Investigation into genetic diversity of the fluted pumpkin Telfairia occidentalis (Hook. F.) in Southern Nigeria. Unpublished M. Tech. Thesis, Federal University of Technology, Akure, Nigeria.

Fayeun, L. S., Odiyi, A. C. Makinde, S. C. O. and Aiyelari, O. P. 2012. Genetic Variability and Correlation Studies in the Fluted Pumpkin (Telfairia occidentalis Hook. F.). Journal of Plant Breeding and Crop Science. 4(10). pp. 156-160

Freeman, G. H., 1985. The analysis and interpretation of interactions. Journal of Applied Statistics 12: 3-10.

Funnah, S. M. and Mak, C. 1980. Yield stability studies in soyabeans. Experimental Agriculture, 16: 387–390.

Gauch, H. G. 1992. Statistical analysis of regional yield trials: AMMI Analysis of Factorial Designs. Elsevier, New York. 278 pp

Gauch, H. G. and Furnas, R. E. 1991. Statistical analysis of yield trial with MATMODEL. Agro. J. 83, 916-920.

Gauch, H. G. and Zobel, R. W. 1989. Accuracy and selection success in yield trial analysis. Theoretical and Applied Genetics.79:751-761.

Gauch, H. G. and Zobel, R. W. 1997. Identifying mega-environments and targeting genotypes. Crop Science 37(2): 311-326.

Gauch, H. G., 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705-715.

Gauch, H.G. and R.W. Zobel, 1988. Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied Genetics 76: 1-10.

GenStat, 2011. GenStat Release 10.3DE, Discovery Edition 4, VSN International Ltd. (Rothamsted Experimental Station).

Gollob, H. F. 1968. A statistical model which contains features of factors of factor analytic and analysis of variance techniques. Psychometrika, 33: 73-115.

Jackson, P., Robertson, M., Cooper, M. and Hammer, G. L., 1998. The role of physiological understanding in Plant Breeding: From a breeding perspective. Field Crops Research 49: 11-37.

Kempton, R. A. 1984. The use of biplots in interpreting variety by environment interaction. J. Agric Science, 103: 123 – 135.

Magari, R. and Kang, M. S., 1993. Genotype selection via a new yield-stability statistics in maize yield trials. Euphytica 70: 105-111.

Makinde, S. C. O., Ariyo, O. J. and Akinbowale, R. I. 2013. Assessment of groundnut performance in different environments using Additive Main effects and Multiplicative Interaction (AMMI) model. Canadian Journal of Plant Breeding. 1: 2. pp. 60-66

Makinde, S.C.O. and Ariyo, O. J. (2011) Analysis of Genotype x Environment interaction of groundnut (Arachis hypogaea L.). Malays. Appl. Biol. 40 (2): 19-26.

Purchase, J. L. 1997. Parametric analysis to describe Genotype x Environment interaction and yield stability in winter wheat. Ph.D. Thesis, Department of Agronomy, Faculty of Agriculture, University of the Free State, Bloemfontein, South Africa.

Putto, W., Patanothai, A., Jogloy, S. and Hoogenboom, G. 2008. Determnation of mega-environments for peanut breeding using the CSM-CROPGRO-Peanut model. Crop Sci., 48: 973-982

RUFORUM, (Regional Universities Forum for Capacity Building in Agriculture) 2010. June Monthly Brief report on the Workshop to analyse human and institutional capacities and needs for Neglected and Underutilized Species (NUS) research and marketing Benin, Cotonou, 8th– 10th June 2010. The Workshop was sponsored by the RUFORUM, EU, ACP, Anafe, IRDCAM, IFS and Bioversity International.

Xu, Y. 2010. Molecular Plant Breeding. CAB International, Wallingford, UK. 755pp

Yan, W. and Hunt, L. A., 1998. Genotype-by-environment interaction and crop yield. Plant Breeding 117: 135-178.

Yan, W. 2001. GGE biplot- a window application for graphical analysis of multi-environmental data and other types of two-way data. Agron. J., 93. 1111-1118.


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