Martin Orawu, Gladys Amoding, Lastus Serunjogi, George Ogwang, Chris Ogwang


Yield and fibre qualities are economically important parameters considered by the majority of stakeholders engaged in the cotton value chain in Uganda. The study objective was to determine the stability and adaptability of advanced cotton lines in diverse agro-ecological zones. Yield potential and fibre traits of cotton genotypes were evaluated in cotton growing agro-ecologies of Uganda. Sixteen genotypes were evaluated for two-year cycles of 2013/2014 and 2014/2015 in Arua, Lira and Serere districts. Additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype by environment interaction (GGE) biplots determined the stability of genotypes for seed cotton yield in different environments. Significant differences were observed among genotype performances for all the traits assessed with exception of ginning out turn. Some genotypes showed good fibre traits and high seed cotton yield across sites in the two-year cycles. The mean yield across sites and years ranged from 1422 to 1883kg/ha with eight genotypes including the check (BPA2002), attained yield above the overall mean of 1729kg/ha. Five genotypes BTAM(13)MO.2 (1883kg/ha), MS(13)MO.1 (1838kg/ha), EZAMMAR(13)MO.1 (1839kg/ha), BTAM(13)MO.3 (1824kg/ha) and BHG(13)MO.2 (1818kg) had higher yield than the check (1777kg/ha). Using AMMI model, the genotype and environment effects revealed significant differences for yield. Genotype by environment interactions was significant, indicating that there is genetic variability among genotypes for yield in the changing environments. The relationships observed among test locations using GGE biplot revealed three mega-environments. This indicated that classifying genotypes into mega-environments implied higher heritability and faster progress for plant breeders and higher yields for growers. AMMI analysis revealed six stable genotypes G11(BPA2002), G15 [BHG(13)MO.2], G7 [BTAM(13)MO.3], G14 [EZAMMAR(13)MO.1], G9 [BPAN(13)MO.2] and G16 [BPAN(02)14] which contributed to relatively lowest interaction. Generally, these results showed that genotypes with above average means of seed cotton yield, good fibre traits and stability were considered for further evaluation in national performance trials prior to release.


AMM1; cotton; fibre traits; genotype; GGE; stability

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Anley, W., H. Zeleke and Y. Dessalegn. 2013. Genotype x environment interaction of maize (Zea mays L. ) across North Western Ethiopia. J. Plant Breed. and Crop Sc. 5:171-181.

Baffes, J. 2009. Comparative analysis of organization and performance of African cotton sectors. The Cotton Sector of Uganda: Africa Region Working Paper Series No. 123, March 2009.

Baloch, M., W. Baloch., M.K. Baloch., A. Mallano., M. Baloch., N.J. Baloch. and S. Abro. 2015. Association and heritability analysis for yield and fibre traits in promising genotypes of cotton (Gossypium hirsutum L.). Sindh Univ. Res. J. l 47:303-306.

Baxevanos, D., C.Goulas,, J. Rossi. and E. Braojos. 2008. Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE biplots. Agron. J. 100:1230-1236.

Blanche, S.B. and G.O. Myers. 2006. Identifying discriminating locations for cultivar selection in Louisiana. Crop Sc. 46:946-949.

Blanche, S.B., G.O. Myers. and M.S. Kang. 2007. GGE biplots and traditional stability measures for interpreting genotype by environment interactions. J. Crop Impr. 20:123-135.

Campbell, B. and M.A. Jones. 2005. Assessment of genotype x environment interactions for yield and fibre quality in cotton performance trials. Euphytica 144:69-78.

Cooper, M., D. R. Woodruff., R.I. Eisemann., P.S. Brennan. and I.H. Delacy. 1995. A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theor. Appl. Genet. 90:492-502.

Crossa, J., P.N. Fox., W.H. Pfeiffer., S. Rajaram. and H.G. Gauch. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theor. Appl. Genet. 81:27-31.

Gauch, H.G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sc. 46:1488-1500.

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

International Cotton Advisory Committee, 2013. Press release and annual cotton production highlights. Washington DC, USA.

Iqbal, M., M.A. Chang., M.Z. Jabbar., M. Hassan. and N. Islam. 2003. Inheritance of earliness and other characters in upland cotton. J. Biol. Sci. 3:585-590.

Kang, M.S. 1993. Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron. J. 85:754-757.

Kaya, Y., C. Palta. and S. Taner. 2002. Additive main effects and multiplicative interactions analysis of yield performances in bread wheat genotypes across environments. Turkey J. Agric. Forest. 26:275-279.

Moreno-Gonzalez, J., J. Crossa. and P.L. Cornelius. 2003. Additive main effects and multiplicative interaction model. I. Theory on variance components for predicting cell means. Crop Sci. 43:1967-1975.

Mukoyi, F., W. Mubvekeri., D. Kutywayo., V. Muripira. and N. Mudada. 2015. Development of elite medium staple cotton (G.hirsutum L.) genotypes for production in middleveld upland ecologies. Afric. J. Plant Sci. 9:1-7.

Ntawuruhunga, P.H., P. Rubaihayo., J.B.A. Whyte., A.G.O. Dixon. and D.S.O. Osiru. 2001. Additive main effects and multiplicative interaction analysis for storage root yield of cassava genotypes evaluated in Uganda. Afric. Crop Sci. J. 9:591-598.

Rasheed, A., W. Malik., A.A. Khan., N. Murtaza., A. Qayyum. and E. Noor. 2009. Genetic evaluation of fibre yield and yield components in fifteen cotton (Gossypium hirsutum) genotypes. Inter. J. Agric. Biol. 11:581-585.

Rauf, S., T.M. Khan., H.A. Sadaqat. and A.I. Khan. 2004. Correlation and path coefficient analysis of yield components in cotton (Gossypium hirsutum L.). Inter. J. Agr. Biol. 6:686-688.

Reddy, V.R., D.N. Baker. and H.F. Hodges. 1991. Temperature effects on cotton canopy growth, photosynthesis and respiration. Agron. J. 83:699-704.

Red-pepper, 2015. Uganda’s cotton production rises to one million bales. Red-pepper of February 24, 2015.

Singh, M. and S.D. Narkhede. 2010. Evaluation of morphological parameters and yield in cotton (Gossypium hirsutum) through correlation and path coefficient analysis. Agric. Sci. Digest 30:202-206.

Sudaric, A.,A.D. Simic. and M. Vrataric. 2006. Characterization of genotype by environment interactions in soybean breeding programmes of southeast Europe. Plant Breed. 125:191-194.

Tukamuhabwa, P., M. Asiimwe., M. Nabasirye., P. Kabayi. and M. Maphosa. 2012. Genotype by environment interaction of advanced generation soybean lines for grain yield in Uganda. Afric. Crop Sci. J. 20:107-115.

Tyagi, A.P., R. Morand. and D.P. Singh. 1988. Path analysis in upland cotton (G. Hirsutum L.). Indian J. Agric. Res. 22:137.

Wamatu, J.N. and E. Thomas. 2002. The influence of genotype-by-environment interaction on the grain yields of 10 Pigeonpea cultivars grown in Kenya. J. Agron. Crop Sci. 188:25-33.

Wortman, C.S. and C.A. Eledu. 1999. Uganda’s agro-ecological zones: A guide to planners and policymakers. Centro Internationale de Agricultural Tropical (CIAT), Kawanda Uganda.

Xu, N., M. Fok., G. Zhang., J. Li. and Z. Zhou. 2013. The application of GGE Bi-plot analysis for evaluating test locations and mega-environment investigation of cotton regional trials. J. Integr. Agric. Adv. Doi: 10.1016/S2095-3119(13)60656-5.

Yan, W. 2015. Mega-environment analysis and test location evaluation based on unbalanced multiyear data. Crop Sci. 55:113-122.

Yan, W., L.A. Hunt., Q.L. Sheng. and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigation based on GGE bi-plot. Crop Sci. 40:596-605.

Yan, W.K. 2001. GGE biplot-A windows application for graphical analysis of multi-environment trial data other types of two-way data. Agron. J. 93:1111-1118.

Yan, W. and M.S. Kang. 2003. GGE Bi-plot analysis: A graphical tool for Breeders, Geneticists and Agronomists. Florida, USA: CRC, Press.

Yan, W., M.S. Kang., B. Ma., S. Woods. and P.L. Cornelius. 2007. GGE Bi-plot vs. AMMI Analysis of Genotype-by-Environment Data. Crop Sci. 47:643-655.

Yan, W. and I. Rajcan. 2002. Bi-plots analysis of the test sites and trait relations of soybean in Ontario. Crop Sci. 42:11-20.

Zeng, L., W.R. Meredith., B.T. Campbell., J.K. Dever., J. Zhang., K.M. Glass., A.S. Jones., G.O. Myers. and F.M. Bourland. 2014. Genotype-by-Environment Interaction Effects on Lint Yield of Cotton Cultivars Across Major Regions in the U.S. Cotton Belt. The J. Cotton Sci. 18:75-84.

Zhang, X. and W. Ni. 2006. Studies on main agronomical and economical traits in cotton breeding lines with high fibre. China Cotton 33:17-19

Zhu, Y., Q. Yu. and T. Sun. 2002. Studies on growth and development habits and cultivation technology of high quality cotton cultivar Yumian No. 1. China Cotton 29:17-19.

Zobel, R.W. and H.G. Gauch. 1988. Statistical analysis of yield trial. J. Agron. 80:388-393.


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