IDENTIFYING BARLEY GENOTYPES FOR OPTIMUM INPUT CONDITIONS IN THE WANA REGIONS
R.P.S. Verma
r.verma@cgiar.orgInternational Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco (Morocco)
A. Visioni
International Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco (Morocco)
S. Gyawali
International Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco (Morocco)
M. Singh
International Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco (Morocco)
Abstract
Barley (Hordeum vulgare L.) is an important cereal crop for its high demand for grain feed and forage/grazing for the animals as well as for human food in the WANA region under the low rainfall situations and thus creating an urgent need for developing high yielding barley genotypes. A total of 542 hulled and hulless genotypes with seven checks were evaluated at three locations with diverse agro-ecological conditions, Terbol in Lebanon and Marchouch and Allal Tazi in Moracco. The aim of this study was to understand the nature of genotype x environment interaction (GEI), quantify the genotypic variability and identify high yielding genotypes. The mixed models were fitted to evaluate heritability and predicted means to identify gentypes with specific adaptation to the locations using GGE-biplot. GEI across locations was significant for days to heading, days to maturity, plant height, spike lenght and grain yield. On a trial-wise analysis, days to maturity was most heritable (49 - 50% broad sense heritability on mean basis) while the grsain yield was the least (5 - 13 %). The genotype G427 (4812 kg/ha) at Marchouch, the check VMorales (4889 kg/ha) at Allal Tazi and G528 (6995 kg/ha) at Trebol were the highest yielding genotypes. Several hulled and hulless genotypes with higher grain yield and early flowering time in the three environments were idntifield for utilization by the national programs globally. The test locations, Marchouch and Allal Razi, were found comprising one mega environment while Terbol showed maximum discrimination of genotypes for grain yield.
Keywords:
Barley, Hordeum vulgare L., GGE-biplot, grain yield, optimum input conditionsReferences
Annichiarico, P. 2002. Genotype x environment interaction: challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Production and Protection Paper 174, Rome: FAO.
Google Scholar
Ceccarelli, S. and Grando, S. 2002. Plant breeding with farmers requires testing the assumptions of conventional plant breeding: Lessons from the ICARDA barley program. In: Farmers, Scientists and Plant Breeding: Integrating Knowledge and Practice, 297-332 (Eds D. A. Cleveland and D. Soleri). Wallingford, Oxon, UK: CAB International.
Google Scholar
Ceccarelli, S. 1989. Wide adaptation: how wide? Euphytica 40: 197-205.
Google Scholar
Ceccarelli, S. 1994. Specific adaptation and breeding for marginal conditions. Euphytica 77: 205-219.
Google Scholar
Ceccarelli, S. and Grando, S. 1996. Drought as a challenge for the plant breeder. Plant Growth Regulation 20:149-155.
Google Scholar
DeLacy, I.H., Basford, K.E., Cooper, M., Bull, J.K. and McLaren, G.C. 1996. Analysis of multi-environment trials – an historical perspective. In: Plant Adaptation and Crop Improvement, 39-124 (Eds Cooper, M. and Hammer, G. L.). Wallingford, Oxon, UK: CAB International.
Google Scholar
FAO, 2010. Statistical database. www.fao.org. (accessed 10/05/2015)
Google Scholar
Finlay, K.W. and Wilkinson, G.N. 1963. Analysis of adaptation in a plant breeding programme. Aust. J. Agric. Res. 14:742-754.
Google Scholar
Kang, M. S. (ed.). 1990. Genotype-by-environment Interaction and Plant Breeding. Baton Rouge, USA: Louisiana State Univ.
Google Scholar
Kempthorne, O. 1983. The design and analysis of experiments. R.E. Krieger Publ., Malabar, FL
Google Scholar
Payne. R.W. 2014 (ed). The guide to GenStat® Command Language (release 17). Part 2: Statistics. VSN International Ltd, Hemel Hempstead, United Kingdom.
Google Scholar
Yan, W. 2011.GGE Biplot vs. AMMI graphs for Genotype-by-Environment Data analysis, J. Indian Soc. Agric. Statist. 65:181-193.
Google Scholar
Verma, R.P.S.; Sharma, R.K. and B. Mishra. 2005. Future of Barley for Malt, Feed and Fodder in India. Directorate of Wheat Research, Karnal-132001, India. Technical Bulletin No.9: P28.
Google Scholar
Authors
R.P.S. Vermar.verma@cgiar.org
International Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco Morocco
Authors
A. VisioniInternational Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco Morocco
Authors
S. GyawaliInternational Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco Morocco
Authors
M. SinghInternational Center for Agricultural Research in the Dry Areas (ICARDA), PO Box 6299, Rabat, Institutes Rue Hafiane Cherkaoui, Rabat, Morocco Morocco
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