Additive Main Effect and Multiplicative Interaction Stability Analysis of Grain Yield Performance in Cowpea Genotypes across Locations — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Additive Main Effect and Multiplicative Interaction Stability Analysis of Grain Yield Performance in Cowpea Genotypes across Locations
Department of Horticulture and Crop Production, University of Energy and Natural Resources, Dormaa Campus, Sunyani, Ghana
,
Department of Crop and Soil Science Education, Akenten Appiah-Menka University of Skill Training and Entrepreneurial Development, Mampong, Ghana
,
Department of Crop and Soil Science Education, Akenten Appiah-Menka University of Skill Training and Entrepreneurial Development, Mampong, Ghana
,
Department of Crop and Soil Science Education, Akenten Appiah-Menka University of Skill Training and Entrepreneurial Development, Mampong, Ghana
,
Department of Horticulture and Crop Production, University of Energy and Natural Resources, Dormaa Campus, Sunyani, Ghana
1 Department of Horticulture and Crop Production, University of Energy and Natural Resources, Dormaa Campus, Sunyani, Ghana
2 Department of Crop and Soil Science Education, Akenten Appiah-Menka University of Skill Training and Entrepreneurial Development, Mampong, Ghana
3 Department of Crop and Soil Science Education, Akenten Appiah-Menka University of Skill Training and Entrepreneurial Development, Mampong, Ghana
4 Department of Crop and Soil Science Education, Akenten Appiah-Menka University of Skill Training and Entrepreneurial Development, Mampong, Ghana
5 Department of Horticulture and Crop Production, University of Energy and Natural Resources, Dormaa Campus, Sunyani, Ghana
Crops are largely influenced by climatic conditions during the growing season and therefore, minor deviation from optimal conditions can seriously threaten yield. In view of this, knowledge on the effect of environmental factors on crop growth and development could reduce the possibilities of significant yield loss. There have been statistical methods which h ave been developed in respect to characterizing crops but the additive main effect and multiplicative interaction (AMMI) method integrates analysis of variance and principal components analysis into a unified approach. AMMI has been used in the analysis of G × E interaction with greater precision in many crops. The objective of this study was to assess the extent of genotype x environment in teraction and to select the stable cowpea genotypes in Ghanaian environments over seasons using AMMI model. Eight genotypes of cowpea released by Crops Research Institute of Ghana over two decades were selected for evaluation in two locations and two seasons using RCBD with 3 replications in forest and transitional zones of Ghana. When the mean yields of various genotypes were subjected to the AMMI model, the results showed that, a highly significant (P < 0.001) genotype by location and by year interaction effects for cowpea grain yield was recorded with 63.1% of the total variation attributable to environmental effects. The AMMI Bi-plot of PC1 and GGE Bi-plot gave 80.8% and 89.3% respectively. Genotype Asontem (G3) had the highest yield and was adapted to all the environments and seasons. Genotypes Asetenapa (G1) and Soronko (G6) were however not stable with consistently low yield across all the environments. It is recommended that farmers in Forest and transitional zones of Ghana should cultivate the highly stable cowpea genotypes in order to get stable yields across environments due to climatic change.
Savannah Agricultural Research Institute (SARI) (2012) Breeding for High Yielding Improved Cowpea Varieties with Resistance to Thrips, Pod Sucking Bugs and Striga gesnerioides in Northern Ghana. Annual Report. 51-60.
Singh, A., Singh, A., Kumari, M., Kumar, S., Rai, M.K., Sharma, A.P. and Varma, A. (2003) Unmassing the Accessible Treasures of the Hidden Unexplored Microbial World. In: Prasad, B.N., Ed., Biotechnology in Sustainable Biodiversity and Food Security, Science Publishers, Enfield, 101-124.
Singh, A., Singh, A., Kumari, M., Rai, M.K. and Varma, A. (2003) Biotechnological Importance of Piriformospora indica Verma—A Novel Symbiotic Mycorrhiza-Like Fungus: An Overview. Indian Journal of Biotechnology, 2, 65-75.
Singh, K.K., Das, M.M., Samanta, A.K., Kundu, S.S. and Sharma, S.D. (2002) Evaluation of Certain Feed Resources for Carbohydrate and Protein Fractions and in Situ Digestion Characteristics. The Indian Journal of Animal Sciences, 72, 794-797.
Boukar, O., Fatokun, C.A., Huynh, B.L., Roberts, P.A. and Close, T.J. (2016) Genomic Tools in Cowpea Breeding Programs: Status and Perspectives. Frontiers in Plant Science, 7, 757. https://doi.org/10.3389/fpls.2016.00757
Langyintuo, A.S., Lowenberg-DeBoer, J., Faye, M., Lambert, D., Ibro, G., Moussa, B., Kergna, A., Kushwaha, S., Musa, S. and Ntoukam, G. (2003) Cowpea Supply and Demand in West and Central Africa. Field Crop Research, 82, 215-231. https://doi.org/10.1016/S0378-4290(03)00039-X
Fatokun, A.C. (2012) Breeding Cowpea for Resistance to Insect Pest; Attempted Crosses between Cowpea and Vigna vexillata. In: Fatokun, C.A.S.A., Tawawali, B.B., Singh, P.M. and Tamo, K.M., Eds., Challenges and Opportunities for Enhancing Sustainable Cowpea Production, International Institute for Tropical Agriculture (IITA), Ibadan, 52-61.
Ravelombola, W.S., Shi, A., Weng, Y., Motes, D., Chen, P., Srivastava, V. and Wingfield, C. (2016) Evaluation of Total Seed Protein Content in Eleven Arkansas Cowpea (Vigna unguiculata (L.) Walp.) Lines. American Journal of Plant Sciences, 7, 2288-2296. https://doi.org/10.4236/ajps.2016.715201
Lambot, C. (2002) Industrial Potential of Cowpea. Agriculture Raw Material, Nestle Research Center, Abidjan, 367-375.
Agyemang, K., Berchie, J.N., Osei-Bonsu, Tetteh, I., Nartey, E. and Fordjour, J.K. (2014) Growth and Yield Performance of Improved Cowpea (Vigna unguiculata L.) Varieties in Ghana. The Journal of Agricultural Science, 2, 44-52. https://doi.org/10.12735/as.v2i4p44
De Carvalho, M.H.C., Laffray, D. and Louguet, P. (1998) Comparison of the Physiological Responses of Phaseolus vulgaris an Vigna unguiculata Cultivars When Submitted to Drought Conditions. Environmental and Experimental Botany, 40, 197-207. https://doi.org/10.1016/S0098-8472(98)00037-9
Daimon, H. and Yoshioka, M. (2001) Responses of Root Nodule Formation and Nitrogen Fixation Activity to Nitrate in a Split-Root System in Peanut (Arachis hypogaea L.). Journal of Agronomy and Crop Science, 187, 89-95. https://doi.org/10.1046/j.1439-037X.2001.00505.x
Baidoo, P.K. and Mochiah, M.B. (2014) Varietal Susceptibility of Improved Cowpea [Vigna unguiculata (L.) Walp.] Cultivars to Field and Storage Pests. Sustainable Agricultural Research, 3, 69-76. https://doi.org/10.5539/sar.v3n2p69
Directorate General for International Co-Operation (DGIC) (2001) Crop Production in Tropical Africa. Edited by Romain H, Raemaekers DGIC. Brussels, 335-338.
Onuh, M.O. and Donald, K.M. (2009) Effects of Water Stress on the Rooting, Nodulation Potentials and Growth of Cowpea (Vigna Unguiculata (L) Walp). Science World Journal, 4, 31-34. https://doi.org/10.4314/swj.v4i3.51858
Gauch, H. (1988) Model Selection and Validation for Yield Trials with Interaction. Biometrics, 44, 705. https://doi.org/10.2307/2531585
Gauch, H.G. and Zobel, R.W. (1988) Predictive and Postdictive Success of Statistical Analyses of Yield Trials. Theoretical and Applied Genetics, 76, 1-10. https://doi.org/10.1007/BF00288824
Zobel, R.W., Wright, M.J. and Gauch, H.G. (1988) Statistical Analysis of a Yield Trial. Agronomy Journal, 80, 388-393. https://doi.org/10.2134/agronj1988.00021962008000030002x
Crossa, J. (1990) Statistical Analysis of Multi-Location Trials. Advances in Agronomy, 44, 55-85. https://doi.org/10.1016/S0065-2113(08)60818-4
Gauch, H.G. (1993) MATMODEL Version 2.0. AMMI and Related Analyses for Two-Way Data Matrices. Soil Crop Atmospheric Science, 48, 104.
Annicchiarico, P. (1997) Joint Regression VRS AMMI Analysis of Genotype-Environment Interactions for Cereals in Italy. Euphytica, 94, 53-62. https://doi.org/10.1023/A:1002954824178
Gauch, H.G. and Zobel, R.W. (1989) Accuracy and Selection Success in Yield Trials Analysis. Theoretical and Applied Genetics, 77, 443-481. https://doi.org/10.1007/BF00274266
Ariyo, O.J. (1999) Use of Additive Main Effects and Multiplicative Interaction Model to Analyze Multiplication Soybean Varietal Trials. Journal of Genetics and Breeding, 53, 129-134.
Bradu, D. (1984) Response Surface Model Diagnosis in Two-Way Tables. Communications in Statistics—Theory and Methods, 13, 3059-3106. https://doi.org/10.1080/03610928408828878
Gauch, H.G. (1990) Full and Reduced Models for Yield Trials. Theoretical and Applied Genetics, 80, 153-160. https://doi.org/10.1007/BF00224379
Crossa, J., Fox, P.N., Pfeiffer, W.H., Rajaram, S. and Gauch, H.G. (1991) AMMI Adjustment for Statistical Analysis of an International Wheat Yield Trial. Theoretical and Applied Genetics, 81, 27-37. https://doi.org/10.1007/BF00226108
Horn, L., Shimelis, H., Sarsuf, F., Mwadzingeni, L. and Laing, M.D. (2017) Genotype-by-Environment Interaction for Grain Yield among Novel Cowpea (Vigna unguiculata L.) Selections Derived by Gamma Irradiation. The Crop Journal, 6, 306-313. https://doi.org/10.1016/j.cj.2017.10.002
FAO/UNESCO (1994) Soil Map of the World Revised Legend, with Corrections. World Soil Resources Report 60, Rome. (Reprinted as Technical Paper 20, ISRIC, Wageningen)
Asiamah, R.D., Adjei-Gyapong, T., Yeboah, E., Fening, J.O., Ampontuah, E.O. and Gaisie, E. (2000) Soil Characterization and Evaluation of Four Primary Cassava Multiplication Sites (Mampong, Wenchi, Asuansi and Kpeve) in Ghana. SRI Technical Report, No. 200, Kumasi.
Meteorological Services Department of Ghana Ashanti Region, Kumasi (2015) Weather Data Report. No. 25, 21-28.
Obeng, H. (1971) Soil Map of Ghana SRI. Kumasi.
(2014) Plant Breeding Tools Software.
Gauch, H.G., Piepho, H.P. and Annicchiaricoc, P. (2008) Statistical Analysis of Yield Trials by AMMI and GGE. Further Considerations. Crop Science, 48, 866-889. https://doi.org/10.2135/cropsci2007.09.0513
Yan, W. and Kang, M.S. (2003) GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists and Agronomists. CRC Press, Boca Raton, 271. https://doi.org/10.1201/9781420040371
Kaya, Y., Palta, C. and Taner, S. (2002) Additive Main Effects and Multiplicative Interactions Analysis of Yield Performance in Bread Wheat Genotypes across Environments. Turkish Journal of Agriculture, 26, 275-279.
Admassu, S., Nigussie, M. and Zelleke, H. (2008) Genotype-Environment Interaction and Stability Analysis for Grain Yield of Maize (Zea mays L.) in Ethiopia. Asian Journal of Plant Science, 7, 163-169. https://doi.org/10.3923/ajps.2008.163.169
Adjebeng-Danquah, J., Manu-Aduening, J., Gracen, V.D., Asante, I.K. and Offei, S.K. (2017) AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana. International Journal of Agronomy, 2017, Article ID: 8075846. https://doi.org/10.1155/2017/8075846
Gauch, H.G. and Zobel, R.W. (1996) AMMI Analysis of Yield Trials. In: Kang, M.S. and Gauch, H.G., Eds., Genotype-by-Environment Interaction, CRC Press, Boca Raton, 85-122. https://doi.org/10.1201/9781420049374.ch4
Kayode, A.S., Ariyo, O.J., Ojo, D.K., Gregorio, G., Somado, E.A., Sanchez, M., Futakuchi, K., Ogunbayo, S.A., Guei, R.G. and Wopereis, M.C.S. (2009) Additive Main Effects and Multiplications Analysis of Grain Yield Performances in Rice Genotypes across Environments. Asian Journal of Plant Sciences, 8, 48-53. https://doi.org/10.3923/ajps.2009.48.53
Egesi, C.N. and Asiedu, R. (2002) Analysis of Yam Yields Using the Additive Main Effects and Multiplicative Interaction (AMMI) Model. African Crop Science Journal, 10, 195-201.