Genotype × environment interaction and adaptation of cowpea genotypes across six planting seasons

Abstract

Cowpea exhibits significantly inconsistent performances across different environments, and hence demands performance evaluation of genotypes prior release or cultivation in every breeding program. Hence, the goal of this study was to compare 16 cowpea genotypes over six planting seasons (2014-2019) in Akungba-Akoko, Nigeria for their stability and adaptation through Finlay and Wilkinson (FW), Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype and Genotype × Environment (GGE) analyses. ANOVA revealed high significant genotype (15.33%), environment (14.71%) and GEI (64.34%) effects for seed yield among genotypes. All analyses were able to pinpoint stable high-yielding genotypes including G14 and G9. Genotypes G14, G3, G4, G5, G6 and G9 were high yielding and stable according to FW; AMMI showed G10, G9, G16, G14 and G13 stable high-yielding while GGE showed G14, G16, G9 and G13 as stable high-yielding. As analyses explored the variation in the data due to GEI, they also complemented one another, in that where one erroneously included a wrong genotype as stable; the other excluded such genotype, making recommendation possible on the basis of consistency to gain reliability.

Keywords

• Adaptation;
• Cowpea
• GxE interaction
• Stability
• Yield

References

1. Agahi, K., Ahmadi, J., Oghan, H. A., Fotokian, M. H., & Orang, S. F. (2020). Analysis of genotype × environment interaction for seed yield in spring oilseed rape using the AMMI model. Crop Breeding and Applied Biotechnology, 20(1), 1-8. https://doi.org/10.1590/1984-70332020v20n1a2.
2. Ajayi, A. T., & Gbadamosi, A. E. (2020). Genetic variability, character association and yield potentials of twenty five accessions of cowpea (Vigna unguiculata L. Walp). Journal of Pure and Applied Agriculture, 5, 1-16. https://doi.org/10.5281/zenodo.3956157.
3. Ajayi, A. T., Gbadamosi, A. E., Olotuah, O. F., & David, E. A. (2020). Crossability and inheritance of seed coat colour in cowpea at F1 generation. Frontiers in Life Sciences and Related Technologies, 1(2), 58-62.
4. Akinde, B. P., Olakayode, A. O., Oyedele, D. J., & Tijani, F. O. (2020). Selected physical and chemical properties of soil under different agricultural land–use types in Ile–Ife, Nigeria. Heliyon, 6, e5090.
5. Aliyu, O. M., Lawal, O. O., Wahab, A. A., & Ibrahim, U. Y. (2019). Evaluation of advanced breeding lines of cowpea (Vigna unguiculata L. Walp) for high seed yield under farmers’ field conditions. Plant Breeding and Biotechnology, 7(1), 12-23. https://doi.org/10.9787/PBB.2019.7.1.12.
6. Almeida, W. S., Fernandes, F. R. B., Teófilo, E. M., & Bertini, C. H. (2012). Adaptability and stability of grain yield in cowpea under different biometrics. Revista Brasileira de Engenharia Agricola, 18, 221-228.
7. Aremu, C., Ige, S. A., Ibirinde, D., Raji, I., Abolusoro, S., Ajiboye, B., Obaniyi, S., Adekiya, A., & Asaleye, A. (2020). Assessing yield stability in African yam bean (Sphenostylis stenocarpa) performance using year effect. Open Agriculture, 5(1), 202-212. https://doi.org/10.1515/opag-2020-0020.
8. Baraki, F., Gebregergis, Z., Belay, Y., Berhe, M., & Zibelo, H. (2020). Genotype × environment interaction and yield stability analysis of mung bean (Vigna radiata (L.) Wilczek) genotypes in Northern Ethiopia. Cogent Food and Agriculture, 6(1), 1-14.

9. Bocianowski, J., & Prazak, R. (2022). Genotype by year interaction for selected quantitative traits in hybrid lines of Triticum aestivum L. with Aegilops kotschyi Boiss. and Ae. Variabilis Eig. Using the additive main effects and multiplicative interaction model. Euphytica, 218, 11.
10. Das, A., Parihar, A. K., Saxena, D., Singh, D., Singha, K. D., Kushwaha, K. P. S., Chand, R., Bal, R. S., Chandra, S., & Gupta, S. (2019). Deciphering genotype-by-environment interaction for targeting test environments and rust resistant genotypes in field pea (Pisum sativum L.). Frontiers in Plant Science, 10, 825. https://doi.org/10.3389/fpls.2019.00825.
11. De Melo, L. F., Pinheiro, M. D. S., De Matos, R. F., Dovale, J. C., & De Magalhães Bertini, C. H. C. (2020). GGE biplot analysis to recommend cowpea cultivars for green grain production. Revista Caatinga, 33(2), 321-331. https://doi.org/10.1590/1983-21252020v33n205rc.
12. FAOSTAT, (2020). Official Website of Food and Agriculture Organization, FAOSTAT_data_8-29-2020-Excel, http://faostat.fao.org, Last Accessed on January 29, 2022.
13. Fayeun, L. S., Hammed, L. A., Oduwaye, O. A., Madike, J. U., & Ushie, E. U. (2016). Estimates of genetic variability for seedling traits in fluted pumpkin (Telfairia occidentalis Hook. F), Plant Breeding and Biotechnology, 4(2), 262-270. https://doi.org/10.9787/pbb.2016.4.2.262.
14. Gerrano, A. S., Adebola, P. O., Jansen van Rensburg, W. S., & Laurie, S. M. (2015). Genetic variability in cowpea (Vigna unguiculata (L.) Walp.) genotypes. South African Journal of Plant and Soil, 32(3), 165-174. https://doi.org/10.1080/02571862.2015.1014435.
15. Gerrano, A. S., Jansen van Rensburg, W. S., Mathew, I., Shayanowako, A. I., Bairu, M. W., Venter, S. L., ... & Labuschagne, M. (2020). Genotype and genotype× environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa. Euphytica, 216(5), 1-11. https://doi.org/10.1007/s10681-020-02611-z.
16. Gomes, A., Nhantumbo, N., Ferreira-Pinto, M., Massinga, R., Ramalho, J. C., & Ribeiro-Barros, A. (2019). Breeding elite cowpea [Vigna unguiculata (L.) Walp] varieties for improved food security and income in Africa: Opportunities and challenges. Legume crops‐Characterization and Breeding for Improved Food Security, 626-640.
17. Horn, L., Shimelis, H., Sarsu, F., Mwadzingeni, L., & Laing, M. D. (2018). Genotype-by-environment interaction for grain yield among novel cowpea (Vigna unguiculata L.) selections derived by gamma irradiation. The Crop Journal, 6, 306-313.
18. Kebede, E., & Bekeko, Z. (2020). Expounding the production and importance of cowpea (Vigna unguiculata (L.) Walp.) in Ethiopia. Cogent Food and Agriculture, 6(1), 1769805. https://doi.org/10.1080/23311932.2020.1769805.
19. Lian, L., & De Los Campos, G. (2016). FW: An R package for Finlay-Wilkinson regression that incorporates genomic/pedigree information and covariance structures between environments. G3: Genes, Genomes, Genetics, 6(3), 589-597. https://doi.org/10.1534/g3.115.026328.
20. Maniruzzaman, I. M. Z., Begum, F., Khan, M. A. A., Amiruzzaman, M., & Hossain, A. (2019). Evaluation of yield stability of seven barley (Hordeum vulgare L.) genotypes in multiple environments using GGE biplot and AMMI model. Open Agriculture, 4(1), 284-293. https://doi.org/10.1515/opag-2019-0027.
21. Morgan, R. F. (2011). A new journal for Torrid Zone. Journal of Tropical Psychology, 1(1), 1-1.
22. Neisse, A. C., Kirch, J. L., & Hongyu, K. (2018). AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data. Biometrical Letters, 55(2), 97-121. https://doi.org/10.2478/bile-2018-0008.
23. Odeseye, A. O., Amusa, N. A., Ijagbone, I. F., Aladele, S. E., & Ogunkanmi, L. A. (2018). Genotype by environment interactions of twenty accessions of cowpea [Vigna unguiculata (L.) Walp] across two locations in Nigeria. Annals of Agrarian Science, 16, 481-489.
24. Oladele, S. O., Adeyemo, A. J., & Awodun, M. A. (2019). Influence of rice husk biochar and inorganic fertilizer on soil nutrients availability and rain–fed rice yield in two contrasting soils. Geoderma, 336, 1-11.
25. Oladele, S. O., Adeyemo, A., Awodun, M., Adegaye, A., & Ingold, M. (2022). Impact of biochar amendment on soil nematode communities in a West African rain–fed rice cropland. Nematology, 24, 159-170.
26. Olayiwola, M. O., Soremi, P. A. S., & Okeleye, K. A. (2015). Evaluation of some cowpea (Vigna unguiculata L. Walp) genotypes for stability of performance over 4 years. Current Research in Agricultural Science, 2(1), 22-30.
27. Oliveira, D. P., Soares, B. L., Ferreira, P. A. A., Passos, T. R., da Silva, J. S., Ferreira, D. F., Messias de Andrade, J. B., & de Souza Moreira, F. M. (2020). Adaptability and phenotypic stability of elite strains of rhizobia for inoculation in cowpea confirmed by biometric techniques. Soil Science Society of America Journal, 84(4), 1125-1138. https://doi.org/10.1002/saj2.20084.
28. Osekita, O. S. (2018). Genotype × Environment interaction and molecular diversity studies of selected rice (Oryza sativa L.) genotypes across three Nigerian agro-ecological zones, Doctoral Dissertation, (pp. 1-194). Federal University of Technology, Akure, Nigeria.
29. Osekita, O. S. (2019). Stability and yield of rice (Oryza sativa L.) genotypes at three agro ecological zones of South west Nigeria. South Asian Research Journal of Biology and Applied Biosciences, 1(2), 38-42. https://doi.org/10.36346/sarjbab.2019.v01i02.002.
30. Owusu, E. Y., Amegbor, I. K., Mohammed, H., Kusi, F., Atopkle, I., Sie, E. K., ... & Nutsugah, S. K. (2020). Genotype × environment interactions of yield of cowpea (Vigna unguiculata (L.) Walp) inbred lines in the Guinea and Sudan savanna ecologies of Ghana. Journal of Crop Science and Biotechnology, 23(5), 453-460. https://doi.org/10.1007/s12892-020-00054-5.
31. Padi, F. K. (2007). Genotype × environment interaction and yield stability in a cowpea-based cropping system. Euphytica, 158, 11-25.
32. PBTools, (2014). PBT App (Version 1.4). Biometrics and Breeding Informatics, https://www.irri.org/bbi/products, Last Accessed on February 13, 2022.
33. Pour-Aboughadareh, A., Barati, A., Koohkan, S. A., Jabari, M., Marzoghian, A., Gholipoor, A., ... & Kheirgo, M. (2022). Dissection of genotype-by-environment interaction and yield stability analysis in barley using AMMI model and stability statistics. Bulletin of the National Research Centre, 46(1), 1-12.
34. Salami, B. T., & Sangoyomi, T. E. (2013). Soil fertility status of cassava fields in South Western Nigeria. American Journal of Experimental Agriculture, 3(1), 152–164.
35. Sharma, M., Ghosh, R., Telangre, R., Rathore, A., Saifulla, M., Mahalinga, D. M., ... & Jain, Y. K. (2016). Environmental influences on pigeonpea-Fusarium udum interactions and stability of genotypes to Fusarium wilt. Frontiers in Plant Science, 7, 253. https://doi.org/10.3389/fpls.2016.00253.
36. SPSS, (2017). SPSS App (Version 20). SPSS Inc., Chicago IL, USA.
37. Sousa, M. B., Damasceno-Silva, K. J., Rocha, M. D. M., De Menezes Júnior, J. Â. N., & Lima, L. R. L. (2018). Genotype by environment interaction in cowpea lines using GGE biplot method. Revista Caatinga, 31(1), 64-71. https://doi.org/10.1590/1983-21252018v31n108rc.
38. Tariku, S., Wassu, M., & Berhanu, A. (2018). Genotype by environment interaction and stability analysis of cowpea [Vigna unguiculata (L.) Walp] genotypes for yield in Ethiopia. Journal of Plant Breeding and Crop Science, 10(9), 249-257. https://doi.org/10.5897/jpbcs2018.0753.
39. Tena, E., Goshu, F., Mohamad, H., Tesfa, M., Tesfaye, D., & Seife, A. (2019). Genotype × environment interaction by AMMI and GGE-biplot analysis for sugar yield in three crop cycles of sugarcane (Saccharum officinirum L.) clones in Ethiopia. Cogent Food and Agriculture, 5, 1–14.
40. United Nation, (2019). World Population Prospects, http://www.ncbi.nlm.nih.gov/pubmed/12283219, Last Accessed on January 15, 2022.
41. Verma, A., Kumar, V., & Kharab, A. S. (2020). G × E interaction analysis by AMMI model for fodder yield of dual purpose barley genotypes. International Journal of Bio-Resource and Stress Management, 11(1), 051–056. https://doi.org/10.23910/ijbsm/2020.11.1.2064.
42. Yan, W., & Kang, M. S. (2003). GGE-biplot analysis: a graphical tool for breeders, geneticists and agronomists. CRD Press, Boca Raton, FL, USA.
43. Yan, W., & Tinker, N. A. (2006). Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, 86(3), 623-645. https://doi.org/10.4141/P05-169.
44. Yan, W., Kang, M. S., Ma, B., Woods, S., & Cornelius, P. L. (2007). GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science, 47, 643-653.