Grain yield stability of maize genotypes grown in paddy fields
Keywords:dry season, GGE, maize, paddy field, stability, multi-environment trials
Breeding work for identifying the high performance and stable genotypes appropriate for environmental situations in different fields is an important task in maize breeding programs. The objective of this study was to identify high yielding and stable maize hybrids in irrigated paddy fields in the dry season in Thailand. Three new hybrid varieties (Suwan 5720, Suwan 5819 and Suwan 5821) from the National Corn and Sorghum Research Center, and three commercial cultivars (Nakhon Sawan 3, Nakhon Sawan 5, and S 7328) from public and private agencies, were planted and evaluated for grain yield stability in paddy fields in the dry season. Fourteen experimental yield trials were conducted in a randomized complete block design (RCBD) with four replications in Saraburi, Chai Nat, and Phra Nakhon Si Ayutthaya provinces in the dry seasons of 2018-2021. The genotype main effect plus genotype by environment interaction (GGE) model was used to analyze yield stability. The combined analysis of variance showed that the effect of the environment, genotype and genotype-environment interaction (GEI) of grain yield had highly significant differences. The commercial cultivar S7328 (b=0.84) performed the highest grain yield and yield stability, followed by the new hybrid, Suwan 5821(b=0.89), then Nakhon Sawan 5 (b=0.91), Suwan 5720 (b=0.81), and Nakhon Sawan 3 (b=0.97) except Suwan 5819 (b=1.58) had more variation. Based on the GGE model, the biplot explained 76.42% of total variation with PC1 (56.91%) and PC2 (19.51%). S7328 and Suwan 5821 had high yield stability and grain yield, whereas Nakhon Sawan 3 and Nakhon Sawan 5 had the highest grain stability with less grain yield. Therefore, the new hybrid Suwan 5821 could be recommended to farmers for planting in paddy fields during the dry season with irrigation.
Al-Naggar, A. M. M., Shafik, M. M., & Musa, R. Y. M. (2020). AMMI and GGE biplot analyses for yield stability of nineteen maize genotypes under different nitrogen and irrigation levels. Plant Archives, 20(2), 4431-4443.
Annicchiarico, P., Bellah, F., & Chiari, T. (2015). Defining subregions and estimating benefits for a specific‐adaptation strategy by breeding programs: A case study. Crop Science, 45(5), 1741-1749. DOI: https://doi.org/10.2135/cropsci2004.0524
Badu-Apraku, B., Oyekunle, M., Obeng-Antwi, K., Osuman, A. S., Ado, S. G., Coulibay, N., ... & Didjeira, A. (2012). Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. The Journal of Agricultural Science, 150(4), 473-483. DOI: https://doi.org/10.1017/S0021859611000761
Eberhart, S. T., & Russell, W. A. (1966). Stability parameters for comparing varieties 1. Crop science, 6(1), 36-40. DOI: https://doi.org/10.2135/cropsci1966.0011183X000600010011x
Ekasingh, B., Gypmantasiri, P., Thong Ngam, K., & Krudloyma, P. (2004). Maize in Thailand: production systems, constraints, and research priorities. Mexico: CIMMYT.
Jompuk, C., Jampatong, S., Boonrumpun, P., Chaiyasit, R., & Jompuk, P. (2019). Field corn inbred lines ‘Ki 61’ and ‘Ki 62’ for single cross hybrids ‘Suwan 5720’ and ‘Suwan 5821’ for growing on irrigated rice fields in dry season. In Proceeding of 39th National Corn and Sorghum Research Conference, Lopburi, Thailand. Department of Agricultural Extension, Bangkok.
Kang, M. S. (1997). Using genotype-by-environment interaction for crop cultivar development. Advances in agronomy, 62, 199-252. https://doi.org/10.1016/S0065-2113(08)60569-6
Kpotor, P., Akromah, R., Ewool, M. B., Kena, A. W., Owusu-Adjei, E., & Tuffour, H. O. (2014). Assessment of the Relative Yielding Abilities and Stability of Maize (Zea mays L) Genotypes under Different Levels of Nitrogen Fertilization across Two Agro-Ecological Zones in Ghana. International Journal of Scientific Research in Agricultural Sciences, 1(7), 128-141.
Kumar, R., Singode, A., Chikkappa, G. K., Mukri, G., Dubey, R. B., Komboj, M. C., ... & Yadav, O. P. (2014). Assessment of genotype × environment interactions for grain yield in maize hybrids in rainfed environments. SABRAO Journal of Breeding & Genetics, 46(2), 284-92.
Macrobert, J. F., Setimela, P. S., Gethi, J., & Regasa, M. W. (2014). Maize hybrid seed production manual. Mexico: CIMMYT.
McPherson, M. (2022). An Application of GGE Biplot to Cotton Variety Development. Crop Breeding, Genetics and Genomics, 4(1), e220001. DOI: https://doi.org/10.20900/cbgg20220001
Mitrović, B., Stanisavljević, D., Treskić, S., Stojaković, M., Ivanović, M., Bekavac, G., & Rajković, M. (2012). Evaluation of experimental maize hybrids tested in multi-location trials using AMMI and GGE biplot analyses. Turkish Journal of Field Crops, 17(1), 35-40.
Mushayi, M., Shimelis, H., Derera, J., Shayanowako, A. I., & Mathew, I. (2020). Multi-environmental evaluation of maize hybrids developed from tropical and temperate lines. Euphytica, 216(5), 1-14. DOI, https://doi.org/10.1007/s10681-020-02618-6
Office of Agricultural Economics (OAE). (2018). Agricultural economic outlook 2019. Bangkok, Thailand: Office of Agricultural Economics, Ministry of Agriculture and Cooperatives.
Office of Agricultural Economics (OAE). (2020). Agricultural statistics of Thailand 2019. Bangkok, Thailand: Office of Agricultural Economics, Ministry of Agriculture and Cooperatives.
Olanrewaju, O. S., Oyatomi, O., Babalola, O. O., & Abberton, M. (2021). GGE Biplot analysis of genotype× environment interaction and yield stability in bambara groundnut. Agronomy, 11(9), 1839. https://doi.org/10.3390/agronomy11091839
Poolsawas, S., & Napasintuwong, O. (2012). Duration analysis of hybrid maize adoption in Thailand [Master thesis]. Kasetsart University, Bangkok.
R Development Core Team. (2021). The R Project for Statistical Computing. Retrieved firm https://www.R-project.org/.
Ruswandi, D., Syafii, M., Maulana, H., Ariyanti, M., Indriani, N. P., & Yuwariah, Y. (2021). GGE biplot analysis for stability and adaptability of maize hybrids in western region of Indonesia. International Journal of Agronomy, 2021. Article ID 2166022. https://doi.org/10.1155/2021/2166022
Sharma, S. P., Leskovar, D. I., Crosby, K. M., & Ibrahim, A. M. H. (2020). GGE biplot analysis of genotype-by-environment interactions for melon fruit yield and quality traits. HortScience, 55(4), 533-542. https://doi.org/10.21273/HORTSCI14760-19
Tester, M., & Langridge, P. (2010). Breeding technologies to increase crop production in a changing world. Science, 327(5967), 818-822. DOI: 10.1126/science.1183700
Tuong, T. P., & Bouman, B. A. (2003). Rice production in water-scarce environments. Water productivity in agriculture: Limits and opportunities for improvement, 1, 13-42. https://doi.org/10.1079/9780851996691.0053
Yan, W., Hunt, L. A., Sheng, Q., & Szlavnics, Z. (2000). Cultivar evaluation and mega‐environment investigation based on the GGE biplot. Crop science, 40(3), 597-605. https://doi.org/10.2135/cropsci2000.403597x
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(2), 643-653. https://doi.org/10.2135/cropsci2006.06.0374
Ye, M., Chen, Z., Liu, B., & Yue, H. (2021). Stability Analysis of Agronomic Traits for Maize (Zea Mays L.) Genotypes Based on Ammi Model. Bangladesh Journal of Botany, 50(2), 343-350. ttps://doi.org/10.3329/bjb.v50i2.54091
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