Stability analysis of pre-commercial hybrid corn developed by the National Corn and Sorghum Research Center
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Abstract
In the process of maize improvement and selecting single-cross hybrid maize varieties, it is essential to conduct trials in multiple environments before promoting new varieties to farmers. This study aimed to analyze the yield stability of pre-commercial single-cross hybrid maize varieties from the National Corn and Sorghum Research Center and select those with higher yield potential than the Suwan 4452 variety. 23 pre-commercial hybrids and 7 commercial varieties from the public and private sectors were evaluated. The experiments were conducted in a randomized complete block design (RCBD) with three replications in 10 environments across 8 provinces, including Nakhon Ratchasima, Saraburi, Nakhon Sawan, Sukhothai, Tak, Lamphun, Chiang Mai, and Phayao. The genotype plus genotype by environment interaction (GGE) was used to analyze the yield stability. The results showed that there were highly significant differences (P≤0.01) in genotype (G), environment (E), and genotype-environment interaction (GE). The pre-commercial hybrids yielded between 1,096 and 1,347 kg/rai. The highest yield was observed in KSX5720 (1,347 kg/rai), followed by KSX5819 (1,336 kg/rai) and KSX6108 (1,278 kg/rai), respectively. The commercial hybrids yielded between 1,094 and 1,448 kg/rai, while the best yield was DK9979C and P3875, yielding 1,448 kg/rai, followed by CP303 at 1,360 kg/rai. The Suwan 4452 check variety yielded 1,197 kg/rai, 17% and 14% lower than KSX5720 and KSX5918, respectively. According to the Eberhart and Russell stability analysis, the regression coefficients (b) for the two hybrid varieties were 1.18 and 1.16, respectively, which did not differ significantly from 1. The GGE biplot explained 56.22% of the total variance (PC1=38.32% and PC2=17.9%). The KSX5720 and KSX5918 varieties demonstrated excellent yield stability and higher yields than Suwan 4452, indicating their potential as new commercial varieties.
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References
ชูศักดิ์ จอมพุก. 2562. วิธีวิเคราะห์ทางพันธุศาสตร์ปริมาณในการปรับปรุงพันธุ์พืช. สำนักพิมพ์มหาวิทยาลัยเกษตรศาสตร์, กรุงเทพฯ.
สำนักงานเศรษฐกิจการเกษตร 2566. สถานการณ์สินค้าเกษตรที่สำคัญและแนวโน้ม ปี 2566. สำนักวิจัยเศรษฐกิจการเกษตร สำนักงานเศรษฐกิจการเกษตร กระทรวงเกษตรและสหกรณ์ .
สุรพล อุปดิสสกุล. 2536. สถิติการวางแผนการทดลอง เล่ม 1. มหาวิทยาลัยเกษตรศาสตร์, กรุงเทพฯ.
Al-Naggar, A., M. Shafik, and R. Musa. 2020. Ammi and gge biplot analyses for yield stability of nineteen maize genotypes under different nitrogen and irrigation levels. Plant Arch. 20: 4431-4443.
Annicchiarico, P., F. Bellah, and T. Chiari. 2005. Defining subregions and estimating benefits for a specific-adaptation strategy by breeding programs: A case study. Crop Science. 45: article ID 1741-9.
Badu-Apraku, B., M. Oyekunle, K. Obeng-Antwi, A. Osuman, S. Ado, N. Coulibay, and A. Didjeira. 2012. Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. The Journal of Agricultural Science. 150: 473-483.
Balestre, M., J. C. de Souza, R. G. Von Pinho, R. L. de Oliveira, and J. M. Paes. 2009. Yield stability and adaptability of maize hybrids based on GGE biplot analysis characteristics. Crop Breeding and Applied Biotechnology. 9: 219-28.
Changizi, M., R. Choukan, E. M. Heravan, M. R. Bihamta, and F. Darvish. 2014. Evaluation of genotype × environment interaction and stability of corn hybrids and relationship among univariate parametric methods. Canadian Journal of Plant Science. 94: 1255-1267.
Eberhart, S.t. and W. Russell. 1966. Stability Parameters for Comparing Varieties 1. Crop science. 6: 36-40.
Gauch, J. H., and R. Zobel. 1996. AMMI analysis of yield trails. Genotype by Environment Interaction (Kang M and Gauch Junior HG, eds.). CRC Press, Boca Raton.
Kang, M. 1998. Using genotype-by-environment interaction for crop cultivar development. Advances in Agronomy. 62: 199-252.
Kpotor, P., R. Akromah, M. B. Ewool, A. W. Kena, E. Owusu-Adjei, and H. O. Tuffour. 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: 128-141.
Kumar, V., A. Rathore, A. Kharub, D. Kumar, and I. Sharma. 2014. GGE biplot analysis of multi-locational yield trials and identification of representative environments for barley (Hordeum vulgare L.) in India. Research on Crops. 15: 871-875.
McPherson, M. 2022. An application of GGE biplot to cotton variety development. Crop Breeding, Genetics and Genomics. 4: article ID e220001.
Mitrović, B., D. Stanisavljević, S. Treskić, M. Stojaković, M. Ivanović, G. Bekavac, and M. Rajković. 2012. Evaluation of experimental maize hybrids tested in multi-location trials using AMMI and GGE biplot analyses. Turkish Journal of Field Crops. 17: 35-40.
Mushayi, M., H. Shimelis, J. Derera, A. I. T. Shayanowako, and I. Mathew. 2020. Multi-environmental evaluation of maize hybrids developed from tropical and temperate lines. Euphytica. 216: 1-14.
Olanrewaju, O. S., O. Oyatomi, O. O. Babalola, and M. Abberton. 2021. GGE biplot analysis of genotype× environment interaction and yield stability in Bambara groundnut. Agronomy. 11: article ID 1839.
Ruswandi, D., M. Syafii, H. Maulana, M. Ariyanti, N. P. Indriani, and Y. Yuwariah. 2021. GGE biplot analysis for stability and adaptability of maize hybrids in western region of Indonesia. International Journal of Agronomy. 2021: article ID 2166022.
Sharma, S. P., D. I. Leskovar, K. M. Crosby, and A. Ibrahim. 2020. GGE biplot analysis of genotype-by-environment interactions for melon fruit yield and quality traits. HortScience. 55: 533-542.
Snedecor, G., and W. G. Cochran. 1980. One way classifications; analysis of variance In Statistical methods. Ames, IA: The Iowa State University Press. Iowa.
Troyer, A., S. Openshaw, and K. Knittle. 1988. Measurement of genetic diversity among popular commercial corn hybrids. Crop Science. 28: 481-485.
Yan, W., L. A. Hunt, Q. Sheng, and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science. 40: 597-605.
Yan, W., and L. A. Hunt. 2002. In: Kang MS, editor. Biplot Analysis of multi-environment trial data. Quantitative genetics, genomics, and plant breeding. 2nd edition. USA: Kansas State University.
Yan, W., M. S. Kang, B. Ma, S. Woods, and P. L. Cornelius. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science. 47: 643-653.
Yan, W., and N. A. Tinker. 2006. Biplot analysis of multi-environment trial data: principles and applications. Canadian Journal of Plant Science. 86: 623-645.
Yan, W. 2011. GGE biplot vs. AMMI graphs for genotype-by-environment data analysis. Journal of the Indian Society of Agricultural Statistics. 65: 181-193.
Ye, M., Z. Chen, B. Liu, and H. Yue. 2021. Stability analysis of agronomic traits for maize (Zea mays L.) genotypes based on ammi model. Bangladesh Journal of Botany. 50: 343-350.
