نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشیار، بخش تحقیقات ذرت و گیاهان علوفه‌ای، موسسه تحقیقات اصلاح و تهیه نهال و بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.
2 استادیار، بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی فارس، سازمان تحقیقات، آموزش و ترویج کشاورزی، شیراز، ایران.
3 دانشیار، بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی کرمان، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرمان، ایران.
4 استادیار، بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران.
5 استادیار، بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی کرمانشاه، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرمانشاه، ایران.
6 استادیار، بخش تحقیقات ذرت و گیاهان علوفه‌ای، موسسه تحقیقات اصلاح و تهیه نهال و بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Multi-environments trails are crucial elements in development and recommendation of new crop cultivars. Crop yield is influenced by genetic, environmental, and genotype × environment interaction (GEI). This study employed a Bayesian probabilistic method to study GEI by integrating adaptation and grain yield stability assessments of 22 maize hybrids within a unified framework. Using grain yield data from 22 maize hybrids tested across five research field stations (Karaj, Shiraz, Kermanshah, Kerman and Mashhad) in two years, genotypes were ranked by success probability under a 20% selection intensity. H15, H10, and H17 hybrids showed the highest marginal probability of superior performance. H15 outperformed all tested hybrids, while H10 exceeded most (p >0.90) except H15. For grain yield stability, H20, H16, and H05 hybrids ranked highest. Combining performance and grain yield stability probabilities, H20, H16, and H15 hybrids were top candidates. The Bayesian approach effectively identified genotypes with high grain yielding and yield stability, providing a robust tool for maize breeding programs. By quantifying probabilistic outcomes, this method enhances decision-making, ensuring precise selection and recommendation of maize hybrids tailored for target environments.
Keywords: Maize, multi-environment trial, probability, genotype × environment interaction, grain yield stability.
Introduction
Genotype × environment interaction (GEI) explains how genotypes perform across different environments, which can be either simple (no change in genotype rankings) or complicated (rankings change inversely). Complicated GEI challenges plant breeders, as it indicates instability in performance of genotype, making cultivar recommendations difficult. To address this challenge, yield stability and adaptability analyses are conducted to identify adapted genotypes with high yield and yield stability across environments. Various methods have been proposed, differing in their statistical approaches and adaptability concepts. A novel approach introduced by Dias et al. (2022) introduces a Bayesian probabilistic method, which integrates prior information and ranks genotypes by superiority using selection intensity. This method addresses critical issues, such as the probability of a new genotype outperforming existing ones and the risk of failure in specific environments. By combining information from multi-environment trails, the Bayesian approach provides greater inferential power, enabling more accurate predictions and decision-making in plant breeding programs. This advancement helps breeders to identify adapted genotypes with high yield and yield stability reducing the risks associated with GEI and improving cultivar development. This study aimed to employ Bayesian probabilistic approach for risk assessment in selection and recommendation of new maize (Zea mays L.) hybrids for target environments.
Materials and Methods
In this study 20 promising maize hybrids and two commercial hybrids (H21 and H22) were evaluated in five research filed stations (Karaj, Shiraz, Kermanshah, Kerman and Mashhad) over two years (2023 and 2024). The experimental design was randomized complete block design with four replications. Each plot consisted four rows of 6.12 meters length with 75 centimeter row spacing, and plant density of 78,000 ha-1. Three seeds were planted in each hill, thinned to two plants at the 4-5 leaf stage. Crop management practices including; irrigation, weed control, and fertilization applications, were followed as recommended for each location. Initial statistical analysis involved simple analysis of variance for each environment to assess genotypic variation, experimental precision, and residual variance homogeneity. Then, combined analysis of variance was performed, which revealed significant genotype × environment interaction (GEI). Adaptability and grain yield stability were estimated using the method introduced by Dias et al. (2022) implemented through the ProbBreed package in R.
Results and Discussion
Combined analysis of variance revealed that the effects of hybrids, environments, and genotype × environment interaction (GEI) were significant (p < 0.01). This highlights the complication of GEI, and indicated that top-performing hybrids in one environment may not be excel in others, necessitating environment-specific cultivar recommendations over general adaptability. Bayesian probabilistic models were justified for more reliable recommendations.
Hybrid H15 as the most promising, had the highest marginal probability of superior performance. This hybrid outperformed other hybrids, including checks, in seven out of nine environments. Hybrids H10 and H17 also ranked high with 98% and 94% probabilities, respectively, of belonging to the top-performing subset. While hybrid H15 had 71% probability of outperforming over Hybrid H10, as it underperformed in environments E03 and E07, where hybrid H10 and H17 were selected and recommended. At the 20% selection intensity, hybrid H15 was the only hybrid common to both the top-performing (H15, H10, H17, H20, H16) and high grain yield stability (H07, H03, H13, H12, H15) groups. High-performing with high grain yield stability hybrids like H15, H10, and H17 reduce risks of new hybrids selection and recommendation for target environments. These findings are in accordance with results reported by Malikouski et al. (2024) and Miranda et al. (2024), validating the reliability of Bayesian approaches in crop breeding strategies. Using the multi-traits stability index (MTSI), hybrids H15, H11, and H10 were the top-ranked hybrids.
Overall, hybrids H15 and H10 combining superior performance, grain yield stability, and adaptability, were identified as the most promising hybrids for recommendation to target environments. Bayesian probabilistic approaches provided precise, reliable tool for hybrids recommendations by directly interpreting genotype performance and grain yield stability across test environments, enhancing decision-making in maize breeding programsu.
References
Dias, K. O. G., Santos, J. P. R., Krause, M. D., Piepho, H.-P., Guimarães, L. J. M., Pastina, M. M. and Garcia, A. A. F. 2022. Leveraging probability concepts for cultivar recommendation in multi-environment trials. Theoretical and Applied Genetics, 135(4), pp.1385–1399. DOI: 10.1007/s00122-022-04041-y
Malikouski, R. G., Ferreira, F. M., Chaves, S. F. S., Couto, E. G. O., Dias, K.O. G., and Bhering, L.L. (2024). Recommendation of Tahiti acid lime cultivars through Bayesian probability models. PLOS ONE, 19(3), e0299290. DOI: 10.1371/journal.pone.0299290
Miranda, I. R., Dias, K. O. G., Júnior, J. D. P., Carneiro, P. C. S., Carneiro, J. E. S., Carneiro, V. Q., Souza, E. A., Melo, L. C., Pereira, H. S., Vieira, R. F. and Martins, F. A. D. 2024. Use of Bayesian probabilistic model approach in common bean varietal recommendation. Crop Science, 64(6), pp.3163-3173. DOI: 10.1002/csc2.21340
کلیدواژه‌ها [English]
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