Molecular Plant Breeding 2024, Vol.15, No.6, 362-370 http://genbreedpublisher.com/index.php/mpb 364 3.2 Field trials and evaluation of new varieties Field trials are indispensable for evaluating the performance of new sorghum hybrids under various environmental conditions. For example, a study on sorghum hybrids for silage production in semiarid conditions utilized a randomized block design to assess fresh matter yield (FMY) and dry matter yield (DMY) across multiple treatments (Perazzo et al., 2017). Similarly, the performance of photoperiod-sensitive sorghum hybrids was evaluated in farmer-managed and on-station trials in Mali, revealing significant genotypic differences and limited genotype × environment interactions (Rattunde et al., 2013). These trials are crucial for identifying hybrids with superior agronomic traits and adaptability to different growing conditions (Fonseca et al., 2021). 3.3 Analysis of agronomic performance The analysis of agronomic performance involves assessing various traits such as yield, plant height, and resistance to diseases. For instance, a study on the genomic prediction of hybrid performance in sorghum demonstrated that including both additive and dominance effects in prediction models significantly improved the accuracy of yield predictions (Hunt et al., 2020). Another study focused on the combining ability and heterosis for grain Fe and Zn concentration, highlighting the potential for developing biofortified sorghum hybrids with enhanced nutritional value (Gaddameedi et al., 2020). These analyses provide valuable insights into the genetic and environmental factors influencing hybrid performance, guiding the selection and breeding of high-yielding, resilient sorghum varieties. 4 Yield Performance Analysis 4.1 Yield trials under different environmental conditions Yield trials for the newly developed hybrid sorghum varieties were conducted across various environmental conditions to assess their performance and stability. Multi-environment trials (MET) were fundamental in evaluating genotype-by-environment interactions (G×E), which significantly influence yield outcomes. For instance, trials conducted in China using the AMMI and GGE biplot models revealed that significant G×E effects (p < 0.001) impacted yield performance, with broad-sense heritability estimates ranging from 0.40 to 0.94 (Wang et al., 2023). Similarly, in Australia, the inclusion of both additive and dominance effects in multi-environment models improved the prediction accuracy of hybrid performance, highlighting the importance of considering environmental variability (Hunt et al., 2020). In Senegal, trials demonstrated that specific genotypes showed particular adaptations to local conditions, with significant G×E interactions affecting both grain and biomass yields (Ndiaye et al., 2019). These findings underscore the necessity of conducting yield trials under diverse environmental conditions to identify high-yielding and stable sorghum hybrids. 4.2 Comparison with existing sorghum varieties The performance of the new hybrid sorghum varieties was compared with existing varieties to determine their relative advantages. In a study conducted in West Africa, new Guinea-race hybrids exhibited 20 to 80% higher yields compared to local varieties under both low and high phosphorus conditions (Kante et al., 2019). Another study in China identified two superior genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), which outperformed other varieties in terms of yield and stability across multiple environments (Wang et al., 2023). In the United States, the evaluation of sorghum hybrids under different water and heat stress patterns revealed that new hybrids could maintain higher yields under stress conditions compared to traditional varieties (Carcedo et al., 2022). These comparisons indicate that the newly developed hybrids have the potential to significantly enhance sorghum production, particularly in challenging environmental conditions. 4.3 Statistical analysis of yield data Statistical analyses were employed to rigorously evaluate the yield data from the trials. The use of AMMI and GGE biplot models in China captured more than 66.3% of the total variance, demonstrating their effectiveness in analyzing G×E interactions and identifying stable, high-yielding genotypes (Wang et al., 2023). In Australia, linear mixed models incorporating both additive and dominance effects provided a more accurate prediction of hybrid performance, with the inclusion of dominance effects increasing prediction accuracies by up to 60% (Hunt et al., 2020). In Senegal, the joint analysis of variance revealed highly significant effects of genotypes,
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