Flowering represents the most important process in the reproductive stage of fruit trees, including olive trees. Previous studies have demonstrated that the genotype-environment interaction (GEI) has a considerable influence on olive flowering time. This study investigated the GEI and genetic parameters influencing olive flowering phenology in Southwestern China (a non-Mediterranean region), using multi-trait-based stability selection methods. Sixteen olive cultivars from five countries were evaluated over two years in two distinct climatic regions of Southwestern China. Flowering phenology was assessed based on three parameters: full-bloom date (FBD), flowering-period length (FP), and full-bloom-period length (FBP). In the analyses, the best linear unbiased prediction (BLUP) to predict genetic value and genotype + genotype by environment interaction (GGE) biplot methods to visualize and assess stability and performance were employed across four environments. The results showed that genotype, environment, and GEI had highly significant effects on flowering traits, with GEI accounting for 54.12% to 89.62% of the variance. Heritability values were low (0.0589 to 0.262), indicating that genetic factors had limited control over flowering phenology compared to environmental factors. A stability analysis using a mean performance and stability (MPS) index identified genotypes with earlier flowering dates and longer flowering periods. Multi-trait selection using a multi-trait mean performance and stability (MTMPS) index further highlighted six superior genotypes with high performance and stability across environments. The findings emphasize the critical role of environmental factors on olive flowering phenology, highlighting the challenges in breeding for stable flowering traits. This study demonstrates the effectiveness of multi-trait selection methods in identifying genotypes with superior performance and stability under different environmental conditions. These results provide valuable insights for olive breeding programs, particularly in non-Mediterranean regions, suggesting that targeted selection and multi-trait evaluation could enhance the adaptability and productivity of olive cultivars under changing climatic conditions.
Keywords: GEI; GGE-biplot; MPS; MTMPS; multi-environment trial (MET); olive tree flowering phenology; weighted average of absolute scores (WAASB).