The screening of poplar varieties that demonstrate tolerance to low nitrogen (N) represents a promising strategy for improving nitrogen-use efficiency in trees. Such an approach could reduce reliance on N fertilizers while mitigating environmental pollution associated with their cultivation. In this study, a total of 87 poplar varieties were evaluated in a controlled greenhouse pot experiment. Under both low-nitrogen (LN) and normal-nitrogen (NN) conditions, 18 traits spanning four categories—growth performance, leaf morphology, chlorophyll fluorescence, and N isotope parameters were measured. For 13 of these traits (growth, leaf morphology, chlorophyll fluorescence), genetic variation and parameters, including genotypic values, were analyzed using best linear unbiased prediction (BLUP) within a linear mixed model (LMM). LN tolerance of tested poplar varieties was comprehensively assessed with three MGIDI strategies by integrating means, BLUPs, and low-nitrogen tolerance coefficient (LNindex) to rank poplar varieties. The results exhibited highly significant differences across all traits between LN and NN experiments, as well as among varieties. LN stress markedly inhibited growth, altered leaf morphology, and reduced chlorophyll fluorescence parameters in young poplar plants. Among the selection strategies, the MGIDI_LNindex approach demonstrated the highest selection differential percent (SD% = 10.5–35.23%). Using a selection intensity (SI) of 20%, we systematically identified 17 superior genotypes across all three strategies. In a thorough, comprehensive MGIDI-based evaluation, these varieties exhibited exceptional adaptability and stability under LN stress. The selected genotypes represent valuable genetic resources for developing improved poplar cultivars with enhanced low-nitrogen tolerance.

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.

Background: Breeding drought-tolerant poplar cultivars necessitates efficient selection strategies that can simultaneously improve multiple traits. This study evaluated the integration of multivariate selection indices with Weighted Rank Aggregation (WRA) to identify superior genotypes in hybrid poplar progenies. Methods: We assessed 372 F ₁ progenies from three families of Populus simonii × P. nigra under controlled drought stress and well-watered conditions. Data on 16 growth, leaf, and photosynthetic traits were analyzed using four multivariate indices: the Smith-Hazel Index (SHI), FAI-BLUP, and two Multi-Trait Genotype-Ideal Genotype Distance Index (MGIDI) variants. The rankings were integrated using WRA. Results: Genetic parameters revealed high heritability for key growth traits. The selection indices exhibited divergent focus, with SHI showing strong directional selection for growth but sensitivity to multicollinearity, while FAI-BLUP and MGIDI enabled more balanced multi-trait improvements. Most indices were weakly correlated (Spearman’s |r| < 0.2), indicating complementary information. Venn analysis identified genotypes (e.g., C4‑246, E4‑70) performing consistently across multiple indices. The final WRA integration selected robust genotypes, including C2‑65, C4‑210, E4‑115, and E4‑70, which combine drought tolerance with desirable growth and physiological characteristics. Conclusions: Integrating multiple selection indices with WRA provides a powerful and reliable strategy for selecting drought-tolerant poplar genotypes at the seedling stage. This approach effectively balances genetic gains across traits, enhancing the efficiency of breeding programs for stress resilience.