## 4 Discussion  
Sugarcane yield is independently affected by the genotype type, location, and year of cultivation, indicating that multiple characteristics act synergistically in its development. Additionally, highly significant differences are observed when genotypes are cultivated in different zones (G \(\times\) S), during a specific period (G \(\times\) Y), and when zones and periods are distinct (S \(\times\) Y). Our results demonstrate that genotypes vary in their yield in response to particular soil characteristics and changing environmental conditions over time. Studies such as those by [PERSON] et al., (2013) [28] and [PERSON] et al., (2014) [29] show that double interactions like genotype by location and genotype by sowing date are highly significant, explaining up to 58% of the variance. Additionally, interactions such as G \(\times\) Y guide future agricultural trials where the evaluation informs researchers whether to include a new site to assess different genotypic responses [30]. On the other hand, a broad spectrum of combinations (G \(\times\) S \(\times\) Y) does not show significant differences in all cases. These results are consistent with those of [PERSON] et al. [29], who suggest that the non-significant triple interaction indicates that yield differences attributed to location and crop type do not interact complexly with genotypes in a statistically predictable pattern, demonstrating that one or several environments are correlated, so the genotypic responses to these locations would be similar [7; 30]. In our study, we demonstrate that decoupling the factors (locations) provides more information, making at least one specific location (Sector 04) a determining factor during cultivation periods for activating certain additive genes in the varieties, causing them to respond differently in terms of yield. The analysis of the sources of variation using the Tukey HSD technique determines that the best sugarcane variety is the CLONES group, the best location is Sector 03, and the best production year is 2020. However, the GGE biplot patterns of the \"which-won-where\" indicate that the yield in CLONES responds well only to the conditions presented by environments D, G, M, and with less stability in environment J, corresponding to Sectors 02, 03, 05, and 04 during 2018, respectively. This shows that during this period, the four sectors were influenced by extrinsic factors such as the reduction of hectares dedicated to this crop [31], positive, negative, and unpredictable climatic anomalies [32], and biotic and abiotic stress interactions [23] that improve the phenotypic development of this variety and affect or maintain that of others. The mean and stability explained by the GGE biplot showed that the CLONES had high yield but high instability across several environments. Therefore, in terms of agronomic value, the genotypes ECU-01, EC-02, EC-07, or EC-08 might be the most ideal for sugarcane production. These results align with those obtained by [PERSON] et al., (2023) [5], who highlighted the productive potential of two clones from breeding programs (Co 15017 and CoSnk 15102) with high yield but high instability across various environments (high values of the sum of squared deviations), emphasizing that standard varieties offer general stability in different environments. The pattern of representativeness and discrimination shows that among all visualizations, the Ecuadorian variety EC-08 has advantages in stability (Figure 4a). However, the variety EC-07 ranks higher in the GGE biplot ranking model with better performance (Figure 4b). These results contradict the decisions made on the part of the producing sector during the 2018 sugar production decline who mostly leaned more towards the EC-08 variety [33]. Another important pattern observed in the discriminativeness vs. representativeness graph is the stability of two additional genotypes besides variety EC-08, specifically the Colombian variety CC-8592 and the Dominican variety CR-74250.