Antonio Lucas Aguiar Lobo

Accurate phenotyping is essential for genetic evaluation in forest breeding, but repeated measurements in large clonal trials are labor-intensive and can be confounded by within-site environmental heterogeneity. Unmanned aerial vehicles (UAVs) equipped with red–green–blue (RGB) sensors provide a scalable approach for repeated structural and spectral phenotyping. We hypothesized spatial correction and RGB covariates may improve prediction precision, while reaction norms reveal genotype-specific environmental sensitivity. Therefore, we evaluated UAV-derived canopy height and RGB vegetation indices in two Brazilian Eucalyptus clonal trials. The site from São José dos Campos (SP) included pure E. grandis and E. grandis × E. urophylla genotypes (45 clones, 2,576 trees; five campaigns at 419, 470, 530, 645, and 956 days after planting [DAP]). The site from Três Lagoas (MS), included pure E. urophylla and E. urophylla × E. grandis genotypes (72 clones, 6,048 trees; four campaigns at 272, 341, 403, and 463 DAP). Four mixed models were compared: a randomized complete block model (M1), M1 plus a spectral covariate (M2), a two-dimensional spatial P-spline model (M3), and the spatial model plus a spectral covariate (M4). UAV canopy height was validated against 777 field measurements (R² = 0.74, r = 0.86, RMSE = 1.61 m, relative error = 23.33%). Adding VARI slightly increased the model fit (AIC) in both locations. Conversely, broad-sense heritability increased substantially from 0.56 (M1) and 0.58 (M2) to 0.84 (M3) and 0.85 (M4), reaching 0.91 in later campaigns using spatial models. Overall, UAV-based RGB phenotyping provided repeatable, high-resolution information across Eucalyptus clonal trials, while spatial modeling produced the largest gains in prediction precision. However, RGB covariates provided limited additional gains after spatial correction. Finally, reaction norms revealed clone-specific responses to environmental factors, highlighting the potential of integrating UAV phenotyping, spatial models, and environmental covariates for high-throughput clonal evaluation.

Using reaction norm components instead of traditional phenotypic data in genetic association studies (GWAS) may allow the identification of genomic regions that are more influenced by environmental variables in terms of tolerance and responsiveness to water stress. To test this hypothesis, we used a public genetic diversity panel of tropical maize inbred lines, evaluated in eight environments, four in well‐watered (WW) and four in water stress (WS) conditions. Most SNPs explained at least 40% of the genetic variability, and some reached 67%. The identified genes and genomic regions revealed physiological responses and direct or indirect molecular mechanisms related to water deficit tolerance and responsiveness. This information will enable more assertive selections and subsidize breeding programs aimed at obtaining cultivars for water deficit conditions while reducing the costs of the evaluation processes of reaction standards.