Associação entre caracteres agronômicos e índices de vegetação na seleção de linhagens de soja

Abstract

The selection of superior soybean lines is a crucial process and involves the evaluation of multiple traits. In this context, high-throughput phenotyping represents an important tool in breeding programs. Vegetation indices constitute a promising technique for predicting grain yield and traits associated with photosynthetic efficiency. However, the adoption of these tools must result in representative and accurate information. In this sense, the repeatability coefficient is a parameter that allows the assessment of the consistency and reliability of trait estimates to be transferred to the offspring. Given the above, the objectives were: (i) to evaluate the repeatability of the vegetation indices NDVI and GLI; (ii) to investigate the potential of these indices in the multi-trait selection of superior soybean lines using the MGIDI index; and (iii) to compare selection criteria with and without the inclusion of spectral indices. Data from Value for Cultivation and Use (VCU) trials conducted in the municipalities of Ijaci, Campanha, Nazareno, and São João del-Rei, during the 2023/24 and 2024/25 growing seasons, were used. The experiments were carried out in a randomized complete block design with three replications. The following traits were evaluated: days to maturity, plant height, lodging, and grain yield. Vegetation indices were obtained from images captured by multispectral (NDVI) and RGB (GLI) cameras mounted on a Mavic drone. The experimental data were subjected to individual and joint analyses of variance. A resampling simulation was also performed to evaluate the effect of combining environments on repeatability estimates. Genotype selection was conducted using a multi-trait approach through the MGIDI index, and the agreement among different selection strategies was assessed using the coincidence index (CI). The integration of vegetation indices with agronomic traits via MGIDI is efficient for selecting superior soybean lines. NDVI shows higher repeatability than GLI. However, GLI stands out as a viable and lower-cost alternative, with high agreement with NDVI. Greater selection gains were obtained in multi-trait analyses involving multiple environments and growing seasons, highlighting that vegetation indices are reliable complementary tools in multi-environment evaluations.