BackgroundIn the early stages of plant breeding programs high-quality phenotypes are still a constraint to improve genetic gain. New field-based high-throughput phenotyping (HTP) platforms have the capacity to rapidly assess thousands of plots in a field with high spatial and temporal resolution, with the potential to measure secondary traits correlated to yield throughout the growing season. These secondary traits may be key to select more time and most efficiently soybean lines with high yield potential. Soybean average canopy coverage (ACC), measured by unmanned aerial systems (UAS), is highly heritable, with a high genetic correlation with yield. The objective of this study was to compare the direct selection for yield with indirect selection using ACC and using ACC as a covariate in the yield prediction model (Yield|ACC) in early stages of soybean breeding. In 2015 and 2016 we grew progeny rows (PR) and collected yield and days to maturity (R8) in a typical way and canopy coverage using a UAS carrying an RGB camera. The best soybean lines were then selected with three parameters, Yield, ACC and Yield|ACC, and advanced to preliminary yield trials (PYT).ResultsWe found that for the PYT in 2016, after adjusting yield for R8, there was no significant difference among the mean performances of the lines selected based on ACC and Yield. In the PYT in 2017 we found that the highest yield mean was from the lines directly selected for yield, but it may be due to environmental constraints in the canopy growth. Our results indicated that PR selection using Yield|ACC selected the most top-ranking lines in advanced yield trials.ConclusionsOur findings emphasize the value of aerial HTP platforms for early stages of plant breeding. Though ACC selection did not result in the best performance lines in the second year of selections, our results indicate that ACC has a role in the effective selection of high-yielding soybean lines.

The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various highthroughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.

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In soybean, stink bugs are considered the most important pest insect as they feed directly from the grain, causing significant losses in seed yield and quality. The use of resistant genotypes is a promising strategy to control these insects. Focusing on selection of soybean lines with resistance and high yield potential, 251 recombinant inbred lines (RILs), derived from a cross between IAC‐100 (resistant) and CD‐215 (susceptible), were evaluated in two experiments, designed as alpha‐lattice, with three replicates in Piracicaba, during the growing seasons of 2012/13 and 2013/14. The evaluated traits were as follows: number of days to maturity (NDM), plant height at maturity (PHM), grain filling period (GFP), lodging (L), agronomic value (AV), grain yield (GY), weight of a hundred seeds (WHS), leaf retention (LR), and healthy seeds weight (HSW). Variance components were estimated by the Restricted Maximum Likelihood method (REML). Heritability and selection gain (SG) parameters were also calculated. Selection was carried out based on 2012/13 season, considering the genotypes that exhibited a minimum HSW of 2908.26 kg ha−1 (acceptable losses of 20% from the average GY). Insect population was monitored by cloth beating. An increase in stink bug population was observed during the grain filling period, with the highest population density occurring in the season 2012/13. Estimates of the variance components demonstrated the elevate influence of the interaction genotype x environment on GY and HSW, which exhibited the lowest estimates of heritability (23 and 34%, respectively). The estimate selection gain, calculated from the predicted means of GY and HSW, was of 665.4 and 482.4 kg ha−1 season 2012/13. Therefore, the applied selection allowed the identification of the genotypes exhibiting higher yields and resistance to the stink bug complex. From the RIL population, lines or genotypes potentially useful to generate novel cultivars were identified.

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.