Abstract
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.
Highlights
Soybean [Glycine max (L.) Merr.] is one of the most economically important crops worldwide, being the primary source of plant-based protein, and the second largest source of vegetable oil (USDA, 2018)
We used two methods to quantify the ability of image-based features to statistically predict the above-ground biomass (AGB) in soybean: Least Absolute Shrinkage and Selection Operator (LASSO) regression and Partial Least Squares Regression (PLSR)
Besides being an important yield component, plant biomass is a foundation for unraveling several complex processes of plant growth, development, and environmental response (De Bruin and Pedersen, 2009; Koester et al, 2014; Balboa et al, 2018; Jumrani and Bhatia, 2018)
Summary
Soybean [Glycine max (L.) Merr.] is one of the most economically important crops worldwide, being the primary source of plant-based protein, and the second largest source of vegetable oil (USDA, 2018). Koester et al (2014) measured above-ground biomass (AGB) every 2 weeks in cultivars released between 1923 and 2007 and observed that biomass production per unit of absorbed light increased with the release year. Information on temporal biomass production provides insights into crop development and responses to multiple abiotic and biotic stressors (Bajgain et al, 2015; Jumrani and Bhatia, 2018). Increased temperatures and water stress have imposed vegetative and reproductive stage reduced AGB significantly and resulted in 28% and 74% reduction in soybean yield, respectively (Jumrani and Bhatia, 2018). Understanding the genetic factors controlling the temporal dynamics of biomass accumulation may contribute to future soybean yield gains and the development of stress-resilient cultivars
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