Abstract
Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping.
Highlights
Phenotyping is currently the bottleneck in breeding for many traits, including drought tolerance (Richards et al, 2010; Montes et al, 2011), mostly due to the cost of genotyping having largely decreased during the last years in relation to that of phenotyping
The drought susceptibility index (DSI) for final Shoot dry weight (SDW) correlated with yield DSIs found by Pardo et al (2015) in greenhouse conditions (Experiments 2 and 3 from Pardo et al, 2015; r = 0.81, p < 0.05 and r = 0.97, p < 0.05, respectively) and in field trials (r = 0.99, p < 0.10)
In Experiment 1, TJ2049 showed the highest DSI for both SDW and leaf area (LA), while Munasqa had the lowest index for both traits (Figures 2A,B)
Summary
Phenotyping is currently the bottleneck in breeding for many traits, including drought tolerance (Richards et al, 2010; Montes et al, 2011), mostly due to the cost of genotyping having largely decreased during the last years in relation to that of phenotyping. High-throughput and reproducible phenotyping is crucial for accelerating the release of improved varieties (Vadez et al, 2012). To help attain this goal, automated phenotyping platforms have been developed, which aim at increasing the capacity for obtaining phenotypic information. Operational costs of commercial and custom-made phenotyping platforms, have not received much attention, but can be crucial for assessing the practical applicability of this technology
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