Assessing the Efficiency of Phenotyping Early Traits in a Greenhouse Automated Platform for Predicting Drought Tolerance of Soybean in the Field

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.

Dissecting new genetic components of salinity tolerance in two-row spring barley at the vegetative and reproductive stages

“…Similarly, Peirone et al . [ 77 ] found that transpiration efficiency measured in greenhouse conditions at the early stages of soybean growth was the most efficient trait in predicting drought tolerance in the field and Knoch et al . [ 75 ] showed that early plant growth of canola was a complex trait that involves several temporally dynamic loci with medium and small effects.…”

“…In the end, the paper was focused on QTL associated with transpiration efficiency [24]. Similarly, Peirone et al [77] found that transpiration efficiency measured in greenhouse conditions at the early stages of soybean growth was the most efficient trait in predicting drought tolerance in the field and Knoch et al [75] showed that early plant growth of canola was a complex trait that involves several temporally dynamic loci with medium and small effects. Furthermore, the heritabilities of TPA-measured traits were relatively low in our experiments, ranging between 0.5 and 0.6 (S6 Table), and these values were based on spatially adjusted means.…”

A Robust Automated Image-Based Phenotyping Method for Rapid Vegetative Screening of Wheat Germplasm for Nitrogen Use Efficiency

“…Associations between greenhouse and field trials using the same varieties is further complicated by other environmental factors such as soil type, microorganisms, N and water availability (Cormier et al, 2016; Nguyen et al, 2017). However, several studies have reported a causal relationship between greenhouse screens and the field performance of crops (Chapuis et al, 2012; Pardo et al, 2015; Peirone et al, 2018). Therefore, results from vegetative screens in greenhouses, like those in the current study, can still be useful indicators of the performance of genotypes for NUE, which can help reduce the time and cost of developing new breeding materials.…”

Detection of quantitative trait loci associated with drought tolerance in St. Augustinegrass

“…Greenhouse experiments provide a controlled, stable environment and reproducible evaluations, but are limited in the size of plots that can be used for evaluation. On the other hand, field experiments reflect ‘real-world’ responses under water deficit stress, but these can also be affected by complicated environmental factors [20]. Given consideration of the environmental difference, both greenhouse and field phenotypic data were collected for QTL analysis in this study, which could enable us to detect drought response QTL in various environments.…”