57 research outputs found
Spectral Phenotyping of Physiological and Anatomical Leaf Traits Related with Maize Water Status
Advancements in phenotyping techniques capable of rapidly and nondestructively detecting impacts of drought on crops are
necessary to meet the 21st-century challenge of food security. Here, we describe the use of hyperspectral reflectance to predict
variation in physiological and anatomical leaf traits related with water status under varying water availability in six maize (Zea
mays) hybrids that differ in yield stability under drought. We also assessed relationships among traits and collections of traits
with yield stability. Measurements were collected in both greenhouse and field environments, with plants exposed to different
levels of water stress or to natural water availability, respectively. Leaf spectral measurements were paired with a number of
physiological and anatomical reference measurements, and predictive spectral models were constructed using a partial leastsquares
regression approach. All traits were relatively well predicted by spectroscopic models, with external validation (i.e. by
applying partial least-squares regression coefficients on a dataset distinct from the one used for calibration) goodness-of-fit (R2)
ranging from 0.37 to 0.89 and normalized error ranging from 12% to 21%. Correlations between reference and predicted data
were statistically similar for both greenhouse and field data. Our findings highlight the capability of vegetation spectroscopy to
rapidly and nondestructively identify a number of foliar functional traits affected by drought that can be used as indicators of
plant water status. Although we did not detect trait coordination with yield stability in the hybrids used in this study, expanding
the range of functional traits estimated by hyperspectral data can help improve trait-based breeding approaches
How can we harness quantitative genetic variation in crop root systems for agricultural improvement?
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