High throughput screening of Leaf Economics traits in six wine grape varieties

Abstract

Reflectance spectroscopy has become a powerful tool for non-destructive and high- throughput phenotyping in crops. Emerging evidence indicates that this technique allows for estimation of multiple leaf traits across large numbers of samples, while alleviating the constraints associated with traditional field- or lab-based approaches. While the ability of reflectance spectroscopy to predict leaf traits across species and ecosystems has received considerable attention, whether or not this technique can be applied to quantify within species trait variation have not been extensively explored. Employing reflectance spectroscopy to quantify intraspecific variation in functional traits is especially appealing in the field of agroecology, where it may present an approach for better understanding crop performance, fitness, and trait-based responses to managed and unmanaged environmental conditions. We tested if reflectance spectroscopy coupled with Partial Least Square Regression (PLSR) predicts rates of photosynthetic carbon assimilation (Amax), Rubisco carboxylation (Vcmax), electron transport (Jmax), leaf mass per area (LMA), and leaf nitrogen (N), across six wine grape (Vitis vinifera) varieties (Cabernet Franc, Cabernet Sauvignon, Merlot, Pinot Noir, Viognier, Sauvignon Blanc). Our PLSR models showed strong capability in predicting intraspecific trait variation, explaining 55%, 58%, 62%, and 64% of the variation in observedJmax,Vcmax, leaf N, and LMA values, respectively. However, predictions ofAmaxwere less strong, with reflectance spectra explaining only 29% of the variation in this trait. Our results indicate that trait variation within species and crops is less well-predicted by reflectance spectroscopy, than trait variation that exists among species. However, our results indicate that reflectance spectroscopy still presents a viable technique for quantifying trait variation and plant responses to environmental change in agroecosystems.