English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data

MPS-Authors
/persons/resource/persons97324

Obata,  T.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

/persons/resource/persons97147

Fernie,  A. R.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

External Resource

Link
(Any fulltext)

Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Vergara-Diaz, O., Vatter, T., Kefauver, S. C., Obata, T., Fernie, A. R., & Araus, J. L. (2020). Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data. The Plant Journal, 102(3), 615-630. doi:10.1111/tpj.14636.


Cite as: http://hdl.handle.net/21.11116/0000-0005-5D3F-E
Abstract
Abstract Hyperspectral techniques are currently used to retrieve information concerning plant biophysical traits, predominantly targeting pigments, water and nitrogen-protein contents, structural elements, and the leaf area index. Even so,hyperspectral data could be more extensively exploited toovercomethe breeding challenges being faced under global climate change by advancing high throughput field phenotyping. In this study, we explore the potential of field spectroscopy to predict the metabolite profiles in flag leaves and ear bracts in durum wheat. The full range reflectance spectra (VIS-NIR-SWIR) of flag leaves, ears and canopies were recorded in a collection of contrasting genotypes grown in four environments under different water regimes. GC-MS metabolite profiles were analysed in the flag leaves, ear bracts, glumes and lemmas. The results from regression models exceeded 50% of the explained variation (adj-R2 in the validation sets) for at least 15 metabolites in each plant organ, whereas their errors were considerably low. The best regressions were obtained for malate (82%), glycerate and serine (63%) in leaves; myo-inositol (81%) in lemmas; glycolate (80%) in glumes; sucrose in leaves and glumes (68%); GABA in leaves and glumes (61% and 71%, respectively); proline and glucose in lemmas (74% and 71%, respectively) and glumes (72% and 69%, respectively). The selection of wavebands in the models and the performance of the models based on canopy and VIS-organ spectra and yield prediction are discussed. We feel that this technique will likely be of interest due to its broad applicability in ecophysiology research, plant breeding programs andthe agri-food industry. Table S1. Descriptive statistics of metabolite content data. Figure S1. Boxplot of metabolite content variation.