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The role of root phenes in nitrogen (N) acquisition and biomass production was evaluated in 10 contrasting natural accessions of Arabidopsis thaliana L. Seedlings were grown on vertical agar plates with two different nitrate supplies. The low N treatment increased the root to shoot biomass ratio and promoted the proliferation of lateral roots and root hairs. The cost of a larger root system did not impact shoot biomass. Greater biomass production could be achieved through increased root length or through specific root hair characteristics. A greater number of root hairs may provide a low-resistance pathway under elevated N conditions, while root hair length may enhance root zone exploration under low N conditions. The variability of N uptake and the expression levels of genes encoding nitrate transporters were measured. A positive correlation was found between root system size and high-Affinity nitrate uptake, emphasizing the benefits of an exploratory root organ in N acquisition. The expression levels of NRT1.2/NPF4.6, NRT2.2, and NRT1.5/NPF7.3 negatively correlated with some root morphological traits. Such basic knowledge in Arabidopsis demonstrates the importance of root phenes to improve N acquisition and paves the way to design eudicot ideotypes., SCOPUS: ar.j, info:eu-repo/semantics/published

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.