High-throughput phenotyping of maize growth dynamics under nitrogen and water stress
Authors
Božinović, SofijaDodig, Dejan
Nikolić, Ana
Zorić, Miroslav
Vančetović, Jelena
Ignjatović-Micić, Dragana
Weigelt-Fischer, Kathleen
Altmann, Thomas
Junker, Astrid
Conference object (Published version)
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Limited water availability and nitrogen deficiency are the most restricting factors for maize growth. Although maize susceptibility to drought is the highest at the reproductive stage, water shortage during vegetative development can significantly reduce grain yield. The aim of this study was to assess the genetic variation of growth dynamics in 20 maize inbred lines (ILs) through automated phenotyping based on visible light (RGB) imaging in a greenhouse under optimal (C), nitrogen (N), water (W), and combined nitrogen and water stress (NW) conditions. Thirteen biomass-related and morpho-physiological image-derived traits acquired at 33 time points were selected, covering developmental stages from five fully developed leaves to 10-13 fully developed leaves, depending on treatment. Estimated biomass volume (EBv), a proxy for biomass in our study, showed to be the most sensitive image-derived morphological trait to water and combined water and nitrogen stress with the maximum reduction o...f 53 and 54% in W and NW compared to C at the time of maximum water stress. Genotypic variation of EBv within each treatment was consistently high and over 80%, while the analysis across treatments showed that as stress got higher the genetic variation got lower (32-38%) and the interaction term became more prominent. Phenotypic correlations between EBv and other image-derived traits at early stages showed that under no stress or mild stress conditions, morphological traits were more appropriate than color-related traits for the prediction of biomass accumulation, while under more severe stress conditions, color-related traits and chlorophyll fluorescence are more useful to differentiate genotypes for high biomass. To investigate whether ILs can express EBv in similar patterns, temporal profiles were clustered by using the fuzzy c-means clustering algorithm, and two temporal dynamics of EBv patterns among the studied ILs in each treatment were identified.