Abstract
Major research efforts are targeting the improved performance of root systems for more efficient use of water and nutrients by crops. However, characterizing root system architecture (RSA) is challenging, because roots are difficult objects to observe and analyse. A model-based analysis of RSA traits from phenotyping image data is presented. The model can successfully back-calculate growth parameters without the need to measure individual roots. The mathematical model uses partial differential equations to describe root system development. Methods based on kernel estimators were used to quantify root density distributions from experimental image data, and different optimization approaches to parameterize the model were tested. The model was tested on root images of a set of 89 Brassica rapa L. individuals of the same genotype grown for 14 d after sowing on blue filter paper. Optimized root growth parameters enabled the final (modelled) length of the main root axes to be matched within 1% of their mean values observed in experiments. Parameterized values for elongation rates were within ±4% of the values measured directly on images. Future work should investigate the time dependency of growth parameters using time-lapse image data. The approach is a potentially powerful quantitative technique for identifying crop genotypes with more efficient root systems, using (even incomplete) data from high-throughput phenotyping systems.
Highlights
The identification of crop genotypes that can efficiently cap- for example, that crops with deeper root systems may have ture soil water and nutrients has become a major focus of better water uptake efficiency, while shallower root sysresearch (White et al, 2013b)
Density estimations based on V-fold cross-validation and Leave-One-Out (LOO) cross-validation were obtained on the root length density both of primary root and of lateral roots
Root growth parameters obtained by optimization were compared with growth parameters estimated directly from the image data and the results showed that optimized parameters matched the experimental parameters
Summary
The identification of crop genotypes that can efficiently cap- for example, that crops with deeper root systems may have ture soil water and nutrients has become a major focus of better water uptake efficiency, while shallower root sysresearch (White et al, 2013b). They must acquire other essential mineral elements (White et al, 2013a) and provide mechanical support to aerial stems (Dupuy et al, 2005a), while delivering nutrients for soil microorganisms through exudation and biomass turnover (van der Putten et al, 2013). They are able to achieve such functions in a vast heterogeneity of soil profiles and a wide range of soil physical, chemical, biotic and abiotic environments. Understanding what makes a root system more efficient is a multifaceted problem, with many aspects remaining poorly understood
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