|Title||Deciphering messy data on plant root physiology to improve breeding practices|
|Presenter||Grace Chiu (Australian National University)|
|Author(s)||Grace S Chiu; Anton P Wasson; Alexander Zwart|
To ensure future food security, a key objective of crop breeding programs is to effectively identify which genetic and physiological characteristics of the plant are associated with high yield and/or resistance to environmental stressors. Regarding physiology, the part of the plant that is above-ground is easily observed and thus commonly emphasized. However, the root system is perceivably more sensitive to soil-related stressors yet notoriously challenging to (a) measure and (b) characterize. For (a), recent imaging technology can evaluate the number of roots at regular depths along soil cores that are sampled from the crop field. This method results in 1-dimensional spatial data on within-core root counts. For (b), we develop an integrative modelling framework that regards the spatial count data as longitudinal in nature, exhibiting a parametric trend that depends on the plant's genotype (or "breed"). Under our framework, we define new measures of heritability — the variability among cores that is due to genetics as opposed to noise. The novelty of our methodology lies in the ability to reflect root architecture as a whole by accounting for within-core root counts collectively (DOI: 10.3389/fpls.2017.00282). Applied to a field study in Australia, our approach indicates an overall heritability of 0.52-0.71 (95% credible interval), which is substantially higher than previous methods. This suggests that our approach is much more effective in discerning root architecture as captured by soil core data.
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