Virtual StatsPD@Waite meeting

Every month, the professional development meetings of statisticians and data scientists at Waite, known as StatsPD@Waite, bring together specialists in various aspects of data sciences in agriculture from Waite, Roseworthy and Adelaide.

The next StatsPD@Waite meeting will take place on 11 August where Chris Brien will present on his latest work on smoothing and extraction of traits method for analysing longitudinal data.

Email Beata Sznajder  for details of the Zoom meeting.

Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data

Chris Brien

Brien et al. (2020) was recently published in the open-access journal Plant Methods (http://dx.doi.org/10.1186/s13007-020-00577-6). We claim to provide a novel, computationally efficient technique for analyzing longitudinal data that is based on smoothing and extraction of traits (SET). We compare SET-based analyses with traditional  longitudinal analyses for a tomato experiment run at the Plant Accelerator in Adelaide and demonstrate that the oft-omitted unequal variances and autocorrelation are required for a valid longitudinal analysis of the example. Two reasons for deploying the SET-based method are (i) biologically relevant growth parameters are required that parsimoniously describe growth, usually focussing on a small number of intervals, and/or (ii) a computationally efficient method is required for which a valid analysis is easier to achieve, while still capturing the essential features of the exhibited growth dynamics. I will describe the methods and results for the tomato experiment and discuss the advantages and disadvantages of the methods.

Brien, C., Jewell, N., Garnett, T., Watts-Williams, S. J., & Berger, B. (2020). Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data. Plant Methods, 16, 36.