Path analysis and near-infrared spectroscopy in canola crop

Abstract

ABSTRACT: This study measured the effect of the association between agronomic traits related to the yield of canola grains grown at different sowing dates through path analysis. Another objective was to obtain a method to predict the oil content in the grains, fitting a multivariate model through near-infrared (NIR) spectroscopy analysis. The experiment was conducted in the field using a randomized block design in plots subdivided by time, with four plots (sowing dates), six subplots (canola hybrids), and four replicates. In each hybrid, phenological observations were performed, and the grain yield was determined. The data were subjected to analysis of variance in the R environment using the F test at 5% probability. The oil content in the grains was determined by the traditional chemical method, and based on the NIR spectral signature of the grain samples, partial least squares regression (PLS-R) was established to estimate the oil content in the canola grains. The sowing dates influenced the production components and oil content of the grains of all hybrids. The trait number of grains in five plants (0.6857) and their height (0.4943) had greater estimates of positive correlations with grain yield, as well as higher values of positive direct effects on yield (0.2494 and 0.1595, respectively). The NIR technique combined with PLS-R was able to predict the oil content in the grains, resulting in good predictive models (R2 of 0.86 and root mean square error (RMSE) of 1.56 in external validation).

Plant phenotyping relevance

カノーラ種子の油含量という植物形質を、NIRスペクトルとPLS-Rで推定する方法を構築し、外部検証しており、形質取得法が研究の中心的目的である。

Code and data availability

The article describes canola phenotyping (yield/phenology traits) and NIR spectroscopy with PLS-R models for oil content, but contains no data availability statement, no public dataset or spectral data deposit, and no author code or model release. Software mentioned (R, Unscrambler, Genes, Opus) are generic tools, and

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