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Path analysis and near-infrared spectroscopy in canola crop

dataset
posted on 20.09.2022, 07:16 authored by Alexsandro Carvalho Santiago, Guilherme Vieira Pimentel, Adriano Teodoro Bruzi, Inara Alves Martins, Paulo Ricardo Gherardi Hein, Michael Douglas Roque Lima, Dyanna Rangel Pereira

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).

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