Path analysis and near-infrared spectroscopy in canola crop

被引：2

作者：

Santiago, Alexsandro Carvalho ^{[1
]}

Pimentel, Guilherme Vieira ^{[1
]}

Bruzi, Adriano Teodoro ^{[1
]}

Martins, Inara Alves ^{[1
]}

Hein, Paulo Ricardo Gherardi ^{[2
]}

Lima, Michael Douglas Roque ^{[2
]}

Pereira, Dyanna Rangel ^{[3
]}

机构：

[1] Univ Fed Lavras UFLA, Dept Agr, BR-37200900 Lavras, MG, Brazil

[2] Univ Fed Lavras UFLA, Dept Ciencias Florestais, Lavras, MG, Brazil

[3] Univ Fed Lavras UFLA, Dept Biol, Lavras, MG, Brazil

来源：

CIENCIA RURAL | 2023年 / 53卷 / 06期

关键词：

Brassica napus L; var; oleifera; oleaginous; association between traits; genetic breeding; NIR; BRASSICA-NAPUS L; OIL CONTENT; CALIBRATION; PRODUCTS;

D O I：

10.1590/0103-8478cr20220071

中图分类号：

S3 [农学（农艺学）];

学科分类号：

0901 ;

摘要：

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).

引用

页数：12