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RESEARCH ARTICLE
Contents Vol 75(3)

Multivariate assumptions and effect of model parameters in path analysis in oat crop

Jaqueline Sgarbossa https://orcid.org/0000-0001-7541-090X A * , Alessandro Dal’Cól Lúcio https://orcid.org/0000-0003-0761-4200 A , José Antonio Gonzalez da Silva https://orcid.org/0000-0002-9335-2421 B , Braulio Otomar Caron https://orcid.org/0000-0002-6557-3294 C , Maria Inês Diel https://orcid.org/0000-0002-7905-2166 D , Tiago Olivoto https://orcid.org/0000-0002-0241-9636 E , Claiton Nardini https://orcid.org/0000-0001-5791-6720 F , Odenis Alessi https://orcid.org/0000-0002-3509-6984 B and Darlei Michalski Lambrecht https://orcid.org/0000-0002-1376-3504 A
+ Author Affiliations
- Author Affiliations

A Department of Plant Science, Federal University of Santa Maria, Santa Maria, Rio Grande do Sul, Brazil.

B Department of Agronomy, Regional University of Northwestern Rio Grande do Sul State, Ijuí, Rio Grande do Sul, Brazil.

C Department of Agronomy and Natural Sciences, Federal University of Santa Maria Campus Frederico Westphalen, Frederico Westphalen, Rio Grande do Sul, Brazil.

D Federal University of Pampa, Itaqui, Rio Grande do Sul, Brazil.

E Department of Plant Science, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil.

F Department of Forest Science, Federal University of Paraná, Curitiba, Paraná, Brazil.

* Correspondence to: sgarbossajs@yahoo.com

Handling Editor: Davide Cammarano

Crop & Pasture Science 75, CP23135 https://doi.org/10.1071/CP23135
Submitted: 12 May 2023  Accepted: 7 February 2024  Published: 1 March 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context

Path analysis (PA) is a widely used multivariate statistical technique. When performing PA, the effects of the parameters of the mathematical model relating to the experimental design are disregarded, working only with the average effects of the treatments.

Aims

We aimed to analyse the implications of statistical assumptions, and of removing mathematical model parameters, on the PA results in oat.

Methods

A field study was conducted in southern Brazil in five crop years. The experimental design employed was a two-factor 22 × 5 randomised complete block design, characterised by 22 cultivars and five fungicide applications, with three repetitions. Six explanatory variables were measured, panicle length, panicle dry mass, panicle spikelet number, panicle grain number, panicle grain dry mass, and harvest index, and the primary variable yield. Initially, normality and multicollinearity diagnoses were carried out and correlation coefficients were calculated. The PA was performed in three ways: traditional, with measures to address multicollinearity (ridge), and traditional with eliminating variables.

Key results and conclusions

The occurrence of multicollinearity resulted in obtaining path coefficients without biological application. Removing the model’s parameters modifies the path coefficients, with average changes of 10.5% and 13.3% in the direction, and 24.7% and 23.0% in the magnitude, of the direct and indirect effects, respectively.

Implications

This new approach makes it possible to remove the influences of treatments and experimental design from observations and, consequently, from path coefficients and their interpretations. Therefore, the researcher will reduce possible bias in the coefficient estimates, highlighting the real relationship between the variables, and making the results and interpretations more reliable.

Keywords: Avena sativa, linear relationships, multicollinearity, multivariate analysis, parameter removal, simple correlation, variable elimination, yield components.

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