Effective use of statistical tools in agricultural sciences: a critical review of multivariate modelling methods

Abstract

Objectives: A critical review was conducted to assess effective use of multivariate modelling methods for data analysis in agricultural sciences and related fields. Methodology and Results: Four main agricultural fields were considered: biology; agronomy; ecology; and food nutrition. Two journals were randomly selected per agricultural field and up to 250 articles were downloaded considering a ten-year period (2008-2017) per journal. From papers, information such as: statistical methods used; and whether multivariate modelling methods were required and used for data analysis or not, was recorded. Basic statistical methods: descriptive statistics, univariate parametric tests and related tests (post-hoc tests, normality test, and homoscedasticity tests) were the most frequently used. Advanced statistical methods such as multivariate descriptive and modelling methods, Bayesian methods, recorded the least use values. Multivariate modelling methods were rarely used though they were sometimes required according to agricultural fields. The highest and lowest effective uses of statistical methods were recorded for the agronomy and biology fields, respectively. Conclusion and application of findings: There is a gap between the development of advanced statistical methods, their usefulness and accessibility to analyse data in applied sciences especially agricultural sciences. Further investigations in statistical methods’ development may integrate and justify their usefulness in applied sciences. Collaborations between applied scientists and statisticians are necessary for better analysis of research data. Keywords: multivariate modelling techniques, effective use, agronomy, critical analysis, statistical methods