R for Agricultural Scientists
R has been the dominant academic statistics environment for agricultural research for over two decades. The agricolae package covers most classical designs, lme4 covers mixed effects, and the tidyverse handles data manipulation. R is free and powerful but has a real learning curve. This page covers the parts agricultural scientists most often need and points to a browser alternative for the rest.
What R is genuinely good at
Anywhere you need to script a reproducible pipeline, fit a non-textbook model, or use a method that is published only as an R package, R is the right answer. Examples: spatial mixed-effects models with anisotropic covariance, AMMI with bootstrap confidence intervals, Bayesian variable selection. R also has the strongest plotting story (ggplot2) and the most peer-reviewed statistical packages. The cost is the learning curve and the install friction.
agricolae quick start
The agricolae package by Felipe de Mendiburu covers CRD (HSD.test, LSD.test, duncan.test, scheffe.test), RBD, Latin square, factorial, split-plot, lattice, augmented designs, and combining ability analyses. It is the closest thing to a one-package solution for classical agronomy. Install with install.packages('agricolae'), then design.crd, design.rbd, design.split and so on for layout, and the tests above for analysis. The reference manual on CRAN is the canonical guide.
When a browser tool is faster
For a single trial that needs a CRD, RBD, factorial RBD, split-plot, Latin square or post-hoc bundle, opening agricolae in R costs more time in install and syntax than the analysis itself. A browser tool that produces the same numbers (StatVeda's calculations are cross-checked against R for the designs in src/cross-check/fixtures/) plus a citation block and a Word or PPT export is faster end to end for that workload. R remains the right answer when the analysis is not in a textbook or when reproducibility scripts must be archived.