Statistical Analysis of Agricultural Data Using R

R for Agricultural Data Analysis – Experimental Designs & Multivariate Statistics

What you’ll learn
Apply biometrical tools and experimental designs such as CRD, RBD, split-plot, strip-plot, and seed testing designs in agricultural research
Analyze agricultural and plant breeding data using R (RStudio), including ANOVA, variance components, genetic parameters, GCA, SCA, and heterosis
Interpret genotype × environment interaction and stability using AMMI, GGE biplot, PCA, and cluster analysis with hands-on implementation in R
Understand biometrical genetics and perform QTL and GWAS analysis using R packages such as GAPIT and rrBLUP for crop improvement studies

Requirements
Basic understanding of agriculture or plant breeding concepts is helpful, but detailed statistical knowledge is not mandatory.
Introductory knowledge of statistics (mean, variance, ANOVA basics) is recommended for better comprehension
A computer or laptop with the ability to install R and RStudio (Windows, macOS, or Linux)
No prior programming experience in R is required; all necessary R concepts will be explained step by step during the course

Description
Develop a strong foundation in using R for the analysis of agricultural experimental data, with a particular focus on real-world field experiments and plant breeding datasets. This program is designed to help learners understand the practical aspects of data handling, statistical modeling, and interpretation within an agricultural research context.

Participants will gain the ability to design, analyze, and interpret experiments using a wide range of experimental designs, including Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), split plot, strip plot, augmented, and lattice designs. Emphasis is placed on implementing these designs in R, understanding the underlying assumptions, and correctly interpreting outputs for scientific reporting.

The course also builds competence in exploratory data analysis and visualization of agricultural traits using base R and ggplot2. Learners will develop skills in data cleaning, summarization, and graphical representation to uncover patterns, trends, and outliers in experimental data.

In addition, advanced statistical techniques such as correlation analysis, path coefficient analysis, and multivariate methods—including Principal Component Analysis (PCA), cluster analysis, and Mahalanobis D² statistics—are introduced. These tools are essential for studying genetic divergence, identifying trait relationships, and selecting suitable parents in breeding programs.

A key focus of the program is to strengthen the ability to translate statistical outputs into biologically meaningful interpretations. Learners will be trained to connect analytical results with practical breeding decisions, ensuring relevance to crop improvement strategies. By integrating statistical knowledge with domain-specific applications, the course prepares learners to handle complex datasets and contribute effectively to agricultural research and plant breeding programs.

Who this course is for
Undergraduate and postgraduate students of agriculture, plant breeding, genetics, and allied sciences who want to strengthen their statistical data analysis skills using R
PhD scholars and agricultural researchers involved in experimental design, data analysis, genotype evaluation, and interpretation of research results
Faculty members, scientists, and extension professionals seeking practical, hands-on training in biometric analysis and statistical genetics using RStudio
Seed technologists, breeding professionals, and agribusiness analysts who work with experimental and field trial data and need reliable statistical tools