Biometry in Agronomy Course

Lesson 1 • Probability Concepts for Agronomists

Introduces probability rules, conditional probability, and common distributions relevant to crop science. Prepares learners for hypothesis testing in later chapters.

Lesson 2 • Descriptive Statistics for Crop Data

Covers measures of central tendency, dispersion, and shape for agronomic variables. Provides tools to summarise and communicate field data effectively.

Lesson 3 • Populations, Samples, and Parameters

Distinguishes statistical populations from samples and parameters from estimates. Grounds learners in the logic of inference from field samples.

Lesson 4 • Types of Agronomic Data

Classifies continuous, discrete, categorical, and censored data common in field studies. Connects data type to appropriate analytical strategy.

Lesson 1 • Stratified and Cluster Sampling

Teaches stratification by soil type, variety, or management zone to improve precision. Cluster sampling is introduced for large or remote field areas.

Lesson 2 • Adaptive and Spatial Sampling

Introduces adaptive cluster sampling for rare pest or disease occurrences. Spatial sampling accounts for geographic autocorrelation in field data.

Lesson 3 • Sampling Error and Quality Control

Quantifies sampling error, non-sampling error, and methods to audit data quality. Connects error management to reproducibility of agronomic studies.

Lesson 4 • Principles of Agricultural Sampling

Explains representativeness, sampling frames, and error sources in field surveys. Links sampling quality directly to validity of agronomic conclusions.

Lesson 1 • Latin Square and Related Designs

Extends blocking to two-directional field gradients using Latin square arrangements. Covers Graeco-Latin squares for additional nuisance factor control.

Lesson 2 • Completely Randomized Design

Introduces the CRD as the simplest valid design for homogeneous experimental units. Learners lay out treatments and compute the ANOVA table for CRD.

Lesson 3 • Sample Size and Power Planning

Guides learners through power analysis to determine adequate replication before field work. Connects Type I and Type II error rates to practical agronomic decisions.

Lesson 4 • Principles of Experimental Design

Covers replication, randomisation, and local control as the three pillars of valid experiments. Establishes how each principle reduces bias and controls error.

Lesson 5 • Factorial Experiments and Interactions

Introduces factorial treatment structures to study main effects and interactions simultaneously. Learners interpret interaction plots in agronomic contexts.

Lesson 1 • Incomplete Block Designs

Introduces balanced incomplete block and partially balanced designs for large treatment sets. Covers recovery of inter-block information to improve efficiency.

Lesson 2 • Design Selection and Optimization

Provides a decision framework for matching design to field constraints and research objectives. Covers D-optimal and response surface designs for resource-limited trials.

Lesson 3 • Augmented and Partially Replicated Designs

Teaches augmented designs for screening large germplasm sets with limited resources. Partially replicated designs balance coverage and precision in breeding trials.

Lesson 4 • Repeated Measures and Longitudinal Designs

Addresses experiments with multiple observations over time on the same experimental unit. Learners apply mixed models to account for within-unit correlation.

Lesson 1 • ANOVA Theory and Assumptions

Derives the ANOVA model, partitions sums of squares, and states model assumptions. Learners verify assumptions before interpreting F-test results.

Lesson 2 • Planned Contrasts and Orthogonal Polynomials

Teaches a priori contrasts for testing specific hypotheses and polynomial contrasts for dose-response. Learners partition treatment degrees of freedom into meaningful components.

Lesson 3 • One-Way and Two-Way ANOVA

Applies one-way ANOVA to single-factor trials and two-way ANOVA to factorial designs. Learners construct and interpret complete ANOVA tables.

Lesson 4 • Multiple Comparison Procedures

Covers LSD, Tukey, Duncan, Bonferroni, and Dunnett tests for pairwise and control comparisons. Learners select the appropriate test based on experimental objectives.

Lesson 1 • Simple Linear Regression Principles

Derives the least-squares regression line and interprets slope and intercept in agronomic terms. Learners assess model fit using R-squared and residual plots.

Lesson 2 • Correlation Analysis and Path Coefficients

Distinguishes Pearson, Spearman, and partial correlations and applies path analysis to decompose yield components. Learners interpret direct and indirect effects.

Lesson 3 • Regression Diagnostics and Validation

Identifies influential observations, leverage points, and heteroscedasticity in regression models. Learners apply cross-validation to assess predictive accuracy.

Lesson 4 • Nonlinear and Curvilinear Regression

Fits polynomial and intrinsically nonlinear models to fertiliser and growth response data. Learners compare linear and nonlinear fits using information criteria.

Lesson 1 • Multivariate ANOVA and Profile Analysis

Tests treatment effects on multiple response variables simultaneously using MANOVA. Profile analysis compares response patterns across treatment groups.

Lesson 2 • Principal Component Analysis

Reduces dimensionality of soil, climate, and yield datasets using PCA. Learners select components, interpret loadings, and visualise scores in agronomic contexts.

Lesson 3 • Introduction to Multivariate Data

Characterises multivariate agronomic datasets and reviews matrix algebra essentials. Learners visualise high-dimensional data using scatter plot matrices and biplots.

Lesson 4 • Cluster Analysis for Genotype Classification

Groups genotypes or environments using hierarchical and k-means clustering methods. Learners choose linkage methods and validate cluster solutions.

Lesson 1 • Geostatistics and Variogram Analysis

Models spatial dependence in soil and yield data using variograms and kriging. Learners fit theoretical variogram models and produce interpolated field maps.

Lesson 2 • Spatial Adjustment in Field Trials

Applies spatial covariance models within field trials to remove fertility trend effects. Learners compare spatially adjusted versus unadjusted treatment means.

Lesson 3 • Variance Component Estimation

Estimates genetic, environmental, and error variance components for heritability calculation. Learners apply ANOVA-based and REML-based methods to breeding data.

Lesson 4 • Multi-Environment Trial Analysis

Analyses genotype-by-environment interaction using mixed models and stability statistics. Learners partition GEI and identify broadly versus specifically adapted genotypes.