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Design and Analysis of Agricultural Experiments

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24 hours
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About Course

Date: 15th – 19th June

Venue: Online

Training fee: $200

Teaching Methods

  • Lectures: Conceptual frameworks for statistical logic and design.
  • Hands-on Labs: Practical data analysis using R (RStudio) with real agricultural datasets.
  • Problem-Based Learning: Group analysis of “failed” experiments to identify design flaws.

What Will You Learn?

  • Select and implement the most efficient experimental designs (RCBD, Split-plot, etc.) based on field constraints.
  • Master the logic of ANOVA and ANCOVA to derive statistically valid conclusions from agricultural data.
  • Account for environmental noise using covariates and random effects within Linear Mixed Models.
  • Evaluate crop performance across different environments using modern G×E stability analysis techniques.
  • Translate research objectives into robust, peer-review-ready experimental protocols and data analysis workflows.

Course Content

Session 1. Data Wrangling

Session 2. Visualization with ggplot2

Session 3. Experimental Designs: CRD, RCBD, Alpha Lattice and Split plot

Session 4. Modern Modeling: Linear Mixed Models (LMM)

Session 5. Genotype × Environment (G×E) Interaction and Stability

Session 6. Capstone: Experimental Design & Analysis
Participants present a complete design and analysis workflow for a specific agricultural problem. Troubleshooting "messy" real-world datasets. Final review and feedback on experimental protocols.

Instructors

LH

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  • Intermediate
  • 24 hours
  • June 8, 2026
  • Certificate of completion

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