Statistical Analysis of Agronomic Experiments

You’ve designed and run your experiment and collected your data. Now what?

Details at a glance

Date: Friday 6th AND Monday 9th of August 2021
Time: 8:45am to 5:00pm
Cost: $360  ($200 for those working on GRDC projects) including GST
Location:  Online via Zoom
Max attendees: 15
Bring: A computer and headphones (headphones with microphone work best)
NB: A basic proficiency with the statistics package R is expected. If you need R training, please email Sam Rogers (s.rogers@adelaide.edu.au) to be notified when the next course will be run.

This two-day workshop covers the basic topics in experimental analysis of agronomic experiments. It is intended for researchers working in the field of agronomy with an understand of the statistical package R (Introduction to R workshop) and the design of agronomic experiments (Introduction to Experimental Design workshop).  In this course we review ANOVA concepts (Linear Models) and learn more complex experimental analysis concepts – Linear Mixed Models – including spatial modelling for the analysis of factorial experiments, blocked designs, and split-plot agronomic experiments. Participants will learn to identify the correct model to use and perform analyses of data resulting from standard agronomic designs using R and ASReml-R and interpret the results.

Why should you do this course?

• SAGI-STH has developed the Biometry Education Initiative to tailor to the needs of researchers in the Australian Grains Industry.
• The course is run by grains industry researchers, for grains industry researchers. Researchers in other agricultural industries are also more than welcome to attend!
• The course is non-threatening, with plenty of examples and hands-on practice to help you get familiar with the material.

What will you get out of it?

• Confidence to perform statistical analysis of your experiments.
• Practice using R and some relevant packages for real world examples.
• A refresher on statistics but with application to your research.
• An understanding of how the analysis of an experiment relates to its design.