Challenges Analysing Combined Agricultural Field Trials With Partially Overlapping Treatments

To make recommendations on which management practices have the potential to increase crop yield there needs to be a consistent pattern demonstrated across trials from many environments.
This presentation considers a case study looking at 31 mungbean trials in northern Australian from 2014 to 2016. The trials did not always have consistent factors (e.g. variety, row spacing or target plant density) or even consistent factor levels. To overcome the issue of inconsistent factors, environments were defined as the combination of site, year and any management factors not common across trials (e.g. time of sowing, irrigation, fertiliser).
There were numerous full factorial combinations within subsets of the data that could be considered for investigation so the first challenge was to determine which factorial combinations to focus on to best address the research questions and reporting requirements. Once this was determined, all the data from the trials that contributed to the factorial were included in a combined analysis using linear mixed models. In this model, the factorial of interest was partitioned in the test of fixed effects while each trials’ design parameters and residual variances were estimated using all the data from each trial.
An example of the above mentioned factorial combinations is environment by row spacing for one particular variety. The next challenge was that with so many environments there was usually an environment by row spacing interaction which was not useful for making recommendations about row spacing.
Clustering of environments allowed forming groups that did not have a significant interaction between row spacing and environment. These groups were then generalised to types of environments with certain responses to row spacing.