Applications: The Analysis of Crop Variety Evaluation Data in Australia

TLDR

Crop variety evaluation aims to predict future performance of varieties. This paper presents routine statistical analysis of late‑stage crop variety testing data in Australia. The analysis uses a two‑stage approach: spatial modeling of current‑year trial data followed by a mixed‑model meta‑analysis of combined yearly tables, weighting estimates by accuracy and treating variety effects as random. The study developed inferential tools that aid interpretation of the mixed‑model results.

Abstract

The major aim of crop variety evaluation is to predict the future performance of varieties. This paper presents the routine statistical analysis of data from late‐stage testing of crop varieties in Australia. It uses a two‐stage approach for analysis. The data from individual trials from the current year are analysed using spatial techniques. The resultant table of variety‐by‐trial means is combined with tables from previous years to form the data for an overall mixed model analysis. Weights allow for the data being estimates with varying accuracy. In view of the predictive aim of the analysis, variety effects and interactions are regarded as random effects. Appropriate inferential tools have been developed to assist with interpretation of the results. Analyses must be conducted in a timely manner so that variety predictions can be published and disseminated to growers immediately after harvest each year. Factors which facilitate this include easy access to historic data and the use of specialist mixed model software.