Effects of systematic data reduction on trend estimation from German registration trials

Abstract Increasing yield trends are seen on-farm in Germany. The increase is based on genetic trend and changes in agronomic practices and climate. To estimate both genetic and non-genetic trends, historical VCU datasets can be analyzed. Such analyses are based on genotypes submitted by breeders and hence represent the post-breeding population of genotypes. By contrast, on-farm genetic trend is based on registered varieties only (post-registration population). To assess post-registration genetic trend, historical VCU trial datasets are often reduced, e.g. to registered varieties only. This kind of drop-out mechanism is statistically informative which affects variance component estimates and which can affect trend estimates. To investigate the effect of this informative drop-out on trend estimates, a simulation study was conducted mimicking the structure of German winter wheat VCU trials. Zero post-breeding trends were simulated. Results showed unbiased estimates of post-breeding trends when using all data. When restricting data to genotypes tested for at least three years, a positive genetic trend of 0.11 dt ha-1 year-1 and a negative non-genetic trend (-0.11 dt ha-1 year-1) were observed. Bias increased with increasing genotype-by-year variance and disappeared with random selection, which we simulated as a benchmark. As selection intensity in VCU trials is lower due to complex decisions that consider many traits compared to the simulated single-trait selection, the observed bias is an upper bound for the bias expected in practice. We conclude that bias relative to post-breeding trends is not dramatic. Unbiased estimates of post-registration genetic trend are best obtained from post-registration trials.