2025/12/15 by Wolf, Karen, Pierre Fernique, Hans‐Peter Piepho +2
Decision Sciences · Environmental Science · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #Optimal Experimental Design Methods #Soil Geostatistics and Mapping
paper · pdf · doi:10.48550/arxiv.2512.13383
openalex publication_date 2025/12/15 · openalex created_date 2025/12/17 · openalex updated_date 2026/07/28
A common assumption in the spatial analysis of agricultural field trials is stationarity. In practice, however, this assumption is often violated due to unaccounted field effects. For instance, in plant breeding field trials, this can lead to inaccurate estimates of plant performance. Based on such inaccurate estimates, breeders may be impeded in selecting the best performing plant varieties, slowing breeding progress. We propose a method to automatically verify the hypothesis of stationarity. The method is sensitive towards mean as well as variance-covariance nonstationarity. It is specifically developed for the two-dimensional grid-structure of field trials. The method relies on the hypothesis that we can detect nonstationarity by partitioning the field into areas, within which stationarity holds. We applied the method to a large number of simulated datasets and a real-data example. The method reliably points out which trials exhibit quality issues and gives an indication about the severity of nonstationarity. This information can significantly reduce the time spent on manual quality control and enhance its overall reliability. Furthermore, the output of the method can be used to improve the analysis of conducted trials as well as the experimental design of future trials.