Geostatistical Models in Agricultural Field Experiments: Investigations Based on Uniformity Trials

Abstract

The probability of detecting treatment differences can often be increased by using geostatistical instead of classical statistical models. Geostatistical approaches require the selection of the best fitted model from a set of alternative models. This additional analysis effort could be reduced if the same model shows consistently the best fit for a given field or crop. To prove whether this reduction can be expected for designed on‐station trials, we analyzed five uniformity trials conducted on the same field. We studied whether different layouts of randomized complete block designs, the positions on the field, and the randomized plans influenced the model decision and analyzed the precision achieved. For this, the designs were shifted across the field, and 1000 randomized plans were projected onto each position. The model fit was evaluated using the corrected Akaike information criterion (AICC). The ranked AICC values were used for assessing model preference. In the means of all crops, designs, and models, the variation of the ranks depended on an individual decision for the combination of position and randomized plan by 62.6%. Therefore, the best fitted model was not predictable for a single experiment. As in the classical approach, the proper layout of a trial determines precision and unbiasedness of treatment differences. Randomization and blocking still should be the basic principles of experiment planning; however, their roles have partially changed. The detected bias of the Type I errors, both of the t‐test and F test, needs further investigation. Basic findings are also valid for on‐farm trials.