Abstract:

Spatial variability often exists among field experimental units because of many factors, such as moisture, fertility, pH, and structure of soil, and the pressure of diseases and pests. Spatial variability can be dealt with in one of two ways: either though designs, by blocking to account for spatial effect, or though statistical adjustment, by nearest neighbor or trend analysis. Recently, models with spatial covariance structures such as those used in geostatistics have been proposed to account for spatial variability of field experiments. The objectives of this study were to investigate the spatial variation of field experiment, to compare the performance of spatial correlation models with classical variance analysis models, and to investigate the influence of spatial covariance structure selection on data analysis, based on fitting and analyzing yield data of 3 trails using wheat and corn varieties. The results showed that the spatial variation significantly existed in all trails; the spatial variation variance amounted to 83.5%–70.4% of the residual variation variance. Compared with classical randomized complete block analysis, the spatial correlation model analysis reduced standard error of effect contrast by 18.4%–14.2%, its average relative efficiency was 1.50–1.36, hence, it was more effective than the blocking in controlling the spatial variation. The results from different spatial correlation models were not identical. The spatial correlation model and selection of the optimal model using Akaike’s information criterion (AIC) are suggested for analysis of field experiments.

Key words: Field experiment, Spatial variation, Model, Efficiency