The problem of the effective control of soil variability is the main limitation on the broader use of factorial or fractional designs in agricultural experimental practice. The objective of the study was to assess the usefulness of some soil (pH, P, K, Mg, organic C) and plant traits (plant height, seed yield) for the estimation of spatial variation of the experimental field and to assess their usefulness in increasing the efficiency of 3⁵ field plot experiment with pea. Geostatistical methods were used in spatial analyses and ANOVA and ANCOVA in the analyses of the data from the experiment. The relative efficiency of the ANCOVA with information on spatial variation compared to the classic ANOVA was calculated. All of the studied soil properties and plant traits were spatially autocorrelated. The experimental semivariograms were transitive, having the range and the sill fitted with the spherical and exponential models. The range of spatial autocorrelation for soil properties was 18.5-73.7 m and for plant traits 1.6-3.7 m, while the share of structural variance (C) in the variance of sill (C₀ + C) was 60% and 90% for soil properties and plant traits, respectively. The values of spatial descriptors predicted for each experimental plot by kriging and used as covariates in ANCOVA resulted in higher efficiency.