Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data

Abstract

Block Kriging (a spatial interpolation method) and log10 transformation were compared for their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate mean values in all harvested maize plants grown in three randomly taken field plots and for harvested plants after removing the “edge or margin” ones. The results showed that log10 transformation reduced CVs of all harvested original fresh weight (FW) plant data in the three plots from 35.6–41.6% (original data) to 6.0–7.5%, while the respective CVs due to Block Kriging were reduced to 14.5–19.9%. The back-log10-transformed means of all harvested FW plant data were reduced by 6.8–9.4%, while the respective reduction for plants excluding the margin ones was 1.3–8.3%. The Block Kriging means for all harvested FW plant data were reduced only by 0.3–0.4%, while the respective means of the harvested plants excluding margin ones were increased by 0.4–4.3%. These findings strongly suggest that Block Kriging should be preferred over the log10 transformation method (used so far by agroscientists) as it managed to effectively reduce variability in crop data and estimate missing values that provide more precise and reliable estimates of corn yield for farmers.

Keywords

• agricultural experimentation
• data variability
• edge effects
• geoinformatics