Weed Mapping with Co-Kriging Using Soil Properties

Our aim is to build reliable weed maps to control weeds in patches. Weed sampling is time consuming but there are some shortcuts. If an intensively sampled variable (e.g. soil property) can be used to improve estimation of a sparsely sampled variable (e.g. weed distribution), one can reduce weed sampling. The geostatistical estimation method co-kriging uses two or more sampled variables, which are correlated, to improve the estimation of one of the variables at locations where it was not sampled. We did an experiment on a 2.1 ha winter wheat field to compare co-kriging using soil properties, with kriging based only on one variable. The results showed that co-kriging Lamium spp. from 96 0.25m2 sample plots ha-1 with silt content improved the prediction variance by 11% compared to kriging. With 51 or 18 sample plots ha-1 the prediction variance was improved by 21 and 15%.