Evaluating field‐scale sampling methods for the estimation of mean plant densities of weeds

The weed flora (comprising seven species) of a field continuously grown with soyabean was simulated for 4 years, using semivariograms established from previous field observations. Various sampling methods were applied and compared for accurately estimating mean plant densities, for differing weed species and years. The tested methods were based on (a) random selection wherein samples were chosen either entirely randomly, randomly with at least 10 or 20 m between samples, or randomly after stratifying the field; (b) systematic selection where samples were placed along diagonals or along zig‐zagged lines across the field; (c) predicted Setaria viridis (L.) P. Beauv seedling maps which were used to divide the field into low‐ and high‐density areas and to choose the largest sample proportion in the high‐density area. For each method, sampling was performed with 5–40 samples. Systematic methods generally resulted in the lowest estimation error, followed by the random methods and finally by the predicted‐map methods. In case of species over‐ or under‐represented along the diagonals or the zig‐zag sampling line, the systematic methods performed badly, especially with low sample numbers. In those instances, random methods were best, especially those imposing a minimal distance between samples. Even for S. viridis , the methods based on predicted S. viridis maps were not satisfactory, except with low sample numbers. The relationships between sampling error and species characteristics (mean density, variability, spatial structures) were also studied.