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Does kriging predict weed distributions accurately enough for site‐specific weed control?
Author(s) -
Rew L J,
Whelan B,
Mcbratney A B
Publication year - 2001
Publication title -
weed research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 74
eISSN - 1365-3180
pISSN - 0043-1737
DOI - 10.1046/j.1365-3180.2001.00235.x
Subject(s) - sampling (signal processing) , weed , kriging , variogram , weed control , statistics , mathematics , geostatistics , consistency (knowledge bases) , environmental science , computer science , ecology , spatial variability , biology , geometry , filter (signal processing) , computer vision
Numerous studies have demonstrated the patchy distribution of weeds within fields. The majority of these studies have used discrete sampling, recording weed densities at the intersections of regular grids. In this study, Avena spp. seedlings were recorded on square grids at four sites. The data were then divided into test and real data sets using the whole, two‐thirds and one‐half of the data to evaluate the consistency of global variogram models and accuracy of ordinary kriging estimates. Kriging provided poor weed density estimates at both very low and high densities, i.e. data were smoothed when compared with true values. Grid sampling took considerable time and, therefore, money to complete, whereas continuous sampling with multispectral imagery (performed at one site) was much quicker and at a finer resolution. It is suggested that sampling systems that collect continuous rather than discrete data are currently more appropriate for site‐specific weed management.