Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-25T19:48:24.263Z Has data issue: false hasContentIssue false
  • English
  • Français

Sampling strategy is important for producing weed maps: a case study using kriging

Published online by Cambridge University Press:  20 January 2017

Roger D. Cousens ,
Roderick W. Brown ,
Alex B. McBratney ,
Brett Whelan  and
Michael Moerkerk
Roderick W. Brown
Affiliation:
Department of Earth Sciences, The University of Melbourne, Victoria 3010, Australia
Alex B. McBratney
Affiliation:
Australian Centre for Precision Agriculture, Faculty of Agriculture, University of Sydney, New South Wales 2006, Australia
Brett Whelan
Affiliation:
Australian Centre for Precision Agriculture, Faculty of Agriculture, University of Sydney, New South Wales 2006, Australia
Michael Moerkerk
Affiliation:
Agriculture Victoria-Horsham, Victorian Institute of Dryland Agriculture, P.O. Box 260, Victoria 3401, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

Weed maps are typically produced from data sampled at discrete intervals on a regular grid. Errors are expected to occur as data are sampled at increasingly coarse scales. To demonstrate the potential effect of sampling strategy on the quality of weed maps, we analyzed a data set comprising the counts of capeweed in 225,000 quadrats completely covering a 0.9-ha area. The data were subsampled at different grid spacings, quadrat sizes, and starting points and were then used to produce maps by kriging. Spacings of 10 m were found to overestimate the geostatistical range by 100% and missed details apparently resulting from the spraying equipment. Some evidence was found supporting the rule of thumb that surveys should be conducted at a spacing of about half the scale of interest. Quadrat size had less effect than spacing on the map quality. At wider spacings the starting position of the sample grid had a considerable effect on the qualities of the maps but not on the estimated geostatistical range. Continued use of arbitrary survey designs is likely to miss the information of interest to biologists and may possibly produce maps inappropriate to spray application technology.

Keywords

Capeweed, Arctotheca calendula (L.) LevynsGeostatisticskrigingsamplingsurveyrangespacingextentspatial distribution
Type
Research Article
Information
Weed Science , Volume 50 , Issue 4 , August 2002 , pp. 542 - 546
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Cardina, J., Sparrow, D. H., and McCoy, E. L. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in no-till soybean (Glycine max). Weed Sci. 43:258–268.Google Scholar
Clay, S. A., Lems, G. J., Clay, D. E., Forcella, F., Ellsbury, M. M., and Carlson, C. G. 1999. Sampling weed spatial variability on a fieldwide scale. Weed Sci. 47:674–681.Google Scholar
Donald, W. W. 1994. Geostatistics for mapping weeds, with a Canada thistle (Cirsium arvense) patch as a case study. Weed Sci. 42:648–657.Google Scholar
Heisel, T., Andreasen, C., and Ersbøll, A. K. 1996. Annual weed distributions can be mapped with kriging. Weed Res. 36:325–337.Google Scholar
Heisel, T., Ersbøll, A. K., and Andreasen, C. 1999. Weed mapping with co-kriging using soil properties. Precision Ag. 1:39–52.Google Scholar
Hess, M., Barralis, G., Bleiholder, H., Buhr, L., Eggers, T., Hack, H., and Stauss, R. 1997. Use of the extended BBCH scale—general for the descriptions of the growth stages of mono- and dicotyledonous weed species. Weed Res. 37:433–441.CrossRefGoogle Scholar
Johnson, D. D. and Howarth, P. J. 1987. The effects of spatial resolution on land cover/land use theme extraction from airborne digital data. Can. J. Remote Sens. 13:68–74.Google Scholar
Jurik, T. W. and Zhang, S. 1999. Tractor wheel traffic effects on weed emergence in central Iowa. Weed Technol. 13:741–746.Google Scholar
Laslett, G. M. and McBratney, A. B. 1990. A further comparison of spatial prediction methods for soil pH. Soil Sci. Soc. Amer. J. 54:1553–1558.Google Scholar
Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
Nordbo, E., Christensen, S., Kristensen, K., and Walter, M. 1994. Patch spraying of weed in cereal crops. Asp. Appl. Biol. 40 (Arable Farming Under CAP Reform): 325–334.Google Scholar
Pettitt, A. N. and McBratney, A. B. 1993. Sampling designs for estimating spatial variance components. Appl. Stats. 42:185–209.Google Scholar
Pielou, E. C. 1957. The effect of quadrat size on the estimation of the parameters of Neyman's and Thomas's distributions. J. Ecol. 45:31–47.Google Scholar
Rew, L. J. and Cousens, R. D. 2001. Spatial distribution of weeds in arable crops: are current sampling and analytical methods appropriate? Weed Res. 41:1–18.Google Scholar
Rew, L. J., Lamb, D. W., Weedon, M. M., Lucas, J. L., Medd, R. W., and Lemerle, D. 1999. Evaluating airborne multispectral imagery for detecting wild oats in a seedling triticale crop. Pages 265–274 In Stafford, J. V., ed. Proceedings of the Precision Agriculture 1999 Conference, Odense, Denmark. Sheffield, England: Sheffield Academic Press.Google Scholar
Rew, L. J., Whelan, B., and McBratney, A. B. 2001. Does kriging predict weed distributions accurately enough for site-specific weed control? Weed Res. 41:245–264.Google Scholar
Sawyer, A. J. 1989. Inconstancy of Taylor's b: simulated sampling with different quadrat sizes and spatial distributions. Res. Popul. Ecol. 31:11–24.Google Scholar
Turner, M. G., O’Neill, R. V., Gardner, R. H., and Milne, B. T. 1989. Effects of changing spatial scale on the analysis of landscape pattern. Landsc. Ecol. 3:153–162.Google Scholar
Webster, R. and Oliver, M. A. 1992. Sample adequately to estimate variograms of soil properties. J. Soil Sci. 43:177–192.Google Scholar
Webster, R. and Oliver, M. A. 2001. Geostatistics for Environmental Scientists. Chichester, U.K.: J. Wiley. 271 p.Google Scholar
Zanin, G., Berti, A., and Riello, L. 1998. Incorporation of weed spatial variability into the weed control decision-making process. Weed Res. 38:107–118.Google Scholar