Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-22T19:56:28.442Z Has data issue: false hasContentIssue false
  • English
  • Français

How good is your weed map? A comparison of spatial interpolators

Published online by Cambridge University Press:  20 January 2017

J. Anita Dille ,
Maribeth Milner ,
Jeremy J. Groeteke ,
David A. Mortensen  and
Martin M. Williams II
Maribeth Milner
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583
Jeremy J. Groeteke
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583
David A. Mortensen
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583
Martin M. Williams II
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent interest in describing the spatial distribution of weeds and studying their association with site properties has increased the use of interpolation to estimate weed seedling density from spatially referenced data. In addition, farmers and consultants adopting elements of site-specific farming practices are using interpolation methods for mapping weed densities as well as soil properties. This study was conducted to compare the performance of four interpolation methods, namely inverse-distance weighting (IDW), ordinary point kriging (OPK), minimum surface curvature (MC), and multiquadric radial basis function (MUL), with respect to their ability to map weed-seedling densities. These methods were evaluated on data from four weed species, velvetleaf, hemp dogbane, common sunflower, and foxtail species, of contrasting biology and infestation levels in corn and soybean production fields in Nebraska. Mean absolute difference (MAD) and root mean square (RMS) between the observed point sample data and the estimated weed seedling density surfaces were used to evaluate the performance of the interpolation methods. Four neighborhood search types were compared within each interpolation method, and Search3 (12 to 16 neighboring locations) generated an interpolated surface with the smallest MAD and RMS indicating the highest precision. IDW with a power coefficient of p = 4 gave the smallest MAD and RMS, as did a test with an elliptical search and no anisotropy. The level of precision of all four interpolation methods was very poor for weed species with low infestation levels (> 75% of field weed-free; MAD ranged from 100 to 187% of the observed mean density), whereas precision was improved for weed species with high infestation levels (< 25% of field weed-free; MAD ranged from 45 to 85%). No single interpolator appears to be more precise than another. Implications of this study indicate that grid sample spacing and quadrat size are more important than the specific interpolation method chosen.

Keywords

Common sunflower, Helianthus annuus L. HELANfoxtail species, Setaria spp. SETSShemp dogbane, Apocynum cannabinum L. APCCAvelvetleaf, Abutilon theophrasti Medicus ABUTHcorn, Zea mays L.soybean, Glycine max (L.) MerrInverse-distance weightingkrigingminimum curvaturemultiquadricradial basis functionABUTHAPCCAHELANSETSS
Type
Research Article
Information
Weed Science , Volume 51 , Issue 1 , February 2003 , pp. 44 - 55
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

Abtew, W., Obeysekera, J., and Shih, G. 1993. Spatial analysis for monthly rainfall in south Florida. Water Res. Bull. 29:179–188.CrossRefGoogle Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735–742.Google Scholar
Briggs, I. C. 1974. Machine contouring using minimum curvature. Geophysics 39:39–48.Google Scholar
Burnside, O. C., Wilson, R. G., Weisberg, S., and Hubbard, K. G. 1996. Seed longevity of 41 weed species buried 17 years in eastern and western Nebraska. Weed Sci. 44:74–86.Google Scholar
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
Cooke, R. A., Mostaghimi, S., and Campbell, J. B. 1993. Assessment of methods for interpolating steady-state infiltrability. Trans. ASAE 36:1333–1341.Google Scholar
Dessaint, F. and Caussanel, J. P. 1994. Trend surface analysis: a simple tool for modelling spatial patterns of weeds. Crop Prot. 13:433–438.Google Scholar
Dieleman, J. A. and Mortensen, D. A. 1999. Characterizing the spatial pattern of Abutilon theophrasti seedling patches. Weed Res. 39:455–467.Google Scholar
Dieleman, J. A., Mortensen, D. A., Martin, A. R., and Wyse-Pester, D. Y. 1999. Influence of velvetleaf (Abutilon theophrasti) and common sunflower (Helianthus annuus) density on weed management outcomes. Weed Sci. 47:81–89. [Errata 47(2):254]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.CrossRefGoogle Scholar
Forcella, F., Wilson, R. G., Renner, K. A., Dekker, J., Harvey, R. G., Alm, D. A., Buhler, D. D., and Cardina, J. 1992. Weed seedbanks in the U.S. corn belt: magnitude, variation, emergence, and application. Weed Sci. 40:636–644.Google Scholar
Gallichand, J., Buckland, G. D., Marcotte, D., and Hendry, M. J. 1992. Spatial interpolation of soil salinity and sodicity for a saline soil in southern Alberta. Can. J. Soil Sci. 72:503–516.CrossRefGoogle Scholar
Gallichand, J. and Marcotte, D. 1993. Mapping clay content for subsurface drainage in the Nile Delta. Geoderma 58:165–179.CrossRefGoogle Scholar
Gerhards, R., Sökefeld, M., Schulze-Lohne, K., Mortensen, D. A., and Kühbauch, W. 1997a. Site specific weed control in winter wheat. J. Agron. Crop Sci. 178:219–225.CrossRefGoogle Scholar
Gerhards, R., Wyse-Pester, D. Y., Mortensen, D. A., and Johnson, G. A. 1997b. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 45:108–119.CrossRefGoogle Scholar
Gotway, C. A., Ferguson, R. B., Hergert, G. W., and Peterson, T. A. 1996. Comparison of kriging and inverse-distance methods for mapping soil parameters. Soil Sci. Soc. Am. J. 60:1237–1247.Google Scholar
Heisel, T., Ersbøll, A. K., and Andreasen, C. 1999. Weed mapping with co-kriging using soil properties. Prec. Agric. 1:39–52.CrossRefGoogle Scholar
Hosseini, E., Gallichand, J., and Marcotte, D. 1994. Theoretical and experimental performance of spatial interpolation methods for soil salinity analysis. Trans. ASAE 37:1799–1807.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. An Introduction to Applied Geostatistics. New York: Oxford University Press. pp. 249–266, 278–322.Google Scholar
Johnson, G. A., Mortensen, D. A., and Gotway, C. A. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci. 44:704–710.Google Scholar
Johnson, G. A., Mortensen, D. A., Young, L. J., and Martin, A. R. 1995. The stability of weed seedling population models and parameters in eastern Nebraska corn (Zea mays) and soybean (Glycine max) fields. Weed Sci. 43:604–611.CrossRefGoogle Scholar
Lindquist, J. L., Dieleman, J. A., Mortensen, D. A., Johnson, G. A., and Wyse-Pester, D. Y. 1998. Economic importance of managing spatially heterogeneous weed populations. Weed Technol. 12:7–13.Google Scholar
Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distribution in agricultural fields. Pages 113–124 In Robert, P. C. and Rust, R. H., eds. Soil Specific Crop Management. Madison, WI: American Society of Agronomy.Google Scholar
Neeser, C., Dieleman, J. A., and Mortensen, D. A. 1999. Density dependant velvetleaf (Abutilon theophrasti) seedling survival in response to postemergence herbicide applications. Page 337 in Proceedings of the Weed Science Society of America. San Diego, CA: Weed Science Society of America.Google Scholar
Nuss, W. A. and Titley, D. W. 1994. Use of multiquadric interpolation for meteorological objective analysis. Monthly Weather Rev. 122:1611–1631.2.0.CO;2>CrossRefGoogle Scholar
Smith, W.H.R. and Wessel, P. 1990. Gridding with continuous curvature splines in tension. Geophysics 55:293–305.Google Scholar
Teo-Sherrell, C.P.A. 1996. The Fates of Weed Seeds. Ph.D. dissertation. University of Nebraska, Lincoln, NE.Google Scholar
Wallinga, J. 1995. The role of space in plant population dynamics: annual weeds as an example. Oikos 74:377–383.Google Scholar
Watson, D. F. 1992. Contouring—A Guide to the Analysis and Display of Spatial Data. Tarrytown, NY: Pergamon Press. pp. 118–130.Google Scholar
Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G. 1992. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci. 40:554–557.Google Scholar
Williams, M. M. II, Gerhards, R., and Mortensen, D. A. 1999. Spatiotemporal outcomes of site-specific weed management in maize. Pages 897–906 In Stafford, J. V., ed. Precision Agriculture ’99. Sheffield, Great Britain: Society of Chemical Industry.Google Scholar
Wilson, B. J. and Brain, P. 1991. Long-term stability of distribution of Alopecurus myosuroides Huds. within cereal fields. Weed Res. 31:367–373.Google Scholar