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Analysis of Spatial Distribution of Common Lambsquarters (Chenopodium album) in No-Till Soybean (Glycine max)

Published online by Cambridge University Press:  12 June 2017

John Cardina ,
Denise H. Sparrow  and
Edward L. McCoy
John Cardina
Affiliation:
Dep. Hortic. and Crop Sci., and Sc. Nat. Res., Oh. Agric. Res. and Dev. Ctr., Ohio State Univ., Wooster, OH 44691
Denise H. Sparrow
Affiliation:
Dep. Hortic. and Crop Sci., and Sc. Nat. Res., Oh. Agric. Res. and Dev. Ctr., Ohio State Univ., Wooster, OH 44691
Edward L. McCoy
Affiliation:
Dep. Hortic. and Crop Sci., and Sc. Nat. Res., Oh. Agric. Res. and Dev. Ctr., Ohio State Univ., Wooster, OH 44691
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Abstract

The nonuniform spatial distribution of weeds complicates sampling, modeling, and management of weed populations. Principles of a rational approach to analysis of weed spatial distribution, combining classical and spatial statistics, are presented using data for cumulative emergence of common lambsquarters in no-tillage soybean fields in 1990 and 1993. Classical statistics, dispersion indices, mean/variance relationships, and frequency histograms confirmed that raw and loge-transformed data were not normally distributed, that populations were aggregated, and that large-scale trends in population means violated assumptions of spatial statistics. Detrending was accomplished by median polishing loge-transformed data and confirmed by evaluation of standardized residuals and frequency histograms. Detrended residuals were used to construct omni-directional and uni-directional semivariograms to describe the spatial structure of the populations. A spherical model fit to the data was verified by cross validation. Semivariograms showed that common lambsquarters density was spatially autocorrelated at distances to 16 m, with more than 30% of the variance in density due to distance between field locations. Comparisons of kriged estimates and their standard deviations with and without detrending indicated that estimates using detrended data were more appropriate and more precise. Kriged estimates of common lambsquarters density were used to draw contour maps of the populations.

Keywords

Common lambsquarters, Chenopodium album L. #3 CHEALsoybean, Glycine max (L.) MerrGeostatisticsvariogrampattern analysiskriging
Type
Weed Biology and Ecology
Information
Weed Science , Volume 43 , Issue 2 , June 1995 , pp. 258 - 268
Copyright
Copyright © 1995 by the Weed Science Society of America 

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References

LITERATURE CITED

1. Auld, B. A. and Tisdell, C. A. 1987. Economic threshold and response to uncertainty in weed control. Agric. Systems 25:219–227.Google Scholar
2. Benoit, D. L. 1986. Methods of sampling seed banks in arable soils with special reference to Chenopodium spp. Ph.D. Thesis. University of Western Ontario. 282 pp.Google Scholar
3. Bigwood, D. W. and Inouye, D. W. 1988: Spatial pattern analysis of seed banks: An improved method and optimized sampling. Ecology 69:497–507.Google Scholar
4. Brain, P. and Cousens, R. 1990. The effect of weed distribution on prediction of yield loss. J. Appl. Ecol. 27:735–742.Google Scholar
5. Chellemi, D. O., Rohrbach, K. G., Yost, R. S., and Sonoda, R. M. 1988. Analysis of the spatial pattern of plant pathogens and diseased plants using geostatistics. Phytopathol. 78:221–226.Google Scholar
6. Clark, I. 1979. Practical geostatistics. Applied Science Publishers, London.Google Scholar
7. Cochran, W. 1977. Sampling techniques. John Wiley and Sons, New York.Google Scholar
8. Cohen, W. B. and Spies, T. A. 1990. Semivariograms of digital imagery for analysis of conifer canopy structure. Remote Sens. Environ. 34:167–178.Google Scholar
9. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
10. Cressie, N. A. C. 1991. Median-polish kriging. Pages 183–199 in Cressie, N. A. C., Statistics for spatial data. John Wiley & Sons, NY.Google Scholar
11. Dent, J. B., Fawcett, R. H., and Thronton, P. K. 1989. Economics of crop protection with reference to weed control. Proc. 1989 Brighton Crop Prot. Conf. Weeds, 917–926.Google Scholar
12. Dessaint, F. and Caussanel, J.-P. 1994. Trend surface analysis: a simple tool for modelling spatial pattern of weeds. Crop Prot. 13:433–438.Google Scholar
13. Dessaint, F., Chadoeuf, R., and Barralis, G. 1991. Spatial pattern analysis of weed seeds in the cultivated seed bank. J. Appl. Ecol. 28:721–730.Google Scholar
14. Donald, W. W. 1994. Geostatistics for mapping shoot and root growth across a Canada thistle (Cirsium arvense) patch. WSSA Proc. 34:72.Google Scholar
15. Halstead, S. J., Gross, K. L., and Renner, K. A. 1990. Geostatistical analysis of the weed seed bank. Proc. NCWSS. 45:123–124.Google Scholar
16. Hamlett, J. M., Horton, R., and Cressie, N. A. C. 1986. Resistant and exploratory techniques for use in semivariogram analyses. Soil Sci. Soc. Am. J. 50:868–875.Google Scholar
17. Isaaks, E. H. and Srivastava, R. M. 1989. Change of support. Pages 458–488 in Isaaks, E. H. and Srivastava, R. M. Applied geostatistics. Oxford Univ. Press., New York.Google Scholar
18. Legendre, P. 1993. Spatial autocorrelation: Trouble or new paradigm? Ecology 74:1659–1673.Google Scholar
19. Legendre, P and Fortin, M.-J. 1989. Spatial pattern and ecological analysis. Vegetation 80:107–138.Google Scholar
20. Lloyd, M. L. 1967. Mean crowding. J. Animal Ecol. 36:1–30.Google Scholar
21. Ludwig, J. A. and Reynolds, J. R. 1988. Spatial pattern analysis. Pages 13–66 in Ludwig, J. A. and Reynolds, J. R., Statistical ecology: A primer on methods and computing. John Wiley & Sons, NY.Google Scholar
22. Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions based on bioeconomic modeling. Weed Sci. 39:124–129.Google Scholar
23. Marshall, E. J. P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
24. Matheron, G. 1963. Principles of geostatistics. Econ. Geol. 58:1246–1266.Google Scholar
25. Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5:445–452.Google Scholar
26. Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distribution in agricultural fields. Pages 113–124 in Robert, P. C., Rust, R. H., and Larson, W. E., eds. Soil specific crop management. American Society of Agronomy, Madison, WI.Google Scholar
27. Noe, J. P. and Barker, K. R. 1985. Relation of within-field spatial variation of plant-parasitic nematode population densities and edaphic factors. Phytopathol. 75:247–252.Google Scholar
28. Robertson, P. G., Huston, M. A., Evans, F. C., and Tiedje, J. M. 1988. Spatial variability in a successional plant community: Patterns of nitrogen availability. Ecology 69:1517–1524.Google Scholar
29. Simard, Y., Legendre, P., Lavoie, G., and Marcotte, D. 1992. Mapping, estimating biomass, and optimizing sampling programs for spatially autocorrelated data: Case study of northern shrimp (Pandalus borealis). Can. J. Fish. Aquat. Sci. 49:32–45.Google Scholar
30. Swinton, S. M. and King, R. P. 1992. A bioeconomic model for weed management in corn and soybean. Staff Paper 92–44, Dept. Agr. Econ., Michigan State Univ., E. Lansing, MI.Google Scholar
31. Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337–342.Google Scholar
32. Wilson, B. J. and Brain, P. 1991. Long-term stability of distribution of Alopercurus myosuroides Huds. within cereal fields. Weed Res. 31:367–373.Google Scholar
33. 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
34. Wiles, L. J., Wilkerson, G. G., Gold, G. J., and Coble, H. D. 1992. Modelling weed distribution for improved postemergence control decisions. Weed Sci. 40:546–553.Google Scholar
35. Yost, R. S., Uehara, G., and Fox, R. L. 1982. Geostatistical analysis of soil chemical properties of large land areas. II. Kriging. Soil Sci. Soc. Am. J. 46:1033–1037.Google Scholar