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Scouting for Weeds, Based on the Negative Binomial Distribution

Published online by Cambridge University Press:  12 June 2017

Harvey J. Gold ,
Jeff Bay  and
Gail G. Wilkerson
Harvey J. Gold
Affiliation:
Biomathematics Program, Dep. Statistics. North Carolina State Univ., Raleigh, NC 27695–8203
Jeff Bay
Affiliation:
Biomathematics Program, Dep. Statistics. North Carolina State Univ., Raleigh, NC 27695–8203
Gail G. Wilkerson
Affiliation:
Crop Sci. Dep. and Biomathematics Program, North Carolina State Univ., Raleigh, NC 27695–7620
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Abstract

Protocols for sampling weeds in fields generally consist of selecting a given number of quadrats of a certain size, randomly located in the field, and counting the number of weeds of each type within each quadrat. Such a procedure is appropriate if weeds are distributed randomly in the field. However, it has been documented that weeds tend to cluster in fields so that the spatial distribution can often be described by a negative binomial. This research was conducted to identify an appropriate scouting protocol for use when weed populations are clumped in such a manner. Binomial, censored, and presence/absence sampling plans were compared through simulated sampling from negative binomial distributions of varying degrees of clustering and varying mean weed densities. Plans were compared in terms of bias and root mean square error. Study results indicate that binomial and presence/absence sampling offer reasonable alternatives to traditional sampling methods, except when there is extreme clumping. There is a trade-off between sampling effort needed per quadrat and number of quadrats needed for the sample. Traditional methods require intensive weed counting in each quadrat sampled, whereas binomial and presence/absence sampling protocols require less effort per quadrat, but more quadrats sampled for comparable results. Censored sampling displayed no advantage over binomial sampling in terms of bias and root mean square error, and is somewhat more difficult to do.

Keywords

Sampling protocolsbinomial samplingcensored samplingpresence/absence sampling
Type
Weed Biology and Ecology
Information
Weed Science , Volume 44 , Issue 3 , September 1996 , pp. 504 - 510
Copyright
Copyright © 1996 by the Weed Science Society of America 

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References

Literature Cited

1. Binns, M. R. and Bostanian, N. J. 1988. Binomial and censored sampling in estimation and decision making for the negative binomial distribution, Biometrics 44: 473–483.CrossRefGoogle Scholar
2. Brain, P. and Cousens, R. 1990. The effect of weed distributions on prediction of yield loss. J. Appl. Ecol. 27: 735–742.CrossRefGoogle Scholar
3. 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
4. Casella, G. and Berger, R. L. 1990. Statistical inference. Wadsworth & Brooks/Cole, Pacific Grove, California.Google Scholar
5. 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
6. Diggle, P. J. 1979. Statistical methods for spatial point patterns in ecology. Pages 96–150 in Cormak, R. M. and Ord, J. K., eds. Spatial and temporal analysis in ecology. International Cooperative Publishing House, Fairland, MD.Google Scholar
7. Good, I. J. 1983. Some history of the hierarchical Bayesian methodology. Pages 95–105 in Good thinking: the foundations of probability and its applications. Univ. of Minnesota Press, Minneapolis.Google Scholar
8. Hughes, G. 1989. Spatial heterogeneity in yield-loss relationships for crop loss assessment. Crop Res. 29: 87–94.Google Scholar
9. Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions based on bioeconomic modeling. Weed Sci. 39: 124–129.CrossRefGoogle Scholar
10. Marshall, E. J. P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28: 191–198.Google Scholar
11. Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5: 445–452.Google Scholar
12. Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distributions 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
13. Pannell, D. J. 1990. An economic response model of herbicide application for weed control. Aust. J. Agric. Econ. 34: 223–241.Google Scholar
14. Pielou, E. C. 1977. Mathematical ecology. Wiley, New York.Google Scholar
15. Southwood, T. R. E. 1966. The sampling programme and the measurement and description of dispersion. Pages 6–56 in Ecological methods with particular reference to the study of insect populations. Methuen & Co. Ltd, London.Google Scholar
16. Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44: 313–335.Google Scholar
17. 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.CrossRefGoogle Scholar
18. Wiles, L. J. 1990. A decision analytic investigation of the influence of weed spatial distribution on postemergence herbicide decisions in soybeans (Glycine max). . North Carolina State Univ., Raleigh. Pages 81–111.Google Scholar
19. Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G. 1992a. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci. 40: 554–557.Google Scholar
20. Wiles, L. J., Wilkerson, G. G., and Gold, H. J. 1992b. Value of information about weed distribution for improving postermergence control decisions. Crop Prot. 11: 547–554.CrossRefGoogle Scholar
21. Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992c. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40: 546–553.Google Scholar
22. Wiles, L. J., Gold, H. J., and Wilkerson, G. G. 1993. Modelling the uncertainty of weed density estimates to improve postemergence herbicide control decisions. Weed Res. 33: 241–252.Google Scholar
23. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybeans. Agron. J. 83: 413–417.CrossRefGoogle Scholar