Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-19T19:24:43.919Z Has data issue: false hasContentIssue false
  • English
  • Français

The Stability of Weed Seedling Population Models and Parameters in Eastern Nebraska Corn (Zea mays) and Soybean (Glycine max) Fields

Published online by Cambridge University Press:  12 June 2017

Gregg A. Johnson ,
David A. Mortensen ,
Linda J. Young  and
Alex R. Martin
Gregg A. Johnson
Affiliation:
Dep. Agron., Univ. Nebraska, Lincoln, NE 68583-0915
David A. Mortensen
Affiliation:
Dep. Agron., Univ. Nebraska, Lincoln, NE 68583-0915
Linda J. Young
Affiliation:
Dep. Biom., Univ. Nebraska, Lincoln, NE 68583-0915
Alex R. Martin
Affiliation:
Dep. Agron., Univ. Nebraska, Lincoln, NE 68583-0915
Get access Rights & Permissions [Opens in a new window]

Abstract

Intensive field surveys were conducted in eastern Nebraska to determine the frequency distribution model and associated parameters of broadleaf and grass weed seedling populations. The negative binomial distribution consistently fit the data over time (1992 to 1993) and space (fields) for both the inter and intrarow broadleaf and grass weed seedling populations. The other distributions tested (Poisson with zeros, Neyman type A, logarithmic with zeros, and Poisson-binomial) did not fit the data as consistently as the negative binomial distribution. Associated with the negative binomial distribution is a k parameter. k is a nonspatial aggregation parameter related to the variance at a given mean value. The k parameter of the negative binomial distribution was consistent across weed density for individual weed species in a given field except for foxtail spp. populations. Stability of the k parameter across field sites was assessed using the likelihood ratio test There was no stable or common k value across field sites and years for all weed species populations. The lack of stability in k across field sites is of concern, because this parameter is used extensively in the development of parametric sequential sampling procedures. Because k is not stable across field sites, k must be estimated at the time of sampling. Understanding the variability in it is critical to the development of parametric sequential sampling strategies and understanding the dynamics of weed species in the field.

Keywords

Corn, Zea mays L.soybean, Glycine max L. Merr.Negative binomial distributionk parameterlikelihood ratio testsampling strategies
Type
Weed Biology and Ecology
Information
Weed Science , Volume 43 , Issue 4 , December 1995 , pp. 604 - 611
Copyright
Copyright © 1995 by the 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

1. Berti, A., Zanin, G., Baldoni, G., Grignoni, C., Mazzoncini, M., Montemucco, P., Tei, F., Vazzana, C., and Viggiani, P. 1992. Frequency distribution of weed counts and applicability of a sequential method to integrated weed management. Weed Res. 32:39–44.CrossRefGoogle Scholar
2. Bliss, C. I. 1953. Fitting the negative binomial distribution to biological data. Biometrics. 9:176–199.Google Scholar
3. Bliss, C. I. and Owen, A.R.G. 1958. Negative binomial distributions with a common k . Biometrika. 45:37–58.CrossRefGoogle Scholar
4. Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735–742.CrossRefGoogle Scholar
5. Gates, C. E. 1988. Discrete: A computer program for fitting discrete frequency distribution, pp. 458–466 in McDonald, L. et al. (eds) Lecture Notes in Statistics. Springer-Verlag, Berlin, Germany.Google Scholar
6. Katti, S. K. 1966. Interrelations among generalized distributions and their components. Biometrics. 22:44–52.Google Scholar
7. Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
8. Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5:445–452.Google Scholar
9. Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distribution in agricultural fields, pp. 113–124 in Site Specific Crop Management, Robert, P. and Rust, R. H. (eds.). Am. Soc. Agron. Google Scholar
10. Nicot, P. C., Rouse, D. I., and Yandell, B. S. 1984. Comparison of statistical methods for studying patterns of soilborne plant pathogens in the field. Phytopathology 74:1399–1402.CrossRefGoogle Scholar
11. Sondgerath, D. and Richter, O. 1990. Parameter Estimation in Ecology. 215 pp.Google Scholar
12. Southwood, T.R.E. 1966. Ecological methods with particular reference to the study of insect populations. Methuen and Co., LTD. pp 6–56.Google Scholar
13. Stevens, L. M., McGuire, J. U., and Steinhauer, A. L. 1976. Simulation and sampling of the negative binomial distribution with special emphasis on the parameter k as applied to the alfalfa weevil. Misc. Publ. Md. Agric. Exp. Stn. pp 1–26.Google Scholar
14. Warren, W. G. and Chen, P. W. 1986. The impact of misspecification of the negative binomial shape parameter in sequential sampling plans. Can. J. For. Res. 16:608–611.Google Scholar
15. 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.CrossRefGoogle Scholar
16. Young, L. J. and Young, J. H. 1990. A spatial view of the negative binomial parameter k when describing insect populations. Applied Stat. in Agric. pp. 13–20.CrossRefGoogle Scholar