Skip to main content Accessibility help
Hostname: page-component-559fc8cf4f-9dmbd Total loading time: 0.216 Render date: 2021-03-07T22:26:56.919Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

Article contents

Spatial and temporal stability of weed populations over five years

Published online by Cambridge University Press:  20 January 2017

Frank Forcella
North Central Soil Conservation Research Laboratory, USDA-ARS, Morris, MN 56267
Gregg A. Johnson
Department of Agronomy and Plant Genetics, Southern Research and Outreach Center, University of Minnesota, Waseca MN 56093
E-mail address:


The size, location, and variation in time of weed patches within an arable field were analyzed with the ultimate goal of simplifying weed mapping. Annual and perennial weeds were sampled yearly from 1993 to 1997 at 410 permanent grid points in a 1.3-ha no-till field sown to row crops each year. Geostatistical techniques were used to examine the data as follows: (1) spatial structure within years; (2) relationships of spatial structure to literature-derived population parameters, such as seed production and seed longevity; and (3) stability of weed patches across years. Within years, densities were more variable across crop rows and patches were elongated along rows. Aggregation of seedlings into patches was strongest for annuals and, more generally, for species whose seeds were dispersed by combine harvesting. Patches were most persistent for perennials and, more generally, for species whose seeds dispersed prior to expected dates of combine harvesting. For the most abundant weed in the field, the annual, Setaria viridis, locations of patches in the current year could be used to predict patch locations in the following year, but not thereafter.

Research Article
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.


Anonymous. 1994. GS+: Geostatistics for the Environmental Sciences. Version 2.3. Plainwell, MI: Gamma Design Software. 44 p.Google Scholar
Auld, B. A., and Tisdell, C. A. 1987. Economic threshold and response to uncertainty in weed control. Agric. Syst. 25:219227.CrossRefGoogle Scholar
Auld, B. A., and Tisdell, C. A. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q. 31:81.Google Scholar
Bigwood, D. W., and Inouye, D. W. 1988. Spatial pattern analysis of seed banks: an improved method and optimized sampling. Ecology 69:497507.CrossRefGoogle Scholar
Brain, P., and Cousens, R. 1990. The effect of weed distribution on prediction of yield loss. J. Appl. Ecol. 27:735742.CrossRefGoogle 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. 44:7486.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. 43:258268.Google Scholar
Cardina, J., Sparrow, D. H., and Mccoy, E. L. 1996. Spatial relationships between seedbank and seedling populations of common lambsquarters (Chenopodium album) and annual grasses. Weed Sci. 44:298308.Google Scholar
Cressie, N. 1991. Statistics for spatial data. New York: J. Wiley. p. 70.Google Scholar
Dent, J. B., Fawcett, R. H., and Thornton, P. K. 1989. Economics of crop protection in Europe with reference to weed control. Proc. Br. Crop Prot. Conf.—Weeds 1989:917926.Google Scholar
Dessaint, F., Barralis, G., Beuret, E., Caixinhas, M. L., Post, B. J., and Zanin, G. 1990. Étude coopérative EWRS: la détermination du stock semencier. I. Recherche d'une relation entre la moyenne et la variance d'échantillonnage. Weed Res. 30:421429.Google Scholar
Deutsch, C. V., and Journel, A. G. 1998. GSLIB: Geostatistical Software Library and User's Guide. New York: Oxford University Press. 369 p.Google Scholar
Doyle, C. J. 1991. Mathematical models in weed management. Crop Prot. 10:432444.CrossRefGoogle Scholar
Forcella, F., Peterson, D. H., and Barbour, J. C. 1996. Timing and measurement of weed seed shed in corn (Zea mays). Weed Technol. 10:535543.Google Scholar
Gerhards, R., Sökefeld, M., Knuf, D., and Kühbauch, W. 1996. Kartierung und geostatistische Analyse der Unkrautverteilung in Zuckerrübenschlägen als Grundlage für eine teilschlagspezifische Bekämpfung. J. Agron. Crop Sci. 176:259266.CrossRefGoogle Scholar
Gerhards, R., Wyse-Pester, D. Y., Mortensen, D., and Johnson, G. A. 1997. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 45:109119.Google Scholar
Ghersa, C. M., and Roush, M. L. 1993. Searching for solutions to weed problems. Do we study competition or dispersion?. BioScience 43:104109.Google Scholar
Gonzalez-Andujar, J. L. and Perry, J. N. 1995. Models for the herbicidal control of the seedbank of Avena sterilis: the effects of spatial and temporal variability. J. Appl. Ecol. 32:578587.CrossRefGoogle Scholar
Goyeau, H., and Fablet, G. 1982. Etude du stock semencier de mauvaises herbes dans le sol: le problème de l’échantillonnage. Agronomie 2:542551.CrossRefGoogle Scholar
Halstead, S. J., Gross, K. L., and Renner, K. A., 1990. Geostatistical analysis of the weed seed bank. Proc. North Cent. Weed Sci. Soc. 45:123124.Google Scholar
Hamlett, J. M., Horton, R., and Cressie, N.A.C. 1986. Resistant and exploratory techiques for use in semivariogram analyses. Soil Sci. Soc. Am. J. 50:868875.CrossRefGoogle Scholar
Hughes, G. 1990. The problem of weed patchiness. Weed Res. 30:223224.CrossRefGoogle Scholar
Johnson, G. A., Mortensen, D. A., and Gotway, C. A. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci. 44:704710.Google Scholar
Johnson, G. A., Mortensen, D. A., and Martin, A. R. 1995. A simulation of herbicide use based on weed spatial distribution. Weed Res. 35:197205.CrossRefGoogle Scholar
Journel, A. G. and Huijbregts, C. 1978. Mining Geostatistics. New York: Academic Press. p. 194.Google Scholar
Kareiva, P. 1990. Population dynamics in spatially complex environments: theory and data. Philos. Trans. R. Soc., London B 330:175190.CrossRefGoogle Scholar
Legendre, P., and Fortin, M. J. 1989. Spatial pattern and ecological analysis. Vegetatio 80:107138.CrossRefGoogle Scholar
Lloyd, M. L. 1967. Mean crowding. J. Anim. Ecol. 36:130.CrossRefGoogle Scholar
Ludwig, J. A. and Reynolds, J. R. 1988. Spatial pattern analysis. Pages 1366 In Ludwig, J. A. and Reynolds, J. R., eds. Statistical Ecology: A Primer on Methods and Computing. New York: J. Wiley.Google Scholar
Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corm based on bioeconomic modeling. Weed Sci. 39:124129.Google Scholar
Marshal, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191198.CrossRefGoogle Scholar
Maxwell, B. D. and Ghersa, C. 1992. The influence of weed seed dispersion versus the effect of competition on crop yield. Weed Technol. 6:196204.Google Scholar
Moloney, K. A. 1988. Fine-scale spatial and temporal variation in the demograph of a pernennial bunchgrass. Ecology 69:15881598.CrossRefGoogle Scholar
Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distribution in agricultural fields. Pages 113124 In Robert, P. C., Rust, R. H., and Larson, W. E., eds. Soil Specific Crop Management. Madison, WI: American Society of Agronomy.Google Scholar
Nordmeyer, H. and Niemann, P. 1992. Möglichkeiten der gezielten Teilflächenbehandlung mit Herbiziden auf der Grundlage von Unkrautverteilung und Bodenvariabilität. Z. Pflkrankh. Pflschutz Sonderheft 13:539547.Google Scholar
[SAS] Statistical Analysis Systems. 1989. SAS/STAT User's Guide. Version 6. Cary, NC: Statistical Analysis Systems Institute. 1028 p.Google Scholar
Streibig, J. C., Gottschau, A., Dennis, B., Haas, H., and Polgaard, P. 1984. Soil properties affecting weed distribution. 7th Int. Symp. Weed Biol. Ecol. Syst. 7:147154.Google Scholar
Thompson, K. 1986. Small-scale heterogeneity in the seed bank of an acidic grassland. J. Ecol. 74:733738.CrossRefGoogle Scholar
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. 9:337342.CrossRefGoogle Scholar
Van Groenendael, J. M. 1988. Patchy distribution of weeds and some implications for modelling population dynamics: a short literature review. Weed Res. 28:437441.CrossRefGoogle Scholar
Wiles, L. J., Barlin, D. H., Schweizer, E. E., Duke, H. R., and Whitt, D. E. 1996. A new soil sampler and elutriator for collecting and extracting weed seeds from soil. Weed Technol. 10:3541.Google Scholar
Wiles, L. J., Gold, H. J., and Wilkerson, G. G. 1993. Modelling the uncertainty of weed density estimates to improve post-emergence herbicide control decisions. Weed Res. 33:241252.CrossRefGoogle Scholar
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. 40:554557.Google Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992b. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40:546553.Google Scholar
Wilson, B. J. and Brain, P. 1990. Weed monitoring on a whole farmpatchiness and the stability of distribiton of Alopecurus myosuroides over a ten-year period. Pages 4552 In Integrated Weed Management in Cereals. Proceedings of the EWRS Symposium, Helsinki. Wageningen, The Netherlands: EWRS.Google Scholar
Wilson, R. G., Kerr, E. D., and Nelson, L. A. 1985. Potential for using weed seed content in the soil to predict future weed problems. Weed Sci. 33:171175.Google Scholar

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 0
Total number of PDF views: 9 *
View data table for this chart

* Views captured on Cambridge Core between 20th January 2017 - 7th March 2021. This data will be updated every 24 hours.

Send article to Kindle

To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Spatial and temporal stability of weed populations over five years
Available formats

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Spatial and temporal stability of weed populations over five years
Available formats

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Spatial and temporal stability of weed populations over five years
Available formats

Reply to: Submit a response

Your details

Conflicting interests

Do you have any conflicting interests? *