Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-24T12:34:18.209Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatial dependence of weed seed banks and strategies for sampling

Published online by Cambridge University Press:  20 January 2017

L. Wiles  and
E. Schweizer
E. Schweizer
Affiliation:
Formerly with U.S. Department of Agriculture, Agricultural Research Service, Water Management Research Unit, Fort Collins, CO 80523
Get access Rights & Permissions [Opens in a new window]

Abstract

Weed management could be more efficient and require less herbicide if growers could afford to estimate the composition, density, and distribution of weed seed banks. Spatial distribution of a weed seed bank will affect the accuracy of both mean estimates and interpolated maps of density. Consequently, information about the general characteristics of spatial distributions of seeds in a seed bank is needed to identify the most efficient strategies for sampling. Seed banks were sampled on 8.4-m square grids in eight irrigated corn fields to identify the common features of distributions of seed banks of annual weeds. Spatial dependence was described with correlograms for four to eight species in each field. Spatial dependence was detected for 36 of 45 distributions, and seed counts were correlated to an average distance of 25 to 150 m for a distribution. Seed banks of different species and fields had common features of spatial correlation: spatial pattern accounted for less than half of the total variability of seed counts, spatial correlation decreased rapidly over short distances, and ranges of spatial dependence varied with direction. For half of the distributions, the maximum range of spatial dependence was at least twice as long as the minimum range. Seed counts were correlated for the longest distances in the direction of the crop row for 16 distributions, and the distance was longer in the direction of the crop row than across rows for 26 of the 36 samples. Researchers should be able to design more efficient sampling plans for growers if the common features of spatial dependence are considered. For seed banks like these, the accuracy of maps and estimates of seed bank density may be improved by collecting multiple cores around each sampling location to mitigate the effect of short-scale spatial variability. In addition, sampling may be more efficient with grids and interpolation methods that account for ranges that are 1.5 to 2 times longer in the direction of the crop row than perpendicular to the row. With a 55- by 30-m sampling grid, adjacent observations would be correlated, and maps could be made for 80% of these seed banks. More closely spaced observations would be needed to describe the rapid decline in spatial correlation with distance for a more accurate or finer-scale map. Whether sampling seed banks for making management decisions will be cost-effective is not clear. However, potential methods to sample and map seed bank distributions more efficiently have not been exhausted.

Keywords

Corn, Zea mays L.Geostatisticsmapspatial distributionspatial correlationredroot pigweedAmaranthus retroflexus L. AMAREhairy nightshadeSolanum sarrachoides SendtnerSOLSAbarnyardgrassEchinochloa crus-galli (L.) BeauvECHCGgreen foxtailSetaria viridis LSETVI
Type
Research Article
Information
Weed Science , Volume 50 , Issue 5 , October 2002 , pp. 595 - 606
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

Ambrosia, L., Dorado, J., and Del Monte, J. P. 1997. Assessment of the sample size to estimate the weed seedbank in soil. Weed Res. 37:129–137.CrossRefGoogle Scholar
Benoit, D. L., Derksen, D. A., and Panneton, B. 1992. Innovative approaches to seedbank studies. Weed Sci. 40:660–669.CrossRefGoogle Scholar
Benoit, D. L., Kenkel, N. C., and Cavers, P. B. 1989. Factors influencing the precision of soil seed bank estimates. Can. J. Bot. 67:2833–2840.CrossRefGoogle Scholar
Bigwood, D. W. and Inouye, D. W. 1988. Spatial pattern analysis of seed banks: an improved method and optimized sampling. Ecology 69:497–507.CrossRefGoogle Scholar
Buhler, D. D., King, R. P., Swinton, S. M., Gunsolus, J. L., and Forcella, F. 1997. Field evaluation of a bioeconomic model for weed management in soybean (Glycine max). Weed Sci. 45:158–165.CrossRefGoogle Scholar
Buhler, D. D. and Maxwell, B. D. 1993. Seed separation and enumeration from soil using K2CO3-centrifugation and image analysis. Weed Sci. 41:298–302.CrossRefGoogle Scholar
Burrough, P. A. 1991. Sampling designs for quantifying map unit composition. Pages 89–125 In Mausbach, M. J. and Wilding, L. P., eds. Spatial Variabilities of Soils and Landforms. SSSA Special Publ. No. 28. Madison, WI: Soil Science Society of America.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45:364–373.CrossRefGoogle Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in no-till soybean (Glycine max). Weed Sci. 43:258–268.CrossRefGoogle Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. 1996. Spatial relationships between seedbank and seedling populations of common lambsquarters (Chenopodium album) and annual grasses. Weed Sci. 44:298–308.CrossRefGoogle Scholar
Chauvel, B., Gasquez, J., and Darmency, H. 1989. Changes of weed seed bank parameters according to species, time and environment. Weed Res. 29:213–220.CrossRefGoogle Scholar
Colbach, N., Forcella, F., and Johnson, G. A. 2000. Spatial and temporal stability of weed populations over five years. Weed Sci. 48:366–377.CrossRefGoogle Scholar
Cousens, R. and Mortimer, M. 1995. Dynamics of Weed Populations. Cambridge, Great Britain: Cambridge University Press. pp. 217–242.CrossRefGoogle Scholar
Davis, P. 1994. Statistics for describing populations. Pages 34–54 In Pedigo, L. P. and Buntion, G. D., eds. Handbook of Sampling Methods for Arthropods in Agriculture. Boca Raton, FL: CRC Press.Google Scholar
Dessaint, F., Barralis, G., Caixinhas, M. L., Mayor, J.-P., Recasens, J., and Zanin, G. 1996. Precision of soil seed bank sampling: how many cores? Weed Res. 36:143–151.CrossRefGoogle Scholar
Dessaint, F., Chadoeuf, R., and Barralis, G. 1991. Spatial pattern analysis of weed seeds in the cultivated soil seed bank. J. Appl. Ecol. 28:721–730.CrossRefGoogle Scholar
Deutsch, C. V. and Journel, A. G. 1992. GSLIB Geostatistical Software Library and User's Guide. New York: Oxford University Press. pp. 13, 24–29.Google Scholar
Flatman, G. T., Englund, E. J., and Yfanis, A. A. 1988. Geostatistical approaches to the design of sampling regimes. Pages 73–84 In Keith, L. H., ed. Principles of Environmental Sampling. Washington, DC: American Chemical Society.Google Scholar
Forcella, F., King, R. P., Swinton, S. M., Buhler, D. D., and Gunsolus, J. L. 1996. Multi-year validation of a decision aid for integrated weed management in row crops. Weed Sci. 44:650–661.CrossRefGoogle Scholar
Gotway, C. A., Ferguson, R. B., and Hergert, G. W. 1996. The effects of mapping and scale on variable-rate fertilizer recommendations for corn. Pages 321–330 In Robert, P. C., Rust, R. H., and Larson, W. E., eds. Precision Agriculture. Minneapolis, MN: American Society of Agronomy.Google Scholar
Gross, K. L. and Renner, K. A. 1989. A new method for estimating seed numbers in the soil. Weed Sci. 37:836–839.CrossRefGoogle Scholar
Halstead, S. J. and Gross, K. L. 1990. Geostatistical analysis of the weed seed bank. Proc. N. Cent. Weed Sci. Soc. 45:123–124.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. An Introduction to Applied Geostatistics. New York: Oxford University Press. pp. 140–182, 369–399.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.CrossRefGoogle Scholar
Jones, N. E. 1998. The number of soil cores required to accurately estimate the seed bank on arable land. Aspects Appl. Biol. 51:1–8.Google Scholar
Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Sci. 39:124–129.CrossRefGoogle Scholar
Oliver, M. A., Frogbrook, Z., Webster, R., and Dawson, C. J. 1997. A rational strategy for determining the number of cores for bulked soil samples. Pages 155–162 In Stafford, J. V., ed. Precision Agriculture ’97. Oxford: Bios Scientific Publishers.Google Scholar
Rew, L. J. and Cussans, G. W. 1997. Horizontal movement of seeds following tine and plough cultivation: implications for spatial dynamics of weed infestations. Weed Res. 37:247–256.CrossRefGoogle Scholar
Rossi, R. R., Mulla, D. J., Journel, A. G., and Franz, E. H. 1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol. Monogr. 62:277–314.CrossRefGoogle Scholar
SAGE2001. 1999. Spatial and Geostatistical Environment for Variography User's Guide. San Mateo, CA: Isaaks and Co. pp. 27–40.Google Scholar
Schweizer, E. E., Westra, P., and Lybecker, D. 1998. Seedbank and emerged weed populations in cornfields (Zea mays) in Colorado. Weed Technol. 12:243–247.CrossRefGoogle Scholar
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44:313–335.CrossRefGoogle Scholar
Trangmar, B. B., Yost, R. S., and Uehara, G. 1985. Application of geostatistics to spatial studies of soil properties. Adv. Agron. 38:45–94.CrossRefGoogle Scholar
VanGessel, M. J., Schweizer, E. E., Lybecker, D. W., and Westra, P. 1996. Integrated weed management systems of irrigated corn (Zea mays) production in Colorado—a case study. Weed Sci. 44:423–428.CrossRefGoogle Scholar
Weisz, R., Fleischer, S., and Smilowitz, Z. 1995. Site-specific integrated pest management for high value crops: sample units for map generation using the Colorado potato beetle (Coleoptera: Chrysomelidae) as a model system. J. Econ. Entomol. 88:1069–1080.CrossRefGoogle ScholarPubMed
Wiles, L. J., Barlin, D. H., Schweizer, E. E., Duke, H. R., and Whitt, D. E. 1996a. A new soil sampler and elutriator for collecting and extracting weed seeds from soil. Weed Technol. 10:35–41.CrossRefGoogle Scholar
Wiles, L. J., King, R. P., Schweizer, E. E., Lybecker, D. W., and Swinton, S. M. 1996b. GWM: general weed management model. Agric. Syst. 50:355–376.CrossRefGoogle Scholar
Wiles, L. J. and Schweizer, E. E. 1999. The cost of counting and identifying weed seeds and seedlings. Weed Sci. 47:667–673.CrossRefGoogle Scholar
Wiles, L. J. and Westra, P. 1999. The spatial distribution of weed seed banks in two pivot-irrigated corn fields. Proc. West. Soc. Weed Sci. 52:19–20.Google Scholar
Wyse-Pester, D., Westra, P., and Wiles, L. J. 1999. Spatial sampling and analysis of crop pests in a center pivot corn field. Pages 485–494 In Proceedings of the 2nd European Conference on Precision Agriculture, Odense, Denmark. London: SCI.Google Scholar