Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-29T02:15:32.406Z Has data issue: false hasContentIssue false
  • English
  • Français

Innovative Approaches to Seedbank Studies

Published online by Cambridge University Press:  12 June 2017

Diane L. Benoit ,
Douglas A. Derksen  and
Bernard Panneton
Diane L. Benoit
Affiliation:
Agric. Canada Res. Stn., Saint-Jean-sur-Richelieu, QUE, Canada J3B 3E6
Douglas A. Derksen
Affiliation:
Agric. Canada Exp. Farm., Indian Head, SK., Canada S0G 2K0
Bernard Panneton
Affiliation:
Agric. Canada Res. Stn., Saint-Jean-sur-Richelieu, QUE, Canada J3B 3E6
Get access Rights & Permissions [Opens in a new window]

Abstract

Seedbank studies often suffer from major methodological inadequacies such as absence of appropriate statistical data analysis and low sampling intensity. Multivariate analysis and computer mapping are innovative ways to treat seedbank data. Computer contour mapping was used to visualize spatial patterns of a population of common lambsquarters at three intervals during a growing season. At one site, high spring seed density of 600 000 seed m-2 was decreased to 18.3% of its original size by July, while at another site, low spring seedbank of common lambsquarters of 25 000 seed m-2 increased to 40 000 seed m-2 by autumn. Seedbank studies usually report results on total seed density or on densities of the most abundant species because of difficulties in analyzing large species matrices using parametric statistics. Multivariate analysis and specifically canonical discriminant analysis (CDA) are well suited for seedbank populations. The seedbanks of six agricultural habitats were demonstrated to be floristically different based on the analysis of the relative abundance of weed species in each site using CDA. Organic soils either under grassland or cultivated had significantly larger total seedbanks than mineral soils. If seedbanks are to be used in predictive population models, quantitative data that are reliable, rapidly obtained with limited resources, and logistically feasible for large sampling protocols are needed. Image analysis may be a potential rapid technique for weed seed recognition of washed soil samples.

Keywords

Common lambsquarters Chenopodium album L. #3 CHEALComputer mappingcontour mappingmultivariate analysiscanonical discriminant analysisseed recognitionimage analysisCHEAL
Type
Special Topics
Information
Weed Science , Volume 40 , Issue 4 , December 1992 , pp. 660 - 669
Copyright
Copyright © 1992 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. Afifi, A. A. and Clark, V. 1984. Discriminant analysis. Chapter 11. Page 246–286 in Computer-aided multivariate analysis. Lifetime Learning Publications. Wadsworth, Inc., Belmont, CA.Google Scholar
2. Ball, D. A. and Miller, S. D. 1989. A comparison of techniques for estimation of arable soil seedbanks and their relationship to weed flora. Weed Res. 29:365–373.CrossRefGoogle Scholar
3. Barralis, G., Chadoeuf, R., and Gouet, J.–P. 1986. Essai de détermination de la taille de l'échantillon pour 1′étude du potentiel semencier d'un sol. Weed Res. 26:291–297.Google Scholar
4. 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.Google Scholar
5. 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
6. Cavers, P. B. and Benoit, D. L. 1989. Seed banks in arable land. Chapter 14. Pages 309–328 in Leck, M. A., Parker, V. T., and Simpson, R. L., eds. Ecology of Soil Seed Banks. Academic Press, Inc., San Diego, CA.CrossRefGoogle Scholar
7. Churchill, D. B., Bilsland, D. M., and Cooper, T. M. 1990. Comparison of machine vision with human measurement of seed dimensions. Am. Soc. Agric. Eng. Paper No. 90-7519. 9 pp.Google Scholar
8. Derksen, D. A., Lafond, G. P., Thomas, A. G., Loeppy, H. A., and Swanton, C. J. 1992. The impact of agronomic practices on weed communities: tillage systems. Weed Sci. (submitted).Google Scholar
9. Dessaint, F., Barralis, G., Beuret, E., Caixinhas, M. L., Post, B. J., and Zanin, G. Etude coopérative EWRS: la détermination du potentiel semencier: I. Recherche d'une relation entre la moyenne et la variance d'échantillonnage. Weed Res. 30:421–429.Google Scholar
10. Dvo$rbák, J. and Krej$cbí$rb, J. 1980. Effects of crop rotation and herbicide application on weed seeds and their distribution in topsoil. (in Czech) Acta Univ. Agric. Fac. Agron. (Brno) 28:25–34.Google Scholar
11. Elliott, J. M. 1977. Some methods for the statistical analysis of samples of benthic invertebrates. Freshwater biological association scientific publications No. 25. Titus Wilson and Sons, Ltd., Kendal. 2nd ed. 160 pp.Google Scholar
12. Gauch, H. G. 1982. Multivariate analysis in community ecology. Cambridge Univ. Press, Cambridge. 298 pp.Google Scholar
13. Goyeau, H. and Fablet, G. 1982. Etude du stock de semences de mauvaises herbes dans le sol: le problème de l'échantillonnage. Agronomie (Paris) 2:545–552.Google Scholar
14. Jain, A. K. 1989. Fundamentals of digital image processing. Prentice-Hall, Englewood Cliffs, NJ. 569 pp.Google Scholar
15. Klecka, W. R. 1980. Discriminant Analysis. Sage Publications, Inc., Beverly Hills, CA. 71 pp.Google Scholar
16. Kropá$cb, Z. 1966. Estimation of weed seeds in arable soils. Pedobiologia 6:105–128.Google Scholar
17. Legendre, L., and Legendre, P. 1983. Structure analysis. Chapter 9. Page 313–338 in Numerical Ecology. Elsevier Scientific Publishing Co., Amsterdam.Google Scholar
18. Lewandowska, A. and Skapski, H. 1979. Evaluation of viable weed seeds in the soil following onion production in six regions of Poland. (in Polish). Biul. Warzywniczy 23:285–305.Google Scholar
19. Lodwick, G. D. and Whittle, J. 1970. A technique for automatic contouring field survey data. Aust. Computer J. 2:104–109.Google Scholar
20. Lopez, C., Abramovsky, P., Verdier, J. L., and Mamarot, J. 1988. Estimation du stock semencier dans le cadre d'un essai étudiant l'influence de systèmes culturaux sur l'évolution de la flore adventice. Weed Res. 28:215–221.Google Scholar
21. Morin, C. and Wojewedka, A. 1985. Evaluation du potentiel semencier d'un sol. Page 55–62 in 7th International symposium on ecology, biology and systematics of weeds.Google Scholar
22. Post, B. J. 1988. Multivariate analysis in weed science. Weed Res. 28:425–430.CrossRefGoogle Scholar
23. Roberts, H. A. 1981. Seed banks in soils. Adv. Appl. Biol. 6:1–55.Google Scholar
24. SAS Institute, Inc. 1982. The CANDISC procedure. Page 369–380 in SAS User's Guide: Statistics. SAS Inst., Inc., Cary, NC.Google Scholar
25. Thompson, K. 1986. Small-scale heterogeneity in the seed bank of an acidic grassland. J. Ecol. 74:733–738.Google Scholar
26. Travis, A. J. and Draper, S. R. 1985. A computer based system for the recognition of seed shape. Seed Sci. Technol. 13:813–820.Google Scholar
27. Westerlind, E. 1988. Seed scanner, a computer-based device for determinations of other seeds by number in cereal seed. Seed Sci. Technol. 16:289–297.Google Scholar
28. Wilkinson, L. 1989. Plot. Chapter 11. Page 212–279 in SYGRAPH: The System for Graphics. Systat, Inc., Evanston, IL.Google Scholar
29. Zar, J. H. 1974. Multiple comparisons. Chapter 12. Page 151–162 in Biostatistical Analysis. Prentice-Hall, Inc., Englewood Cliffs, NJ.Google Scholar