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A Comparison of Methods to Predict Weed Seedling Populations from the Soil Seedbank

Published online by Cambridge University Press:  12 June 2017

John Cardina  and
Denise H. Sparrow
John Cardina
Affiliation:
Dep. Hortic, and Crop Sci., Agric. Res. and Dev. Ctr. and Ohio State Univ., Wooster, OH 44691
Denise H. Sparrow
Affiliation:
Dep. Hortic, and Crop Sci., Agric. Res. and Dev. Ctr. and Ohio State Univ., Wooster, OH 44691
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Abstract

Accurate prediction of potential weed seedling density would allow growers to implement control measures more effectively and could help avoid inappropriate and over application of preemergence herbicides. We compared three methods for handling soil samples to predict potential weed seedling emergence in plow-disk and no-tillage corn: seedling emergence from greenhouse trays, emergence from intact cores, and seed extraction following sieving. Seedbank numbers were highest for common lambsquarters followed by annual grasses and redroot pigweed, and seed numbers were higher in no-tillage than plow-disk plots. Coefficients of variation typically exceeded 60%. Density of seedling emergence from cores was more similar to field density than was emergence from trays. The percent of seeds in the seedbank that emerged was commonly more than 90% for annual grasses and usually less than 20% for common lambsquarters. All methods gave equivalent and relatively poor predictions of field population density. Spearman rank correlation between predicted and actual populations ranged from low negative values for common lambsquarters in no-tillage to 0.60 for annual grass emergence from trays in 1991. No method consistently gave highest correlations in both years and both tillage systems. Seedling emergence from intact cores, evaluated for 4 yr in plow-disk and no-tillage soybean fields, was significantly correlated (rs = 0.15 to 0.68) with emergence in the field. Pooling data from three to five neighboring sample sites increased the correlation between core and field emergence densities.

Keywords

Common lambsquarters, Chenopodium album L. ♯ CHEALredroot pigweed, Amaranthus retroflexus L. ♯AMAREcorn, Zea mays L. ‘Dekalb 534’soybean, Glycine max L. Merr. ‘Century 84.’Buried seedsoil seed reserveseedling emergence
Type
Weed Biology and Ecology
Information
Weed Science , Volume 44 , Issue 1 , March 1996 , pp. 46 - 51
Copyright
Copyright © 1996 by the Weed Science Society of America 

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References

Literature Cited

1. 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.Google Scholar
2. Barralis, G., Chadoeuf, R., and Gouet, J. P. 1986. Essai de determination de la taille de l'echantillon pour l'etude du potentiel semencier d'un sol. Weed Res. 26: 291–297.CrossRefGoogle Scholar
3. 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
4. Bigwood, D. W. and Inouye, D. W. 1988. Spatial pattern analysis of seed banks: An improved method and optimized sampling. Ecology 69: 497–507.Google Scholar
5. 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.Google Scholar
6. Fay, P. K. and Olson, W. A. 1978. Technique for separating weed seed from soil. Weed Sci. 26: 530–533.Google Scholar
7. Forcella, F. 1992. Prediction of weed seedling densities from buried seed reserves. Weed Res. 32: 29–38.Google Scholar
8. Forcella, F., Wilson, R. G., Renner, K. A., Dekker, J., Harvey, R. G., Alm, D. A., Buhler, D. D., and Cardina, J. 1992. Weed seedbanks of the U.S. corn belt: Magnitude, variation, emergence, and application. Weed Sci. 40: 636–644.Google Scholar
9. Goyeau, H. and Fablet, G. 1982. Etude du stock de semences de mauvaises herbes dans le sol: le probleme de l'echantillonnage. Agronomie 2: 542–551.Google Scholar
10. Gross, K. L. 1990. A comparison of methods for estimating seed numbers in the soil. J. Ecol. 78: 1079–1093.Google Scholar
11. Gross, K. L. and Renner, K. A. 1989. A new method for estimating seed numbers in the soil. Weed Sci. 37: 836–839.Google Scholar
12. Malone, C. R. 1967. A rapid method for enumeration of viable seeds in the soil. Weed Sci. 15: 381–382.Google Scholar
13. Roberts, H. A. 1981. Seed banks in soils. Adv. Appl. Biol. 6: 1–55.Google Scholar
14. Roberts, H. A. and Ricketts, M. E. 1979. Quantitative relationships between the weed flora after cultivation and the seed population in the soil. Weed Res. 19: 269–275.Google Scholar
15. Schweizer, E. E., Lybecker, D. W., Wiles, L. J., and Westra, P. 1993. Bioeconomic weed management: Models in crop production. International Crop Sci. 1: 103–107.Google Scholar
16. Standifer, L. C. 1980. A technique for estimating weed seed populations in cultivated soil. Weed Sci. 28: 134–138.Google Scholar
17. 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
18. Thorsen, J. A. and Crabtree, G. 1977. Washing equipment for separating weed seed from soil. Weed Sci. 25: 41–42.Google Scholar
19. Villiers, T. A., 1972. Seed dormancy. Pages 219–281 in Kozlowski, T. T., ed. Seed biology, Vol II. Academic Press. New York.Google Scholar
20. 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.Google Scholar
21. 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: 171–175.Google Scholar