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Solaria Help Predict In-Crop Weed Densities
Published online by Cambridge University Press: 20 January 2017
Abstract
At locations in Argentina and the United States, solaria (miniature, portable, plastic greenhouses or a plastic sheet approximately 1 m2) were placed on field soils in autumn or late winter in an attempt to predict summer annual weed densities. Initial emergence of summer annual weeds covered by solaria commenced weeks before that of weeds in exposed seedbeds. Cumulative emergence of many species in solaria reached asymptotes before crops were sown. At asymptotic cumulative emergence, densities of dominant weeds in solaria (common lambsquarters, green foxtail, and large crabgrass) were correlated with weed densities occurring 4 wk after sowing, the typical time for making postemergence weed control decisions. These results indicate that solaria may supplement seedbank-sampling techniques for predicting weed densities in crops.
Keywords
Common lambsquarters, Chenopodium album L. #3 CHEALgreen foxtail, Setaria viridis (L.) Beauv. # SETVIlarge crabgrass, Digitaria sanguinalis (L.) Scop. # DIGSADecision makingemergencepostemergence controlseedlingssolarizationCWD, in-crop weed densityOM, organic matterPPI, preplant incorporatedPRE, preemergenceSWD, weeds inside solariaTWDthreshold weed density
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References
Literature Cited
Abu-Irmailleh, B. E.
1991. Soil solarization controls broomrapes (Orobanche spp.) in host vegetable crops in the Jordan Valley. Weed Technol.
5: 575–581.CrossRefGoogle Scholar
Al Masoom, A. A., Saghir, A. R., and Itani, S.
1993. Soil solarization for weed management in U.A.E.
Weed Technol.
7: 507–510.CrossRefGoogle Scholar
Beckett, T. H., Stoller, E., and Wax, L. M.
1988. Interference of four annual weeds in corn (Zea mays). Weed Sci.
36: 764–769.CrossRefGoogle Scholar
Bishop, Y. M., Feinberg, S. E., and Holland, P. W.
1975. Discrete Multivariate Analysis. Theory and Practice. Cambridge, MA: MIT. 557 p.Google Scholar
Cabria, F. and Culot, P.
1994. Selección y utilización de características edáficas para describir series de Argiudoles en el Sudeste Bonaerense. Ciencia del Suelo
12: 41–46.Google Scholar
Cardina, J. and Sparrow, D.
1996. A comparison of methods to predict weed seedling populations from the soil seed bank. Weed Sci.
44: 46–51.CrossRefGoogle Scholar
Cohen, J.
1960. A coefficient of agreement for nominal scales. Educ. Psych. Meas.
20: 37–46.CrossRefGoogle Scholar
Elmore, C. D., Brown, A., and Flint, E. P.
1983. Early interference between cotton (Gossypium hirsutum) and four weed species. Weed Sci.
31: 200–207.CrossRefGoogle Scholar
Eyherabide, J. J.
1993. Evaluation of pre-emergence applications of flumioxazin alone and with imazaquin against weeds in soybeans. Tests Agrochem. Cultiv.
14: 62–63.Google Scholar
Fehr, W. R. and Caviness, J.
1977. Stages of Soybean Development. Cooperative Extension Service, Agricultural and Home Economics Experiment Station, Iowa State University of Science and Technology, Ames, IA.Google Scholar
Forcella, F.
1992. Prediction of weed seedling densities from buried seed reserves. Weed Res.
12: 29–38.CrossRefGoogle Scholar
Lewis, R. R., De Martelaere, D. E., and Miller, E. L.
1971. Soil Survey of Stevens County, Minnesota. USDA-SCS, U.S. Government Printing Office, Washington, DC. 88 p.Google Scholar
Lindquist, J. L., Mortensen, D. A., and Westra, P. et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci.
47: 195–200.CrossRefGoogle Scholar
Lybecker, D. W., Schweiser, E. E., and King, R. P.
1991. Weed management decisions in corn based on bioeconomic modelling. Weed Sci.
39: 124–129.CrossRefGoogle Scholar
Mahrer, Y. and Katan, J.
1981. Spatial soil temperature regime under transparent polyethylene mulch: numerical and experimental studies. Soil Sci.
131: 82–87.CrossRefGoogle Scholar
McGiffen, M. E., Forcella, F., Lindstrom, M. J., and Reicosky, D. C.
1997. Covariance of cropping systems and foxtail density as predictors of weed interference. Weed Sci.
45: 388–396.CrossRefGoogle Scholar
Morrison, J. E. Jr., Huang, C-H., Lightle, D. T., and Daughtry, C. S. T.
1993. Residue measurement techniques. J. Soil Water Conserv.
48: 478–483.Google Scholar
Robinson, E. L., Langdale, G. W., and Stuedemann, J. A.
1984. Effect of three weed control regimes on no-till and tilled soybeans (Glycine max). Weed Sci.
32: 17–19.CrossRefGoogle Scholar
Shurtleff, J. L. and Coble, H. D.
1985. Interference of certain broadleaf species in soybean (Glycine max). Weed Sci.
33: 654–657.CrossRefGoogle Scholar
Standifer, L. C., Wilson, P. W., and Porche-Sorbet, R.
1984. Effects of solarization on soil weed seed populations. Weed Sci.
32: 569–573.CrossRefGoogle Scholar
Vizantinopoulus, S. and Katranis, N.
1993. Soil solarization in Greece. Weed Res.
33: 225–230.CrossRefGoogle Scholar
Walker, K. L. and Williams, D. J.
1989. Annual grass interference in container-grown bush cinquefoil (Potentilla fruticosa). Weed Sci.
37: 73–75.CrossRefGoogle Scholar
Wiles, L. J., Barlin, D. H., Schweizer, E. E., Duke, H. R., and Whitt, D. E.
1996a. A new sampler and elutriator for collecting and extracting weed seeds from soil. Weed Technol.
10: 35–41.CrossRefGoogle Scholar
Wiles, L. J., King, R. P., Schweiser, E. E., Lybecker, D. W., and Swinton, S. M.
1996b. GWM general weed management. Agric. Syst.
50: 355–376.CrossRefGoogle Scholar
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