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Number of Solaria Needed to Predict Weed Seedlings in Two Summer Crops

Published online by Cambridge University Press:  20 January 2017

Juan J. Eyherabide ,
María G. Cendoya ,
Frank Forcella  and
Marisol Irazazábal
Juan J. Eyherabide*
Affiliation:
Department of Crop Production, Soils and Agricultural Engineering, College of Agricultural Sciences, National University of Mar del Plata, Ruta 226 km 73,5, Balcarce, 7620 Buenos Aires, Argentina
María G. Cendoya
Affiliation:
Department of Crop Production, Soils and Agricultural Engineering, College of Agricultural Sciences, National University of Mar del Plata, Ruta 226 km 73,5, Balcarce, 7620 Buenos Aires, Argentina
Frank Forcella
Affiliation:
U.S. Department of Agriculture–Agricultural Research Service, 803 Iowa Ave, Morris, MN 56267
Marisol Irazazábal
Affiliation:
San Isidro 1105, Temperley, Buenos Aires, Argentina
*
Corresponding author's E-mail: jymeyherabide@gmail.com
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Abstract

The utility of solaria (1 by 1-m plastic sheets) to predict densities of a few weed species in summer crops has been demonstrated previously, but needed further research to be adopted by farmers and advisors. We tested the method to detect important weeds in Argentina and Minnesota, and determined the minimum number of solaria required to predict the presence of emerged weed seedlings in the forthcoming growing season. Three experiments were performed in Buenos Aires Province, Argentina, and one in Minnesota. Solaria were placed in fields with different previous crops and soil management: no tillage (two fields) and conventional tillage (two fields). Preceding crops were corn (one field), wheat (one field), and double-cropped wheat/soybean (two fields). After weeds were enumerated, solaria were removed, sunflower (one field) and soybean (three fields) were planted, and weeds later assessed in each crop. Results indicate that one solarium per 1.9 ha can detect common lambsquarters with 95% confidence within the next summer crop. For other species, one solarium per 4.2, 1.2, 1.0, and 1.8 to 2.7 ha (depending upon field site) for large crabgrass, prostrate knotweed, wild buckwheat, and green foxtail, respectively, was required. The low cost and simplicity of assessment make this technique more suitable than that of soil seed-bank samples to predict weed emergence. The number of solaria required to forecast weed infestation levels confidently is sufficiently low that their use may be justified, especially in small fields of high-value crops.

La utilidad del uso de las solaria (láminas de plástico de 1 por 1m), para predecir las densidades de algunas especies de maleza en cultivos de verano ha sido demostrada previamente, pero se requiere más investigación para que esta práctica sea adoptada por los agricultores y sus asesores. Evaluamos el método para detectar malezas importantes en Argentina y Minnesota EE UU y determinamos el número mínimo de solaria requerido para predecir la presencia de plántulas emergidas de malezas en la temporada productiva siguiente. Se llevaron a cabo tres experimentos en la Provincia de Buenos Aires, Argentina y uno en Minnesota. Las solaria fueron colocadas en lotes que difirieron en sus cultivos y sistemas de manejo de suelo previos, a saber: cero labranza en dos lotes y labranza convencional en otros dos. Los cultivos precedentes fueron: maíz (un lote), trigo (un lote), y doble-cultivo trigo/soja (dos lotes). Después que las malezas fueron enumeradas y las solaria fueron removidas, se sembró girasol (un lote) y soja (tres lotes) y posteriormente las malezas fueron valoradas en cada cultivo. Los resultados indican que un solarium por cada 1.9 ha, puede detectar Chenopodium album con 95% de confiabilidad en el cultivo del verano siguiente. Para otras especies se requirió un solarium por cada 4.2, 1.2, 1.0, y 1.8 a 2.7 ha (dependiendo de lote), para Digitaria sanguinalis, Polygonum aviculare, Polygonum convolvulus y Setaria viridis respectivamente. El bajo costo y la facilidad de la evaluación hacen que esta técnica sea más adecuada que la toma de muestras del banco de semillas para predecir la emergencia de malezas. El número de solaria requeridas para obtener pronósticos confiables sobre los niveles de infestación de malezas es suficientemente bajo para que su uso sea justificado, especialmente en lotes pequeños de cultivos de alto valor.

Keywords

Common lambsquarters, Chenopodium album L. CHEALgreen foxtail, Setaria viridis (L.) Beauv. SETVIlarge crabgrass, Digitaria sanguinalis (L.) Scop. DIGSAprostrate knotweed, Polygonum aviculare L. POLAVwild buckwheat, Polygonum convolvulus L. POLCOcorn, Zea mays L.soybean, Glycine max (L.) Merrsunflower, Helianthus annus L.wheat, Triticum aestivum L.Bioeconomic modelssite-specific weed managementweed controlweed seedling predictionweed thresholds
Type
Weed Management—Techniques
Information
Weed Technology , Volume 25 , Issue 1 , March 2011 , pp. 113 - 118
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bishop, Y. M., Feinberg, S. E., and Holland, P. W. 1975. Discrete Multivariate Analysis. Theory and Practice. Cambridge, MA MIT. 557 p.Google Scholar
Buhler, D. D. 1999. Expanding the context of weed management. J. Crop Prod 2:1–7.Google Scholar
Calviño, P. and Eyherabide, J. J. 2006. Use of solaria to predict weed density and floristic composition in no-till cropping systems. Pesq. Agrop. Bras 41:409–413.Google Scholar
Cardina, J., Johnson, A. G., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45:364–373.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.Google Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. L. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in no till soybeans (Glycine max). Weed Sci. 43:258–268.Google Scholar
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psych. Meas 20:37–46.Google Scholar
Cousens, R. 1998. Misinterpretations of results in weed research through inappropriate use of statistics. Weed Res. 28:282–289.Google Scholar
Cousens, R. D., Brown, R. W., McBratney, A. B., Whelan, B., and Moerkerk, M. 2002. Sampling strategy is important for producing weed maps: a case study using kriging. Weed Sci. 50:542–546.Google Scholar
Eyherabide, J. J., Calviño, P., Forcella, F., Cendoya, M. G., and Oskoui, K. E. 2003. Solaria help predict in-crop weed densities. Weed Technol. 17:166–172.Google Scholar
Eyherabide, J. J. and Cendoya, M. G. 2002. Critical periods of weed control in soybean for full field and in-furrow interference. Weed Sci. 50:162–166.Google Scholar
Eyherabide, J., Peterson, D., and Forcella, F. 2004. Solaria provide pre plant information on weed densities, distributions and management. Proc. North Cent. Weed Sci. Soc 59:52.Google Scholar
Forcella, F. 1992. Prediction of weed seedling densities from buried seed reserves. Weed Res. 32:29–38.Google Scholar
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
Gerhards, R., Wyse-Pester, D. Y., Mortensen, D. A., and Johnson, G. 1997. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 45:108–119.Google Scholar
Gold, H. J., Bay, J., and Wilkerson, G. G. 1996. Scouting for weeds, based on the negative binomial distribution. Weed Sci. 44:504–510.Google Scholar
R Development Core Team 2009. R: A language and environment for statistical computing. Vienna, Austria R Foundation for Statistical Computing. http://www.R-project.org.Google Scholar
Schweizer, E. E. and Zimdahl, R. L. 1984. Weed seed decline in irrigated soil after six years of continuous corn (Zea mays) and herbicides. Weed Sci. 32:76–83.Google Scholar
Spokas, K. and Forcella, F. 2009. Software tools for weed seed germination modeling. Weed Sci. 57:216–227.Google Scholar
Swinton, S. M. 2005. Economics of site–specific weed management. Weed Sci. 53:259–263.Google Scholar
Van Acker, R., Swanton, C. J., and Weise, S. F. 1993. The critical period of weed control in soybean (Glyicine max). Weed Sci. 41:194–200.Google Scholar
Wiles, L. J. 2005. Sampling to make maps for site-specific weed management. Weed Sci. 53:228–235.Google Scholar
Wiles, L. J., Barlin, D. H., Schweizer, E. E., Duke, H. R., and Whitt, D. E. 1995. A new soil sampler and elutriator for collecting and extracting weed seeds from soil. Weed Technol. 10:35–41.Google Scholar
Wiles, L. J., King, R. P., Schweizer, E. E., Lybecker, D. W., and Swinton, S. M. 1996. GWM: general weed management. Model. Agric. Syst 50:355–376.Google Scholar
Wiles, L. J. and Schweizer, E. E. 2002. Spatial dependence of weed seed banks and strategies for sampling. Weed Sci. 50:595–606.Google Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modeling weed distribution for improved postemergence control decision. Weed Sci. 40:546–553.Google Scholar
Wyse-Pester, D. Y., Wiles, L. J., and Westra, P. 2002. Infestation and spatial dependence of weed seedling and mature weed populations in corn. Weed Sci. 50:54–63.Google Scholar
Zanin, G., Berti, A., and Riello, L. 1998. Incorporation of spatial variability into the weed control decision process. Weed Res. 38:107–118.Google Scholar