Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-06-03T10:54:09.062Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatial distribution and mapping of crenate broomrape infestations in continuous broad bean cropping

Published online by Cambridge University Press:  20 January 2017

José Luis González-Andújar ,
Antonio Martínez-Cob ,
Francisca López-Granados  and
Luis García-Torres
Antonio Martínez-Cob
Affiliation:
Experimental Station of Aula Dei, C.S.I.C., Apartado 202, 50080-Zaragoza, Spain
Francisca López-Granados
Affiliation:
Institute for Sustainable Agriculture, C.S.I.C., Apdo.4084, 14080-Córdoba, Spain
Luis García-Torres
Affiliation:
Institute for Sustainable Agriculture, C.S.I.C., Apdo.4084, 14080-Córdoba, Spain
Get access Rights & Permissions [Opens in a new window]

Abstract

Geostatistical techniques were used to describe and map the spatial distribution of crenate broomrape populations parasitizing broad bean over 6 yr (from 1985 to 1990). In the first year, the spatial distribution was random, but from 1986 to 1989, crenate broomrape populations were clearly aggregated. The crenate broomrape infection severity (IS: number of emerged broomrape m−2) increased every year, from an average of 0.45 in 1985 to 29.4 in 1989, with a slight decrease the following year (IS = 27.4). Spherical functions provided the best fit because the cross-validation criteria were accomplished in all study cases. Kriged estimates were used to draw contour maps of the populations. About 34.3, 43.3, and 74.3% of the field plot surface exhibited an IS ≥ 1 (economic threshold) in 1985, 1986, and 1987, respectively, and nearly 100% of the area exceeded the economic threshold from 1988 to 1990; 1985 and 1986 were key years for control of the parasitic weed population. The percentage of infested area at different IS intervals in each year's map obtained by kriging was used to estimate the percentage of yield losses in each infested area (YA) with the equation: YA = A ∗ Ymax ∗ (1 − IS ∗ 0.124), where A is the infested area at a given IS interval and Ymax is the expected broomrape-free broad bean yield. Yield losses under different IS intervals were compared with yield loss attributable to a uniform distribution of crenate broomrape. Results showed that yield loss assuming a uniform distribution of crenate broomrape was clearly overestimated, which is important to avoid overuse of herbicides.

Keywords

Crenate broomrape, Orobanche crenata Forsk. ORACRVicia faba L.Geostatisticskrigingparasitic weedpatchprecision farming
Type
Research Article
Information
Weed Science , Volume 49 , Issue 6 , December 2001 , pp. 773 - 779
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

Auld, B. A. and Tisdell, C. A. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q. 31:81.Google Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. 1995. Analysis of spatial distribution of common lambsquarters (Chenopdium album) in no-till soybean (Glycine max). Weed Sci. 43:258–268.CrossRefGoogle Scholar
Clark, S., Perry, J. N., and Marshall, E.J.P. 1996. Estimating Taylor's power law parameters for weed and the effect of spatial scale. Weed Res. 36:405–417.CrossRefGoogle Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.CrossRefGoogle Scholar
Cressie, N. 1991. Statistics for spatial data. New York: Wiley.Google Scholar
Dessaint, F., Chadoeuf, R., and Barralis, G. 1991. Spatial pattern analysis of weed seed in the cultivated soil seed bank. J. Appl. Ecol. 28:721–730.CrossRefGoogle Scholar
Donald, W. W. 1994. Geostatistics for mapping weeds, with a Canada thistle (Cirsium arvense) patch as a case study. Weed Sci. 42:648–657.CrossRefGoogle Scholar
González-Andújar, J. L. and Fernandez-Quintanilla, C. 1991. Modelling the population dynamics of Avena sterilis under dry-land cereal cropping systems. J. Appl. Ecol. 28:16–27.CrossRefGoogle Scholar
González-Andújar, J. L., Perry, J. N., and Moss, S. R. 1999. Modeling effects of spatial pattern on the seed bank dynamics of Alopecurus myosuroides . Weed Sci. 47:697–705.CrossRefGoogle Scholar
Heisel, T., Andreasen, C., and Ersbøll, A. K. 1996. Annual weed distribution can be mapped with kriging. Weed Res. 36:325–337.CrossRefGoogle Scholar
Heisel, T., Ersbøll, A. K., and Andreasen, C. 1999. Weed mapping with co-kriging using soil properties. Prec. Agric. 1:39–52.CrossRefGoogle Scholar
Hevesi, J. A., Istok, J. D., and Flint, A. L. 1992. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis. J. Appl. Meteor. 31:661–676.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. An Introduction to Applied Geostatistics. New York: Oxford University.Google Scholar
Journel, A. G. and Huijbregts, C. J. 1978. Mining Geostatistics. London: Academic Press.Google Scholar
Kristensen, K. and Ersbøll, A. K. 1995. The use of geostatistic methods in variety trials where some variety is unreplicated. Fifth Working Seminar on Statistical Methods in Variety Testing. Zakopane, Poland, June 12–16, 1995.Google Scholar
Lindquist, J. L., Dieleman, J. A., Mortensen, D. A., Johnson, G. A., and Wyse-Pester, D. Y. 1998. Economic importance of managing spatially heterogeneous weed populations. Weed Technol. 12:7–13.CrossRefGoogle Scholar
López-Granados, F. and García-Torres, L. 1993a. Population dynamics of crenate broomrape (Orobanche crenata) in faba bean (Vicia faba). Weed Sci. 41:563–567.CrossRefGoogle Scholar
López-Granados, F. and García-Torres, L. 1993b. Seed bank and other demographic parameters of broomrape (Orobanche crenata Forsk.) population in faba bean (Vicia faba L.). Weed Res. 33:319–327.CrossRefGoogle Scholar
López-Granados, F. and García-Torres, L. 1997. Modelling the demography of crenate broormape (Orobanche crenata Forsk.) as affected by broad bean planting dates and cropping frequency. Weed Sci. 46:261–268.CrossRefGoogle Scholar
López-Granados, F. and García-Torres, L. 1998. Short and long term implications of controlling broomrape (Orobanche crenata Forsk.) population in faba bean (Vicia faba L.). Crop Prot. 17:139–143.CrossRefGoogle Scholar
López-Granados, F. and García-Torres, L. 1999. Longevity of crenate broomrape (Orobanche crenata Forsk.) seed under soil and laboratory conditions. Weed Sci. 47:161–166.CrossRefGoogle Scholar
Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.CrossRefGoogle Scholar
Mesa-García, J. and García-Torres, L. 1984. A competition index for Orobanche crenata Forsk effects on broad bean (Vicia faba L.). Weed Res. 24:379–382.CrossRefGoogle Scholar
Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distribution in agricultural fields. Pages 113–124 In Robert, P. C., Rust, R. H. and Larson, W. E., eds. Soil Specific Crop Management. American Society of Agronomy, Madison, WI.Google Scholar
Parker, C. 1994. The present state of the Orobanche problem. Pages 17–26 in Proceedings of the Third International Workshop on Orobanche and related Striga research.Google Scholar
Webster, R. and Oliver, M. A. 1990. Statistical methods in soil and land resource survey. Oxford: Oxford University Press.Google Scholar
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. 40:547–554.CrossRefGoogle Scholar