Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-19T13:44:25.217Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-species weed spatial variability and site-specific management maps in cultivated sunflower

Published online by Cambridge University Press:  20 January 2017

Montserrat Jurado-Expósito ,
Francisca López-Granados ,
Luis García-Torres ,
Alfonso García-Ferrer ,
Manuel Sánchez de la Orden  and
Silvia Atenciano
Francisca López-Granados
Affiliation:
Institute for Sustainable Agriculture, CSIC, Apdo. 4084, 14080, Córdoba, Spain
Luis García-Torres
Affiliation:
Institute for Sustainable Agriculture, CSIC, Apdo. 4084, 14080, Córdoba, Spain
Alfonso García-Ferrer
Affiliation:
Department of Remote Sensing, University of Córdoba, Avda. Menendez Pidal s/n. 14004, Córdoba, Spain
Manuel Sánchez de la Orden
Affiliation:
Department of Remote Sensing, University of Córdoba, Avda. Menendez Pidal s/n. 14004, Córdoba, Spain
Silvia Atenciano
Affiliation:
Institute for Sustainable Agriculture, CSIC, Apdo. 4084, 14080, Córdoba, Spain
Get access Rights & Permissions [Opens in a new window]

Abstract

Geostatistical techniques were used to describe and map weed spatial distribution in two sunflower fields in Cabello and Monclova, southern Spain. Data from the study were used to design intermittent spraying strategies. Weed species, overall infestation severity (IS) index, and spatial distribution varied considerably between the two sites. Weed species displayed differences in spatial dependence regardless of IS. The IS mapping of each single weed and of the overall infestation was achieved by kriging, and site-specific application maps were then drawn based on the multi-species weed map and the estimated economic threshold (ET). Herbicide treatment was assumed to be needed for an overall IS score of 2 or 3, and the infested “area exceeding the economic threshold” was determined. The overall weed-infested area varied considerably between locations. About 99 and 38% of the total area was moderately infested (IS ≥ 2) at Monclova and Cabello, respectively. Therefore, if a given herbicide were applied just to the areas exceeding the ET, a significant herbicide saving would be realized in Cabello but not in Monclova. A multi-species spatial analysis provides an opportunity to make site-specific management recommendations from a map of the distribution of IS of the total infestation. Furthermore, only in fields with hard-to-control weed species (e.g., nodding broomrape and corn caraway) would site-specific herbicide application maps developed from total weed infestations need to be complemented with targeted site-specific herbicide treatments to prevent further spread of these species, although their IS might be low.

Keywords

GlyphosateBristly oxtongue, Picris echioides L. PICECcatchweed bedstraw, Gallium aparine L. GALAPcommon lambsquarters, Chenopodium album L. CHEALcorn caraway, Ridolfia segetum Morris, CRYRIcowcockle, Vaccaria pyramidata Medik. VAAPYEuropean heliotrope, Heliotropium europaeum L. HEOEUfield bindweed, Convolvulus arvensis L. CONARlittleseed canarygrass, Phalaris paradoxa L. PHAPAnodding broomrape, Orobanche cernua Loefl. ORACEprostrate knotweed, Polygonum aviculare L. POLAUrapeseed, Brassica napus L.sunflower, Helianthus annuus L.tumble pigweed, Amaranthus albus L. AMAALwild mustard, Sinapis arvensis L. SINAREconomic thresholdgeostatisticskrigingoverall infestationprecision agriculture
Type
Research Article
Information
Weed Science , Volume 51 , Issue 3 , June 2003 , pp. 319 - 328
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

Berti, A. and Zanin, G. 1997. GESTINF, a decision model for post-emergence weed management in soybean (Glycine max (L.) Merr). Crop Prot. 16:109–116.CrossRefGoogle Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735–742.Google Scholar
Bregt, A. K., Gesing, H. J., and Alkasuma, A. 1992. Mapping the conditional probability of soil variables. Geoderma. 53:15–29.CrossRefGoogle Scholar
Cambardella, C. A. and Karlen, D. L. 1999. Spatial analysis of soil fertility parameters. Prec. Agric. 1:5–11.Google Scholar
Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. K., Turco, R. F., and Konopka, A. E. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Sci. Soc. Am. J. 58:1501–1511.CrossRefGoogle Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. L. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in no-till soybean (Glycine max). Weed Sci. 43:258–268.Google Scholar
Carranza, P., Saavedra, M., and García-Torres, L. 1995. Competition between Ridolfia segetum and sunflower. Weed Res. 35:369–375.Google Scholar
Castro-Tendero, A. J. and García-Torres, L. 1995. SEMAGI—an expert system for weed control decision making in sunflowers. Crop Prot. 14:543–548.Google Scholar
Christensen, S. T., Heisel, T., and Walter, A. M. 1996. Patch spraying in cereals. Pages 936–968 In Proceedings of the 2nd International Weed Control Conference. Copenhagen, Denmark: Bios Scientific.Google Scholar
Clay, S. A., Lems, G. J., Clay, D. E., Forcella, F., Ellsbury, M. M., and Carlson, C. G. 1999. Sampling weed spatial variability on a fieldwide scale. Weed Sci. 47:674–681.CrossRefGoogle Scholar
Cousens, R. and Croft, A. M. 2000. Weed populations and pathogens. Weed Res. 40:63–82.Google Scholar
Cressie, N.A.C. 1991. Median polish kriging. Pages 183–199 In Cressie, N.A.C., ed. Statistics for Spatial Data. New York: J. Wiley.Google Scholar
Dammer, K.-H., Schweigert, T., and Wittmann, C. H. 1999. Probability maps for risk assessment in a patchy weed control. Prec. Agric. 1:185–198.Google Scholar
Dieleman, J. A. and Mortensen, D. A. 1999. Characterizing the spatial pattern of Abutilon theophrasti seedling patches. Weed Res. 39:455–467.Google 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.Google Scholar
Doyle, C. J. 1991. Mathematical models in weed management. Crop Prot. 10:432–444.Google Scholar
Ersbøll, A. K., Kristensen, K., Nordbo, E., and Christensen, S. 1993. Estimating the number of weed plants using kriging. Pages 140–151 In Boelskifte, S., ed. Symposium i Anvendt Statistik. Ålborg, Denmark: UNIC.Google Scholar
García-Torres, L. 1980. Una revisión de los sistemas de control de malas hierbas en el girasol, con especial referencia a Andalucía Occidental. Helia. 3:47–52.Google Scholar
García-Torres, L., Castejón-Muñoz, M., Jurado-Expósito, M., and López-Granados, F. 1996. Modeling the economics of controlling nodding broomrape (Orobanche cernua) in sunflower (Helianthus annuus). Weed Sci. 44:591–595.Google Scholar
García-Torres, L., López-Granados, F., Jurado-Expósito, M., and Díaz-Sanchez, J. 1998. The present state of Orobanche spp. infestation in Andalusia and the prospects for its management. Pages 141–145 In Proceedings of the 6th European Weed Research Society Mediterranean Symposium. Montpellier, France: Agro Montpellier.Google Scholar
Gerhards, R., Wyse-Pester, D. Y., Mortensen, D., and Johnson, G. A. 1997. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 45:108–119.Google Scholar
González-Andújar, J. L., Martínez-Cob, A., López-Granados, F., and García-Torres, L. 2001. Spatial distribution and mapping of Orobanche crenata infestation in continuous Vicia faba cropping for six years. Weed Sci. 49:773–779.Google Scholar
Heisel, T., Andersen, C., and Ersbøll, A. K. 1996a. Annual weed can be mapped with kriging. Weed Res. 36:325–337.Google Scholar
Heisel, T., Christensen, S., and Walter, A. M. 1996b. Weed managing model for patch spraying in cereal. Precision agriculture. Pages 999–1007 In Robert, P. C., Rust, R. H., and Larson, W. E., eds. Proceedings of the 3rd International Conference. Minneapolis, MN: University of Minnesota.Google Scholar
Hevesi, J. A., Istok, J. D., and Flint, A. L. 1992. Precipitation estimation in mountains terrain using multivariate geostatistics. Part I: structural analysis. J. Appl. Meteorol. 31:661–676.Google Scholar
Hidalgo, B., Saavedra, M., and García-Torres, L. 1990. Weed flora of dryland crop in the Córdoba region (Spain). Weed Res. 30:309–318.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. An Introduction to Applied Geostatistics. New York: Oxford University. 561 p.Google Scholar
Jiménez-Hidalgo, M. J., Saavedra, M., and García-Torres, L. 1990. Dynamic of Phalaris brachystachys and P. paradoxa populations in winter wheat. Pages 37–43 In Proceedings of the Symposium on Integrated Weed Management in Cereals. Helsinki, Finland: European Weed Research Society.Google Scholar
Johnson, G. A., Mortensen, D. A., and Gotway, C. A. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci. 44:704–710.Google Scholar
Journel, A. G. and Huijbregts, C. J. 1978. Mining Geostatistics. London: Academic Press. 600 p.Google Scholar
Kristensen, K. and Ersbøll, A. K. 1995. The use of geostatistics methods in variety trials where some variety is unreplicated. Pages 12–16 In Proceedings of the 5th Working Seminar on Statistical Methods in Variety Testing. Zakopane, Poland: Polish Academy of Sciences.Google Scholar
Lamb, D. W. and Weedon, M. 1998. Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imaging: Panicum effesum in oilseed rape stubble. Weed Res. 38:443–451.Google Scholar
López-Granados, F., Jurado-Expósito, M., Atenciano, S., García-Ferrer, A., Sanchez de la Orden, M., and García-Torres, L. 2003. Spatial variability of agricultural soil parameters in southern Spain. Plant soil. 246:97–105.Google Scholar
Marshall, E.J.P. 1988. Field scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
Mortensen, D. A., Johnson, G. A., Wyse, D. Y., and Martin, A. R. 1995. Managing spatially variable weed populations. Pages 397–415 In Robert, P. C., Rust, R. H., and Larson, W. E., eds. Proceedings of the 2nd International Conference on Site Specific Management for Agricultural Systems; March 27–30, 1994; Minneapolis, MN. Madison, WI: American Society for Agronomy.Google Scholar
Nordbo, E., Christensen, S., Kristensen, K., and Walter, M. 1994. Patch spraying of weed in cereal crops. Asp. Appl. Biol. 40:325–334.Google Scholar
Nordmeyer, H., Hausler, A., and Niemann, P. 1997. Patchy weed control as an approach in precision farming. Pages 307–314 In Stafford, J., ed. Precision Agriculture ’97. First European Conference on Precision Agriculture, Warwick, U.K.: Bios Scientific.Google Scholar
Nordmeyer, H. and Niemann, P. 1992. Möglichkeiten der gezielten Teilflächenbehandlung mit Herbiziden auf der Grundlage von Unkrautverteilung und Bodenvariabilität. Z. Pflanzenkr. Pflanzenschutz Soderheft. XIII:539–547.Google Scholar
Pérez, A. J., Lóepez, F., Benlloch, J. V., and Christensen, S. 2000. Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electronics Agric. 25:197–212.Google Scholar
Renner, K. A. and Black, J. R. 1991. SOYHERB, a computer program for soybean herbicide decision making. Agron. J. 83:921–925.Google Scholar
Rew, L. J. and Cousens, R. D. 2001. Spatial distribution of weeds in arable crops: are current sampling and analytical methods appropriate? Weed Res. 41:1–18.Google Scholar
Rew, L. J., Cussans, G. W., Mugglestone, M. A., and Miller, P.C.H. 1996. A technique for mapping the spatial distribution of Elymus repens with estimates of the potential reduction in herbicide usage from patch spraying. Weed Res. 36:283–292.Google Scholar
Roberts, E. H., Raulin, F. W., and Fleischer, S. J. 1993. Spatial data representation for integrated pest management programs. Am. Entomol. 39:92–107.Google Scholar
Saavedra, M., García-Torres, L., Hernández-Bermejo, E., and Hidalgo, B. 1989. Weed flora in the middle valley of Guadalquivir, Spain. Weed Res. 29:167–179.Google Scholar
Stigliani, L. and Resina, C. 1993. SELOMA: expert system for weed management in herbicide-intensive crops. Weed Technol. 7:550–559.Google Scholar
Streibig, J. C., Combellack, J. H., Pritchard, G. H., and Richarson, R. G. 1989. Estimation of threshold for weed control in Australian Cereals. Weed Res. 29:117–126.Google Scholar
Thompson, J. F., Stafford, J. V., and Miller, P.C.H. 1991. Potential for automatic weed detection and selective herbicide application. Crop Prot. 10:254–259.Google Scholar
Tian, L., Reid, J. F., and Hummerl, J. W. 1999. Development of a precision sprayer for site-specific weed management. Trans. Am. Soc. Agric. Eng. 42:893–900.CrossRefGoogle Scholar
Timmermann, C., Gerhards, R., Krohmann, P., Sökefeld, M., and Kühbauch, W. 2001. The economical and ecological impact of the site-specific weed control. Pages 563–568 In Grenier, G. and Blackmore, S., eds. Third European Conference on Precision Agriculture. Montpellier, France: Agro Montpellier.Google Scholar
Van Groenendael, J. M. 1988. Patchy distribution of weeds and some implications for modeling population dynamics: a short literature review. Weed Res. 28:437–441.Google Scholar
Wartenberg, G. and Dammer, K. H. 2001. Site-specific real time application of herbicides in practice. Pages 617–622 In Grenier, G. and Blackmore, S., eds. Third European Conference on Precision Agriculture. Montpellier, France: Agro Montpellier.Google Scholar
Webster, R. and Oliver, M. A. 2001. Geostatistics For Environmental Scientists. Chichester, U.K.: J. Wiley. pp.11–35; 219–242.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB, a decision model for post-emergence weed control in soybean. Agron. J. 83:413–417.Google Scholar
Wilson, B. J. and Brain, P. 1991. Long-term stability of distribution of Alopecurus myosuroides Huds. within cereal fields. Weed Res. 31:367–373.Google Scholar
Zanin, G., Berti, A., and Riello, L. 1998. Incorporation of weed spatial variability into the weed control decision-making process. Weed Res. 38:107–118.Google Scholar