Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-23T20:35:36.636Z Has data issue: false hasContentIssue false

Spatial Pattern of Weeds Based on Multispecies Infestation Maps Created by Imagery

Published online by Cambridge University Press:  20 January 2017

Louis Longchamps*
Affiliation:
Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523
Bernard Panneton
Affiliation:
Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, Quebec, Canada
Robin Reich
Affiliation:
Forest and Rangeland Stewardship Department, Colorado State University, Fort Collins, CO 80523
Marie-Josée Simard
Affiliation:
Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, Quebec, Canada
Gilles D. Leroux
Affiliation:
Département de Phytologie, Université Laval, Québec, Canada
*
Corresponding author's E-mail: louis.longchamps@colostate.edu

Abstract

Weeds are often spatially aggregated in maize fields, and the level of aggregation varies across and within fields. Several annual weed species are present in maize fields before postemergence herbicide application, and herbicides applied will control several species at a time. The goal of this study was to assess the spatial distribution of multispecies weed infestation in maize fields. Ground-based imagery was used to map weed infestations in rain-fed maize fields. Image segmentation was used to extract weed cover information from geocoded images, and an expert-based threshold of 0.102% weed cover was used to generate maps of weed presence/absence. From 19 site-years, 13 (68%) demonstrated a random spatial distribution, whereas six site-years demonstrated an aggregated spatial pattern of either monocotyledons, dicotyledons, or both groups. The results of this study indicated that monocotyledonous and dicotyledonous weed groups were not spatially segregated, but discriminating these weed groups slightly increased the chances of detecting an aggregated pattern. It was concluded that weeds were not always spatially aggregated in maize fields. These findings emphasize the need for techniques allowing the assessment of weed aggregation prior to conducting site-specific weed management.

Type
Weed Biology and Ecology
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.)

Footnotes

Associate Editor for this paper: Anita Dille, Kansas State University.

References

Literature Cited

Backes, M, Jacobi, J (2006) Classification of weed patches in Quickbird images: verification by ground truth data. Eur Assoc Remote Sens Lab eProc 5: 173179 Google Scholar
Baddeley, A, Turner, R (2005) Spatstat: an R package for analyzing spatial point patterns. J Statistical Software 12: 142 Google Scholar
Barroso, J, Fernandez-Quintanilla, C, Maxwell, BD, Rew, LJ (2004) Simulating the effects of weed spatial pattern and resolution of mapping and spraying on economics of site specific management. Weed Res 44: 460468 Google Scholar
Barroso, J, Navarrete, L, Sánchez del Arco, MJ, Fernandez-Quintanilla, C, Lutman, PJW, Perry, NH, Hull, RI (2006) Dispersal of Avena fatua and Avena sterilis patches by natural dissemination, soil tillage and combine harvesters. Weed Res 46: 118128 Google Scholar
Bassett, IJ, Crompton, CW (1978). The biology of Canadian weeds: 32 Chenopodium album L. Can J Plant Sci 58: 10611072 Google Scholar
Benvenuti, S (2007). Weed seed movement and dispersal strategies in the agricultural environment. Weed Biol Manag 7: 141157 Google Scholar
Bolker, BM, Pacala, SW, Neuhauser, C (2003) Spatial dynamics in model plant communities: what do we really know? Amer Nat 162: 135148 Google Scholar
Cardina, J, Johnson, GA, Sparrow, DH (1997) The nature and consequence of weed spatial distribution. Weed Sci 45: 364373 Google Scholar
Cardina, J, Sparrow, DH, McCoy, EL (1996) Spatial relationships between seedbank and seedling populations of common lambsquarters (Chenopodium album) and annual grasses. Weed Sci 44: 298308 Google Scholar
Cavers, PB, Harper, JL (1967) The comparative biology of closely related species living in the same area: IX. Rumex: The nature of adaptation to a sea-shore habitat. J Ecol 55: 7382 Google Scholar
Colbach, N, Roger-Estrade, J, Chauvel, B, Caneill, J (2000) Modelling vertical and lateral seed bank movements during mouldboard ploughing. Eur J Agron 13: 111124 Google Scholar
Cousens, RD, Woolcock, JL (1997) Spatial dynamics of weeds: an overview. Pages 613618 in Proceedings of the Brighton Crop Protection Conference Weeds (British Crop Protection Council). Farnham, UK: British Crop Protection Council Google Scholar
Cuzick, J, Edwards, R (1990) Spatial clustering for inhomogeneous populations. J R Stat Soc. Series B (Methodol) 52: 73104 Google Scholar
Dieleman, JA, Mortensen, DA (1999) Characterizing the spatial pattern of Abutilon theophrasti seedling patches. Weed Res 39: 455467 Google Scholar
Dieleman, JA, Mortensen, DA, Buhler, DD, Cambardella, CA, Moorman, TB (2000) Identifying associations among site properties and weed species abundance. I. Multivariate analysis. Weed Sci 48: 567575 Google Scholar
Efloras. 2015. Chenopodium album . Page 296 in Flora of North America. Vol. 4. http://www.efloras.org/florataxon.aspx?flora_id=1&taxon_id=200006809. Accessed December 7, 2015Google Scholar
[EWRS–SSWM] European Weed Research Society–Site Specific Weed Management (2005) Site-Specific Weed Management Working Group. http://web.agrsci.dk/jbt/sch/ewrs/. Accessed June 24, 2015Google Scholar
Fortin, M-J, Dale, MRT (2005) Spatial Analysis: A Guide for Ecologists. Cambridge, UK: Cambridge University Press. 365 pGoogle Scholar
Gerhards, R, Christensen, S (2003) Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Res 43: 385392 Google Scholar
Gerhards, R, Oebel, H (2006) Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Res 46: 185193 Google Scholar
Gonzalez-Andujar, JL, Saavedra, M (2003) Spatial distribution of annual grass weed populations in winter cereals. Crop Prot 22: 629633 Google Scholar
Goudy, HJ, Bennett, KA, Brown, RB, Tardif, FJ (2001) Evaluation of site-specific weed management using a direct-injection sprayer. Weed Sci 49: 359366 Google Scholar
Greig-Smith, P (1983) Quantitative Plant Ecology. 3rd edn. Berkeley, CA: University of California Press. 359 pGoogle Scholar
Hamouz, P, Soukup, J, Holec, J, Jursík, M (2004) Field-scale variability of weediness on arable land. Plant Soil Environ 50: 134140 Google Scholar
Heijting, S, Van der Werf, W, Stein, A, Kropff, MJ (2007) Are weed patches stable in location? Application of an explicitly two-dimensional methodology. Weed Res 47: 381395 Google Scholar
Jhala, AJ, Knezevic, SZ, Ganie, ZA, Singh, M (2014) Integrated weed management in maize. Pages 177196 in Chauhan, BS, Mahajan, G, eds. Recent Advances in Weed Management. New York: Springer-Verlag Google Scholar
Johnson, GA, Mortensen, DA, Gotway, CA (1996) Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci 44: 704710 Google Scholar
Jurado-Expósito, M, López-Granados, F, García-Torres, L, García-Ferrer, A, Sánchez de la orden, M, Atenciano, S (2003) Multi-species weed spatial variability and site-specific management maps in cultivated sunflower. Weed Sci 51: 319328 Google Scholar
Longchamps, L, Panneton, B, Simard, M-J, Leroux, GD (2012) Could weed sensing in corn interrows result in efficient weed control? Weed Technol 26: 649656 Google Scholar
Longchamps, L, Panneton, B, Simard, M-J, Leroux, GD (2013) A technique for high-accuracy ground-based continuous weed mapping at field scale. Trans Am Soc Agric Biol Eng 56: 15231533 Google Scholar
Longchamps, L, Panneton, B, Simard, M-J, Leroux, GD (2014) An imagery-based weed cover threshold established using expert knowledge. Weed Sci 62: 177185 Google Scholar
Marshall, EJP, Brain, P (1999) The horizontal movement of seeds in arable soil by different soil cultivation methods. J Appl Ecol 36: 443454 Google Scholar
Marshall, G, Kirkwood, RC, Martin, DJ (1987) Studies on the mode of action of asulam, aminotriazole, and glyphosate in field horsetail Equisetum arvense L. (field horsetail). II. The metabolism of [14C] asulam,[14C] aminotriazole, and [14C] glyphosate. Pestic Sci 18: 6577 Google Scholar
McGrew, JC, Lembo, AJ, Monroe, CB (2000). An Introduction to Statistical Problem Solving in Geography. 2nd edn. Boston, MA: McGraw Hill. 254 pGoogle Scholar
Meagher, TR, Burdick, DS (1980) The use of nearest neighbor frequency analyses in studies of association. Ecology 61: 12531255 Google Scholar
Mountford, MD (1961) On EC Pielou's index of non-randomness. J Ecol 49: 271275 Google Scholar
Nordmeyer, H (2006) Patchy weed distribution and site-specific weed control in winter cereals. Precis Agric 7: 219231 Google Scholar
Norsworthy, JK, Griffith, G, Griffin, T, Bagavathiannan, M, Gbur, EE (2014) In-field movement of glyphosate-resistant Palmer amaranth (Amaranthus palmeri) and its impact on cotton lint yield: evidence supporting a zero-threshold strategy. Weed Sci 62: 237249 Google Scholar
Pacala, SW (1997) Dynamics of plant communities. Pages 532555 in Crawley, MJ, ed. Plant Ecology. 2nd edn. Oxford, UK: Blackwell Scientific Google Scholar
Petit, S, Fried, G (2012) Patterns of weed co-occurrence at the field and landscape level. J Veg Sci 23: 11371147 Google Scholar
Pielou, EC (1959) The use of point-to-plant distances in the study of the pattern of plant populations. J Ecol 47: 607613 Google Scholar
Pielou, EC (1961) Segregation and symmetry in two-species populations as studied by nearest-neighbor relationships. J Ecol 49: 255269 Google Scholar
R Development Core Team (2012) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/. Accessed April 1, 2016Google Scholar
Schenk, HJ, Callaway, RM, Mahall, BE (1999) Spatial Root Segregation: Are Plants Territorial? Adv Ecol Res 28: 145180 Google Scholar
Schuster, I, Nordmeyer, H, Rath, T (2007). Comparison of vision-based and manual weed mapping in sugar beet. Biosys Eng 98: 1725 Google Scholar
Slaughter, DC, Giles, DK, Downey, D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61: 6378 Google Scholar
Tardif-Paradis, C, Simard, M-J, Leroux, GD, Panneton, B, Nurse, R, Vanasse, A (2015) Effect of planter and tractor wheels on row and inter-row weed populations. Crop Prot 71: 6671 Google Scholar
Timmermann, C, Gerhards, R, Küthbauch, W (2003) The economic impact of site-specific weed control. Precis Agric 4: 249260 Google Scholar
[USDA–NRCS] U.S. Department of Agriculture–Natural Resources Conservation Service 2010. The PLANTS Database. http://plants.usda.gov. Accessed December 7, 2015Google Scholar
Walter, AM, Christensen, S, Simmelsgaard, SE (2002) Spatial correlation between weed species densities and soil properties. Weed Res 42: 2638 Google Scholar
Wiles, LJ, Oliver, GW, York, AC, Gold, HJ, Wilkerson, GG (1992). Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci 40: 554557 Google Scholar
Zanin, G, Berti, A, Riello, L (1998) Incorporation of weed spatial variability into the weed control decision-making process. Weed Res 38: 107118 Google Scholar
Zimdahl, RL (2004) Weed–Crop Competition: A Review. 2nd ed. Ames, IA: Blackwell Publishing. 220 pGoogle Scholar