Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-22T22:16:53.907Z Has data issue: false hasContentIssue false

Comparison of methods to estimate weed populations and their performance in yield loss description models

Published online by Cambridge University Press:  20 January 2017

Mathieu Ngouajio
Affiliation:
Botany and Plant Science Department, University of California, Riverside, CA 92521-0124
Shane Mansfield
Affiliation:
Botany and Plant Science Department, University of California, Riverside, CA 92521-0124
Edmund Ogbuchiekwe
Affiliation:
Botany and Plant Science Department, University of California, Riverside, CA 92521-0124

Abstract

Accurate weed population estimation and yield loss prediction are important components of integrated weed management. Field experiments using Italian ryegrass as a weed in broccoli were conducted from 1994 to 1997 to compare weed density to other methods of weed population estimation, to evaluate the performance of weed population estimates in yield description models, and to study the affect of environmental variability on the predictive ability of models. A strong linear relationship was obtained between Italian ryegrass density and direct leaf area (r 2 = 0.60 to 0.99). For Italian ryegrass, density and estimates of canopy from the optical device (crosswire device) had a hyperbolic relationship with high coefficients of determination (r 2 > 0.72). Both direct leaf area and canopy estimates described broccoli yield as well as or better than Italian ryegrass density. The Li-Cor LAI-2000 Plant Canopy Analyzer (PCA) provided poor estimates of Italian ryegrass population (r 2 from 0.00 to 0.63) that failed to describe broccoli yield. No relationship was observed between estimates of light interception through the plant canopy obtained with the Li-Cor LI-191-S Line Quantum Sensor (LQS) and either Italian ryegrass density or broccoli yield. The low performance of the PCA and lack of performance of the LQS were likely due to the smaller size of the plants and larger gaps in the plant canopy caused by wide bed spacing. At similar densities, Italian ryegrass competition with broccoli was stable from year to year. Under high Italian ryegrass density, water supply affected competition. This may limit construction of robust yield prediction models, especially in areas where water is mainly from rainfall.

Type
Research Article
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

Andreasen, C., Rudemo, M., and Sevestre, S. 1997. Assessment of weed density at an early stage by use of image processing. Weed Res. 37:518.CrossRefGoogle Scholar
Andrieu, B., Allirand, J. M., and Jaggard, K. 1997. Ground cover and leaf area index of maize and sugar beet crops. Agronomie 17:315321.Google Scholar
Baeumer, D. and de Wit, C. T. 1968. Competitive interference of plant species in monocultures and mixed stands. Neth. J. Agric. Sci. 16:103122.Google Scholar
Bell, C. E. 1995. Broccoli (Brassica oleracea var. botrytis) yield loss from Italian ryegrass (Lolium perenne) interference. Weed Sci. 43:117120.Google Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735742.Google Scholar
Bussler, B. H., Maxwell, B. D., and Puettmann, K. J. 1995. Using plant volume to quantify interference in corn (Zea mays) neighborhoods. Weed Sci. 43:586594.CrossRefGoogle Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed (Hieracium pratense) with high resolution multispectral digital imagery. Weed Technol. 9:477483.Google Scholar
Cavero, J., Zaragoza, C., Suso, M. L., and Pardo, A. 1999. Competition between maize and Datura stramonium in an irrigated field under semi-arid conditions. Weed Res. 39:225240.Google Scholar
Comeau, P. G., Gendron, F., and Letchford, T. 1998. A comparison of several methods for estimating light under a paper birch mixed wood stand. Can. J. For. Res. 28:18431850.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.Google Scholar
Cousens, R., Peters, N.C.B., and Marshall, C. J. 1984. Models of yield loss-weed density relationships. Pages 367374 In Proceedings of the 7th International Symposium on Weed Biology, Ecology and Systematics. Paris: Columa-European Weed Research Society.Google Scholar
Dew, D. A. 1972. An index of competition for estimating crop loss due to weeds. Can. J. Plant Sci. 52:921927.Google Scholar
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus spp.) interference in soybean (Glycine max). Weed Sci. 43:612618.Google Scholar
Florez, A. J., Fischer, A. J., Ramirez, H., and Duque, M. C. 1999. Predicting rice yield losses caused by multispecies weed competition. Agron. J. 91:8792.Google Scholar
Ghersa, C. M. and Martinez-Ghersa, M. A. 1991. A field method for predicting yield losses in maize caused by johnsongrass (Sorghum halepense). Weed Technol. 5:279285.Google Scholar
Hicks, S. K. and Lascano, R. J. 1995. Estimation of leaf area index for cotton canopies using the LI-COR LAI-2000 plant canopy analyzer. Agron. J. 87:458464.CrossRefGoogle Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize (Zea mays L.). Weed Res. 35:207214.Google Scholar
Kropff, M. J. and Lotz, L.A.P. 1992. Optimization of weed management systems: the role of ecological models of interplant competition. Weed Technol. 6:462470.Google Scholar
Kropff, M. J., Lotz, L.A.P., Weaver, S. E., Bos, H. J., Wallinga, J., and Migo, T. 1995. A two-parameter model for prediction of crop loss by weed competition from early observations of relative leaf area of weeds. Ann. Appl. Biol. 126:329346.CrossRefGoogle Scholar
Kropff, M. J. and Spitters, C.J.T. 1991. A simple model of crop loss by weed competition from early observations of relative leaf area of the weeds. Weed Res. 31:97105.Google Scholar
Lindquist, J. L., Mortensen, D. A., Sharon, S. A., Schmenk, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)-velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci. 44:309313.Google Scholar
Lotz, L.A.P., Kropff, M. J., Bos, H. J., and Wallinga, J. 1992. Prediction of yield loss based on relative leaf cover of weeds. Pages 290292 In Proceedings of the 1st International Weed Control Congress, Melbourne. Volume 2.Google Scholar
Lotz, L.A.P., Kropff, M. J., Wallinga, J., Bos, H. J., and Groeneveld, R.M.W. 1994. Techniques to estimate relative leaf area and cover of weeds in crops for yield prediction. Weed Res. 34:167175.CrossRefGoogle Scholar
Lotz, L.A.P., Wallinga, J., and Kropff, M. J. 1995. Crop-weed interaction: quantification and prediction. Pages 3147 In Glen, D. M., Greaves, M. P., and Anderson, H. M., eds. Ecology and Integrated Farming Systems. Melbourne: Weed Science Society of America.Google Scholar
Lutman, P.J.W. 1992. Prediction of the competitive ability of weeds on the yield of several spring-sown arable crops. Pages 337345. In Actes IXème colloque international sur la biologie des mauvaises herbes, Dijon, Paris, France. Paris: Association Nationale pour la Protection des Plantes (ANPP).Google Scholar
McGiffen, M. E. Jr., Forcella, F., Lindstrom, M. J., and Reicosky, D. C. 1997. Covariance of cropping systems and foxtail density as predictors of weed interference. Weed Sci. 45:388396.Google Scholar
Ngouajio, M., Lemieux, C., Fortier, J. J., Careau, D., and Leroux, G. D. 1998. Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technol. 12:446453.Google Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999. Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Sci. 47:297304.Google Scholar
Vitta, J. I. and Quintanilla, C. F. 1996. Canopy measurements as predictors of weed-crop competition. Weed Sci. 44:511516.CrossRefGoogle Scholar
Vitta, J. I., Satorre, E. H., and Leguizamon, E. S. 1993. Using canopy attributes to evaluate competition between Sorghum halepense (L.) Pers. and soybean. Weed Res. 33:8997.Google Scholar
Wiles, L. J., Gold, H. J., and Wilkerson, G. G. 1993. Modeling the uncertainty of weed density estimates to improve post-emergence herbicide control decisions. Weed Res. 33:241252.CrossRefGoogle Scholar