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Performance of INTERCOM for predicting corn–velvetleaf interference across north-central United States

Published online by Cambridge University Press:  20 January 2017

John L. Lindquist
John L. Lindquist*
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln NE, 68583-0817; jlindquist1@unl.edu
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Abstract

Cost-effective weed management requires accurate estimates of yield and the potential yield loss resulting from weed infestations. However, crop yield and the effects of weeds are highly variable across years and locations. Ecophysiological models may be useful for predicting the effects of environment and management on crop and weed growth and competitive ability. Ability of the model INTERCOM to predict corn (Zea mays) growth and yield, velvetleaf (Abutilon theophrasti) interference on corn yield loss, and single-year economic threshold velvetleaf density (Te) was evaluated using 13 data sets collected in four states. Predicted and observed monoculture corn total aboveground biomass and leaf area index were in close agreement for most of the growing season. Predicted and observed weed-free corn yields were in agreement for yields ranging from 8 to 13 Mg ha−1 but were over- and underpredicted under low-yielding and near-optimal production conditions, respectively. Predicted and observed corn yield loss agreed well across the full range of observed velvetleaf densities for five to nine location years, depending on the performance criterion used. Estimates of Te calculated from predicted weed-free yield and yield loss relationships were an average of 6% smaller than those calculated from observed data, indicating that the model predicts a conservative value of Te in most cases. Although results are encouraging, they indicate that further research is needed to improve the capacity of INTERCOM for predicting weed-free yield and corn–velvetleaf interference.

Keywords

Corn, Zea mays L.velvetleaf, Abutilon theophrasti Medic. ABUTHCrop modelingcompetitionecophysiologyleaf area indexsimulationABUTH
Type
Weed Biology and Ecology
Information
Weed Science , Volume 49 , Issue 2 , April 2001 , pp. 195 - 201
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Boote, K. J., Jones, J. W., and Pickering, N. B. 1996. Potential uses and limitations of crop models. Agron. J. 88:704–716.CrossRefGoogle Scholar
Bridges, D. C. 1992. Crop Losses Due to Weeds in the United States—1992. Weed Science Society of America. pp. 75–147.Google Scholar
Bunce, J. A. 1982. Low humidity effects on photosynthesis in single leaves of C4 plants. Oecologia 54:233–235.CrossRefGoogle Scholar
Cardina, J., Regnier, E., and Sparrow, D. 1995. Velvetleaf (Abutilon theophrasti) competition and economic thresholds in conventional and no-tillage corn (Zea mays). Weed Sci. 43:81–87.CrossRefGoogle Scholar
Caton, B. P., Foin, T. C., and Hill, J. E. 1999a. A plant growth model for integrated weed management in direct-seeded rice. I. Development and sensitivity analyses of monoculture growth. Field Crops Res. 62:129–143.Google Scholar
Caton, B. P., Foin, T. C., and Hill, J. E. 1999b. A plant growth model for integrated weed management in direct-seeded rice. II. Validation testing of water-depth effects and monoculture growth. Field Crops Res. 62:145–155.Google Scholar
Caton, B. P., Foin, T. C., and Hill, J. E. 1999c. A plant growth model for integrated weed management in direct-seeded rice. III. Interspecific competition for light. Field Crops Res. 63:47–61.CrossRefGoogle Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6:191–195.CrossRefGoogle Scholar
Coleman, J. S., Rochefort, L., Bazzaz, F. A., and Woodward, F. I. 1991. Atmospheric CO2, plant nitrogen status and the susceptibility of plants to an acute increase in temperature. Plant Cell Environ. 14:667–674.CrossRefGoogle Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.CrossRefGoogle Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2:13–20.Google Scholar
de Wit, C. T., Goudriaan, J., van Laar, H. H., Penning de Vries, F.W.T., Rabbinge, R., van Keulen, H., Sibma, L., and de Jonge, C. 1978. Simulation of Assimilation, Respiration and Transpiration of Crops. Simulation Monographs. Wageningen, The Netherlands: Pudoc. p. 35.Google Scholar
El-Sharkawy, A. M., Cock, J. H., and Del Pilar Hernandez, A. 1985. Stomatal response to air humidity and its relation to stomatal density in a wide range of warm climate species. Photosynth. Res. 7:137–149.CrossRefGoogle Scholar
Graf, B., Gutierrez, A. P., Rakotobe, O., Zahner, P., and Delucchi, W. 1990. A simulation model for the dynamics of rice growth and development: Part II—The competition with weeds for nitrogen and light. Agric. Syst. 32:367–392.CrossRefGoogle Scholar
Graf, B. and Hill, J. E. 1992. Modelling the competition for light and nitrogen between rice and Echinochloa crus-galli . Agric. Syst. 40:345–359.CrossRefGoogle Scholar
Janssen, P.H.M. and Heuberger, P.S.C. 1995. Calibration of process-oriented models. Ecol. Model. 83:55–66.CrossRefGoogle Scholar
Jordan, N. 1992. Weed demography and population dynamics: implications for threshold management. Weed Technol. 6:184–190.CrossRefGoogle Scholar
Kiniry, J. R., Williams, J. R., Gassman, P. W., and Debaeke, P. 1992. A general, process oriented model for two competing plant species. Trans. Am. Soc. Agric. Eng. 35:801–810.CrossRefGoogle Scholar
Kiniry, J. R., Williams, J. R., Vanderlip, R. L., Atwood, J. D., Reicosky, D. C., Mulliken, J., Cox, W. J., Mascagni, H. J., Hollinger, S. E., and Wiebold, W. J. 1997. Evaluation of two maize models for nine U.S. locations. Agron. J. 89:421–426.CrossRefGoogle Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus L.) in corn (Zea mays L.). Weed Sci. 42:568–573.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. 35:207–214.CrossRefGoogle Scholar
Kropff, M. J. 1993. Mechanisms of competition for light. Pages 33–61 In Kropff, M. J. and van Laar, H. H., eds. Modelling Crop-Weed Interactions. Wallingford, Great Britain: CAB International and International Rice Research Institute.Google Scholar
Kropff, M. J., Moody, K., Lindquist, J. L., Migo, T. R., and Fajardo, F. F. 1994. Models to predict yield loss due to weeds in rice ecosystems. Philipp. J. Weed Sci. (Special Issue):29–44.Google Scholar
Kropff, M. J. and Spitters, C.J.T. 1992. An eco-physiological model for interspecific competition, applied to the influence of Chenopodium album L. on sugar beet. I. Model description and parameterization. Weed Res. 32:437–450.Google Scholar
Kropff, M. J., Spitters, C.J.T., Schnieders, B. J., Joenje, W., and de Groot, W. 1992. An eco-physiological model for interspecific competition, applied to the influence of Chenopodium album L. on sugar beet. II. Model evaluation. Weed Res. 32:451–464.Google Scholar
Kropff, M. J. and van Laar, H. H. 1993. Modelling crop-weed interactions. Wallingford, Great Britain: CAB International and International Rice Research Institute. 274 p.Google Scholar
Lindquist, J. L. and Kropff, M. J. 1996. Applications of an ecophysiological model for irrigated rice (Oryza sativa)-Echinochloa competition. Weed Sci. 44:52–56.CrossRefGoogle Scholar
Lindquist, J. L. and Mortensen, D. A. 1998. Tolerance and velvetleaf (Abutilon theophrasti) suppressive ability of two old and two modern corn (Zea mays) hybrids. Weed Sci. 46:569–574.CrossRefGoogle Scholar
Lindquist, J. L. and Mortensen, D. A. 1999. Ecophysiological characteristics of four maize hybrids and Abutilon theophrasti . Weed Res. 39:271–285.CrossRefGoogle Scholar
Lindquist, J. L., Mortensen, D. A., Clay, 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. 44:309–313.CrossRefGoogle Scholar
Lindquist, J. L., Mortensen, D. A., Westra, P., et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci. 47:195–200.CrossRefGoogle Scholar
Maxwell, B. D. 1992. Weed thresholds: the space component and considerations for herbicide resistance. Weed Technol. 6:205–212.CrossRefGoogle Scholar
Mitchell, P. L. 1997. Misuse of regression for empirical validation of models. Agric. Syst. 54:313–326.CrossRefGoogle Scholar
Montgomery, D. C. 1991. Design and analysis of experiments. 3rd ed. New York: J. Wiley. pp. 42–45.Google Scholar
Morison, J.I.L. and Gifford, R. M. 1983. Stomatal sensitivity to carbon dioxide and humidity. Plant Physiol. 71:789–796.CrossRefGoogle ScholarPubMed
Patterson, D. T. 1992. Temperature and canopy development of velvetleaf (Abutilon theophrasti) and soybean (Glycine max). Weed Technol. 6:68–76.CrossRefGoogle Scholar
Ryel, R., Barnes, P. W., Beyschlag, W., Caldwell, M. M., and Flint, S. D. 1990. Plant competition for light analyzed with a multispecies canopy model. I. Model development and influence of enhanced UV-B conditions on photosynthesis in mixed wheat and wild oat canopies. Oecologia 82:304–310.Google ScholarPubMed
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44:313–335.CrossRefGoogle Scholar
Tollenaar, M. 1989. Response of dry matter accumulation in maize to temperature: II. Leaf photosynthesis. Crop Sci. 29:1275–1279.Google Scholar
Wilkerson, G. G., Jones, J. W., Coble, H. D., and Gunsolus, J. L. 1990. SOYWEED: a simulation model of soybean and common cocklebur growth and competition. Agron. J. 82:1003–1010.CrossRefGoogle Scholar