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Cropping system effects on giant foxtail (Setaria faberi) demography: II. Retrospective perturbation analysis

Published online by Cambridge University Press:  20 January 2017

Adam S. Davis ,
Philip M. Dixon  and
Matt Liebman
Philip M. Dixon
Affiliation:
Department of Statistics, Iowa State University, Ames, IA 50011
Matt Liebman
Affiliation:
Department of Agronomy, Iowa State University, Ames, IA 50011
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Abstract

Cropping system characteristics affect weed management by altering key demographic rates of weeds, including recruitment, seedling survival, fecundity, and seed survival. To facilitate the design and improvement of cropping systems that limit weed population growth, analytical methods are needed to identify weed management “choke points” (weed life stages that vary in response to management and whose variation strongly affects weed population growth rate). The objectives of this study were to (1) determine whether wheat–red clover green manure can limit giant foxtail population growth rate (λ) in a wheat–corn–soybean crop sequence and (2) identify choke points in the giant foxtail life cycle with respect to the green manure treatment. Demographic data were used to construct a periodic matrix model of giant foxtail population growth in a wheat–corn–soybean crop sequence, with either a wheat sole crop (W) or a wheat–red clover intercrop (R) in the wheat phase. Identification of choke points was accomplished by adapting the life-table response experiment (LTRE) design for retrospective perturbation analysis of the periodic matrix model. The difference in λ (Δλ) between the two treatments was decomposed into contributions from each parameter in each rotation phase of the periodic model. Each LTRE contribution was equal to the product of the sensitivity of λ to changes in a given parameter by the treatment difference in that parameter. Those parameters making large contributions to Δλ represented weed management choke points. Giant foxtail population growth rate in the simulation was more than twice as great in the W treatment (λ = 2.54) than in the R treatment (λ = 1.16). Retrospective perturbation analysis indicated that the proportion of seeds surviving predation in the wheat phase made the largest LTRE contribution (0.55) to Δλ, followed by seedling recruitment in the soybean (0.41) and corn (0.20) phases. By identifying weed management choke points within a given system, retrospective perturbation analysis can target research and management efforts for greater reductions in weed population growth.

Keywords

Giant foxtail, Setaria faberi Herrm. SETFAcorn, Zea mays L. ‘Pioneer 3512’red clover, Trifolium pratense L. ‘Cherokee’soybean, Glycine max L. ‘IA 2039’spring wheat, Triticum aestivum L. ‘Sharp’Life-table response experimentsimulation modelintegrated weed managementseed predationcrop rotationlegume green manure
Type
Weed Biology and Ecology
Information
Weed Science , Volume 51 , Issue 6 , December 2003 , pp. 930 - 939
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bastiaans, L., Kropff, M. J., Goudriaan, J., and Van Laar, H. H. 2000. Design of weed management systems with a reduced reliance on herbicides poses new challenges and prerequisites for modeling crop-weed interactions. Field Crop Res 67:161–179.Google Scholar
Buhler, D. D., Gunsolus, J. L., and Ralston, D. F. 1992. Integrated weed management techniques to reduce herbicide inputs in soybean. Weed Sci 84:973–978.Google Scholar
Buhler, D. D. and Hartzler, R. G. 2001. Emergence and persistence of seed of velvetleaf, common waterhemp, woolly cupgrass, and giant foxtail. Weed Sci 49:230–235.Google Scholar
Burnham, K. P. and Anderson, D. R. 1998. Model Selection and Inference: A Practical Information-Theoretic Approach. New York: Springer Verlag. Pp. 23–74.CrossRefGoogle Scholar
Bussan, A. J., Boerboom, C. M., and Stoltenberg, D. E. 2000. Response of Setaria faberi demographic processes to herbicide rates. Weed Sci 48:445–453.Google Scholar
Caswell, H. 2000. Prospective and retrospective perturbation analyses: their roles in conservation biology. Ecology 81:619–627.Google Scholar
Caswell, H. 2001. Matrix Population Models: Construction, Analysis and Interpretation. 2nd ed. Sunderland, MA: Sinauer. Pp. 206–262.Google Scholar
Caswell, H. and Trevisan, M. C. 1994. Sensitivity analysis of periodic matrix models. Ecology 75:1299–1303.Google Scholar
Conklin, A. E., Erich, M. S., Liebman, M., Lambert, D., Gallandt, E. R., and Halteman, W. A. 2002. Effects of red clover (Trifolium pratense) green manure and compost soil amendments on wild mustard (Brassica kaber) growth and incidence of disease. Plant Soil 238:245–256.Google Scholar
Cromar, H. E., Murphy, S. D., and Swanton, C. J. 1999. Influence of tillage and crop residue on postdispersal predation of weed seeds. Weed Sci 47:184–194.CrossRefGoogle Scholar
Davis, A. S. and Liebman, M. 2001. Nitrogen source influences wild mustard growth and competitive effect on sweet corn. Weed Sci 49:558–566.Google Scholar
Davis, A. S. and Liebman, M. 2003. Cropping system effects on giant foxtail demography: I. Green manure and tillage timing. Weed Sci. 51:919–929.Google Scholar
Gonzalez-Andujar, J. L. and Fernandez-Quintanilla, C. 1991. Modeling the population dynamics of Avena sterilis under dry-land cereal cropping systems. J. Appl. Ecol 28:16–27.Google Scholar
Jordan, N. 1996. Weed prevention: priority research for alternative weed management. J. Prod. Agric 9:485–490.CrossRefGoogle Scholar
Jordan, N., Mortensen, D. A., Prenzlow, D. M., and Cox, K. C. 1995. Simulation analysis of crop rotation effects on weed seedbanks. Am. J. Bot 82:390–398.CrossRefGoogle Scholar
Kegode, G. O., Forcella, F., and Durgan, B. R. 1999. Limiting green and yellow foxtail (Setaria viridis and S. glauca) seed production following spring wheat (Triticum aestivum) harvest. Weed Technol 13:43–47.Google Scholar
Liebman, M. and Gallandt, E. R. 1997. Many little hammers: ecological approaches for management of crop-weed interactions. Pages 291–343 in Jackson, L. E. ed. Ecology in Agriculture. San Diego, CA: Academic.Google Scholar
MathWorks. 1997. MATLAB. Version 5.0. Natick, MA: The MathWorks.Google Scholar
Menalled, F. D., Marino, P. C., Renner, K. A., and Landis, D. A. 2000. Post-dispersal weed seed predation in Michigan crop fields as a function of agricultural landscape structure. Agric. Ecosyst. Environ 77:193–202.Google Scholar
Mertens, S. K., Van Den Bosch, F., and Heesterbeek, J. A. P. 2002. Weed populations and crop rotations: exploring dynamics of a structured system. Ecol. Appl 12:1125–1141.Google Scholar
Mittelbach, G. G. and Gross, K. L. 1984. Experimental studies of seed predation in old fields. Oecologia 65:7–13.CrossRefGoogle ScholarPubMed
Mulder, T. A. and Doll, J. D. 1993. Integrating reduced herbicide use with mechanical weeding in corn (Zea mays). Weed Technol 7:382–389.Google Scholar
Rydgren, K., DeKroon, H., Økland, H., and Van Groenendael, J. 2001. Effects of fine-scale disturbances on the demography and population dynamics of the clonal moss Hylocomium splendens . J. Ecol 89:395–405.Google Scholar
Ullrich, S. D. 2000. Weed Population Dynamics in Potato Cropping Systems as Affected by Crop, Cultivation and Primary Tillage. Ph.D. dissertation. University of Maine, Orono, ME. 171 p.Google Scholar
Wiles, L. J., Barlin, D. H., Schweizer, E. E., Duke, H. R., and Whitt, D. E. 1996. A new soil sampler and elutriator for collecting and extracting weed seeds from soil. Weed Technol 20:35–41.Google Scholar