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WeedTurf: a predictive model to aid control of annual summer weeds in turf

Published online by Cambridge University Press:  20 January 2017

Roberta Masin ,
Maria Clara Zuin ,
David W. Archer ,
Frank Forcella  and
Giuseppe Zanin
Maria Clara Zuin
Affiliation:
Istituto di Biologia Agroambientale e Forestale IBAF-CNR, Viale dell'Università, 16 35020 Legnaro (PD), Italy
David W. Archer
Affiliation:
USDA-ARS, North Central Soil Conservation Research Laboratory, 803 Iowa Avenue, Morris, MN 56267
Frank Forcella
Affiliation:
USDA-ARS, North Central Soil Conservation Research Laboratory, 803 Iowa Avenue, Morris, MN 56267
Giuseppe Zanin
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, Viale dell'Università, 16 35020 Legnaro (PD), Italy
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Abstract

Predicting weed emergence is useful for planning weed management programs. Unfortunately, our ability to anticipate initial emergence and subsequent levels of emergence from simple field observations or weather reports is often inadequate to achieve optimal control. Weed emergence models may provide predictive tools that help managers anticipate best management options and times and, thereby, improve weed control. In this study, the germination characteristics of four annual grass weeds (large crabgrass, goosegrass, green foxtail, and yellow foxtail) were investigated under different temperatures and water stresses to calculate base temperatures and base water potentials. These parameters were used to develop a mathematical model describing seedling emergence processes in terms of hydrothermal time. Hydrothermal time describes seed germination in a single equation by considering the interaction of soil water potential and soil temperature. The model, called WeedTurf, predicted emergence with some accuracy, especially for large crabgrass (lowest efficiency index [EF] value 0.95) and green foxtail (lowest EF value 0.91). These results suggest the possibility of developing interactive computer software to determine the critical timing of weed removal and provide improved recommendations for herbicide application timing.

Keywords

Goosegrass, Eleusine indica (L.) Gaertn. ELEINgreen foxtail, Setaria viridis (L.) Beauv. SETVIlarge crabgrass, Digitaria sanguinalis (L.) Scop. DIGSAyellow foxtail, Setaria glauca (L.) Beauv. SETLUAnnual grass weedsemergence predictionhydrothermal timeturf
Type
Weed Management
Information
Weed Science , Volume 53 , Issue 2 , April 2005 , pp. 193 - 201
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Alvarado, V. and Bradford, K. J. 2002. A hydrothermal time model explains the cardinal temperatures for seed germination. Plant Cell Environ 25:1061–1069.Google Scholar
Archer, D. W., Eklund, J., Walsh, M., and Forcella, F. 2002. WEEDEM: a user-friendly package for predicting annual ryegrass and wild radish emergence. Pages 252–253 in Proceedings of the 13th Australian Weeds Conference. Perth, Australia.Google Scholar
Archer, D. W., Forcella, F., Eklund, J. J., and Gunsolus, J. 2001. WeedCast Version 2.0. www.morris.ars.usda.gov.Google Scholar
Baskin, C. C. and Baskin, J. M. 1998. Seeds: Ecology, Biogeography, and Evolution of Dormancy and Germination. New York: Academic.Google Scholar
Bingham, S. W., Chism, W. J., and Bhowmik, P. C. 1995. Weed management systems for turfgrass. Pages 603–665 in Smith, A. E. ed. Handbook of Weed Management Systems. New York: Marcel Dekker.Google Scholar
Bradford, K. J. 1995. Water relations in seed germination. Pages 351–396 in Kigel, J. and Galili, G. eds. Seed Development and Germination. New York: Marcel Dekker.Google Scholar
Bradford, K. J. 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci 50:248–260.Google Scholar
Bussoni, E. and Mecella, G. 1997. Ritenzione idrica. Pages 55–65 in Pagliai, M. ed. Analisi fisica del suolo. Milano, Italy: Società italiana della scienza del suolo.Google Scholar
Chang, Z. and Bradford, K. J. 1999. Hydrothermal time analysis to tomato seed germination responses to priming treatments. J. Exp. Bot 50:89–99.CrossRefGoogle Scholar
Dehal, P. and Bradford, K. J. 1994. Hydrothermal time analysis of tomato seed germination at suboptimal temperature and reduced water potential. Seed Sci. Res 4:71–80.CrossRefGoogle Scholar
Efron, B. 1979. Bootstrap methods: another look at the jackknife. Ann. Stat 7:1–26.Google Scholar
Ekeleme, F., Forcella, F., Archer, D. W., Chikoye, D., and Akobundu, I. O. 2004. Simulation of shoot emergence pattern of cogongrass (Imperata cylindrica) in the humid tropics. Weed Sci. 52:961–967.Google Scholar
Flerchinger, G. N. and Hardegree, S. P. 2004. Modelling near-surface soil temperature and moisture for germination response predictions of post-wildfire seedbeds. J. Arid Environ. 59:369–385.CrossRefGoogle Scholar
Flerchinger, G. N. and Saxton, K. E. 1989a. Simultaneous heat and water model of a freezing snow-residue-soil system I. Theory and development. Trans. ASAE 32:565–571.Google Scholar
Flerchinger, G. N. and Saxton, K. E. 1989b. Simultaneous heat and water model of a freezing snow-residue-soil system II. Field verification Trans. ASAE 32:573–578.CrossRefGoogle Scholar
Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res 8:201–209.Google Scholar
Forcella, F., Benech-Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modelling seedling emergence. Field Crop Res 67:123–139.CrossRefGoogle Scholar
Gaussoin, R. and Martin, A. 1994. Turfgrass Weed Prevention and Management. http://ianrpubs.unl.edu/Weeds/g1045.htm.Google Scholar
Grundy, A. C. 2002. Predicting weed emergence: a review of approaches and future challenges. Weed Res 43:1–11.Google Scholar
Grundy, A. C., Peters, N. C. B., Rasmussen, I. A., Hartmann, K. M., Sattin, M., Andersson, L., Mead, A., Murdoch, A. J., and Forcella, F. 2003. Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions. Weed Res 43:163–176.CrossRefGoogle Scholar
Grundy, A. C., Phelps, K., Reader, R. J., and Burston, S. 2000. Modelling the germination of Stellaria media using the concept of hydrothermal time. New Phytol 148:433–444.CrossRefGoogle ScholarPubMed
Gummerson, R. J. 1986. The effect of constant temperatures and osmotic potential on the germination of sugar beet. J. Exp. Bot 41:1431–1439.Google Scholar
Ismail, B. S., Chuah, T. S., Salmajah, S., Teng, Y. T., and Schumacher, R. W. 2002. Germination and seedling emergence of glyphosate-resistant and susceptible biotypes of goosegrass (Eleusine indica [L.] Gaertn). Weed Biol. Manag 2:177–185.Google Scholar
King, C. A. and Oliver, L. R. 1994. A model for predicting large crabgrass (Digitaria sanguinalis) emergence as influenced by temperature and water potential. Weed Sci 42:561–567.Google Scholar
Loague, K. and Green, R. E. 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol 7:51–73.Google Scholar
Onofri, A. 2001. BIOASSAY97: A New EXCEL® VBA Macro to Perform Statistical Analyses on Pesticide Dose-Response Data. http://www.agr.unipg.it/disaprov/bioassay97/bioassay97.htm.Google Scholar
Oriade, C. and Forcella, F. 1999. Maximizing efficacy and economics of mechanical weed control in row crops through forecasts of weed emergence. J. Crop Prod 2:189–205.Google Scholar
Pierson, F. B., Flerchinger, G. N., and Wright, J. R. 1992. Simulating near-surface soil temperature and water on sagebrush rangelands: a comparison of models. Trans. ASAE 35:1449–1455.CrossRefGoogle Scholar
Roch&eacute, C. T., Thill, D. C., and Shafii, B. 1997. Estimation of base and optimum temperatures for seed germination in common crupina (Crupina vulgaris). Weed Sci 45:529–533.Google Scholar
Roman, E. S., Shrestha, A., Thomas, A. G., and Swarton, C. J. 1999. Modelling germination and shoot-radicle elongation of Ambrosia artemisifolia . Weed Sci 47:557–562.Google Scholar
Rowse, H. R. and Finch-Savage, W. E. 2003. Hydrothermal threshold models can describe the germination response of carrot (Daucus carota) and onion (Allium cepa) seed populations across sub- and supra-optimal temperatures. New Phytol 158:101–108.CrossRefGoogle Scholar
Swanton, C. J., Huang, J. Z., Deen, W., Tollenaar, M., Sharestha, A., and Rahimain, H. 1999. Effects of temperature and photoperiod on Setaria viridis. Weed Sci 47:446–453.Google Scholar
Walsh, M., Forcella, F., Archer, D., and Eklund, J. 2002. WEEDEM: turning information into action. Pages 446–449 in Proceedings of the 13th Australian Weeds Conference. Perth, Australia.Google Scholar
Willmott, C. J. 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc 63:1309–1313.Google Scholar
Wood, I. M., Dart, I. K., and So, H. B. 1993. Measurement of the total water potential of aqueous solutions of polyethylene glycol: a comparison between osmometer, thermocouple psychrometer and equilibrated soil cores. Soil Phys. Hydrol 31:1–11.Google Scholar
Yelverton, F. 1996. Strategies for Turfgrass Weed Control with Preemergence Herbicides. http://www.turffiles.ncsu.edu/pubs/weeds/ln&lnd.html.Google Scholar