Hostname: page-component-84b7d79bbc-4hvwz Total loading time: 0 Render date: 2024-07-30T20:08:26.089Z Has data issue: false hasContentIssue false
  • English
  • Français

Temperature and Water Potential as Parameters for Modeling Weed Emergence in Central-Northern Italy

Published online by Cambridge University Press:  20 January 2017

Roberta Masin ,
Donato Loddo ,
Stefano Benvenuti ,
Maria Clara Zuin ,
Mario Macchia  and
Giuseppe Zanin
Roberta Masin*
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Donato Loddo
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Stefano Benvenuti
Affiliation:
Dipartimento di Biologia delle Piante Agrarie, Viale delle Piagge 23, 56100, Pisa, Italy
Maria Clara Zuin
Affiliation:
Istituto di Biologia Agroambientale e Forestale–CNR, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Mario Macchia
Affiliation:
Dipartimento di Agronomia e Gestione dell'Agroecosistema, Via S. Michele 2, 56124, Pisa, Italy
Giuseppe Zanin
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
*
Corresponding author's E-mail: roberta.masin@unipd.it
Get access Rights & Permissions [Opens in a new window]

Abstract

Predicting weed emergence dynamics can help farmers to plan more effective weed control. The hydrothermal time concept has been used to model emergence as a function of temperature and water potential. Application of this concept is possible if the specific biological thresholds are known. This article provides a data set of base temperature and water potential of eight maize weeds (velvetleaf, redroot pigweed, common lambsquarters, large crabgrass, barnyardgrass, yellow foxtail, green foxtail, and johnsongrass). For five of these species, two ecotypes from two extreme regions of the predominant maize-growing area in Italy (Veneto and Tuscany), were collected and compared to check possible differences that may arise from using the same thresholds for different populations. Seedling emergence of velvetleaf and johnsongrass were modeled using three different approaches: (1) thermal time calculated assuming 5 C as base temperature for both species; (2) thermal time using the specific estimated base temperatures; and (3) hydrothermal time using the specific, estimated base temperatures and water potentials. All the species had base temperatures greater than 10 C, with the exception of velvetleaf (3.9 to 4.4 C) and common lambsquarters (2.0 to 2.6 C). All species showed a calculated base-water potential equal or up to −1.00 MPa. The thresholds of the two ecotypes were similar for all the studied species, with the exception of redroot pigweed, for which the Veneto ecotype showed a water potential lower than −0.41 MPa, whereas it was −0.62 MPa for the Tuscany ecotype. Similar thresholds have been found to be useful in hydrothermal time models covering two climatic regions where maize is grown in Italy. Furthermore, a comparison between the use of specific, estimated, and common thresholds for modeling weed emergence showed that, for a better determination of weed control timing, it is often necessary to estimate the specific thresholds.

Keywords

Barnyardgrass, Echinochloa crus-galli (L.) Beauv. ECHCGcommon lambsquarters, Chenopodium album L. CHEALgreen foxtail, Setaria viridis (L.) Beauv. SETVIjohnsongrass, Sorghum halepense (L.) Pers. SORHAlarge crabgrass, Digitaria sanguinalis (L.) Scop. DIGSAredroot pigweed, Amaranthus retroflexus L. AMAREvelvetleaf, Abutilon theophrasti Medik. ABUTHyellow foxtail, Setaria pumila (Poir.) Roemer & J. A. Schultes SETLUBase temperaturebase water potentialemergence predictionhydrothermal timemodels
Type
Weed Biology and Ecology
Information
Weed Science , Volume 58 , Issue 3 , September 2010 , pp. 216 - 222
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

Allen, P. 2003. When and how many? hydrothermal models and the prediction of seed germination. New Phytol. 158:1–9.Google Scholar
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
Alvarado, V. and Bradford, K. J. 2005. Hydrothermal time analysis of seed dormancy in true (botanical) potato seeds. Seed Sci. Res. 15:77–88.Google Scholar
Archer, D. W., Forcella, F., Eklund, J. J., and Gunsolus, J. 2001. WeedCast Version 4.0. http://www.ars.usda.gov/services/software/software.htm. Accessed: November 2, 2009.Google Scholar
Benech-Arnold, R. L., Ghersa, C. M., Sanchez, R. A., and Insausti, P. 1990a. Temperature effects on dormancy release and germination rate in Sorghum halepense (L.) Pers. seeds: a quantitative analysis. Weed Res. 30:81–89.Google Scholar
Benech-Arnold, R. L., Ghersa, C. M., Sanchez, R. A., and Insausti, P. 1990b. A mathematical model to predict Sorghum halepense (L.) Pers. seedlings emergence in relation to soil temperature. Weed Res. 30:90–99.Google Scholar
Bewick, T. A., Binming, L. K., and Yandell, B. 1988. A degree-day model for predicting the emergence of swamp dodder in cranberry. J. Am. Soc. Hortic. Sci. 113:839–841.Google Scholar
Bradford, K. J. 1995. Water relations in seed germination. Pages 351–396. in Kigen, 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
Colbach, N., Chauvel, B., Dür, C., and Richard, G. 2002. Effect of environmental conditions on Alopecurus myosuroides germination, I: effect of temperature and light. Weed Res. 42:210–221.Google Scholar
Del Monte, J. P. and Tarquis, A. M. 1997. The role of temperature in the seed germination of two species of the Solanum nigrum complex. J Exp. Bot. 48:2087–2093.Google Scholar
Dorado, J., Fernández-Quintanilla, C., and Grundy, A. C. 2009. Germination patterns in naturally chilled and non-chilled seeds of fierce thornapple (Datura ferox) and velvetleaf (Abutilon theophrasti). Weed Sci. 57:155–162.Google Scholar
Efron, B. 1979. Bootstrap methods: another look at the jackknife. Ann. Statist. 7:1–26.Google Scholar
Ekeleme, F., Forcella, F., Archer, D. W., Akobundu, I. O., and Chikoye, D. 2005. Seedling emergence model for tropic ageratum (Ageratum conyzoides). Weed Sci. 53:55–61.Google Scholar
Forcella, F., Benech-Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedlings emergence. Field Crop Res. 67:123–139.Google Scholar
Gardarin, A., Guillemin, J. P., Munier-Jolain, N. M., and Colbach, N. 2010. Estimation of key parameters for weed population dynamics models: base temperature and base water potential for germination. Eur. J. Agronomy. 32:162–168.Google Scholar
Gardarin, A., Dürr, C., and Colbach, N. 2009. Which model species for weed seedbank and emergence studies? a review. Weed Res. 49:117–130.Google Scholar
Grundy, A. C. 2003. 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.Google Scholar
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
Holt, J. S. and Orcutt, D. R. 1996. Temperature thresholds for bud sprouting in perennial weeds and seed germination in cotton. Weed Sci. 44:523–533.Google Scholar
Kennedy, J. R., Keefer, T. O., Paige, G. B., and Barnes, E. 2003. Evaluation of dielectric constant-based soil moisture sensors in a semi-arid rangeland. Pages 503–508. in. Proceedings from the 1st Interagency Conference on Research in the Watersheds. Washington, DC: Consortium of Universities for the Advancement of Hydrologic Science, Inc.Google Scholar
Kochy, M. and Tielborger, K. 2007. Hydrothermal time model of germination: parameters for 36 Mediterranean annual species based on a specified approach. Basic Appl. Ecol. 8:171–182.Google Scholar
[IPCC] Intergovernmental Panel on Climate Change 2007. Climate change 2007: impacts, adaptation and vulnerability. summary for policymakers: the IPCC 4th assessment report, Brussels, Belgium: IPCC.Google Scholar
Larsen, S. U., Bailly, C., Come, D., and Corbineau, F. 2004. Use of hydrothermal time model to analyse interacting effects of water and temperature on germination of three grass species. Seed Sci. Res. 14:35–50.Google Scholar
Leguizamon, E. S., Fernandez-Quintanilla, C., Barroso, J., and Gonzalez-Andujiar, J. L. 2005. Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Res. 45:149–156.Google Scholar
Leguizamon, E. S., Rodriguez, N., Rainero, H., Perez, M., Perez, L., Zorza, E., and Fernandez-Quintanilla, C. 2009. Modelling the emergence pattern of six summer annual weed grasses under no tillage systems in Argentina. Weed Res. 49:98–106.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
Martinkova, Z., Honek, A., and Lukas, J. 2006. Seed age and storage conditions influence germination of barnyardgrass (Echinochloa crus-galli). Weed Sci. 54:298–304.Google Scholar
Masin, R., Zuin, M. C., Archer, D. W., Forcella, F., and Zanin, G. 2005. Weedturf: a predictive model to aid control of annual summer weeds in turf. Weed Sci. 53:193–201.Google Scholar
Michel, B. E. and Kaufmann, M. R. 1973. The osmotic potential of polyethylene glycol 6000. Plant Physiol. 51:914–916.Google Scholar
Myers, W. M., Curran, W. S., Van Gessel, M. J., Calvin, D. D., Mortensen, D. A., Majec, B. A., Karsten, H. D., and Roth, G. W. 2004. Predicting weed emergence for eight annual species in the northeastern United States. Weed Sci. 52:913–919.Google Scholar
Onofri, A. 2001. Bioassay97: A New Excel® VBA Macro to Perform Statistical Analyses on Pesticide Dose–Response Data. http://www.unipg.it/∼onofri/Bioassay97/Bioassay97.htm. Accessed: June 23, 2009.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
Roman, E. S., Shrestha, A., Thomas, A. G., and Swarton, C. J. 1999. Modelling germination and shoot-radicle elongation of Ambrosia artemisiifolia . Weed Sci. 47:557–562.Google Scholar
Satorre, E. H., Ghersa, C. M., and Pataro, A. M. 1985. Prediction of Sorghum halepense (L.) Pers. rhizome sprout emergence in relation to air temperature. Weed Res. 25:103–109.Google Scholar
Sibony, M. and Rubin, B. 2003. The ecological fitness of ALS-resistant Amaranthus retroflexus and multiple-resistant Amaranthus blitoides . Weed Res. 43:40–47.Google Scholar
Steinmaus, S. J., Prather, T. S., and Holt, J. S. 2000. Estimation of base temperatures for nine weed species. J. Exp. Bot. 51:275–286.Google Scholar
Vleeshouwers, L. M. and Kropff, M. J. 2000. Modelling field emergence patterns in arable weeds. New Phytol. 148:445–457.Google Scholar
Wassom, J. J. and Tranel, P. J. 2005. Amplified fragment length polymorphism-based genetic relationships among weedy Amaranthus species. J. Hered. 96:410–416.Google Scholar
WeedCast Version 4.0 Technical Documentation. http://www.ars.usda.gov. Accessed: June 23, 2009.Google Scholar
Winter, D. M. 1960. The development of the seed of Abutilon theophrasti, II: seed coat. Am. J. Bot. 47:157–162.Google Scholar