Predicting Prostrate Knotweed (Polygonum aviculare L.) and Wild Barley (Hordeum spontaneum [C. Koch]) Seedling Emergence Using Thermal Model in Karaj

Document Type : Research Article


1 Islamic Azad University-Science and Research Branch

2 University of Tehran

3 Science and Research Branch, Islamic Azad University, Tehran


Introduction: Increasing public awareness and concern about the impacts of herbicides on the environment, development of herbicide-resistant weeds, and high economic cost of herbicides have increased the need to reduce the application of herbicides in agriculture. The prediction of weed emergence timing would help to reduce herbicides through the optimization of the timing of weed control. Seedling emergence is probably the most important phenological stage that influences the success of an annual weed. Wild barley (Hordeum spontaneum [C. Koch]) grows in diverse habitats in the eastern Mediterranean and in south-western Asia and is widely distributed in winter fields of Iran. Based on the recent weed surveys, it is now present in more than 16 provinces in Iran and it is increasing in in winter wheat fields. Prostrate knotweed (Polygonum aviculare L.) is one of the most widespread weeds in nearly all the temperate regions of the world. It is an annual weed that colonize open, human-made habitats and able to adapt to different ecological conditions.
Materials and Methods: In order to predict the emergence of wild barley and prostrate knotweed using the temperature model, an experiment was conducted at the research field of college of agriculture and natural resources of the University of Tehran located in Karaj during 2012-13. The first objective was to determine whether accumulated heat degree-days after wheat planting can adequately predict shoot of wild barley and prostrate knotweed emergence. The other objective of this work is that how the presence or absence of wheat affects the emergence patterns and total emergence of aforementioned weeds under growing conditions in Karaj. The experimental area was infested with natural weed populations of two species. No herbicides were used during the course of the experiment to allow the greatest number of weeds to emerge. Ten quadrats 1.0 by 1.0 m were used. Four quadrats placed at the center of the field, four and two quadrats placed at outer and inner margins of field, respectively. Half of the quadrats were assigned for sampling of seed bank and the half of them for emergence recording. Three soil samples were taken from depth of 5 cm. Soil samples were initially poured sieve (9-mesh) and the residues were placed in oven at 65ºC for 24 hours. After that, they were placed in cloth bags under running water. Weed seedling emergence was recorded every week. Weed seedlings were counted by species and cut at the soil surface to minimize soil disturbance. Weather data were obtained from weather stations located next to the experimental field. Heat sums (temperature units or cumulative growing degree days) were calculated based on the base temperature (Tbase) of 5ºC for both species. The nonlinear regression model (logistic 3 parameters) emergence versus cumulative growing degree days was used.
Results and Discussion: Results showed that the emergence of prostrate knotweed occurred earlier than wild barley in the outer margin and the center of the field at fourteen weeks after planting. Also based on the results, emergence of wild barley in outer margin of the field was happened at lower mean GDD and seedlings were appeared over fourteen weeks in 35.8 GDD. Cumulative emergence of wild barely was increased after received 50 GDD in the absence of wheat (outer margin of field) but decreased at inner margin and the center of wheat field. The presence of wheat affected the emergence of wild barely. Wheat canopy was probably developed to affect light levels or soil temperature needed for weed germination and, consequently, seedling emergence. Required GDD for 50% seedling emergence of wild barely was higher than prostrate knotweed at outer and inner margin of field and center of the field. Therefore, it is expected that wild barely was emerged later than prostrate knotweed.
Conclusion: Integrated weed management systems require a comprehensive knowledge of weed biology. Timing of the emergence often determines whether a plant competes successfully with its neighbors, is consumed by herbivores, infected with diseases, and whether its flowers reproduce, and mature properly by the end of the growing season. The simplicity and accuracy of this model would make it an excellent tool to predict wild barley and prostrate knotweed seedling emergence in field situations, facilitating the determination of the timing of scouting in integrated weed management systems. More prostrate knotweed seedling emerged at the margin of field before 100 GDD, thus, control methods such as herbicide must done as soon as possible at early of growing season in the field infested with this species.


1- Baghestani M.A., Zand E., Soufizadeh S., Jamali M., and Maighani M. 2007. Evaluation of sulfosulfuron for broadleaved and grass weed control in wheat (Triticum aestivum L.) in Iran. Crop Protection 26:1385–1389.
2- Blackshow R.E. 1991. Soil temperature and moisture effects on downy brome Vs. winter canola, wheat, and rye emergence. Crop Science 31: 1034-1040.
3- Buhler D.D., Liebman M., and Obrycki J.J. 2000. Theoretical and practice challenges to an IPM approach to weed management. Weed Science 48: 274-280.
4- Bulcke R., Willemijns P., Stryckers J., and Himme M. 1987. Weed competition in maize. Mededelingen van de Faculteit Landbouwwetenschappen, Rijksuniversiteit Gent 52: 1185–1194.
5- Catullo J.C., Sosa C.A., Rodriguez M.L., and Colombo I. 1983. Weed incidence in wheat in relation to fertilization. Malezas, 11: 179–203. )In Spanish with English abstract(
6- Chantre G.R., Blanco A.M., Forcella F., Van Acker R.C., Sabbatini M.R., and Gonzalez-Andujar J.L. 2013. A comparative study between non-linear regression and artificial neural network approaches for modeling wild oat (Avena fatua) field emergence. Journal of Agricultural Science 152: 1-9.
7- Donald W.W. 2000. A degree-day model of Cirsium arvense shoot emergence from adventitious root buds in spring. Weed Science 48: 333-341.
8- Dorado J., Sousa E., Calha I.M., Gonzalez-Andujar J.L., and Fernandez-Quintanilla C. 2009. Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Research 49: 251-260.
9- Forcella F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Science Research 8: 201–209.
10- Gan Y., Stobbe E.H., and Moes J. 1992. Relative date of wheat seedling emergence and its impact on grain yield. Crop Science 32: 1275-1281.
11- Hamidi R., Mazaheri D., and Rahimian mashhadi H. 2009. Wild Barley (Hordeum spontaneum Koch) Seed Germination as Affected by Dry Storage Periods, Temperature Regimes, and Glumellae Characteristics Iranian Journal of Weed Science 5: 1-12.
12- Harlan J.R., and Zohary D. 1966. Distribution of wild wheats and barley. Science 153: 1074–1080.
13- Keshtkar E., Kordbachehm F., Mesgaran M.B., Mashhadi H.R., and Alizadeh H.M. 2009. Effects of the sowing depth and temperature on the seedling emergence and early growth of wild barley (Hordeum spontaneum) and wheat. Weed Biology and Management 9: 10–19.
14- Kruk B., Insausti P., Razul A., and Benech-Arnold R. 2006. Light and thermal environments as modified by a wheat crop: effects on weed seed germination. Journal of Applied Ecolology 43: 227–236.
15- Leblanc M.L., Cloutier D.C., Stewart K.A., and Hamel C. 2003. The use of thermal time to model common lambsquarters (Chenopodium album) seedling emergence in corn. Weed Science 51:718–724.
16- Leguizamon E.S., Rodriguez N., Rainero H., Perez M., Perez L., Zorza E., and Fernandez-Quintanilla C. 2009. Modeling the emergence pattern of six summer annual weed grasses under no tillage systems in Argentina. Weed Research 49: 98-106.
17- Leguizamon E.S., Fernandez-Quintanilla C., Barros J., and Gonzalez-Andujar J.L. 2005. Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Research 45: 149–156.
18- Lopez J.A., and Mattiacci M.R. 1983. Damage caused by prostrate knotweed (Polygonum aviculare L.) during the establishment of a sown pasture. Malezas 11: 246–251. (In Spanish with English abstract)
19- Mohler C.L., and Calloway M.B. 1992. Effects of tillage and mulch on the emergence and survival of weeds in corn. Journal of Applied Ecology 29: 21–34.
20- Myers M.W., Curran W.S., VanGessel M.J., Calvin D.D., Mortensen D.A., Majek B.A., Karsten H.D., and Roth G.W. 2004. Predicting weed emergence for eight annual species in the Northeastern United States. Weed Science 52: 913-919.
21- Norsworthy J.K., and Oliveira M.J. 2007. A model predicting common cocklebur (Xanthium strumarium) emergence in soybean. Weed Science 55: 341–345.
22- Oryokot J.O.E., Hunt L.A., Murphy S., and Swanton C.J. 1997. Simulation of pigweed (Amaranthus spp.) seedling emergence in different systems. Weed Science 45: 684–690.
23- Paolini R., Del Puglia S., Principi M., Barcellona O., and Riccardi E. 1998. Competition between safflower and weeds as influenced by crop genotype and sowing time. Weed Research 38: 247–255.
24- Roman E.S., Murphy S.D., and Swanton C.J. 2000. Simulation of Chenopodium album emergence. Weed Science 48: 217–224.
25- Roman E.S., Murphy S.D., and Swanton C.J. 1999. Effect of tillage and Zea mays on Chenopodium album seedling emergence and density. Weed Science 47: 551–556.
26- Smutny V., and Kren J. 2002. Improvement of an elutriation method for estimation of weed seed bank in the soil. Rostlinna Vyroba 48: 271–278.
27- Tardif M.C., and Francois J. 2005. The biology of Canadian weeds. 131. Polygonum aviculare L. Canadian Journal of Plant Science 85: 481-506.
28- Wright D., and Baloch M.K. 1999. Effects of seven common arable weeds on the yield of normal and semi-leafless pea varieties. Tests Agrochemical Culture 20: 54–55.