Short communication: Evaluation of a model for predicting Avena fatua and Descurainia sophia seed emergence in winter rapeseed
Abstract
Avena fatua and Descurainia sophia are two important annual weeds throughout winter rapeseed (Brassica napus L.) production systems in the semiarid region of Iran. Timely and more accurate control of both species may be developed if there is a better understanding of its emergence patterns. Non-linear regression techniques are usually unable to accurately predict field emergence under such environmental conditions. The objectives of this research were to evaluate the emergence patterns of A. fatua and D. sophia and determine if emergence could be predicted using cumulative soil thermal time in degree days (CTT). In the present work, cumulative seedling emergence from a winter rapeseed field during 3 years data set was fitted to cumulative soil CTT using Weibull and Gompertz functions. The Weibull model provided a better fit, based on coefficient of determination (R2sqr), root mean square of error (RMSE) and Akaike index (AICd), compared to the Gompertz model between 2013 and 2016 seasons for both species. Maximum emergence of A. fatua occured 70-119 days after sowing or after equals 329-426 °Cd, while in D. sophia it occurred 119-134 days after sowing rapeseed equals 373-470 °Cd. Both models can aid in the future study of A. fatua and D. sophia emergence and assist growers and agricultural professionals with planning timely and more accurate A. fatua and D. sophia control.
Downloads
References
Baskin JM, Baskin CC, 1989. Germination responses of buried seeds of Capsella bursa-pastoris exposed to seasonal temperature changes. Weed Res 29: 205-212. https://doi.org/10.1111/j.1365-3180.1989.tb00860.x
Best KF, 1977. The biology of Canadian weeds. Descurainia sophia (L.) Webb. Can J Plant Sci 57: 499-507. https://doi.org/10.4141/cjps77-073
Blackshaw RE, Stobbe EH, Sturko ARW, 1981. Effect of seeding dates and densities of green foxtail (Setaria viridis) on the growth and productivity of spring wheat (Triticum aestivum). Weed Sci 29: 212-217.
Bradford KJ, 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci 50: 248-260. https://doi.org/10.1614/0043-1745(2002)050[0248:AOHTTQ]2.0.CO;2
Cardina J, Hook JE, 1989. Factors influencing germination and emergence of Florida beggarweed (Desmodium tortuosum). Weed Technol 3: 402-407.
Chantre GR, Blanco AM, Lodovichi MV, Bandoni AJ, Sabbatini MR, Lopez R.L, Vigna MR, Gigon R, 2012. Modeling Avena fatua seedling emergence dynamics: An artificial neural network approach. J Comput Electron 88: 95-102. https://doi.org/10.1016/j.compag.2012.07.005
Chikoye DS, Weise SF, Swanton CJ, 1995. Influence of common ragweed (Ambrosia artenissiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci 43: 375-380.
Cousens R, Weaver SE, Porter JR, Rooney JM, Butler DR, Johnson MP, 1992. Growth and development of Avena fatua L. in the field. Ann Appl Biol 120: 339-351. https://doi.org/10.1111/j.1744-7348.1992.tb03430.x
Ekeleme F, Forcella F, Archer DV, Akobundu IO, Chikoye D, 2005. Seedling emergence model for tropic ageratum (Ageratum conyzoides). Weed Sci 53: 55-61. https://doi.org/10.1614/WS-03-147R1
Forcella F, Benech-Arnold RL, Sanchez R, Ghersa CM, 2000. Modeling of seedling emergence. Field Crop Res 67: 123-139. https://doi.org/10.1016/S0378-4290(00)00088-5
Gallagher RS, Kathryn JS, Crawford AD, 2004. Alleviation of dormancy in annual ryegrass (Lolium rigidum) seeds by hydration and after-ripening. Weed Sci 52: 968-975. https://doi.org/10.1614/WS-04-075R
Gonzalez-Diaz L, Leguizamon E, Forcella F, Gonzalez-Andujar JL, 2007. Short communication: Integration of emergence and population dynamic models for long term weed management using wild oat (Avena fatua L.) as an example. Span J Agric Res 5: 199-203. https://doi.org/10.5424/sjar/2007052-245
Grundy AC, 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res 43: 1-11. https://doi.org/10.1046/j.1365-3180.2003.00317.x
Holm LG, Plunknett DL, Pancho JV, Herberger JP, 1977. The world's worst weeds: distribution and biology. Hawaii Univ. Press; Honolulu, Hawaii, USA.
Imam AG, Allard RW, 1965. Population studies in predominantly self-pollinated species. VI. Genetic variability between and within natural populations of wild oats from differing habitats in California. Genetics 51: 49-62.
Izquierdo J, Bastida F, Lezaun JM, Sanchez del Arco MJ, Gonzalez-Andujar JL, 2013. Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area. Weed Res 53: 1-10. https://doi.org/10.1111/wre.12023
Kiemnce GL, Mcinnis ML, 2002. Hoary cress (Cardaria draba) root extract reduces germination and root growth of five plant species. Weed Technol 16: 231-234. https://doi.org/10.1614/0890-037X(2002)016[0231:HCCDRE]2.0.CO;2
Knezevic SZ, Horak MJ, Vanderlip RL, 1997. Relative time of redroot pigweed (Amaranthus retroflexus L.) emergence is critical in pigweed-sorghum [Sorghum bicolor (L.) Moench] competition. Weed Sci 45: 502-508.
Leblanc ML, Cloutier DC, Stewart K, Hamel C, 2003. The use of thermal time to model common lambsquarters (Chenopodium album) seedling emergence in corn. Weed Sci 51: 718-724. https://doi.org/10.1614/P2002-108
Leguizamon ES, Fernandez QC, Barroso J, Gonzalez-Andujar JL, 2005. Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Res 45 149-156. https://doi.org/10.1111/j.1365-3180.2004.00444.x
Martinson K, Durgan B, Forcella F, Wiersma J, Spokas K, Archer D, 2007. An emergence model for wild oat (Avena fatua). Weed Res 55: 584-591. https://doi.org/10.1614/WS-07-059.1
Mickelson JA, Grey WE, 2006. Effect of soil water content on wild oat (Avena fatua) seed mortality and seedling emergence. Weed Sci 54: 255-262. https://doi.org/10.1614/WS-05-007R.1
Moechnig MJ, Stoltenberg DE, Boerboom CM, Binning LK, 2003. Empirical corn yield loss estimation from common lambsquarters (Chenopodium album) and giant foxtail (Setaria faberi) in mixed communities. Weed Sci 51: 386-393. https://doi.org/10.1614/0043-1745(2003)051[0386:ECYLEF]2.0.CO;2
Ogg AG, Dawson JH, 1984. Time of emergence of eight weed species. Weed Sci 32: 327-335.
Probert RJ (ed), 1992. The role of temperature in germination ecophysiology. In: Seeds: The ecology of regeneration in plant communities; Fenner M (ed), pp: 285-325. CABI Publ., Wallingford, UK.
Qi M, Zhang GP, 2001. An investigation of model selection criteria for neural network time series forecasting. Eur J Oper Res 132: 666-680. https://doi.org/10.1016/S0377-2217(00)00171-5
Royo-Esnal A, Torra J, Conesa JA, Forcella F, Recasens J, 2010. Modeling the emergence of three arable bedstraw (Galium) species. Weed Sci 58: 10-15. https://doi.org/10.1614/WS-09-063.1
Royo-Esnal A, Necajeva J, Torra J, Recasens J, Gesch RW, 2015. Emergence of field pennycress (Thlaspi arvense L.): Comparison of two accessions and modelling. Ind Crops Prod. 66:161-169. https://doi.org/10.1016/j.indcrop.2014.12.010
Sexsmith JJ, 1969. Dormancy of wild oat seed produced under various temperature and moisture conditions. Weed Sci 17: 405-407.
Sharma MP, McBeath DK, Vanden Born, WH, 1976. Studies of the biology of wild oat. I. Dormancy, germination and emergence. Can J Plant Sci 56: 611-618. https://doi.org/10.4141/cjps76-097
Sharma MP, Vanden Born WH, 1978. The biology of Canadian weeds. 27. Avena fatua L. Can J Plant Sci 58: 141-157. https://doi.org/10.4141/cjps78-022
Yousefi AR, Rastgoo M, Ghanbari Motlagh M, Ebrahimi M, 2013. Predicting seedling emergence of flixweed (Descurainia sophia (L.) Webb.) and Hoary cress (Cardaria draba (L.) Desv.) in rapeseed (Brassica napus) field in Zanjan conditions. J Plant Protec 27: 48-54. [In Persian with English abstract].
Yousefi AR, Oveisi M, Gonzalez-Andujar, JL, 2014. Prediction of annual weed seed emergence in garlic (Allium sativum L.) using soil thermal time. Sci Hortic 168:189-192. https://doi.org/10.1016/j.scienta.2014.01.035
© CSIC. Manuscripts published in both the print and online versions of this journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. You may read the basic information and the legal text of the licence. The indication of the CC BY 4.0 licence must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the final version of the work produced by the publisher, is not allowed.
