Short communicationA predictive model for the time course of seedling emergence of Phalaris brachystachys (short-spiked canary grass) in wheat fields

  1. Bastida, Fernando 1
  2. Lezaun, Juan A. 2
  3. Gonzalez-Andujar, Jose L. 3
  1. 1 Universidad de Huelva, Campus El Carmen, Dept. Ciencias Agroforestales, Avda. Fuerzas Armadas s/n, 21007 Huelva
  2. 2 INTIA, Edificio Peritos, Avda. Serapio Huici 22, 31610 Villava/Atarrabia, Navarra
  3. 3 Instituto de Agricultura Sostenible (CSIC), Avda. Menéndez Pelayo s/n, 14004 Córdoba
Mostrar afiliaciones +
Revista:
Spanish journal of agricultural research

ISSN: 1695-971X,  2171-9292

Año de publicación: 2021

Volumen: 19

Número: 3

Tipo: Artículo

DOI: 10.5424/SJAR/2021193-17876 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Spanish journal of agricultural research

Resumen

Aim of study: A predictive model of the seedling emergence pattern of Phalaris brachystachys Link (short-spiked canary grass) was developed, aimed to contribute to support a more efficient management of this troublesome, competitive weed in winter cereal crops around its native Mediterranean range and in different areas of the world where it is introduced.Area of study: Southern (Andalusia) and northern Spain (Navarra).Material and methods: A model describing the emergence pattern of P. brachystachys in cereal fields based on accumulation of hydrothermal time in soil was developed and validated. For model development, cumulative emergence data were obtained in an experimental field, while an independent validation of the model was conducted with data collected in two commercial wheat fields from climatically contrasting regions of Spain.Main results: The relationship between cumulative emergence and cumulative hydrothermal time (CHT) was well described by a Logistic model. According to model predictions, 50% and 95% seedling emergence takes place at 108 and 160 CHT above base water potential for seed germination, respectively. The model accurately predicted the seedling emergence time course of P. brachystachys in the two commercial wheat fields (R2 ≥ 0.92).Research highlights: This model is a new tool that may be useful to improve the timing of control measures to maximize efficiency in reducing P. brachystachys infestations in cereal crops.Phalaris brachystachys Link (short-spiked canary grass) is a competitive weed that affects winter cereal crops around its native Mediterranean basin and in different areas of the world where it is introduced. The development of a predictive model of the seedling emergence pattern may contribute to support a more efficient management of this species. In this work, a model describing the emergence time course of P. brachystachys in cereal fields based on accumulation of hydrothermal time in soil was developed and validated. For model development, cumulative emergence data were obtained in an experimental field, while an independent validation of the model was conducted with data collected in two commercial wheat fields from climatically contrasting regions of Spain. The relationship between cumulative emergence and cumulative hydrothermal time (ΘCHTT) was well described by a Logistic model. According to model predictions, 50% and 95% seedling emergence takes place at 108 and 160 ΘCHTT above base water potential for seed germination, respectively. The model accurately predicted the seedling emergence time course of P. brachystachys in the two commercial wheat fields (R2 ≥ 0.92).Research highlights: This model is a new tool that may be useful for fine-tuning the timing of control measures to maximize efficiency in reducing P. brachystachys infestations in cereal crops. 

Referencias bibliográficas

  • Afentouli CG, Eleftherohorinos IG, 1996. Littleseed canarygrass (Phalaris minor) and short-spiked canarygrass (Phalaris brachystachys) interference in wheat and barley. Weed Sci 44: 560-565. https://doi.org/10.1017/S0043174500094339
  • Afentouli CG, Eleftherohorinos IG, 1999. Competition between wheat and canarygrass biotypes and their response to herbicides. Weed Sci 47: 55-61. https://doi.org/10.1017/S0043174500090652
  • Alcántara C, Fuentes M, Carranza R, Sánchez-Gamonoso M, Saavedra M, 2017. Aplicaciones de herbicidas de postemergencia temprana y tardía contra alpisteras (Phalaris spp.) en trigo duro. Consejería Agricultura, Pesca y Desarrollo Rural, Instituto de Investigación y Formación Agraria y Pesquera, Sevilla, Spain.
  • Chikoye DS, Weise SF, Swanton CJ, 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci 43: 375-380. https://doi.org/10.1017/S0043174500081352
  • Cudney DW, Hill JE, 1979. The response of wheat grown with three population levels of canarygrass to various herbicide treatments. Proc West Soc Weed Sci 32: 55-56.
  • Dubois J, Cheptou PO, 2012. Competition/colonization syndrome mediated by early germination in non-dispersing achenes in the heteromorphic species Crepis sancta. Ann Bot 110: 1245-1251. https://doi.org/10.1093/aob/mcs203
  • Egea-Cobrero V, Bradley K, Calha I, Davis AS, Dorado J, Forcella F, Lindquist JL, Sprague CL, Gonzalez-Andujar JL, 2020 Validation of predictive empirical weed emergence models of Abutilon theophrasti Medik. based on intercontinental data. Weed Res 6: 297-302. https://doi.org/10.1111/wre.12428
  • Fernández-Moreno P, Bastida F, De Prado R, 2017. Evidence, mechanism and alternative chemical seedbank-level control of a glyphosate resistant rigid ryegrass (Lolium rigidum) biotype from Southern Spain. Front Plant Sci 8: 1-16. https://doi.org/10.3389/fpls.2017.00450
  • Golmohammadzadeh S, Rojano-Delgado AM, Vazquez-Garcia JG, Romano Y, Osuna MD, Gherekhloo J, De Prado R, 2020. Cross-resistance mechanisms to ACCase-inhibiting herbicides in short-spike canarygrass (Phalaris brachystachys). Plant Physiol Biochem 151: 681-688. https://doi.org/10.1016/j.plaphy.2020.03.037
  • Gonzalez-Andujar JL, 2020. Introduction to decision suport systems. In: Decision support systems for weed management; Chantre G & Gonzalez-Andujar JL (eds.), pp: 25-38. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-030-44402-0_2
  • Gonzalez-Andujar JL, Saavedra M, 2003. Spatial distribution of annual grass weed populations in winter cereals. Crop Prot 22: 629-633. https://doi.org/10.1016/S0261-2194(02)00247-8
  • Gonzalez-Andujar JL, Jimenez-Hidalgo M, Garcia-Torres L, Saavedra M, 2005. Demography and population dynamic of the arable weed Phalaris brachystachys L. (short-spiked canary grass) in winter wheat. Crop Prot 24: 581-584. https://doi.org/10.1016/j.cropro.2004.09.019
  • Gonzalez-Andujar JL, Chantre GR, Morvillo C, Blanco A, Forcella F, 2016a. Predicting field weed emergence with empirical models and soft computing techniques. Weed Res 56: 415-423. https://doi.org/10.1111/wre.12223
  • Gonzalez-Andujar JL, Francisco-Fernandez M, Cao R, Reyes M, Urbano JM, Forcella F, Bastida F, 2016b. A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence. Weed Res 56: 367-376. https://doi.org/10.1111/wre.12216
  • Gonzalez-Diaz L, Bastida F, Gonzalez-Andujar JL, 2009. Modelling of the population dynamics of Phalaris brachystachys Link under various herbicide control scenarios in a Mediterranean climate. Span J Agric Res 7: 155-159. https://doi.org/10.5424/sjar/2009071-414
  • Gummerson R, 1986. The effect of constant temperatures and osmotic potentials on the germination of sugar beet. J Exp Bot 37: 729-741. https://doi.org/10.1093/jxb/37.6.729
  • Hidalgo B, Saavedra M, Garcia-Torres L, 1990. Weed flora of dryland crops in the Cordoba region (Spain). Weed Res 30: 309-318. https://doi.org/10.1111/j.1365-3180.1990.tb01718.x
  • 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: 269-278. https://doi.org/10.1111/wre.12023
  • Jimenez-Hidalgo MJ, Saavedra M, Garcia-Torres L, 1997. Phalaris sp. L. in cereal crops. In: Biología de las malas hierbas de España; Sans X & Fernandez-Quintanilla C (eds.), pp: 77-89. Phytoma España and Soc Esp de Malherbología, Valencia (Spain).
  • 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. https://doi.org/10.1017/S0043174500088731
  • Loddo D, Bozic D, Calha IM, Dorado J, Izquierdo J, Šćepanović M, et al., 2018. Variability in seedling emergence for European and North American populations of Abutilon theophrasti. Weed Res 59: 15-27. https://doi.org/10.1111/wre.12343
  • Masin R, Loddo D, Gasparini V, Otto S, Zanin G, 2014. Evaluation of weed emergence model AlertInf for maize in soybean. Weed Sci 62: 360-369. https://doi.org/10.1614/WS-D-13-00112.1
  • Royo-Esnal A, Torra J, Conesa JA, Recasens J, 2012. Emergence and early growth of Galium aparine and Galium spurium. Weed Res 52: 458-466. https://doi.org/10.1111/j.1365-3180.2012.00939.x
  • Schutte BJ, Regnier EE, Harrison SK, Schmoll JT, Spokas K, Forcella F, 2008. A hydrothermal seedling emergence model for Giant Ragweed (Ambrosia trifida). Weed Sci 56: 555-560. https://doi.org/10.1614/WS-07-161.1
  • Spokas K, Forcella F, 2009. Software tools for weed seed germination modeling. Weed Sci 57: 216-227. https://doi.org/10.1614/WS-08-142.1
  • Waheed A, Qureshi R, Jakhar GS, Tareen H, 2009. Weed community dynamics in wheat crop of district Rahim Yar Khan, Pakistan. Pak J Bot 41: 247-254.
  • Zambrano-Navea CL, Bastida F, Gonzalez-Andujar JL, 2012. Herbicidal strategies to control Phalaris brachystachys in a wheat-sunflower rotation: a simulation approach. Span J Agric Res 10: 1101-1106. https://doi.org/10.5424/sjar/2012104-578-11
  • Zambrano-Navea CL, Bastida F, Gonzalez-Andujar JL, 2013. A hydrothermal seedling emergence model for Conyza bonariensis. Weed Res 53: 213-220. https://doi.org/10.1111/wre.12020
Fuente de los datos: Dialnet