Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-26T08:09:18.054Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of Weed Emergence Model AlertInf for Maize in Soybean

Published online by Cambridge University Press:  20 January 2017

Roberta Masin ,
Donato Loddo ,
Valentina Gasparini ,
Stefan Otto  and
Giuseppe Zanin
Roberta Masin*
Affiliation:
Department of Agronomy, Food, Natural Resources, Animals & Environment, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Donato Loddo
Affiliation:
Department of Agronomy, Food, Natural Resources, Animals & Environment, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Valentina Gasparini
Affiliation:
Department of Agronomy, Food, Natural Resources, Animals & Environment, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Stefan Otto
Affiliation:
Institute of Agro-environmental and Forest Biology-CNR, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Giuseppe Zanin
Affiliation:
Department of Agronomy, Food, Natural Resources, Animals & Environment, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

AlertInf is a recently developed model to predict the daily emergence of three important weed species in maize cropped in northern Italy (common lambsquarters, johnsongrass, and velvetleaf). Its use can improve the effectiveness and sustainability of weed control, and there has been growing interest from farmers and advisors. However, there are two important limits to its use: the low number of weed species included and its applicability only to maize. Consequently, the aim of this study was to expand the AlertInf weed list and extend its use to soybean. The first objective was to add another two important weed species for spring-summer crops in Italy, barnyardgrass and large crabgrass. Given that maize and soybean have different canopy architectures that can influence the interrow microclimate, the second objective was to compare weed emergence in maize and soybean sown on the same date. The third objective was to evaluate if AlertInf was transferable to soybean without recalibration, thus saving time and money. Results showed that predictions made by AlertInf for all five species simulated in soybean were satisfactory, as shown by the high efficiency index (EF) values, and acceptable from a practical point of view. The fact that the algorithm used for estimating weed emergence in maize was also efficient for soybean, at least for crops grown in northeastern Italy with standard cultural practices, encourages further development of AlertInf and the spread of its use.

Keywords

Common lambsquarters, Chenopodium album L., CHEALbarnyardgrass, Echinochloa crus-galli (L.) Beauv., ECHCGjohnsongrass, Sorghum halepense (L.) Pers, SORHAlarge crabgrass, Digitaria sanguinalis (L.) Scop., DIGSAvelvetleaf, Abutilon theophrasti Medik., ABUTHmaize, Zea mays L.soybean, Glycine max (L.) Merr.Hydrothermal timemodelingpredicting weed emergence dynamicsweed control
Type
Weed Management
Information
Weed Science , Volume 62 , Issue 2 , June 2014 , pp. 360 - 369
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

Alvarado, V, Bradford, KJ (2002) A hydrothermal time model explains the cardinal temperatures for seed germination. Plant Cell Environ. 25:1061–1069 Google Scholar
Archer, DW, Forcella, F, Eklund, JJ, Gunsolus, J (2001) WeedCast Version 4.0. http://www.ars.usda.gov/services/software/software.htm. Accessed July 30, 2013Google Scholar
Batlla, D, Kruk, BG, Benech-Arnold, RL (2000) Very early detection of canopy presence by seeds through perception of subtle modifications in R:FR signals. Funct Ecol. 14:195–202 Google Scholar
Baumann, DT, Bastiaans, L, Kropff, MJ (2001) Effects of intercropping on growth and reproductive capacity of late-emerging Senecio vulgaris L., with special reference to competition for light. Ann Bot. 87:209–217 Google Scholar
Benjamin, LR, Milne, AE, Parsons, DJ, Lutman, PJW (2010) A model to simulate yield losses in winter wheat caused by weeds, for use in a weed management decision support system. Crop Prot. 29:1264–1273 Google Scholar
Bradford, KJ (2002) Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50:248–260 Google Scholar
Chantre, GR, Blanco, AM, Lodovichi, MV, Bandoni, AJ, Sabbatini, MR, López, RL, Vigna, MR, Gigón, R (2012) Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach. Comput Electron Agr. 88:95–102 Google Scholar
Colbach, N, Chauvel, B, Gauvrit, C, Munier-Jolain, NM (2007) Construction and evaluation of ALOMYSYS modelling the effects of cropping systems on the blackgrass life-cycle: from seeding to seed production. Ecol Model. 201:283–300 Google Scholar
Dorado, J, Sousa, E, Calha, IM, Gonzalez-Andujar, JL, Fernandez-Quintanilla, C (2009) Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Res. 49:251–260 Google Scholar
Forcella, F, Benech-Arnold, RL, Sanchez, R, Ghersa, CM (2000) Modeling seedling emergence. Field Crop Res. 67:123–139 Google Scholar
Gardarin, A, Guillemin, JP, Munier-Jolain, NM, Colbach, N (2010) Estimation of key parameters for weed population dynamics models: base temperature and base water potential for germination. Eur J Agron. 32:162–168 Google Scholar
Grundy, AC (2003) Predicting weed emergence: a review of approaches and future challenges. Weed Res. 43:1–11 Google Scholar
Gummerson, RJ (1986) The effect of constant temperatures and osmotic potential on the germination of sugar beet. J Exp Bot. 37:729–741 Google Scholar
Hock, SM, Knezevic, SZ, Martin, AR, Lindquist, JL (2005) Influence of soybean row width and velvetleaf emergence time on velvetleaf (Abutilon theophrasti). Weed Sci. 53:160–165 Google Scholar
Huarte, HR, Benech Arnold, RL (2003) Understanding mechanisms of reduced annual weed emergence in alfalfa. Weed Sci. 51:876–885 Google Scholar
Knezevic, SZ, Evans, SP, Blankenship, EE, van Acker, RC, Lindquist, JL (2002) Critical period for weed control: the concept and data analysis. Weed Sci. 50:773–786 Google Scholar
LeBlanc, ML, Cloutier, DC, Legere, A, Lemieux, C, Assemat, L, Benoit, DL, Hamel, C (2002) Effect of the presence or absence of corn on common lambsquarters (Chenopodium album L.) and barnyardgrass (Echinochloa crus-galli (L.) Beauv.) emergence. Weed Technol. 16:638–644 Google Scholar
Loague, K, Green, RE (1991) Statistical and graphical methods for evaluating solute transport models: overview and application. J Cont Hydrol. 7:51–73 Google Scholar
Loddo, D, Sousa, E, Masin, R, Calha, I, Zanin, G, Fernandez-Quintanilla, C, Dorado, J (2013) Estimation and comparison of base temperatures for germination of European populations of velvetleaf (Abutilon theophrasti) and jimsonweed (Datura stramonium). Weed Sci. 61:443–451 Google Scholar
Manidool, C (1992) Echinochloa crus-galli (L.) P. Beauv. Pages 126–127 in t'Mannetje, L and Jones, RM, eds. Plant resources of south-east Asia. No. 4. Forages. Wageningen, The Netherlands Pudoc Scientific Publishers Google Scholar
Masin, R, Cacciatori, G, Zuin, MC, Zanin, G (2010a) AlertInf: emergence predictive model for weed control in maize in Veneto. Ital J Agrometeorol. 1:5–9 Google Scholar
Masin, R, Loddo, D, Benvenuti, S, Otto, S, Zanin, G (2012) Modeling weed emergence in Italian maize field. Weed Sci. 60:254–259 Google Scholar
Masin, R, Loddo, D, Benvenuti, S, Zuin, MC, Macchia, M, Zanin, G (2010b) Temperature and water potential as parameters for modeling weed emergence in central-northern Italy. Weed Sci. 58:216–222 Google Scholar
Masin, R, Vasileiadis, VP, Loddo, D, Otto, S, Zanin, G (2011) A single-time survey method to predict the daily weed density for weed control decision-making. Weed Sci. 59:270–275 Google Scholar
Mohler, CL (1996) Ecological bases for the cultural control of annual weeds. J Prod Agric. 9:468–474 Google Scholar
Myers, MW, Curran, WS, VanGessel, MJ, Calvin, DD, Mortensen, DA, Majek, BA, Karsten, HD, Roth, GW (2004) Predicting weed emergence for eight annual species in the northeastern United States. Weed Sci. 52:913–919 Google Scholar
Norsworthy, JK (2004) Soybean canopy formation effects on pitted morningglory (Ipomoea lacunosa), common cocklebur (Xanthium strumarium), and sicklepod (Senna obtusifolia) emergence. Weed Sci. 52:954–960 Google Scholar
Nyamusamba, RP, Moeching, MJ, Deneke, DL (2008) Simulating weed emergence under different crop canopies. Page 103 in 63rd Proceedings of the North Central Weed Science Society Symposium. Las Cruces, NM North Central Weed Science Society Google Scholar
Otto, S, Masin, R, Casari, G, Zanin, G (2009) Weed–corn competition parameters in late-winter sowing in northern Italy. Weed Sci. 57:194–201 Google Scholar
Rahman, M, Ungar, IA (1990) The effect of salinity on seed germination and seedling growth of Echinochloa crus-galli . Ohio J Sci. 90:13–15 Google Scholar
Ramanarayanan, TS, Williams, JR, Dugas, WA, Hauck, LM, McFarland, AMS (1997) Using APEX to Identify Alternative Practices for Animal Waste Management. Part I: Model Description and Validation. St. Joseph, MI American Society of Agricultural Engineers, ASAE Paper No. 972209Google Scholar
Royo-Esnal, A, Torra, J, Antoni Conesa, J, Forcella, F, Recasens, J (2010) Modeling the emergence of three arable bedstraw (Galium) species. Weed Sci. 58:10–15 Google Scholar
Shipley, B, Parent, M (1991) Germination responses of 64 wetland species in relation to seed size, minimum time to reproduction and seedling relative growth rate. Funct Ecol. 5:111–118 Google Scholar
Spokas, K, Forcella, F (2009) Software tools for weed seed germination modeling. Weed Sci. 57:216–227 Google Scholar
Spokas, K, Forcella, F, Archer, D, Peterson, D, Miller, S (2007) Improving weed germination models by incorporating seed microclimate and translocation by tillage. Proceedings Weed Science Society of America. 44:60 Google Scholar
Steinmaus, SJ, Prather, TS, Holt, JS (2000) Estimation of base temperatures for nine weed species. J Exp Bot. 51:275–286 Google Scholar
Sweeney, AE, Renner, KA, Laboski, C, Devis, A (2008) Effect of fertilizer nitrogen on weed emergence and growth. Weed Sci. 56:714–721 Google Scholar
Vina, A, Gitelson, AA, Nguy-Robertson, AL, Peng, Y (2011) Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens Environ. 115:3468–3478 Google Scholar
Wallach, D (2006) Evaluating crop models. Pages 11–54 in Wallach, D, Makowski, D, and Jones, JW, eds. Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications. Amsterdam Elsevier Google Scholar
Willmott, CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc. 63:1309–1313 Google Scholar
Zhang, H, Irving, LJ, Tian, Y, Zhou, D (2012) Influence of salinity and temperature on seed germination rate and the hydrotime model parameters for the halophyte, Chloris virgata, and the glycophyte, Digitaria sanguinalis . S Afr J Bot. 78:203–210 Google Scholar