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Evaluation of Weed Emergence Model AlertInf for Maize inSoybean

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: roberta.masin@unipd.it
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Abstract

AlertInf is a recently developed model to predict the daily emergence ofthree important weed species in maize cropped in northern Italy (commonlambsquarters, johnsongrass, and velvetleaf). Its use can improve theeffectiveness and sustainability of weed control, and there has been growinginterest from farmers and advisors. However, there are two important limitsto its use: the low number of weed species included and its applicabilityonly to maize. Consequently, the aim of this study was to expand theAlertInf weed list and extend its use to soybean. The first objective was toadd another two important weed species for spring-summer crops in Italy,barnyardgrass and large crabgrass. Given that maize and soybean havedifferent canopy architectures that can influence the interrow microclimate,the second objective was to compare weed emergence in maize and soybean sownon the same date. The third objective was to evaluate if AlertInf wastransferable to soybean without recalibration, thus saving time and money.Results showed that predictions made by AlertInf for all five speciessimulated in soybean were satisfactory, as shown by the high efficiencyindex (EF) values, and acceptable from a practical point of view. The factthat the algorithm used for estimating weed emergence in maize was alsoefficient for soybean, at least for crops grown in northeastern Italy withstandard cultural practices, encourages further development of AlertInf andthe 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

Information

Type
Weed Management
Information
Weed Science , Volume 62 , Issue 2 , June 2014 , pp. 360 - 369
Copyright
Copyright © Weed Science Society of America 

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References

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