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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: roberta.masin@unipd.it
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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 

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