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Weed Decision Threshold as a Key Factor for Herbicide Reductions in Site-Specific Weed Management

Published online by Cambridge University Press:  23 February 2017

Carolina San Martín ,
Dionisio Andújar ,
Judit Barroso ,
Cesar Fernández-Quintanilla  and
José Dorado
Carolina San Martín*
Affiliation:
Department of Crop Protection, Instituto de Ciencias Agrarias, CSIC, Serrano 115 B, 28006 Madrid, Spain
Dionisio Andújar
Affiliation:
Department of Crop Protection, Instituto de Ciencias Agrarias, CSIC, Serrano 115 B, 28006 Madrid, Spain
Judit Barroso
Affiliation:
Department of Crop and Soil Science, Columbia Basin Agricultural Research Center, Oregon State University, Pendleton, OR, 97801
Cesar Fernández-Quintanilla
Affiliation:
Department of Crop Protection, Instituto de Ciencias Agrarias, CSIC, Serrano 115 B, 28006 Madrid, Spain
José Dorado
Affiliation:
Department of Crop Protection, Instituto de Ciencias Agrarias, CSIC, Serrano 115 B, 28006 Madrid, Spain
*
Corresponding author's E-mail: carolina.smh@ica.csic.es
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Abstract

The objective of this research was to explore the influence that weed decision threshold (DT; expressed as plants m−2), weed spatial distribution patterns, and spatial resolution of sampling have on potential reduction in herbicide use under site-specific weed management. As a case study, a small plot located in a typical corn field in central Spain was used, constructing very precise distribution maps of the major weeds present. These initial maps were used to generate herbicide prescription maps for each weed species based on different DTs and sampling resolutions. The simulation of herbicide prescription maps consisted of on/off spraying decisions based on information from two different approaches for weed detection: ground-based vs. aerial sensors. In general, simulations based on ground sensors resulted in higher herbicide savings than those based on aerial sensors. The extent of herbicide reductions derived from patch spraying was directly related to the density and the spatial distribution of each weed species. Herbicide savings were potentially high (up to 66%) with relatively sparse patchy weed species (e.g., johnsongrass) but were only moderate (10 to 20%) with abundant and regularly distributed weed species (e.g., velvetleaf). However, DT has proven to be a key factor, with higher DTs resulting in reductions in herbicide use for all the weed species and all sampling procedures and resolutions. Moreover, increasing DT from 6 to 12 plants m−2 resulted in additional herbicide savings of up to 50% in the simulations for johnsongrass and up to 28% savings in the simulations for common cocklebur. Nonetheless, since DT determines the accuracy of patch spraying, the consequences of using higher DTs could be leaving areas unsprayed, which could adversely affect crop yields and future weed infestations, including herbicide-resistant weeds. Considering that the relationship between DT and accuracy of herbicide application depends on weed spatial pattern, this work has demonstrated the possibility of using higher DT values in weeds with a clear patchy distribution compared with weeds distributed regularly.

El objetivo de esta investigación fue explorar la influencia que tienen el umbral de decisión para el control de malezas (DT; expresado como plantas m−2), los patrones de distribución espacial de malezas, y la resolución espacial del muestreo, sobre la reducción potencial en el uso de herbicidas con un manejo de malezas de sitio-específico. Como un caso de estudio, se usó una parcela pequeña localizada en un campo de maíz típico en el centro de España, para construir mapas muy precisos de distribución de las principales malezas presentes. Estos mapas iniciales fueron usados para generar mapas de prescripción de herbicidas para cada especie de maleza con base en diferentes DTs y resoluciones de muestreo. La simulación de mapas de prescripción de herbicidas consistió de decisiones de iniciar/detener la aspersión con base en la información proveniente de dos estrategias diferentes para la detección de malezas: sensores terrestres vs. aéreos. En general, las simulaciones con base en sensores terrestres resultaron en mayores ahorros de herbicidas que aquellas basadas en sensores aéreos. La magnitud de las reducciones en el uso de herbicidas derivadas de las aspersiones localizadas estuvieron directamente relacionadas a la densidad y la distribución espacial de cada especie de malezas. Los ahorros de herbicidas fueron potencialmente altos (hasta 66%) con especies de malezas relativamente esparcidas en patrones agregados (e.g., Sorghum halepense), pero fueron solamente moderados (10 a 20%) con especies de malezas abundantes y distribuidas en forma regular (e.g., Abutilon theophrasti). Sin embargo, el DT ha probado ser un factor clave, y DTs altos resultan en reducciones en el uso de herbicidas para todas las especies de malezas y todos los procedimientos y resoluciones de muestreo. Además al incrementar el DT de 6 a 12 plantas m−2 resultó en ahorros adicionales de herbicidas en hasta 50% en las simulaciones para S. halepense y hasta 28% de ahorros en las simulaciones para Xanthium strumarium. Sin embargo, como el DT determina la exactitud de la aspersión del agregado de malezas, las consecuencias de usar DTs altos podría ser el dejar áreas sin asperjar, lo que podría afectar adversamente los rendimientos de los cultivos y las infestaciones futuras de las malezas, incluyendo malezas resistentes a herbicidas. Considerando que la relación entre el DT y la exactitud de la aplicación del herbicida depende del patrón de distribución espacial de la malezas, este trabajo ha demostrado la posibilidad de usar valores más altos de DT en malezas con un patrón claro de distribución agregada al compararse con malezas distribuidas en forma regular.

Keywords

Common cocklebur, Xanthium strumarium L. XANSTjohnsongrass, Sorghum halepense (L.) Pers. SORHAvelvetleaf, Abutilon theophrasti Medik. ABUTHcorn, Zea mays L.Patch sprayingprescription map errorsweed decision thresholdweed mappingweed spatial distribution
Type
Research Article
Information
Weed Technology , Volume 30 , Issue 4 , December 2016 , pp. 888 - 897
Copyright
Copyright © Weed Science Society of America 

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Footnotes

Associate Editor for this paper: Andrew Kniss, University of Wyoming.

References

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