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Agronomy 2013, 3(1), 13-27; doi:10.3390/agronomy3010013

Development of a Geo-Referenced Database for Weed Mapping and Analysis of Agronomic Factors Affecting Herbicide Resistance in Apera spica-venti L. Beauv. (Silky Windgrass)

1
Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, 70599 Stuttgart, Germany
2
Agency for Health Technology Assessment, Institute of Health Carlos III, 28029 Madrid, Spain
3
Proplanta GmbH & Co. KG, 70599 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Received: 10 September 2012 / Revised: 18 December 2012 / Accepted: 18 December 2012 / Published: 4 January 2013
(This article belongs to the Special Issue Weed Management and Herbicide Resistance)
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Abstract

In this work, we evaluate the role of agronomic factors in the selection for herbicide resistance in Apera spica-venti L. Beauv. (silky windgrass). During a period of three years, populations were collected in more than 250 conventional fields across Europe and tested for resistance in the greenhouse. After recording the field history of locations, a geo-referenced database has been developed to map the distribution of herbicide-resistant A. spica-venti populations in Europe. A Logistic Regression Model was used to assess whether and to what extent agricultural and biological factors (crop rotation, soil tillage, sowing date, soil texture and weed density) affect the probability of resistance selection apart from the selection pressure due to herbicide application. Our results revealed that rotation management and soil tillage are the factors that have the greatest influence on the model. In addition, first order interactions between these two variables were highly significant. Under conventional tillage, a percentage of winter crops in the rotation exceeding 75% resulted in a 1280-times higher risk of resistance selection compared to rotations with less than 50% of winter crops. Under conservation tillage, the adoption of >75% of winter crops increased the risk of resistance 13-times compared to rotations with less than 50% of winter crops. Finally, early sowing and high weed density significantly increased the risk of resistance compared to the reference categories (later sowing and low weed density, respectively). Soil texture had no significant influence. The developed model can find application in management programs aimed at preventing the evolution and spread of herbicide resistance in weed populations.
Keywords: farm management; geographic information system; logistic regression model (LRM) farm management; geographic information system; logistic regression model (LRM)
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Massa, D.; Kaiser, Y.I.; Andújar-Sánchez, D.; Carmona-Alférez, R.; Mehrtens, J.; Gerhards, R. Development of a Geo-Referenced Database for Weed Mapping and Analysis of Agronomic Factors Affecting Herbicide Resistance in Apera spica-venti L. Beauv. (Silky Windgrass). Agronomy 2013, 3, 13-27.

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