Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications
This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems.
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Multidisciplinary Digital Publishing Institute
2020-10-21
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| Subjects: | Weed seedling emergence, Crop-weed competition, Weed population dynamics, Gene flow, Herbicide resistance, Decision-support tools, Predictive models, |
| Online Access: | http://hdl.handle.net/10261/226833 http://dx.doi.org/10.13039/100004913 http://dx.doi.org/10.13039/501100005740 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100003074 http://dx.doi.org/10.13039/501100000780 |
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dig-ias-es-10261-2268332021-01-17T04:47:30Z Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications Bagavathiannan, Muthukumar V. Beckie, Hugh J. Chantre, Guillermo R. González-Andújar, José Luis León, Ramón G. Neve, Paul Poggio, Santiago L. Schutte, Brian J. Somerville, Gayle J. Werle, Rodrigo Acker, Rene C. van Texas AgriLife Research European Commission Ministerio de Economía y Competitividad (España) Agencia Nacional de Promoción Científica y Tecnológica (Argentina) Universidad Nacional del Sur Weed seedling emergence Crop-weed competition Weed population dynamics Gene flow Herbicide resistance Decision-support tools Predictive models This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems. In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making. Muthukumar V. Bagavathiannan’s research program is supported by state and federal funds appropriated to Texas A&M AgriLife Research. Gayle J. Somerville was supported by the UKRI project of evaluating people-environment trade-offs through low-tech intensification of livestock management in communal grazing systems in South Africa (BB/S014683/1) whilst working at Rothamsted. Brian J. Schutte is supported by state and federal funds appropriated to the New Mexico Agricultural Experiment Station. JLG-A was funded by FEDER (European Regional Development Funds) and the Spanish Ministry of Economy and Competitiveness grants (projects AGL2015-64130-R). Guillermo R. Chantre received funding from Agencia Nacional de Promoción Científica y Tecnológica MINCyT (PICT-2016-1575) and Universidad Nacional del Sur of Argentina (PGI 24/A225). 2021-01-15T11:50:05Z 2021-01-15T11:50:05Z 2020-10-21 2021-01-15T11:50:05Z artículo de revisión http://purl.org/coar/resource_type/c_dcae04bc doi: 10.3390/agronomy10101611 e-issn: 2073-4395 Agronomy 10(10): 1611 (2020) http://hdl.handle.net/10261/226833 10.3390/agronomy10101611 http://dx.doi.org/10.13039/100004913 http://dx.doi.org/10.13039/501100005740 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100003074 http://dx.doi.org/10.13039/501100000780 #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2015-64130-R Publisher's version http://doi.org/10.3390/agronomy10101611 Sí open Multidisciplinary Digital Publishing Institute |
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Weed seedling emergence Crop-weed competition Weed population dynamics Gene flow Herbicide resistance Decision-support tools Predictive models Weed seedling emergence Crop-weed competition Weed population dynamics Gene flow Herbicide resistance Decision-support tools Predictive models |
| spellingShingle |
Weed seedling emergence Crop-weed competition Weed population dynamics Gene flow Herbicide resistance Decision-support tools Predictive models Weed seedling emergence Crop-weed competition Weed population dynamics Gene flow Herbicide resistance Decision-support tools Predictive models Bagavathiannan, Muthukumar V. Beckie, Hugh J. Chantre, Guillermo R. González-Andújar, José Luis León, Ramón G. Neve, Paul Poggio, Santiago L. Schutte, Brian J. Somerville, Gayle J. Werle, Rodrigo Acker, Rene C. van Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications |
| description |
This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems. |
| author2 |
Texas AgriLife Research |
| author_facet |
Texas AgriLife Research Bagavathiannan, Muthukumar V. Beckie, Hugh J. Chantre, Guillermo R. González-Andújar, José Luis León, Ramón G. Neve, Paul Poggio, Santiago L. Schutte, Brian J. Somerville, Gayle J. Werle, Rodrigo Acker, Rene C. van |
| format |
artículo de revisión |
| topic_facet |
Weed seedling emergence Crop-weed competition Weed population dynamics Gene flow Herbicide resistance Decision-support tools Predictive models |
| author |
Bagavathiannan, Muthukumar V. Beckie, Hugh J. Chantre, Guillermo R. González-Andújar, José Luis León, Ramón G. Neve, Paul Poggio, Santiago L. Schutte, Brian J. Somerville, Gayle J. Werle, Rodrigo Acker, Rene C. van |
| author_sort |
Bagavathiannan, Muthukumar V. |
| title |
Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications |
| title_short |
Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications |
| title_full |
Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications |
| title_fullStr |
Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications |
| title_full_unstemmed |
Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications |
| title_sort |
simulation models on the ecology and management of arable weeds: structure, quantitative insights, and applications |
| publisher |
Multidisciplinary Digital Publishing Institute |
| publishDate |
2020-10-21 |
| url |
http://hdl.handle.net/10261/226833 http://dx.doi.org/10.13039/100004913 http://dx.doi.org/10.13039/501100005740 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100003074 http://dx.doi.org/10.13039/501100000780 |
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