Simulation models on the ecology and management of arable weeds structure, quantitative insights, and applications
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, especi...
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| Otros Autores: | , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Materias: | |
| Acceso en línea: | http://ri.agro.uba.ar/files/download/articulo/2020bagavathiannan.pdf LINK AL EDITOR |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
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| 022 | |a 2073-4395 | ||
| 024 | |a 10.3390/agronomy10101611 | ||
| 040 | |a AR-BaUFA |c AR-BaUFA | ||
| 245 | 1 | 0 | |a Simulation models on the ecology and management of arable weeds |b structure, quantitative insights, and applications |
| 520 | |a 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. | ||
| 650 | |2 Agrovoc |9 26 | ||
| 653 | |a WEED SEEDLING EMERGENCE | ||
| 653 | |a CROP - WEED COMPETITION | ||
| 653 | |a WEED POPULATION DYNAMICS | ||
| 653 | |a GENE FLOW | ||
| 653 | |a HERBICIDE RESISTANCE | ||
| 653 | |a DECISION - SUPPORT TOOLS | ||
| 653 | |a PREDICTIVE MODELS | ||
| 700 | 1 | |a Bagavathiannan, Muthukumar V. |u Texas A and M University. Department of Soil and Crop Sciences. College Station. USA. |9 73386 | |
| 700 | 1 | |a Beckie, Hugh J. |u The University of Western Australia. School of Agriculture and Environment. Western Australia, Australia. |9 72344 | |
| 700 | 1 | |9 47366 |a Chantre, Guillermo Rubén |u Universidad Nacional del Sur. Departamento de Agronomía. CERZOS. Bahía Blanca, Buenos Aires, Argentina. |u CONICET - Universidad Nacional del Sur. Departamento de Agronomía. CERZOS. Bahía Blanca, Buenos Aires, Argentina. | |
| 700 | 1 | |a González Andújar, José L. |u Instituto de Agricultura Sostenible (CSIC). Cordoba, Spain. |9 4026 | |
| 700 | 1 | |a León, Ramón G. |u North Carolina State University. Department of Crop and Soil Sciences. Center for Environmental Farming Systems, Genetic Engineering and Society Center. Raleigh, USA. |9 73387 | |
| 700 | 1 | |a Neve, Paul |u Agriculture and Horticulture Development Board. Stoneleigh Park, Kenilworth, UK. |9 69391 | |
| 700 | 1 | |a Poggio, Santiago Luis |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal.Cátedra de Producción Vegetal.Buenos Aires, Argentina. |9 9496 | |
| 700 | 1 | |a Schutte, Brian J. |u New Mexico State University. Department of Entomology, Plant Pathology and Weed Science. Las Cruces, USA. |9 73388 | |
| 773 | |t Agronomy |g Vol.10, no.10 (2020), art.1611, 24 p., grafs. | ||
| 856 | |f 2020bagavathiannan |i En internet |q application/pdf |u http://ri.agro.uba.ar/files/download/articulo/2020bagavathiannan.pdf |x ARTI202204 | ||
| 856 | |u http://www.mdpi.com/ |z LINK AL EDITOR | ||
| 942 | |c ARTICULO | ||
| 942 | |c ENLINEA | ||
| 976 | |a AAG | ||