Predicting field weed emergence with empirical models and soft computing techniques
Seedling emergence is one of the most important phenological processes that influence the success of weed species. Therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict se...
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| Otros Autores: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Materias: | |
| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2016gonzalezandujar.pdf LINK AL EDITOR |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
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| 024 | |a 10.1111/wre.12223 | ||
| 040 | |a AR-BaUFA |c AR-BaUFA | ||
| 245 | 1 | 0 | |a Predicting field weed emergence with empirical models and soft computing techniques |
| 520 | |a Seedling emergence is one of the most important phenological processes that influence the success of weed species. Therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict seedling emergence patterns for weed species under field conditions. Empirical emergence models have been the most common tools used for this purpose. They are based mainly on the use of temperature, soil moisture and light. In this review, we present the more popular empirical models, highlight some statistical and biological limitations that could affect their predictive accuracy and, finally, we present a new generation of modelling approaches to tackle the problems of conventional empirical models, focusing mainly on soft computing techniques. We hope that this review will inspire weed modellers and that it will serve as a basis for discussion and as a frame of reference when we proceed to advance the modelling of field weed emergence. | ||
| 650 | |2 Agrovoc |9 26 | ||
| 653 | |a ARTIFICIAL NEURAL NETWORKS | ||
| 653 | |a GENETIC ALGORITHMS | ||
| 653 | |a PREDICTIVE MODELLING | ||
| 653 | |a NONLINEAR REGRESSION | ||
| 653 | |a WEED CONTROL | ||
| 653 | |a DAY DEGREES | ||
| 700 | 1 | |a González Andújar, José L. |u Instituto de Agricultura Sostenible (CSIC), Córdoba, Spain. |9 4026 | |
| 700 | 1 | |a Chantre, Guillermo Rubén |u Universidad Nacional del Sur. Departamento de Agronomía (CERZOS). Bahíıa Blanca, Buenos Aires, Argentina. |u CONICET – Universidad Nacional del Sur. Bahíıa Blanca, Buenos Aires, Argentina. |9 47366 | |
| 700 | 1 | |a Morvillo, Claudia Mariela |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Buenos Aires, Argentina. |u Instituto de Agricultura Sostenible (CSIC), Córdoba, Spain. |9 14518 | |
| 700 | 1 | |a Blanco, A. M. |u Universidad Nacional del Sur. Planta Piloto de Ingeniería Quíımica. Bahía Blanca, Buenos Aires, Argentina. |u CONICET - Universidad Nacional del Sur. Bahía Blanca, Buenos Aires, Argentina. |9 67586 | |
| 700 | 1 | |a Forcella, Frank |u USDA-ARS North Central Soil Conservation Research Laboratory, Morris, MN, USA. |9 58067 | |
| 773 | 0 | |t Weed research |w SECS000181 |g Vol.56, no.6 (2016), p.415-423, grafs. | |
| 856 | |f 2016gonzalezandujar |i En reservorio |q application/pdf |u http://ri.agro.uba.ar/files/intranet/articulo/2016gonzalezandujar.pdf |x ARTI201808 | ||
| 856 | |u http://www.blackwellpublishing.com |z LINK AL EDITOR | ||
| 942 | |c ARTICULO | ||
| 942 | |c ENLINEA | ||
| 976 | |a AAG | ||