English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/158070
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Predicting field weed emergence with empirical models and soft computing techniques

AutorGonzález-Andújar, José Luis ; Chantre, G. R.; Morvillo, C. M.; Blanco, Antonio M.; Forcella, Frank
Palabras claveArtificial neural networks
Genetic algorithms
Predictive modelling
Nonlinear regression
Weed control
Day degrees
d °C
Fecha de publicacióndic-2016
EditorJohn Wiley & Sons
CitaciónWeed Research 56(6): 415-423 (2016)
ResumenSeedling 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.
Versión del editorhttp://doi.org/10.1111/wre.12223
Aparece en las colecciones: (IAS) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Mostrar el registro completo

NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.