Predicting field weed emergence with empirical models and soft computing techniques

• Published in: Weed research 2016-12, Vol.56 (6), p.415-423
• Main Authors: Gonzalez-Andujar, J L, Chantre, G R, Morvillo, C, Blanco, A M, Forcella, F
• Format: Article
• Language: English
• Subjects: artificial neural networks; day degrees; day degrees, d °C; d °C; genetic algorithms; nonlinear regression; predictive modelling; weed control
• ISSN: 0043-1737; 1365-3180
• DOI: 10.1111/wre.12223

Summary 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.