Decision Support Systems for Weed Management

Editors: Guillermo R. Chantre, José L. González-Andújar.Weed management Decision Support Systems (DSS) are increasingly important computer-based tools for modern agriculture. Nowadays, extensive agriculture has become highly dependent on external inputs and both economic costs, as well the negative environmental impact of agricultural activities, demands knowledge-based technology for the optimization and protection of non-renewable resources. In this context, weed management strategies should aim to maximize economic profit by preserving and enhancing agricultural systems. Although previous contributions focusing on weed biology and weed management provide valuable insight on many aspects of weed species ecology and practical guides for weed control, no attempts have been made to highlight the forthcoming importance of DSS in weed management. This book is a first attempt to integrate 'concepts and practice' providing a novel guide to the state-of-art of DSS and the future prospects which hopefully would be of interest to higher-level students, academics and professionals in related areas

A quantitative analysis of temperature-dependent seasonal dormancy cycling in buried Arabidopsis thaliana seeds can predict seedling emergence in a global warming scenario

Understanding how the environment regulates seed-bank dormancy changes is essential for forecasting seedling emergence in actual and future climatic scenarios, and to interpret studies of dormancy mechanisms at physiological and molecular levels. Here, we used a population threshold modelling approach to analyse dormancy changes through variations in the thermal range permissive for germination in buried seeds of Arabidopsis thaliana Cvi, a winter annual ecotype. Results showed that changes in dormancy level were mainly associated with variations in the higher limit of the thermal range permissive for germination. Changes in this limit were positively related to soil temperature during dormancy release and induction, and could be predicted using thermal time. From this, we developed a temperature-driven simulation to predict the fraction of the seed bank able to germinate in a realistic global warming scenario that approximated seedling emergence timing. Simulations predicted, in accordance with seedling emergence observed in the field, an increase in the fraction of the seed bank able to emerge as a result of global warming. In addition, our results suggest that buried seeds perceive changes in the variability of the mean daily soil temperature as the signal to change between dormancy release and induction according to the seasons