Titlebook: Decision Support Systems for Weed Management; Guillermo R. Chantre,José L. González-Andújar Book 2020 Springer Nature Switzerland AG 2020

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Site-Specific Based Models-DSS), recent advances in the use of remote and ground platforms and sensors for information gathering and processing, and initial experiences translating this information into chemical and physical weed control actuations through decision algorithms and models.

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Modelling Weed Seedbank Dormancy and Germinationbanks are regulated by the environment. This framework is based on the distinction between those factors that regulate seasonal changes in the seedbank dormancy level (i.e. temperature in interaction with seed moisture content) and those factors that terminate dormancy (i.e. light and alternating te

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Weed Emergence Modelsoped using nonlinear regression (NLR) techniques. NLR sigmoidal type models which are based on cumulative thermal or hydrothermal time have become the most popular approach as they are easy to develop and use. However, some statistical and bioecological limitations arise, for example, the lack of in

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Weed Interference Models a descriptive approach. Weed interference models are an important part of the decision support systems to establish recommendations based on the economic quantification of different weed management strategies. Thus, these models are very useful tools for the development of integrated weed managemen

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How to Use a “Virtual Field” to Evaluate and Design Integrated Weed Management Strategies at Differeies, plant morphology and stage. To simplify the addition of new species, difficult-to-measure model parameters are estimated with functional relationships from easily measured species traits, trait databases and expert opinion. To simplify the comparison of cropping systems, the detailed daily and

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