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      Modelling the impacts of pests and diseases on agricultural systems

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          Abstract

          The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

          Highlights

          • Overview of the current state of development in coupling pest and disease models to crop models

          • Technical and scientific challenges in coupling pest and disease model to crop models

          • Roadmap to improve the simulation of the impacts caused by plant diseases and pests

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          Most cited references112

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          The NCEP Climate Forecast System Version 2

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            The DSSAT cropping system model

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              The generic genome browser: a building block for a model organism system database.

              The Generic Model Organism System Database Project (GMOD) seeks to develop reusable software components for model organism system databases. In this paper we describe the Generic Genome Browser (GBrowse), a Web-based application for displaying genomic annotations and other features. For the end user, features of the browser include the ability to scroll and zoom through arbitrary regions of a genome, to enter a region of the genome by searching for a landmark or performing a full text search of all features, and the ability to enable and disable tracks and change their relative order and appearance. The user can upload private annotations to view them in the context of the public ones, and publish those annotations to the community. For the data provider, features of the browser software include reliance on readily available open source components, simple installation, flexible configuration, and easy integration with other components of a model organism system Web site. GBrowse is freely available under an open source license. The software, its documentation, and support are available at http://www.gmod.org.
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                Author and article information

                Contributors
                Journal
                Agric Syst
                Agric Syst
                Agricultural Systems
                Elsevier Applied Science [etc.]
                0308-521X
                1 July 2017
                July 2017
                : 155
                : 213-224
                Affiliations
                [a ]CREA - Council for Agricultural Research and Economics, Research Center for Agriculture and Environment, via di Corticella 133, I-40128, Bologna, Italy
                [b ]Center for Integrated Pest Management, North Carolina State University, Raleigh, NC 27606, USA
                [c ]AGIR, Université de Toulouse, INRA, INPT, INP- EI PURPAN, Castanet-Tolosan, France
                [d ]CSIRO Agriculture and Food, 203 Tor St Toowoomba, Qld 4350, Australia
                Author notes
                [* ]Corresponding author. marcello.donatelli@ 123456crea.gov.it
                Article
                S0308-521X(17)30104-X
                10.1016/j.agsy.2017.01.019
                5485649
                28701814
                b3576d3f-585e-48ff-bc06-9e4be4bcae96
                © 2017 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 February 2016
                : 26 January 2017
                : 30 January 2017
                Categories
                Article

                model coupling,model integration,process-based models,yield loss,modelling frameworks

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