Literature DB >> 23682914

The use and role of predictive systems in disease management.

David H Gent1, Walter F Mahaffee, Neil McRoberts, William F Pfender.   

Abstract

Disease predictive systems are intended to be management aids. With a few exceptions, these systems typically do not have direct sustained use by growers. Rather, their impact is mostly pedagogic and indirect, improving recommendations from farm advisers and shaping management concepts. The degree to which a system is consulted depends on the amount of perceived new, actionable information that is consistent with the objectives of the user. Often this involves avoiding risks associated with costly disease outbreaks. Adoption is sensitive to the correspondence between the information a system delivers and the information needed to manage a particular pathosystem at an acceptable financial risk; details of the approach used to predict disease risk are less important. The continuing challenge for researchers is to construct tools relevant to farmers and their advisers that improve upon their current management skill. This goal requires an appreciation of growers' decision calculus in managing disease problems and, more broadly, their overall farm enterprise management.

Entities:  

Mesh:

Year:  2013        PMID: 23682914     DOI: 10.1146/annurev-phyto-082712-102356

Source DB:  PubMed          Journal:  Annu Rev Phytopathol        ISSN: 0066-4286            Impact factor:   13.078


  7 in total

1.  Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model.

Authors:  K N Neufeld; A P Keinath; B K Gugino; M T McGrath; E J Sikora; S A Miller; M L Ivey; D B Langston; B Dutta; T Keever; A Sims; P S Ojiambo
Journal:  Int J Biometeorol       Date:  2017-11-25       Impact factor: 3.787

2.  Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae.

Authors:  Elisa González-Domínguez; Josep Armengol; Vittorio Rossi
Journal:  PLoS One       Date:  2014-09-18       Impact factor: 3.240

3.  Application of the Maryblyt Model for the Infection of Fire Blight on Apple Trees at Chungju, Jecheon, and Eumsung during 2015-2020.

Authors:  Mun-Il Ahn; Sung Chul Yun
Journal:  Plant Pathol J       Date:  2021-12-01       Impact factor: 1.795

4.  Coupling Spore Traps and Quantitative PCR Assays for Detection of the Downy Mildew Pathogens of Spinach (Peronospora effusa) and Beet (P. schachtii).

Authors:  Steven J Klosterman; Amy Anchieta; Neil McRoberts; Steven T Koike; Krishna V Subbarao; Hermann Voglmayr; Young-Joon Choi; Marco Thines; Frank N Martin
Journal:  Phytopathology       Date:  2014-12       Impact factor: 4.025

5.  Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

Authors:  Lucky K Mehra; Christina Cowger; Kevin Gross; Peter S Ojiambo
Journal:  Front Plant Sci       Date:  2016-03-30       Impact factor: 5.753

6.  A Mechanistic Model of Botrytis cinerea on Grapevines That Includes Weather, Vine Growth Stage, and the Main Infection Pathways.

Authors:  Elisa González-Domínguez; Tito Caffi; Nicola Ciliberti; Vittorio Rossi
Journal:  PLoS One       Date:  2015-10-12       Impact factor: 3.240

Review 7.  A Systematic Map of the Research on Disease Modelling for Agricultural Crops Worldwide.

Authors:  Giorgia Fedele; Chiara Brischetto; Vittorio Rossi; Elisa Gonzalez-Dominguez
Journal:  Plants (Basel)       Date:  2022-03-09
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.