Literature DB >> 24585689

Herbicide resistance modelling: past, present and future.

Michael Renton1, Roberto Busi, Paul Neve, David Thornby, Martin Vila-Aiub.   

Abstract

Computer simulation modelling is an essential aid in building an integrated understanding of how different factors interact to affect the evolutionary and population dynamics of herbicide resistance, and thus in helping to predict and manage how agricultural systems will be affected. In this review, we first discuss why computer simulation modelling is such an important tool and framework for dealing with herbicide resistance. We then explain what questions related to herbicide resistance have been addressed to date using simulation modelling, and discuss the modelling approaches that have been used, focusing first on the earlier, more general approaches, and then on some newer, more innovative approaches. We then consider how these approaches could be further developed in the future, by drawing on modelling techniques that are already employed in other areas, such as individual-based and spatially explicit modelling approaches, as well as the possibility of better representing genetics, competition and economics, and finally the questions and issues of importance to herbicide resistance research and management that could be addressed using these new approaches are discussed. We conclude that it is necessary to proceed with caution when increasing the complexity of models by adding new details, but, with appropriate care, more detailed models will make it possible to integrate more current knowledge in order better to understand, predict and ultimately manage the evolution of herbicide resistance.
© 2014 Society of Chemical Industry.

Entities:  

Keywords:  computer; evolution; genetics; integration; simulation

Mesh:

Substances:

Year:  2014        PMID: 24585689     DOI: 10.1002/ps.3773

Source DB:  PubMed          Journal:  Pest Manag Sci        ISSN: 1526-498X            Impact factor:   4.845


  8 in total

Review 1.  The Evolution and Ecology of Resistance in Cancer Therapy.

Authors:  Robert Gatenby; Joel Brown
Journal:  Cold Spring Harb Perspect Med       Date:  2018-03-01       Impact factor: 6.915

2.  Simulating changes in cropping practices in conventional and glyphosate-resistant maize. II. Weed impacts on crop production and biodiversity.

Authors:  Nathalie Colbach; Henri Darmency; Alice Fernier; Sylvie Granger; Valérie Le Corre; Antoine Messéan
Journal:  Environ Sci Pollut Res Int       Date:  2017-04-06       Impact factor: 4.223

Review 3.  Application of Evolutionary Principles to Cancer Therapy.

Authors:  Pedro M Enriquez-Navas; Jonathan W Wojtkowiak; Robert A Gatenby
Journal:  Cancer Res       Date:  2015-11-02       Impact factor: 12.701

4.  Glyphosate-Resistant Parthenium hysterophorus in the Caribbean Islands: Non Target Site Resistance and Target Site Resistance in Relation to Resistance Levels.

Authors:  Enzo Bracamonte; Pablo T Fernández-Moreno; Francisco Barro; Rafael De Prado
Journal:  Front Plant Sci       Date:  2016-12-06       Impact factor: 5.753

5.  Pollen-Mediated Movement of Herbicide Resistance Genes in Lolium rigidum.

Authors:  Iñigo Loureiro; María-Concepción Escorial; María-Cristina Chueca
Journal:  PLoS One       Date:  2016-06-23       Impact factor: 3.240

6.  Inheritance of Mesotrione Resistance in an Amaranthus tuberculatus (var. rudis) Population from Nebraska, USA.

Authors:  Maxwel C Oliveira; Todd A Gaines; Amit J Jhala; Stevan Z Knezevic
Journal:  Front Plant Sci       Date:  2018-02-02       Impact factor: 5.753

7.  Syngenta's contribution to herbicide resistance research and management.

Authors:  Shiv Shankhar Kaundun
Journal:  Pest Manag Sci       Date:  2020-09-21       Impact factor: 4.845

Review 8.  Senescence-Induced Chemoresistance in Triple Negative Breast Cancer and Evolution-Based Treatment Strategies.

Authors:  Anindita Chakrabarty; Shayantani Chakraborty; Ranjini Bhattacharya; Goutam Chowdhury
Journal:  Front Oncol       Date:  2021-06-24       Impact factor: 6.244

  8 in total

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