Literature DB >> 23534680

Deconstructing crop processes and models via identities.

John R Porter1, Svend Christensen.   

Abstract

This paper is part review and part opinion piece; it has three parts of increasing novelty and speculation in approach. The first presents an overview of how some of the major crop simulation models approach the issue of simulating the responses of crops to changing climatic and weather variables, mainly atmospheric CO2 concentration and increased and/or varying temperatures. It illustrates an important principle in models of a single cause having alternative effects and vice versa. The second part suggests some features, mostly missing in current crop models, that need to be included in the future, focussing on extreme events such as high temperature or extreme drought. The final opinion part is speculative but novel. It describes an approach to deconstruct resource use efficiencies into their constituent identities or elements based on the Kaya-Porter identity, each of which can be examined for responses to climate and climatic change. We give no promise that the final part is 'correct', but we hope it can be a stimulation to thought, hypothesis and experiment, and perhaps a new modelling approach.
© 2013 John Wiley & Sons Ltd.

Entities:  

Keywords:  Kaya-Porter identity; crop models; deconstruction; resource use efficiency

Mesh:

Substances:

Year:  2013        PMID: 23534680     DOI: 10.1111/pce.12107

Source DB:  PubMed          Journal:  Plant Cell Environ        ISSN: 0140-7791            Impact factor:   7.228


  2 in total

1.  Challenges for a Massive Implementation of Phenomics in Plant Breeding Programs.

Authors:  Gustavo A Lobos; Félix Estrada; Alejandro Del Pozo; Sebastián Romero-Bravo; Cesar A Astudillo; Freddy Mora-Poblete
Journal:  Methods Mol Biol       Date:  2022

2.  Phenotyping field-state wheat root system architecture for root foraging traits in response to environment×management interactions.

Authors:  Xinxin Chen; Yinian Li; Ruiyin He; Qishuo Ding
Journal:  Sci Rep       Date:  2018-02-08       Impact factor: 4.379

  2 in total

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