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BioCro II: a Software Package for Modular Crop Growth Simulations

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BioCro II: a Software Package for Modular Crop Growth Simulations. / Lochocki, Edward B; Rohde, Scott; Jaiswal, Deepak et al.
In: in silico Plants, Vol. 4, No. 1, 30.06.2022, p. 1-13.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Lochocki, EB, Rohde, S, Jaiswal, D, Matthews, ML, Miguez, F, Long, SP & McGrath, JM 2022, 'BioCro II: a Software Package for Modular Crop Growth Simulations', in silico Plants, vol. 4, no. 1, pp. 1-13. https://doi.org/10.1093/insilicoplants/diac003

APA

Lochocki, E. B., Rohde, S., Jaiswal, D., Matthews, M. L., Miguez, F., Long, S. P., & McGrath, J. M. (2022). BioCro II: a Software Package for Modular Crop Growth Simulations. in silico Plants, 4(1), 1-13. https://doi.org/10.1093/insilicoplants/diac003

Vancouver

Lochocki EB, Rohde S, Jaiswal D, Matthews ML, Miguez F, Long SP et al. BioCro II: a Software Package for Modular Crop Growth Simulations. in silico Plants. 2022 Jun 30;4(1):1-13. Epub 2022 Feb 12. doi: 10.1093/insilicoplants/diac003

Author

Lochocki, Edward B ; Rohde, Scott ; Jaiswal, Deepak et al. / BioCro II : a Software Package for Modular Crop Growth Simulations. In: in silico Plants. 2022 ; Vol. 4, No. 1. pp. 1-13.

Bibtex

@article{f04f3e098c72401b837208ae77a3084c,
title = "BioCro II: a Software Package for Modular Crop Growth Simulations",
abstract = "The central motivation for mechanistic crop growth simulation has remained the same for decades: To reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.",
keywords = "Dynamical systems, mechanistic crop growth simulation, modular modelling, multiscale modelling",
author = "Lochocki, {Edward B} and Scott Rohde and Deepak Jaiswal and Matthews, {Megan L} and Fernando Miguez and Long, {Stephen P} and McGrath, {Justin M}",
year = "2022",
month = jun,
day = "30",
doi = "10.1093/insilicoplants/diac003",
language = "English",
volume = "4",
pages = "1--13",
journal = "in silico Plants",
issn = "2517-5025",
publisher = "Oxford University Press (OUP)",
number = "1",

}

RIS

TY - JOUR

T1 - BioCro II

T2 - a Software Package for Modular Crop Growth Simulations

AU - Lochocki, Edward B

AU - Rohde, Scott

AU - Jaiswal, Deepak

AU - Matthews, Megan L

AU - Miguez, Fernando

AU - Long, Stephen P

AU - McGrath, Justin M

PY - 2022/6/30

Y1 - 2022/6/30

N2 - The central motivation for mechanistic crop growth simulation has remained the same for decades: To reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.

AB - The central motivation for mechanistic crop growth simulation has remained the same for decades: To reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.

KW - Dynamical systems

KW - mechanistic crop growth simulation

KW - modular modelling

KW - multiscale modelling

U2 - 10.1093/insilicoplants/diac003

DO - 10.1093/insilicoplants/diac003

M3 - Journal article

VL - 4

SP - 1

EP - 13

JO - in silico Plants

JF - in silico Plants

SN - 2517-5025

IS - 1

ER -