README.md
In ebimodeling/biocro: Modular Crop Growth Simulations

BioCro is a model that predicts plant growth over time given crop-specific parameters and environmental data as input.

It uses models of key physiological and biophysical processes underlying plant growth (Humphries and Long, 1995), and has previously been used for predicting biomass yield and leaf area index of switchgrass and miscanthus (Miguez et al., 2009). In 2022, BioCro was reorganized to take a truly modular approach to modeling (Lochocki et al., 2022) and a new soybean model was developed (Matthews et al., 2022).

BioCro has also been integrated into a suite of tools that link the model directly to crop trait and yield data (LeBauer et al., 2013). The Predictive Ecosystem Analyzer (PEcAn) couples BioCro to the Biofuel Ecophysiological Traits and Yields database.

See References below for a full list of scientific publications using the BioCro framework.

The run_biocro() function accepts initial values, parameters, climate variables, and sets of modules to run. It returns the results in a data frame.

library(BioCro)
library(lattice)

result <- with(soybean, {run_biocro(
  initial_values,
  parameters,
  soybean_weather$'2002',
  direct_modules,
  differential_modules,
  ode_solver
)})

xyplot(Stem + Leaf ~ TTc, data = result, type='l', auto = TRUE)

There are parameters and modules for soybean (Glycine max), miscanthus (Miscanthus x giganteus), and willow (Saliceae salix).

Requirements

The R environment version 3.6.0 or greater.
On Windows, a version of Rtools appropriate for your version of R.
On Linux, gcc and g++ version 4.9.3 or greater (consult documentation for your distribution for installation instructions).
On MacOS, Xcode.

Installation steps

Obtain a local copy of this repository, making sure to include the Git submodule code. This can be accomplished using either of two methods:
If you are new to Git, the easiest way to get a local copy is to install GitHub Desktop and use the "Open with GitHub Desktop" option in the "Code" dropdown on the GitHub page for this repository.
Alternatively, clone the repository using Git on the command line in the usual fashion by running git clone https://github.com/biocro/biocro The repository contains a Git submodule, so you will need to take the additional step of running git submodule update --init to obtain it.
Install the BioCro R package using one of the following sets of comands. These assume that the source files are in a directory named "biocro" residing in a parent directory located at "path_to_source_code_parent_directory".
To install from the command line: cd path_to_source_code_parent_directory R CMD INSTALL biocro
To install from within R: setwd('path_to_source_code_parent_directory') install.packages('biocro', repos=NULL, type='SOURCE')

Please see the contribution guidelines before submitting changes. These may be found in Chapter One of the Developer's Manual on the public BioCro Documentation web site.

See the public BioCro Documentation web site. There will be found not only the usual package documentation, but also documentation of the C++ code, including notes on the biological models used in BioCro and their implementation. Also included is documentation for BioCro package developers and maintainers.

There is also a separate page that documents all of the quantities used by the Standard BioCro Module Library.

The BioCro R package is licensed under the MIT license, while the BioCro C++ framework is licensed under version 3 or greater of the GNU Lesser General Public License (LGPL). This scheme allows people to freely develop models for any use (public or private) under the MIT license, but any changes to the framework that assembles and solves models must make source code changes available to all users under the LGPL. See LICENSE.note for more details.

Appropriate references for BioCro are Miguez et al. (2009) and Lochocki et al. (2022), with details given below. To cite the package itself, use citation('BioCro') in R to get details for the current installed version.

Humphries S and Long SP (1995) WIMOVAC: a software package for modelling the dynamics of plant leaf and canopy photosynthesis. Computer Applications in the Biosciences 11(4): 361-371.
Miguez FE, Zhu XG, Humphries S, Bollero GA, Long SP (2009) A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus × giganteus: description, parameterization and validation. Global Change Biology Bioenergy 1: 282-296.
LeBauer D, Wang D, Richter K, Davidson C, Dietze M (2013) Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs 83(2): 133-154.
Wang D, Jaiswal D, Lebauer DS, Wertin TM, Bollero GA, Leakey ADB, Long SP (2015) A physiological and biophysical model of coppice willow (Salix spp.) production yields for the contiguous USA in current and future climate scenarios. Plant, Cell & Environment 38(9): 1850-1865.
Larsen S, Jaiswal D, Bentsen NS, Wang D, Long SP (2015) Comparing predicted yield and yield stability of willow and Miscanthus across Denmark. GCB Bioenergy 8(6): 1061-1070.
Jaiswal D, de Souza AP, Larsen S, LeBauer D, Miguez FE, Sparovek G, Bollero G, Buckeridge MS, Long SP (2017) Brazilian sugarcane ethanol as an expandable green alternative to crude oil use. Nature Climate Change 7(11): 788-792.
Lochocki EB, Rohde S, Jaiswal D, Matthews ML, Miguez FE, Long SP, McGrath JM (2022) BioCro II: a software package for modular crop growth simulations. in silico Plants 4(1): diac003.
Matthews ML, Marshall-Colón A, McGrath JM, Lochocki EB, Long SP (2022) Soybean-BioCro: a semi-mechanistic model of soybean growth. in silico Plants 4(1): diab032.
He Y, Jaiswal D, Liang XZ, Sun C, Long SP (2022) Perennial biomass crops on marginal land improve both regional climate and agricultural productivity. GCB Bioenergy 14(5): 558-571.
He Y, Matthews ML (2023) Seasonal climate conditions impact the effectiveness of improving photosynthesis to increase soybean yield. Field Crops Research 296: 108907.
Holland B, Matthews ML, Bota P, Sweetlove LJ, Long SP, diCenzo GC (2023) A genome-scale metabolic reconstruction of soybean and Bradyrhizobium diazoefficiens reveals the cost–benefit of nitrogen fixation. New Phytologist 240(2): 744-756.