The DynACof process-based model computes plot-scale Net Primary Productivity, carbon allocation, growth, yield, energy, and water balance of coffee plantations according to management, while accounting for spatial effects using metamodels from the 3D process-based MAESPA. The model also uses coffee bud and fruit cohorts for reproductive development to better represent fruit carbon demand distribution along the year.
Informations about installation, version release, source code and news are available on the GitHub repository. This source is always the most up-to-date. Informations about model description (equations, state of the art, history) can be found on the third chapter of the thesis manuscript (Vezy, 2017). NB : the manuscript is partly in French, but this chapter is in English.
The development version from GitHub can be installed with:
# install.packages("devtools") devtools::install_github("VEZY/DynACof")
Or using the lightweight remotes package:
# install.packages("remotes") remotes::install_github("VEZY/DynACof")
For the moment, there is no CRAN released version of DynACof, but we work on that. You will soon be able to run this command to install the package:
This is a basic example using all defaults (parameters and meteorology) over 2 years :
rm(list = ls()) library("DynACof") Sys.setenv(TZ="UTC") DynACof(Period= as.POSIXct(c("1979-01-01", "1980-12-31")))
To use your own data, you have to tell DynACof where to find it using
Inpath parameter, and what are the files names with the
FileName parameter list:
rm(list = ls()) library("DynACof") Sys.setenv(TZ="UTC") DynACof(WriteIt = T, Period= as.POSIXct(c("1979-01-01", "1980-12-31")),Inpath = "1-Input/Aquiares/",Simulation_Name = "Test1",FileName = list(Site = "1-Site.R", Meteo ="2-Meteorology.txt", Soil = "3-Soil.R",Coffee = "4-Coffee.R", Tree = NULL))
Note that the Meteo file can be of any regular format because the model uses the
data.table::fread function internally.
The DynACof model was mainly developed thanks to the MACCAC project, which was funded by the french ANR (Agence Nationale de la Recherche). The authors were funded by CIRAD and INRA. The authors are grateful for the support of CATIE for the long-term coffee agroforestry trial, the SOERE F-ORE-T which is supported annually by Ecofor, Allenvi and the French national research infrastructure ANAEE-F; the CIRAD-IRD-SAFSE project (France) and the PCP platform of CATIE. CoffeeFlux observatory was supported and managed by CIRAD researchers. We are grateful to the staff from Costa-Rica, in particular Alvaro Barquero, Alejandra Barquero, Jenny Barquero, Alexis Perez, Guillermo Ramirez, Rafael Acuna, Manuel Jara, Alonso Barquero for their technical and field support.
Vezy, R., Simulation de pratiques de gestion alternatives pour l’adaptation des plantations pérennes aux changements globaux, in École doctorale science de l’environnement, spécialité physique de l’environnement. 2017, UNIVERSITÉ DE BORDEAUX: Bordeaux. p. 270. Link
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