APSIM: Importing APSIM Classic and NewGeneration files
Rai Schwalbert & Adrian Correndo
2023-03-27
Source:vignettes/apsim_open.Rmd
apsim_open.Rmd1. Introduction
The metrica package was developed to assess the
prediction performance of, among other, crop simulation models such as
APSIM.
This vignette introduces the functionality of the
metrica package applied to facilitate opening
APSIM output files in R.
Import data from APSIM
1. APSIM Classic (.out)
# Obtaining filepath from package folder
apsim_out_filepath <- system.file("extdata/soybean.out", package = "metrica")
# Use import_apsim_out for APSIM Classic output
soybean.out <- metrica::import_apsim_out(filepath = apsim_out_filepath)
head(soybean.out)
#> Date (dd/mm/yyyy) yield (kg/ha) biomass (kg/ha) grain_protein (%)
#> 1 16/05/2001 1449.8 2915.9 37.115
#> 2 10/05/2002 1092.5 2965.1 37.115
#> 3 23/04/2003 1368.9 3529.6 37.115
#> 4 26/04/2004 2121.6 4710.3 37.115
#> 5 09/05/2005 1950.3 4303.9 37.115
#> 6 20/04/2006 1670.6 3806.6 37.115
#> GrainN (g/m^2) lai (m^2/m^2) oilGrainN (g/m^2) grain_oil_conc (%)
#> 1 0.00 0 0 0.4
#> 2 7.10 0 0 0.4
#> 3 8.90 0 0 0.4
#> 4 13.79 0 0 0.4
#> 5 12.68 0 0 0.4
#> 6 10.86 0 0 0.41. APSIM NextGeneration (.db)
# Obtaining path from package folder
apsim_db_folderpath <- system.file("extdata", package = "metrica")
# Use import_apsim_db for APSIM NextGeneration output
soybean.db <- metrica::import_apsim_db(filename = "soybean.example.db", folder = apsim_db_folderpath)
head(soybean.db)
#> CheckpointID SimulationID Zone Clock.Today Soybean.Phenology.Stage
#> 1 1 1 paddock 2018-01-01 12:00:00 1
#> 2 1 1 paddock 2018-01-02 12:00:00 1
#> 3 1 1 paddock 2018-01-03 12:00:00 1
#> 4 1 1 paddock 2018-01-04 12:00:00 1
#> 5 1 1 paddock 2018-01-05 12:00:00 1
#> 6 1 1 paddock 2018-01-06 12:00:00 1
#> Soybean.Phenology.CurrentStageName Soybean.AboveGround.Wt Yield
#> 1 0 0
#> 2 0 0
#> 3 0 0
#> 4 0 0
#> 5 0 0
#> 6 0 0
#> Soybean.Grain.Size Soybean.Grain.Number Soybean.Total.Wt Soybean.Leaf.FRGR
#> 1 0 0 0 0
#> 2 0 0 0 0
#> 3 0 0 0 0
#> 4 0 0 0 0
#> 5 0 0 0 0
#> 6 0 0 0 0
#> Soybean.Leaf.Fw Soybean.Leaf.Photosynthesis.FT Soybean.Leaf.Photosynthesis.FW
#> 1 1 0 1
#> 2 1 0 1
#> 3 1 0 1
#> 4 1 0 1
#> 5 1 0 1
#> 6 1 0 1
#> Soybean.WaterUptake(1) Soybean.WaterUptake(2) Soybean.WaterUptake(3)
#> 1 NA NA NA
#> 2 NA NA NA
#> 3 NA NA NA
#> 4 NA NA NA
#> 5 NA NA NA
#> 6 NA NA NA
#> Soybean.WaterUptake(4) Soybean.WaterUptake(5) Soybean.WaterUptake(6)
#> 1 NA NA NA
#> 2 NA NA NA
#> 3 NA NA NA
#> 4 NA NA NA
#> 5 NA NA NA
#> 6 NA NA NA
#> Soybean.WaterUptake(7) Soybean.WaterUptake(8) Soybean.WaterUptake(9)
#> 1 NA NA NA
#> 2 NA NA NA
#> 3 NA NA NA
#> 4 NA NA NA
#> 5 NA NA NA
#> 6 NA NA NA
#> Soybean.WaterUptake(10) Date
#> 1 NA 2018-01-01
#> 2 NA 2018-01-02
#> 3 NA 2018-01-03
#> 4 NA 2018-01-04
#> 5 NA 2018-01-05
#> 6 NA 2018-01-06
# If observed.data is already as a dataframe, the user may do the match using a simple code like this:
# PO.dataframe <- simulated.data %>% left_join(., observed.data) *by = "col" arg. could be required*