DyMEP {DyMEP}

Dynamic Multi Environment Phenology-Model

Description

Empirically models/predicts the phenology (macro-phases) of 10 crop plants (trained on a big dataset over 80 years derived from the German weather service (DWD)). Can be applied for remote sensing purposes, environmental inputs can be chosen from a range of pre-trained response curves and applied to the trained crops and phenological phases. No retraining is done within the use of this package.

Examples

available <- available_crops_and_phases()
#what is the best environmental covariates for one or multiple phases?
# check what covairates are implemented in the model
available_covariates <- available_environmental_covariates()

best_DyMEP_model(env_covariates = c("tas","tasmin","VPD","SPI",
"global_radiation","tasmax","RH"),
                pheno_phases = c("sowing-emergence","jointing-heading"),
                crop_abbrev = "WW")
# create a list of wanted phases and corresponding environmental covariates
phase_covariate_list <- list("sowing-emergence" = c("tasmin","VPD","SPI"),
                         "emergence-jointing"= c("tas","tasmin","VPD","SPI"),
                     "jointing-heading" = c("global_radiation","tas","SPI"))

# alternatively you can create this input list directly like this with the
# best available model:
phase_covariate_list <- best_DyMEP_model(env_covariates =
c("tas","tasmin","VPD","SPI","global_radiation","tasmax","RH"),
pheno_phases = c("sowing-emergence","emergence-jointing","jointing-heading"),
crop_abbrev = "WW",
output_list_for_prediction = TRUE)


# create dummy environmental data
environmental_data <- data.frame("DATE" = seq.Date(
              from = as.Date("2021-01-01"), to = as.Date("2023-12-31"),by=1),
                          "tas"=runif(1095,min=-10,max=40),
                           "RH"=runif(1095,min=0,max=100),
                           "tasmin"=runif(1095,min=-10,max=40),
                           "tasmax"=runif(1095,min=-5,max=40),
                           "VPD" = runif(1095,min=0,max=40),
                           "SPI"= runif(1095,min=-1,max=4),
                           "global_radiation"= runif(1095,min=0,max=3500))


pheno_phase_prediction(phase_covariate_list = phase_covariate_list,
                      environmental_data = environmental_data,
                      phase_starting_date =as.Date("2021-01-01"),
                      crop_abbrev = "WW")

 # you can also get a more detailed output, containing detailed predictions
 # and the parameters of the used DRC curves:
 detailed_output <- pheno_phase_prediction(
            phase_covariate_list = phase_covariate_list,
            environmental_data = environmental_data,
            phase_starting_date =as.Date("2021-01-01"),
            crop_abbrev = "WW",
            output_type = "detailed_information")

 #  this output can be visualised like:
 # get overview plot of the prediction
 DyMEP_prediction_visualizer(detailed_output)
 # check the DRC curves of the used model
 DyMEP_DRC_visualizer(detailed_output)