diff --git a/DESCRIPTION b/DESCRIPTION
index 83b09c9..6483a42 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: BBWPC
 Type: Package
 Title: Calculator for BedrijfsBodemWaterPlan (BBWP)
-Version: 0.3.2
+Version: 0.4.0
 Authors@R: c(
     person("Gerard", "Ros", email = "gerard.ros@nmi-agro.nl", role = c("aut","cre")),
     person("Sven", "Verweij", email = "sven.verweij@nmi-agro.nl", role = c("aut")),
diff --git a/NAMESPACE b/NAMESPACE
index e76d84d..9e1b8dc 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+export(bbwp)
 export(bbwp_check_meas)
 export(bbwp_farm_score)
 export(bbwp_field_indicators)
@@ -8,6 +9,7 @@ export(bbwp_field_scores)
 export(bbwp_meas_rank)
 export(bbwp_meas_score)
 export(cdf_rank)
+export(ecoregeling)
 export(er_croprotation)
 export(er_farm_aim)
 export(er_farm_score)
diff --git a/NEWS.md b/NEWS.md
index dbbcee9..0ee0d6a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,11 @@
 # Changelog BBWPC
 
+## 0.4.0
+### Added
+- add wrapper function `bbwp` to run BBWP for a series of fields
+- add wrapper function `ecoregeling` to run Ecoregeling for a series of fields
+- add associated test functions for `bbwp` and `ecoregeling`
+
 ## 0.3.2
 ### Added
 - add argument `B_SLOPE` to `bbwp_field_properties`,`bbwp_field_indicators`, issue #4
diff --git a/R/bbwp_main.R b/R/bbwp_main.R
new file mode 100644
index 0000000..89a3f5c
--- /dev/null
+++ b/R/bbwp_main.R
@@ -0,0 +1,200 @@
+#' Calculate the BBWP scores on field and farm level
+#'
+#' Estimate the potential to contribute to agronomic and environmental challenges in a region for a farm and assess the impact of farm measures taken.
+#' A high BBWP score is indicative for the number of opportunities to improve soil quality and land use.
+#'
+#' @param B_SOILTYPE_AGR (character) The type of soil
+#' @param B_LU_BRP (numeric) The crop type (conform BRP coding, preferable the most frequent crop on the field)
+#' @param B_LU_BBWP (numeric) The BBWP category used for allocation of measures to BBWP crop categories
+#' @param B_GWL_CLASS (character) The groundwater table class
+#' @param B_SC_WENR (character) The risk for subsoil compaction as derived from risk assessment study of Van den Akker (2006)
+#' @param B_HELP_WENR (character) The soil type abbreviation, derived from 1:50.000 soil map
+#' @param B_SLOPE (numeric) The slope of the field (degrees)
+#' @param B_GWP (boolean) is the field located in a groundwater protected area (options: TRUE or FALSE)
+#' @param B_AREA_DROUGHT (boolean) is the field located in an area with high risks for water deficiencies (options: TRUE or FALSE)
+#' @param B_CT_PSW (numeric) the critical target for required reduction in P loss from agriculture (kg P / ha) to reach targets of KRW
+#' @param B_CT_NSW (numeric) the critical target for required reduction in N loss from agriculture (kg N / ha) to reach targets of KRW
+#' @param B_CT_PSW_MAX (numeric) the max critical target for P reduction loss (kg P / ha)
+#' @param B_CT_NSW_MAX (numeric) the max critical target for N reduction loss (kg N / ha)
+#' @param A_CLAY_MI (numeric) The clay content of the soil (\%)
+#' @param A_SAND_MI (numeric) The sand content of the soil (\%)
+#' @param A_SILT_MI (numeric) The silt content of the soil (\%)
+#' @param A_SOM_LOI (numeric) The organic matter content of the soil (\%)
+#' @param A_N_RT (numeric) The organic nitrogen content of the soil (mg N / kg)
+#' @param A_FE_OX (numeric) The aluminium content of soil (mmol+ / kg)
+#' @param A_AL_OX (numeric) The iron content of soil (mmol+ / kg)
+#' @param A_P_CC (numeric) The plant available P content, measured via 0.01M CaCl2 extraction (mg / kg)
+#' @param A_P_AL (numeric) The plant extractable P content, measured via ammonium lactate extraction (mg / kg)
+#' @param A_P_WA (numeric) The P-content of the soil extracted with water
+#' @param A_P_SG (numeric) The P-saturation index (\%)
+#' @param D_WP (numeric) The fraction of the parcel that is surrounded by surface water
+#' @param D_RO_R (numeric) The risk that surface water runs off the parcel
+#' @param D_AREA (numeric) the area of the field (\ m2 or \ ha) 
+#' @param M_DRAIN (boolean) is there tube drainage present in the field
+#' @param LSW (data.table) The surface water polygon for catchment or polder
+#' @param measures (data.table) the measures planned / done per fields
+#' @param sector (string) a vector with the farm type given the agricultural sector (options: options: 'diary', 'arable', 'tree_nursery', 'bulbs')
+#' @param output (string) a vector specifying the output type of the function. Options: scores, measures 
+#'  
+#' @import data.table
+#' @import OBIC
+#'
+#' @export
+bbwp <- function(B_SOILTYPE_AGR, B_LU_BRP, B_LU_BBWP,B_GWL_CLASS, B_SC_WENR, B_HELP_WENR,B_SLOPE,
+                 A_CLAY_MI, A_SAND_MI, A_SILT_MI, A_SOM_LOI, A_N_RT,A_FE_OX, A_AL_OX, A_P_CC, A_P_AL, A_P_WA, A_P_SG,
+                 B_GWP, B_AREA_DROUGHT, B_CT_PSW, B_CT_NSW,B_CT_PSW_MAX = 0.5, B_CT_NSW_MAX = 5.0, 
+                 D_WP, D_RO_R, D_AREA, 
+                 M_DRAIN, LSW, 
+                 measures, sector,output = 'scores'){
+  
+  # add visual binding
+  field_id = NULL
+  
+  # check wrapper inputs that are not checked in the bbwp functions
+  checkmate::assert_character(output)
+  checkmate::assert_subset(output,choices = c('scores','measures'))
+  
+  # convert soil properties to a BBWP risk indicator
+  dt <- bbwp_field_properties(B_SOILTYPE_AGR = B_SOILTYPE_AGR, 
+                              B_LU_BRP = B_LU_BRP, 
+                              B_GWL_CLASS = B_GWL_CLASS, 
+                              B_SC_WENR = B_SC_WENR, 
+                              B_HELP_WENR = B_HELP_WENR,
+                              B_SLOPE = B_SLOPE,
+                              A_CLAY_MI = A_CLAY_MI, 
+                              A_SAND_MI = A_SAND_MI, 
+                              A_SILT_MI = A_SILT_MI, 
+                              A_SOM_LOI = A_SOM_LOI, 
+                              A_N_RT = A_N_RT,
+                              A_FE_OX = A_FE_OX, 
+                              A_AL_OX = A_AL_OX, 
+                              A_P_CC = A_P_CC, 
+                              A_P_AL = A_P_AL, 
+                              A_P_WA = A_P_WA, 
+                              A_P_SG = A_P_SG,
+                              D_WP = D_WP, 
+                              D_RO_R =  D_RO_R, 
+                              LSW = LSW)
+  
+  # Aggregate BBWP risk indicators into five indicators
+  dt.ind <- bbwp_field_indicators(D_NGW_SCR = dt$ngw_scr,
+                                  D_NGW_LEA = dt$ngw_lea,
+                                  D_NGW_NLV = dt$ngw_nlv,
+                                  D_NSW_SCR = dt$nsw_scr,
+                                  D_NSW_GWT = dt$nsw_gwt,
+                                  D_NSW_RO = dt$nsw_ro,
+                                  D_NSW_SLOPE = dt$nsw_slope,
+                                  D_NSW_WS = dt$nsw_ws,
+                                  D_NSW_NLV = dt$nsw_nlv,
+                                  D_PSW_SCR = dt$psw_scr,
+                                  D_PSW_GWT= dt$psw_gwt,
+                                  D_PSW_RO = dt$psw_ro,
+                                  D_PSW_SLOPE = dt$psw_slope,
+                                  D_PSW_WS = dt$psw_ws,
+                                  D_PSW_PCC = dt$psw_pcc,
+                                  D_PSW_PSG = dt$psw_psg,
+                                  D_PSW_PRET = dt$psw_pret,
+                                  D_NUE_WRI = dt$npe_wri,
+                                  D_NUE_PBI = dt$npe_pbi,
+                                  D_NUE_WDRI = dt$npe_wdri,
+                                  D_NUE_NLV = dt$npe_nlv,
+                                  D_WUE_WWRI = dt$wue_wwri,
+                                  D_WUE_WDRI = dt$wue_wdri,
+                                  D_WUE_WHC = dt$wue_whc
+                                )
+  
+  # Calculate BBWP field scores
+  
+    # when measures are requested as output, then BBWP field scores are derived for situation without measures
+    if(output == 'measures'){measures <- NULL}
+  
+    # calculate BBWP field scores
+    dt.fields  <- bbwp_field_scores(B_SOILTYPE_AGR = B_SOILTYPE_AGR,
+                                    B_GWL_CLASS = B_GWL_CLASS,
+                                    A_P_SG = A_P_SG,
+                                    B_SLOPE = B_SLOPE,
+                                    B_LU_BRP = B_LU_BRP,
+                                    B_LU_BBWP = B_LU_BBWP,
+                                    M_DRAIN = M_DRAIN,
+                                    D_WP = D_WP,
+                                    D_RISK_NGW = dt.ind$D_RISK_NGW,
+                                    D_RISK_NSW = dt.ind$D_RISK_NSW,
+                                    D_RISK_PSW = dt.ind$D_RISK_PSW,
+                                    D_RISK_NUE = dt.ind$D_RISK_NUE,
+                                    D_RISK_WB = dt.ind$D_RISK_WB,
+                                    B_GWP = B_GWP,
+                                    B_AREA_DROUGHT = B_AREA_DROUGHT,
+                                    B_CT_PSW = B_CT_PSW,
+                                    B_CT_NSW = B_CT_NSW,
+                                    B_CT_PSW_MAX = B_CT_PSW_MAX, 
+                                    B_CT_NSW_MAX = B_CT_NSW_MAX,
+                                    measures = measures,
+                                    sector = sector
+                                  )
+  
+  # Calculate the BBWP farm score
+  dt.farm <- bbwp_farm_score(D_OPI_TOT = dt.fields$D_OPI_TOT,
+                             D_OPI_NGW = dt.fields$D_OPI_NGW,
+                             D_OPI_NSW = dt.fields$D_OPI_NSW,
+                             D_OPI_PSW = dt.fields$D_OPI_PSW,
+                             D_OPI_NUE = dt.fields$D_OPI_NUE,
+                             D_OPI_WB = dt.fields$D_OPI_WB,
+                             D_AREA = D_AREA)
+                              
+  
+  # Retreive the best measures to improve
+  dt.meas <- bbwp_meas_rank(B_SOILTYPE_AGR = B_SOILTYPE_AGR,
+                            B_GWL_CLASS = B_GWL_CLASS,
+                            A_P_SG = A_P_SG,
+                            B_SLOPE = B_SLOPE,
+                            B_LU_BRP = B_LU_BRP,
+                            B_LU_BBWP = B_LU_BBWP,
+                            M_DRAIN = M_DRAIN,
+                            D_WP = D_WP,
+                            D_OPI_NGW = dt.fields$D_OPI_NGW,
+                            D_OPI_NSW = dt.fields$D_OPI_NSW,
+                            D_OPI_PSW = dt.fields$D_OPI_PSW,
+                            D_OPI_NUE = dt.fields$D_OPI_NUE,
+                            D_OPI_WB = dt.fields$D_OPI_WB,
+                            measures = NULL,
+                            sector = sector
+                          )
+  
+  # return output when preferred measures are requested
+  if(output == 'measures'){
+    
+    # convert names of dt.meas
+    setnames(dt.meas,gsub('\\.','_',colnames(dt.meas)))
+    
+    # convert dt.meas to a splitted list
+    out <- split(dt.meas,by='id',keep.by = FALSE)
+    
+    # covnert each list again to a list
+    out <- lapply(out,function(x) as.list(na.omit(x)))
+    
+    # set output object
+    out <- data.table(field_id = sort(unique(dt.meas$id)),
+                      measures = out)
+    
+  }
+  
+  # return output when BBWP field and farm scores are requested
+  if(output == 'scores'){
+    
+    # Set the column names to lowercase
+    setnames(dt.fields, colnames(dt.fields), tolower(colnames(dt.fields)))
+    setnames(dt.farm, colnames(dt.farm), tolower(colnames(dt.farm)))
+    
+    # Add field id
+    dt.fields[,field_id := .I]
+    
+    # set output object
+    out <- list(farm = as.list(dt.farm),fields = dt.fields)
+    
+  }
+  
+  
+  # return output
+  return(out)
+  
+}
diff --git a/R/er_main.R b/R/er_main.R
new file mode 100644
index 0000000..0e12908
--- /dev/null
+++ b/R/er_main.R
@@ -0,0 +1,118 @@
+#' Calculate the Ecoregeling scores on field and farm level
+#'
+#' Estimate the potential to contribute to agronomic and environmental challenges in a region for a farm and assess the impact of farm measures taken.
+#' A high Ecoregeling score is indicative for the number of opportunities to improve soil quality, water quality, climate biodiversity and landscape.
+#'
+#' @param B_SOILTYPE_AGR (character) The type of soil
+#' @param B_LU_BRP (numeric) The crop type (conform BRP coding, preferable the most frequent crop on the field)
+#' @param B_LU_BBWP (numeric) The BBWP category used for allocation of measures to BBWP crop categories
+#' @param B_GWL_CLASS (character) The groundwater table class
+#' @param B_SLOPE (numeric) The slope of the field (degrees)
+#' @param A_P_SG (numeric) The P-saturation index (\%)
+#' @param D_WP (numeric) The fraction of the parcel that is surrounded by surface water
+#' @param D_AREA (numeric) the area of the field (\ m2 or \ ha) 
+#' @param M_DRAIN (boolean) is there tube drainage present in the field
+#' @param farmscore (numeric) The desired total ER score on farm level
+#' @param measures (data.table) the measures planned / done per fields
+#' @param sector (string) a vector with the farm type given the agricultural sector (options: options: 'diary', 'arable', 'tree_nursery', 'bulbs')
+#' @param output (string) a vector specifying the output type of the function. Options: scores, measures 
+#'  
+#' @import data.table
+#' @import OBIC
+#'
+#' @export
+ecoregeling <- function(B_SOILTYPE_AGR, B_LU_BRP, B_LU_BBWP,B_GWL_CLASS, B_SLOPE,
+                        A_P_SG,D_WP, D_AREA,M_DRAIN, farmscore, 
+                        measures, sector,output = 'scores'){
+  
+  # check wrapper inputs that are not checked in the bbwp functions
+  checkmate::assert_character(output)
+  checkmate::assert_subset(output,choices = c('scores','measures'))
+  
+  # Calculate the minimum required ER scores on Farm level
+  dt.farm.aim <- er_farm_aim(B_SOILTYPE_AGR = B_SOILTYPE_AGR, 
+                             D_AREA = D_AREA, 
+                             farmscore = 100)
+   
+  # when measures are requested as output, then field scores are derived for situation without measures
+  if(output == 'measures'){measures <- NULL}
+  
+  # Calculate the aggregated ER scores per field
+  dt.fields <- er_field_scores(B_SOILTYPE_AGR = B_SOILTYPE_AGR, 
+                                B_LU_BRP = B_LU_BRP, 
+                                B_LU_BBWP = B_LU_BBWP,
+                                D_AREA = D_AREA,
+                                B_CT_SOIL = dt.farm.aim$B_CT_SOIL, 
+                                B_CT_WATER = dt.farm.aim$B_CT_WATER,
+                                B_CT_CLIMATE = dt.farm.aim$B_CT_CLIMATE,
+                                B_CT_BIO = dt.farm.aim$B_CT_BIO,
+                                B_CT_LANDSCAPE = dt.farm.aim$B_CT_LANDSCAPE,
+                                measures = measures, 
+                                sector)
+  
+  # Calculate the ER farm score
+  dt.farm <- er_farm_score(D_OPI_TOT = dt.fields$D_OPI_TOT,
+                           D_OPI_SOIL = dt.fields$D_OPI_SOIL,
+                           D_OPI_WATER = dt.fields$D_OPI_WATER,
+                           D_OPI_CLIMATE = dt.fields$D_OPI_CLIMATE,
+                           D_OPI_BIO = dt.fields$D_OPI_BIO,
+                           D_OPI_LANDSCAPE = dt.fields$D_OPI_LANDSCAPE,
+                           D_AREA = D_AREA)
+ 
+  # Retreive the best measures to improve
+  dt.meas <- er_meas_rank(B_SOILTYPE_AGR = B_SOILTYPE_AGR,
+                          B_GWL_CLASS = B_GWL_CLASS,
+                          A_P_SG = A_P_SG,
+                          B_SLOPE = B_SLOPE,
+                          B_LU_BRP = B_LU_BRP,
+                          B_LU_BBWP = B_LU_BBWP,
+                          M_DRAIN = M_DRAIN,
+                          D_WP = D_WP,
+                          D_AREA = D_AREA,
+                          B_CT_SOIL = dt.farm.aim$B_CT_SOIL, 
+                          B_CT_WATER = dt.farm.aim$B_CT_WATER,
+                          B_CT_CLIMATE = dt.farm.aim$B_CT_CLIMATE,
+                          B_CT_BIO = dt.farm.aim$B_CT_BIO,
+                          B_CT_LANDSCAPE = dt.farm.aim$B_CT_LANDSCAPE,
+                          measures = measures,
+                          sector = sector
+                          )
+  
+  # return output when preferred measures are requested
+  if(output == 'measures'){
+    
+    # convert names of dt.meas
+    setnames(dt.meas,gsub('\\.','_',colnames(dt.meas)))
+    
+    # convert dt.meas to a splitted list
+    out <- split(dt.meas,by='id',keep.by = FALSE)
+    
+    # covnert each list again to a list
+    out <- lapply(out,function(x) as.list(na.omit(x)))
+    
+    # set output object
+    out <- data.table(field_id = sort(unique(dt.meas$id)),
+                      measures = out)
+    
+  }
+  
+  # return output when BBWP field and farm scores are requested
+  if(output == 'scores'){
+    
+    # Set the column names to lowercase
+    setnames(dt.fields, colnames(dt.fields), tolower(colnames(dt.fields)))
+    setnames(dt.farm, colnames(dt.farm), tolower(colnames(dt.farm)))
+    
+    # Add field id
+    setnames(dt.fields,'id','field_id')
+    
+    # set output object
+    out <- list(farm = as.list(dt.farm),fields = dt.fields)
+    
+  }
+  
+  
+  # return output
+  return(out)
+  
+}
diff --git a/man/bbwp.Rd b/man/bbwp.Rd
new file mode 100644
index 0000000..455ee76
--- /dev/null
+++ b/man/bbwp.Rd
@@ -0,0 +1,110 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/bbwp_main.R
+\name{bbwp}
+\alias{bbwp}
+\title{Calculate the BBWP scores on field and farm level}
+\usage{
+bbwp(
+  B_SOILTYPE_AGR,
+  B_LU_BRP,
+  B_LU_BBWP,
+  B_GWL_CLASS,
+  B_SC_WENR,
+  B_HELP_WENR,
+  B_SLOPE,
+  A_CLAY_MI,
+  A_SAND_MI,
+  A_SILT_MI,
+  A_SOM_LOI,
+  A_N_RT,
+  A_FE_OX,
+  A_AL_OX,
+  A_P_CC,
+  A_P_AL,
+  A_P_WA,
+  A_P_SG,
+  B_GWP,
+  B_AREA_DROUGHT,
+  B_CT_PSW,
+  B_CT_NSW,
+  B_CT_PSW_MAX = 0.5,
+  B_CT_NSW_MAX = 5,
+  D_WP,
+  D_RO_R,
+  D_AREA,
+  M_DRAIN,
+  LSW,
+  measures,
+  sector,
+  output = "scores"
+)
+}
+\arguments{
+\item{B_SOILTYPE_AGR}{(character) The type of soil}
+
+\item{B_LU_BRP}{(numeric) The crop type (conform BRP coding, preferable the most frequent crop on the field)}
+
+\item{B_LU_BBWP}{(numeric) The BBWP category used for allocation of measures to BBWP crop categories}
+
+\item{B_GWL_CLASS}{(character) The groundwater table class}
+
+\item{B_SC_WENR}{(character) The risk for subsoil compaction as derived from risk assessment study of Van den Akker (2006)}
+
+\item{B_HELP_WENR}{(character) The soil type abbreviation, derived from 1:50.000 soil map}
+
+\item{B_SLOPE}{(numeric) The slope of the field (degrees)}
+
+\item{A_CLAY_MI}{(numeric) The clay content of the soil (\%)}
+
+\item{A_SAND_MI}{(numeric) The sand content of the soil (\%)}
+
+\item{A_SILT_MI}{(numeric) The silt content of the soil (\%)}
+
+\item{A_SOM_LOI}{(numeric) The organic matter content of the soil (\%)}
+
+\item{A_N_RT}{(numeric) The organic nitrogen content of the soil (mg N / kg)}
+
+\item{A_FE_OX}{(numeric) The aluminium content of soil (mmol+ / kg)}
+
+\item{A_AL_OX}{(numeric) The iron content of soil (mmol+ / kg)}
+
+\item{A_P_CC}{(numeric) The plant available P content, measured via 0.01M CaCl2 extraction (mg / kg)}
+
+\item{A_P_AL}{(numeric) The plant extractable P content, measured via ammonium lactate extraction (mg / kg)}
+
+\item{A_P_WA}{(numeric) The P-content of the soil extracted with water}
+
+\item{A_P_SG}{(numeric) The P-saturation index (\%)}
+
+\item{B_GWP}{(boolean) is the field located in a groundwater protected area (options: TRUE or FALSE)}
+
+\item{B_AREA_DROUGHT}{(boolean) is the field located in an area with high risks for water deficiencies (options: TRUE or FALSE)}
+
+\item{B_CT_PSW}{(numeric) the critical target for required reduction in P loss from agriculture (kg P / ha) to reach targets of KRW}
+
+\item{B_CT_NSW}{(numeric) the critical target for required reduction in N loss from agriculture (kg N / ha) to reach targets of KRW}
+
+\item{B_CT_PSW_MAX}{(numeric) the max critical target for P reduction loss (kg P / ha)}
+
+\item{B_CT_NSW_MAX}{(numeric) the max critical target for N reduction loss (kg N / ha)}
+
+\item{D_WP}{(numeric) The fraction of the parcel that is surrounded by surface water}
+
+\item{D_RO_R}{(numeric) The risk that surface water runs off the parcel}
+
+\item{D_AREA}{(numeric) the area of the field (\ m2 or \ ha)}
+
+\item{M_DRAIN}{(boolean) is there tube drainage present in the field}
+
+\item{LSW}{(data.table) The surface water polygon for catchment or polder}
+
+\item{measures}{(data.table) the measures planned / done per fields}
+
+\item{sector}{(string) a vector with the farm type given the agricultural sector (options: options: 'diary', 'arable', 'tree_nursery', 'bulbs')}
+
+\item{output}{(string) a vector specifying the output type of the function. Options: scores, measures}
+}
+\description{
+Estimate the potential to contribute to agronomic and environmental challenges in a region for a farm and assess the impact of farm measures taken.
+A high BBWP score is indicative for the number of opportunities to improve soil quality and land use.
+}
diff --git a/man/ecoregeling.Rd b/man/ecoregeling.Rd
new file mode 100644
index 0000000..17e4ed4
--- /dev/null
+++ b/man/ecoregeling.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/er_main.R
+\name{ecoregeling}
+\alias{ecoregeling}
+\title{Calculate the Ecoregeling scores on field and farm level}
+\usage{
+ecoregeling(
+  B_SOILTYPE_AGR,
+  B_LU_BRP,
+  B_LU_BBWP,
+  B_GWL_CLASS,
+  B_SLOPE,
+  A_P_SG,
+  D_WP,
+  D_AREA,
+  M_DRAIN,
+  farmscore,
+  measures,
+  sector,
+  output = "scores"
+)
+}
+\arguments{
+\item{B_SOILTYPE_AGR}{(character) The type of soil}
+
+\item{B_LU_BRP}{(numeric) The crop type (conform BRP coding, preferable the most frequent crop on the field)}
+
+\item{B_LU_BBWP}{(numeric) The BBWP category used for allocation of measures to BBWP crop categories}
+
+\item{B_GWL_CLASS}{(character) The groundwater table class}
+
+\item{B_SLOPE}{(numeric) The slope of the field (degrees)}
+
+\item{A_P_SG}{(numeric) The P-saturation index (\%)}
+
+\item{D_WP}{(numeric) The fraction of the parcel that is surrounded by surface water}
+
+\item{D_AREA}{(numeric) the area of the field (\ m2 or \ ha)}
+
+\item{M_DRAIN}{(boolean) is there tube drainage present in the field}
+
+\item{farmscore}{(numeric) The desired total ER score on farm level}
+
+\item{measures}{(data.table) the measures planned / done per fields}
+
+\item{sector}{(string) a vector with the farm type given the agricultural sector (options: options: 'diary', 'arable', 'tree_nursery', 'bulbs')}
+
+\item{output}{(string) a vector specifying the output type of the function. Options: scores, measures}
+}
+\description{
+Estimate the potential to contribute to agronomic and environmental challenges in a region for a farm and assess the impact of farm measures taken.
+A high Ecoregeling score is indicative for the number of opportunities to improve soil quality, water quality, climate biodiversity and landscape.
+}
diff --git a/tests/testthat/test-bbwp.R b/tests/testthat/test-bbwp.R
new file mode 100644
index 0000000..bf83c72
--- /dev/null
+++ b/tests/testthat/test-bbwp.R
@@ -0,0 +1,232 @@
+require(testthat)
+
+  # # default input for testing
+  # B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei')
+  # B_GWL_CLASS = c('GtIII', 'GtI', 'GtV')
+  # B_SC_WENR = c(4, 2,2)
+  # B_HELP_WENR = c('AZW1AwF', 'AZW1AwF','AZW1AwF')
+  # A_P_SG = c(0.4, 0.8, 1)
+  # A_CLAY_MI = c(15, 5,8)
+  # A_SAND_MI = c(45, 65,15)
+  # A_SILT_MI = c(40, 30,45)
+  # A_SOM_LOI = c(5, 15,3) 
+  # A_N_RT = c(4200, 1000,3000)
+  # A_FE_OX = c(500, 500,800)
+  # A_AL_OX = c(150, 150,145)
+  # A_P_CC = c(5, 1,6)
+  # A_P_AL = c(65, 5,40)
+  # A_P_WA = c(52, 5,45)
+  # B_SLOPE = c(1.5,4,1.5)
+  # B_LU_BRP = c(265, 1932, 266)
+  # B_LU_BBWP = c(1,4,1)
+  # M_DRAIN = c(TRUE, FALSE, TRUE)
+  # D_WP = c(0, 0.5, 1)
+  # D_RO_R = c(0.5, 0,0.9)
+  # D_AREA = c(100,80,2.5)
+  # B_GWP = c(TRUE, FALSE, TRUE)
+  # B_AREA_DROUGHT = c(TRUE, FALSE, TRUE)
+  # B_CT_PSW = c(0, 25, 50)
+  # B_CT_NSW = c(0, 50, 100)
+  # B_CT_PSW_MAX = 0.5
+  # B_CT_NSW_MAX = 5.0
+  # measures = NULL
+  # sector = c('dairy', 'arable')
+  # output = 'scores'
+
+  LSW = data.table(sd_wp = rep(0.27, 3),
+                   mean_wp = 0.33,
+                   sd_n_rt = 371.4,
+                   sd_p_al = 10.6,
+                   sd_p_cc = 1.34,
+                   sd_p_sg = 7.92,
+                   sd_p_wa = 12.5,
+                   sd_ro_r = 0.305,
+                   sd_al_ox = 3.99,
+                   sd_fe_ox = 6.25,
+                   mean_n_rt = 1599.9,
+                   mean_p_al=  63.2,
+                   mean_p_cc =3.27,
+                   mean_p_sg = 59.5,
+                   mean_p_wa = 56.781,
+                   mean_ro_r = 0.471,
+                   mean_al_ox = 36.401,
+                   mean_fe_ox = 27.877,
+                   sd_clay_mi = 0.583,
+                   sd_sand_mi = 5.93,
+                   sd_silt_mi = 4.158,
+                   sd_som_loi = 0.799,
+                   mean_clay_mi = 2.00,
+                   mean_sand_mi = 83.96,
+                   mean_silt_mi = 12.81,
+                   mean_som_loi = 3.92
+  )
+# run example 1 without any measures taken
+test <- bbwp(B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei'),
+              B_GWL_CLASS = c('GtIII', 'GtI', 'GtV'),
+              B_SC_WENR = c(4, 2,2),
+              B_HELP_WENR = c('AZW1AwF', 'AZW1AwF','AZW1AwF'),
+              A_P_SG = c(0.4, 0.8, 1),
+              A_CLAY_MI = c(15, 5,8),
+              A_SAND_MI = c(45, 65,15),
+              A_SILT_MI = c(40, 30,45),
+              A_SOM_LOI = c(5, 15,3) ,
+              A_N_RT = c(4200, 1000,3000),
+              A_FE_OX = c(500, 500,800),
+              A_AL_OX = c(150, 150,145),
+              A_P_CC = c(5, 1,6),
+              A_P_AL = c(65, 5,40),
+              A_P_WA = c(52, 5,45),
+              B_SLOPE = c(1.5,4,1.5),
+              B_LU_BRP = c(265, 1932, 266),
+              B_LU_BBWP = c(1,4,1),
+              M_DRAIN = c(TRUE, FALSE, TRUE),
+              D_WP = c(0, 0.5, 1),
+              D_RO_R = c(0.5, 0,0.9),
+              D_AREA = c(100,80,2.5),
+              B_GWP = c(TRUE, FALSE, TRUE),
+              B_AREA_DROUGHT = c(TRUE, FALSE, TRUE),
+              B_CT_PSW = c(0, 25, 50),
+              B_CT_NSW = c(0, 50, 100),
+              B_CT_PSW_MAX = 0.5,
+              B_CT_NSW_MAX = 5.0,
+              measures = NULL,
+              sector = c('dairy', 'arable'),
+             output = 'scores',
+             LSW = LSW
+            )
+
+    # run tests on format and output values
+    test_that("check bbwp", {
+      expect_equal(
+        object = names(test),
+        expected = c('farm','fields'))
+    })
+    
+    test_that("check bbwp", {
+      expect_equal(
+        object = colnames(test$fields),
+        expected = c("d_opi_ngw", "d_opi_nsw", "d_opi_psw", "d_opi_nue", "d_opi_wb" , "d_opi_tot", "field_id"))
+    })
+    
+    test_that("check bbwp", {
+      expect_equal(
+        object = test$fields$d_opi_tot,
+        expected = c(35,17,8),
+        tolerance = 0.01)
+    })
+    
+    test_that("check bbwp", {
+      expect_equal(
+        object = as.numeric(unlist(test$farm)),
+        expected = c(27,42,54,54,24,92),
+        tolerance = 0.01)
+    })
+
+
+# run example 2 without any measures taken
+test <- bbwp(B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei'),
+             B_GWL_CLASS = c('GtIII', 'GtI', 'GtV'),
+             B_SC_WENR = c(4, 2,2),
+             B_HELP_WENR = c('AZW1AwF', 'AZW1AwF','AZW1AwF'),
+             A_P_SG = c(0.4, 0.8, 1),
+             A_CLAY_MI = c(15, 5,8),
+             A_SAND_MI = c(45, 65,15),
+             A_SILT_MI = c(40, 30,45),
+             A_SOM_LOI = c(5, 15,3) ,
+             A_N_RT = c(4200, 1000,3000),
+             A_FE_OX = c(500, 500,800),
+             A_AL_OX = c(150, 150,145),
+             A_P_CC = c(5, 1,6),
+             A_P_AL = c(65, 5,40),
+             A_P_WA = c(52, 5,45),
+             B_SLOPE = c(1.5,4,1.5),
+             B_LU_BRP = c(265, 1932, 266),
+             B_LU_BBWP = c(1,4,1),
+             M_DRAIN = c(TRUE, FALSE, TRUE),
+             D_WP = c(0, 0.5, 1),
+             D_RO_R = c(0.5, 0,0.9),
+             D_AREA = c(100,80,2.5),
+             B_GWP = c(TRUE, FALSE, TRUE),
+             B_AREA_DROUGHT = c(TRUE, FALSE, TRUE),
+             B_CT_PSW = c(0, 25, 50),
+             B_CT_NSW = c(0, 50, 100),
+             B_CT_PSW_MAX = 0.5,
+             B_CT_NSW_MAX = 5.0,
+             measures = NULL,
+             sector = c('dairy', 'arable'),
+             output = 'measures',
+             LSW = LSW
+)
+
+    # run tests on format and output values
+    test_that("check bbwp", {
+      expect_equal(
+        object = names(test$measures[[1]]),
+        expected = c("top_tot","top_ngw","top_nsw","top_psw","top_wb", "top_nue"))
+    })
+    
+    test_that("check bbwp", {
+      expect_equal(
+        object = test$measures[[1]]$top_tot,
+        expected = c("G17","G16", "G18", "G19", "G88"))
+    })
+
+
+
+# get internal table with measures
+dt.measures <- as.data.table(BBWPC::bbwp_measures)
+dt.measures <- dt.measures[!is.na(effect_psw)]
+
+# make measurement list for 2 of the 4 fields
+measures <- rbind(data.table(id = 1, dt.measures[c(2,5,18,28,32,3,38,43,62)]),
+                  data.table(id = 3, dt.measures[c(7,21,30,46,5)]))
+
+# run example 3 without any measures taken
+test <- bbwp(B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei'),
+             B_GWL_CLASS = c('GtIII', 'GtI', 'GtV'),
+             B_SC_WENR = c(4, 2,2),
+             B_HELP_WENR = c('AZW1AwF', 'AZW1AwF','AZW1AwF'),
+             A_P_SG = c(0.4, 0.8, 1),
+             A_CLAY_MI = c(15, 5,8),
+             A_SAND_MI = c(45, 65,15),
+             A_SILT_MI = c(40, 30,45),
+             A_SOM_LOI = c(5, 15,3) ,
+             A_N_RT = c(4200, 1000,3000),
+             A_FE_OX = c(500, 500,800),
+             A_AL_OX = c(150, 150,145),
+             A_P_CC = c(5, 1,6),
+             A_P_AL = c(65, 5,40),
+             A_P_WA = c(52, 5,45),
+             B_SLOPE = c(1.5,4,1.5),
+             B_LU_BRP = c(265, 1932, 266),
+             B_LU_BBWP = c(1,4,1),
+             M_DRAIN = c(TRUE, FALSE, TRUE),
+             D_WP = c(0, 0.5, 1),
+             D_RO_R = c(0.5, 0,0.9),
+             D_AREA = c(100,80,2.5),
+             B_GWP = c(TRUE, FALSE, TRUE),
+             B_AREA_DROUGHT = c(TRUE, FALSE, TRUE),
+             B_CT_PSW = c(0, 25, 50),
+             B_CT_NSW = c(0, 50, 100),
+             B_CT_PSW_MAX = 0.5,
+             B_CT_NSW_MAX = 5.0,
+             measures = measures,
+             sector = c('dairy', 'arable'),
+             output = 'scores',
+             LSW = LSW
+)
+
+  # run tests on format and output values
+  test_that("check bbwp", {
+    expect_equal(
+      object = test$fields$d_opi_tot,
+      expected = c(100,17,8),
+      tolerance = 0.01)
+  })
+  
+  test_that("check bbwp", {
+    expect_equal(
+      object = as.numeric(unlist(test$farm)),
+      expected = c(62,92,55,55,72,96),
+      tolerance = 0.01)
+  })
diff --git a/tests/testthat/test-er.R b/tests/testthat/test-er.R
new file mode 100644
index 0000000..3ee820b
--- /dev/null
+++ b/tests/testthat/test-er.R
@@ -0,0 +1,130 @@
+require(testthat)
+
+  # # default input for testing
+  # B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei')
+  # B_GWL_CLASS = c('GtIII', 'GtI', 'GtV')
+  # A_P_SG = c(0.4, 0.8, 1)
+  # B_SLOPE = c(1.5,4,1.5)
+  # B_LU_BRP = c(265, 1932, 266)
+  # B_LU_BBWP = c(1,4,1)
+  # M_DRAIN = c(TRUE, FALSE, TRUE)
+  # D_WP = c(0, 0.5, 1)
+  # D_AREA = c(100,80,2.5)
+  # measures = NULL
+  # farmscore = 100
+  # sector = c('dairy', 'arable')
+  # output = 'measures'
+
+# run example 1 without any measures taken
+test <- ecoregeling(B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei'),
+                    B_GWL_CLASS = c('GtIII', 'GtI', 'GtV'),
+                    A_P_SG = c(0.4, 0.8, 1),
+                    B_SLOPE = c(1.5,4,1.5),
+                    B_LU_BRP = c(265, 1932, 266),
+                    B_LU_BBWP = c(1,4,1),
+                    M_DRAIN = c(TRUE, FALSE, TRUE),
+                    D_WP = c(0, 0.5, 1),
+                    D_AREA = c(100,80,2.5),
+                    farmscore = 100,
+                    measures = NULL,
+                    sector = c('dairy', 'arable'),
+                    output = 'scores'
+                   )
+
+  # run tests on format and output values
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = names(test),
+      expected = c('farm','fields'))
+  })
+  
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = colnames(test$fields),
+      expected = c("field_id","d_opi_soil","d_opi_water","d_opi_climate","d_opi_bio","d_opi_landscape","d_opi_tot"))
+  })
+  
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = test$fields$d_opi_tot,
+      expected = c(6,6,6),
+      tolerance = 0.01)
+  })
+  
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = as.numeric(unlist(test$farm)),
+      expected = c(6,9,6,11,6,0),
+      tolerance = 0.01)
+  })
+
+
+# get internal table with measures
+dt.measures <- as.data.table(BBWPC::bbwp_measures)
+dt.measures <- dt.measures[!is.na(eco_id)]
+
+# make measurement list for 2 of the 4 fields
+measures <- rbind(data.table(id = 1, dt.measures[c(2,5,18,28,32,3,38,43,62)]),
+                  data.table(id = 3, dt.measures[c(7,21,30,46,5)]))
+
+
+# run example 2 with any measures taken
+test <- ecoregeling(B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei'),
+                    B_GWL_CLASS = c('GtIII', 'GtI', 'GtV'),
+                    A_P_SG = c(0.4, 0.8, 1),
+                    B_SLOPE = c(1.5,4,1.5),
+                    B_LU_BRP = c(265, 1932, 266),
+                    B_LU_BBWP = c(1,4,1),
+                    M_DRAIN = c(TRUE, FALSE, TRUE),
+                    D_WP = c(0, 0.5, 1),
+                    D_AREA = c(100,80,2.5),
+                    farmscore = 100,
+                    measures = measures,
+                    sector = c('dairy', 'arable'),
+                    output = 'scores'
+)
+
+  # run tests on format and output values
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = test$fields$d_opi_tot,
+      expected = c(42,6,42),
+      tolerance = 0.01)
+  })
+  
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = as.numeric(unlist(test$farm)),
+      expected = c(26,34,30,61,16,15),
+      tolerance = 0.01)
+  })
+
+# run example 3 with any measures taken
+  test <- ecoregeling(B_SOILTYPE_AGR = c('dekzand', 'loess', 'rivierklei'),
+                      B_GWL_CLASS = c('GtIII', 'GtI', 'GtV'),
+                      A_P_SG = c(0.4, 0.8, 1),
+                      B_SLOPE = c(1.5,4,1.5),
+                      B_LU_BRP = c(265, 1932, 266),
+                      B_LU_BBWP = c(1,4,1),
+                      M_DRAIN = c(TRUE, FALSE, TRUE),
+                      D_WP = c(0, 0.5, 1),
+                      D_AREA = c(100,80,2.5),
+                      farmscore = 100,
+                      measures = measures,
+                      sector = c('dairy', 'arable'),
+                      output = 'measures'
+  )
+  
+  # run tests on format and output values
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = names(test$measures[[1]]),
+      expected = c("top_tot","top_soil","top_water","top_climate","top_biodiversity", "top_landscape"))
+  })
+  
+  test_that("check ecoregeling", {
+    expect_equal(
+      object = test$measures[[1]]$top_tot,
+      expected = c('B156','B133','G50'))
+  })
+  
\ No newline at end of file