Merge branch 'calibtests' of github.com:vartika271987/magpie into f_f…

…secCHA

 Inclusion of AQUASTAT calibration factors for yields magpiemodel#457

CHANGELOG.md

@@ -9,6 +9,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### changed
+- **scripts** fix in start_functions for the calibration setting `ifneeded`
+- **config** best_calib set to FALSE in default
+- **42_water_demand** account for multiple cropping in water requirements
 - **config** new switches `s36_minimum_wage`, `s36_scale_productivity_with_wage`, and `s38_fix_capital_need`
 - **38_factor_costs** included labor cost scaling in case of wage scenario
 - **70_livestock** included labor cost scaling in case of wage scenario
@@ -50,6 +53,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - **scripts** added output scripts for FSEC FSDP runs
 - **15_food** added new realization with country level exogenous diets, product-specific intake estimates, new scenarios for exogenous BMI and decomposition switches for EAT Lancet diets. Simplified code and improved iteration procedure.
 - **57_maccs** added new Marginal Abatement Cost Curve (MACCs) data set from PBL (PBL2022)
+- **14_yields** added input file containing AQUASTAT yield calibration factors and switch `s14_calib_ir2rf` in default.cfg to activate this yield calibration
 
 ### removed
 - **38_factor_costs** removed `mixed_reg_feb17` realization

config/default.cfg

@@ -26,20 +26,11 @@ cfg$input <- c(regional    = "rev4.77_h12_magpie.tgz",
                cellular    = "rev4.77_h12_fd712c0b_cellularmagpie_c200_MRI-ESM2-0-ssp370_lpjml-8e6c5eb1.tgz",
                validation  = "rev4.77_h12_validation.tgz",
                additional  = "additional_data_rev4.30.tgz",
-               calibration = "calibration_H12_per_ton_fao_may22_28May22.tgz")
+               calibration = "calibration_H12+ir2rf_05Oct22.tgz")
 
-# Options for calibration tgz files for different factor costs realizations, with default settings and rev4.73:
-# * (mixed_reg_feb17, global fac req)              "calibration_H12_mixed_reg_feb17_28May22.tgz"
-# * (mixed_reg_feb17, regional fac req)            "calibration_H12_mixed_reg_feb17_reg_13Jun22.tgz"
-# * (per_ton_fao_may22, global fac req)            "calibration_H12_per_ton_fao_may22_28May22.tgz"
-# * (per_ton_fao_may22, regional fac req)          "calibration_H12_per_ton_fao_may22_reg_13Jun22.tgz"
-# * (sticky_feb18, global fac req)                 "calibration_H12_sticky_feb18_28May22.tgz"
-# * (sticky_feb18, regional fac req)               "calibration_H12_sticky_feb18_reg_13Jun22.tgz"
-# * (sticky_labor, global fac req)                 "calibration_H12_sticky_feb18_28May22.tgz"
-# * (sticky_labor, regional fac req)               "calibration_H12_sticky_feb18_reg_13Jun22.tgz"
-
-# Calibration factors for 31_past (pasture management module)
-# * (grasslands_mar22)           "calibration_H12_grassland_mar22.tgz"
+# NOTE: It is recommended to recalibrate the model when changing cellular input data
+#       as well as for any other setting that would affect initial values in the model,
+#       e.g. changes in costs structure, NPI policies, etc.
 
 #a list of repositories (please pay attention to the list format!) in which the
 #files should be searched for. Files will be searched in all repositories until
@@ -54,7 +45,8 @@ cfg$input <- c(regional    = "rev4.77_h12_magpie.tgz",
 #below it will get merged into cfg$repositories
 
 cfg$repositories <- append(list("https://rse.pik-potsdam.de/data/magpie/public"=NULL),
-                            getOption("magpie_repos"))
+                               getOption("magpie_repos"))
+
 
 
 # Should input data be downloaded from source even if cfg$input did not change?
@@ -82,7 +74,7 @@ cfg$crop_calib_max <- 1.5            # def= 1.5
 # Selection type of calibration factors.
 # If FALSE, calibration factors from the last iteration are used.
 # If TRUE, calibration factors from the iteration with the lowest standard deviation are used.
-cfg$best_calib <- TRUE
+cfg$best_calib <- FALSE   # def = FALSE
 
 # Settings for land conversion cost calibration (cropland)
 # The calibration routine derives regional calibration factors for
@@ -308,6 +300,11 @@ cfg$gms$s14_yld_past_switch <- 0.25           # def = 0.25
 # *          0 (pure relative calibration)
 cfg$gms$s14_limit_calib <- 1       # def = 1
 
+# * Switch on scaling of irrigated yields to AQUASTAT irrigated-to-rainfed yield
+# * ratio on regional scale
+# NOTE: It is recommended to recalibrate the model when changing this setting!
+cfg$gms$s14_calib_ir2rf <- 1       # def = 1
+
 # ***---------------------    15_food    ---------------------------------------
 # * (anthropometrics_jan18): estimates food using scenario dependent regression
 # *                          and demography drivers
@@ -597,6 +594,7 @@ cfg$gms$ageclass    <- "feb21"               # def = feb21
 # * (endo_apr21): Hard rotational constraints. Dynamic cropland and detailed cropland availability data at grid cell level.
 # * (rotation_apr22): hard rotational constraints and fallow constraints with the option of different future scenarios. Dynamic cropland and detailed cropland availability data at grid cell level.
 # * (penalty_apr222): rotational and fallow constraints are incentivized via penalty payments. Dynamic cropland and detailed cropland availability data at grid cell level.
+# NOTE: It is recommended to recalibrate the model when changing this setting!
 cfg$gms$crop    <- "endo_apr21"               # def = endo_apr21
 # * (c30_bioen_type): switch for type of bioenergy crops; options: begr, betr, all
 cfg$gms$c30_bioen_type <- "all"               # def = "all"
@@ -651,6 +649,7 @@ cfg$gms$policy_countries30  <- all_iso_countries
 # * (static):     static pasture
 # * (endo_jun13): dynamic pasture
 # * (grasslands_apr22): Grassland management separating managed pastures from rangelands
+# NOTE: It is recommended to recalibrate the model when changing this setting!
 cfg$gms$past <- "endo_jun13"               # def = endo_jun13
 
 # * Factor requirements (USD04 per ton DM)
@@ -862,12 +861,12 @@ cfg$gms$employment <- "exo_may22"        # default = "exo_may22"
 # * global minimum wage in USDMER05 per hour that needs to be reached in all countries by 2050
 cfg$gms$s36_minimum_wage <- 0            # default = 0 (no minimum wage)
 
-# * A scenario that increases wages can either be fully related to productivity increase 
+# * A scenario that increases wages can either be fully related to productivity increase
 # * (leading to lower employment for the same total labor costs), or keep productivity
-# * constant (leading to the same employment for higher total labor costs), or to a 
-# * mixture between productivity increase an total labor cost increase. 
-# * The scalar `s36_scale_productivity_with_wage` describes how high the labor productivity 
-# * gain should be relative to the increase in hourly labor costs. If set to 0, 
+# * constant (leading to the same employment for higher total labor costs), or to a
+# * mixture between productivity increase an total labor cost increase.
+# * The scalar `s36_scale_productivity_with_wage` describes how high the labor productivity
+# * gain should be relative to the increase in hourly labor costs. If set to 0,
 # * productivity stays constant, if set to 1 productivity scales proportional to hourly
 # * labor costs. Other positive values lead to a mixture of productivity gain and increase
 # * in total costs.
@@ -899,16 +898,19 @@ cfg$gms$c37_labor_uncertainty <- "ensmean"	# default = "ensmean"
 # * (per_ton_fao_may22)    factor costs fixed per ton
 # * (sticky_feb18)         factor costs including investments in capital
 # * (sticky_labor)         based on sticky_feb18 + labor productivity factor included
+# NOTE: It is recommended to recalibrate the model when changing this setting!
+# NOTE: It is recommended to use best_calib only for sticky factor cost realizations!
 cfg$gms$factor_costs <- "per_ton_fao_may22"        # default = per_ton_fao_may22
 
 # * Regional (reg) or global (glo) factor requirements (applicable for per_ton_fao_may22,
 # * sticky_feb18, sticky_labor). The regional version keeps factor requirements constant
 # * after 2010, the global version uses values from 2005 for all years. Typically, the
 # * same version (glo or reg) should be chosen for c70_fac_req_regr for consistency.
+# NOTE: It is recommended to recalibrate the model when changing this setting!
 cfg$gms$c38_fac_req <- "glo"        # default "glo"
 
 # * Only relevant for sticky_labor implementation: scalar to determine until which year
-# * the capital need (per unit of output) should be fixed to start value (i.e. not 
+# * the capital need (per unit of output) should be fixed to start value (i.e. not
 # * allowing for subsititution between labor and capital).
 
 cfg$gms$s38_fix_capital_need <- 2020    # default 2020
@@ -1567,15 +1569,15 @@ cfg$gms$c60_bioenergy_subsidy <- 300              # def = 300
 cfg$gms$material <- "exo_flexreg_apr16"
 
 # * Biomass demand for bioplastics is based on a logistic curve projecting
-# * bioplastic demand, which matches the historic demand in 2020, and is 
+# * bioplastic demand, which matches the historic demand in 2020, and is
 # * defined by the maximum demand for bioplastics s62_max_dem_bioplastics
-# * (in mio. tonnes) and the midpoint s62_midpoint_dem_bioplastics (i.e. 
+# * (in mio. tonnes) and the midpoint s62_midpoint_dem_bioplastics (i.e.
 # * where bioplastic demand is half of the maximum demand).
 # * If maximum demand is 0, biomass demand for bioplastic production is not included.
-# * Projected total plastic use in the "Global Ambiton" scneario of the OECD is 
-# * about 600 mio. tonnes in 2050 -> E.g. 200 mio. tonnes in 2050 could be a 
-# * reasonable (but ambitious) bioplastic scenario. With midpoint 2050 this 
-# * would mean s62_max_dem_bioplastic = 400.  
+# * Projected total plastic use in the "Global Ambiton" scneario of the OECD is
+# * about 600 mio. tonnes in 2050 -> E.g. 200 mio. tonnes in 2050 could be a
+# * reasonable (but ambitious) bioplastic scenario. With midpoint 2050 this
+# * would mean s62_max_dem_bioplastic = 400.
 cfg$gms$s62_max_dem_bioplastic      <- 0                 # def = 0
 cfg$gms$s62_midpoint_dem_bioplastic <- 2050              # def = 2050
 

literature.bib

@@ -1037,6 +1037,14 @@ @article{siebert_FAO_2007
 	year = {2007},
 }
 
+@book{fao_aquastat_2016,
+    address = {Rome},
+    title = {{AQUASTAT} main database},
+    publisher = {Food and Agriculture Organization of the United Nations (FAO)},
+    author = {{FAO}},
+    year = {2016},
+}
+
 @techreport{worldbank_irrigation_1995,
 	title = {World Bank and Irrigation},
 	institution = {World Bank},

modules/14_yields/managementcalib_aug19/declarations.gms

@@ -19,6 +19,10 @@ parameters
  p14_growing_stock_initial(j,ac,forest_land,forest_type) Initial Forest growing stock (tDM per ha per yr)
  pm_timber_yield_initial(j,ac,forest_land)               Initial Forest yield (tDM per ha per yr)
  pm_yields_semi_calib(j,kve,w)                           Potential yields calibrated to FAO regional levels (tDM per ha per yr)
+ i14_calib_yields_hist(i,w)                              Calibrated yields average over region and crop type at the historical reference year (tDM per ha per yr)
+ i14_calib_yields_ratio(i)                               Irrigated to rainfed yield ratio for calibrated yields (1)
+ i14_target_ratio(i)                                     Target irrigated to rainfed ratio as upper bound (1)
+ i14_modeled_yields_hist2(i,knbe14)                      Calibrated yields average over region and water supply type at the historical reference year (tDM per ha per yr)
  ;
 
 positive variables

modules/14_yields/managementcalib_aug19/input.gms

@@ -12,6 +12,8 @@ $setglobal c14_yields_scenario  cc
 
 scalar s14_limit_calib   Relative managament calibration switch (1=limited 0=pure relative) / 1 /;
 
+scalar s14_calib_ir2rf   Switch to calibrate rainfed to irrigated yield ratios (1=calib 0=not calib) / 0 /;
+
 scalars
   s14_yld_past_switch  Spillover parameter for translating technological change in the crop sector into pasture yield increases  (1)     / 0.25 /
 ;
@@ -54,6 +56,14 @@ $offdelim
 ;
 m_fillmissingyears(f14_fao_yields_hist,"i,kcr");
 
+parameter f14_ir2rf_ratio(i) AQUASTAT ratio of irrigated to rainfed yields per region (1)
+/
+$ondelim
+$include "./modules/14_yields/managementcalib_aug19/input/f14_ir2rf_ratio.cs4"
+$offdelim
+/
+;
+
 table f14_ipcc_bce(clcl,forest_type) IPCC Biomass Conversion and Expansion factors (1)
 $ondelim
 $include "./modules/14_yields/input/f14_ipcc_bce.cs3"

modules/14_yields/managementcalib_aug19/input/files

@@ -1,4 +1,5 @@
 * list of files that are required here
 f14_region_yields.cs3
+f14_ir2rf_ratio.cs4
 f14_ipcc_bce.cs3
 f14_aboveground_fraction.csv

modules/14_yields/managementcalib_aug19/preloop.gms

@@ -92,7 +92,6 @@ loop(t,
      );
 );
 
-
 *' The calibrated cellular yield 'i14_yields_calib' is calculated for each time step depending
 *' on the constant values 'i14_modeled_yields_hist', 'i14_fao_yields_hist', 'i14_lambda_yields'
 *' and the uncalibrated, cellular yield 'f14_yields' following the idea of eq. (9) in @Heinke.2013:
@@ -108,6 +107,37 @@ i14_yields_calib(t,j,knbe14,w)    = i14_managementcalib(t,j,knbe14,w) * f14_yiel
 pm_yields_semi_calib(j,knbe14,w)  = i14_yields_calib("y1995",j,knbe14,w);
 
 *' Note that the calculation is split into two parts for better readability.
+
+*' Irrigated yields can be optionally calibrated to meet the country-level
+*' ratio between irrigated and rainfed yields reported by Aquastat
+*' by activating the switch `s14_calib_ir2rf`.
+if ((s14_calib_ir2rf = 1),
+
+* Weighted yields
+  i14_calib_yields_hist(i,w)
+     = sum((cell(i,j), knbe14), fm_croparea("y1995",j,"irrigated",knbe14) * i14_yields_calib("y1995",j,knbe14,w)) /
+       sum((cell(i,j), knbe14), fm_croparea("y1995",j,"irrigated",knbe14));
+
+* Use irrigated-rainfed ratio of Aquastat if larger than our calculated ratio
+  i14_calib_yields_ratio(i) = i14_calib_yields_hist(i,"irrigated") / i14_calib_yields_hist(i,"rainfed");
+  i14_target_ratio(i) = max(i14_calib_yields_ratio(i), f14_ir2rf_ratio(i));
+  i14_yields_calib(t,j,knbe14,"irrigated") = sum((cell(i,j)), i14_target_ratio(i) / i14_calib_yields_ratio(i)) *
+                                               i14_yields_calib(t,j,knbe14,"irrigated");
+
+* Calibrate newly calibrated yields to FAO yields
+  i14_modeled_yields_hist2(i,knbe14)
+   = (sum((cell(i,j),w), fm_croparea("y1995",j,w,knbe14) * i14_yields_calib("y1995",j,knbe14,w)) /
+      sum((cell(i,j),w), fm_croparea("y1995",j,w,knbe14)))$(sum((cell(i,j),w), fm_croparea("y1995",j,w,knbe14))>0)
+   + (sum((cell(i,j),w), i14_croparea_total("y1995",w,j) * f14_yields("y1995",j,knbe14,w)) /
+      sum((cell(i,j),w), i14_croparea_total("y1995",w,j)))$(sum((cell(i,j),w), fm_croparea("y1995",j,w,knbe14))=0);
+
+  i14_yields_calib(t,j,knbe14,w) = sum((cell(i,j)), i14_fao_yields_hist("y1995",i,knbe14) /
+                                                      i14_modeled_yields_hist2(i,knbe14)) *
+                                   i14_yields_calib(t,j,knbe14,w);
+
+  pm_yields_semi_calib(j,knbe14,w)  = i14_yields_calib("y1995",j,knbe14,w);
+);
+
 *' @stop
 
 

modules/14_yields/managementcalib_aug19/realization.gms

@@ -12,6 +12,8 @@
 *' yields achieved under the highest currently observed value of the $\tau$ factor
 *' representing agricultural land-use intensity. Secondly, pasture yields are calculated
 *' based on pasture demand to account for in- and extensification of managed grasslands.
+*' Thirdly, irrigated yields are scaled to meet the irrigated-to-rainfed yield
+*' ratio as provided by AQUASTAT [@fao_aquastat_2016].
 *' Finally, crop yields are calibrated to FAO [@FAOSTAT] regional yield levels of the
 *' initial time step. An additional feature of this realization is to allow crop yields
 *' technological change from the precedent times step to spillover to pasture areas. This

modules/14_yields/module.gms

@@ -17,6 +17,8 @@
 *' [@FAOSTAT]. For the simulation of the temporal development of agricultural
 *' yields, the module receives information about the agricultural land use
 *' intensity represented by the $\tau$ factor coming from the module [13_tc].
+*' Irrigated yields can optionally be calibrated to meet irrigated-rainfed
+*' country-level yield ratios as reported by Aquastat [@fao_aquastat_2016].
 *'
 *' The module returns yields for all crops and for pasture, which is then used
 *' by the modules [30_crop] and [31_past].

modules/18_residues/flexreg_apr16/equations.gms

@@ -14,7 +14,7 @@
  q18_prod_res_ag_reg(i2,kcr,attributes) ..
                  vm_res_biomass_ag(i2,kcr,attributes)
                  =e=
-                 (sum((cell(i2,j2),w), vm_area(j2,kcr,w)) * sum(ct,f18_multicropping(ct,i2)) * f18_cgf("intercept",kcr)
+                 (sum((cell(i2,j2),w), vm_area(j2,kcr,w)) * sum(ct,fm_multicropping(ct,i2)) * f18_cgf("intercept",kcr)
                  + vm_prod_reg(i2,kcr)*f18_cgf("slope",kcr))
                  *  f18_attributes_residue_ag(attributes,kcr);
 

modules/18_residues/flexreg_apr16/input.gms

@@ -8,7 +8,7 @@
 $setglobal c18_burn_scen  phaseout
 *   options:    phaseout,constant
 
-table f18_multicropping(t_all,i) Multicropping indicator as ratio of area harvested by physical area (1)
+table fm_multicropping(t_all,i) Multicropping indicator as ratio of area harvested by physical area (1)
 $ondelim
 $include "./modules/18_residues/input/f18_multicropping.csv"
 $offdelim;

modules/18_residues/off/input.gms

@@ -0,0 +1,13 @@
+*** |  (C) 2008-2021 Potsdam Institute for Climate Impact Research (PIK)
+*** |  authors, and contributors see CITATION.cff file. This file is part
+*** |  of MAgPIE and licensed under AGPL-3.0-or-later. Under Section 7 of
+*** |  AGPL-3.0, you are granted additional permissions described in the
+*** |  MAgPIE License Exception, version 1.0 (see LICENSE file).
+*** |  Contact: magpie@pik-potsdam.de
+
+table fm_multicropping(t_all,i) Multicropping indicator as ratio of area harvested by physical area (1)
+$ondelim
+$include "./modules/18_residues/input/f18_multicropping.csv"
+$offdelim;
+
+*** EOF input.gms ***

modules/36_employment/exo_may22/declarations.gms

@@ -15,7 +15,7 @@ positive variables
 ;
 
 parameters
- p36_hourly_costs_iso(t,iso, wage_scen) Hourly labor costs in agriculture on iso level before and after including wage scenario (USDMER05 per hour)
+ p36_hourly_costs_iso(t_all,iso, wage_scen) Hourly labor costs in agriculture on iso level before and after including wage scenario (USDMER05 per hour)
  p36_hourly_costs_increase(iso)         Difference between minimum hourly labor costs and actual hourly labor costs in 2050 (USDMER05 per hour)
  pm_hourly_costs(t,i, wage_scen)        Hourly labor costs in agriculture on regional level before and after including wage scenario (USDMER05 per hour)
  pm_productivity_gain_from_wages(t,i)   Multiplicative factor describing productivity gain related to higher wages (1)