Lpjml2magpie

config/default.cfg

@@ -22,11 +22,11 @@ cfg$model <- "main.gms"   # def = "main.gms"
 #### input settings ####
 
 # which input data sets should be used?
-cfg$input <- c(regional    = "rev4.132_h12_magpie.tgz",
-               cellular    = "rev4.132_h12_1b5c3817_cellularmagpie_c200_MRI-ESM2-0-ssp245_lpjml-8e6c5eb1.tgz",
-               validation  = "rev4.132_h12_92e02314_validation.tgz",
-               additional  = "additional_data_rev4.67.tgz",
-               calibration = "calibration_H12_FAO_01Apr26.tgz")
+cfg$input <- c(regional    = "rev4.131l2m_v5-10-0m2_may2026+BEdata2_h12_magpie.tgz",
+               cellular    = "rev4.131l2m_v5-10-0m2_may2026+BEdata2_h12_00e02813_cellularmagpie_c200_MRI-ESM2-0-ssp370_lpjml-a0c283bd.tgz",
+               validation  = "rev4.131l2m_v5-10-0m2_may2026+BEdata2_h12_92e02314_validation.tgz",
+               additional  = "additional_data_rev4.65.tgz",
+               calibration = "calibration_H12_FAO_01Apr26.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,
@@ -287,9 +287,10 @@ cfg$gms$s12_hist_interest_hic_noselect <- "0.04"    # def = 0.04
 # ***---------------------    13_tc    -----------------------------------------
 # * (endo_jan22): endogenous technological change with full cost accounting and
 # *               stepwise updated crop and managed pastures area information
-# * (exo): exogenous technological change (removes non-linearities from the model);
-# *     requires an existing model run with endo tc for generating the input file
-# *     f13_tau_scenario.csv
+# *               including account of growing period adaptation in yields and tau
+# * (exo):        exogenous technological change (removes non-linearities from the model);
+# *               requires an existing model run with endo tc for generating the input file
+# *               f13_tau_scenario.csv
 cfg$gms$tc <- "endo_jan22"              # def = endo_jan22
 
 # * tc cost scenario crops: low, medium or high
@@ -348,20 +349,27 @@ cfg$gms$s13_croparea_consv_target <- 2030       # def = 2030
 
 
 # ***---------------------    14_yield    --------------------------------------
-# * (managementcalib_aug19): calibrate potential LPJmL-yields to FAO regional numbers,
-# *                          pasture yields increase based on exogenous demand-side proxy
-# *                          for growth rate of cattle stocks
-# * (dynRegPastrTau_apr26):  as managementcalib_aug19, but the pasture yield spillover
-# *                          from crop-sector TC is a regional, time-varying parameter
-# *                          f14_yld_past_switch(t_all,i) read from a CSV input file
-cfg$gms$yields <- "managementcalib_aug19"           # def = managementcalib_aug19
+# * (gsadapt_dynPastrTau_jul26): calibrate potential LPJmL-yields to FAO regional numbers,
+# *                              pasture yields increase based on exogenous demand-side proxy
+# *                              for growth rate of cattle stocks yields. Optionally, include 
+# *                              future growing period adaptation based on LPJmL simulations.
+# *                              Pasture yield spillover from crop-sector TC can be controlled 
+# *                              via regional, time-varying parameter (see `s14_past_spillover_mode`)
+cfg$gms$yields <- "gsadapt_dynPastrTau_jul26"          # def = gsadapt_dynPastrTau_jul26
 
 # * yield scenario
 # *   options:   cc        (climate change)
 # *             nocc       (no climate change)
 # *             nocc_hist  (no climate change after year defined by sm_fix_cc)
 cfg$gms$c14_yields_scenario  <- "cc"   # def = "cc"
 
+# * Switch for pasture spillover mode in gsadapt_dynPastrTau_jul26 realization
+# * (0): static scalar mode - uses s14_yld_past_switch for all regions and timesteps
+# * (1): regional dynamic mode - uses f14_yld_past_switch(t_all,i) per region and timestep
+# * This switch allows switching between a uniform spillover effect across all regions
+# * and region-specific, time-varying spillover effects read from input data.
+cfg$gms$s14_past_spillover_mode <- 0           # def = 1 (regional dynamic mode)
+
 # * switch determing the effectivity of translating crop tc into pasture yield
 # * increase. Value has to be in the range of 0 (no pasture yield growth)
 # * and 1 (pasture yields increase linearily with tau). Only used in the realizations:
@@ -399,12 +407,24 @@ cfg$gms$s14_degradation <- 0      # def = 0
 # For other realizations, it is recommended not to use this yield calibration.
 cfg$gms$s14_use_yield_calib <- 0    # def = 0
 
+# Growing period adaption for yield under climate change
+# Options: 1 (growing period adaption is included in yield projections), 
+#          0 (growing period adaption is not included in yield projections; i.e. constant growing periods are assumed)
+cfg$gms$s14_use_gsadapt <- 1 # def = 0
+
+# Accounting for growing period adaption (gsadapt) in tau with consequences for tc costs
+# Only relevant if s14_use_gsadapt is activated (1).
+# Options: 1 (gsadapt is considered in tau, which leads to higher tc costs), 
+#          0 (gsadapt is not considered in tau, which implies that tau comes free of charge)
+cfg$gms$s14_gsadapt2tau <- 1 # def = 0
+
 # Minimum growing stock for timber harvest in natural vegetation (tDM per ha).
 # Age classes below this threshold cannot be harvested.
 # 5 tDM/ha ~ 10 m3/ha, i.e. very sparse woodland where commercial timber
 # harvest would be unrealistic (expert guess).
 cfg$gms$s14_minimum_growing_stock <- 5   # def = 5
 
+
 # ***---------------------    15_food    ---------------------------------------
 # * (anthropometrics_jan18): estimates food using scenario dependent regression
 # *                          and demography drivers

core/sets.gms

@@ -55,32 +55,32 @@ sets
       VEN, VGB, VIR, VNM, VUT, WLF, WSM, YEM, ZAF, ZMB, ZWE /
 
   j number of LPJ cells
-    / CAZ_1*CAZ_5,
-      CHA_6*CHA_24,
-      EUR_25*EUR_36,
-      IND_37*IND_48,
-      JPN_49*JPN_55,
-      LAM_56*LAM_88,
-      MEA_89*MEA_113,
-      NEU_114*NEU_121,
-      OAS_122*OAS_137,
-      REF_138*REF_149,
-      SSA_150*SSA_182,
-      USA_183*USA_200 /
+    / CAZ_1*CAZ_14,
+      CHA_15*CHA_31,
+      EUR_32*EUR_43,
+      IND_44*IND_57,
+      JPN_58*JPN_65,
+      LAM_66*LAM_99,
+      MEA_100*MEA_117,
+      NEU_118*NEU_126,
+      OAS_127*OAS_153,
+      REF_154*REF_164,
+      SSA_165*SSA_186,
+      USA_187*USA_200 /
 
   cell(i,j) number of LPJ cells per region i
-    / CAZ . (CAZ_1*CAZ_5)
-      CHA . (CHA_6*CHA_24)
-      EUR . (EUR_25*EUR_36)
-      IND . (IND_37*IND_48)
-      JPN . (JPN_49*JPN_55)
-      LAM . (LAM_56*LAM_88)
-      MEA . (MEA_89*MEA_113)
-      NEU . (NEU_114*NEU_121)
-      OAS . (OAS_122*OAS_137)
-      REF . (REF_138*REF_149)
-      SSA . (SSA_150*SSA_182)
-      USA . (USA_183*USA_200) /
+    / CAZ . (CAZ_1*CAZ_14)
+      CHA . (CHA_15*CHA_31)
+      EUR . (EUR_32*EUR_43)
+      IND . (IND_44*IND_57)
+      JPN . (JPN_58*JPN_65)
+      LAM . (LAM_66*LAM_99)
+      MEA . (MEA_100*MEA_117)
+      NEU . (NEU_118*NEU_126)
+      OAS . (OAS_127*OAS_153)
+      REF . (REF_154*REF_164)
+      SSA . (SSA_165*SSA_186)
+      USA . (USA_187*USA_200) /
 
   i_to_iso(i,iso) mapping regions to iso countries
     / CAZ . (AUS, CAN, HMD, NZL, SPM)

literature.bib

@@ -1794,3 +1794,23 @@ @article{difulvio2016logging
   year = {2016},
   doi = {10.1080/02827581.2016.1221128}
 }
+
+@article{li_global_2018,
+  title = {A global yield dataset for major lignocellulosic bioenergy crops based on field measurements},
+  author = {Wei Li and Philippe Ciais and David Makowski and Shushi Peng},
+  journal = {Scientific Data},
+  volume = {5},
+  number = {1},
+  year = {2018},
+  doi = {10.1038/sdata.2018.169}
+}
+
+@article{li_mapping_2020,
+  title = {Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale},
+  author = {Wei Li and Philippe Ciais and Elke Stehfest and Detlef van Vuuren and Alexander Popp and Almut Arneth and Fulvio Di Fulvio and Jonathan Doelmann and Florian Humpenöder and Anna B. Harper and Taejin Park and David Makowski and Petr Havlik and Michael Obersteiner and Jingmeng Wang and Andreas Krause and Wenfeng Liu},
+  journal = {Earth System Science Data},
+  number = {2},
+  pages = {789-804},
+  year = {2020},
+  doi = {10.5194/essd-12-789-2020}
+}

main.gms

@@ -154,7 +154,7 @@ $title magpie
 * 
 * Number of cells per region:
 *   CAZ  CHA  EUR  IND  JPN  LAM  MEA  NEU  OAS  REF  SSA  USA
-*     5   19   12   12    7   33   25    8   16   12   33   18
+*    14   17   12   14    8   34   18    9   27   11   22   14
 * 
 * Regionscode: 62eff8f7
 *###################### R SECTION END (VERSION INFO) ###########################
@@ -195,7 +195,7 @@ $setglobal land  landmatrix_dec18
 $setglobal costs  default
 $setglobal interest_rate  select_apr20
 $setglobal tc  endo_jan22
-$setglobal yields  managementcalib_aug19
+$setglobal yields  gsadapt_dynPastrTau_jul26
 
 $setglobal food  anthro_iso_jun22
 $setglobal demand  sector_may15

modules/13_tc/endo_jan22/declarations.gms

@@ -6,7 +6,7 @@
 *** |  Contact: magpie@pik-potsdam.de
 
 positive variable
- v13_tau_core(h,tautype)                   Agricultural land use intensity tau for conventional cropland (1)
+ v13_tau_core(h,tautype)              Agricultural land use intensity tau for conventional cropland (1)
  vm_tech_cost(i)                      Total Annuitized costs of TC (mio. USD17MER per yr)
  v13_cost_tc(i,tautype)               Technical change costs per region (mio. USD17MER)
  v13_tech_cost(i,tautype)             Annuitized costs of TC for crops and pasture (mio. USD17MER per yr)
@@ -20,6 +20,7 @@ equations
  q13_tech_cost_sum(i)                 Total Total annuitized costs for TC (mio. USD17MER per yr)
  q13_tau(j, tautype)                  Overall agricultural land use intensity tau (1)
  q13_tau_consv(h, tautype)            Tau for cropland within conservation priority areas (1)
+ q13_lower_bound_tau(j)               Lower limit constraint for tau (1)
 ;
 
 parameters
@@ -52,5 +53,6 @@ parameters
  oq13_tech_cost_sum(t,i,type)     Total Total annuitized costs for TC (mio. USD17MER per yr)
  oq13_tau(t,j,tautype,type)       Overall agricultural land use intensity tau (1)
  oq13_tau_consv(t,h,tautype,type) Tau for cropland within conservation priority areas (1)
+ oq13_lower_bound_tau(t,j,type)   Lower limit constraint for tau (1)
 ;
 *##################### R SECTION END (OUTPUT DECLARATIONS) #####################

modules/13_tc/endo_jan22/equations.gms

@@ -44,6 +44,9 @@ q13_tech_cost(i2, tautype) ..
 q13_tech_cost_sum(i2) ..
  vm_tech_cost(i2) =e= sum(tautype, v13_tech_cost(i2, tautype));
 
+*' Enforce gsadapt yield increases in tau, so that tc costs apply
+q13_lower_bound_tau(j2) ..
+  vm_tau(j2,"crop") =g= pcm_tau(j2,"crop") * sum((ct, cell(i2,j2)), pm_yields_gsadapt_ratio_increment(ct,i2));
 
 *' The overall land use intensity factor `vm_tau` is a linear combination between the
 *' land use intensity factors `v13_tau_core` for regular cropland and `v13_tau_consv`

modules/13_tc/endo_jan22/postsolve.gms

@@ -28,6 +28,7 @@ pcm_tau(j, tautype) = vm_tau.l(j, tautype);
  oq13_tech_cost_sum(t,i,"marginal")     = q13_tech_cost_sum.m(i);
  oq13_tau(t,j,tautype,"marginal")       = q13_tau.m(j,tautype);
  oq13_tau_consv(t,h,tautype,"marginal") = q13_tau_consv.m(h,tautype);
+ oq13_lower_bound_tau(t,j,"marginal")   = q13_lower_bound_tau.m(j);
  ov13_tau_core(t,h,tautype,"level")     = v13_tau_core.l(h,tautype);
  ov_tech_cost(t,i,"level")              = vm_tech_cost.l(i);
  ov13_cost_tc(t,i,tautype,"level")      = v13_cost_tc.l(i,tautype);
@@ -39,6 +40,7 @@ pcm_tau(j, tautype) = vm_tau.l(j, tautype);
  oq13_tech_cost_sum(t,i,"level")        = q13_tech_cost_sum.l(i);
  oq13_tau(t,j,tautype,"level")          = q13_tau.l(j,tautype);
  oq13_tau_consv(t,h,tautype,"level")    = q13_tau_consv.l(h,tautype);
+ oq13_lower_bound_tau(t,j,"level")      = q13_lower_bound_tau.l(j);
  ov13_tau_core(t,h,tautype,"upper")     = v13_tau_core.up(h,tautype);
  ov_tech_cost(t,i,"upper")              = vm_tech_cost.up(i);
  ov13_cost_tc(t,i,tautype,"upper")      = v13_cost_tc.up(i,tautype);
@@ -50,6 +52,7 @@ pcm_tau(j, tautype) = vm_tau.l(j, tautype);
  oq13_tech_cost_sum(t,i,"upper")        = q13_tech_cost_sum.up(i);
  oq13_tau(t,j,tautype,"upper")          = q13_tau.up(j,tautype);
  oq13_tau_consv(t,h,tautype,"upper")    = q13_tau_consv.up(h,tautype);
+ oq13_lower_bound_tau(t,j,"upper")      = q13_lower_bound_tau.up(j);
  ov13_tau_core(t,h,tautype,"lower")     = v13_tau_core.lo(h,tautype);
  ov_tech_cost(t,i,"lower")              = vm_tech_cost.lo(i);
  ov13_cost_tc(t,i,tautype,"lower")      = v13_cost_tc.lo(i,tautype);
@@ -61,4 +64,5 @@ pcm_tau(j, tautype) = vm_tau.l(j, tautype);
  oq13_tech_cost_sum(t,i,"lower")        = q13_tech_cost_sum.lo(i);
  oq13_tau(t,j,tautype,"lower")          = q13_tau.lo(j,tautype);
  oq13_tau_consv(t,h,tautype,"lower")    = q13_tau_consv.lo(h,tautype);
+ oq13_lower_bound_tau(t,j,"lower")      = q13_lower_bound_tau.lo(j);
 *##################### R SECTION END (OUTPUT DEFINITIONS) ######################

modules/13_tc/endo_jan22/presolve.gms

@@ -5,7 +5,6 @@
 *** |  MAgPIE License Exception, version 1.0 (see LICENSE file).
 *** |  Contact: magpie@pik-potsdam.de
 
-
 pc13_land(i,"pastr") = sum(cell(i,j),pcm_land(j,"past"));
 pc13_land(i,"crop") = sum(cell(i,j),pcm_land(j,"crop"));
 
@@ -20,8 +19,9 @@ else
 
 if(m_year(t) > sm_fix_SSP2 AND s13_max_gdp_shr <> Inf,
 
-* We constrain tech cost to a defined share of regional GDP to avoid unrealistically
-* high endogenous tech investments
+*' Technological change costs are constrainted to a 
+*' pre-defined share of regional GDP to avoid unrealistically
+*' high endogenous tech investments
   vm_tech_cost.up(i) =
     sum((i_to_iso(i,iso),ct), im_gdp_pc_ppp_iso(ct,iso) * im_pop_iso(ct,iso)) * s13_max_gdp_shr;
 

modules/13_tc/endo_jan22/realization.gms

@@ -5,7 +5,7 @@
 *** |  MAgPIE License Exception, version 1.0 (see LICENSE file).
 *** |  Contact: magpie@pik-potsdam.de
 
-*' @description The endo realization stands for endogenous implementation of
+*' @description The endo_jan22 realization stands for endogenous implementation of
 *' technological change and land use intensification. The intensification rates
 *' are calculated endogenously based on an interplay between land use intensity
 *' $\tau$ and technological change costs (as shown schematically in the figure

modules/13_tc/endo_jan22/scaling.gms

@@ -5,7 +5,6 @@
 *** |  MAgPIE License Exception, version 1.0 (see LICENSE file).
 *** |  Contact: magpie@pik-potsdam.de
 
-vm_tech_cost.scale(i) = 1e3;
-v13_cost_tc.scale(i,tautype) = 1e6;
-v13_tech_cost.scale(i,tautype) = 1e3;
-q13_cost_tc.scale(i,tautype) = 1e4;
+vm_tech_cost.scale(i) = 10e3;
+v13_cost_tc.scale(i,tautype) = 10e6;
+v13_tech_cost.scale(i,tautype) = 10e3;

modules/13_tc/exo/not_used.txt

@@ -1,3 +1,4 @@
 name,type,reason
 im_pop_iso,input,questionnaire
 im_gdp_pc_ppp_iso,input,questionnaire
+pm_yields_gsadapt_ratio_increment,input,questionnaire