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the_capri_data_base [2020/05/01 08:38] – matszthe_capri_data_base [2022/11/07 10:23] (current) – external edit 127.0.0.1
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 ** Figure 2: Overview on key elements in the consolidation of European data at the Member state level (in coco1.gms) ** ** Figure 2: Overview on key elements in the consolidation of European data at the Member state level (in coco1.gms) **
-{{:fig02.png?nolink|}}+{{:figure_02.png?nolink|}}
  
 Source: Own illustration Source: Own illustration
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 ** Table 3: Data items and their main sources ** ** Table 3: Data items and their main sources **
 ^ Data items ^ Source ^ ^ Data items ^ Source ^
-| Activity levels | Eurostat: Crop production statistics, Land use statistics, herd size statistics, slaughtering statistics, statistics on import and export of live animals\\ \\ For Western Balkan Countries and Turkey: Eurostat supplemented with national statistical yearbooks, data from national ministries, FAOstat production statistics and others | +| Activity levels | Eurostat: Crop production statistics, Land use statistics, herd size statistics, slaughtering statistics, statistics on import and export of live animals For Western Balkan Countries and Turkey: Eurostat supplemented with national statistical yearbooks, data from national ministries, FAOstat production statistics and others | 
-| Production, farm and market balance positions | Eurostat: Farm and market balance statistics, crop production statistics, slaughtering statistics, statistics on import and export of live animals\\ \\ For Western Balkan Countries and Turkey: Eurostat supplemented with national statistical yearbooks, data from national ministries,  FAOstat production statistics and others | +| Production, farm and market balance positions | Eurostat: Farm and market balance statistics, crop production statistics, slaughtering statistics, statistics on import and export of live animals For Western Balkan Countries and Turkey: Eurostat supplemented with national statistical yearbooks, data from national ministries,  FAOstat production statistics and others | 
-| Sectoral revenues, costs, and producer prices | Eurostat: Economic Accounts for Agriculture (EAA) and price indices for gap filling, otherwise unit value calculation\\ \\ For Western Balkan Countries and Turkey: Supplemented with national statistical yearbooks, data from national ministries, results from AgriPolicy, FAOstat price statistics |+| Sectoral revenues, costs, and producer prices | Eurostat: Economic Accounts for Agriculture (EAA) and price indices for gap filling, otherwise unit value calculation For Western Balkan Countries and Turkey: Supplemented with national statistical yearbooks, data from national ministries, results from AgriPolicy, FAOstat price statistics |
 | Consumer prices | Derived from macroeconomic expenditure data (Eurostat, supplemented with UNSTATS) and food price information from various sources | | Consumer prices | Derived from macroeconomic expenditure data (Eurostat, supplemented with UNSTATS) and food price information from various sources |
 | Output coefficients | Derived from production and activity levels, engineering knowledge | | Output coefficients | Derived from production and activity levels, engineering knowledge |
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 ** Table 4: Temporal coverage of national data by region ** ** Table 4: Temporal coverage of national data by region **
 ^ Member State ^ Range ^ ^ Member State ^ Range ^
-| EU15 Member States without Germany | 1984 – 2014 | +| EU15 Member States without Germany | 1984 – 2018/2019 | 
-| Germany and (12) New Member States | 1989 – 2014 | +| Germany and (12) New Member States | 1989 – 2018/2019 | 
-| Western Balkan (WB) Countries and Turkey | 1995 – 2014 | +| Western Balkan (WB) Countries and Turkey | 1995 – 2018/2019 | 
-| Norway | 1984 – 2014 |+| Norway | 1984 – 2017 |
  
 === Eurostat data === === Eurostat data ===
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 ** Second step: data selection and code mapping ** ** Second step: data selection and code mapping **
 +
 The second step is data selection and code mapping performed by the GAMS program //‘coco_input.gms’.// Cross sets linking Eurostat codes to COCO codes define the subset of data series subsequently used. The second step is data selection and code mapping performed by the GAMS program //‘coco_input.gms’.// Cross sets linking Eurostat codes to COCO codes define the subset of data series subsequently used.
  
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   * //‘eurostat_ econfinc_mapping.gms’// for the tables from Eurostat’s “Economy and Finance” Statistics   * //‘eurostat_ econfinc_mapping.gms’// for the tables from Eurostat’s “Economy and Finance” Statistics
  
-Example from file //‘Eurostat _agriculture_mapping.gms’//+Example from file //‘Eurostat _agriculture_mapping.gms’//. The results of the program run are gdx-files loaded by files (e.g. coco/coco1_eurostat.gms) which are in turn loaded by coco1.gms or coco2.gms. 
 + 
 +<code fortran> 
 +SET EcoActMAP(ASS_COLS,ASS_ROWS,eco_act_ori_eurostat) "mapping" / 
 +EAAP.CERE. aact_eaa01_01000_PROD_PP_MIO_EUR 
 +EAAP.SWHE. aact_eaa01_01110_PROD_PP_MIO_EUR 
 +EAAP.DWHE. aact_eaa01_01120_PROD_PP_MIO_EUR /; 
  
-| SET AgriProdOriEurostat / \\ apro_acs_a_C1000_AR "CEREALS-EXCLUDING RICE-AREA"\\ apro_acs_a_C1110_AR "COMMON WHEAT AND SPELT - AREA" \\ \\ SET AgriProd_MAP(ASS_COLS,ASS_ROWS,AgriProdOriEurostat) / \\ CERE.LEVL. apro_acs_a_C1000_AR \\ SWHE.LEVL. apro_acs_a_C1110_AR | 
  
-The results of the program run are gdx-files loaded by files (e.g. coco/coco1_eurostat.gms) which are in turn loaded by coco1.gms or coco2.gms.+SET AgriProdMAP(ASS_COLS,ASS_ROWS,agri_prod_ori_eurostat) "mapping" / 
 +CERE.LEVL.( apro_cpnh1_C1000_AR,apro_cpnh1_h_C1000_AR) 
 +SWHE.LEVL.( apro_cpnh1_C1110_AR,apro_cpnh1_h_C1110_AR) 
 +SWH1.LEVL.( apro_cpnh1_C1111_AR,apro_cpnh1_h_C1111_AR) /; 
 +</code>
  
 === Western Balkan Countries and Turkey === === Western Balkan Countries and Turkey ===
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 === Other additional input data === === Other additional input data ===
  
-COCO1: Biofuels+COCO1: Biofuels FIXME (most links are not working anymore, remove or re-link)
  
   * Production, market balance and feedstock quantities for biodiesel and bioethanol are collected from a multitude of sources:   * Production, market balance and feedstock quantities for biodiesel and bioethanol are collected from a multitude of sources:
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   - The key parameters at a certain point in the program flow (above: p_agriProd, p_agriPri, p_ecoAct) are copied to a debugging parameter “debug” (better name would be: “p_debug”). At the end of a coco1 run (or if desired also at this point) the parameter is unloaded into a file “results/coco/debug/debug_%MS%.gdx” such that the various assignments, corrections, deletions that have occurred up to a certain program line may be inspected in one file.   - The key parameters at a certain point in the program flow (above: p_agriProd, p_agriPri, p_ecoAct) are copied to a debugging parameter “debug” (better name would be: “p_debug”). At the end of a coco1 run (or if desired also at this point) the parameter is unloaded into a file “results/coco/debug/debug_%MS%.gdx” such that the various assignments, corrections, deletions that have occurred up to a certain program line may be inspected in one file.
   - The next command “$batinclude “util/debug” %system.fn% %system.incline%  unloads the whole memory, incuding all parameters but also sets and other symbols, at this point into a debugging file in the gams/temp folder. This may be useful to analyse “difficult” cases of debugging.   - The next command “$batinclude “util/debug” %system.fn% %system.incline%  unloads the whole memory, incuding all parameters but also sets and other symbols, at this point into a debugging file in the gams/temp folder. This may be useful to analyse “difficult” cases of debugging.
-  - +
 Finally the biofuel sector is prepared. Finally the biofuel sector is prepared.
  
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     * To acknowledge that the Corine Classes may be mapped to several LUCAS categories we multiplied them with the “profiles”, giving the distribution of each Corine category according to the LUCAS classes. In this case, only 26.7% of the “complexCultiv” area is mapped to annual crops, but 7.3% are mapped to “temporary pastures”, 6.4% to “permanent  grassland with sparse tree/shrub vegetation” and so forth. The transformed Corine data often give the most detailed area coverage and thus assume a role as a kind of fall back information in case that other information is missing.     * To acknowledge that the Corine Classes may be mapped to several LUCAS categories we multiplied them with the “profiles”, giving the distribution of each Corine category according to the LUCAS classes. In this case, only 26.7% of the “complexCultiv” area is mapped to annual crops, but 7.3% are mapped to “temporary pastures”, 6.4% to “permanent  grassland with sparse tree/shrub vegetation” and so forth. The transformed Corine data often give the most detailed area coverage and thus assume a role as a kind of fall back information in case that other information is missing.
   * **LEVRegio** - Eurostat regional land use data (Eurostat Table: “agr_r_landuse”, discontinued). Inspite of using the same codes as for the national data, the national totals, aggregated from the NUTS2 regions are not always in line with LEVAgriProd. Furthermore a few categories are missing (no inland waters, no other wooded land). However there are few alternative annual series available to regionalise the national data in CAPREG.   * **LEVRegio** - Eurostat regional land use data (Eurostat Table: “agr_r_landuse”, discontinued). Inspite of using the same codes as for the national data, the national totals, aggregated from the NUTS2 regions are not always in line with LEVAgriProd. Furthermore a few categories are missing (no inland waters, no other wooded land). However there are few alternative annual series available to regionalise the national data in CAPREG.
-  * **LEVFAO** - Land use data from the resource FAOSTAT domain FIXME ((See [[http://faostat3.fao.org/home/index.html#DOWNLOAD]].)) with annual time series on agricultural land use but also some non agricultural area categories (forest, inland waters, other land, total area).+  * **LEVFAO** - Land use data from the resource FAOSTAT domain FIXME ((See [[http://faostat3.fao.org/home/index.htmlDOWNLOAD]].)) with annual time series on agricultural land use but also some non agricultural area categories (forest, inland waters, other land, total area).
   * **LEVLucas** – directly using the LUCAS data is an option that has been considered but not implemented in CAPRI so this code is not used at the moment.   * **LEVLucas** – directly using the LUCAS data is an option that has been considered but not implemented in CAPRI so this code is not used at the moment.
   * **LEVLandCov** - Eurostat land cover data for 2009, 2012, 2015 at the MS level. Agricultural land is only distinguished into cropland CROP and grassland GRAS, but 5 nonagricultural areas are neatly aggregating up to the total country (Artificial ARTIF, shrubland (considered similar to “other wooded land” OWL), bare land & wetlands (mapped to “other sparcely vegetated or bare OSPA) and waters WATER.   * **LEVLandCov** - Eurostat land cover data for 2009, 2012, 2015 at the MS level. Agricultural land is only distinguished into cropland CROP and grassland GRAS, but 5 nonagricultural areas are neatly aggregating up to the total country (Artificial ARTIF, shrubland (considered similar to “other wooded land” OWL), bare land & wetlands (mapped to “other sparcely vegetated or bare OSPA) and waters WATER.
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 **Figure 3: Overview on main estimations in for the consolidation of national data in Europe (in coco1.gms)** **Figure 3: Overview on main estimations in for the consolidation of national data in Europe (in coco1.gms)**
  
-{{::figure3.png?600|}}+{{::figure_03.png?600|}}
  
 Results are not always fully satisfactory (perhaps impossible given some raw data). For example the resulting prices (unit values) are far from a priori expectations for a number of series, in particular less important ones. This is because, apart from some additional security checks, unit values are by and large considered a free balancing variable calculated to preserve the identity between largely fixed EAA values and fixed production (in coco1_estimb). The priority for EAA values has been reduced somewhat in recent years but a more thorough revision would require to estimate production, market balances and EAA simultaneously rather than consecutively (first $(a)$, then $(c)$ for crops). As this is infeasible for all crops at the same time the whole estimation would need to be split up differently in the crop sector, perhaps first for the aggregates and then within those. Results are not always fully satisfactory (perhaps impossible given some raw data). For example the resulting prices (unit values) are far from a priori expectations for a number of series, in particular less important ones. This is because, apart from some additional security checks, unit values are by and large considered a free balancing variable calculated to preserve the identity between largely fixed EAA values and fixed production (in coco1_estimb). The priority for EAA values has been reduced somewhat in recent years but a more thorough revision would require to estimate production, market balances and EAA simultaneously rather than consecutively (first $(a)$, then $(c)$ for crops). As this is infeasible for all crops at the same time the whole estimation would need to be split up differently in the crop sector, perhaps first for the aggregates and then within those.
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   * In some cases it is convenient to have the completed COCO1 results of all countries at hand for comparison purposes and in order to achieve a balanced picture across MS. This is the main motive for the assignments of consumer loss rates (Section 3.2.7.1).   * In some cases it is convenient to have the completed COCO1 results of all countries at hand for comparison purposes and in order to achieve a balanced picture across MS. This is the main motive for the assignments of consumer loss rates (Section 3.2.7.1).
-  * Whenever averages of consolidated data (from COCO1) across several or all MS are involved, a solution in a loop requires certain sequence (such as first solving for non-candidate countries to form the averages that are input to candidate countries) or is better solved in a new module like COCO2. This applies to the expenditure allocation problem (Section [[the capri data base#The Complete and Consistent Data Base (COCO) for the national scale#COCO2: Data Preparation]]), to completions for certain feedstuffs (Section 3.2.7.2, EU averages used due to the scarcity of data), and to corrections of LULUCF coefficients (Section 3.2.7.3). FIXME+  * Whenever averages of consolidated data (from COCO1) across several or all MS are involved, a solution in a loop requires certain sequence (such as first solving for non-candidate countries to form the averages that are input to candidate countries) or is better solved in a new module like COCO2. This applies to the expenditure allocation problem (Section [[the capri data base#COCO2: Data Preparation]]), to completions for certain feedstuffs (Section 3.2.7.2, EU averages used due to the scarcity of data), and to corrections of LULUCF coefficients (Section 3.2.7.3). FIXME
  
 ===Assignment of consumer loss rates and nutrient intake per head === ===Assignment of consumer loss rates and nutrient intake per head ===
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 \begin{align} \begin{align}
 \begin{split} \begin{split}
- 
 &fatngday_r^{HEIF}\\ &fatngday_r^{HEIF}\\
 &= min \left[ DAYS_{up}^{HEIF},max \left\{ DAYS_{lo}^{HEIF},\frac{BEEF_r^{HEIF}/carcassSh_{HEIF}-startWgt_{HEIF}}{dailyIncrease_r^{HEIF}} \right\} \right] &= min \left[ DAYS_{up}^{HEIF},max \left\{ DAYS_{lo}^{HEIF},\frac{BEEF_r^{HEIF}/carcassSh_{HEIF}-startWgt_{HEIF}}{dailyIncrease_r^{HEIF}} \right\} \right]
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 After this last include file the completions in module COCO2 are finished and the main output file (coco2_output.gdx) is unloaded. This file is loaded in subsequent modules (main use in CAPREG, but also in CAPTRD for nowcasting and in CAPMOD for update of LULUCF coefficients).   After this last include file the completions in module COCO2 are finished and the main output file (coco2_output.gdx) is unloaded. This file is loaded in subsequent modules (main use in CAPREG, but also in CAPTRD for nowcasting and in CAPMOD for update of LULUCF coefficients).  
 +
 +
 +
 +====Annex: Code lists for the COCO database====
 +
 +This section includes detailed code lists, which are in use in the COCO database.
 +
 +**Table: Codes used for storing the original REGIO tables in the database and their description (rows)**
 +
 +^ Codes used in CAPRI REGIO tables ^ Original REGIO description ^
 +| TOTL | Territorial area |
 +| FORE | Forest land |
 +| AGRI | Utilized agricultural area |
 +| GARD | Private gardens |
 +| GRAS | Permanent grassland |
 +| PERM | Permanent crops |
 +| VINE | Vineyards |
 +| OLIV | Olive plantations |
 +| ARAB | Arable land |
 +| GREF | Green fodder on arable land |
 +| CERE | Cereals (including rice) |
 +| WHEA | Soft and durum wheat and spelt |
 +| BARL | Barley |
 +| MAIZ | Grain maize |
 +| RICE | Rice |
 +| POTA | Potatoes |
 +| SUGA | Sugar beet |
 +| OILS | Oilseeds (total) |
 +| RAPE | Rape |
 +| SUNF | Sunflower |
 +| TOBA | Tobacco |
 +| MAIF | Fodder maize |
 +| CATT | Cattle (total) |
 +| COWT | Cows (total) |
 +| DCOW | Dairy cows |
 +| CALV | Other cows |
 +| CAT1 | Total cattle under one year |
 +| CALF | Slaughter calves |
 +| CABM | Male breeding calves (\&lt;1 year) |
 +| CABF | Female breeding calves (\&lt;1 year) |
 +| BUL2 | Male cattle (1-2 years) |
 +| H2SL | Slaughter heifers (1-2 years) |
 +| H2BR | Female cattle (1-2 years) |
 +| BUL3 | Male cattle (2 years and above) |
 +| H3SL | Slaughter heifers (2 years and above) |
 +| H3BR | Breeding heifers |
 +| BUFF | Total buffaloes |
 +| PIGS | Total pigs (total) |
 +| PIG1 | Piglets under 20 kg |
 +| PIG2 | Piglets under 50 kg and over 20 kg |
 +| PIG3 | Fattening pigs over 50 kg |
 +| BOAR | Breeding boars |
 +| SOW2 | Total breeding sows |
 +| SOW1 | Sows having farrowed |
 +| GILT | Gilts having farrowed for the first time |
 +| SOWM | Maiden sows |
 +| GILM | Maiden gilts |
 +| SHEP | Sheep total) |
 +| GOAT | Goats (total) |
 +| EUQI | Equidae (total) |
 +| POUL | Poultry (total) |
 +| OUTP | Final production |
 +| CROP | Total crops production |
 +| DWHE | Durum wheat |
 +| PULS | Pulses |
 +| ROOT | Roots and tubers |
 +| INDU | Industrial crops |
 +| TEXT | Textile fibre plants |
 +| HOPS | Hops |
 +| VEGE | Fresh vegetables |
 +| TOMA | Tomatoes |
 +| CAUL | Cauliflowers |
 +| FRUI | Fresh fruit |
 +| APPL | Apples |
 +| PEAR | Pears |
 +| PEAC | Peaches |
 +| CITR | Citrus fruit (total) |
 +| ORAN | Oranges |
 +| LEMN | Lemons |
 +| MAND | Mandarins |
 +| GRAP | Table grapes |
 +| WINE | Wine |
 +| TABO | Table olives |
 +| OLIO | Olive oil |
 +| NURS | Nursery plants |
 +| FLOW | Flowers and ornamental plants |
 +| OCRO | Other crops |
 +| ANIT | Total animal production |
 +| ANIM | Animal |
 +| SHGO | Sheep and goats |
 +| ANIP | Animal products |
 +| MILK | Milk |
 +| EGGS | Eggs |
 +| INPU | Intermediate consumption (total) |
 +| FEED | Animal feeding stuffs |
 +| FDGR | Animal compounds for grazing livestock |
 +| FDPI | Animal compounds for pigs |
 +| FDPO | Animal compounds for poultry |
 +| FODD | Straight feeding stuffs |
 +| FERT | Fertilizers and enrichments |
 +| ENER | Energy and lubricants |
 +| INPO | Other inputs |
 +| GVAM | Gross value added at market prices |
 +| SUBS | Subsidies |
 +| TAXS | Taxes linked to production (including VAT balance) |
 +| GVAF | Gross value added at factor costs |
 +| DEPM | Depreciation |
 +| LABO | Compensation and social security contributions of employees |
 +| RENT | Rent and other payments |
 +| INTE | Interests |
 +| GFCF | Total of gross fixed capital formation |
 +| BUIL | Buildings and other structures |
 +| MACH | Transport equipment and machinery |
 +| GFCO | Other gross fixed capital formation |
 +
 +**Table: Codes used for storing the original REGIO tables in the data base and their description (columns)**
 +
 +^ Codes used in CAPRI REGIO tables ^ Original REGIO description ^
 +| LEVL | Herd size / Area / # of persons |
 +| LSUN | Live stock units |
 +| PROP | Physical production |
 +| YILD | Yield |
 +| VALE | EAA position in ECU |
 +| VALN | EAA position in NC |
 +
 +**Table: Connection between CAPRI and REGIO crop areas, crop production and herd sizes**
 +
 +^ SPEL-code ^ REGIO-code ^ REGIO-code ^ REGIO-code ^ REGIO-code ^ Description of SPEL activity ^
 +| SWHE | WHEA | CERE | ARAB | | Soft wheat |
 +| DWHE | WHEA | CERE | ARAB | | Durum wheat |
 +| RYE | | CERE | ARAB | | Rye |
 +| BARL | BARL | CERE | ARAB | | Barley |
 +| OATS | | CERE | ARAB | | Oats |
 +| MAIZ | MAIZ | CERE | ARAB | | Maize |
 +| OCER | | CERE | ARAB | | Other cereals (excl. rice) |
 +| PARI | RICE | CERE | ARAB | | Paddy rice |
 +| PULS | | | ARAB | | Pulses |
 +| POTA | POTA | | ARAB | | Potatoes |
 +| SUGB | SUGA | | ARAB | | Sugar beet |
 +| RAPE | RAPE | OILS | ARAB | | Rape and turnip rape |
 +| SUNF | SUNF | OILS | ARAB | | Sunflower seed |
 +| SOYA | | OILS | ARAB | | Soya beans |
 +| OLIV | | OLIV | PERM | | Olives for oil |
 +| OOIL | | OILS | ARAB | | Other oil seeds and oleaginous fruits |
 +| FLAX | | | ARAB | | Flax and hemp \*\*\* (faser) \*\*\* |
 +| TOBA | TOBA | | ARAB | | Tobacco, unmanufactured, incl. dried |
 +| OIND | | | ARAB | | Other industrial crops |
 +| CAUL | | | ARAB | | Cauliflowers |
 +| TOMA | | | ARAB | | Tomatoes |
 +| OVEG | | | ARAB | | Other vegetables |
 +| APPL | | | PERM | | Apples, pears and peaches |
 +| OFRU | | | PERM | | Other fresh fruits |
 +| CITR | | | PERM | | Citrus fruits |
 +| TAGR | | VINE | PERM | | Table grapes |
 +| TABO | | OLIV | PERM | | Table olives |
 +| TWIN | | VINE | PERM | | Table wine |
 +| OWIN | | VINE | PERM | | Other wine |
 +| NURS | | | PERM | | Nursery plants |
 +| FLOW | | | ARAB | | Flowers,ornamental plants, etc. |
 +| OCRO | | | ARAB | | Other final crop products |
 +| MILK | DCOW | | | | Dairy cows |
 +| BEEF | BUL2 | BUL3 | | | Bulls fattening |
 +| CALF | CALF | | | | Calves fattening (old VEAL) |
 +| PORK | PIG3 | PIG2 | PIG1 | | Pig fattening |
 +| MUTM | GOAT | SHEP | | | Ewes and goats |
 +| MUTT | GOAT | SHEP | | | Sheep and goat fattening |
 +| EGGS | POUL | | | | Laying hens |
 +| POUL | POUL | | | | Poultry fattening |
 +| OANI | | | | | Other animals |
 +| OROO | | | ARAB | | Other root crops |
 +| GRAS | GRAS | | | | Green fodder |
 +| SILA | GREF | | ARAB | | Silage |
 +| CALV | CALV | | | | Suckler cows |
 +| RCAL | CABM | CABF | | | Calves, raising |
 +| HEIF | H2SL | H2BR | H3SL | H3BR | Heifers |
 +| PIGL | SOW2 | | | | Pig breeding |
 +| FALL | | | FALL | | Fallow land |
 +
 +**Tables: Codes of the input allocation estimation**
 +
 +^FADN inputs (FI) ^Label  ^
 +| TOIN | total inputs |
 +| COSA | animal specific inputs |
 +| FEDG | self grown feedings |
 +| ANIO | other animal inputs |
 +| FEDP | purchased feedings |
 +| COSC | crop specific inputs |
 +| SEED | seeds |
 +| PLAP | plant protection |
 +| FERT | fertilisers |
 +| TOIX | other inputs (overheads) |
 +
 +
 +^CAPRI inputs (CI) used in the reconciliation ^label ^
 +| TOIN | total inputs |
 +| FEED | feedings |
 +| IPHA | other animal inputs |
 +| COSC | crop specific inputs |
 +| SEED | seeds |
 +| PLAP | plant protection |
 +| FERT | fertilisers |
 +| REPA | repairs |
 +| ENER | energy |
 +| SERI | agricultural services input |
 +| INPO | other inputs |
 +
 +1. The set of //Other// activities that had been omitted from the econometric estimation: 
 +
 +  * OTHER={OCER, OFRU, OVEG, OCRO, OWIN, OIND, OOIL, OFAR, OANI}
 +
 +2. The set of activity groups, and their elements, used in the replacement or missing/negative coefficients
 +
 +  * GROUPS = {YOUNG, VEGE, SETT, PULS, PIG, OILS, MILK, MEAT, INDS, HORSE, GOAT, FRU, FOD, FLOWER, DENNY, COW, CHICK1, CHICK2, CHICK3, CERE, ARAB}
 +  * YOUNG={YBUL, YCOW},
 +  * VEGE={TOMA},
 +  * SETT={SETA, NONF, FALL, GRAS},
 +  * PULS=PULS
 +  * PIG={PIGF, SOWS},
 +  * OILS={RAPE, SOYA, SUNF, PARI, OLIV},
 +  * INDS={TOBA, TEXT, TABO},
 +  * GOAT={SHGM, SHGF},
 +  * FRU={APPL, CITR, TAGR, TWIN},
 +  * FOD={ROOF, MAIF},
 +  * FLOWER={FLOW, NURS},
 +  * DENNY={PORK, SOWS},
 +  * COW={DCOW, SCOW, HEIF, HEIR, CAMF, CAFF, BULF, CAMR, CAFR},
 +  * CHICK1={HENS, POUF},
 +  * CERE={SWHE, DWHE, BARL, OATS, RYEM, MAIZ},
 +  * ARAB={POTA, SUGB}
 +
 +3. The sets of Northern European, Southern European countries:
 +
 +  * NEUR={NL000, UK000, AT000, BL000, DE000, DK000, FI000, FR000, SE000}
 +  * SEUR={El000, ES000, PT000, IT000, IR000}
 +
 +
 +** Table: Codes of land use classes (Set LandUse)**
 +
 +^Code  ^Label  ^
 +| OART | artificial |
 +| ARAO | (other) arable crops - all arable crops excluding rice and fallow (see also definition of ARAC below) |
 +| PARI | paddy rice (already defined) |
 +| GRAT | temporary grassland (alternative code used for CORINE data, definition identical to TGRA |
 +| FRCT | fruit and citrus |
 +| OLIVGR | Olive Groves |
 +| VINY | vineyard (already defined) |
 +| NUPC | nursery and permanent crops (Note: the aggregate PERM also includes flowers and other vegetables |
 +| BLWO | board leaved wood |
 +| COWO | coniferous wood |
 +| MIWO | mixed wood |
 +| POEU | plantations (wood) and eucalyptus |
 +| SHRUNTC | shrub land - no tree cover |
 +| SHRUTC | shrub land - tree cover |
 +| GRANTC | Grassland - no tree cover |
 +| GRATC | Grassland - tree cover |
 +| FALL | fallow land (already defined) |
 +| OSPA | other sparsely vegetated or bare |
 +| INLW | inland waters |
 +| MARW | marine waters |
 +| KITC | kitchen garden |
 +
 +
 +** Table: Codes of land use aggregates (Set LandUseAgg)**
 +
 +^Code  ^Label  ^
 +| OLND | other land - shrub, sparsely vegetated or bare |
 +| ARAC | arable crops |
 +| FRUN | fruits, nursery and (other) permanent crops |
 +| WATER | inland or marine waters |
 +| ARTIF | artificial - buildings or roads |
 +| OWL | other wooded land - shrub or grassland with tree cover (definition to be discussed) |
 +| TWL | total wooded land - forest + other wooded land |
 +| SHRU | shrub land |
 +| FORE | forest (already defined) |
 +| GRAS | grassland (already defined) |
 +| ARAB | arable (already defined) |
 +| PERM | permanent crops (already defined) |
 +| UAAR | utilizable agricultural area (already defined) |
 +| ARTO | total area - total land and inland waters |
 +| ARTM | total area including marine waters |
 +| CROP | crop area - arable and permanent |
 +
 +**Table: Codes of mutually exclusive subset adding up to total area - ARTO (Set LandUseARTO)**
 +
 +^Code  ^Label  ^
 +| OLND | other land - shrub, sparsely vegetated or bare |
 +| ARTIF | artificial - buildings or roads |
 +| FORE | forest |
 +| UAAR | utilizable agricultural area |
 +| INLW | Inland waters |
 +====Annex: Detailed description of Eurostat data processing in COCO (coco1_eurostat.gms)====
 +
 +The program starts by importing pre-processed data from Eurostat. The pre-processing includes simple data selection routines and also manual checks. The Eurostat domains are processed one by one, and the corrections are done for each Member State ((Eurostat offers data for Belgium and Luxembourg separately, whereas the database combines both countries to the model region "BL000" (Belgium and Luxembourg). The key reason is that Eurostat offers data mainly for the aggregate Belgium and Luxembourg up to the year 1999, especially for all market balances. Furthermore, Luxembourg has a rather small agricultural sector (2004 total output was about EUR 250 million) with some similarities to Belgium.))
 +
 +Below we discuss the specific data-processing tasks related to Eurostat table groups.
 +The first Eurostat Table Group is “p_AgriProd” covering market balances and activity levels. 
 +
 +//Corrections and complements for all MS://
 +
 +
 +  * The following  data are not anymore available form Eurostat, starting with the 2010 data extractionBeginning with Eurostat selection 2010 some data are missing from the Eurostat website: 
 +    * DWH1, RAP1, POT1, POT2, ROO1 and ROO2 are not longer supported 
 +    * data for slaughter heads and slaughter tons for calves are only available for recent years  
 +    * deliveries to dairy of RMLK missing for earlier years in selection starting with February 2018
 +For an Interim solution, data for the missing data points are collected from an earlier Eurostat selection (March 2010). 
 +  * UNFCCC data is included, here sheep and goats population, to prolong data of some countries where Eurostat data collection stopped 2008/2009.
 +  * Recent dairy sector data from Eurostat via DG supplements the ordinary dairy data downloaded from the website of Eurostat.
 +  * Sugar trade data from the market balances of Eurostat is extended with Comext (Eurostat) data.
 +  * For the milk products WMIO, SMIP, FRMI and COCM some market balance positionpositions are corrected: “industrial use” is added to “feed on market and “processing” is added to “human consumption.
 +  * COCO code "FRUI" is aggregated from auxiliary data for fruit trees, plus soft fruits, plus strawberries.
 +  * All activities for the aggregate ILAM are added up from SHEP and GOAT.
 +  * The units for wine balance sheets are converted from 1000hl to 10000hl=1000000l
 +  * A rice milled equivalent balance without paddy rice (separate product) is constructed.
 +  * Survey data on buffaloes are used to increase the bovine stock data to cover the whole cattle herd.
 +
 +//Corrections and complements for specific MS://
 +
 +Due to years of database updates, a number of corrections on input data are carried out. For special cases in some MS, data are read in from additional data sources:
 +
 +  * Belgium-Luxemburg: trade for potatoes (Eurostat: EU trade since 1988 by HS2-HS4 [DS-016894])
 +  * France: market balances for cereal products (Agreste, Direction générale des douanes et droits indirects (DGDDI))
 +  * Denmark: market balances for some cereal products (StatBank Denmark)
 +  * Finland: market balances for some cereal products (Natural Resources Institute Finland, Balance sheet for food commodities)
 +  * Germany: activity levels for textile crops (BMELF)
 +  * Ireland: trade for citrus fruits and some milk products (Eurostat: EU trade since 1988 by HS2-HS4 [DS-016894]) and activity levels for grass land (StatBank Ireland)
 +  * Austria: production of cow milk, fruit products and potatoes (Statistisches Amt Österreich)
 +  * Czechia: trade of life animals (Eurostat: EU trade since 1988 by CN8 [DS-016890])
 +  * Lithuania: human consumption cereal products (calculated from data from statistical yearbook 2018)
 +  * Slovenia: slaughtering (SiStat Slovenia)
 +  * Romania: data for the meat and in the milk sectors (Romanian experts)
 +  * Trade data for sugar are collected from Eurostat COMEXT data.
 +
 +
 +The remaining domains/table groups only require a few case-by-case corrections: 
 +
 +  * The second Eurostat Table Group is “p_ExchRate” covering exchange rates
 +  * The third Eurostat Table Group is “p_EcoAct” covering the economic accounts for agriculture. 
 +  * The fourth Eurostat Table Group is “p_AgriPri” covering agricultural producer prices.
 +
 +
 +
 +====Annex: Testing procedure and checking intermediate steps in COCO (biofuels)====
 +
 +The COCO module produces various reporting files on the intermediate data processing steps. These files can be used to trace back potential errors in the COCO database to their origin. These debugging files also contain meta-information on the input data and settings used for producing the COCO database. 
 +
 +The following example is a walk-through on the typical data processing steps, covering biofuels data preparation in France.
 +
 +The reporting file 'output/results/coco/biof_data_with_prep/chk_biof_data_with_prep_FR000000.gdx' reports on the data preparation for biofuels for France (FR000) in COCO1. The file includes the set ‘meta_prepare_biofuel_data’, with meta-information on the recent coco1 run (e.g. creation date of file, GAMS version used).
 +
 +{{coco_biof_1.png?nolink|}}
 +
 +The set //biofCheckItems// in the same reporting .gdx file shows all biofuel items potentially filled with numbers.
 +
 +{{coco_biof_2.png?nolink|}}
 +
 +The complete list of the biofuel items in //biofCheckItems// includes codes which are additional to the CAPRI activity codes (see Annex on code lists above). The full code list includes the following items: 
 +
 +| bioECere | Ethanol processed from cereals |
 +| bioESuga | Ethanol processed from sugar beets |
 +| bioETwin | Ethanol processed from wine |
 +| bioEFrui | Ethanol processed from fruits |
 +| bioEOcro | Ethanol processed from other agricultural crops |
 +| bioEExog | Ethanol processed from crops not explicit in biofuel modelling (fruits, potatoes, other crops) |
 +| bioARES | Biofuels processed from crops residues |
 +| bioORES | Biofuels processed from forest residues and waste material (municipal waste, waste oil, other waste) |
 +| SECG | Biofuel quantities from second generation |
 +| MAPRagr | Ethanol production from agricultural sources |
 +| EloBio | Biofuel production and demand data from DG Energy project EloBio |
 +| DG_Agri | Ethanol data from DGAgri website and supplementary files |
 +| ProdCom | Eurostat: PRODCOM ANNUAL SOLD (NACE Rev. 2.) [DS-066341] |
 +| EIA | Independent Statistics &amp; Analysis, US Energy Information Administration |
 +| comext | Eurostat: Comext |
 +| Energy_bal | Eurostat: Supply, transformation, consumption - renewable energies - annual data [nrg_107a] |
 +| Energy_dem | Eurostat: Supply, transformation, consumption - renewable energies - annual data [nrg_102a, nrg_1073a] |
 +| final | results of the calculations |
 +| ODOM | other domestic use (activity from biostock calculations |
 +| INDt | Sum of model results for BIOF and INDM |
 +| BIOi, INDi, DOMi | intermediate activities to save data from model initialisation for later documentation. |
 +
 +
 +Biofuels production (levels) are calculated for biodiesel (BIOD) and bioethanol (BIOE). Input data and final initialization values before the consistency models are run are documented on the parameter //p_prepare_biofuelsMS// (see examples below). The results of the consistency models m_bioFitD (BIOD) and m_bioFitE (BIOE) are documented on the parameter //p_biofDatatMS// (see examples below).
 +
 +
 + 
 +**Example 1: Bioethanol**
 +
 +The screenshot demonstrates the input data and final initialization values collected on parameter //p_prepare_biofuelsMS//. The first column of the table indicates the data source, respectively the processing status of the data. Data sources for bioethanol (BIOE) include data from EloBio, DG_Agri, ProdCom, EIA, Engergy_bal and Energy_dem. The second column of the table shows the activity.
 +
 +{{coco_biof_3.png?nolink|}}
 +
 +The results of the model m_bioFitE (BIOE) are documented on the parameter p_biofDatatMS.
 +
 +{{coco_biof_4.png?nolink|}}
 +
 +We take soft wheat (SWHE) as an example for biofuel feedstock, and walk through the initialization and consistency model results. From data input (Eurostat and FAO) we received in 2002 an industrial use of 894 1000t, saved on INDi. For production of bio-ethanol 631 1000t were initialized, saved on BIOi. The results of the breakdown by use for bio-ethanol and others industrial use, are saved on BIOF and INDM. BIOE shows the yield of soft wheat for bio-ethanol.
 +
 +
 +
 +**Example 2: Biodiesel**
 +
 +
 +The first dimension of the reporting parameter //p_prepare_biofuels// shows the data source (processing status). 
 +The second dimension of the parameter shows the activity.
 +
 +{{coco_biof_5.png?nolink|}}
 +
 +For Bio-diesels, PRIMES model results are used as an additional data source. 
 +
 +^Data source code ^Data source description ^
 +|Primes |PRIMES MODEL, EC3MLAB of ICCS, National University of Athens|
 +
 +{{coco_biof_6.png?nolink|}}
 +
 +The parameter //p_biofDataMS// reports on production (MAPR), trade (import:IMPT, export:EXPT), production from non-agricultural sources (NAGR), prices (UVAD, UVAP) and consumer taxes (CTAX). The distribiution of total biodiesel processing to the feedstock is also reported, for rapeseed oil (RAPO), sunflower oil (SUNO), soya oil (SOYO) and palm oil (PLMO).
 +
 +
 +
 +====Annex: Testing procedure and checking intermediate steps in COCO (dairy)====
 +
 +The following three examples show how to use the intermediate reporting files to trace the data preparation steps. Screenshots demonstrate the arrangement of the reporting parameters by using the CAPRI Graphical User Interface. COCO automatically produces the reporting files in the folder // results/coco/res_estima/ //
 +
 + 
 +**Example 1: Production of cow (COMI) and sheep (SGMI) milk**
 +
 +In order to document the procedure of data consolidation and rebooking, we look at the reporting file for France “chk_estima_FR000.gdx”.
 +
 +{{:wiki:coco_dairy_1.png?nolink|}}
 +
 +The codes in the rows show the activity code, the product code and its status. For activity codes see Annex 1: Code list.
 +
 +Status codes:
 +
 +  * INI: initial value
 +  * COCO1: estimation value
 +
 +The initialization of the production of COMI and SGMI is done in the module //coco1_milk.gms// (see section 3.1.3). Additional remarks to better understand the example:
 +
 +  * COMI: Milk from cows (CMLK) and buffaloes (BMLK) is added up. 
 +  * SGMI: Milk from ewes (EMLK) and goats (GMLK) is added up. 
 +  * If data on cow or sheep and goat milk is not available separately, but total milk production (RMLK) is available, then production of COMI is set equal to total milk production. 
 +  * Only COMI and SGMI are included in the estimation in //coco1_estima.gms//
 +  * The production of RMLK and its components CMLK, BMLK, EMLK and GMLK are only copied from raw data tables into this check parameter for documentation purposes.
 +
 +
 +** Example 2: data consolidation for cow milk**
 +
 +The procedure of data consolidation and rebooking of all activities for the CAPRI product “COMI” (cow milk) is shown in the following screenshot (only part of the reporting parameter p_estimAnimMS is shown, but the full scope of the table is visible in the GUI).
 +
 +{{:wiki:coco_dairy_2.png?nolink|}}
 +
 +
 +The codes in the rows show the activity code and its status. For activity codes see Annex 1: Code list.
 +Additional codes for status include the following.
 +
 +^Status code   ^Status code description  ^
 +|StdeData |Final (small) Stde (standard deviation) attached to priors from raw data |
 +|StdeScale |Final (large) Stde attached to priors from trends but not from raw data  |
 +|Upplim |Soft upper limits triggering extra penalties if violated                 |
 +|Lowlim |Soft lower limits triggering extra penalties if violated                 |
 +|Supps |Prior value = support: comes from raw data or trends plus HP filter      |
 +|Err2rev |Original error term from preest: to steer speed of bound opening         |
 +
 +
 +Under activity dairy cows (DCOW) the following items are reported: yield, total production (GROF), feed use (FEDM) and losses on market (LOSM). Eurostat’s //National Accounts of Agriculture (EAA)// only supply data for the aggregate milk (MILK). The equation //e_EAAMLK// in the consolidation model //AnimNSSQ// ensures the consistency of EAA values for MILK, as they are split up between cow milk (COMI) and sheep and goat milk (SGMI).
 +
 +<code fortran>
 +e_EAAMLK("%MS%000",T) 
 +     $ (p_NobsP("%MS%000","EAAP","MILK") AND ESTR("MILK") and 
 +        (p_NobsP("%MS%000","EAAP","COMI") or p_NobsP("%MS%000","EAAP","SGMI"))) ..
 +*
 +        v_EstimY("%MS%000","EAAP","MILK",T) =E=
 +                             v_EstimY("%MS%000","EAAP","COMI",T) $ p_NobsP("%MS%000","EAAP","COMI")
 +                           + v_EstimY("%MS%000","EAAP","SGMI",T) $ p_NobsP("%MS%000","EAAP","SGMI") ;  
 +
 +</code>
 +
 +Finally. the producer prices (UVAP) are calculated directly from the monetary EEA values and production. 
 +The following picture shows the data processing steps (states) for the EAA values for milk.
 +
 +{{:wiki:coco_dairy_3.png?nolink|}}
 +
 +From the example for COMI above you can also understand the influence of the standard deviation from raw data (e.g. FEDM.StdeData), and standard deviation from trends (e.g. FEDM.StdeScale) Standard deviations are calculated both for raw data and the trends. For years where FEDM.StdeData is given, the results are very close to the prior values FEDM.Supps, whereas they are deviating sizeably for years where only FEDM.StdeScale is available.
 +
 +The first initialisation of //StdeData// and //StdeScale// is done in module coco1_preest.gms, which is a pre-step for the data consolidation models (crops, animals, market balances), using a Hodrick-Prescott filter to smooth the combination of given values and trend line. Both standard deviations enter the objective function (see chapter 3.1.4).
 +
 +
 +
 +**Example 3: data consolidation for cow dairy cow activity (DCOW)**
 +
 +The procedure of data consolidation and booking intermediate data processing results for the dairy cow activity (DCOW) is demonstrated in the following screenshot. 
 +
 +{{:wiki:coco_dairy_4.png?nolink|}}
 +
 +The rows of the table show the product item code for the production activity DCOW, and the data processing steps (status). The first two lines show the coco1 results for slaughtering. The items starting with Y and I stand for the output and input of calves. The initialization, the estimation steps and the final results are all documented on the reporting parameter //p_estimAnimMS//. Items COMI and BEEF show the yields for cow milk and beef. Item DAYS is the process length, initialized by 365 days (equals one year). Finally, the item HERD models the herd size of dairy cows.
 +
  
 ===== The Regionalised Data Base (CAPREG) ===== ===== The Regionalised Data Base (CAPREG) =====
Line 1051: Line 1556:
 In the last major update of 2015 the original data had been first stored in the TSV format designed by EUROSTAT: In the last major update of 2015 the original data had been first stored in the TSV format designed by EUROSTAT:
   * Unordered List ItemIn a first step, these files had been converted by an excel macro into csv format and an overall set with all items including their long text has been created to prepare further processing.    * Unordered List ItemIn a first step, these files had been converted by an excel macro into csv format and an overall set with all items including their long text has been created to prepare further processing. 
-  * In a second step these alredy GAMS readable files are stored in GDX format in folder “dat\capreg” and under version control. Meta data are added in the process as well.+  * In a second step these alredy GAMS readable files are stored in GDX format in folder “dat/capreg” and under version control. Meta data are added in the process as well.
  
  
Line 1262: Line 1767:
 |//Sum of slaughtered cows and stock change//| | |235,45|  |//Sum of slaughtered cows and stock change//| | |235,45|
 |GROFYCOW| Numer of heifers raised to young cows| 235,45 |227,16 |229,4| |GROFYCOW| Numer of heifers raised to young cows| 235,45 |227,16 |229,4|
-|HEIRLEVL| Activity level of the heifers raising process |235,45 |227,16 |229,4| +|HEIRLEVL| Activity level of the heifers raising process |235,45 |227,16 |229,4| \\ Source: CAPRI Modelling System
- \\ Source: CAPRI Modelling System+
  
  
Line 1273: Line 1777:
 |Dairy cows (DCOW) |DCOL: 60% milk yield of average, variable inputs besides feed an young animals at 60% of average |DCOH: 140% milk yield of average, variable inputs besides feed an young animals at 140% of average| |Dairy cows (DCOW) |DCOL: 60% milk yield of average, variable inputs besides feed an young animals at 60% of average |DCOH: 140% milk yield of average, variable inputs besides feed an young animals at 140% of average|
 |Bull fattening (BULF) |BULL: 20% lower meat output, variable inputs besides feed an young animals at 80% of average |BULH: 20% higher meat output, variable inputs besides feed an young animals at 120% of average| |Bull fattening (BULF) |BULL: 20% lower meat output, variable inputs besides feed an young animals at 80% of average |BULH: 20% higher meat output, variable inputs besides feed an young animals at 120% of average|
-|Heifers fattening (HEIF)| HEIL: 20% lower meat output, variable inputs besides feed an young animals at 80% of average |HEIH: 20% higher meat output, variable inputs besides feed an young animals at 120% of average| +|Heifers fattening (HEIF)| HEIL: 20% lower meat output, variable inputs besides feed an young animals at 80% of average |HEIH: 20% higher meat output, variable inputs besides feed an young animals at 120% of average| \\ Source: CAPRI Modelling System
- \\ Source: CAPRI Modelling System+
  
 ====Input allocation for feed==== ====Input allocation for feed====
Line 1702: Line 2205:
  
 |  **Ex-post**  |  **Ex-ante**  | |  **Ex-post**  |  **Ex-ante**  |
-|**Given:**\\ -Herd sizes\\  => Manure output\\ -Crop areas and yields\\  => Export with harvest\\ -National anorganic application\\ **Estimated:**\\ -Regional anorganic application\\ -Factor for Fertilization beyond N export\\ -Manure availability |**Model result:**\\ -Herd sizes\\  => manure output\\ -Crop areas and yields\\ => Export with harvest\\ -National and Regional anorganic application\\ **Given:** \\ -Factor for Fertilization beyond export (trended)\\ -Manure availability (trended)|+|**Given:**|**Model result:**| 
 +|-Herd sizes|-Herd sizes| 
 +| => Manure output|=> manure output| 
 +|-Crop areas and yields|-Crop areas and yields| 
 +|=> Export with harvest|=> Export with harvest| 
 +|-National anorganic application|-National and Regional anorganic application| 
 +|**Estimated:**|**Given:**| 
 +|-Regional anorganic application|-Factor for Fertilization beyond export (trended)| 
 +|-Factor for Fertilization beyond N export|-Manure availability (trended)| 
 +|-Manure availability| |
  
 A good overview on how the Nitrogen balances are constructed and can be used for analysis can be found in: Leip A., Britz W., de Vries W. and Weiss F. (2011): Farm, land, and soil nitrogen budgets for agriculture in Europe calculated with CAPRI, Environmental Pollution 159(11), 3243-3253 and Leip, A., Weiss, F. and Britz, W. (2011): Agri-Environmental Nitrogen Indicators for EU27, in: Flichman G. (ed.), Bio-Economic Models applied to Agricultural Systems, p. 109-124, Springer, Netherlands. A good overview on how the Nitrogen balances are constructed and can be used for analysis can be found in: Leip A., Britz W., de Vries W. and Weiss F. (2011): Farm, land, and soil nitrogen budgets for agriculture in Europe calculated with CAPRI, Environmental Pollution 159(11), 3243-3253 and Leip, A., Weiss, F. and Britz, W. (2011): Agri-Environmental Nitrogen Indicators for EU27, in: Flichman G. (ed.), Bio-Economic Models applied to Agricultural Systems, p. 109-124, Springer, Netherlands.
Line 1872: Line 2384:
 |::: |Manure management|CH4Man| |::: |Manure management|CH4Man|
 |::: |Rice production|CH4Ric| |::: |Rice production|CH4Ric|
-|::: |Land use change emissions from\\ biomass burning|CH4bur|+|::: |Land use change emissions from biomass burning|CH4bur|
 |**Nitrous Oxide**|Manure management|N2OMan| |**Nitrous Oxide**|Manure management|N2OMan|
 |::: |Manure excretion on grazings|N2OGra| |::: |Manure excretion on grazings|N2OGra|
Line 1878: Line 2390:
 |::: |Application of manure|N2OApp| |::: |Application of manure|N2OApp|
 |::: |Crop residues|N2OCro| |::: |Crop residues|N2OCro|
-|::: |Indirect emissions from ammonia \\ losses|N2OAmm| +|::: |Indirect emissions from ammonia losses|N2OAmm| 
-|::: |Indirect emissions from leaching \\ and runoff|N2OLea|+|::: |Indirect emissions from leaching and runoff|N2OLea|
 |::: |Cultivation of histosols|N2Ohis| |::: |Cultivation of histosols|N2Ohis|
-|::: |Land use change emissions from the \\ burning of biomass|N2Obur|+|::: |Land use change emissions from the burning of biomass|N2Obur|
 |**Carbon dioxide**|Cultivation of histosols|CO2his| |**Carbon dioxide**|Cultivation of histosols|CO2his|
 |::: |Applicaton of ureum|CO2urea| |::: |Applicaton of ureum|CO2urea|
 |::: |Liming|CO2lim| |::: |Liming|CO2lim|
-|::: |Land use change emissions from above \\ and below ground biomass|CO2bio| +|::: |Land use change emissions from above and below ground biomass|CO2bio| 
-|::: |Land use change emissions from soil \\ carbon changes|CO2soi| \\ Source: CAPRI Modelling System+|::: |Land use change emissions from soil carbon changes|CO2soi| \\ Source: CAPRI Modelling System
  
 For a detailed analysis of these single emission sources refer to Pérez 2006: Greenhouse Gases: Inventories, Abatement Costs and Markets for Emission Permits in European Agriculture -A Modelling Approach and Leip et al 2010: Evaluation of livestock sector’s contribution to the RU greenhouse gas emissions (GGELS). For a detailed analysis of these single emission sources refer to Pérez 2006: Greenhouse Gases: Inventories, Abatement Costs and Markets for Emission Permits in European Agriculture -A Modelling Approach and Leip et al 2010: Evaluation of livestock sector’s contribution to the RU greenhouse gas emissions (GGELS).
Line 1958: Line 2470:
  
 |  Region  |  crop or aggregate  |  Econometric estimation  |||  HPD solution including  ||| |  Region  |  crop or aggregate  |  Econometric estimation  |||  HPD solution including  |||
-|:::| |  regional  |  national- \\ including yield  |  national - \\ without yield  |  regional, \\ national, crop \\ aggregates  |  + expert assumption  |  + regional \\ labour supply  |+|:::| |  regional  |  national- including yield  |  national - without yield  |  regional, national, crop  aggregates  |  + expert assumption  |  + regional labour supply  |
 |Belgium (BL24)|Soft wheat| 31.49| 31.26| 31.49| 24.99| 32.73| 53.88| |Belgium (BL24)|Soft wheat| 31.49| 31.26| 31.49| 24.99| 32.73| 53.88|
 |:::|Sugar beet |  76.25| 77.39| 76.25| 62.19| 48.27| 68.36| |:::|Sugar beet |  76.25| 77.39| 76.25| 62.19| 48.27| 68.36|
Line 1997: Line 2509:
 The head section of the sub-module comprises (a) initialization of FAOSTAT-related and mapping sets which are used in all futher consolidation sections, (b) loading union sets from the CommodityBalances and ProductionAndRessources data files, (c) introducing the land categories relevant for the land use consolidation, (d) introduction of multiplication factors for the mapping of units between FAOSTAT and CAPRI items, and (e) initialization of parameters. The (c) land categories relevant for the land use consolidation are as follows: \\ The head section of the sub-module comprises (a) initialization of FAOSTAT-related and mapping sets which are used in all futher consolidation sections, (b) loading union sets from the CommodityBalances and ProductionAndRessources data files, (c) introducing the land categories relevant for the land use consolidation, (d) introduction of multiplication factors for the mapping of units between FAOSTAT and CAPRI items, and (e) initialization of parameters. The (c) land categories relevant for the land use consolidation are as follows: \\
  
-{{::code_p_96.png?600}} \\+{{::code_p_96.png?600}}
  
 The first consolidation section is on “Production and Ressources”. After loading the raw data at the beginning, the FAOSTAT units are mapped to CAPRI units via the “unit_map” set and corresponding multiplication factors as provided under (d) in the head section of the program to harmonise the units. After that the data is checked for completeness and various heuristic rules are applied to fill gaps in the data: \\ The first consolidation section is on “Production and Ressources”. After loading the raw data at the beginning, the FAOSTAT units are mapped to CAPRI units via the “unit_map” set and corresponding multiplication factors as provided under (d) in the head section of the program to harmonise the units. After that the data is checked for completeness and various heuristic rules are applied to fill gaps in the data: \\
  
-{{:code_p_96_2.png?600}} \\+{{:code_p_96_2.png?600}}
  
 After aggregating data for China and some reporting on missing data the consolidated production data is written to the /fao folder in the restart-directory for usage in the following consolidation steps. After aggregating data for China and some reporting on missing data the consolidated production data is written to the /fao folder in the restart-directory for usage in the following consolidation steps.
Line 2007: Line 2519:
 The next stept consolidates “Commodity Balances” and introduces the sets for the main balance components and demand positions as well as the mapping between the original FAOSTAT item codes and the commodity balance codes. This is another example that any data consolidation combining different data sets (even when coming from the same agency like FAO) needs to consider different coding systems used in those data sets: \\ The next stept consolidates “Commodity Balances” and introduces the sets for the main balance components and demand positions as well as the mapping between the original FAOSTAT item codes and the commodity balance codes. This is another example that any data consolidation combining different data sets (even when coming from the same agency like FAO) needs to consider different coding systems used in those data sets: \\
  
-{{:code_p_97.png?600}} \\+{{:code_p_97.png?600}}
  
 In addition to the item code and unit matching and the removal of flags, negative observations are removed (except for stock changes) from the data. Gap filling is based on weighted averages and smoothed interpolation. Total demand is added up from single demand positions if missing and single demand positions are scaled to given total demand in case they do not sum up consistently. Finally, stock changes are adjusted to ensure that market balances are closed. The consolidated commodity balance data is written to the /fao-folder in the restart directory for further usage inside the fao_balance_consolidation. In addition to the item code and unit matching and the removal of flags, negative observations are removed (except for stock changes) from the data. Gap filling is based on weighted averages and smoothed interpolation. Total demand is added up from single demand positions if missing and single demand positions are scaled to given total demand in case they do not sum up consistently. Finally, stock changes are adjusted to ensure that market balances are closed. The consolidated commodity balance data is written to the /fao-folder in the restart directory for further usage inside the fao_balance_consolidation.
Line 2013: Line 2525:
 The next stept combines production and ressources with the data on commodity balances in order to consolidate the land use data. The consolidation procedure for land use categories is a separate sub-routine included under this section:\\ The next stept combines production and ressources with the data on commodity balances in order to consolidate the land use data. The consolidation procedure for land use categories is a separate sub-routine included under this section:\\
  
-{{:code_p_97_2.png?600}} \\+{{:code_p_97_2.png?600}} 
  
 The land use consolidation step takes care of the mapping between FAOSTAT and CAPRI land use categories, imposes gap filling routines, introduces auxiliary data from UNFCCC and UNSTATS and ensures that nested land use categories consistently sum up to their totals.  The land use consolidation step takes care of the mapping between FAOSTAT and CAPRI land use categories, imposes gap filling routines, introduces auxiliary data from UNFCCC and UNSTATS and ensures that nested land use categories consistently sum up to their totals. 
Line 2019: Line 2531:
 The land use consistency is solved as an optimization problem ensuring (a) adding up of single crop areas to land use aggegates and (b) imposes constraints stemming from transition probabilities between different UNFCCC land use categories: \\ The land use consistency is solved as an optimization problem ensuring (a) adding up of single crop areas to land use aggegates and (b) imposes constraints stemming from transition probabilities between different UNFCCC land use categories: \\
  
-{{:code_p_98.png?600}}\\+{{:code_p_98.png?600}}
  
 Finally, crop area levels are rescaled based on the solution from the optimization problem and yields are recalculated accordingly. The consolidated land use data is written to the /fao-folder in the restart directory. Finally, crop area levels are rescaled based on the solution from the optimization problem and yields are recalculated accordingly. The consolidated land use data is written to the /fao-folder in the restart directory.
Line 2031: Line 2543:
 The consolidation of trade flows is split up across product specific groups to keep the task feasible in terms of computational complexity. The task is split up among 29 groups in total:\\ The consolidation of trade flows is split up across product specific groups to keep the task feasible in terms of computational complexity. The task is split up among 29 groups in total:\\
  
-{{:code_p_99.png?600}}\\+{{:code_p_99.png?600}}
  
 The whole procedure for creating a consistent data base as a starting point for the CAPRI task “Build global database” consists of two major tasks that are called the “groupSpecific” and “nongroupSpecific” tasks. The first one is the actual consolidation part that is done for each commodity group separately but executed in parallel. The second one is necessary for exporting the results such that they may be exploited via the GUI or be used as major input for the GLOBAL module. \\ The whole procedure for creating a consistent data base as a starting point for the CAPRI task “Build global database” consists of two major tasks that are called the “groupSpecific” and “nongroupSpecific” tasks. The first one is the actual consolidation part that is done for each commodity group separately but executed in parallel. The second one is necessary for exporting the results such that they may be exploited via the GUI or be used as major input for the GLOBAL module. \\
  
-{{:code_p_99_2.png?600}}\\+{{:code_p_99_2.png?600}}
  
 The group specific task starts 29 separate consolidation processes in parallel where the actual consolidation processes are defined in the separate include file “/fao/do_trade_consolidation_for_one_group.gms”.  The group specific task starts 29 separate consolidation processes in parallel where the actual consolidation processes are defined in the separate include file “/fao/do_trade_consolidation_for_one_group.gms”. 
  
 The trade consolidation part requires specific FAOSTAT trade data related sets that are loaded at the beginning of the include file. There are 18 different types of output reported in the result array.\\ The trade consolidation part requires specific FAOSTAT trade data related sets that are loaded at the beginning of the include file. There are 18 different types of output reported in the result array.\\
-{{::code_p_100.png?600}}\\+{{::code_p_100.png?600}}
  
 There are also 25 different statistics reported for the time series that are important intermediate indicators for the trade consolidation process. \\ There are also 25 different statistics reported for the time series that are important intermediate indicators for the trade consolidation process. \\
  
-{{:code_p_100_2.png?600}}\\+{{:code_p_100_2.png?600}}
  
 The trade consolidation consists of eight steps in sequence that are dependent on each other, i.e. each step produces an intermediate output file that is written to the /fao folder in the restart directory for usage in the follow-up steps.  The trade consolidation consists of eight steps in sequence that are dependent on each other, i.e. each step produces an intermediate output file that is written to the /fao folder in the restart directory for usage in the follow-up steps. 
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 In the following step (4) STATRADE a trust indicator is computed that allows to assign a trade flow value in case of conflicting notifications between trade partners. It is based on the sum of absolute differences to partner notifications relative to total notified trade. In the following step (4) STATRADE a trust indicator is computed that allows to assign a trade flow value in case of conflicting notifications between trade partners. It is based on the sum of absolute differences to partner notifications relative to total notified trade.
  
-{{:code_p_101.png?600}} \\+{{:code_p_101.png?600}} \
  
 The next step (5) TRDTRADE calculates national linear trend lines for quantities, values, unit values and price indices.  The next step (5) TRDTRADE calculates national linear trend lines for quantities, values, unit values and price indices. 
Line 2060: Line 2572:
 Step (6) INITRADE prepares the trade data for the final consolidation procedure by calculating expected means of imports, exports and unit values, and by computing the trust indicator, standard errors and expected standard errors for trade quantity and units, and unit values. The trust indicator is used for adjusting the standard errors in the estimation of trade flows between partners. Higher trust indicators result in lower standard errors and lower standard errors lead to smaller deviations from reported trade, i.e. the outcome from the estimation will deviate less from the reportings for more trustworthy partners, and vice versa. Step (6) INITRADE prepares the trade data for the final consolidation procedure by calculating expected means of imports, exports and unit values, and by computing the trust indicator, standard errors and expected standard errors for trade quantity and units, and unit values. The trust indicator is used for adjusting the standard errors in the estimation of trade flows between partners. Higher trust indicators result in lower standard errors and lower standard errors lead to smaller deviations from reported trade, i.e. the outcome from the estimation will deviate less from the reportings for more trustworthy partners, and vice versa.
  
-{{:code_p_101_2.png?600}} \\+{{:code_p_101_2.png?600}}
  
 The computations are accomplished for each commodity separately. The computations are accomplished for each commodity separately.
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 The second “nongroupSpecific” task in the trade consolidation part takes care of exporting the consolidated trade data to the /fao-folder in the results directory. This output is a major input for the CAPRI task “Build global database” (“fao_trade_for_global…gdx”). The trade data is complemented with data on conversion coefficients, on extraction rates, mappings between product equivalent and product codes, and between raw and processed goods, production data on the animal sector, and caseinTrade. The export job is included as a separate program under the nongroupSpecific task. The second “nongroupSpecific” task in the trade consolidation part takes care of exporting the consolidated trade data to the /fao-folder in the results directory. This output is a major input for the CAPRI task “Build global database” (“fao_trade_for_global…gdx”). The trade data is complemented with data on conversion coefficients, on extraction rates, mappings between product equivalent and product codes, and between raw and processed goods, production data on the animal sector, and caseinTrade. The export job is included as a separate program under the nongroupSpecific task.
  
-{{:code_p_102.png?600}} \\+{{:code_p_102.png?600}}
  
 ====Task: Build global database==== ====Task: Build global database====
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 **Figure 9: Overview on key elements in the consolidation of global data (in global_database.gms)** **Figure 9: Overview on key elements in the consolidation of global data (in global_database.gms)**
  
-{{:figure_8_1_.png?600|Source: own illustration}} +{{:figure_09.png?600|Source: own illustration}} 
  
 The program starts with including three general programs also present (possibly in task specific form) in other main programms plus the steering file (runglobal.gms) with more precise settings for the current run which may come from the GUI or from a batch file((A batch file is a steering file to execute a CAPRI task with all settings that are usually made in the GUI (say which simulation years) expressed equivalently in a certain language in a text file.)): The program starts with including three general programs also present (possibly in task specific form) in other main programms plus the steering file (runglobal.gms) with more precise settings for the current run which may come from the GUI or from a batch file((A batch file is a steering file to execute a CAPRI task with all settings that are usually made in the GUI (say which simulation years) expressed equivalently in a certain language in a text file.)):
  
-{{:code_p_103.png?600}} \\+{{:code_p_103.png?600}}
  
 After these general settings the programm continues in a rather standard manner with a section collecting various declarations of sets and parameters. Among these are the general sets of CAPRI (sets.gms), and the sets specific to the market model (arm_sets.gms) because the purpose of the task is to compile the data needed for the market model at the global level of CAPRI:  After these general settings the programm continues in a rather standard manner with a section collecting various declarations of sets and parameters. Among these are the general sets of CAPRI (sets.gms), and the sets specific to the market model (arm_sets.gms) because the purpose of the task is to compile the data needed for the market model at the global level of CAPRI: 
  
-{{:code_p_104.png?600}} \\+{{:code_p_104.png?600}}
  
 The most important data source for task “Build global database” is FAOstat which involves a fairly long file (FAO_codes_new.gms) with sets and cross-sets to map from FAO regions, items, and products into the CAPRI world (defined by the code system in the annex). This serves to map some key data from FAO compiled in the previous task: population (fao_population.gms), commodity balances combined with production and landuse statistics. Furthermore special balances for dairy products are loaded (all in load_fao_data_new.gms). The most important data source for task “Build global database” is FAOstat which involves a fairly long file (FAO_codes_new.gms) with sets and cross-sets to map from FAO regions, items, and products into the CAPRI world (defined by the code system in the annex). This serves to map some key data from FAO compiled in the previous task: population (fao_population.gms), commodity balances combined with production and landuse statistics. Furthermore special balances for dairy products are loaded (all in load_fao_data_new.gms).
  
-{{:code_p_104_2.png?600}} \\+{{:code_p_104_2.png?600}} 
  
 The second most important group of data, both historical as well as projections, for the global market model of CAPRI come from the Aglink-Cosimo model((This model is also used by DG Agri for its own outlook and provides important inputs to the CAPRI baseline.)), including its ex post database.  The second most important group of data, both historical as well as projections, for the global market model of CAPRI come from the Aglink-Cosimo model((This model is also used by DG Agri for its own outlook and provides important inputs to the CAPRI baseline.)), including its ex post database. 
  
-{{:code_p_104_3.png?600}} \\+{{:code_p104_3.png?600}}
  
   * The first $include file (load_%aglink%_new.gms((A string like %textname% is a placeholder in GAMS code for some other text to be substituted for %textname% during the program execution. In this example it holds the name for the specific Aglink-Cosimo version that should be loaded.)) ) includes the relevant sets to handle the Aglink data, including the cross-sets to map to CAPRI. In addition it also merges a special data set on fish markets with other original Aglink data.    * The first $include file (load_%aglink%_new.gms((A string like %textname% is a placeholder in GAMS code for some other text to be substituted for %textname% during the program execution. In this example it holds the name for the specific Aglink-Cosimo version that should be loaded.)) ) includes the relevant sets to handle the Aglink data, including the cross-sets to map to CAPRI. In addition it also merges a special data set on fish markets with other original Aglink data. 
Line 2103: Line 2616:
 The next three $include files cover additional macroeconomic data from UNstats (load_gdp_unstats_new.gms), include and map long run projections beyond the Aglink horizon from the GLOBIOM((The GLOBIOM model is the second model providing key inputs to the CAPRI baseline. It is mainly developed and operated at IIASA.)) model (create_longrun_info.gms, comment “merge FAO and IMPACT 2050 projections is obsolete), and collect prior values for demand elasticities from the literature (collect_literature_elas.gms, whereas demand elasticties from Aglink-Cosimo are ignored). The next three $include files cover additional macroeconomic data from UNstats (load_gdp_unstats_new.gms), include and map long run projections beyond the Aglink horizon from the GLOBIOM((The GLOBIOM model is the second model providing key inputs to the CAPRI baseline. It is mainly developed and operated at IIASA.)) model (create_longrun_info.gms, comment “merge FAO and IMPACT 2050 projections is obsolete), and collect prior values for demand elasticities from the literature (collect_literature_elas.gms, whereas demand elasticties from Aglink-Cosimo are ignored).
  
-{{:code_p_105.png?600}} \\+{{:code_p_105.png?600}}
  
 It may be seen that “create_longrun_info.gms” is active or not depending on a setting from the GUI or a batch file. Similar to the code processing Aglink information it includes sets and mappings to handle the GLOBIOM information. Another similarity with the Aglink related files is that this code basically needs annual adjustments, because some definitions are changing from year to year and there are two GLOBIOM versions to distinguish, one with a certain EU focus, the other one with a perfectly global orientation. Finally, it may be mentioned that the projections are introduced into the CAPRI world mostly in the form of growth factors.  It may be seen that “create_longrun_info.gms” is active or not depending on a setting from the GUI or a batch file. Similar to the code processing Aglink information it includes sets and mappings to handle the GLOBIOM information. Another similarity with the Aglink related files is that this code basically needs annual adjustments, because some definitions are changing from year to year and there are two GLOBIOM versions to distinguish, one with a certain EU focus, the other one with a perfectly global orientation. Finally, it may be mentioned that the projections are introduced into the CAPRI world mostly in the form of growth factors. 
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 The CAPRI market model is spatial and therefore requires data on bilateral trade flows. These are covered in two include files, the first one dealing with the special case of biofuel trade flows, the second one with the general case. \\ The CAPRI market model is spatial and therefore requires data on bilateral trade flows. These are covered in two include files, the first one dealing with the special case of biofuel trade flows, the second one with the general case. \\
  
-{{:code_p_106.png?600}} \\+{{:code_p_106.png?600}}
  
 Biofuel trade requires a special treatment again because FAOstat does not cover these. Instead, bilateral trade flows are constructed using total exports and imports from AGLINK and trade data from COMEXT, USDA and FO-Licht. By contrast the data for the trade matrix for other commodities is from FAOstat.  Biofuel trade requires a special treatment again because FAOstat does not cover these. Instead, bilateral trade flows are constructed using total exports and imports from AGLINK and trade data from COMEXT, USDA and FO-Licht. By contrast the data for the trade matrix for other commodities is from FAOstat. 
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 The second is a transport cost matrix estimation using the original FAOstat trade matrix (so before gap filling and consolidation) and distance related information from CEPII. Together with price information the transport costs are estimated to provide a link between CIF and FOB prices for bilateral tradeflows. The second is a transport cost matrix estimation using the original FAOstat trade matrix (so before gap filling and consolidation) and distance related information from CEPII. Together with price information the transport costs are estimated to provide a link between CIF and FOB prices for bilateral tradeflows.
  
-{{:code_p_106_2.png?600}} \\+{{:code_p_106_2.png?600}} 
  
 The next $include file extends the Aglink-Cosimo projections to 2030, if needed, with a trend estimation involving a number of pragmatic modifications (such as the trend line passing trough the last observation). Then the the growth factors computed previously or the default trends are used to estimate a medium term outlook projections for global market balances, prices or GDP. These projections do however not include any consictency checks on closed market balances or similar properties. This is achieved in the baseline calibration only. The next $include file extends the Aglink-Cosimo projections to 2030, if needed, with a trend estimation involving a number of pragmatic modifications (such as the trend line passing trough the last observation). Then the the growth factors computed previously or the default trends are used to estimate a medium term outlook projections for global market balances, prices or GDP. These projections do however not include any consictency checks on closed market balances or similar properties. This is achieved in the baseline calibration only.
  
-{{:code_p_107.png?600}} \\+{{:code_p_107.png?600}} 
  
 Finally, data on trade policy variables such as applied and scheduled tariffs, tariff rate quotas or bilateral trade agreements are collected from the Agricultural Market Access Database (AMAD, obsolete current version) or from the MacMaps database (%macMap%)((See GAMS Documentation on The GAMS Call and Command Line Parameters (https://www.gams.com/latest/docs/UG_GamsCall.html))==on, but not yet activated under Star2.4). Finally, data on trade policy variables such as applied and scheduled tariffs, tariff rate quotas or bilateral trade agreements are collected from the Agricultural Market Access Database (AMAD, obsolete current version) or from the MacMaps database (%macMap%)((See GAMS Documentation on The GAMS Call and Command Line Parameters (https://www.gams.com/latest/docs/UG_GamsCall.html))==on, but not yet activated under Star2.4).
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 The very last include file is probably also the least important one: FAPRI projections had a more important role several years ago, are not updated anymore and presumable affect less than a dozen numbers (if any at all) in the global database compiled in this task: The very last include file is probably also the least important one: FAPRI projections had a more important role several years ago, are not updated anymore and presumable affect less than a dozen numbers (if any at all) in the global database compiled in this task:
  
-{{:code_p_107_2.png?600}} \\+{{:code_p_107_2.png?600}} 
  
 =====Policy data===== =====Policy data=====
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 ===Tariffs and Tariff Rate Quotas=== ===Tariffs and Tariff Rate Quotas===
  
-Data on trade policy instruments other than tariffs (Tariff Rate Quotas, export subsidies, entry price system and flexible levies) enter CAPRI directly in the market model calibration workstep. Note that the ad valorem equivalent tariff rates in MacMap already include an estimated equivalent tariff rate for TRQs. Nevertheless, the CAPRI market model separates TRQs from fixed tariff rates by using a sigmoid function-representation of the TRQ regime switch mechanism((Tariff rates under TRQ vary between the lower in-quota and the higher out-of-quota rates, depending on the quota fill rates. For more details on the methodological approach please visit section [[scenario simulation#Market module for agricultural outputs#Endogenous tariffs under Tariff Rate Quotas, flexible levies and the minimum import price regime for fruits and vegetables of the EU]])). +Data on trade policy instruments other than tariffs (Tariff Rate Quotas, export subsidies, entry price system and flexible levies) enter CAPRI directly in the market model calibration workstep. Note that the ad valorem equivalent tariff rates in MacMap already include an estimated equivalent tariff rate for TRQs. Nevertheless, the CAPRI market model separates TRQs from fixed tariff rates by using a sigmoid function-representation of the TRQ regime switch mechanism((Tariff rates under TRQ vary between the lower in-quota and the higher out-of-quota rates, depending on the quota fill rates. For more details on the methodological approach please visit section [[scenario simulation#Endogenous tariffs under Tariff Rate Quotas, flexible levies and the minimum import price regime for fruits and vegetables of the EU]])). 
  
 The TRQ system of the EU is included in great detail, based on DG AGRI.information. Data on  TRQ orders are aggregated to the geographical and commodity definitions of CAPRI in dat/arm/TRQ_orderds.gms. Specific GAMS routines convert some of the compound TRQs into ad valorem TRQs if necessary((Compound TRQs are TRQs applying a compound tariff (combination of specific and ad valorem) on the in-quota or out-of-quota imports. For methodological reasons, the compound tariffs might need to be converted into their ad valorem equivalent rates.))(gams/arm/convert_compound_trqs.gms). The TRQ system of the EU is included in great detail, based on DG AGRI.information. Data on  TRQ orders are aggregated to the geographical and commodity definitions of CAPRI in dat/arm/TRQ_orderds.gms. Specific GAMS routines convert some of the compound TRQs into ad valorem TRQs if necessary((Compound TRQs are TRQs applying a compound tariff (combination of specific and ad valorem) on the in-quota or out-of-quota imports. For methodological reasons, the compound tariffs might need to be converted into their ad valorem equivalent rates.))(gams/arm/convert_compound_trqs.gms).
the_capri_data_base.1588322299.txt.gz · Last modified: 2022/11/07 10:23 (external edit)
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