Differences

This shows you the differences between two versions of the page.

--- the_complete_and_consistent_data_base_coco_for_the_national_scale [2020/02/11 15:42] – [COCO1 Estimation procedure] matsz
+++ the_complete_and_consistent_data_base_coco_for_the_national_scale [2020/02/13 10:34] – [COCO2: Final completions] matsz
@@ Line 327: / Line 327: @@
 & + \sum_{i,t} wgt^{up}((max(y_{i,t}^{up},y_{i,t})-y_{i,t}^{up}))/abs(y_{i,t}^{up}))^2\\
 & + \sum_{i,t} wgt^{lo}((min(y_{i,t}^{lo},y_{i,t})-y_{i,t}^{lo}))/abs(y_{i,t}^{lo}))^2\\
-\text {s.t.}\\
-y_{i,t}^{LO}<y_{i,t}<y_{i,t}^{UP}\\
-\text {Accounting identities defined on} y_{i,t}\\
-\text {Identity of land use from different sources}
 \end{split}
 \end{align}
+\begin{align*}
+\begin{split}
+&\text {s.t.}\\
+&y_{i,t}^{LO}<y_{i,t}<y_{i,t}^{UP}\\
+&\text {Accounting identities defined on} y_{i,t}\\
+&\text {Identity of land use from different sources}
+\end{split}
+\end{align*}
 where //i// represents the index of the elements to estimate (crop production activities or groups, herd sizes etc.), //t// stands for the year, wgtx are weights attached to the different parts of the objective (\(wgt^{dat} = wgt^{hp} = 10, wgt^{ini} = 1, wgt^{up} = wgt^{lo} = 100)\), and
@@ Line 416: / Line 420: @@
 \(CORF\) =	ratio of on farm content to the standard content
-and CORF is contrained to equal to one except that we permit CORF  1 for FRMI.
+and CORF is contrained to equal to one except that we permit CORF $\neq$ 1 for FRMI.
 Production in dairies and on farm may be added to obtain the total production that enters the market balances:
@@ Line 486: / Line 490: @@
 This procedure has developed as a path dependent compromise between computation time and presumed quality. It starts with an estimation of land use in combination with agricultural land balance, including the land transition between LU classes. This determines the utilisable agricultural area (UAA) and non-agricultural land use. Step 2 distributes crop areas within the fixed UAA from step 1 and estimates crop production and yields. Step 3 only tackles the complete animal sector data (activities, markets, EAA). The crop production is taken as given, when market balance and EAA are estimated for the crops and derived processed products (step 4). However, with all steps completed some final checks may modify the results (e.g. delete tiny activity levels or estimate another crop area from another crop output value and thus change the UAAR). Furthermore the crop estimation may have slightly changed the ratio of cropland to productive grassland. Therefore the accounting identities ensured in steps 1 are not necessarily fulfilled in a strict sence anymore. Hence a final reconciliation of land use is added for full consistency:
-**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|}}
-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.
 Furthermore it should be mentioned that the main parts of COCO are handled in a program (‘coco1.gms’) looping over MS because there are no direct linkages between them. However, for practical reasons it will be useful to run COCO in country groups that have the same coverage of years. The longest series (as off 1984) can be established for EU15((Belgium and Luxembourg are aggregated in COCO for reasons of data availability.)) countries except Germany. For the New MS it turned out that data before 1989 are often very unreliable and create considerable burden in the data maintenance. These countries (and Germany) are only completed for years from 1989 onwards therefore. Norway also offers reliable series as of 1984. In the case of the Western Balkan countries it is rather hopeless to provide very recent data as key data are still missing such that the series can only be completed from 1995 onwards. Furthermore for the Western Balkan counties it was necessary to transfer certain coefficients and shares from (previously consolidated) neighbouring countries to the Western Balkan, such that a certain sequence is necessary for a reasonable application of COCO1:
@@ Line 498: / Line 502: @@
 ====COCO2: Data Preparation====
+The data consolidation in COCO2 only covers a few special topics:
+  * producer prices of dairy products and vegetable oils
+  * consumer prices
+  * consumer losses and nutrient intake after losses
+  * feed stuff quantities without market balances (by-product, fish emal)
+  * loss rates of fodder for preliminary balancing of animal nutrients
+  * corrections of certain LULUCF coefficients based on UNFCCC
+An overview is given in the following figure.
+**Figure 4: Overview on main elements in the finalisation step for the consolidation of national data in Europe (in coco2.gms)**
+{{::figure_4.png?600|}}
+In spite of only limited subtasks tackled in coco2.gms, the multitude of different data inputs is comparable to that in COCO1.
+**Include file //‘coco2_collect.gms’//**
+Various input files are collected with some adjustments to match to CAPRI definitions and with some gap filling. As the consumer prices follow from a top down expenditure allocation problem, the input data range from macroeconomic information to very detailed prices of food items.
+  * Consolidated data from COCO1
+  * Macroeconomic information from Eurostat and UNSTATS: Exchange rates, population, GDP deflator, private consumption of households in current prices.
+  * Price index information: Aggregate food price index, relative (to EU) food price index, harmonised indices of consumer prices (HICPs) with item weights all from Eurostat
+  * Expenditure by product groups (from Eurostat and national sources)
+  * Auxiliary data for special cases (Prices for some milk products in selected countries, fish meal information etc)
+  * Country Sheets of the Western Balkan and Turkey: Exchange rate, inhabitants, inflation rate, food expenditure shares
+  * Disaggregate absolute consumer prices for selected narrowly defined food items (ILO and Eurostat)
+Where available, producer prices for milk products were already included from Eurostat statistics (Agricultural prices and price indices) in COCO1. Completeness was not achieved in COCO1, however, because processed dairy products are not part of the EAA. Here we complete some gaps using price information for some Member States and (partly assumed) relationships among dairy product prices and their fat and protein contents.
+Data on total consumer expenditures as well as expentitures by food groups are included from various sources as described in Chapter 2.2.2.5, partly extended using general price index information.
+Consumer price index weights and price indices for food aggregates (2005=100) are coming from Eurostat tables on HICP. Supplementary information for Albania, Bosnia and Croatia comes from national agencies. The price index weights are used to extend older series on food expenditure by product groups (say “meat”) which have been discontinued (see below under file coco2_shares.gms).
+Finally we use very narrowly defined absolute consumer prices (e.g. for spaghetti) and price indices. The earlier years (before 2008) had been provided by ILO which has discontinued this activity. For a subset of those Eurostat offers matching information as “detailed average prices (table prc_dapYY) that has been used to extend the ILO series. These prices are mapped to CAPRI regions, products and units (//‘coco2_ilo_addup.gms’//).
+Price indices for food and non-alcoholic beverages from HICP as well as the general food price index are used to complete the disaggregate ILO prices for single typical food items.  (like “Wheat bread white unsliced not wrapped”) using a Hodrick-Prescott filter and the expectation that their changes should follow the price index informaiton collected.
+Finally another HPD estimator is used to adjust the dissagregate prices to be (somewhat) in line with Eurostat information on relative food price levels across Europe.
+**Include file //‘coco2_shares.gms’//**
+Expenditure shares are defined and completed top-down using simple OLS estimates against related statistical expenditure information or, as a last fall back option, based on a trend.
+The food expenditure share completions start with data from COICOP level 3 giving results on food and non-alcoholic beverages. Further disaggregation relies on historical Eurostat data (HIST), on the above mentioned index weights from HICP and partly national data (Germany and Spain).
+A conveninent expenditure group is potatoes as these expenditure shares may be extrapolated based on COCO1 human consumption multiplied by producer price as regressors for OLS.
+====COCO2: Estimation procedure====
+**Include file //‘coco2_def.gms’//**
+The approach to determine consumer prices is to distribute food expenditure on groups with consumption quantities given from COCO1 results such that endogenous consumer prices link endogenous expenditure with exogenous quantities. Deviations of estimated expenditure and consumer prices from their supports is penalised in an entropy framework. Estimation is done year by year, starting with the most recent year where hard data are usually available to a greater extent than for the oldest years in the database. Including consumer price changes (always relative to the previously solved year) serves to stabilise the results to some extent such that the objective does not only have supports for the consumer prices, but also for their changes. The entropy problem is solved by maximizing:
+\begin{align}
+\begin{split}
+max_t &- \sum_{m,j,k} CPS_{m,j,2}*HCOM_{m,j,k}/1000/TOFO_{m,t}*\\
+&PE_{m,j,k}*LOG(PE_{m,j,k}/PQ_k)\\
+&-\sum_{m,j,k} CPS_{m,j,2}*HCOM_{m,j,k}/1000/TOFO_{m,t}*\\
+&PED_{m,j,k}*LOG(PED_{m,j,k}/PQ_k)\\
+&-\sum_{m,FOPOS,k} EXS_{m,FOPOS,2}/TOFO_{m,t}*\\
+&PEX_{m,FOPOS,k}*LOG(PEX_{m,FOPOS,k}/PQ_k)\\
+&-\sum_{m,j,k} PFAC_{m,k}*LOG(PFAC_{m,,k}/PQ_k)*1000\\
+\end{split}
+\end{align}
+where //m// represents the region, //j// the food item with consumer price, FOPOS the food group, //t// stands for the current estimation year, t_1 for the year estimated before and k for the number of support points (=3).
+Parameters are
+| \(HCOM_{m,j,t}\) |Human consumption, result from COCO1|
+| \(UVAD_{m,j,t\_1}\)	|Consumer price from last simulation of year t+1|
+|\(CPS_{m,j,k}\)	|Support points for consumer prices |
+|\(DCPS_{m,j,k}\)	|Support points for consumer price changes|
+|\(EXS_{m,FOPOS,k}\)	|Support points for group expenditures|
+|\(TOFACS_{m,k}\)	|Support points for total food expenditure slack|
+|\(PQ_k\)		|A priori probabilities for support points|
+|\(TOFO_{m,t}\)	|Total food expenditure |
+|and entropy variables||
+|\(PE_{m,j,t}\)	|Probability of support points for consumer prices|
+|\(PED_{m,j,t}\)	|Probability of support points for consumer price changes|
+|\(CP_{m,j}\)	|Consumer prices|
+|\(DCP_{m,j}\)	|Consumer price changes|
+|\(PEX_{m,FOPOS,t}\)	|Probability of support points for group expenditure|
+|\(PFAC_{m,k}\)	|Probability of support points for food expenditure slack|
+|\(EX_{mFOPOS}\)	|Group expenditures|
+|\(TOFAC_m\)	|Food expenditure slack|
+Constraints are as follows:
+Summing up probabilities for support points
+\begin{equation}
+\sum_{k\forall_{m,j}(CP.L_{m,j}\ge 0\wedge HCOM_{m,j,i}\ge 0)} PE_{m,j,k}=1
+\end{equation}
+\begin{equation}
+\sum_{k\forall_{m,j}(DCPS_{m,j}\ge 0\wedge HCOM_{m,j,i}\ge 0)} PE_{m,j,k}=1
+\end{equation}
+\begin{equation}
+\sum_{k\forall_{m,j}(EX.L_{m,FOPOS}\ge 0)} PE_{m,FOPOS,k}=1
+\end{equation}
+\begin{equation}
+\sum_{k\forall_{m}(TOFAC.LO_m\ge TOFAC.UP_m)} PFAC_{m,k}=1
+\end{equation}
+Define consumer price changes from support points
+\begin{equation}
+DCP_{m,j} = \sum_{k\forall_{m,j}(CP.L_{m,j}\ge 0\wedge HCOM_{m,j,i}\ge 0 \wedge DCPS_{m,j,2}\ge 0)} PED_{m,j,k}*DCPS_{m,j,k}
+\end{equation}
+Of course consumer prices changes are also related to the last simulation result (which is for T+1 due to backward looping)
+\begin{equation}
+DCP_{m,j} =UVAD_{m,j,t\_1}-CP_{m,j}
+\end{equation}
+Define consumer prices from support points and probabilities
+\begin{equation}
+CP_{m,j} = \sum_{k\forall_{m,j}(CP.L_{m,j}\ge 0\wedge HCOM_{m,j,i}\ge 0)} PE_{m,j,k}*CPS_{m,j,k}
+\end{equation}
+Define group expenditure from support points and probabilities
+\begin{equation}
+EX_{m,FOPOS} = \sum_{k\forall_{m,j}(EX_{m,FOPOS}\ge 0)} PEX_{m,FOPOS,k}*EXS_{m,FOPOS,k}
+\end{equation}
+Define total expenditure slack from support points and probabilities
+\begin{equation}
+TOFAC_m=\sum_{k\forall_{m}(TOFAC.LO_m\ge TOFAC.UP_m)} PFAC_{m,k}*TOFACS_m
+\end{equation}
+Exhaustion of food expenditure may be relaxed with a slack factor different from one. However, this “last resort” to achieve feasibility in the expenditure allocation problem is limited to years and countries with precarious data and subject to strong penalties.
+\begin{equation}
+\sum_{FOPOS} EX_{m,FOPOS}=TOFO_{m,t}*TOFAC_{m,k}
+\end{equation}
+Consistency of group expenditure
+\begin{equation}
+EX_{m,FOPOS}=\sum_{j\forall_{m,FOPOS}(j\in FOPOS\wedge HCOM_{m,j} \ge 0)}CP_{m,j}*HCOM_{m,j}/1000
+\end{equation}
+For most countries the exhaustion of total expenditure is the only evident hard constraint (and even this is relaxed in problem cases). However, as the penalties for group expenditure are set high, and furthermore as the range of expenditure supports defines additional implicit hard constraints, the problem may turn out infeasible (typically solved by additional leeway). To meet the expenditure constraints the solver would tend to concentrate deviations from supports on the most important expenditure items while setting the less important items close to their supports. A more balanced distribution of deviations from supports was achieved in practice by weighting all contributons to the overall objective (except the last one for the total expenditure slack) with expected expenditure shares. The weights may be interpreted as expected expenditure shares because supports are specified in a symmetric way such that the central, second (of three) supports, which is used in the objective function, is equal to the expectation.
+**Include file //‘coco2_solve.gms’//**
+The initialisation, solving, reporting and storage is organised in the next include files with a few elements worth mentioning
+  * The initialisation tries to ensure positive consumer margins by the assignments of expected values and by specifying bounds on estimated consumer prices. The reference point for these margins is an average of EU and national prices that reflects the importance of domestic sales vs. imports.
+  * Bounds and spread of supports around expected consumer prices are set high for items without ILO style prices (say “table olives” TABO) or where the fit of available price information is questionable (e.g. cabbage prices for “OVEG”).
+  * A checking parameter (“p_checks”) permits to check the iniitalisation in case of infeasibilites. The most frequent case observed in the last years is that lower bounds on oils expenditure become binding, suggesting the need for some systematic mismatch of price and expenditure information for this group.
+====COCO2: Final completions====
+At this point it may be motivated why there is at all a need for a COCO2 module instead of handling all further topics in COCO1, that is MS by MS. There are basially two motives:
+  * 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 3.2.5), 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).
+===Assignment of consumer loss rates and nutrient intake per head ===
+Since a number of years diet shift scenarios have increase in importance and therefore the plausibility of per capita consumption projectios and hence their starting values, per capita consumption in the data base. A common yardstick to assess plausibility is nutrient (e.g. calorie) consumption per head where the nutrition literature offers guidance in terms of recommendable as well as “observed” consumption. For nutrition issues it is intake, so consumption after losses, which matters, such that the assignment of these loss rates becomes a critical element of the database. The starting values are due to an FAO study and stored in the \dat folder
+{{::code_p53.png?600|}}
+The aggregate food share (= 1-loss shares) links intake (INHA(i)) to total consumption (sum(i, HCOM(i)*foodSh(i)) / INHA(levl) and is therefore stored in the database as well.
+{{:code_p53_2.png?600|}}
+In spite of the FAO study the real loss rates are highly uncertain. Therefore they are reduced if the estimate of calorie intake based on the FAO loss rates strongly falls short of recommendations (most strongly in a set of “low calory regions”). Conversely loss rates are increased, if the estimate of calorie intake based on the FAO loss rates strongly exceeds recommendations (e.g. in Turkey).
+{{:code_p53_3.png?600|}}
+=== Completion of feed related data in coco2_feed ===
+The first sections of coco2_feed handle completions for certain by-products and other product so far ignored in coco1. These are by-products of the milling and the brewing industry and for corn gluten feed, sugarbeet pulp, manioc and fish meal where the database is completed for market balance positions production, imports, exports and feed. This relies on discontinued Eurostat tables (collected on p_feedAgri) which are extended using national data and external trade data from Comext. After completion the detailed by-products are aggregated to the CAPRI rows FENI (Rich energy fodder imported or industrial) and FPRI (Rich protein fodder imported or industrial). Based on completed data for all feedingstuffs nutrient contents for the CAPRI feed “bulks” (cereal feed FCER, protein feed FPRO etc) are assigned as an aggregate of their components.
+These completions are useful as such but they also permit a balancing of (preliminary) total nutrient supply and demand in the animal sector that ultimately serves to adjust loss rates for fodder with the help of a number of include files:
+**Include files //‘feed_decl.gms’// and //‘req_or_man_fcn.gms’//**
+These files are not only active in COCO2, but also in CAPREG, and in the baseline calibration of CAPMOD. This “reuse” of the same files in different modules is efficient and ensures consistency, but usually also requires some adaptations of set definitions:
+{{::code_p54.png?600|}}
+The previous snippet from coco2_feed gives an example that some sets (RS, R_RAGG) are assigned specifically to ensure functionality in different modules (here COCO2).
+As the name should signal file //‘feed_decl.gms’// mainly collects a number of declarations but it also specifies some bounds for process length DAYS and daily growth DAILY that are imposed throughout of CAPRI (example: maximum daily growth for male cattle = 1.5kg/day). The second include file (//‘req_or_man_fnc.gms’//) specifies the requirement functions (with the argument “req” passed on) for animal activities of CAPRI.
+Requirement functions are specified that determine:
+  * ENNE	Net energy for ruminants as sum of
+    * NEL	net energy for lactation (cows, ewes, goats)
+    * NEM	net energy for maintenance (cows, calves, bulls, heifers, ewes, goats)
+    * NEA	net energy for activity (cows, calves, bulls, heifers, ewes, goats)
+    * NEP	net energy for pregnancy (cows)
+    * NEG	net energy for growth (calves, bulls, heifers)
+  * ENMC	Net energy chicken
+  * ENMP	Net energy pigs
+  * CRPR   crude protein (all categories) and LISI lysine aminoacid (sows, poultry)
+  * DRMA dry matter (all categories with min and max requirements)
+  * Various fiber measures (irrelevant for COCO2)
+There are three main sources for these functions:
+  * IPCC 2006 guidelines for the estimation of emissions ([[http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf]])
+  * Kirchgessner Tierernährng, 7th edition,  1987
+  * CAPRI working paper 97-12 ([[http://www.ilr.uni-bonn.de/agpo/publ/workpap/pap97-12.pdf]])
+These functions are one the one hand quite complex. They are composed of various parts that finally give the requirements, for example for energy, as a function of various parameters that may be specific to the region (often the final weights, process length, daily growth) or uniform across regions (carcass ratio). In spite of several components these are typically linked in a straightforward fashion as will be illustrated with a relatively easy example (energy for maintenance of heifers for fattening).
+As a starting point, the daily growth from COCO is forced into the range defined in //‘feed_decl.gms’//. At the same time regions with a stocking rate above the MS average are assumed to rely on more intensive technologies, such that their daily growth is also above average (but within the range [\(DAILY_{lo},DAILY_{up}\)]). This is irrelevant in COCO (r=MS, no subnational regions) but relevant for CAPREG and CAPMOD calling the same //‘req_or_man_fnc.gms’//:
+\begin{align}
+\begin{split}
+&dailyIncrease_r^{HEIF}\\
+&= min [DAILY_{up}^{HEIF},max(DAILY_{lo}^{HEIF},\frac {stockingrate_r} {stockingrate_{MS}} DAILY_{MS}^{HEIF})]
+\end{split}
+\end{align}
+The daily increase is then used to determine the process length (rearrangement of equation below with empty days EDAYS = 0)
+\begin{align}
+\begin{split}
+&fatngday_r^{HEIF}\\
+&= min [DAYS_{up}^{HEIF},max\{DAYS_{lo}^{HEIF},\\
+& \quad (BEEF_r^{HEIF}/carcassSh_{HEIF}-startWgt_{HEIF})/dailyIncrease_r^{HEIF}\}]
+\end{split}
+\end{align}
+The daily increase and process length may be conbined to estimate the mean live weight,
+\begin{equation}
+meanWgt_r^{HEIF}=startWgt_{HEIF}+\frac {dailyIncrease_r^{HEIF}\cdot fatngdays_r^{HEIF}} 2
+\end{equation}
+which in turn is the last information to estimate energy requirements for maintenance according to the IPCC guidelines:
+\begin{equation}
+NEM_r^{HEIF}=(meanWgt_{HEIF})^{0.75}\cdot 0.322 \cdot fatngdays_r^{HEIF}
+\end{equation}
+Other energy requirements (for growth and activity) are calculated in a similar fashion as well as those for other animals. Important aspects to note are
+  * Fixed bounds for DAYS and DAILY ensure reasonable requirements, but require that the same constraints are anticipated in COCO and CAPREG to avoid inconsistencies.
+  * Regional coefficients are derived from the MS level information
+**Include file //‘coco2_gras.gms’//**
+With animal requirements specified the results of COCO1 for grass, other fodder and as a last resort cereals might be revised in terms of losses on farm to achieve an acceptable relationship of energy and protein requirements of total herds compared to the intake with feed. For gras and other fodder on arable land the contents may be adjusted in certain limits as well. The corrections do not eliminate the typical oversupply of nutrients compared to the requirements based on the literature, but they should give reasonable starting values for the feed allocation addressed in module CAPREG.
+===Compare COCO1 results with UNFCCC and compute correction factors in coco2_lulufc_carbon===
+In COCO1, an assignment of LULUCF effects (totals and per ha) has taken place, mostly relying on IPCC coefficients. These assignments are compared in coco2_lulucf_carbon with the reportings from EU MS to UNFCCC. For forestry and any transitions involving forestry, the standard IPCC reporting appears rather coarse, as it implies, for example, that management of forest land remaining forest has zero carbon effects. By contrast most EU countries report that there is still a considerable gain in biomass from forest management because the forests have not yet achieved a stable state (as implied by IPCC standard methodology).
+To pick up the detailed knowledge of management practices, disturbances, age and species structure embededed in the country level UNFCCC reporting the forest management coefficients per ha for the remaining class (FORFOR) have been already adopted in COCO1. Here we also compute correction factors for the default per ha effects from transitions involving forestry. These are ultimately stored on the data(.) array unloaded in the main result file to be used in LULUCF accounting of CAPMOD.
+===Complete prices for vegetable oil in coco2_oil_price===
+The EU prices for vegetable oils relevant for biofuel processing functions are assigned using prices from a USDA source. These assignments refer to prices at the wholesale level (relevant for the processing industry), not to consumer prices which have been determined previously.
+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).