Differences

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

--- forecast_tool_captrd [2020/02/27 09:15] – [Step 2.1: Consistency constraints in the trend projection tool] matsz
+++ forecast_tool_captrd [2022/11/07 10:23] (current) – external edit 127.0.0.1
@@ Line 268: / Line 268: @@
 \begin{align}
 \begin{split}
-X_{r,resimp,t}^{MAPR,Trend}= & \sum_{o \rightarrow resimp} \left(X_{r,0,t}^{HCOM,Trend} +X_{r,o,t}^{PRCM,Trend} +X_{r,o,t}^{INDM,Trend} +X_{r,o,t}^{BIOF,Trend}\right) \\
+X_{r,resimp,t}^{MAPR,Trend}= & \sum_{o \rightarrow resimp} \left(X_{r,o,t}^{HCOM,Trend} +X_{r,o,t}^{PRCM,Trend} +X_{r,o,t}^{INDM,Trend} +X_{r,o,t}^{BIOF,Trend}\right) \\
 & \cdot \frac {X_{r,resimp,t}^{MAPR,bas}}{ \sum_{o \rightarrow resimp} \left( X_{r,o,t}^{HCOM,bas} +X_{r,o,t}^{PRCM,bas}+ X_{r,o,t}^{INDM,bas}+ X_{r,o,t}^{BIOF,bas}\right)}
 \end{split}
@@ Line 274: / Line 274: @@
 ===Constraints relating to bio-fuel production===
+Marketable production (//MAPR//) of biofuels (//seco_biof//) derives (according to Equation //BIOF_//) from non agricultural production NAGR (e.g biodiesel from waste oil), from second generation production //SECG// , or through processing yields in terms of biofuels ((Note that the processing yields PRCY (say X tons of rape oil per ton of rape) are associated with the outputs, because there is just one possible input for the given output (say PRCY.RAPO = yield of rape in terms of rape oil). But in the case of bio-ethanol, for example, there are several feedstocks (wheat, barley etc) producing one output (ethanol). Hence the output coefficients PRCB are associated with the inputs (say PRCB.BARL = yield of barley in terms of ethanol) and we need different types of coefficients.)) (//PRCB//) from biofuel use of first generation feedstocks (//BIOF//):
+\begin{equation}
+X_{r,seco\_biof,t}^{MAPR,Trend}=\sum_{stocks \rightarrow seco\_biof } X_{r,stocks,t}^{BIOF,Trend} X_{r,stocks,t}^{PRCB,Trend}
+\end{equation}
+In case of ethanol there is another by-product, //DDGS//, which is usable as a feedstuff and produced according to by-product coefficients from cereals (//DDGS_//):
+\begin{equation}
+X_{r,DDGS,t}^{MAPR,Trend}=\sum_{stocks \rightarrow DDGS} X_{r,stocks,t}^{BIOF,Trend} X_{r,stocks,t}^{PRCBY,Trend}
+\end{equation}
+===Constraints relating to policy===
+There are only a few constraints directly taken from an EU regulation: firstly, the acreage under compulsatory set-aside (abolished in the CAP Health Check of 2008) must be equal to the set-aside obligations of the individual crops (//OSET_//):
+\begin{equation}
+X_{r,"levl",t}^{"OSET",Trend}=\sum_{cact} X_{r,"levl",t}^{cact,Trend} \frac {0.01 X_{r,"setr",t}^{cact,Trend}}{\left(1-0.01X_{r,"setr",t}^{cact,Trend}\right)}
+\end{equation}
+Secondly, we have the quota products milk and sugar. The milk quotas on deliveries are acknowledged with a fixing on processing of cow milk without an explicit equation, taking into account that there are countries with persistent under- or over-deliveries. Given the expiry of milk quotas after 2015 this is largely irrelevant for current applications of CAPTRD. The sugar quotas, by contrast, are included as an upper bound (//SugaQuot_//) that may be relaxed (see Regulation 318/2006, Article 12) through industrial or biofuel use of sugar (and losses of sugar):
+\begin{equation}
+X_{r,SUGA,t}^{MAPR,Trend} \le  X_{r,SUGA,t}^{QUTS,Trend}+ X_{r,SUGA,t}^{INDM,Trend}+ X_{r,SUGA,t}^{BIOF,Trend}+ X_{r,SUGA,t}^{LOSM,Trend}
+\end{equation}
+Finally, there are upper bounds on new plantings of vineyards according to the CMO for wine from Regulation 1493/1999
+===Constraints relating to growth rates===
+During estimation, a number of safeguards regarding the size of the implicit growth rates had been introduced in the course of various past CAPRI projects (bounds mainly found in //‘captrd\fix_est.gms’//):
+  * In general, input or output coefficients (yields) are not allowed to change by more than +/- 2.5 % per annum, with a higher ranges for feed input coefficients (+/- 10 % and +/  5 % for non-marketable fodder).
+  * The number of calves born per cow is may only change up to +/- 10 % around the base period value until the last projection year.
+  * The number of young cows (or sows) needed for replacement may only change up to +/  20 % around the base period value until the last projection year.
+  * Final fattening weights must fall into a corridor of +/- 20% around the base period value.
+  * Milk yields are assumed to increase at least by 0.25% and at most by 1.25% near the EU average with some correction for below or above average initial yields (in //‘captrd\comibounds.gms’//).
+  * Crop yields (except those of very hererogeneous crops like “other fruits” or “other fodder on arable land) should have a minimum yield growth of 0.5%.
+  * Specific (and quite generous) upper limits are applied to prevent unrealistic crop yields (for example: 15 tons/ha for cereals)
+  * Technical coefficients like contents of milk products or processing yields are also subject to plausible bounds.
+  * Strong increases in pork and poultry production in the past are restricted by environmental legislation in force, notably the nitrate directive. Accordingly, yearly increases were restricted to +1% for pork in EU15 Member States (even more stringent for Denmark and The Netherlands) and to 1.5% for poultry. In the new MS these maximum growth rates are assumed to be half a percentage point larger, in line with a weaker implementation of environmental legislation. The same bounds are also applied to the corresponding activity levels.
+  * A strong decrease of animal activity levels (below 20% of the base year) is not allowed.
+  * Total agricultural area is not allowed to decline at a rate exceeding -0.2 % per annum.
+  * Shares of arable crop on total arable area are bounded by a formula which allows small shares to expand or shrink more compared to crops with a high share. A crop with a base year share of 0.1% is allowed to expand to 2.5%, one of 10% only to 25%, and one of 50% to only 70%:
+\begin{align}
+\begin{split}
+X_{r,"levl",t}^{arab,Trend} .up / lo = & X_{r,"levl",bas}^{arab,Trend} \\
+& \pm 1/4 \left( \frac {X_{r,"levl",bas}^{arab,Trend}} {X_{r,"levl",bas}^{"arab",Trend}} \right)^{1/4} X_{r,"levl",bas}^{"arab",Trend} \; max\left(0.2,\frac {t-bas} {last-bas} \right)
+\end{split}
+\end{align}
+  * However, in line with cross-compliance constraints from the CAP, permanent grass land must not decrease by more than 10% compared to the base year.
+  * An upper bound of 1% applies to the yearly growth of the area of “other oils” (for unclear reasons)
+  * Total labour must not deviate by more than 5% from forecasts based on coefficients estimated in an earlier study (“CAPRI-DYNASPAT”).
+  * Changes in human consumption per caput for each of the products cannot exceed a growth rate of +/- 2% per annum. Due to some strong and rather implausible trends for total meat and total cereals consumption, the growth rate was restricted to +/- 0.8 % per annum for meat and +/- 0.4% per annum for cereals assuming that trend shifts between single items are more likely than strong trends in aggregate food groups.
+  * A downward sloping corridor is defined for subsistence consumption of raw milk (in ‘captrd\comibounds.gms’).
+  * Changes in prices are not allowed to exceed a growth rate of +/- 2% per annum, usually.
+  * Expert supports for biofuel related variables are given high priority with mostly tight corridors around these supports (in //‘captrd\biobounds.gms’//).
+  * If a variable has dropped to zero according to recent COCO data it will be fixed to zero.
+====Step 2.2: Integration of specific expert support (Member State level or lower)====
+The definition of expert “supports” allows for provision of a mean and a standard deviation for all elements, and it is particularly useful for items for which the AgLink forcasts in step 3 are missing, or where there are other reasons for stability problems, such as missing historical data or very short time series
+The expert supports are dealt with in //’captrd\expert_support.gms’//. Currently, mainly three sources can be distinguished:
+  * Support for the development of the sugar and sugar beet sectors, evolved from a small study with the seed production company KWS
+  * Expert on the development of bio-fuel production (bio-ethanol, bio-diesel), and the input demand for the related feedstocks, mainly based on results from the PRIMES model
+  * Expert supports for some key time series impacting on GHG emission for some Member States provided by the EC4MACS projects
+The standard deviation is expressed by a “trust level” between 1 and 10.
+The following table presents selected results related to the EU27 biomass feedstock for bioenergy production from the PRIMES((PRIMES is a modelling tool for the EU energy system projections and impact assessment of the respective policies (see [[https://ec.europa.eu/clima/policies/strategies/analysis/models_en]]))) biomass component (also given for each MS):
+**Table 22: Selected results related to the EU27 biomass feedstock for bioenergy production from the PRIMES biomass component**
+^**Unit: ktoe (unless specified otherwise)**^  2000^  2005^  2010^
+^**Domestic Production of Biomass Feedstock**^  69,087^  87,595^  101,303^
+|Crops|  1,228|  5,419|  12,500|
+| - Wheat|  0|  601|  2,462|
+| - Sugarbeet|  0|  1,291|  4,518|
+| - Sunflower/Rapeseed|  1,228 |  3,527|  5,520|
+| - Lign. Crops	|  0|  0|  0|
+|Agricultural Residues|  4,194|  6,428|  7,200|
+|Waste	|  19,990|  26,002|  28,054|
+^**Net imports of Biomass Feedstock**^  239^  1,598^  4,289^
+|Pure Vegetable Oil as feedstock for bioenergy production|  239|  1,598|  4,289|
+^**Cultivated Land (Kha)**^  896^  3,022^  5,422^
+|Starch crops|  0|  320|  1,218|
+|Oil crops|  896|  2,654|  4,031|
+|Sugar Crops|  0|  48|  172|
+|Lignocellulosic crops	|  0|  0|  0| \\Source: PRIME model
+The above information on the biomass production is NOT used as the immediate input for CAPRI for several reasons. Converting from ktoe to 1000 tons (using 0.37 ktoe/1000t for cereals, 0.05 ktoe/1000t for sugar beet, 0.52 ktoe/1000t for rape seed) gives the production //for the bio-fuel sector// which matches with the market position “BIOF” = processing to biofuels. For cereals we have indeed 6.7 million tons from PRIMES in 2010 and 7.0 million tons according to CAPRI. For oilseeds we have to convert the PRIMES information in terms of oilseeds into a quantity of vegetable oil, giving approximately 5.5 mtoe / 0.52 ktoe/1000t * 0.4 [rape oil/ rape seed] = 4.2 million tons which is considerably larger than the results from CAPRI((It appears that the CAPRI bio-fuel results of August 2011 are affected by reporting errors in the oilseeds and sugar sectors.)) 1.8 million tons. A similar comparison for the sugar sector may point at conversion problems with the units. The PRIMES sugar beet production should correspond to a sugar quantity of 4.5 mtoe / 0.05 ktoe/1000t * 0.15 [sugar/sugar beet] = 13.5 million tons of sugar equivalents which is close to the //total// sugar production in CAPRI of 15.7 million tons. Apart from these unresolved differences in the ex post data the main reason for NOT using these biomass production quantities from PRIMES is conceptual: They are given from supply functions specific to the bio-fuel sector whereas CAPRI covers the whole production (mostly for food purposes) such that the use of exogenous information for parts of the total may create problems for the CAPRI market balances.
+A similar consideration also applies to the area information from PRIMES which refers to the specific areas used for biofuel purposes, except for the area for lignocellulosic crops.
+Basically, the information “close” to agriculture (feed stock use and required areas) has not been taken from PRIMES assuming that it is preferable to estimate those in the context of the agricultural sector model CAPRI. On the other hand, the information on the production of bioenergy, including its main technologies and pathways, was supposed to be given reliably from the PRIMES biomass component exactly because it covers beyond agriculture also forestry and various forms of waste. The next table focuses on those results that will be used as the immediate inputs for CAPRI (thus omitting bio-energy from forestry, for example).
+First of all PRIMES offers net imports, production and demand quantities for the biofuels itself. Production of biodiesel is split up according to the technology in first generation and second generation technologies (FT diesel, HTU diesel, pyrolysis diesel). For ethanol such a breakdown is not given in terms of production volumes, but the PRIMES output includes among the installed capacities also those for fermentation of sugar crops, starchy crops and lignocellulosic crops, the latter identifying the share for second generation production of ethanol. The input for first generation production of biodiesel (through esterification) is “bioheavy” which includes pure vegetable oil from domestic production, but also from various forms of waste oil (recovered oils, biocrude, pyrolysis oil). In addition the market balance for bioheavy includes imports (pure vegetable oil, the larger part according to the previous table for biodiesel production, a smaller part for direct use as fuel) and demand quantities of bioheavy. These are the key inputs for CAPRI, plus the area of lignocellulosic crops that is also a direct input to CAPRI.
+In addition, there is more information that may be used in the future. Biogas production is mainly based on sewage systems but in part it also relies on animal manure (whereas the German particularity of biogas from green maize is not yet included). Biogas production from manure might be coordinated between PRIMES and CAPRI in the future. Equally the PRIMES assumptions on the amount of crop residues usable for bio-energy are not yet cross-checked with CAPRI. Finally, it should be mentioned that the use of waste in the PRIMES tables refers to other sources of bioenergy (like municipal waste).
+**Table 23: Results on biofules of PRIMES model**
+^**Unit: ktoe (unless specified otherwise)**^	2000^	2005^ 	2010^
+^**Net imports of Bioenergy**^  400^  1,731^  5,820^
+|Biodiesel	|  0|  0|  1,948|
+|Bioethanol|  0|  20|  1,130|
+|Pure Vegetable Oil|  8|  390|  505|
+^**Bioenergy Production**^  67,971^   84,554^  95,430^
+|Biodiesel|  610|  2,548|  6,578|
+| - Biodiesel (1st gen.)|  610|  2,548|  6,578|
+| - FT diesel|  0|  0|  0|
+| - HTU diesel|  0|  0|  0|
+| - Pyrolysis diesel|  0|  0|  0|
+|Bioethanol|  0|  561|  2,193|
+|BioHeavy|  1|  83|  605|
+| - Recovered Oils|  0|  43|  589|
+| - Pure Vegetable Oil|  1|  40 |  15|
+| - BioCrude|  0|  0|  0|
+| - Pyrolysis oil	|  0|  0|  0|
+|BioGas|  352|  871|  2,049|
+| - Bio-gas	|  352|  871|  2,049|
+| - Synthetic Natural Gas |  0|  0|  0|
+|Waste Solid|  12,353|  13,985|  14,654|
+|Waste Gas|  1,898|  3,537|  4,538|
+^**Demand**^  68,372^  86,285^  101,250^
+|Biodiesel	|  610|  2,548|  8,526|
+|Bioethanol	|  0|  581|  3,234|
+|BioKerosene	|  0|  0|  0|
+|BioHydrogen	|  0|  0|  0|
+|BioHeavy	|  9|  473|  1,110|
+|BioGas	|  352|  871|  2,049|
+|Waste Solid	| 12,353|  13,985|  14,654|
+|Waste Gas	| 1,898|  3,537|  4,538|
+^**Capacities (Ktoe/yr)**^  10,440^ 	16,067^	26,754^
+|Fermentation	|  134|  1,127|  4,104|
+| - Sugar	|  0|  551|  2,103|
+| - Starch	|  134|  576|  2,001|
+| - Lignocellulosic	|  0|  0|  0|
+|Esterification	|  1,141|  4,170|  9,021| \\Source: PRIMES model
+In technical terms the PRIMES results are given as a set of Excel tables that is usually amended with each release in some detail. To extract these data a small GAMS program (//‘merge.gms’//) prepares strings that, when saved and reload with Excel, are interpreted as external links to the PRIMES files using the “Vlookup” function of Excel. The relevant data are written to a parameter p_PRIMESresults, including the following:
+P_PRIMESresults(MS,BIOEshare,SECG,year) \\
+= capacity, lignocellulosic / capacity fermentation
+Otherwise the selection addresses directly certain lines of the PRIMES output.
+====Step 3: Adding comprehensive sets of supports from AGLINK or other agencies====
+In Step 3, results from external projections on market balance positions (production, consumption, net trade etc.) and on activity levels for EU aggregates (EU15, EU12) are added. Currently, these projections are provided by Aglink-COSIMO model projections. The baseline of Aglink-COSIMO integrates the market outlook results from DG-AGRI, but is also globally harmonised, so that it also enters the baseline generation for the market model of CAPRI.
+Integration of results from another modelling system is a challenging exercise as neither data nor definitions of products and market balance positions are fully harmonized. That holds especially for Aglink-COSIMO, where at least in the past the mnemonics had even not been harmonized across equations of the model itself. After a restructuring exercise in 2010, that had somewhat been improved. The ingredients in the mapping process are first a list of the codes for the regions, products and items used in Aglink-COSIMO (‘//baseline\aglink*_sets.gms//’, where * can be 2009 or 2010 to differentiate the versions before and after the restructuring). A second program, (//‘baseline\aglink*_mappings.gms//’) links the CAPRI regions, products and items to the mnemonics and Aglink-COSIMO, and a larger program (‘//baseline\loag_aglink*.gms//’) then uses the mapping to assign them to the CAPRI code world.
+Aglink-COSIMO currently features results at EU15 and EU12 level. It is hence not possible to funnel the Aglink-COSIMO results into Step 2 above without an assumption of the share of the individual Member States.
+As DG-AGRI is often the main client of the CAPRI projections for the EU, it was deemed sensible to pull the projections towards the DG-AGRI baseline wherever the constraints of the estimation problem and potentially conflicting other expert sources allow for it. That is achieved by two assignments related to the objective function:
+  - Step 2 results (except those steered by other expert supports) are scaled proportionally to give MS level supports for step 3 that are consistent with the Aglink-COSIMO baseline (after adjusting for different definitions in the respective databases).
+  - The standard errors from the default trends are replaced with a special formula reflecting a high confidence in the Aglink-COSIMO derived supports.
+More precisely, the weighted variance is replaced with the following setting for external supports (//“XSupport”// = AGLINK or expert supports):
+\begin{equation}
+X_{r,i,"varErr"}^{j,"XSupport"}=\left(X_{r,i,"exante"}^{j,"XSupport"}\cdot 0.05/3 \cdot \left(10/X_{r,i,"trustlevl"}^{j,"XSupport"}\right)\right)^2
+\end{equation}
+The “trust level” in the last denominator is a scaling factor for the implied coefficient of variation. A higher trust level translates into a lower error variance of the external information. With a normal distribution we would have
+  * at “trust level” = 10:  X ∈ [-0.055*Mean, +0.055*Mean] with probability 99.9%
+  * at “trust level” = 5:  X ∈ [-0.275*Mean, +0.275*Mean] with probability 99.9%
+  * at “trust level” = 1:  X ∈ [-0.55*Mean, +0.55*Mean] with probability 99.9%
+The default setting for "DGAgri" supports is a “trust level” of 5, which is a moderately high value to leave some distance for special cases that should be pulled very tightly towards their supports.
+The Aglink-COSIMO projections currently run to 2020 or a few years beyond. For climate related applications CAPRI has to tackle projections up to 2030 or even 2050. CAPRI projections up to 2030 have been prepared in the context of EC4MACS project ([[http://www.ec4macs.eu]]). The methodology was quite simple: The year 2020 projection (usually prepared in the same run of CAPTRD) has been extrapolated in a nonlinear dampened (logistic) fashion (in //‘define_eu_supports.gms’//) with some additional bounds to prevent unreasonable increases of certain variables (nonnegativity already provided a good lower bound). Together with the information in the time series database this has been an ad hoc but operational procedure to address the 2030 horizon, but it would have been inappropriate for a move to the long run up to 2050 as required for a recent study on behalf of DG CLIMA((Service contract on “Model based assessment of EU energy and climate change policies for post-2012 regime” (Tender DG ENV.C.5/SER/2009/0036), coordinated by the Energy-Economy-Environment Modelling Laboratory (E3MLab), National Technical University of Athens with the International Institute for Applied Systems Analysis (IIASA) and EuroCARE as subcontractors.)).
+For the long run evolution of food production a link has been established to long run projections from two major agencies (FAO 2006 and the IMPACT projections in Rosegrant et al 2009, see also Rosegrant et al 2008). This linkage required mappings to bridge differences in definitions (see //‘gams\global\f2050_impact.gms’// called when running //‘gams\global.gms’//).
+Furthermore, methodology was needed to avoid a break in the projections at the transition of medium run expert information (Aglink-COSIMO, up to 2020) and long run information (FAO/IFPRI for 2050). For this purpose a variable weighting scheme is introduced (in //‘gams\captrd\expert_support.gms’//) that gives an increasing weight to our “long run” sources (FAO/IFPRI) as the projection horizon approaches 2050. This tends to give projections that gradually approach the long run sources, for example as in the case of pork production in Hungary (taken from a baseline established in November 2011).
+**Figure 11: Pork production in Hungary as an example for merging medium run and long run a priori information in the CAPRI baseline approach**
+{{::figure11.png?600|}} \\ Source: own elaboration
+The example has been chosen because historical trends (and Aglink-COSIMO projections) on the one hand and long run expectations differ markedly. This is not unusual because medium run forecasts often give a stronger weight to recent production trends, often indicating a stagnating or declining production in the EU, whereas the long run studies tend to focus on the global growth of food demand in the coming decades. The simple trends (filled triangles) would evidently give unreasonable, even negative forecasts after 2030. Already the imposition of constraints from relationships to other series would stabilise the projections and imply some recovery after 2030 (filled squares). The year 2020 supports from Aglink-COSIMO (not shown) produces some upward correction of the step 2 results for 2020, giving a final projection (filled circles) of about 375 ktons for pork production in Hungary. This is also the starting point for the specification of the long run support (empty circles) which is a weighted average of two components. The first is a linear interpolation to the external projection from FAO/IFPRI for 2050 (empty triangles).  The second is a nonlinear damped extrapolation of the medium run projection beyond 2020 (empty squares). Changing the weight for the first component (FAO/IFPRI support) with increasing projection horizon creates a long run target value (empty circles) that gives a smooth transition from the medium to the long run. As the final projections (filled circles) tend to follow these target values, they show a turning point in the future evolution of pork production in Hungary that ultimately reflects the consideration of increasing global demand underlying the FAO/IFPRI projections.
+Evidently this approach is quite removed from economic modelling and it is not intended to be. Instead it tries to synthesize the existing projections from various agencies, each specialised in particular fields and time horizons, in a technically consistent and plausible manner. The specification of a constraint set and penalties of the objective function translates plausibility in an operational form. Technical consistency is imposed through the system of constraints active during the estimation.
+====Step 4: Breaking down results from Member State to regional and farm type level====
+Even if it would be preferable to add the regional dimension already during the estimation of the variables discussed above, the dimensionality of the problem renders such an approach infeasible. Instead, the step 3 projection results regarding activity levels and production quantities are taken as fixed and given, and are distributed to the regions minimizing deviation from regional supports. The aggregation conditions for this step (and correspondingly for the disaggregation of NUTS2 regions to farm types) are:
+  * Adding up of regional production to Member State production (//MSGROF_//)
+  * Adding up of regional agricultural and non-agricultural areas to Member State areas (eqs. //MSLEVL_// and //MSLANDUSE_//)
+  * Adding up of regional feed use by animal types to Member State values (//MSFEEDI_//).
+The results at Member State level are thus broken down to regional level, ensuring adding up of production, areas and feed use:
+\begin{equation}
+X_{MS,i,t}^{GROF,Trend}=\sum_{r\in MS}X_{r,i,t}^{GROF,Trend}
+\end{equation}
+\begin{equation}
+X_{MS,"levl",t}^{j,Trend}=\sum_{r\in MS}X_{r,"levl",t}^{j,Trend}
+\end{equation}
+\begin{equation}
+X_{MS,"levl",t}^{j,Trend}\cdot \left(X_{MS,"feed",t}^{j,Trend}+10 \right)=\sum_{r\in MS}X_{r,"levl",t}^{j,Trend}\cdot \left(X_{r,"feed",t}^{j,Trend}+10 \right)
+\end{equation}
+The addition of the “10” (kg/animal) considerably improves the scaling in case of very small quantities (say 1 gram per animal). This is an example of a technical detail that may be crucial for numerical stability but usually cannot be reported fully in this documentation.
+In addition to the above aggregation conditions, the lower level (NUTS2 or farm type) models only require the following constraints (as the market variables are already determined at the MS level):
+  * Related to areas: area balance (Equation 57 FIXME), obligatory set aside (Equation 80 FIXME), aggregation to groups like cereals (0).
+  * Related to yields: linkage of production, activity levels and yields (Equation 55 FIXME), stabilisation of straw yields (//STRA_//)
+  * Related to animals: Nutrient balances (Equation 65 FIXME), local use of fodder (//EFED_//), definition of livestock density (//LU_//).
+In order to keep developments at regional and national level comparable, relative changes in activity levels are not allowed to deviate very far from the national development. These bounds are widened in cases of infeasibilities.
+Table below contains an example of the final output of the trends estimation task (C:\....CAPRI\STAR\star_2.4\output\results\baseline\results_BBYY.gdx), where BB stands for base year and YY for simulation year). Its main purpose is to provide with explanations on the variables of this output and, thus, a possibility to review the results in a step-by-step manner.
+**Table 24: Example of the final output of the trends estimation task and description of the variables**
+^Product code^  Activity code  ^  Variables  ^  Years  ^^^^^^^^^  Explanations  ^
+^  ^ ^ ^1984^…^2009^2010^2011^2012^2013^2014^2015^:::^
+^SWHE^	SWHE^	BASM	| 	 | | | | | | |	|   	 	 	 	 	8337|Base year value from Build database workstep.|
+^ ^ ^Penalty	| 	 | | | | | | |	|  	 	 	 	 	 	0.2|"squared root" difference between actual estimate and support value. The larger the value, the farer the estimate from support.|
+^ ^ ^Lo	 	 | 	 | | | | | | |	|  	 	 	 	 	 	8080|	Lower estimation bound.|
+^ ^ ^	 	DGAgri1	 | | |	 	8876|	8385	|8046	|8109|	8632|	8996|	9167|	Projection of Aglink-Cosimo for the EU15 aggregate scaled to fit the CAPRI database.((Aglink-Cosimo model produces projections not for each EU MS, but for the EU aggregates: EU, EU "old" MSs and EU "new" MSs. During the calibration process these values are first scaled to better fit the CAPRI database. These scaled values are then used for the calibration procedure.))|
+^ ^ ^ 	 	TrustLevl	| 	 | | | | | | |	|  	 	 	 	3|	Exogeneous value used for restricting min and max values of the support values. It is used in calculating lower and upper bounds (up and lo) of the projections.|
+^ ^ ^ 	 	data	 | 	 | | | | | | |	| | |
+^ ^ ^	 	BAST	 | 	 | | | | | | |	|  	 	 	 	8579|	Simple average of the last 3 observation years available: 2012-2014.|
+^ ^ ^ 	 	B2000	| 	 | | | | | | |	|  	  	 	 	7988| |
+^ ^ ^ 	 	support	 | 	 | | | | | | |	|  	 	 	 	9167|	Values estimated as linear combination of Step1 and BAST (BASM) with R2 as weight. They are replaced with expert support where applicable and then scaled. They are then stored as Support1. Support is then redefined based on the Aglink-Cosimo value.((The final version of the support value at MS level (if calibration to the projections of Aglink-Cosimo takes place), is calibration value derived from DgAgri1.))|
+^ ^ ^	 	support1	| 	 | | | | | | |	|    	 	 	8943|	(expert) support value, before introduction of Aglink-Cosimo calibration values. |
+^ ^ ^ 	 	step1	 	| 	 | | | | | | |	|  	 	 	8918|	1) Result of estimation of unconstrined trends|
+^ ^ ^ 	 	step2	 	| 	 | | | | | | |	|  	 	 	8851|2) Results of solving the trend model with constraints at MS level and with support1|
+^ ^ ^ 	 	step3	 | 	 | | | | | | |	|  	 	 	 	8949|3) First, it is defined as results of solving trend model with constraints at MS level and with support (defined with Aglink-Cosimo value). Then, it is redifined with the results from solving this trend model with additional constraints at NUTS2 level.  |
+^ ^ ^ 	 	wVarErr	 | 	 | | | | | | |	|  	 	 	 	259353|	Error variance.
+^ ^ ^	 	CoefVarErr	| 	 | | | | | | |	| 	 	 	0.1| |
+^ ^ ^	 	Extrap	 	 | 	 | | | | | | |	| |  |
+^ ^ ^ 	 	Longrun| 	 | | | | | | 	8553 | 8579	| 	8633| |
+^ ^ ^ 	 	Longrun1	| 	 | | | | | | |	| | |
+^ ^ ^ 	 	P_Data	|6975|	…|	9061|	8614|	8078	|8139|	8810|	8789|  |	Historical data – output of Build database. The last observation year – 2014.|
+^ ^ ^ 	 	series	|6975|	…	|9061	|8614	|8078	|8139|8810	|8789	|  8949| 	Historical values (until 2014) and projected values (starting from 2015 with 5-year step, as defined in the GUI setting for the Trends projection task). The projected values are "copied" from Step 3.((Because the last observation year is 2014, values in 2015 are prjections.))((Values in 2020, 2025 and 2030 are projections as well but are not presented in this example.))|
+^ ^ ^ 	 	up	| 	 | | | | | | |	|     	 	 	 	 	8978	|Upper estimation bound|
+Source: own compilation. Comments: SWHE in Product code column indicates soft wheat commodity. SWHE in Activity code indicates yield of soft wheat. The CAPRI model used for this example was calibrated to the projections of Aglink-Cosimo model.