forecast_tool_captrd
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forecast_tool_captrd [2020/02/27 11:11] – [Step 3: Adding comprehensive sets of supports from AGLINK or other agencies] matsz | forecast_tool_captrd [2020/03/01 07:19] – [Step 4: Breaking down results from Member State to regional and farm type level] matsz | ||
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The default setting for " | The default setting for " | ||
- | 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:// | + | 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 |
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 // | 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 // | ||
Furthermore, | Furthermore, | ||
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+ | **Figure 11: Pork production in Hungary as an example for merging medium run and long run a priori information in the CAPRI baseline approach** | ||
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+ | {{:: | ||
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+ | 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, | ||
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+ | 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. | ||
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+ | ====Step 4: Breaking down results from Member State to regional and farm type level==== | ||
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+ | 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: | ||
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+ | * Adding up of regional production to Member State production (// | ||
+ | * Adding up of regional agricultural and non-agricultural areas to Member State areas (eqs. //MSLEVL_// and // | ||
+ | * Adding up of regional feed use by animal types to Member State values (// | ||
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+ | The results at Member State level are thus broken down to regional level, ensuring adding up of production, areas and feed use: | ||
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+ | \begin{equation} | ||
+ | X_{MS, | ||
+ | \end{equation} | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{MS," | ||
+ | \end{equation} | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{MS," | ||
+ | \end{equation} | ||
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+ | 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. | ||
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+ | 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): | ||
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+ | * 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 (// | ||
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+ | 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. | ||
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+ | Table below contains an example of the final output of the trends estimation task (C: | ||
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+ | **Table 24: Example of the final output of the trends estimation task and description of the variables** | ||
+ | ^Product code^ Activity code ^ Variables | ||
+ | ^ ^ ^ ^1984^…^2009^2010^2011^2012^2013^2014^2015^::: | ||
+ | ^SWHE^ SWHE^ BASM | | | | | | | | | 8337|Base year value from Build database workstep.| | ||
+ | ^ ^ ^Penalty | | | | | | | | | 0.2|" | ||
+ | ^ ^ ^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 " | ||
+ | ^ ^ ^ 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| | ||
+ | ^ ^ ^ series |6975| … |9061 |8614 |8078 |8139|8810 |8789 | | ||
+ | ^ ^ ^ up | | | | | | | | | 8978 |Upper estimation bound| | ||
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forecast_tool_captrd.txt · Last modified: 2022/11/07 10:23 by 127.0.0.1