forecast_tool_captrd
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forecast_tool_captrd [2020/02/27 10:04] – [Step 2.1: Consistency constraints in the trend projection tool] matsz | forecast_tool_captrd [2020/02/27 11:57] – [Step 4: Breaking down results from Member State to regional and farm type level] matsz | ||
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* 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. | * 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. | * 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’). | + | * 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%. | * 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) | * Specific (and quite generous) upper limits are applied to prevent unrealistic crop yields (for example: 15 tons/ha for cereals) | ||
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* A downward sloping corridor is defined for subsistence consumption of raw milk (in ‘captrd\comibounds.gms’). | * 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. | * 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’). | + | * 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. | * 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)==== | ====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 // | ||
+ | |||
+ | * 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, | ||
+ | * 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:// | ||
+ | |||
+ | **Table 22: Selected results related to the EU27 biomass feedstock for bioenergy production from the PRIMES biomass component** | ||
+ | |||
+ | ^**Unit: ktoe (unless specified otherwise)**^ | ||
+ | ^**Domestic Production of Biomass Feedstock**^ | ||
+ | |Crops| | ||
+ | | - Wheat| | ||
+ | | - Sugarbeet| | ||
+ | | - Sunflower/ | ||
+ | | - Lign. Crops | | ||
+ | |Agricultural Residues| | ||
+ | |Waste | | ||
+ | ^**Net imports of Biomass Feedstock**^ | ||
+ | |Pure Vegetable Oil as feedstock for bioenergy production| | ||
+ | ^**Cultivated Land (Kha)**^ | ||
+ | |Starch crops| | ||
+ | |Oil crops| | ||
+ | |Sugar Crops| | ||
+ | |Lignocellulosic crops | | ||
+ | |||
+ | 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/ | ||
+ | |||
+ | 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**^ | ||
+ | |Biodiesel | | ||
+ | |Bioethanol| | ||
+ | |Pure Vegetable Oil| 8| 390| 505| | ||
+ | ^**Bioenergy Production**^ | ||
+ | |Biodiesel| | ||
+ | | - Biodiesel (1st gen.)| | ||
+ | | - FT diesel| | ||
+ | | - HTU diesel| | ||
+ | | - Pyrolysis diesel| | ||
+ | |Bioethanol| | ||
+ | |BioHeavy| | ||
+ | | - Recovered Oils| 0| 43| 589| | ||
+ | | - Pure Vegetable Oil| 1| 40 | 15| | ||
+ | | - BioCrude| | ||
+ | | - Pyrolysis oil | 0| 0| 0| | ||
+ | |BioGas| | ||
+ | | - Bio-gas | | ||
+ | | - Synthetic Natural Gas | 0| 0| 0| | ||
+ | |Waste Solid| | ||
+ | |Waste Gas| 1, | ||
+ | ^**Demand**^ | ||
+ | |Biodiesel | | ||
+ | |Bioethanol | | ||
+ | |BioKerosene | | ||
+ | |BioHydrogen | | ||
+ | |BioHeavy | | ||
+ | |BioGas | | ||
+ | |Waste Solid | 12, | ||
+ | |Waste Gas | 1, | ||
+ | ^**Capacities (Ktoe/ | ||
+ | |Fermentation | | ||
+ | | - Sugar | | ||
+ | | - Starch | | ||
+ | | - Lignocellulosic | | ||
+ | |Esterification | | ||
+ | |||
+ | 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 (// | ||
+ | |||
+ | P_PRIMESresults(MS, | ||
+ | = 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, | ||
+ | |||
+ | 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, | ||
+ | |||
+ | 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 (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \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, | ||
+ | * at “trust level” = 5: X ∈ [-0.275*Mean, | ||
+ | * at “trust level” = 1: X ∈ [-0.55*Mean, | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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, | ||
+ | |||
+ | **Figure 11: Pork production in Hungary as an example for merging medium run and long run a priori information in the CAPRI baseline approach** | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | 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, | ||
+ | |||
+ | 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 (// | ||
+ | * 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 (// | ||
+ | |||
+ | 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, | ||
+ | \end{equation} | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{MS," | ||
+ | \end{equation} | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{MS," | ||
+ | \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 (// | ||
+ | |||
+ | 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: | ||
+ | |||
+ | **Table 24: Example of the final output of the trends estimation task and description of the variables** | ||
+ | |||
+ |
forecast_tool_captrd.txt · Last modified: 2022/11/07 10:23 by 127.0.0.1