baseline_generation
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* The second example is that of FAPRI model, where a so-called melting down meeting is organised where the modellers responsible for specific parts of the system come together with market experts. Results are discussed, parameters and assumptions changed until there is consensus. Little is known about how the process works exactly, but both examples underline the interaction between model mechanisms and ex-ante expectations of market experts. | * The second example is that of FAPRI model, where a so-called melting down meeting is organised where the modellers responsible for specific parts of the system come together with market experts. Results are discussed, parameters and assumptions changed until there is consensus. Little is known about how the process works exactly, but both examples underline the interaction between model mechanisms and ex-ante expectations of market experts. | ||
- | As is the case in other agencies, the CAPRI baseline is also fed by external (“expert”) forecasts, as well as by trend forecasts using data from the national ‘COCO’ and regionalized CAPREG databases (Chapters 3.2 and 3.3). The purpose of these trend estimates is, on the one hand, to compare expert forecasts with a purely technical extrapolation of time series and, on the other hand, to provide a ‘safety net’ position in case no values from external projection are available. Usually the projections for a CAPRI baseline are a combination of expert data (e.g. from FAO, European Commission, World Bank, other research teams and even private entreprises) and simple statistical trends of data contained in the CAPRI database. | + | As is the case in other agencies, the CAPRI baseline is also fed by external (“expert”) forecasts, as well as by trend forecasts using data from the national ‘COCO’ and regionalized CAPREG databases (sections [[the capri data base#The Complete |
=====Overview of CAPRI baseline processes===== | =====Overview of CAPRI baseline processes===== | ||
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**Figure 10: Overview of CAPRI baseline process** | **Figure 10: Overview of CAPRI baseline process** | ||
- | {{:: | + | {{:: |
The forecast tool CAPTRD uses the consolidated national and regional time series from COCO and CAPREG together with external projections from the AgLink model. The result is a projection for the key variables in the agricultural sector (activity levels and market balances) of all regions in the supply models (EU+) that is consistent with the supply model equations. | The forecast tool CAPTRD uses the consolidated national and regional time series from COCO and CAPREG together with external projections from the AgLink model. The result is a projection for the key variables in the agricultural sector (activity levels and market balances) of all regions in the supply models (EU+) that is consistent with the supply model equations. | ||
- | - Next task is the market model calibration. That task uses the same AgLink projections, | + | - Next task is the market model calibration. That task uses the same AgLink projections, |
- The third task is the calibration of the supply models. This step also uses the regional data base, regional trends, and policy files, and calibrates various technical and behavioural economic parameters of the supply models so that the projected regional production is the optimal production at the producer prices coming from the market model calibration. | - The third task is the calibration of the supply models. This step also uses the regional data base, regional trends, and policy files, and calibrates various technical and behavioural economic parameters of the supply models so that the projected regional production is the optimal production at the producer prices coming from the market model calibration. | ||
- Finally, the modeller typically wants to perform a simulation using all the calibrated parameters and projected data. The purpose is twofold: to verify that the calibration of the baseline worked as intended and to generate all reports for inspection in the GUI. | - Finally, the modeller typically wants to perform a simulation using all the calibrated parameters and projected data. The purpose is twofold: to verify that the calibration of the baseline worked as intended and to generate all reports for inspection in the GUI. | ||
- | =====Forecast tool CAPTRD ===== | + | =====Forecast tool CAPTRD===== |
+ | The tool providing projections for the European regions (the EU as of 2019, Turkey, Norway, Albania, Northern Macedonia, Montenegro, Bosnia and Herzegovina, | ||
+ | * Step 1 involves independent trends on all series, providing initial forecasts and statistics on the goodness of fit or indirectly on the variability of the series. | ||
+ | * Step 2 imposes constraints like identities (e.g. production = area * yield) or technical bounds (like non-negativity or maximum yields) and introduces specific expert information given on the MS level. | ||
+ | * Step 3 includes expert information on aggregate EU markets, typically coming from the AgLink model or GLOBIOM. This external data is not available for all individual countries in CAPRI, but for larger regions. Therefore, several countries must be simultaneously estimated in order to ensure proper use of this important prior information. | ||
+ | * Step 4 Depending on the aggregation level chosen the MS result may be disaggregated in subsequent steps to the regional level (NUTS2) or even to the level of farm types. | ||
+ | |||
+ | The trends estimated in CAPTRD are subject to consistency restrictions in steps 2 and 3. Hence they are not independent forecasts for each time series and the resulting estimator is hence a system estimator under constraints (e.g. closed area and market balances). Nonetheless, | ||
+ | |||
+ | CAPTRD results are in turn only the first of several steps before a full CAPRI baseline is ready to use. The rest of this chapter focuses on CAPTRD. | ||
+ | |||
+ | ====Step 1: Independent, | ||
+ | |||
+ | Before entering into the details it should be stated that ultimately almost any projection may be reduced to a particular type of trend projections, | ||
+ | |||
+ | The first ingredient in the estimator is the trend curve itself which is defined as: | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | where the parameters a, b and c are to be estimated so that the squared deviation between given and estimated data are minimized. The X stands for the data and represents a five dimensional array, spanning up products i and items j (as feed use or production), | ||
+ | |||
+ | This form has the advantage of ensuring monotonic developments whereas quadratic trends often gave increasing yields for the first part of the projection period and afterwards a decrease. Another conclusion from the early explorations was that it is useful to define the trend variable \(t_{1984} = 0.1, t_{1985} = 0.2, t_{1986} = 0.3 \) etc., giving a potentially strong nonlinearity in the early years of the database (where the frequency of high changes, possibly due to data weaknesses was high) and a rather low nonlinearity in the projection period. | ||
+ | |||
+ | The ex-post period usually covers the period from 1985 towards the end of the underlying CAPREG output (file // | ||
+ | |||
+ | * //Expost//: defined from the length of the series in CAPREG output // | ||
+ | * //Exante//: covering any sequence of intermediate result years up to the user specified final year((For technical reasons some years are “obligatory” result years, for example the year immediately following after the last ex post year.)). | ||
+ | * // | ||
+ | * // | ||
+ | |||
+ | The estimator minimises the weighted sum of squares of errors using the trend variable as weights: | ||
+ | |||
+ | \begin{equation} | ||
+ | wSSE_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | The weighting with the trend was introduced in the exploration phase based on the following considerations and experience. First of all, it reflects the fact that statistics from the early years (mid eighties) are often less reliable then those from later years. Secondly, even if they are reliable, older data will tend to contribute less useful information than more recent ones due to ongoing structural change. For this reason we have discarded any years before 1992 for the New MS, for example, but the data from the mid 90ies may nonetheless represent a situation of transition that should count less than the recent past. In technical terms the step 1 estimates are found by a grid search over selected values of parameter c with analytical OLS estimates for parameters a and b (see // | ||
+ | |||
+ | ====Step 2.1: Consistency constraints in the trend projection tool==== | ||
+ | |||
+ | Step 2 adds the consistency conditions and thus transforms the naïve independent trends into a system estimator. In almost all cases, the unrestricted trend estimates from the first step would violate one or several of the consistency conditions. We want to find estimates that both fit into the consistency constraints and exploit the information comprised in the ex-post development in a technical feasible way. Consider the identity that defines production as hectares/ | ||
+ | |||
+ | When consolidating simultaneously the different Step1 estimates, we will minimise the squared deviations from values computed in Step 1, in the following called “supports”, | ||
+ | |||
+ | The confidence interval from the Step 1 trend estimation will not help, as it will be centred around the last projection value and as it will simply be quite large in case of a bad R². However, we may use the idea underlying the usual test statistics for the parameters related to the trend (// | ||
+ | |||
+ | This reasoning is the basis for the supports derived from the Step 1 estimates in CAPTRD (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | where | ||
+ | \begin{equation} | ||
+ | wR_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | and the weighted total sum of squares is defined analogous to equation below: | ||
+ | \begin{equation} | ||
+ | wSST_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | with a trend weighted average | ||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | How is this rule motivated? If R² for a certain time series is 100%, in other words: for a perfect fit, the restricted trend estimate is fully drawn towards the unrestricted Step 1 estimate. If R² is zero, the trend curve does not explain any of the weighted variance of the series. Consequently, | ||
+ | |||
+ | The above definition of supports works for series with //expost// data from CAPREG only as well as for those series with an extended set of observations (// | ||
+ | |||
+ | Our objective function for Step 2 will be the sum of squared deviations from the supports defined above, weighted with the variance of the error terms from the first step: | ||
+ | \begin{equation} | ||
+ | Penalty=\sum_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | where the weighted variance of errors is | ||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | The variance of the error term is used to normalise the squared deviations from all series which serves two purposes. First the weighted error variance is decreasing with the mean of the explanatory variable. Normalizing with it will hence ensure that the penalty targets relative rather than absolute deviations. Otherwise the solver would only tackle the deviations from “large” crops, say soft wheat, and more or less ignore the deviations of oats, for example. Secondly the deviations from the support are penalized stronger where the Step 1 trend had a high explanatory power and therefore a low variance of the error term. | ||
+ | |||
+ | The constraints in the trend projection enforce mutual compatibility between baseline forecasts for individual series in the light of relations between these series, either based on definitions as ‘production equals yield times area’ or on technical relations between series as the balance between energy deliveries from feed use and energy requirements from the animal herds. The set of constraints is deemed to be exhaustive in the sense as any further restriction would either not add information or require data beyond those available. The underlying data set takes into account all agricultural activities and products according to the definition of the Economic Accounts for Agriculture. | ||
+ | |||
+ | The constraints discussed in the following (from // | ||
+ | |||
+ | ===Constraints relating to market balances and yields=== | ||
+ | |||
+ | Closed market balances (CAPTRD eq. MBAL_ ) define the first set of constraints and state that the sum of imports (IMPT) and production (GROF) must be equal to the sum of feed (FEDM) and seed (SEDM) use, human consumption (HCOM), processing (INDM, | ||
+ | |||
+ | \begin{align} | ||
+ | \begin{split} | ||
+ | X_{r, | ||
+ | \\ & | ||
+ | \end{split} | ||
+ | \end{align} | ||
+ | |||
+ | Where //r// are the Member States of the EU, //i// are the products, //t// the different forecasting years, corresponding to the equation. In the case of secondary products (dairy products, oils and oilcakes, for example) production is given on item //MAPR//. Domestic use //DOMM// (sum of the right hand side without exports) and net trade //NTRD// are defined in separate equations (//DOMM_//, //NTRD_//) not reproduced here. They do not act as constraints but permit a link to expert projections for EU markets in Step 3. | ||
+ | |||
+ | Secondly, production of agricultural raw products (//GROF//) is equal to yield times area/herd size (//LEVL//) where acts are all production activities (eq. //GROF_//): | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | The market balance positions for certain products enter adding up equations for groups of products (cereals, oilseeds, industrial crops, vegetables, fresh fruits, fodder production, meat, eq. // | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | ===Constraints relating to land use and cropping area=== | ||
+ | |||
+ | Adding up over the individual crop areas defines the total utilizable agricultural area (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Adding up over the individual crop areas defines (in // | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Adding up over mutually exclusive land use (in // | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | ===Constraints relating to agricultural production=== | ||
+ | |||
+ | Another Equation (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Where //oyani// stands for the different young animals defined as outputs (young cows, young bulls, young heifers, male/female calves, piglets, lambs and chicken). These outputs are produced by raising processes, and apart from stock changes //STCM// (defined in Equation // | ||
+ | |||
+ | For those activites that have been split up in the database into a high and low yielding variant (DCOW, BULF, HEIF, GRAS) with 50% for each, this split is maintained (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | The purpose of this split has been to permit an endogenous variation of yields also for animal activites, but so far no statistical information on the distribution of intensities has been available. Hence “intensive” has been //defined// to represent the upper 50% of the total distribution and it makes sense to maintain this split also in the baseline. | ||
+ | |||
+ | Animal herds (HERD) are related to animal activity levels through the process length in days (DAYS) via //HERD_//. | ||
+ | | ||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | The process length is fixed to 365 days for female breeding animals (activities DCOL, DCOH, SCOW, SOWS, SHGM, HENS) such that the activity level is equal to the herd size((The wording for animal numbers is a continuous source of confusion that may also affect older parts of this documentation or table headings from the CAPRI GUI. It is therefore recommendable to reserve the term “herd” strictly to stock variables (animals countable at a particular day) whereas the flow variable “produced heads per year” is the activity level for fattening activities.)). For fattening activites the process length, net of any empty days (relevant for seasonal sheep fattening in Ireland, for example) times the daily growth should give the final weight after conversion into live weight with the carcass share // | ||
+ | |||
+ | \begin{align} | ||
+ | \begin{split} | ||
+ | X_{r, | ||
+ | &\cdot (X_{r, | ||
+ | \end{split} | ||
+ | \end{align} | ||
+ | |||
+ | As the daily growth is an important input into the livestock sector requirement functions it turned out useful to explicitely link it to the yields in terms of meat, both in the expost data (accounting identities in COCO) and here in the projections. Heavier animals require in this way a higher daily growth and/or a longer fattening period. | ||
+ | For all inputs into the requirement functions hard constraints have been imposed (without the possibility to relax them in the solution process) to ensure that projected variables are fully in line with these contraints, mostly over bounds in // | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | While all information for the requirement functions of CAPRI is projected consistently, | ||
+ | |||
+ | \begin{equation} | ||
+ | \sum_{feed}X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | where //Cont// are the contents in terms of energy and crude protein. The left hand side of the equation defines total delivery of energy or protein from the current feeding practise per animal activity in region r, whereas the right hand side the need per animal derived from requirement functions depending on the main output (meat, milk, eggs, piglets born). The parameters a and b of the requirement functions are estimated from engineering functions as implemented in the CAPRI modelling system, and scaled so that the balance holds for the base year. The factor in front of the requirements introduces some input saving technical progress of -0.2% per annum. | ||
+ | |||
+ | The feeding coefficients multiplied with the herd sizes define total feed use for the different feeding stuffs ‘bulks’ (cereals, protein rich, energy rich, dairy based, other) and single nontradable feed items (grass, maize silage, fodder root crops, straw, milk for feeding, other fodder from arable land), technically in the same (//GROF_//) equation as equation below: | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Feed use of individual products must add up to the feed use of the ‘bulks’ mentioned above (in //FEED_//): | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Additional equations impose that certain stable relationships of agricultural technology are also maintained in projections: | ||
+ | |||
+ | * Equation //EFED_// ensures that feed use of non-tradable fodder items must be equal to production after accounting for losses. | ||
+ | * Other equations (// | ||
+ | * Production has to exceed seed use and losses (//SEED_//) | ||
+ | * The ratio of straw to cereal yields is maintained at base year values (//STRA_//) | ||
+ | * Livestock units per hectare are calculated (//LU_//) and may thus be subject to constraints (limiting their deviations from the supports, for example). | ||
+ | |||
+ | Finally there is an Equation (//LABO_//) ensuring that projections of family (//LABH//) and hired labour (//LABN//) in agriculture add up to total labour (//LABO//): | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | In the first place projections of family and hired labour follow from input coefficients combined with the activity levels, but the previous equation permits to apply bounds to the total. | ||
+ | |||
+ | ===Constraints relating to prices, production values and revenues=== | ||
+ | |||
+ | The check of external forecasts revealed that for some products, external price projections are not available. It was decided to include prices, value and revenues per activity in the constrained estimation process. The first Equation (//EAAG_//) defines the value (//EAAG//, position from the Economic Accounts for Agriculture) of each product and product group as the product of production (//GROF//) times the unit value prices (//UVAG//): | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | The revenues of the activities (//TOOU//, total output) for each activity and group of activities //acts// are defined in Equation //REVE_// as: | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Consumer prices (//UVAD//) are equal to producer prices (//UVAG//) plus a margin (//CSSP// ) according to Equation //UVAD_//: ((The symbol CSSP (initially for “consumer surplus”) is usually used for the welfare effects related to final consumers (currently expressed as equivalent variation). Consumer margins are stored on CMRG in the market model. This misuse of code CSSP in CAPTRD is due to historical reasons. )) FIXME (fußnote61) | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | ===Constraints relating to consumer behaviour=== | ||
+ | |||
+ | Human consumption (//HCOM//) is defined as per head consumption multiplied with population (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Consumer expenditures per caput (//EXPE//) are equal (via //EXPE_//) to human consumption per caput (//INHA//) times consumer prices (//UVAD//): | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Total per caput expenditure (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | ===Constraints relating to processed products=== | ||
+ | |||
+ | Marketable production (//MAPR//) of secondary products (//sec//) - cakes and oils from oilseeds, molasses and sugar, rice and starch - is linked in Equation //MAPR_// to processing of primary products (//PRCM//) by processing yields (//PRCY//): | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | In case of products from derived milk (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Marketable production of by-products from the brewery, milling and sugar industry (set //RESIMP// = { //FENI//, //FPRI//}) are derived from corresponding uses of related products (cereals and sugar, Equation // | ||
+ | |||
+ | \begin{align} | ||
+ | \begin{split} | ||
+ | X_{r, | ||
+ | & \cdot \frac {X_{r, | ||
+ | \end{split} | ||
+ | \end{align} | ||
+ | |||
+ | ===Constraints relating to bio-fuel production=== | ||
+ | |||
+ | Marketable production (//MAPR//) of biofuels (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \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 (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \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 (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r," | ||
+ | \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 (// | ||
+ | |||
+ | \begin{equation} | ||
+ | X_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Finally, there are upper bounds on new plantings of vineyards according to the CMO for wine from Regulation 1493/ | ||
+ | |||
+ | ===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 // | ||
+ | |||
+ | * 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 // | ||
+ | * 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, | ||
+ | * 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," | ||
+ | & \pm 1/4 \left( \frac {X_{r," | ||
+ | \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, | ||
+ | * A downward sloping corridor is defined for subsistence consumption of raw milk (in ‘captrd/ | ||
+ | * 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 // | ||
+ | * 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 // | ||
+ | |||
+ | * 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** | ||
+ | ^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| | ||
+ | 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. | ||
+ | |||
+ | =====Calibrating the global trade model===== | ||
+ | |||
+ | After the Task on Trends generation have been successfully completed, meaning that the projections for the defined (in GUI or a batch file) future years (currently, 2015, 2020, 2025 and 2030 are available) have been produced, the next step in the Baseline generation process (" | ||
+ | |||
+ | The calibration of the market model is steered by the C:/ | ||
+ | |||
+ | ====Stage I: Data preparation and balancing==== | ||
+ | |||
+ | The CAPRI database is composed of many different data sources, and requires data processing before the market model equations can be calibrated against the data set. Sources of potential problems include missing data and price-quantity framework that is inconsistent with the behavioural assumptions (e.g. profit maximizing producers, utility maximizing consumers). | ||
+ | |||
+ | Stage I of the market model calibration makes the CAPRI database consistent, and creates a dataset for the global agri-food markets against which the market model can be calibrated. As CAPRI is a comparative static model, the market model is calibrated only against the simulation year. But technically the CAPRI dataset is first made consistent to the model structure in the base year, and then shifted to the simulation year. More specifically the main steps in this stage include: | ||
+ | |||
+ | - Prepare the necessary data by | ||
+ | - loading them from various intermediate data files; | ||
+ | - mapping them to correct code lists; | ||
+ | - adjusting if necessary, often by applying security bounds; | ||
+ | - Ensure the consistency of the dataset to the market model structure for the base year (BAS) | ||
+ | - Shift the consistent dataset from the base year to the simulation year | ||
+ | - Ensure the consistency of the dataset to the market model structure for the simulation year (SIMY) | ||
+ | |||
+ | ===Data preparation=== | ||
+ | |||
+ | Before actually performing the calibration of the market model parameters, CAPRI first loads the necessary sets, parameters and data. These refer to periods (years), regions, activities, commodities, | ||
+ | |||
+ | Constraints, | ||
+ | |||
+ | Next, FAO data on the non-European countries as well as the trade flows among all of the countries (country trade blocks) accounted for in CAPRI are loaded. These FAO data together with the European data, which has already been subjected to certain adjustments as described in the previous paragraph, undergo the, so-called, data preparation step. This process is controlled by C:/ | ||
+ | |||
+ | Together with the data, equations of the CAPRI market module are loaded. They are described in detail in section [[scenario simulation# | ||
+ | |||
+ | ===Data balancing=== | ||
+ | |||
+ | After data preparation, | ||
+ | |||
+ | //Data balancing for the base year// \\ | ||
+ | |||
+ | Data calibration for the base year aims at modifying the base year data to fit the system of equations of the market module. Some of the parameters defined in Stage I (e.g., p_rhoX) as well as parameter values and bounds defined at this stage are used. For example, starting points and corridors for quantity variables are set (e.g., calculating of world production to define correction corridor for calibration of production/ | ||
+ | |||
+ | With the file C:/ | ||
+ | |||
+ | The model that calibrates base year data (MODEL m_calMarketBas) is defined in cal_models.gms file as well and includes almost all equations of the market model. In particular: equations for processing margin for dairy products (ProcMargM_), | ||
+ | |||
+ | The NSSQ equation is crucial to the data calibration as it, in its essence, minimizes the difference between the estimated and the observed (already adjusted at the previous stage) data of the base year. Its logic is analogues to the one of equation below: | ||
+ | |||
+ | \begin{equation} | ||
+ | SSQ\cdot \sum_{RMS} \sum_{XXX} p\_weight_{RMS}^i=\sum_{RMS} \sum_{XXX} \left( \frac{v_{RMS, | ||
+ | \end{equation} | ||
+ | |||
+ | where SSQ is an artificial variable to be minimized, indices RMS, XXX, BAS and i indicate, respectively, | ||
+ | |||
+ | The process of model solving is navigated with C:/ | ||
+ | |||
+ | After solving the MODEL m_calMarketBas, | ||
+ | |||
+ | //Data balancing for the simulation year// \\ | ||
+ | |||
+ | Aim of data calibration for the simulation year aims at generating such quantity, price and other market values (see list below) for the simulation year that they fit the system of equations of the market module and variable and parameter lower and upper bounds, as well as remain as close as possible to the values to which they are calibrated (e.g., trends, estimated with growth rates from the base year, Aglink-COSIMO values, GLOBIOM values etc.). Thus process, basically, follows similar approach as for the base year. There are, however, a few differences. The main is that the model used for calibration is MODEL m_calMarketFin. As the model for base year calibration (MODEL m_calMarketBas), | ||
+ | |||
+ | Before MODEL m_calMarketFin is solved, values of DATA parameter for the simulation year are defined. For example, administrative prices for dairy products and cereals and minimum import prices for cereals (in C:/ | ||
+ | |||
+ | As m_calMarketBas model, m_calMarketFin model is solved by minimizing SSQ value by applying the approach of assuring feasibility via data_fit.gms file. After the solution is found and energy conversion factors for animal products are defined with MODEL m_fitFeedConv, | ||
+ | |||
+ | ====Stage II: Elasticity trimming==== | ||
+ | |||
+ | Elasticity trimming in CAPRI aims at adjusting prior estimations of elasticities so that | ||
+ | * the behavioural functions can be parameterized/ | ||
+ | * the calibrated elasticities satisfy regulatory conditions (homogeneity, | ||
+ | * the calibrated elasticities are as close as possible to prior elasticities (minimize deviation). | ||
+ | |||
+ | At first, parameters for land use market are calculated based on data from FAO world food market model. Among them are land use classes, crop yields, land demand of non-crop activities, areas used for fodder and average land price, total energy use for feeding and producer price of feed. Next, starting elasticity values, as well as their lower and upper bounds are loaded (e.g., demand elasticities used in SPEL/MFSS). Finally, elasticities are trimmed. | ||
+ | |||
+ | Elasticities trimming is controlled by C:/ | ||
+ | |||
+ | Human consumption elasticities are estimated with MODEL m_trimDem by minimizing absolute squares between given and calibrated elasticities (FitElas_). Apart from the objective function the model includes several equations related to the definition of the demand system as Generalized Leontief, homogeniety of degree zero for elasticities in prices, additivity of income elasticities weighted with budget shares and elasticities for total calorie intake. | ||
+ | |||
+ | ====Stage III: Feed and fertilizer calibration==== | ||
+ | |||
+ | In this stage, the feed system is calibrated against the primary product prices of the market model (both marketable and non-marketable feed). The nutrient requirements of the crops are calculated together with the nutrient and energy requirement of the animal production activities. | ||
+ | |||
+ | The fertilizer flows are also calibrated here. The prior parameters for the fertilizer flows are defined based on the // | ||
+ | |||
+ | The file C:/ | ||
+ | |||
+ | ====Stage IV: Initialization and test run==== | ||
+ | |||
+ | After the behavioural blocks of the market model are calibrated (one-by-one), | ||
+ | |||
+ | At the final stage, some of the starting values and bounds for the market model are set, and agricultural policy data are loaded, adjusted and extended to the simulation year. The policy data include single area payment scheme, set-aside regulations, | ||
+ | |||
+ | ====Technical remarks==== | ||
+ | |||
+ | Note that the task " | ||
+ | |||
+ | Technically, | ||
+ | |||
+ | =====Calibrating the supply models to the CAPTRD projection===== | ||
+ | |||
+ | ====Introduction==== | ||
+ | |||
+ | The supply side models of the CAPRI simulation tool are programming models with an objective function. If we want the optimal solution to coincide with the forecast produced by the projection tools of CAPTRD, we need to ensure that first and second order optimality conditions (marginal revenues equal to marginal costs, all constraints feasible, and the solution is a maximum point) hold in the calibration point for each of the NUTS 2 or farm type models. The consequences regarding the calibration are threefold: | ||
+ | |||
+ | - Elements not projected so far but entering the constraints of the supply models (e.g. feed, fertilization) must be defined in such way that constraints are feasible, | ||
+ | - The cost function of the models must be shifted so that marginal costs and marginal revenues are equal in the calibration point. | ||
+ | - The curvature of the functions must be such that the solution obtained is a maximum, not a minimum or a saddle point. | ||
+ | |||
+ | ====Calibrating feed and fertilizer restrictions==== | ||
+ | |||
+ | The calibration of feed and fertilization restrictions happens in the file // | ||
+ | |||
+ | It is hence necessary to find a //feed mix// in the projected point which exhausts the projected production of non-tradable feed and the projected feed mix of marketable bulk feeds (cereals, protein feed, …), fits in the requirement constraints and leads to plausible feed cost. In order to do so, the feed allocation framework used to construct the base year allocation of feedstuff to animals is re-used. The resulting factors are stored in external files and reloaded by counterfactual runs. | ||
+ | |||
+ | Similar to animal feed balance, the crop nutrient needs must be consistent with available projected nutrients from various sources. To find such a feasible point, the distribution of various fertilizer sources (manure, mineral fertilizers and crop residues) to crops estimated in the database (CAPREG), is shifted with changes in crop areas to make a first best guess (prior) of the allocation to crops in the baseline. This prior is used as the modal value of a probability density function of a Bayesian estimation, similar to the CAPREG procedure described in a previous section of the documentation. Thus, a crop nutrient allocation is sought that is in some sense “as similar” to the base year estimate as possible. The result of the fertilizer calibration for the baseline is stored in a GDX file for each country, found in the directory “results/ | ||
+ | |||
+ | ====Calibrating the marginal cost functions==== | ||
+ | |||
+ | Since the very first CAPRI version, ideas based on Positive Mathematical Programming were used to achieve perfect calibration to observed behaviour – namely regional statistics on cropping pattern, herds and yield – and data base results as the input or feed distribution. The basic idea is to interpret the ‘observed’ situation as a profit maximising choice of the agent, assuming that all constraints and coefficients are correctly specified with the exemption of costs or revenues not included in the model. Any difference between the marginal revenues and the marginal costs found at the base year situation is then mapped into a non-linear cost function, so that marginal revenues and costs are equal for all activities. In order to find the difference between marginal costs and revenues in the model without the non-linear cost function, calibration bounds around the choice variables are introduced. | ||
+ | |||
+ | The reader is now reminded that marginal costs in a programming model without non-linear terms comprise the accounting cost found in the objective and opportunity costs linked to binding resources. The opportunity costs in turn are a function of the accounting costs found in the objective. It is therefore not astonishing that a model where marginal revenues are not equal to marginal revenues at observed activity levels will most probably not produce reliable estimates of opportunity costs. The CAPRI team responded to that problem by defining exogenously the opportunity costs of two major restrictions: | ||
+ | |||
+ | The development, | ||
+ | |||
+ | The two possible competitors are standard duality based approaches with a following calibration step or estimates based directly on the Kuhn-Tucker conditions of the programming models. Both may or may not require a priori information to overcome missing degrees of freedom or reduce second or higher moments of estimated parameters. The duality based system estimation approach has the advantage to be well established. Less data are required for the estimation, typically prices and premiums and production quantities. That may be seen as advantage to reduce the amount of more or less constructed information entering the estimation, as input coefficients. However, the calibration process is cumbersome, and the resulting elasticities in simulation experiments will differ from the results of the econometric analysis. | ||
+ | |||
+ | The second approach – estimating parameters using the Kuhn-Tucker-conditions of the model – leads clearly to consistency between the estimation and simulation framework. However, for a model with as many choice variables as CAPRI that straightforward approach may require modifications as well, e.g. by defining the opportunity costs from the feed requirements exogenously. | ||
+ | |||
+ | The dissertation work of Torbjoern Jansson (Jansson 2007) focussed on estimating the CAPRI supply side parameters. The results have been incorporated in the current version. The milk study (2007/08) contributed additional empirical evidence on marginal costs related to milk production, see also Kempen, M., Witzke. P., Pérez-Dominguez. I., Jansson, T. and Sckokai, P. (2011): Economic and environmental impacts of milk quota reform in Europe, Journal of Policy Modeling, 33(1), pp 29-52. | ||
+ | |||
+ | ====Calibration tests with supply models==== | ||
+ | |||
+ | After calibrating the various functions of the supply models, a test for successful calibration is carried out. The purpose of the test is to ensure that the models are really properly calibrated, and to avoid that a disequilibrium in the baseline is misinterpreted as the effect of some policy change in a scenario. | ||
+ | |||
+ | To test for successful calibration, | ||
+ | |||
+ | ====Sensitivity experiments with the supply models==== | ||
+ | |||
+ | The market model of CAPRI is solved with a simplified representation of the supply model behaviour (see model overview). Even in countries where we do have a detailed supply model representation of agriculture, | ||
+ | |||
+ | If the linearized supply models would replicate the behaviour of the supply models exactly, then no iterations would be needed. In fact, no programming models of supply would be needed either. However, the approximation is not perfect, and hence the model needs to iterate between supply and demand. Since these iterations with re-calibrations are time consuming, it is desirable to have as good an approximation as possible. | ||
+ | |||
+ | The functional form of the approximation is derived from a ”normalized quadratic profit function”, | ||
+ | |||
+ | =====Baseline reproduction run===== | ||
+ | |||
+ | Not formally a component of the baseline calibration procedure, it has become an established habit to validate the calibration of supply using the simulation models themselves. There are many conceivable circumstances where the build-in calibration tests would pass, but a normal simulation nevertheless would not replicate the calibration point, for instance if some necessary and calibrated data is not properly loaded. | ||
+ | Furthermore, | ||
+ | |||
+ | In order to facilitate the evaluation of the calibration point, we run the same scenario as the one used to calibrate the model, but under a different name. “cal” and “ref” are frequently used name suffixes. Since the calibration is done country-wise, | ||
+ | |||
+ | |||
+ | **Figure 12: CAPRI settings: read in all the country specific gdx files from the results directory (capmod); load a specified symbol (dataout); store the data back into a gdx file with the same name but without country suffix** | ||
+ | {{:: | ||
+ | |||
+ | Then, the GUI can be used in a standard fashion to manually compare the activity levels reported after calibration with those computed in a baseline reproduction run. | ||
baseline_generation.1582628340.txt.gz · Last modified: 2022/11/07 10:23 (external edit)