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--- the_regionalised_data_base_capreg [2020/02/09 07:34] – [Methodology applied in the regional data consolidation] matsz
+++ the_regionalised_data_base_capreg [2022/11/07 10:23] (current) – external edit 127.0.0.1
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 ===Production and yields ===
+The proceedure for gross output (GROF) is similar to the one for activity levels, as correction factors are applied to line up regional yields with given national production:
+\begin{align}
+\begin{split}
+CORR_{GROF,o} &= \sum_{j,r}{Levl_{j,r}O_{j,r}}/GROF_{o,n}\\
+O_{j,r}^*&=O_{j,r} \cdot CORR_{GROF,o}
+\end{split}
+\end{align}
+In case of missing statistical information for regional yields, national yields are used. A special rule is used for fodder maize yields, where regional yields are derived from national fodder maize yields, and the relation between regional and national average cereal yields.
+For grassland and fodder from arable land, missing yields are derived from national ones using the relation between regional and national stocking densities of ruminants, in combination with assumed share of concentrates in terms of a weighted sum of energy and protein per ruminant activity in CAPRI. Those shares are then scaled with a uniform factor to exhaust on average the available energy and protein from concentrates at the national level. Accordingly, higher fodder yields are expected where ruminant stocking densities are high, acknowledging differences in concentrate shares. If e.g. the stocking densities solely stem from sheep and goat, the assumed impacts on yields is higher. In order to avoid unrealistic low or high yields, those are bounded to a 25%-400% range compared to the regional aggregate.
+The input allocation in any given year should not be linked to realised, but to expected yields. Expected yields are constructed using the following modified Hodrick-Prescott filter:
+\begin{equation}
+\text{min} \quad hp=1000 \sum_{1<t<T-1}({y_{t+1}^*-y_{t-1}^*})^2 + \sum_{t}({y_t^*-y_t})^2
+\end{equation}
+where y covers all output coefficients in the data base. The Hodrick-Prescott filter is applied both at the national and regional level after any gaps in the time series had been closed.
+====Final steps of regional data completion====
+The regional database modules also cover some aspects which are discussed in other parts of this documentation.
+  * For policy data at the regional level (mostly premium related data) see Section [[Policy data]]. These policy related assignments require a good part of the CAPREG module
+  * For the fertiliser and feed allocations and environmental indicators, also important elements of the regional database, see the next Section [[Input Allocation]]
+  * Towards the end of the regional data base consolidation supply side PMP parameters are calibrated as a final test of consistency and sometimes to serve as starting values for the subsequent baseline calibration (in //gams\capreg\pmp.gms//)
+====Build and compare time series of GHG inventories====
+The regionalised data base module CAPREG runs in two steps:
+  * The first steps prepares regional time series covering activities, production, land use and the fertiliser allocation
+  * The second step involves more time consuming processing steps which are therefore only executed for the selected base year: feed allocation, computation of GHG results, and the final calibration test
+To assess the reliability of the CAPRI database in terms of GHG results against official UNFCCC notifications, results from the first step (time series) were insufficient, as the GHG accounting also requires information on the feed allocation. This problem was addressed within the scope of the IDEAg (Improving the quantification of GHG emissions and flows of reactive nitrogen) project((The IDEAg project was commissioned by the JRC-IES in Ispra in 2015 and was carried out by the Thünen Institute in cooperation with the JRC-IES (August 2015 – August 2016). A more detailed explanation of the CAPRI task “Build GHG inventories” and its use has been prepared by the Thünen contributors at the time, Sandra Marquardt and Alexander Gocht, see capri\doc\GHG_inventory_module.docx. )), where an option has been introduced to allow for a consistent accounting of GHG emissions over time. This is able to combine input information from CAPREG time series runs as well as (short run, nowcasting-style) CAPMOD simulation results. Furthermore, an R-based tool was introduced to the CAPRI GUI that maps GHG emissions data from CAPRI to the GHG emission balances contained in the National Inventory Reports (NIRs) that are submitted annually by countries in compliance with UNFCCC GHG reporting obligations.