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--- premium_module [2020/03/13 14:12] – [Pillar I] matsz
+++ premium_module [2020/04/07 09:35] – [Pillar I] matsz
@@ Line 138: / Line 138: @@
   *land receiving payments should be kept in good agricultural and environmental condition (G.A.E.C). “Good agricultural condition is generally interpreted to mean that the land will not be abandoned and environmental problems such as erosion will be avoided” this requirement could be interpreted as re-establishing the link between the payment and the factors of production employed (land management practices) and ultimately current production; some form of management of the land should be maintained;
   *entitlements are tradable within the EU member states (not among them) but certain limitations are imposed (Ciaian, Kancs and Swinnen, 2010). For example, in the Netherlands, entitlements can be transferred among farmers only when the farmer has land without entitlements;
-  *areas already under permanent pasture should must remain so; in practise, certain reductions at regional level were accepted before Member States would be forced to
+  *areas already under permanent pasture should must remain so; in practise, certain reductions at regional level were accepted before Member States would be forced to interact.
-  *interact.
   *a strengthened rural development policy based on expanded EU budget outlayswith more EU money, new measures to promote the environment, quality and animal welfare and to help farmers to meet EU production standards starting in 2005,
   *a reduction in direct payments ("modulation") for bigger farms to contribute to finance the new rural development policy,
@@ Line 353: / Line 352: @@
 ===Overview===
+Modelling of Pillar II for an EU-wide assessment provides a specific challenge given data availability regarding measures which are programmed and implemented at Member State or even regional level. Official reporting of measures under Pillar II in standardized data bases uses a rather rough categorisation, so are e.g. all agri-environmental measures grouped into one category. But even at that rather aggregated level, there is no ready to use data base available to researchers, especially with a regional resolution. A first task therefore consisted in building up a suitable data base on funds for Rural Development measures, a task undertaken by a team which had already compiled such data in the past (BALDOCK et.al. 2002).
+The most important measures from a budgetary view point are the Agri-Environmental Schemes and the Less Favourite Area Payments. Therefore some care must be taken to model these measures accurately. The project draws here on the work of a study by LEI and IEEP (LEI and IEEP 2009) for DG-AGRI, which use in the case of the agri-environmental payments analysis based on FADN and for LFA based on FADN and CLUE (Verburg et.al. 2010) results.
+**Table 26: Overview of pillar II measures modelled in CAPRI**
+|Measure type	|Measure codes EU| Modelling approach in CAPRI|
+|LFAs	|211-212	|Regional direct support. Distribution over sectors and regions based on FADN data and CLUE results.|
+|Natura 2000|213,224	|Regional direct support. Distribution over sectors and regions based on FADN data and CLUE results. Conditional on extensive technology being used.|
+|Agri-environment|214-215| Regional direct support. Distribution over sectors and regions based on FADN data. 50% of the support directed towards TF8 farm types 1, 2, 3, 4 and 8 is conditional on extensive technology being used, for remaining amounts extensive as well as intensive technology is eligible.| \\ Source: Capri Modelling System
+===Modelling of LFA===
+The LFA measure was implemented as a direct payment to cropping and grassland, with the same amount for all cropping activities except for fallow land. The first challenge encountered when implementing the LFA premiums is that the regions do not coincide with the administrative regions used in CAPRI. – Recall that CAPRI only has one single representative firm in each nuts 2 region (or up to nine farm types). In reality, thus, only a share, generally much less than 100%, of the land in a nuts region is eligible for LFA payments, and it may very well be the case that the agricultural production on that land is different from the regional average. For example, one may expect that a mountainous LFA area contains more grass land than the surrounding flat land agricultural areas in the same nuts 2 region. In order to capture a possible bias of this nature, a GIS tool (CLUE-model) was used to compute the shares \(S_{ij}\) of LFA in different broad land use classes //j// \(\in\) {non-irrigated arable land, irrigated arable land, pasture, permanent crops} in each region //i//. Those shares were used to compute a (potentially) different nominal premium amount for crops belonging to each class //j//. The so computed different amounts were taken to reflect the biased distribution of crops inside and outside of LFA regions. Since CLUE does not distinguish “Mountainous” and “Other” LFA, the nominal amount //A// to which the shares //S// were applied was assumed the same everywhere: 250 euro, the maximum amount in mountainous LFA regions.
+\begin{equation}
+P_{ij} =AS_{ij}
+\end{equation}
+where \\
+//P// = Premium per hectare	\\
+//i// = Region	\\
+//j// = Group of crops \\
+//A// = Maximum amount per hectare, 250 euro \\
+//S// = Share of LFA in all land of class //j// \\
+A value ceiling for the premium was computed by adding the budgets for the component measures. Recall that the premium module of CAPRI will apply a cut factor to the amount P such that the ceiling is not overshot.
+In economic terms, the potentially different premium rates for different groups of crops has a production effect, so that the type of production in CAPRI that receives the higher rates may expand on the expense of other activities. The interpretation would be that more farmers in the LFA areas comply with the LFA eligibility rules and modify their production plans to comply with the criteria there. Nevertheless, this is a simplification, because in CAPRI, no special technical restrictions are required in order to comply with the payment.
+=== Modelling of N2k ===
+Each production activity in CAPRI is split into a low and high yield variant with adjusted input coefficients, and thus own costs and revenues, such that their weighted average recovers regional resp. farm type means. The N2k premiums are modelled in pretty much the same way as the LFA premiums, but now with the additional assumption that the payments are conditional on extensification. This was implemented using the two alternative technologies. Thus, only the technological alternative with yield 20% below nuts2 average and lower input requirements (following a yield function) was made eligible for the payment. This is based on no empirical investigation, but is a pure assumption based on the frequently stated fact that the N2k payments are conditional on extensive management practices.
+The interpretation is the following: If more money is spent on the measure, more farmers within the designated areas may switch to extensive agriculture. Then the average payment per hectare of the nuts 2 region would increase, reflecting that a larger share of the farmers now participate in the measure. It is today indeed the case that not all farms within an N2k area receive support.
+===Modelling agri-environmental payments===
+The agri-environmental payments is a very diverse set of measures, which accounts for the largest share of the second pillar. In the modelling approach opted for here, with one aggregated measure “05 agri-environment”, a uniform implementation across member states cannot be used, which is in contrast to the LFA and N2k measures. Instead, a way of capturing the national or even regional preferences within the agri-environmental schemes must be sought.
+The method for “nationalization” of the AE scheme employed here is to use the distribution of the sum of the AE measures, i.e. the measure allocated to class 05, to agricultural sectors using the receipts by farm types according to FADN in 2005 as key. This implies linking the support to production. Whether this corresponds to reality is an empirical question. It is doubtless the case for some measures in some regions, but certainly not so for all AE measures in all regions. Refining the implementation would thus involve conditioning the support on technical constraints. Nevertheless, the implementation described above has the merit that it allocates the correct budget, resulting from the LEI budget model, to approximately the right group of farmers.
+**Table 27: Mapping from aggregated farm types in FADN (TF8) to activity groups in CAPRI**
+|TF8 type|Group of activities in CAPRI|
+|1|Grandes Cultures|
+|2|Vegetables|
+|3|Wine|
+|4|Permanent crops|
+|5|Dairy cows including pastures|
+|6|Suckler cows, sheep and goats, including pastures|
+|7|Pigs and poultry|
+|8|All agricultural activities| \\Source: Capri Modelling System
+The agri-environmental (AE) payments are implemented as extensification subsidies, and they are distributed to regions based on the distribution of less-favoured areas across regions. However, the distribution is modulated by the national level shares of farms in or out of LFA receiving AE support. We needed the following two probabilities:
+p_landPartition(ru,lcAgri,”LFA”): The share of land of each land class that is LFA (computed based on extrations from the DYNA-CLUE database).
+p_aeLfa(ms,tf8): The probability of a farm of type tf8 having agri-environmental support conditional on the farm being in an LFA region or not, computed based on the FADN sample.
+Then, the payment rate for each region is set in proportion to the weighted share of farms likely to have some AE support, predicted by the regional share of each land class (grass, arable) being classified as LFA in each region times the share of farms in/out of LFA having AE-support in the national FADN sample. The computation takes place in policy\rd_logic.gms:
+{{::code_p179.png?600|}}
+Note that the code does not know how high the absolute level of payments shall be for each region, but allocates the relative levels. Then, the national ceiling for AE payments are applied to adjust all regional payments until the ceiling is respected.
+Finally, an extensification effect to the AE payments is introduced using the possibility to make technological variants differently eligible.
+{{:code_p179_2.png?600|}} \\
+{{:code_p180.png?600|}}
+====Co-financing rates, assignment of premiums to pillars, WTO boxes and PSE-types====
+**EU and national budget contribution**
+The reporting part of the system was expanded to account for (co-)financing rates of the different schemes, so that contributions from EU and national budgets can be differentiated. The underlying factors are currently defined in //‘policy\policy_sets.gms’//:
+{{:code_p180_2.png?600|}}
+**PSEs**
+The mapping to the PSE-types is defined in //‘policy\policy_sets.gms’//:
+{{:code_p180_3.png?600|}}
+**WTO boxes**
+In a similar fashion, the premiums are allocated to the WTO boxes. The following payments are allocated to the green box (‘//policy\policy_sets.gms’//):
+{{:code_p181.png?600|}}
+The blue box, i.e.g payments under supply control or only paid up to certain upper limits, is defined as along with remaining amber box payments in Norway:
+{{:code_p182.png?600|}}
+Currently, the following budget categories are supported (see ‘//sets.gms//’ and ‘//policy\policy_sets.gms//’):
+{{:code_p182_2.png?600|}}
+In ‘//reports\feoga.gms//’, these categories are first aggregated for each activities from actual schemes (“PRME” = actual payment rate, p_budToPsdpay: distribution key):
+{{:code_p182_3.png?600|}}
+In order to come to a product based accounting scheme as used by the PSEs and WTO, these payments are assigned to the main outputs of the activities. Payments to obligatory set-aside are allocated to the activities according to set-aside rates:
+{{:code_p183.png?600|}}
+For a discussion about the WTO and PSE Boxes and their implementation in CAPRI see: Mittenzwei, K., Britz, W. und Wieck, C. (2012): Studying the effects of domestic support provisions on global agricultural trade: WTO and OECD policy indicators in the CAPRI model, selected paper presented at the 15th Annual Conference on Global Economic Analysis, "New Challenges for Global Trade and Sustainable Development", June 27-29,2012, Geneva (Switzerland).