From land use shares to spatial policy impact assessment

Transcription

1 From land use shares to spatial policy impact assessment Wolfgang Britz CAPRI-Dynaspat final meeting,, DG-AGRI, Brussels 27.February 2007

2 Outlay Context Estimation of stocking densities Estimation of I/O coefficients at spatial level Environmental and Economic indicators Outlook and Summary 2

3 Context Proposal was concentrating on land use cover: Development concentrated on cropping shares Yields were added later First attempts to distribute fertilizers W. Britz joining JRC/IES in 9/2006: Resource available to estimate animal stocking densities, input use, and improve fertilizer distribution Work on meta model from DNDC 3

4 Motivation: Why going spatial? Certain impacts need to be evaluated in a spatial context: Water pressures in the context of Nitrate Vulnerable Zones Effects on bio-diversity in the contexts of habitats Providing data in rather a high spatial solution allows for re-aggregation, e.g. to River basins LFA areas High Nature Value Farmland 4

5 Estimation of stocking densities I In opposite to LUCAS, no Pan-European data set in high spatial resolution available on animals stocking densities Alternative approach chosen Data set used: Farm Structure Survey data on 530 regions Regression analysis for animal stocking densities Explanatory variables: cropping shares, yields, economic performance indicators for crops, altitude, slope, climatic parameters, all linear and quadratic Automatic variable selection by backward elimination Estimation for group of countries Generally good fit, problems in some countries with pig & poultry 5

6 Estimation of stocking densities Problem: Explanatory variables at HSMU level often out of observed range Solution: Application of Highest Posterior Density Estimator FSS data on herd sizes are recovered are estimated stocking densities of HSMUs Simultaneous estimation for all type of animals, and for total LU, L and of LU of ruminants and of nonn on-ruminants Expected mean for the different stocking densities for each HSMU is a distance and area weighted average of estimates of surrounding HSMU Variance is based on variance of the forecast error unstable estimation results receive low weight Additional security bounds on results 6

7 LU in the Netherlands Livestock units LU/ha

8 Estimation of I/O coefficients and economic performance indicators Output coefficients: For major crops: water/non water limited yields from MARS, from there, index derived for other crops For animals: identical output coefficients at NUTS II level Input coefficients: Derived from linear input demand functions per ha, as in CAPREG Economic performance indicators: Using identical prices inside NUTS II regions, derived from estimated I/O coefficients Using pillar I premiums from NUTS II Work ahead: integration of Pillar II payments 8

9 Distribution of gross margin plus pillar I premiums in Austria Agricultural revenues including pillar I premiums minus variable costs/ha Euro/ha

10 Environmental indicators Currently available: N balance N removals estimated from yields Inorganic and manure N application depends on N removals and manure availability Recovers NUTS II application rates per crop estimated by CAPREG Meta model estimation for nutrient fate (NOx( NOx, leaching, ammonia, soil stocks) from DNDC in development: Variant of non-parametric estimator Aim to include results already in estimation at NUTS II level in CAPREG 10

11 Example: Nitrogen surplus for Germany Surplus at soil level (without NH3 losses) kg N/ha

12 Summary and outlook The spatial layer provides major indicators at 1x1 km grid for EU25 Allows re-aggregation to different spatial extents (administrative regions, river basins, nitrate vulnerable zones, LFA..) Basis to define additional indicators for landscape characterisation, bio-diversity and rural development Opens up new avenues to develop project ideas 12

13 Thanks for your attention! 13