Institut für Ländliche Räume Paper presented at the 122th EAAE Seminar 17./ Ancona MUNICIPALITY DISAGGREGATION OF GERMAN'S AGRICULTURAL SECTOR MODEL RAUMIS Norbert Röder & Alexander Gocht

Röder & Gocht, Outline Motivation Aim Method Results Discussion

Röder & Gocht, Need for high resolution data RAUMIS: German sector model for agricultural environmental impact assessment County as highest spatial resolution (>15 years of history) For environmental impact assessment frequently data with a higher spatial resolution needed: Phytosanitary issues Erosion Biodiversity Water protection Greenhouse gas emissions Motivation Aim Method Results Discussion

Röder & Gocht, Legal limitations on the availability of data Data published by Statistical offices must comply with data protection regulations (cf. EUROSTAT, 2009), i.e. data are censored if a value is aggregated of less than 3 observations single observation contributes more than 80% to the aggregate  Primary confidentiality Additional data are censored to avoid a univocal retrival of censored data  Secundary confidentiality  The higher the reolution the more data are censored  FSS is generally not available on local level for scientific use (exemptions U.K.: EDINA; Austria: IACS) Motivation Aim Method Results Discussion

Röder & Gocht, The aim (from county to municipality)  Build a data set for 31 agricultural activities on local level  Data should be consistent to the agriculural statistics on county level  Not only distribute already published agricultural activities according to a priory rules  Extract as much information from the Statistical office as legally feasible Motivation Aim Method Results Discussion

Röder & Gocht, The aim (from county to municipality) n = 316n ~ 9000 Motivation Aim Method Results Discussion

Röder & Gocht, Data flow of the algorithm Motivation Aim Method Results Discussion

Röder & Gocht, The Clustering Step Calculate 3 independent kMeans-cluster models (general landuse; arable farming livestock husbandry) Clustering based on relative data Each model has 8 to 13 Variables In each Model ~ 9,000 municipalities are atttributed to ~ 180 clusters Median and std. of the clusters are used as parameters in the further processing Problem Sum(Cluster(median) * GIS(extent)) ≠ County data  Estimation of a consistent data set Motivation Aim Method Results Discussion

Röder & Gocht, Estimation (Highest Posterior Density (HPD)) Algorithm Search production level that comply with the constraints and mimize the deviation to the prior information Prior Information Relative values for 31 groups of Activities from (Cluster) (Median and Std.) Size of some types of land use (arable land, grass land …) (GIS) Constraints Aggregated activity levels per county (published statistic) Motivation Aim Method Results Discussion

Röder & Gocht, Objective function (Highest Posterior Density (HPD)) Objective function s e :relative share (estimated) s p : relativer share (prior) X P :activity level (prior) M:municipality A:activity E:confidence in the prior share (e.g. std.) Motivation Aim Method Results Discussion

Röder & Gocht, Self-consistency of the data Deviation from Motivation Aim Method Results Discussion

Röder & Gocht, Deviation from the observed values Deviation weighted with the observed stocks on municipality level Motivation Aim Method Results Discussion

Röder & Gocht, Difference in the estimated shares (e.g. Maize 2007) Motivation Aim Method Results Discussion

Röder & Gocht, Distribution and development of maize cultivation Motivation Aim Method Results Discussion

Röder & Gocht, Summary Validated map of agriculture on municipality level from ’99 till ’07 Data set consistent to agricultural census (county) Compared to GIS complete mapping of livestock husbandry Better estimates compared to county averages In most cases the deviation to the censored data on local level is negligible The approach allows disaggregation of RAUMIS-modelling results to the local level Particular relevance for environmental indicators Analysis of land use change with high resolution Motivation Aim Method Results Discussion

Röder & Gocht, Outlook Include computation of envrionmental indicators Modification of the symetric penalty function (distribution of municipalities in the clusters frequently skewed) Modify GIS confidence estimator (Currently ad-hoc approach) Use in information of the other years in the estimation framework Motivation Aim Method Results Discussion

Röder & Gocht, Thank you for your attention. Further information: