1. agriregionieuropa A minimum cross entropy model to generate disaggregated agricultural data at the local level António Xavier 1, Maria de Belém Martins 1 and Rui Fragoso 2 1 Sciences and Technology Faculty-University of Algarve and CEFAGE-UE, 2 Management Department, University of Évora The authors gratefully acknowledge partial financial support from FCT, program FACC. 122 nd European Association of Agricultural Economists Seminar Evidence-Based Agricultural and Rural Policy Making Methodological and Empirical Challenges of Policy Evaluation February 17 th – 18 th, 2011, Ancona (Italy) associazioneAlessandroBartola studi e ricerche di economia e di politica agraria Centro Studi Sulle Politiche Economiche, Rurali e Ambientali Università Politecnica delle Marche

2. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)  Introduction – The problem’s description – Previous studies – The methodological approach – The empirical implementation – Results – Validation  Conclusions Contents

3. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Introduction  The lack of data-Portugal  The objective of the presentation – Overcome Lack of disaggregated agricultural data – Necessity of methods for different situations – A method for certain specific situations in Portugal the approach presented here results from a series of experiences carried out by the authors and it’s still under development Relevant for policy evaluation

4. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) The problem’s formulation  In Portugal the specific situation may be described as follows: – Existence of aggregated data – Other co-variables: Land use cartography Biophysical data –Slope –Soils Meteorological data …  Necessity of disaggregated data at the following levels: County Parish Local (pixel,...)

5. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) DATA DISAGGREGATION ????????? Administrative units Local level FARMS’DATA-REGIONAL LEVEL CLIMATE DATA MAPS LAND USE MAPS SLOPE HIPSOMETRY METEO STATIONS DATA SOIL CAPACITY The problem’s formulation

6. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Previous studies Howitt and Reynaud (2003) presented a two stages dynamic disaggregation process which was able to recover a complete sequence of disaggregated data. Kempen et al. (2005) You and Wood (2006) have used a spatial disaggregation procedure combining a logit model with posterior density estimators to break down production data available at the regional level to a homogeneous spatial mapping unit level (HSMU) proposed a spatial disaggregation model for crop production statistics based on a cross-entropy approach.

7. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Chakir (2009) Fragoso et al. (2008) Martins et al. (2010) proposed a model which estimates incomplete information at disaggregated level through an entropy approach used agricultural data in conjunction with biophysical processes to break down agricultural FADN regional data into 100m × 100m pixel spatial units. presented disaggregated data regarding land use for the Montado ecosystem area. In Portugal: Previous studies

8. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) The methodological framework  BASED MOSTLY IN THE WORKS OF C HAKIR (2009), Y OU AND W OOD (2006) AND Y OU ET AL. (2007, 2009) – COMBINATION OF THE DIFFERENT EXPOSED IDEAS, IN ORDER TO VALORIZE ALL THE EXISTING INFORMATION  It’s composed by 2 steps: – 1ºstep-Prior information database creation – 2ºStep-The cross entropy approach

9. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) The methodological framework

10. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) 1ºstep description  There are several ways of creating this prior and the diversity of information leaded to the exclusion of some predefined methods  combination of the following information: land use cartographical data, soil capacity maps, climate data, and other biophysical data, namely slope and hypsometric data. – Information is reclassified in a Geographical Information System (GIS) – accurate estimation for the biophysical conditions for which the use may be developed experts’ opinions or the available cartographical land use data-prior estimation

11. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) 2ºstep description Subject to: Land use Biophysical restrictions

12. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Calculation of the area 2ºstep description

13. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Empirical implementation  it was necessary to define concretely the study area: – Region of Algarve – 16 counties and 84 parishes

14. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Region of Algarve Empirical implementation

15. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) The data used Empirical implementation

16. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)  Considered the following farms’ uses: – cereals (CC); – Other temporary crops (OCT); – Fallows (PO); – citrines (CT); – other fresh fruits (OFF); – olive trees (OL); – almond trees (AM); – Other permanent crops (OCP); – permanent pastures (PP) – “other occupations” (OO) Empirical implementation

17. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Applied to the year 1999 Used the Agricultural Census data Necessity of validation Empirical implementation

18. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) The model was applied following two variants: – 1) disaggregation of data of the different administrative units (counties); – 2) disaggregation at a local pixel level (1 km2) The errors’ limits definition The lack of data Some considerations Other adaptations Empirical implementation

19. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Results  1º Variant of the model

20. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Results  2ºvariant of the model  Data at pixel level Olive treesCitrines

21. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Validation  Deviation measures

22. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Validation  1ºvariant of the model

23. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Validation  1ºvariant of the model – The WPADi – The WPAD 17,787%. The counties‘ WPADi

24. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Validation  2ºVariant – Limited to a sample of counties – It’s under development in some aspects

25. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Validation

26. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Conclusions The model proved for the first time for Portugal a new way of disaggregating data Some problems need still solution Some results of the studies being carried out and now provide better results Some examples The future Innovations

27. agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Thank you for your atention!