1. Federico Spanna: Regione Piemonte - Agrometeorological Service federico.spanna@regione.piemonte.it federico.spanna@regione.piemonte.it Alberto Rainero: S.I.T. – Alessandria County Council albertorainero@libero.it Workshop on climatic analysis and mapping for agriculture (14-17 june 2005, Bologna, Italy)

2. Contents Context, aim, method Multivariate analysis Spatial representation

3. Territorial representation Contoured map showing elevation 50 % mountainous 30 % plain 20 % hill

4. Distribution of meteorological stations 150 agrometeorological stations (RAM) 300 hydrographic station

5. Aim Georepresentation of agrometeorological variables as influenced by land morphology

6. Methodology Analysis and selection of main morphological informations Individuation of homogeneous agrometeorological areas (multivariate analysis) Spatial representation (statistical multiregressive analysis)

7. Contents Context, aim, method Multivariate analysis Spatial representation

8. Morphological features: 1- agrarian landscape map 3 perceptive levels Scale 1:100.000 Cultivation Agrarian trend

9. Morphological features: 2 – soil yield Scale 1:100.000 9 classes Potential soil use for crops

10. Morphological features: 3 – Corine coverage Actual soil use Scale 1:100.000 44 classes

11. Morphological features: 4 - morphology Height Slope Exposure Distance from valley bottom Piedmont Digital Elevation Model (DEM) Scale 1:100.000

12. Territorial information found Multivariate analysis Homogeneous areas features Slope Exposure Height Yield soil use Corine coverage Description of morphological and topological parameters Categorical qualitative table

13. Aggregation classes 92 stations 91 typologies 8 cluster (homogeneous areas)

14. Objective function 8 areas

15. Contents Context, aim, method Multivariate analysis Spatial representation

16. Homogeneous areas representation Watershed Borough boundaries

17. Spatial interpolation Algorithm Station cluster Influence territorial area Meteo information M Morphological parameters x i Morphological parameters Meteo information synthesis M=F(x i ) ?

18. Multiregressive analysis M = F(x i ) M = kp*H + kd*S + ke*E + kq*D Meteo information: dependent variableM Morphological variables: independentH, S, E, D Multiple regression Height, Slope, Exposure, River bed distance

19. +H *kh +S *ks Substrata superposition M +E *ke +D *kd

20. Coefficient exploration Sample Dependent variable Performance (R 2 ) Period All (92) station Mean of T min 20030,139 Area 1 Stations Mean of T min february0,791 Area 1 Stations Mean of T max autumn0,784

21. Traditional representation Field of Temperature range

22. Asti Area 1 Mean of T min - 2003

23. Barolo Area Mean of T min – 02/03

24. Barolo Area

25. Asti and Cuneo Province Area 2 Mean of T mean - Spring 02/03

26. ASTI and Cuneo Province Area 2

27. Conclusions Innovative and significant methodology for a youngagrometeorological region Map developing of the most important climatic indexes (ex. Winkler, Huglin, Thermal excursions etc.) Production of useful supports for local advisors and farmers

29. Backup

32. Area del Barolo

33. Aree dellAstigiano e del Cuneese

34. Area dellAstigiano