Workshop on climatic analysis and mapping for agriculture (14-17 june 2005, Bologna, Italy)

1. Workshop on climatic analysis and mapping for agriculture (14-17 june 2005, Bologna, Italy) The multivariate and multi-regressive techniques in the spatial representation of agrometeorological data for the Piedmont (North-West Italy) areas Federico Spanna: Regione Piemonte - Agrometeorological Service federico.spanna@regione.piemonte.it Alberto Rainero: S.I.T. – Alessandria County Council albertorainero@libero.it

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 9 classes Scale 1:100.000 Potential soil use for crops

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

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

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

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

14. 8 areas Objective function

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

16. Homogeneous areas representation Watershed Borough boundaries

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

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

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

20. All (92) station Mean of T min 2003 0,139 Area 1 Stations Mean of T min february 0,791 Area 1 Stations Mean of T max autumn 0,784 Coefficient exploration Dependent variable Performance (R2 ) Sample Period

21. Traditional representation Field of Temperature range

22. Mean of T min - 2003 AstiArea 1

23. Mean of T min – 02/03 “Barolo” Area

24. “Barolo” Area

25. Mean of T mean - Spring 02/03 Asti andCuneo ProvinceArea 2

26. ASTI andCuneo ProvinceArea 2

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

29. Backup

32. Area del Barolo

33. Aree dell’Astigiano e del Cuneese

34. Area dell’Astigiano