Automatic procedures of agrometeorological data spatial interpolation for the application of simulation models

1. Automatic procedures of agrometeorological data spatial interpolation for the application of simulation models S. Orlandini(1), A. Dalla Marta(1), A. Cicogna(2) (1) DISAT - University of Florence (2) ARPA - Friuli Venezia Giulia

2. Agrometeorological variables are directly involved in plant growth and development, but also in the damages due to pests and diseases • For this reason the knowledge and the monitoring of such variable distribution and variability on the territory represent the basis for a correct management of agricultural activities • The integration between spatial interpolation methods and simulation models can provide useful information disseminated to the farmers, using texts, figures and maps

3. Minimum temperature Stable in time Variable in space Maximum temperature Variable in time Stable in space

4. AIM The aim of this work was the set up of an integrated and automatic system for the production of agrometeorological thematic maps at farm level. Maps provide information concerning weather variables and the output of simulation models for crop, pest and disease distribution on the territory. The system was created with the idea to directly involve the final users.

5. The integrated system • Collection of agrometeorological and crop data • Elaboration of weather data • Spatial interpolation of data • Application of simulation models • Production of thematic maps of weather and agrometeorological model output • Information to the growers

6. The study was carried on in the farm “Poggio Casciano” located in the North part of Chianti region (South of Florence)

7. AVAILABLE DATA • 40 weather stations for temperature and relative humidity • 10 years of data • Latitude and longitude • Altitude • Aspect • Distance from valleys bottom • Slope

8. Weather data were analysed to identify the climatic characteristics of the farm areas. Different approaches were used, based on the mean difference and the correlation coefficient. On these bases, three locations were identified as the warmest, the coldest and the most representative of the entire farm conditions. Standard agrometeorological stations were installed in these locations. THE AGROCLIMATIC CLASSIFICATION

9. COLDEST WARMEST REPRESENTATIVE

10. Station for agroclimatic monitoring Station for agrometeorological monitoring

11. THE PROBLEM of LEAF WETNESS • No standard for sensor positioning (top of the canopy; within the canopy; north; south; etc.) • No standard for measurement (0-1; 0-15; 0-100; minutes; etc.) • No standard for sensors design

12. For this reason the use of LWD simulation models, based on agrometeorological variables, represents a valuable alternative to field measurements. • One literature review listed at least 16 models capable of simulating surface wetness both with empirical and physical approach.

13. SWEB model • In this work a physical model based on the energy balance was used. The model, developed in the United States, was calibrated on Sangiovese variety and transferred in Visual Basic language to be part of the integrated system • The inputs are: Air temperature (°C), Relative humidity (%), Wind Speed (m s-1), Precipitations (mm), Net radiation (kW m-2) • The output is the duration of leaf wetness in minutes.

14. PLASMO • PLASMO is a mathematical model set up in the University of Florence and well validated for Sangiovese variety. • The model simulates the infection of Plasmopara viticola through its principal stages of incubation, sporulation, spore survival and inoculation, jointly with the simulation of leaf area growth. • The input are: air temperature (°C), relative humidity (%), precipitations (mm), leaf wetness (0-1)

15. Main symptoms oil spot on upper surface of leaf It affects the leaves, fruits and shoots. When weather is favourable and protection against the disease is not provided, downy mildew can easily destroy 50-75% of the production in one season. mildew on under surface of leaf

16. The integrated system • The system was created using Visual Basic for Application (Excel). • The creation of grids and maps was done with Surfer7. • It is composed by different parts and the final result is represented by both text files and thematic maps of the most important parameters.

17. 1st MODULE - Loading of weather and geo-topographical data (“.csv”) • Reading of weather and geo-topographical data (creation of territorial matrices, control of data gaps) • Spatial interpolation of weather data • Calculation of solar radiation 2nd MODULE - Simulation of leaf wetness - Plasmo simulation 3rd MODULE • Creation and export of thematic maps (“.png”) • Creation of grids (“.grd”)

19. Distance from valleys bottom Altitude Correction factor based on the deviations from the average of the 40 stations during the 10 years Multiple regression with altitude and distance from the valleys bottom to interpolate temperature

20. Calculation of solar radiation. The spatial variability is mainly due to slope and aspect.

21. SWEB Model

22. Plasmo

23. Grid creation

24. Output “.txt”

25. Output “.grd” Grid of relative humidity (day 157)

26. Output “.png” Map of relative humidity (day 157)

27. Map of temperature (day 140)

28. Map of leaf wetness (day 153)

29. Map of number of current infections (day 154)

30. Map of number of days for the outbreak of the current infection (day 154)

31. Available thematic maps • Minimum, maximum and mean temperature • Relative humidity • Leaf wetness • Global radiation, PAR • Number of current and total infections • Number of days for the outbreak of infections • Severity of each infection cycle • Area of infected and healthy leaf tissue

32. Future improvements • Implementation of the system in a GIS and creation of the related database • Spatial interpolation of wind speed and precipitation • Personalization of the products and application of other simulation models • Creation of standard procedures to apply agrometeorological monitoring at farm level • Use of future scenarios for agronomical purposes (shift of cultivation areas, increase of early frost, etc.)