GIS, SPATIAL INTERPOLATION AND MODELS FOR DECISION MAKING IN AGRICULTURE Anna Dalla Marta, Simone Orlandini Department of Agronomy and Land Management University of Florence
Nowadays, the existing relationships between biological organisms and agrometeorological variables are well known and deeply investigated in many different fields: - Plant protection - Insect control - Human health - etc.
Study variable Study territory Simulation models GIS Interpolation techniques Data base and thematic maps BASE OF DATA AND INFORMATION SUPPORT FOR DECISION MAKING
Cases study - Olive fly (Bactrocera oleae) - Simulation model - Spatial interpolation (output interpolation) - Grapevine downy mildew (Plasmopara viticola) -Simulation models - Plasmo - Fuzzy - Integrated system (input interpolation) - Tiger mosquito (Aedes albopictus) - Relations with environmental parameters - Spatial interpolation
CASE STUDY 1: Olive Fly
The model is based on thermal summation. The driving variable is mean daily temperature: the number of generation is calculated through the summation of degree day taking in consideration two constants of development at variable temperature (8.99 C for zero development and 379.01 C for thermal constant). The model begins the simulation starting from the stone hardening (oviposition) and finishes at the harvest time.
Olifly model Fly Simulator - Main Menu - Ver. 1.0
Model output
Kriging
Multiple Regression
Smart Kriging
Kriging Multiple Regression Smart Kriging Residual distribution
Interpolation errors MAE MA%E RMSE Neural Network 0.48 16.59 0.66 Kriging 0.24 14.35 0.30 Informed Kriging 0.22 12.78 0.30 Multiple Regression 0.32 19.34 0.40
Identification of micro areas with different risk of attack: possibility to find zones where the organic olive oil production is practicable Optimization/identification of intervention time The management of technical assistance can be improved
CASE STUDY 2: Grapevine Downy Mildew
PLASMO Leaf area growth T Primary inoculum Sporulation F0 Survival F1 T, RH T, RH Inoculation Incubation m T, LW T, RH
http://agromet agromet-cost.bo.ibimet.cnr.it
Oilspot FUZZY MODEL F devitalization sporulation advancement F Sporangia F devitalization germination advancement F Zoospores F devitalization inoculation Mycelium incubation advancement F
Fuzzy logic handles the concept of partial truth - truth values between "completely true" and "completely false". Fuzzy logic includes 0 and 1 as extreme cases of truth but also includes the various states of truth in between. T RH fuzzification fuzzification fuzzy inference defuzzification Adv or Dev Quantitative information Qualitative information & base of rules
Simulation models, GIS and spatial interpolation techniques can be integrated in more or less complex systems The systems outputs represent an additional information by which the decision making activity can be improved
LOADING AND READING OF WEATHER, CROP AND GEO-TOPOGRAPHICAL DATA SPATIAL INTERPOLATION LEAF WETNESS SIMULATION P. VITICOLA SIMULATION GRIDS TEXT FILES MAPS The integrated system
Map of relative humidity (day 157)
Map of temperature (day 140)
Map of leaf wetness (day 153)
Map of number of current infections (day 154)
Map of number of days for the outbreak of the current infection (day 154)
Available thematic maps Minimum, maximum and mean temperature Relative humidity Leaf wetness Global radiation Number of current and total infections Number of days for the outbreak of infections Severity
CASE STUDY 3: Tiger Mosquito In the last years, Florence, like many other urban centers, is experiencing the always larger presence of Tiger mosquito. This species is considered dangerous for human health because its prerogative to be a carrier of many diseases (dengue, etc.). The use of GIS allows to study the relations between the mosquito distribution and some important parameters, such as the proximity of public parks and gardens, the population density, the air humidity, etc. Such a study can be used by public administration to face the problem and to find some solution to stem the infestation.
CONDOMINIAL/PRIVATE GARDENS ARNO RIVER SOWED FIELDS PUBLIC PARKS
Identification of the infestation beginning (where and when) Identification of the relations between the insect and the other parameters (water sources, public gardens, etc.) Planning for prevention and intervention Production of warnings and suggestions for the population (watering, presence of backwater in the gardens, identification of places suitable for oviposition, etc.).
The use of models allows the users to know the current situation (real time), to know how it can evolve (forecasts), or how it is stabilized during years (historical series). The use of GIS and spatial interpolation techniques allows the user to have the information spread on the study area and to find the interactions between the variable and the territory parameters (altitude, slope, distance from valleys bottom, etc.). The integration between models and GIS, then, allows to produce a complete information that is the basis for decision making