1. Experiences from Data retrieval to crop disease modelling
Experiences from Data retrieval to crop disease modelling
Hanna Huitu
MMEA result seminar

2. Model development and data input
Biology and epidemiology check-up. Model compatibility with different input weather data sources.
Framework for agro models – model was implemented in CropInfra Framework, containing interfaces for meteorological data retrieval (MMEA), field crop information, and in future model result output (MMEA)
Data 1: Interface provided by FMI (MMEA 25.5.result seminar), LAPS (Limited Area Prediction System) data
Data 2: SoilWeather – a-Lab in-situ measurements (XML data; MMEA platform)
Experiences so far
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3. Implementation of a disease forecast model to the
Implementation of a disease forecast model to the
pilot environment
BASE RISK
Weather input data
(MMEA)
Cultivation history +
DAILY RISK
Cultivation
Sporulation
Infection
THRESHOLD
VALUES
FOR WARNING +
Itiönmuodostus; ilmankosteuden gradientti
Variety

4. Feeding the model with epidemiological data
Feeding the model with epidemiological data
INFECTION SPEED AT DIFFERENT TEMPERATURES
NET BLOTCH
TAN SPOT
Infection development - spot size mm in 7 days
Weather data used in model development vs weather data available in operational use (instrumented fields, or random farm in location X)

5.
CropInfra Platform
Internet-based networked production infrastructure, ”Sandbox”
Service Oriented Architecture
Project co-operation at national and EU levels: design and carrying out case studies (AgroXML, AgriXchange, Smart Agri-Food...)
Co-operation with agricultural machinery manufacturers; studying present and emerging ecosystems
Assisted decision making in agriculture: Science -> knowledge models -> practise
Implemented onto an operational plant production farm (150 ha) with instrumentation to monitor both environment and machinery
Research themes Internet of things & Future internet
Esim. Infektion leviämisnopeustutkimus tulokset käytäntöön
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6. System of services framework DPS Service
System of services framework DPS Service
SOA
Interfaces
(ESB)
Optional
SOA
Interfaces
Independent Services
FMIS Service Framework (Cloud)
Discovery Services
(USDL based)
Embedded DPS
Trusted Log In Services
Trusted Profile Services
Data Storage Services
AgroXML
Weather Services
PMML
Model Services
FMIS Service Framework (Cloud Proxy aka Local)
Disease Pressure Service Framework
Embedded DPS
Limited Functionality
Communication Enabler
User Interface
for stand-alone usage
SOA Interfaces
for service integration
Säätietopalvelu – tähän pakataan MMEA tietolähteet
Model services- tilaa mallikirjastolle
Service Registration Enabler
Framework Specific Services – User Specific Services
Framework Specific Application Logic Enabler
Framework Specific Data Model Enabler
Service Registry Enabler
Public – Private - System

7.
FMI data retrieval
Interface presented in MMEA result seminar
Real-time LAPS data
Partial implementation of WCS 2.0
XML data retrieval; air temperature, relative humidity, wind speed, wind direction, precipitation 1 hour
Textual format, suitable for small grid datasets
Data delivery was working out very well!
Problems with model time synchronization (NTP) in MTT influenced data retrieval in 2012.
Aikojen kohdistaminen, mallin säätietopalvelu kellotettu hakemaan dataa eri lähteistä
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8. SoilWeather (in situ) weather station data retrieval
SoilWeather observation network in Karjaanjoki area, part of CropInfra in Vihti
Network monitors air, soil and water (air temperature, air humidity, wind, precipitation, soil moisture, water level, water temperature, turbidity, nitrate content..)
We used the data from automatic weather station on Hovi field (temperature and air humidity)
Data measured in 15 min time interval; station-specific xml request from a-Lab server every 1 hour
Data delivery was established and worked out well
Built procedure to replace data gaps with value from database to enable gradient calculations in the model
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9. Temperature
Modelled (LAPS) temperature and relative air humidity were well correlated with in situ measurements on the field

10. Comparison of two weather input datasets
Model results (cumulated risk value) for individual field plots in Finland
Comparison of two weather input datasets
Cumulated risk value
Based on in situ measurement
Cumulated risk value based on FMI (1 km2) data
© Maa- ja elintarviketalouden tutkimuskeskus

11.
Agricultural (biological) models and data interoperability – first experiences
Having choice over weather input data to agro models is excellent, and FMI weather data was seen as a fine source of information to replace or complement data from compact in-situ weather stations
Easy access to data from FMI / MMEA interfaces.
Biological expertise must be included in implementation process together with technical expertise.
Continuous, good communication is needed because ways of thinking can differ and terminologies are often domain-specific.
Information connected to measured value: parameter, unit, time and location of measurement, estimate of uncertainty of measurement...Here Input data values behaved nicely, but work with other information (especially time, location) needed and will need more attention than we expected
Weather data used in model development vs weather data available in operational use (instrumented fields, or random farm in location X)
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12. Work continues...
Model testing , and model output for growth season 2013
Data quality assessment
Weather data comparison in agricultural applications (Scientific article, 2012 manuscript)
CropInfra environment will be developed towards supporting flexible service use, service relaying and service integration
Support for model simulations (Monte Carlo approach)
Our work will be useful for integration of other crop disease and pest models
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