WP2 — “Climate Trends Modeling”
The objective of this WP is to investigate climate changes trends and its relation to agricultural parameters based on the data collected within WP1. Four methods will be used in this study: time series analysis, correlation analysis, regression analysis and principal component analysis (PCA). Time series analysis will be applied to identify trends in the 30-year AVHRR, meteorological and agricultural data. Trend will be approximated by the first or/and second degree polynomial. Correlation and regression analysis will be used to model yield dependence on meteorological and satellite-based proxy indices. In addition, correlation and PCA will be used to match the patterns of environmental and agricultural data.
Tasks within WP2 are as follows:
2.1. Evaluate current trend in climate, land surface and agricultural records.
2.2. Modeling drought and low winter temperature affecting winterkill by combining satellite proxy and climate data.
Milestones & Deliverables within WP2 (KO = project start, Q = quarter):
D2.1 – KO + Q3: Report on trends in climate, land surface and agricultural records.
D2.1 – KO + Q4: Report on droughts modeling.
M2.1 – KO + Q4: Matching the systems’ components characterizing land cover, atmospheric phenomena near the ground and socioeconomic indicators.
M2.2 – KO + Q4: Modeling environmental impacts (both satellite and weather data) on grain production for: short term simulations and long term prediction based on climate forcing (ENSO and oscillations).
M2.3 – KO + Q4: Regression modeling of drought by combining satellite proxy and climate data.