This WP is devoted to provide spatial data at the highest spatial resolution available for the Northern Mediterranean basin, including present and future climatology, soil and topographic data, olive tree current distribution, main management practices, main olive trees pests and pathogen outbreaks. Spatial data will be gathered from pre-existent datasets and organized onto a comprehensive geodatabase. A downscaling procedures will be adopted to respect spatial and temporal resolution standards of the data (e.g. climatic variables). A set of indicators, specifically designed for oil production will be identified and derived. Finally experimental data for model calibration will be also gathered in this WP.
Task 1.1 Data collection and data harmonization
leader ARI, partners involved ALL
In this task all partners (including the Greek IOSV subcontractor) will cooperate to retrieve pre-existent database in order to spatially identify current olive tree distribution across Mediterranean countries, and inside of them the main soil (pH, texture, water content, etc.) and climate characteristics. Moreover, information on main management practices (i.e. planting density, main cultivars, pruning, irrigation, fertilization), carbon balance, olive production, pest, diseases and pathogen outbreaks (with a focus on olive fruit fly) will be also investigated, and mapped. All dataset will be acquired at the highest spatial resolution available. Moreover, the dataset will be qualitatively checked in order to highlight and delete errors and produce reliable data.
Once acquired, all dataset will be spatially harmonized and integrated within a geodatabase at the highest spatial resolution. Metadata will also prepared for each dataset gathered. Where original dataset will be at lower spatial resolution than 10km, downscaling procedures for resolution harmonization will be applied. From the geodatabase, relevant variables will be extracted to calculate indicators (task 1.4) to be used as model input in WP2.
Aiming at a fully calibration of the model spanning a wide range of olive tree varieties, management practices and environmental conditions, experimental data, already available from each partner (see table 1), will be collected, shared and standardized according to a specific protocol with respect to the standard requested by the model.
Task 1.2 Climate scenarios
leader CNR, partners involved CREA and UCO
In this task, monthly climate data for future scenarios, including minimum and maximum temperature, rainfall and global radiation (Tmin, Tmax, R, Rad), will be produced at highest spatial resolution in consideration of their different use.
The newest climate IPCC climate scenarios produced by Regional Circulation Models (RCMs) at a spatial resolution ranging from 25 km to 12.5 km, will be collected for the period 1951 to 2100 and different downscaling procedures will be adopted in consideration of a different spatial and time resolution required by indicators (WP4) and models (WP2).
In the specific, where average monthly climate data are required, future climate data will be downscaled over the relevant observed climatological dataset using delta change approach. According to this methodology, monthly average differences between the baseline (1980-2010) and future periods (2031-2040, 2041-2070, 2071-2100) for Tmin, Tmax, R and Rad will be added to the observed climatology in order to derive relevant informative layers at a spatial resolution of 1km+1km.
A different approach will be used to produce climate scenarios at a daily time step to feed the simulations of the process-based model OLIVECAN over the Mediterranean basin. In this case, Tmin, Tmax, R and Rad will be statistically downscaled over the existing meteorological dataset produced by MARS project (http://mars.jrc.ec.europa.eu/mars/About-us/AGRI4CAST/Data-distribution), which contains daily meteorological data with a daily time step from 1951 to 2014 gridded over the EU at a spatial resolution of 25×25 km, using a weather generator. According to this technique, the available observed daily weather data for each grid will be used to calibrate the weather generator. Once the weather generator will be calibrated over each grid cell, it will be then used to generate a stochastically homogenous synthetic weather time series for the present and future climate over the European domain. The results of RCMs will be used to derive the perturbing factors for the downscaling procedure. Likewise in delta change approach, these will be computed for each one of RCM grid cell covering the European domain as monthly average differences of Tmin, Tmax, R and Rad between the future (2031-2040, 2041-2070, 2071-2100) and the relevant baseline period (1980-2010). In addition, for temperature and rainfall the relative changes in standard deviation and in duration of wet and dry spell will be also calculated to consider also change in climate variability. Finally, forcing factors calculated for each RCM grid will be applied in the downscaling procedure to perturb the relevant climatology of the observed dataset generating stochastically 100 years of daily data for each 25×25 km grid point.
Task 1.3 List of indicators
leader CREA, partners involved CNR, UCO
In this task, a literature review will be performed to define a set of environmental sustainability indicators (ESI) and economic viability indicators (EVI), that allow the identification of the interconnections existing between the olive production and the surrounding system. Meanwhile, thresholds, which will allow us to establish environmental sustainability references, will be defined on the basis of the European legislation as well as on specific critical issues (of environmental nature) previously identified by stakeholder consultations in tasks 3.2 and 3.3. Indicators and thresholds will be used to compare different olives agro-ecosystems management options under current and future climate (WP4). The EVI will be selected consulting the main widespread indicators reported by EUROSTAT. This will help to identify sustainable olive tree management strategies, and assess their profitability on a local scale.
As an example, amongst environmental indicators the simulation of soil loss via RUSLE for which soil type, field steepness, slope length and soil conservation practice factor will be derived from task 1.1, will be compared to the threshold generally acknowledged as “acceptable” in a soil conservation context, especially
when considering an anthropic system such as agriculture (Schertz, 1983). The economic viability will be assessed for instance analyzing the economic impact of a warmer climate in terms of missing/increasing income of those farms currently involved in the olive production, considering a series of indicators, e.g. final yield, economic changes due to new management practices.
D1.1 (Month 8) Report on data collected and relevant metadata →Read
D.1.2 (Month 8) Report on future climate scenarios (changes in temperature and precipitation) →Read
D.1.3 (Month 12) List of indicators and relevant thresholds
M.1.1 (Month 3) Analysis of available dataset
M.1.2 (Month 7) Outputs of downscaling procedures (delta changes and weather generator)
M.1.3 (Month 11) Feedbacks on indicators list from stakeholders consultation.