WP2 – Modelling framework

The WP2 aims at calibrating and validating models that will be applied for the impacts of climate change on olive tree cultivation. These activities comprise the calibration/validation of OLIVECAN process based model to be used for simulating olive tree phenology, yield and quality. In addition, a modelling solution based on recent research advances, will be developed to simulate the effect of temperature and moisture on the fly-olive interaction dynamics. Finally, land cultivation suitability will be produced on the basis of a statistical model.

Task 2.1- Model calibration

Leader UCO, partners involved CNR, CREA
Duration M9-M15

Some routines of OLIVECAN will benefit from specific calibration work toward sites, soils and cultivars. The calibrated parameters will improve the model capacity to fit the reality of a wider range of olive farming systems with respect to those of southern Spain where the model was developed. All the partners have extended experimental experience and have conducted olive trials in the past; these results will contribute to the quality of the model simulation.

The calibration activity will be focused on the cultivar specific parameters acting over the water use and carbon assimilation (relation between canopy conductance and water potential, root distribution, photosynthesis parameters) and on those related to the local canopy management (volume, leaf area density, pruning frequency, etc.), which, by modulating the radiation interception, have a strong impact on the performance of an orchard, both for biomass produced and yield.

Although OLIVECAN sub-units were calibrated over the years against many different experimental datasets, some of the units underwent calibration against environmentally-limited data combinations (i.e, restricted to a single relatively short-term experiment in a single place). This is especially the case routines dealing with live biomass respiration and soil organic matter fate and distribution, and the parameters related with the dynamics of the cover crop. For some of these parameters, pre-existent experimental data will be collected (see table 2) by each partner.

A crucial point for a reliable simulation of the olive cropping systems under future climatic conditions is the accuracy of the phenological model unit for the main cultivars in use. For this reason, the partners will contribute with their phenological observations to the improvement of this sub-model, by reinforcing the calibration with a wider set of data and, and by evaluating to-date alternative modelling approaches. In particular, the description of the dynamics of the chilling and forcing phases is crucial to capture the effect of shorter and milder winters, which are expected to become more frequent in the short to medium time scenarios as a consequence of climate change.

Conventional GDD-based models typically assume that physiological bud endo-dormancy expires before the 1st January, the date which is normally taken as starting point for computing temperature summation. This condition has always been met so far, but it is likely to become outdated in the forthcoming years. Chilling-forcing models are in principle more suitable to describe changing scenarios, but due to their higher complexity they are highly sensible to calibration, which must be performed against a wide base of data to confer models an adequate robustness. The calibration work will be conducted in strict association with the development of appropriate model validation procedures based on multi-metric indicators. Due to the complexity and heterogeneity of model outputs, model quality assessment is a multifaceted issue, where different aspects of model response can take different importance according to end-users’ needs and objectives.

Table 2 – List of model inputs and outputs and agro-environmental performance indicators of actual olive farming

Model INPUTS  


·       Canopy dimension and tree spacing

·       Fraction of soil wetted and irrigation frequency

·       Irrigation strategy (regulation of continuous deficit, rainfed, start and end of the irrigation season, amounts)

·       Soil organic matter concentration at the beginning of simulation

·       Meteorological variables and CO2 atmospheric concentration


·       Water Use Efficiency (WUE) calculated as :

o   Biomass/Transpiration

o   Biomass/ET

o   Yield/(Irrigation + effective rainfall)

·       Yield (as mass of fruit or oil/ha)

·       Fruit number

·       Water used (soil evaporation and transpiration)

·       Maximum irrigation water requirements

·       Net Ecosystem Exchange

·       Gross assimilation

·       Net assimilation (and respirations)

·       Pruning biomass

·       Flowering date and flowering occurrence

·       Above-ground biomass

·       Cover crop biomass

·       Soil loss

·       Deep percolation

·       Soil Organic Carbon (SOC)


Variables useful to initialize the simulations and/or indicators of actual performance
·       Yield  (as mass of fruit or oil/ha)

·       Irrigation applied

·       Maximum irrigation water requirement (if any experimental data is available)

·       Water Use Efficiency (WUE) [Yield / (Irrigation + effective rainfall)]

·       Soil Water Balance (SWB) estimations

·       Soil organic carbon (SOC) (fractions – categories…)

·       Biomass of cover crop (when present)

·       Pruning biomass

·       Leaf Area Index and/or Leaf Area Density (if measured)

·       Phenological indicators on cultivar basis: date of dormancy release, flowering, ripening (oil accumulation measurements?)

Net Ecosystem Exchange (from EC measurements) for given orchard systems (site specific)

Task 2.2 – Modelling of quality characteristics

Leader CREA, partners involved ARI, UCO
Duration M9-M15

The economic success of olive cultivation is strictly dependent on the achievement of a high oil quality, hence concern is rising about climate change impact on the oil organoleptic characteristics, which is likely to put at risk established brands and Denominations of Origin. Indeed, it happens to record significant lowering of oil quality during seasons characterized by anomalous temperature and precipitation regimes. Including quality aspects in climate change impact assessments is therefore highly desirable for more accurate and comprehensive descriptions of olive productive systems in future scenarios.

However, the quantitative relationship between weather variables and quality parameters are not well understood yet, which makes it difficult to develop reliable models for producing impact estimates.

In this task the quality modelling problem will be approached by developing a structural equation, out of a set of working hypotheses to be evaluated, about causal relationships between weather variables and olive oil quality characteristics and trade standards.

The models will be numerically fitted against an extended dataset, assembled from experimental observations gathered from the partners and other institutions, and it will provide information about the actual significance and relative strength of the hypothesised relationships. They will therefore form a knowledge-base to improve our comprehension of eco-physiology of quality, but they also will be utilized to establish empirical relationship to be incorporated in olive process-based models.

Task 2.3 – Impact of biotic stressors

Leader ARI, partners involved CREA, UCO
Duration M9-M15

Climate changes are expected to significantly modify olive vulnerability to pests, which therefore should be considered in impact assessment on olive culture.

A modelling study will be undertaken to analyze how climatic change will affect pests geographical distribution and their relationships with the crop, in the short- to medium –term. The analysis will focus in particular on the olive fruit fly (Bactrocera oleae Rossi), which is currently one of the major cause of yield losses.

A modelling solution based on recent research advances, will be developed to simulate the effect of temperature and moisture on the fly-olive interaction dynamics. This will be utilized to study the overwintering population sensitivity to winter temperatures, which affects the infestation for the next season, and the population growth and spatial spread in response to temperature and moisture time course. The inter annual weather variability, whose effect is not so straightforward in population dynamics, due to cumulative effects and erratic occurrence of spring frosts, which is expected to increase in the future, will be also studied.

The relationship between the pest and crop phenology will be also analyzed, as the magnitude of yield losses is strictly associated with the timing of pest attacks during fruit development.

A number of software components from the BioMA framework initiative (http://bioma.jrc.ec.europa.eu/) which have been already used in similar studies, are available to be assembled in the modeling, to simulate air temperature, solar radiation, soil temperature, plus a generic disease simulation component,. This will allow to concentrate the modelling efforts on the domain topic.

Task 2.4 – Suitability of olive tree distribution

Leader CNR, partners involved CREA, UCO, ARI
Duration M14-M17 and M32-M35

a comprehensive suite of spatially informative layers, including thermal and hydrologic indices derived from WP1 and WP2 will be firstly exploited to examine the spatial structure, distribution and variability of many of the current olive tree cultivated area, In particular, these parameters will be used as driving variables for calibrating a classification algorithm. Once calibrated to current conditions, the classification algorithm will be used to outline possible changes in the cultivated area of olive tree for future time slices (2031-2040, 2041-2070, 2071-2100).


D2.1 (Month 15): Report on a scenario analysis concerning the impact of olive fruit fly infestations on olive production system.

D2.2 (Month 17): Suitability of olive tree distribution for the main cultivars in Europe under present condition

D2.3 (Month 35): Suitability of olive tree distribution for the main cultivars in Europe under future climate scenarios


M2.1 (Month 12): Calibration of the OLIVECAN parameters related to the soil carbon respiration and phenology

M2.2 (Month 15)  Simulation of olive farming systems under future climate scenarios

M2.3 (Month 15) Development of a BioMa-based modelling solution for simulating olive fruit fly impact on olive production.