The olive (Olea europaea L.) is the leading commercial tree crop in the Mediterranean area. Its reproductive cycle displays considerable variations due to inherent genetic factors but also to climate response. This thesis provides a detailed analysis and modelling of the olive reproductive cycle and its response to a range of environmental variables in the southern Iberian Peninsula. Analysis was based on phenological, aerobiological and meteorological data recorded over the last 30 years in the province of Córdoba (Andalusia, Spain), the second-largest olive-oil-producing province in Andalusia, which is in turn the world’s largest producing region. A more thorough knowledge of the factors governing year-on-year changes in olive flowering and fruit production is clearly of agricultural interest. It is also useful for medical purposes—since olive pollen is highly allergenic—and for ecological reasons, given that the wild olive Olea europaea var. sylvestris Brot., is a characteristic shrub used as a bioindicator for Mediterranean ecosystem. Although the thesis focuses mainly on the behaviour and phenological response of the olive tree in the province of Córdoba, Chapter IV offers an overview of olive production in the Mediterranean area, drawing on data for Andalusia (Spain), Italy and Tunisia.
A statistical analysis is made of the correlations between various environmental factors and critical features of the olive reproductive cycle (flowering intensity, floral phenology and fruit production). The results are used in the construction of models to describe and predict the reproductive cycle, from the earliest phases through to harvest. These models are of considerable scientific interest and can readily be transferred for social applications, since they enable the prediction—several months in advance—of major biological events such as the timing, duration and intensity of flowering and the volume of fruit production.
Chapters I and II “Biometeorological and autoregressive indices for predicting olive pollen intensity” and “Year clustering analysis for modelling olive flowering phenology” analyse variables relating to flowering intensity, expressed in this anemophilous species by the Pollen Index. The first chapter focuses specifically on the construction of indices to account for year-on-year variations in olive flowering intensity, while in the second chapter a cluster analysis is used to group years with similar meteorological and phenological characteristics and to distinguish those variables most influencing floral phenology, and more particularly flowering intensity.
In Chapter 1, pollen production is modelled using bioclimatic and autoregressive indices which account for the influence and impact of a range of environmental variables. The method designed for the construction of these indices enables analysis of the role played by extreme weather events in flowering. The autoregressive index provides crucial information on the dynamics of the olive reproductive cycle. For the mathematical modelling of flowering intensity in Chapter II, a “three-step method” is used, consisting in grouping using a clustering technique, classification using artificial neural networks, and modelling using partial least squares regression. The findings show that the environmental variables most affecting and governing flowering intensity in the province of Córdoba are rainfall during the period prior to flowering and temperature during the month of March. Effective models are also constructed to predict pollen emission intensity; these are especially valuable in preventing symptoms in patients allergic to olive pollen.
Chapter III “Modelling olive phenological response to weather and topography” examines the environmental factors affecting each phase of the olive’s floral phenology in the province of Córdoba. The topographical and meteorological variables most influencing the response of different local olive populations and varieties are identified. The topographical factors most affecting reproductive phenology are altitude and East-west orientation of the steepest slope, while the most important weather-related variables are winter temperature, spring temperature and water availability during reproductive organ development.
Finally, Chapter IV “Better prediction of Mediterranean olive production using pollen-based models” analyses the factors that determine olive fruit production in the Mediterranean Basin, focussing on Andalusia—Spain’s largest olive-producing region—Italy and Tunisia; for this purpose, for this purpose, flowering intensity and weather-related factors are taken as the major variables Harvest size is modelled using data from the world’s largest olive-oil-producing regions, in order to draw overall conclusions regarding the bioclimatological characterization of the olive, with a view to enabling the construction of regional prediction models applicable to the Mediterranean Basin as a whole.
The results of this thesis help to improve the bioclimatic characterisation of the olive, as well as contributing to our knowledge both of the olive’s response to environmental conditions and of the biology of its reproductive cycle. The results were obtained using new, purpose-designed statistical methods which will be useful in future research both in the study area and in other olive-producing regions, and may provide a basis for the phenological modelling of other tree species.
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