The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2 –enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed.

Modelling olive trees and grapevines in a changing climate / Moriondo, Marco; Ferrise, Roberto; Trombi, Giacomo; Brilli, Lorenzo; Dibari, Camilla; Bindi, Marco. - In: ENVIRONMENTAL MODELLING & SOFTWARE. - ISSN 1364-8152. - STAMPA. - 72:(2015), pp. 387-401. [10.1016/j.envsoft.2014.12.016]

Modelling olive trees and grapevines in a changing climate

FERRISE, ROBERTO;TROMBI, GIACOMO;DIBARI, CAMILLA;BINDI, MARCO
2015

Abstract

The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2 –enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed.
2015
72
387
401
Moriondo, Marco; Ferrise, Roberto; Trombi, Giacomo; Brilli, Lorenzo; Dibari, Camilla; Bindi, Marco
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1013499
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