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A dynamic model of potential growth of olive (Olea europaea L.) orchards

AutorMorales, Alejandro; Leffelaar, Peter A.; Testi, Luca ; Orgaz Rosua, Francisco ; Villalobos, Francisco J.
Palabras claveSuper-high density
Radiation use efficiency
Crop model
Climate change
Fecha de publicaciónmar-2016
CitaciónEuropean Journal of Agronomy 74: 93-102 (2016)
ResumenA model of potential olive oil production is presented, based on a three-dimensional model of canopy photosynthesis and respiration and dynamic distribution of assimilates among organs. The model is used to analyse the effects of planting density (high and super-high density orchards with 408 and 1667 trees ha−1, respectively) and climate change (ΔT of 4 °C and CO2 concentration of 740 ppm) on olive oil production. To evaluate its predictive power, the simulations were tested with published measurements of leaf area, growth and yield for a high density olive orchard cv. ‘Arbequina’ in Cordoba, Spain. The model slightly overestimated (less than 7%) the different measurements reported in the experiment. For all simulations, the maximum yields obtained were in agreement with literature. Simulations showed that climate change had a very small effect on yields due to compensation of the negative and positive effects of temperature and CO2 on photosynthesis and respiration. However, high temperatures led to some sterile years due to lack of vernalization. The model predicts that super-high density olive orchards achieve higher potential yields than high-density systems and that maximum yields are reached on the third year of the orchard. The advantage of a higher density is a higher interception of solar radiation, especially during the first years of the orchard. In all the simulations, the model predicted a small decrease of the radiation use efficiency for oil production with the age of the orchard as well as an important inter-annual variability (range of 0.11–0.19 g (MJ PAR)−1), indicating that the use of a constant radiation use efficiency may not be adequate to predict oil production.
Versión del editorhttp://doi.org/10.1016/j.eja.2015.12.006
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