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International Journal of Agronomy
Volume 2014 (2014), Article ID 672123, 12 pages
Research Article

Modelling the Effects of Soil Conditions on Olive Productivity in Mediterranean Hilly Areas

1International Center for Agricultural Research in the Dry Areas, P.O. Box 5466, Aleppo, Syria
2University of Guelph, Kemptville Campus, P.O. Box 2003, Kemptville, ON, Canada K0G 1J0
3Research Group of Nature and Society, Research Institute for Nature and Forest, 1070 Brussels, Belgium
4General Commission for Scientific Agricultural Research, Department of Olive Research, Idleb, Syria

Received 18 July 2014; Accepted 18 September 2014; Published 19 October 2014

Academic Editor: David Clay

Copyright © 2014 Ashraf Tubeileh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The majority of olive (Olea europaea L.) production in Mediterranean environments is characterized by low external inputs and is practiced in hilly areas with shallow soils. This study aimed to study the yield and nutritional status for olive (cv. “Zeiti”) trees in northwestern Syria and establish correlations between yield, on the one hand, and soil/land factors and tree nutrition, on the other hand, to determine the most yield-affecting factors. Land and soil fertility parameters (field slope, soil depth, and soil nutrients) and concentrations of leaf minerals were determined. As olive roots can go deep in the soil profile to extract nutrients, the total available nutrients per tree (over the whole profile) were estimated. Multiple regression analyses were performed to determine the model that best accounts for yield variability. Total available soil potassium amount (), soil total N amount (), and soil depth () had the highest correlations with olive fruit yields. Available soil potassium amount and soil depth explained together 77% of the yield variability observed. In addition to these two factors, adding leaf B and Fe concentrations to the model increased the variability explained to 83%.