Modeling Olive Crop Yield in Andalusia, Spain

• Published in: Agronomy journal Vol. 100; no. 1; pp. 98 - 104
• Main Authors: Galan, C, García-Mozo, H, Vazquez, L, Ruiz, L, Diaz de la Guardia, C, Domínguez-Vilches, E
• Format: Journal Article
• Language: English
• Published: Madison, WI American Society of Agronomy 01.01.2008
• Subjects: Agronomy. Soil science and plant productions; air temperature; Biological and medical sciences; crop models; Crop yield; flowering; Fundamental and applied biological sciences. Psychology; meteorological parameters; model validation; Olea europaea; olives; phenology; pollen; rain; seasonal variation; simulation models; temporal variation; yield forecasting; Spain; Southern Europe; Europe; Olive; Vegetals; Andalusia Spain; Agronomy; Models; Yield; Cultivated plant; Mediterranean region; Temperate zone; Modeling
• ISSN: 0002-1962; 1435-0645; 1435-0645
• DOI: 10.2134/agrojnl2006.0345

Andalusia (southern Spain) is the largest olive (Olea europaea L.) oil producing region in the world. This study sought to identify the main factors influencing olive fruit production in this region, by modeling pollen release as an index of flowering intensity, field floral phenology data, and meteorological data over the fruiting season in three main olive-producing provinces of Andalusia: Jaén, Córdoba, and Granada. Field floral phenological data were used to determine the anthesis phase, to ascertain which period of the pollination records should be included in the models. Forecast models indicated that annual prepeak pollen index (PI) was the variable most influencing the final olive crop. Spring and summer rainfall and both maximum and minimum temperatures in summer and autumn were the major weather-related parameters affecting final fruit production, although statistical analysis revealed differences between sites with regard to the timing and the degree of their influence. Validation results confirmed the validity and accuracy of the widely-used Hirst volumetric trap as a tool for olive crop yield forecasting in high-density olive-growing areas. The models used here provided earlier and more accurate crop estimates than those traditionally used by the Andalusian regional government, and may thus enable farmers, as well as government departments, to improve both crop yield management and olive oil marketing strategies and policies.