Modeling olive-crop forecasting in Tunisia

• Published in: Theoretical and applied climatology Vol. 128; no. 3-4; pp. 541 - 549
• Main Authors: Ben Dhiab, Ali, Ben Mimoun, Mehdi, Oteros, Jose, Garcia-Mozo, Herminia, Domínguez-Vilches, Eugenio, Galán, Carmen, Abichou, Mounir, Msallem, Monji
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.05.2017; Springer; Springer Nature B.V
• Subjects: Agricultural production; Air temperature; Analysis; Annual variations; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Citrus; Climatology; Crop yield; Crop yields; Earth and Environmental Science; Earth Sciences; Forecasting; Forecasting techniques; Fruits; Harvest; Harvesting; Humidity; Olive oil; Original Paper; Pollen; Rain; Temperature; Variables; Waste Water Technology; Water Management; Water Pollution Control; Weather; Tunisia; MED, Tunisia; MED, Tunisia, Zarzis; Airborne Pollen; Olive Production; Partial Little Square Regression; Pollen Index; Main Pollen Season
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-015-1726-1

Tunisia is the world’s second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia’s five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993–2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.