Forecasting olive crop yields based on long-term aerobiological data series and bioclimatic conditions for the southern Iberian Peninsula

Abstract

In the present study, bio-meteorological models for predicting olive-crop production in the southern Iberian Peninsula were developed. These covered a 16-year period: 1994-2009. The forecasting models were constructed using the partial least-squares regression method, taking the annual olive yield as the dependent variable, and both aerobiological and meteorological parameters as the independent variables. Two regression models were built for the prediction of crop production prior to the final harvest at two different times of the year: July and November. The percentage variance explained by the models was between 83% and 93%. Through these forecasting models, the main factors that influence olive-crop yield were identified. Pollen index and accumulated precipitation, especially as rain recorded during the pre-flowering months, were the most important parameters for providing an explanation of fluctuations in fruit production. The temperature recorded during the two months preceding budburst was another important variable, which showed positive effects on the final yield. The July model that provides accurate predictions of fruit production eight months prior to the final harvest is proposed as an optimal model to forecast fruit produced by olive trees in western Mediterranean areas.

Plant Responses to Climate Change: The Case Study of Betulaceae and Poaceae Pollen Seasons (Northern Italy, Vignola, Emilia-Romagna)

“…The 15-year set of data represents a long series offering the possibility to compare pollen trends and temperatures over a relatively long timespan. Other examples of long series published in the last couple of years are: 16-year period (1994–2009) to predict olive crop production [21]; 15-year period (1993–2007, coupled with another 1992–2011) to model and predict the dynamic of Vitis flowering in the field [22]; an 11-year study (2003–2013) to quantify the effects of environmental factors including pollen on asthma hospitalization [23]; a 30-year series (1981–2010) is useful to compare historical data with atmospheric dispersion model for airborne pathogen concentrations [24]. …”

Olive Yields Forecasts and Oil Price Trends in Mediterranean Areas: A Comprehensive Analysis of the Last Two Decades

“…Numerous studies confirmed the importance of airborne pollen and weather requirement during the months successive to the olive flowering for olive yield forecasting (Aguilera and Ruiz-Valenzuela, 2014;Fornaciari et al, 2002;Galán et al, 2004Galán et al, , 2008García-Mozo et al, 2012;Orlandi et al, 2014a;Oteros et al, 2014). These models offer more precise forecasts but delay the statistical modelization till the end of summer or during autumn, depending on the site (Galán et al, 2008;Oteros et al, 2014).…”

“…However, today the most widely used method for yield estimation is by visual observation prior to collect crop. Recent studies have shown better results in anemophilous species by modelling airborne pollen as an index of floral intensity and as a good bio-indicator of the future yields (Aguilera and Ruiz-Valenzuela, 2014;García-Mozo et al, 2012;Orlandi et al, 2010). In wind pollinated species, the annual airborne pollen has been demonstrated as a good indicator for both flowering intensity and plant behaviour during reproductive phenological stages (Aguilera and Ruiz-Valenzuela, 2009;Galán et al, 2004Galán et al, , 2008Orlandi et al, 2012Orlandi et al, , 2013Oteros et al, 2013aOteros et al, , 2013b.…”

Identificación y conteo de aceitunas en imágenes digitales tomadas en el olivar mediante morfología matemática y redes neuronales convolucionales

“…De entre la información deducible del olivar, la estimación temprana y precisa de la cosecha destaca en interés, ya que sería una valiosa herramienta de apoyo al sector [6]. Y es que, en efecto, una estimación precisa de la producción tendría aplicaciones prácticas en muy diversos aspectos, tales como: eficiencia en la transformación del aceite de oliva, gestión del stock, o la optimización de los recursos humanos necesarios para la recolección [7]. También tendría una importancia crucial en la regulación del precio de mercado del aceite de oliva.…”