Modelling potential growth and yield of olive (Olea europaea L.) canopies

Self Cite

Economic valuation of irrigation water is done through the use of production functions for the case of the olive grove. In order to do so the integration of an agronomic model (based on the production function) and an economic model linked to the profitability of the crop (the ratio of revenue and operating costs) in the area under study is proposed. The study case encompasses the Guadalbullon River Sub-basin area, belonging to the Guadalquivir River Hydrologic Demarcation (Southern Spain). Within the overall deficit of the Guadalquivir River basin, the Guadalbullon River poses a special problem as it is unregulated and there are important irrigated fields on its banks, most of them olive groves. Net marginal value of water obtained (having deducted the variable costs of production including harvesting and irrigation) is€ 0.60 m–3 for the allocation of 1,000 m3 ha–1 and € 0.53 m–3 for the water right allowance of 1,500 m3 ha–1 (average for period 2005/2008). The results obtained support the recommendation by other authors suggesting the use of deficit irrigation in olive, additionally the high value of water estimated contributes to explain the substantial increase in irrigated olive area in Andalucia.

Section: A) Agronomic Modelmentioning

confidence: 81%

Estimating marginal value of water for irrigated olive grove with the production function method

Mesa-Jurado¹,

Berbel²,

Orgaz

1970

Self Cite

“…Similarly, the traditional dryfarming olive orchard, with low density, less than 100 trees ha -1 , frequent tillage to better conserve water by controlling weeds and manual harvest , shifted to a new model, with densities going from 200 to 400 trees ha -1 , very much used in Spain and other countries nowadays, and later to even 2,000 trees ha -1 (Pastor et al, 2007). Drip irrigation, mechanical harvest and better fertilization and pest and disease control are already used in the medium-density model, while they are a must in the high-density one (Villalobos et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

Planting density affects vigour and production of ‘Arbequina’ olive

Larbi¹,

Ayadi²,

Dhiab³

et al. 2012

The hedgerow orchard type is being increasingly used with the olive although not much information is still available about its suitability to this species. The objective of this study was to assess the influence of planting density (312, 416, 625 and 1,250 trees ha -1 ) on vigour and productive characteristics of 'Arbequina' olive trees planted in 2003. Significant linear regressions have been observed between planting density and tree width, trunk cross section area and canopy volume. Increasing planting density showed positive linear correlation (R 2 = 0.63) with canopy volume per hectare. There was a negative correlation between planting density and production per tree in the five first crops, but it was positive with production per hectare both in olive and olive oil production with coefficients of determination ranging from 0.16 to 0.43 and from 0.28 to 0.46, respectively. A significant linear regression (R 2 = 0.31 and 0.48) was found between planting density and fruit size on two of the studied cropping years. Our results have not allowed establishing any relationship between planting density and fruit oil content. Finally, the studied densities did not affect the oil fatty acid composition. The production increase observed at the highest tried density is linked to the increase in canopy volume per hectare, but these results should be checked at later stages of tree development, as this study covers only until the fifth harvested crop.Additional key words: fruit and oil traits; Olea europaea L; yield. ResumenInfluencia de la densidad de plantación sobre el comportamiento del olivo 'Arbequina'El cultivo en seto está siendo cada vez más usado en olivo aunque todavía no hay demasiada información acerca de su idoneidad para esta especie. El objetivo de este estudio fue evaluar el efecto de la densidad de plantación (312, 416, 625 y 1.250 árboles ha -1 ) sobre las características de vigor y productivas de árboles de 'Arbequina' plantados en 2003. Se han observado regresiones significativas entre la densidad de plantación y la anchura del árbol, el área de su sección de tronco y su volumen de copa. La densidad de plantación ha mostrado una relación lineal positiva (R 2 = 0,63) con el volumen de copa por hectárea. Hubo una correlación negativa entre la densidad de plantación y la producción por árbol de los cinco primeros años, pero fue positiva con la producción por hectárea, tanto de aceituna como de aceite, con coeficientes de determinación que variaron entre 0,16 y 0,43 y entre 0,28 y 0,46, respectivamente. Se encontró una regresión significativa (R 2 = 0,31 y 0,48) entre la densidad de plantación y el tamaño de los frutos en dos de las cosechas estudiadas. Nuestros resultados no han permitido establecer ninguna relación entre la densidad de plantación y el contenido en aceite del fruto. Finalmente, las densidades de plantación empleadas no afectaron a la composición ací-dica del aceite. El aumento de producción observado a las mayores densidades de plantación está asociado al incremento de volumen d...

“…The temperatures recorded during the spring were relevant for the interpretation of the olive production variability in central and southern Italy (Orlandi et al, 2010). Moreover, models based on the radiation-use efficiency have been developed, with optimal results, as this index is an interesting parameter that can be used in future studies (Villalobos et al, 2006). Summer evapotranspiration is another important variable that has an influence on crop yield.…”

Section: Discussionmentioning

confidence: 99%

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

Mendías

Valenzuela

2014

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.