Importance of dietary arachidonic acid for the growth,survival and stress resistance of larval European sea bass (Dicentrarchus labrax) fed high dietary docosahexaenoic and eicosapentaenoic acids

Together with docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), arachidonic acid (ARA) is being considered to be an essential fatty acid in marine fish larval diets. The objective of the present study was to determine the importance of dietary ARA levels for larval European sea bass performance, when EPA and DHA are also present in the diet. Eighteen‐day‐old larvae were fed, for 14 days, gelatine‐based microdiets containing the following ARA levels: 0.3%, 0.6% or 1.2%. Elevation of dietary ARA up to 1.2% showed a positive correlation with larval survival and a significant improvement in the specific growth rates, body weight and total length. Arachidonic acid was efficiently incorporated into larval lipids, even at a higher proportion than that in the diets. Increased accumulation of ARA did not affect the incorporation of DHA or EPA from the diet into larval total lipids. A significant positive correlation was found between dietary ARA levels and survival after handling stress, indicating the importance of this fatty acid in sea bass larvae response to acute stressors. The results show the importance of ARA for sea bass larvae, but higher dietary levels should be tested to determine whether there is a negative effect of ARA in sea bass as reported for other species.     

Field-testing and characterization of Technosols made from industrial and agricultural residues for restoring degraded slopes in semiarid SE Spain

Journal of Soils and Sediments - Many industries and agriculture generate huge amounts of wastes either mineral, organic or both, with environmental impact. Some of these residues have the...     

Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)

Reference evapotranspiration (ETo) determination is a key factor for water balance and irrigation scheduling. Evapotranspiration can be measured directly by high-cost micrometeorological techniques, or estimated by mathematical models. The combination equation of Penman–Monteith, modified by Allen et al. [Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage, Paper no. 56. FAO, Rome] (PM56), is the reference equation for ETo estimation. This method is also appropriate for the calibration of other ETo estimation equations. The utilization of these calibrated ETo equations is recommended in the absence of data of any of the meteorological parameters necessary for the application of PM56. In addition to the use of classic ETo equations, the adoption of artificial neural network (ANN) models for the estimation of daily ETo has been evaluated in this study. ANNs are mathematical models, whose architecture has been inspired by biological neural networks. They are highly appropriate for the modelling of non-linear processes, which is the case of the evapotranspiration process. Seven ANNs (with different input combinations) have been implemented and compared with ten locally calibrated empirical and semi-empirical ETo equations and variants of these equations (with estimated meteorological parameters as inputs). The comparisons have been based on statistical error techniques, using PM56 daily ETo values as a reference. ANNs have obtained better results than the locally calibrated ETo equations in the three groups of evaluated methods: temperature and/or relative humidity-based methods (0.385 mm d⁻¹ of root mean square error (RMSE)), solar radiation-based methods (0.238 mm d⁻¹ of RMSE), and methods based on similar requirements to those of PM56 except for the estimation of solar radiation and/or relative humidity (0.285 mm d⁻¹ of RMSE).