F. Ramírez de Cartagena

Wet soil volume and strategy effects on drip-irrigated olive trees (cv. ‘Arbequina’)

The effect on productive and vegetative behavior and on the quality of oil from Olea europaea L. when applying two distinct irrigation techniques, full irrigation (FI) and regulated deficit irrigation (RDI), was studied. A total of five wet soil volumes (WSVs, 12, 24, 35, 47 and 59%) expressed in terms of the potential root exploration volume were established for each strategy. The experiment was performed on cv. ‘Arbequina’ in an olive grove in Tarragona (Spain). Results obtained suggest that a 20% reduction in the irrigation dose (RDI) had no significant effect either on olive fruit and oil production or on oil content. Likewise, no significant increase in irrigation water-use efficiency was observed for FI with respect to RDI. A tendency for olive and oil production per hectare to increase with increased WSV percentage was observed, although there were no significant differences between FI and RDI except for 59% WSV in the RDI strategy, producing the best response.

New mathematical model for computing head loss across sand media filter for microirrigation systems

Dimensional analysis was used to develop a new mathematical model that can describe head loss across sand filters for microirrigation using parameters that are easy to estimate. The developed model was compared with others previously developed. The study revealed that the new mathematical model had an adjusted coefficient of determination of 0.995 with no obvious pattern in its residual plot, in addition to other statistical parameters that revealed high precision and accuracy. Furthermore, the study exposed that the new developed model and the previously developed ones are adequate for computing head loss across sand filters. The selection among various models depends primarily on the available information about the microirrigation system and the applied effluent characteristics.

A new predictive model for the filtered volume and outlet parameters in micro-irrigation sand filters fed with effluents using the hybrid PSO-SVM-based approach

Prediction of sand filter outlet values allows assessing drip emitter clogging risk.A hybrid model based on SVMs with the PSO technique was used for this prediction.The developed model predicted satisfactorily sand filter outlet parameters.Performance of the PSO-SVM model was better than with other techniques. Filtration is a key operation in micro-irrigation for removing the particles carried by water that could clog drip emitters. Currently, there are not sufficiently accurate models available to predict the filtered volume and outlet parameters for the sand filters used in micro-irrigation systems. The aim of this study was to obtain a predictive model able to perform an early detection of the filtered volume and sand filter outlet values of dissolved oxygen (DO) and turbidity, both related to emitter clogging risks. This study presents a novel hybrid algorithm, based on support vector machines (SVMs) in combination with the particle swarm optimization (PSO) technique, for predicting the main filtration operation parameters from data corresponding to 769 experimental filtration cycles in a sand filter operating with effluent. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. To this end, the most important physical-chemical parameters of this process are monitored and analyzed: effective sand media size, head loss across the filter and filter inlet values of dissolved oxygen (DO), turbidity, electrical conductivity (Ec), pH and water temperature. The results of the present study are two-fold. In the first place, the significance of each physical-chemical variables on the filtration is presented through the model. Secondly, a model for forecasting the filtered volume and sand filter outlet parameters is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.74 for outlet turbidity, 0.82 for filtered volume and 0.97 for outlet dissolved oxygen were obtained when this hybrid PSO-SVM-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.