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Artificial neural networks (NN) have been widely used for both prediction and classification tasks in many fields of knowledge; however, few studies are available on dairy science. In this work, we use NN models to predict next week's goat milk based on the current and previous milk production. A total of 35 Murciano-Granadina dairy goats were selected from a commercial farm according to number of lactation, litter size and body weight. Input variables taken into account were diet, milk production, stage of lactation and days between partum and first control. From the 35 goats, 22 goats were used to build the neural model and 13 goats were used to validate the model. It is important to emphasize that these 13 goats were not used to build the model in order to demonstrate the generalization capability of the network. Afterwards, the neural models that provided better prediction results were analysed in order to determine the relative importance of the input variables of the model. We found that the most important inputs are present and previous milk production, followed by days between parturition, and first milk control, and type of diet. Besides, we benchmark NN to other widely used prediction models, such as autoregressive system modelling or naı¨ve prediction. The results obtained with the neural models are better than with the rest of models. The best neural model in terms of accuracy provided a root mean square error equal to 0.57 kg/day and a low bias mean error equal to À 0.05 kg/day. Dairy goat farmers could make management decisions during current lactation from one week to the next (present time), based on present and/or previous milk production and dairy goat factors, without waiting until the end of lactation.

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The physical properties (specific gravity, moisture content, thickness swelling and water absorption) and mechanical properties (internal bond strength, bending strength and modulus of elasticity) were determined on 93 Spanish-manufactured standard particleboards of different thicknesses selected randomly at the end of the production process. The testing methods of the corresponding European standards (EN) were used, except in the case of the thickness swelling and absorption tests, for which the Spanish UNE standard was used. The thickness and the values obtained for the physical properties were entered into an artificial neural network in order to predict the mechanical properties of the board. The fit was compared with the usual multivariate regression models. The use of a neural network made it possible to obtain the values of bending strength, modulus of elasticity and internal bond strength of the boards utilizing the known data, not only of thickness, moisture content and specific gravity, but also of thickness swelling and water absorption. The neural network proposed is much better adapted to the observed values than any of the multivariate regression models obtained.Key words: wood-based panels, physico-mechanical properties, ANN, regression fit, predictive model. ResumenPredicción de propiedades mecánicas del tablero de partículas estándar mediante una red neuronal artificial y comparación con un modelo de regresión multivariante Se han determinado las propiedades físicas (densidad, humedad, hinchazón en espesor y absorción de agua) y mecánicas (tracción perpendicular a las caras, resistencia a flexión y módulo de elasticidad) de 93 tableros de partículas estándar de diferentes espesores, de fabricación española, elegidos aleatoriamente a la salida del proceso de producción, utilizando los métodos de ensayo recogidos en las normas EN correspondientes, excepto en los ensayos de hinchazón y absorción que se ha utilizado norma UNE (española). El espesor y los valores obtenidos de las propiedades físicas han sido introducidos en una red neuronal artificial (RNA) para predecir las propiedades mecánicas del tablero. El ajuste se ha comparado con los habituales modelos de regresión multivariante. La utilización de una red neuronal ha permitido obtener los valores de resistencia a flexión, módulo de elasticidad y resistencia a la tracción perpendicular a las caras de los tableros de partículas a través de los datos conocidos, no sólo de espesor, humedad y densidad sino también de hinchazón en espesor y absorción de agua. La red neuronal propuesta tiene una adecuación a los valores experimentales muy superior a cualquiera de los modelos de regresión multivariante obtenidos.Palabras clave: tableros derivados de la madera, propiedades físico-mecánicas, RNA, ajuste por regresión, modelo predictivo.