RESUMEN: Este estudio pretendió caracterizar y modelar las fases de crecimiento y producción de aves productoras de huevo comercial, por medio de la toma de información, medición, análisis de variables productivas y generación de modelos de predicción. Como resultados se presenta la evaluación de la capacidad para ajustar la curva de crecimiento de los modelos no lineales mixtos Von Bertalanffy, Richards, Gompertz, Brody, y Logístico. Como resultado, el modelo no lineal mixto que mejor ajustó la curva de crecimiento fue el de Gompertz, seguido por Richards y Von Bertalanffy. Para la modelación de la curva del crecimiento de aves de la línea Lohmann LSL en el capítulo 2, se compararon los modelos no lineal Von Bertalanffy (MNL), no lineal mixto Von Bertalanffy (MNLM) y redes neuronales artificiales (RNA). Se encontró que el modelo más preciso fue el MNLM, seguido por la RNA y en último lugar el MNL. Señalando a las RNA como alternativa en la modelación del crecimiento. Para el ajuste de la curva de producción de huevos se utilizaron los modelos Adams-Bell, Lokhorst y de distribución con retardo (Delay). Los modelos Delay y Lokhorst presentaron el mejor ajuste, siendo los más eficientes para predecir la curva de las estirpes probadas. Continuando con la definición del modelo para la curva de producción de huevos en el capítulo 4 se compararon el modelo perceptrón multicapa (redes neuronales artificiales (RNA)) y el modelo Lokhorst. Ambos modelos proporcionaron ajustes adecuados para la curva de producción, aunque por la facilidad de configuración y de ajuste se recomendó el uso de las RNA. En contraste a los modelos mencionados se utilizaron las redes neuronales recurrentes de Elman y Jordan, y el perceptrón multicapa (MLP) para construir un modelo de predicción de la curva de producción.ABSTRACT: This project pretended to characterize and model the growth and production phases of commercial laying hens, by gathering information, measuring and analyzing productive variables and creating prediction models. This final thesis document presents the results of the research process and is comprised of an introduction where concepts alluding to the problem that motivated the development of the research are discussed. Next the reader will encounter the theoretical framework with information on the commercial egg production system in Colombia, production parameters of the genetic strains, and modeling concepts and their use in poultry, along with the definition of the functionality and structure of a support system for decision making culminating with the specification of neural networks emphasising on their morphology and use in modeling. In Chapter 1 the evaluation on the adjustment capacity presents assessing the ability to adjust the curve of growth of nonlinear mixed models: Von Bertalanffy, Richards, Gompertz, Brody and Logistic. As a result, the mixed nonlinear model that best fitted the growth curve was Gompertz model, followed by Richards and Von Bertalanffy. In Chapter 2, the non linear model Von Bertalanffy (MNL), non linear mixed model Von Bertalanffy (MNLM) and the artificial neural networks (ANN) were compared for the modeling of the growth curve of hens from the Lohmann LSL line. The most precise model was the MNLM, followed by the ANN and in last place the MNL. This shows ANN as an alternative in growth modeling. In Chapter 3, in order to model the egg production curve, the models Adams-Bell, Lokhorst and delay distribution (Delay) were used. The Delay and Lokhorst models presented the best fit, being the most efficient in the prediction of the curve of the strains tested. Continuing on with the model definition for the egg production curve in Chapter 4 the multilayer perceptron (artificial neural networks (ANN)) and the Lokhorst models were compared. Both models provide adequate fits for the production curve, although due to the ease of configuration and adjustment, the ANN is recommended. In the second part of this chapter the recurrent neural networks of Elman and Jordan and the multilayer perceptron (MLP) were used to build a prediction model of the production curve. It was possible to obtain a functional model that predicts the daily egg production, but it needs to include more variables to adjust the variability presented in the yield curve.
In the fifth chapter the theoretical and practical concepts of modeling of the previous four chapters are incorporated to give life to the software tool called "Information Management System For Poultry Farms", as a support system to farmers to facilitate and expedite the collection, storage, processing and analysis of information, and also serves as a management support decision making in real time
