On supervised learning to model and predict cattle weight in precision livestock breeding

Abstract

Livestock production efficiency is essential to improve the world food chain in terms of making meat available to more people and reducing producer costs, while supporting environmental sustainable solutions. In this context, predicting cattle weights supports the decision making process to optimize the beef cattle supply chain animals and improving feed efficiency. Current body weight analyses are typically performed using predetermined models based on a set of differential equations (e.g. Davis Growth model), however they are not easily adaptable to accept new influencing variables made available in the current technological scenario. This study, proposes two fully adaptable approaches to build up models and forecast cattle body weights while considering related variables (e.g. temperature, atmospheric pressure, global radiation, wind speed, air humidity and dry matter intake (DMI). Our approaches explore two complementary scientific branches: (i) Stochastic Processes, where we employ the Autoregressive Integrated Moving Average (ARIMA) and Seazonal Autoregressive Integrated Moving Average (SARIMA) models only on the variable weight; and, (ii) Deterministic Dynamical Systems, with reconstruct at multidimensional spaces representing the relationships among between daily body weights while being influenced by climatic, management and diet variables. Takens’ embeded theorem was used to represent phase spaces, which work as input for a weights regression model based on Multi-Layer Perceptron (MLP) – Artificial Neural Network (ANN) base. A dataset comprising 71 Nelore (Bos indicus) cattle were used in this study and the leave-one-out was used as a cross-validation strategy. Models were evaluated using the Mean-Distance from the Diagonal Line (MDDL) technique. MDDL results for 14,21 and 28 days of prediction were, respectively, for MLP: 0.2216,0.3947 and 0.0025 (with 5 hidden layer neurons). For ARIMA, MDDL results were 0.8763,0.9494 and 0.8299 for 14,21 and 28 days of prediction horizon, respectively; and for SARIMA 0.5912,0.5614 and 0.4884 for 14,21 and 28 days of prediction horizon, respectively. This study demonstrates that by integrating different data sources in a deterministic model, one can predict meat production, surpassing the ARIMA and SARIMA models. Further studies on decomposition analyses to support the individual modeling of animals based on stochastic and deterministic influences are warranted.