Improvement of feedlot operations through statistical learning and business analytics tools

Gradient boosting and random forest regression are used topredict future cattle growth.An ensemble method further improves prediction accuracy fromindividual modeling outputs.Hierarchical clustering is used for group homogeneity whenassigning cattle to feeding pens.The method estimates the optimal time each individual cattle should remain in the system.A case study based on an operation in Sonora, Mexico ispresented. A decision-support, modeling tool is developed that can project future cattle growth patterns in a feedlot based on a low dimensionality dataset available at the start of the feeding process. This work adapts the predictive performance of two well-known statistical machine modeling tools, gradient boosting and random forest regression, to predict future cattle growth. Time series analysis techniques are then used to create an ensemble method that further improves prediction accuracy from individual modeling outputs. Hierarchical clustering techniques are used to leverage projected growth patterns to increase group homogeneity when assigning cattle to different feeding pens. Finally, a profit maximization method is developed that estimates the optimal time each individual cattle should remain in the system under different revenue and cost estimates.The purpose of this work is to incentivize the implementation of modern statistical learning tools in cattle management operations, especially within low-to-mid scale operations that traditionally rely on the expertise of its workers and have limited cattle and process information. Access to off-the-shelf statistical learning tools, requiring minimal user-interaction, not only enhances prediction accuracy but helps automate operational decisions. This results in higher process efficiencies and improved standardization practices, while also helping identify profit opportunities. Finally, integrating these components into a single operating framework allows the tool to adapt to changes in data characteristics, which is especially important within non-standardized processes. We show the application of this tool through a case study implementation on a mid-scale operation in the northwestern state of Sonora, Mexico. From our case-study results, it was found that the modeling tool can satisfactorily predict growth patterns based on a low-dimensional set. It also can also capture historic decision-making when segmenting cattle into homogenous groups during their feeding process. Furthermore, it can help identify profit opportunities when estimating optimal cattle system times under varying market conditions.