Towards an Effective Decision-making System based on Cow Profitability using Deep Learning

Life-time profitability is a leading factor in the decision to keep a cow in a herd, or sell it, that a dairy farmers face regularly. A cow's profit is a function of the quantity and quality of its milk production, health and herd management costs, which in turn may depend on factors as diverse as animal genetics and weather. Improving the decision making process, e.g. by providing guidance and recommendation to farmers, would therefore require predictive models capable of estimating profitability. However, existing statistical models cover only partially the set of relevant variables while merely targeting milk yield. We propose a methodology for the design of extensive predictive models reflecting a wider range of factors, whose core is a Long Short-Term Memory neural network. Our models use the time series of individual features corresponding to earlier stages of cow's life to estimate target values at following stages. The training data for our current model was drawn from a dataset captured and preprocessed for about a million cows from more than 6000 different herds. At validation time, the model predicted monthly profit values for the fifth year of each cow (from data about the first four years) with a root mean squared error of 8.36 $/cow/month, thus outperforming the ARIMA statistical model by 68% (14.04 $/cow/month). Our methodology allows for extending the models with attention and initializing mechanisms exploiting precise information about cows, e.g. genomics, global herd influence, and meteorological effects on farm location.