Traditional mixed linear modelling versus modern machine learning to estimate cow individual feed intake

Three modelling approaches were used to estimate cow individual feed intake (FI) using feeding trial data from a research farm, including weekly recordings of milk production and composition, live-weight, parity, and total FI. Additionally, weather data (temperature, humidity) were retrieved from the Dutch National Weather Service (KNMI). The 2014 data (245 cows; 277 parities) were used for model development. The first model (M1) applied an existing formula to estimate energy requirement using parity, fat and protein corrected milk, and live-weight, and assumed this requirement to be equal to energy intake and thus FI. The second model used ‘traditional’ Mixed Linear Regression, first using the same variables as in M1 as fixed effects (MLR1), and then by adding weather data (MLR2). The third model applied Boosted Regression Tree, a ‘modern’ machine learning technique, again once with the same variables as M1 (BRT1), and once with weather information added (BRT2). All models were validated on 2015 data (155 cows; 165 parities) using correlation between estimated and actual FI to evaluate performance. Both MLRs had very high correlations (0.91) between actual and estimated FI on 2014 data, much higher than 0.46 for M1, and 0.73 for both BRTs. When validated on 2015 data, correlations dropped to 0.71 for MLR1 and 0.72 for MLR2, and increased to 0.71 for M1 and 0.76 for both BRTs. FI estimated by BRT1 was, on average, 0.35kg less (range: -7.61 – 13.32kg) than actual FI compared to 0.52kg less (range: -11.67 – 19.87kg) for M1. Adding weather data did not improve FI estimations.