Quantifying the economic and animal welfare trade-offs of classification models in precision livestock farming for sub-optimal mobility management

Precision livestock farming (PLF) offers a sensor-based management approach to potentially mitigate the negative economic and animal welfare consequences of sub-optimal mobility (SOM). Human-based SOM classification is often done using more than two classes (i.e., mobility scores 1-5, where 1 = optimal and 5 = severely impaired mobility), while binary classification is ultimately used in sensor-based classification. Previous economic research shows that classifying SOM as a binary problem in sensor-based management has little to no economic value while non-binary SOM classification may be more economically beneficial. However, the animal welfare implications of a non-binary SOM classification approach are unknown. In this study, we assess whether economic and welfare gains can be achieved by using 3-class SOM classifiers (i.e., sensors) for sensor-based SOM management compared with the current no-sensor SOM management. With respect to mobility scores 1-5, three SOM classes (K1 = non -SOM, K2 = SOM, and K3 = severe -SOM) along with two management scenarios, with four different classifiers each, were defined. Mobility scores 1-5 were grouped into one of three SOM classes depending on the classifier. In management scenario one, mobility scores 1 and 2, were grouped to K1, while mobility score 3 was grouped to K2 and mobility scores 4 and 5 to K3. In management scenario two, mobility scores 2 and 3 were grouped to K2. In both management scenarios, alerts for cows classified to SOM class K2 were generated every 7 days based on an alert prioritisation criterion, while alerts for cows classified to SOM class K3 were generated daily. Treatment options followed the generation of either weekly or daily alerts. For each of the eight classifiers (i.e., 4 classifiers per management scenario) 600 classification outcomes were defined. A bioeconomic simulation model was used to simulate the economic and welfare effects of the various classifiers and classification outcomes respective of management scenarios. Comparisons were made with a no-classifier scenario. The simulated output data was first analysed using an exploratory approach. Second, a novel method accounting for the highly interactive classification outcomes was developed to quantify the trade-offs in classification outcomes and how they affected the economic and welfare gains. Among the tested classifiers, all showed economic and welfare gains on average. Classifiers with larger separations between non -SOM and SOM classes showed the highest economic gains. Including mobility score 2 into the SOM class K2 showed meaningful welfare gains on average as opposed to when mobility score 2 was included in the non -SOM class K1. Economic gains were more sensitive to trade-offs in classification outcomes compared to welfare gains. Larger increases in economic gains were often achieved at the cost of smaller reductions in welfare gains for changes in classification outcomes. This study provides valuable insights on designing appropriate 3-class SOM classifiers to be used in practice that could also be beneficial when designing classifiers for health disorders other than SOM. It also demonstrates the value in using simulation models to test classifiers by highlighting interesting classification outcomes that can be further validated in practice.