Feeding intake estimation in sheep based on ingestive chewing sounds

Accurate estimation of sheep feed intake is essential for optimal pasture management and understanding the patterns of grass-livestock ecosystems. Although many studies have been reported, fewer studies have comprehensively considered animal body weight, grass species, and grass moisture content. This study designed a 29-test experiment with three classes of sheep weight, two grass species, and three levels of grass moisture content combinations. Segment samples were constructed from each test, and many explanatory variables (44 in total) associated with chewing were extracted from each segment sample. Correlations between each explanatory variable and intake were examined under every test, and the slopes of their regression lines were also recorded. A statistical analysis was then used to reveal the effects of sheep bodyweight class, grass species, and grass moisture content class on the slopes of the explanatory variables. On the test sample set (one test sample corresponding to one test), intake was predicted using a single explanatory variable, all explanatory variables, all explanatory variables and factor variables (sheep weight, grass moisture content, grass NDF content, grass ADF content). The results showed that the chew_ZCTOverLegth variable (number of chewing waveforms over the mean divided by waveform length and then accumulated over all chews) and intake had the strongest correlation with a mean R² score of 0.8796. The influence of grass moisture content class on the slope of the variable was approximately equal to that of the sheep weight class and less than that of the differences in individual sheep. In the test sample set, the LogMel_power variable (sum of squares of waveform log-Mel) was the best single explanatory variable for predicting intake (when the type of intake is fresh grass matter). Adding factor variables significantly increased the R² score of the model and reduced the number of variables used. Introducing sheep bodyweight to intake further increased the R² score of the model and reduced the number of variables used. When the type of intake was FMI_W (fresh matter multiplied by the square of the sheep bodyweight value), intake was accurately estimated using all explanatory and factor variables with an R² score of 0.9734. This result demonstrates that chewing-derived acoustic variables acoustically can accurately estimate intake despite the complexity of the experimental conditions. This study will make an outstanding contribution to the development of general and accurate models for estimating intake.