In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST − mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature-independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.

Maintenance energy requirements (MER) of mice selected for high (H) or low (L) oxygen consumption (OC) were compared. Forty-four mice from H and L OC lines were weaned at 3 weeks and divided into four experimental groups: group A were sacrificed at 4 weeks; group B were fed ad libitum, and groups C and D were fed 2.8 and 2.4 g/day, respectively, from 4 to 8 weeks of age. Groups B-D were sacrificed at 8 weeks. Chemical components were estimated for all groups. MER was estimated using a model that partitioned metabolizable energy intake into that used for maintenance, and protein and fat deposition. The feed conversion ratio for the B group was significantly higher in the H than in the L line. Feed intake for metabolic energy content per metabolic body size was significantly also higher in the H line, whereas accumulated energy content per metabolic body size was significantly higher in the L line. MER of the H line was greater than that of the L line (P < 0.10). These results suggest that selection for H or L OC produced differences in chemical components, feed efficiency, and MER between the H and L lines.

The aims of the present study were to identify the differences between two mouse lines (high (H)‐ and low (L)‐oxygen consumption) in terms of mitochondrial respiratory activity when GMP (glutamate, malate, and pyruvate) and succinic acid are used as substrates and to examine the relationship between mitochondrial respiration activity and feed efficiency in both lines. The average daily feed intake, feed conversion ratio (FCR), and residual feed intake (RFI) were significantly higher in the H than the L line. The correlation between FCR and RFI was significant (r = 0.60, p < 0.05). RFI was effective as an indicator of feed efficiency. When succinic acid was used as a substrate, mitochondrial respiration states 2–4, ACR, and proton leak were significantly higher in the H than the L line. When GMP was used as a substrate, respiration states 3 and 4 in the H line were significantly higher than those in the L line, and there were significant positive correlations between FCR and RFI and mitochondrial respiration states 2–4. The results indicated that selection for high or low OC changed the basal metabolic rates estimated from liver mitochondrial respiration activity and feed efficiency.

We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cattle (six Holsteins and five Japanese Blacks) using the tail-attached sensor. Three estrus detection models were constructed with the training data (n = 17) using machine learning techniques (random forest, artificial neural network, and support vector machine) based on 13 features extracted from sensing data (indicative of estrus-associated ST changes). Estrus detection abilities of the three models on test data (n = 34) were not statistically different among models in terms of sensitivity and precision (range 50.0% to 58.8% and 60.6% to 73.1%, respectively). The relatively poor performance of the models might indicate the difficulty of separating estrus-associated ST changes from estrus-independent fluctuations in ST.