A survey was conducted to investigate willingness to eat (WTE) and willingness to pay (WTP) for chicken meat and eggs produced by termite-fed broilers and layers and those factors influencing WTE and WTP in two scenarios; with or without information that supplemented termites have a component for human health benefit (healthy-termite information). Data was collected through an online consumer survey with 982 valid responses in Japan. Factors influencing WTE and WTP were analyzed using least squares ANOVA. Both WTE and WTP for chicken meat and eggs were enhanced with the healthy-termite information. There were higher scales of WTE for male consumers, higher education levels, and respondents who have experienced eating or rearing insects. There were interactions between the region and residential population on WTE for meat and region and gender on WTE for healthy-termite meat and healthy-termite eggs. In contrast, WTP was scarcely influenced by these attributes, suggesting that proofing components for human health benefits in termites would be needed to generate price premiums for such chicken products.

Dynamic body acceleration (DBA) derived from the data obtained by tri-axial accelerometer has been widely used in various animal species as a proxy for estimating energy expenditure (EE) under free-moving conditions. More recently, the use of 9-axis sensor equipped with tri-axial accelerometer, tri-axial gyroscope and tri-axial magnetometer is examined to estimate EE as well as tri-axial accelerometer. In order to estimate EE correctly from DBA, sensors should be placed near the center of mass of animals. However, in practice, livestock farmers attach sensors usually on collar because the place is easier to be attached. Therefore, the aims of this study were to examine the relationship between the sensors attached on back and collar of animals and to develop EE estimation models from the sensor data attached on collar in cattle and sheep. Four pregnant Japanese Black cows and four castrated Corriedale sheep were used in this study. Twenty-four hours of sensor data in grazing conditions were collected. First, the correlations of the sensors data attached on back and collar were analyzed by each behavioral classification. Then, the models to estimate the calculated EE derived from DBA gained from the sensor placed on back were developed by regression analyses by cows and sheep. The independent variables in the models were behavioral indicators calculated from 9-axis sensors attached on collar and the dependent variables in the models were calculated EE from the sensor data on back. As a result, the distinctive relationship of behavioral indicators between collar and back in each livestock species were found. As for the development of EE estimation models, although the effect of animals in the model was significant in each species, it was found that the models without the effect of animals could also estimate EE with some degree of accuracy; there is a possibility to estimate EE with the indicators derived from the sensor attached on collar. Also, the coefficients of determination (R2) of the models using 9-axis sensor data from collar were similar with those of the model using only tri-axial accelerometer data from collar, indicating that the use of tri-axial accelerometer data is enough for EE estimation of grazing livestock animals.