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Experimental and comparative studies suggest that the striped coats of zebras can prevent biting fly attacks. Biting flies are serious pests of livestock that cause economic losses in animal production. We hypothesized that cows painted with black and white stripes on their body could avoid biting fly attacks and show fewer fly-repelling behaviors. Six Japanese Black cows were assigned to treatments using a 3 × 3 Latin-square design. The treatments were black-and-white painted stripes, black painted stripes, and no stripes (all-black body surface). Recorded fly-repelling behaviors were head throw, ear beat, leg stamp, skin twitch, and tail flick. Photo images of the right side of each cow were taken using a commercial digital camera after every observation and biting flies on the body and each leg were counted from the photo images. Here we show that the numbers of biting flies on Japanese Black cows painted with black-and-white stripes were significantly lower than those on non-painted cows and cows painted only with black stripes. The frequencies of fly-repelling behaviors in cows painted with black-and-white stripes were also lower than those in the non-painted and black-striped cows. These results thus suggest that painting black-and-white stripes on livestock such as cattle can prevent biting fly attacks and provide an alternative method of defending livestock against biting flies without using pesticides in animal production, thereby proposing a solution for the problem of pesticide resistance in the environment. [ABSTRACT FROM AUTHOR]

Body condition score (BCS) is a proxy for evaluating body fat reserves. However, monitoring BCS is a time-consuming and subjective task. Thus, we aimed to develop a method for estimating the BCS of beef cows using three-dimensional (3D) body features of cows' rump area derived from 3D camera data. Three-dimensional surface data of the rump area from 39 multiparous cows were obtained using a 3D camera, and four 3D body features were extracted. The BCSs of the cows were scored by experts, and models for predicting BCS by 3D features were developed using machine learning algorithms. The derived model yielded an overall accuracy, precision, sensitivity, and F-measure of 90%, 88%, 90%, and 88%, respectively. Additionally, we evaluated a simple practical method to estimate BCS using the difference between heart girth (HG) and tightened heart girth (THG) for 118 multiparous cows. A cumulative logistic regression model for estimating BCS by the difference was developed, and the derived generalized coefficient of determination was 0.81. These results suggest that 3D images are useful for estimating the BCS of beef cows and that the difference between HG and THG can be used to estimate BCS as a simple practical method. [ABSTRACT FROM AUTHOR]