Research on the computer processing of the biological information in farm animals. (2) Analysis of data on calving and estrous period in cattle.

在牛的生理信息中，体温可以作为预测产犊时间和检测发情的最适用参数。因此，本系列实验主要研究在实际养殖条件下，利用便携式体温记录仪测量牛的体温波动情况.发情期温度变化：使用7头荷斯坦小母牛和8个发情期。一天内最低体温（阴道温度）在围情期变化明显，结果如下。(1)最低气温从发情前2 ~ 3天开始下降。下降的范围从0.1到0.8摄氏度不等。(2)动情日上午气温较前一天上升0.3至0.6摄氏度。(3)如果发情期延长两天，第二天的体温仍保持在较高水平。(4)在发情后，温度立即下降到以前的水平。在产犊试验中，使用了24头荷斯坦奶牛，结果如下。(1)产犊前3 ~ 1天体温升高，产犊后体温下降。(2)夜间气温上升更明显。(3)初步探讨了产犊时间与体温波动的关系.作为额外的结果，发现体温波动由三个分量组成，如下所示。而使用移动平均的滤波方法，很好地实现了这一目的。(1)昼夜节律;早晨最低，晚上最高。(2)与进食间隔相对应的节律;进食后热量增加。(3)波动间隔3至5小时;由躺着和站立引起。根据这些结果，正在开发更精确的产热检测和产犊时间预测的软件程序。

Out of the physiological informations of cattle, the body temperature can be the most applicable parameter for predicting the time of calving and detecting the estrus. Hence we focused on the body temperature fluctuations measured by the portable data logger on cattle under the practical farm condition in this series of experiments.1. Temperature changes in estrus: 7 Holstein heifers and 8 estrous phases were used. The lowest body temperature (vaginal temperature) in a day changed clearly in peri-estrus period and results are summarized as follow. (1) The lowest temperature began falling from two or three days before estrus. The range of the drop varied from 0.1 to 0.8 ﾟC. (2) The temperature rose by 0.3 to 0.6 ﾟC in the morning of the estrous day compared with the day before. (3) If the estrus prolonged for two days, in the second day the temperature remained in a higher level. (4) In the post-estrus the temperature fell to the previous levels immediately.2. In the calving trial, 24 Holstein cows were used and the results were as follow. (1) The Body temperature rose between 3 and 1 days before calving and dropped thereafter. (2) Temperature rose more clearly during the night hours. (3) Some relationships between the calving time and body temperature fluctuations were suggested.3. As additional results it was found that the body temperature fluctuations consisted of three components as shown bellow. And the filtering method using the moving average, worked well for this purpose. (1) Circadian rhythm ; lowest in the morning and highest in the evening. (2) Rhythm which correspond with feeding interval ; heat increment after eating. (3) Fluctuations of 3 to 5 hours interval ; caused by lying and standing. The software program for more precise heat detection and calving time prediction are developing according to these results.