This is a prospective observational study that evaluates the effects of body condition score (BCS) changes in primiparous Holstein cows during peripartum on their NEFA and BHBA concentrations, hormone levels, postpartum health, and production performance. The cows under study (n = 213) were assessed to determine their BCS (5-point scale; 0.25-point increment) once a week during the whole peripartum by the same researchers; backfat was used for corrections. Blood samples were collected 21 and 7 days before calving and 7, 21, and 35 days after calving, and were assayed for NEFA, BHBA, growth hormone (GH), insulin, leptin, and adiponectin concentrations. The incidence of disease and milk yield were recorded until 84 days after calving. Cows were classified according to their BCS changes during peripartum as follows: Those that gained BCS (G; ΔBCS ≥ 0.25), maintained BCS (M; ΔBCS = 0-0.25), or lost BCS (L; ΔBCS ≥ 0.5). The BCS at -21 days and at 7, 14, and 21 days were different (p < 0.01), but trended toward uniformity in all groups at calving. The L group had higher NEFA and BHBA concentrations and hormone levels (p < 0.01) than the M and G groups at 21 and 35 days after calving, and had a higher incidence of uterine and metabolic diseases; however, there were no differences in production performance between the various groups. In conclusion, a lower BCS in primiparous cows during peripartum influences the NEFA and BHBA concentrations, hormone levels, and occurrence of health problems postpartum. The postpartum effects of BCS changes appear prior to calving.

Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.

Residual feed intake (RFI) is an effective indicator to evaluate the feed utilization efficiency.Compared with the indexes of feed to gain ratio and gain to feed ratio, RFI can avoid to the inherent difference in animals of different shapes and different growth stages, and promote scientificity of the evaluation on feed utilization efficiency.With the development of science and technology, the pressure sensor system and image information system can help to collect relevant data of feed intake more quickly and effectively.The studies found that ruminant animals with low RFI had a stronger roughage digestive ability.Meanwhile, there were differences in the rumen bacterial composition in different levels of RFI, and the key genes for the transport of volatile fatty acids in the rumen were correlated with the level of RFI.Although the differences in slaughtering performance and carcass traits of different RFI have not been consistent in studies on different animals, breeding livestock with low RFI in reproduction can accelerate production efficiency and lower feed cost.Therefore, the application of RFI in large-scale breeding can improve the management level of ranching and provide significantly economic benefits.

<span id="ChDivSummary" name="ChDivSummary" class="abstract-text">为了解个体识别在奶牛生产中记录产奶量、监控采食活动、监测卧床行为、追踪活动轨迹以及其他生产项目中的研究现状,以"奶牛"、"个体识别"、"识别方法"和"生产应用"为关键词,对2008—2018年的文献进行检索,并根据识别过程的不同特点对三类识别方法,即人工机械识别、接触式电子识别和图像生物识别进行归纳和总结并对识别方法、现代化生产应用以及国内进展等3个方面进行归纳和总结。结果表明:1)生物识别技术相较于传统方法对奶牛个体的伤害较小,可以在很多方面克服环境的干扰。2)目前智能识别技术与奶牛行为活动监测相关联,可以全面掌握个体健康状况和生产性能。3)国内在计算机视觉方面取得了长足的进步,为个体的智能化识别打下了坚实基础。今后个体识别技术研究应该着重于提高对环境的适应性和系统的兼容性,为建立完整的自动化奶牛监测体系提供依据。</span>

奶牛体况评分(BCS)是反映奶牛能量蓄积程度、营养状况和营养管理水平的实用工具.目前,国内外奶牛生产现场以触摸和目测的人工方法测量BCS,这些方法主观性强,虽然最新的研究利用折叠量角器可以有效测量BCS,但总体上,仍然具有稳定性和准确性差的缺点.现在,最新研究由超声波成像技术和图像信息系统替代人工方法.超声波测定显示,BCS每增加1分,奶牛尻部厚度就会增加10 mm;而图像信息系统在奶牛BCS方面有较好的应用,不同相机类型可以满足多种生产需求,可见光相机以分析动物轮廓为主要依据,热成像相机通过接收和测量物体表面的红外辐射作为BCS的判断依据,而深度相机可以提取奶牛背部更多细节信息从而提高准确率.在奶牛生产中,应该更加关注奶牛泌乳后期及干奶期的BCS,该时期的BCS过高会增加疾病和繁殖障碍的风险,BCS过低则会使奶牛泌乳初期处于能量负平衡状态.因此,合理的方法加上科学的管理,可以使BCS成为奶牛生产中不可缺少的实用工具.

: The purpose of this experiment is to explore the effect of different heat treated on the molecular structure of carbohydrates in okara and its relationship with nutritional value and ruminal degradation characteristics. The nutrient value of different heat treated okara was evaluated by conventional chemical analysis combined with Cornell net carbohydrate-protein system (CNCPS) and nylon bag technology, changes of the molecular structure of carbohydrates in okara was analyzed by Fourier transform infrared spectroscopy (FTIR), then explore the correlation between the molecular structure of carbohydrates and nutritional value and ruminal degradation characteristics. The results showed as follows: 1) heat treatment reduced the nutritional value of okara and the rumen degradability of neutral detergent fibers (NDF). 2) Different heat treatment had significants effect on the peak area of structural carbohydrates (STCHO), fiber composites (CELC), total carbohydrates (CHO) and the corresponding peak heights in the three peak areas in the molecular structure of carbohydrates in okara ( P <0.05). 3) The peak area ratio of STCHO to CHO and

为了解5种引进燕麦[林纳(Avena sativa Lena)、青海444(A.sativa Qinghai No.444)、青引1号(A.sativa Qingyin No.1)、青引2号(A.sativa Qingyin No.2)、甜燕麦(A.sativa Qinghai)]异地自繁后种子对盐胁迫的适应性,采用不同浓度的单盐(NaCl、Na2CO3)和混盐(NaCl、Na2 SO4、Na2CO3、NaiHCO3)溶液对5个燕麦品种进行盐胁迫处理,通过培养皿纸上发芽法测定种子发芽率,再根据发芽率的变化得出燕麦品种耐盐适宜范围、耐盐半致死浓度和耐盐极限浓度.结果表明:最耐NaCl盐胁迫的是青引2号和青引1号,极限浓度分别是1.29％、1.18％;最耐Na2CO3盐胁迫的是青引1号和青引2号,极限浓度为0.49％;最耐混盐胁迫的是青引2号,极限浓度为1.05％.说明5种燕麦中的青引2号燕麦对轻度盐碱有较强的适应能力.