畜牧兽医学报  2024, Vol. 55 Issue (4): 1381-1388. DOI: 10.11843/j.issn.0366-6964.2024.04.004    PDF    
母牛发情期间躯体不同部位温度变化规律研究进展
修豪宇1,2, 李迎军3, 原开敏1,4, 汪超1,4, 杨书含1, 吕丽华2, 王栋1     
1. 中国农业科学院北京畜牧兽医研究所,北京 100193;
2. 山西农业大学动物科学学院,太谷 030801;
3. 内蒙古自治区赤峰市敖汉旗四道湾子镇党群服务中心,赤峰 024328;
4. 吉林农业大学动物科学技术学院,长春 130118
摘要:高效发情鉴定是提高母牛繁殖力、提升牛场经济效益的重要途径。随着奶牛养殖规模越来越大,传统人工观察已难以适应规模场奶牛发情鉴定需要。计步器发情鉴定难以检出安静发情,无法提高发情检出率。虽然体温参数发情鉴定潜力大,但母牛全身被毛,体表测温不理想,而接触式测温采集部位有限,可穿戴设备研发难度较大,不同采集部位牛只体温数据差别很大,为此,本文综述了前人利用奶牛躯体不同部位体温数据发情鉴定的结果,比较分析了发情奶牛各部位体温变化规律与发情的关系,为研发高效母牛发情鉴定技术提供科学参考。
关键词母牛    不同部位体温    发情    
Research Progress of Temperature Variation in Different Parts of Body During Estrus in Cows
XIU Haoyu1,2, LI Yingjun3, YUAN Kaimin1,4, WANG Chao1,4, YANG Shuhan1, LÜ Lihua2, WANG Dong1     
1. Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China;
2. College of Animal Science, Shanxi Agricultural University, Taigu 030801, China;
3. Party and Mass Service Center, Sidaowanzi Town, Aohan Banner, Chifeng City, Inner Mongolia Autonomous Region, Chifeng 024328, China;
4. College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
Abstract: Efficient estrus identification is an important way to improve the fertility of heifers and enhance the economic efficiency of cattle farms. With the increasing scale of dairy farming, the traditional manual observation of estrus identification method has been difficult to adapt to the needs of estrus identification of dairy cows in large-scale farms. Pedometer estrus identification is difficult to detect quiet estrus and does not improve estrus detection rates. Although the body temperature parameters of estrus identification potential, but the cow body hair, body surface temperature measurement is not ideal, and contact temperature measurement collection site is limited, wearable equipment research and development difficulties, different parts of the collection of cattle body temperature data vary greatly. For this reason, this paper summarizes the results of the previous use of different parts of the cow′s body temperature data estrus identification, comparative analysis of the relationship between the pattern of change of the body temperature of each part of the cow in estrus and estrus, for the research and development of efficient cow estrus identification technology to provide scientific reference.
Key words: cows    body temperature in different parts    estrus    

传统养殖条件下进行母牛发情鉴定,饲养员每日需巡场3次以上,每次不低于30 min[1],养殖场需投入大量人力物力,不仅费时费力,并且奶牛发情类型[2]、发情强度[3-4]与持续时间[5-7]存在个体差异,受人力视听及活动时空范围等因素限制,通过人工观察进行发情鉴定很容易漏检。并且,发情鉴定工作量随养殖规模的增加而增大,但鉴定效果却因群体增大而降低。截至2020年底,全国百头以上规模养殖场占比67.2%[8-10],使用传统人工观察进行发情鉴定已难以适应当前的奶牛养殖形势[11]。随着生物传感器技术的快速发展,母牛行为以及生理参数可进行自动化采集分析,为研究这些参数的变化规律提供了便利[12]。基于传感器建立的母牛发情鉴定自动化技术更加准确便捷[13-14]

采用计步器发情鉴定实现了发情鉴定自动化,但受活动空间大小[15]、蹄病[16]、安静发情[17]等因素影响较大,发情检出率较低,而温度传感器揭示的生理性变化受上述因素影响较小,自动化发情鉴定效果更好[18]。基于温度传感器的自动化发情鉴定技术,可能会突破多年来一直困扰产业的安静发情鉴定难这一世界难题,具有更大研发与应用潜能。但母牛全身被毛,非接触式测温效果不理想,而接触式测温可采集的部位有限,穿戴设备研发难度较大,并且,采集部位不同,监测到的牛只体温数据也有所差别。为此,本文综述了前人利用母牛躯体不同部位体温数据进行的发情鉴定结果,比较分析了发情母牛各部位体温变化及体温变化规律与发情的关系,希望这些分析结果能够促进后续母牛发情鉴定技术的研发,提高监测效果。

1 基于体温的母牛发情自动化鉴定技术研发进展

人工发情检测费时费力,对繁殖员发情鉴定技术要求较高,并且,因夜间发情无人值守还存在发情漏检问题。面对越来越多、越来越大的技术与管理压力,“用数据说话”实行科学管理已是产业发展必然趋势。为此,很多基于活动量传感器的发情监测设备被研发和全世界推广应用,并催生了阿菲金、利拉伐等跨国公司,在发情鉴定方面取得了良好的效果。但是,活动量检测却无法突破安静发情鉴定瓶颈,而体温检测却展示了很好的安静检出效果[19]。利用温度传感器借助于互联网的高效数据分析,基于母牛发情体温变化规律[20-21],构建自动化发情鉴定技术有望突破安静发情检出瓶颈。目前,基于体温的母牛发情鉴定自动化技术,根据设备与身体的接触方式,可分为接触式与非接触式两种[22]

1.1 接触式体温监测

传统使用水银体温计或电子测温计进行直肠测温,无法实现连续、自动监测,测温时间长、费力,还易引起应激反应或造成损伤,更无法对牛群实行批量化体温连续监测,远不能满足规模不断扩大的现代养殖业发展需求。研发母牛体温自动采集技术,并比较母牛不同部位体温发情周期变化规律,是发情鉴定自动化技术研发的基础。

随着传感器技术的不断发展,人们将温度传感器与牛只的身体部位相接触,依靠传感器技术来获取牛只的体温信息,即通过电子元件的参数变化检测温度,实现体温信息的自动化监测。田富洋等[23]将温度监测探头紧贴母牛后腿跟内侧的皮肤上,连续采集5头牛的体温变化,其设计的监测系统和神经网络辨识算法检测母牛发情的准确率可达100%,发情预测率达到70%以上。Wang等[19]将温度传感器安装于阴道栓内,对42头牛进行连续阴道温度监测,预测到39头发情,其发情检出率为92%。

接触式设备的优点在于设备的温度采集端直接与需要采集温度数据的牛只躯体直接接触,准确性高,且结构简单,成本低,被各大养殖场广泛使用。但是,接触式设备与牛只的躯体直接接触,佩戴设备会对母牛舒适度带来影响。牛舍和运动场中污物相对较多,粪便和垫料的附着、堆积会对设备造成影响,由于设备长期暴露在较为恶劣的环境下,母牛日常活动对设备的碰撞、水分渗入、天气变化等因素都会造成损坏,进而影响其使用寿命[10]

1.2 非接触式体温监测

随着温度监测技术的不断发展,非接触式的体温监测方法也逐渐运用到牛只体温监测方面。非接触式测温主要以红外热成像技术(infrared thermography, IRT)为主。红外热成像技术的原理是基于物体发射红外辐射的特性,根据物体的温度差异来进行热成像[24-26]。随着热成像技术的发展和热红外相机的广泛应用,这种对母牛无损、无接触的测温方式,为体温检测研究提供了新的方向[27]。Hellebrand等[28]通过IRT发现,母牛排卵前24 h的体温升高与发情的时间相吻合。Talukder等[29]使用热成像仪连续2个月对20头健康母牛的阴道、鼻镜温度进行检测,监测发情检出率为92%。

红外热成像技术的优点在于设备安装方便,仅需少量设备即可实现对牛群的体温监测,同时,监测母牛体温时不需接触,可以避免动物受到惊吓,也减少了传染疾病的风险。但是,动物身上沾染的垫料、泥污或粪便等污物可能会对需要监测的部位进行遮挡,影响设备对该部位的热辐射采集,降低数据监测的准确性,同时,监测设备与牛只之间角度和距离的变化也会对温度判断产生影响[30]。而且,相较于接触式体温监测设备,外界环境的温度以及湿度变化会对热红外相机的数据搜集准确性产生较大的影响[31]

2 母牛发情时不同部位温度变化 2.1 直肠温度监测与发情鉴定

因为直肠接近深部组织,温度相对稳定且容易测定,所以,直肠温度成为最常用的家畜核心体温(core body temperature,CBT)评价指标[32],广泛用于大家畜体温监测。

葛利江等[33]使用温度计对22头发情母牛早中晚各一次测量直肠温度发现,母牛发情时直肠温度升高到39.2 ℃,比发情前后直肠温度平均高0.52 ℃(P < 0.01),持续1 d后逐渐下降,接近排卵时达最低值(38.50 ℃),随后逐渐上升到(38.70±0.20)℃,并持续到下个发情期前。Piccione等[34]使用水银温度计每日两次(早晨8:00,傍晚17:00)监测母牛发情期间直肠温度,同时提出了一个进行发情预测的计算方法,将过去5 d母牛早晨平均体温与当日早晨体温进行对比,如果当日体温超过前5 d平均值3个标准差,则第2天牛只发情,使用此方法,该团队在41次发情中32次正确预测了母牛发情,检出率为78%。Liles等[35]发现,母牛定时输精时,直肠温度与妊娠结果呈正相关(P < 0.000 1),直肠温度每增加1 ℃,妊娠率增加1.9倍。Reuter等[36]设计了可佩戴在牛只尾根处的支架,搭载热敏传感器温度监测设备,可连续监测牛只直肠温度,但由于造价昂贵且会造成牛只不适,后续并未广泛应用。Suthar等[37]将温度传感器置于阴道栓内,将牛只麻醉后放入直肠中,实现了对直肠温度的连续记录。但是,该方法的操作要求较高,且无法长时间、大批量地进行牛群体温监测。李淦[38]对45头高产荷斯坦奶牛呼吸频率及直肠温度进行测量分析发现,平均呼吸速率(mean respiration rate,MRR)与平均直肠温度(mean rectal temperature,MRT)高度相关,且MRT=0.021×MRR+37.6(R2=0.925),这表明MRR每增加4.8 bpm,MRT就会增加0.1 ℃。相较于直肠温度无法连续稳定监测,呼吸频率则可长时间监测且不影响牛只日常活动,该设备已初见雏形,但并没有对母牛发情期间呼吸频率变化的研究,或许利用二者之间的相互联系可以寻找出新的发情鉴定指标。

2.2 阴道温度监测与发情鉴定

监测阴道温度为母牛发情鉴定技术提供了重要技术支撑。Wang等[19]研究发现,阴道温度在发情前逐渐升高,发情结束前4 h达到峰值,而后逐渐恢复至正常水平。对不同季节母牛间情期体温变化规律研究发现,夏季阴道温度((39.13±0.51)℃)显著高于秋季((38.68±0.19)℃)以及冬季((38.71±0.25)℃)(P < 0.05),同时,自然发情母牛阴道温度显著高于同期发情牛((38.92±0.44)℃ vs. (38.63±0.19)℃,P < 0.05)[21]。有研究发现,当阴道温度较前1 d同期升高0.30 ℃,且持续升高3 h以上时,可判定母牛发情,发情时体温最早升高0.30 ℃前的温度采集时间点记为发情开始,当两个连续时间点温度变化小于0.30 ℃时,应记为发情结束[39-41]。Higaki等[42]使用可穿戴式无线传感器设备连续监测17头牛发情周期阴道温度,并开发发情检测模型,结果表明,使用人工神经网络(artificial neural network,ANN)具有最佳检测性能,检出率为94.1%,灵敏度、精确率为94%。Gavan和Riza[40]将温度传感器安装到阴道栓(controlled internal drug release,CIDR)中进行阴道温度连续监测,并同时佩戴计步器记录发情期间活动量数据,发现前列腺素(prostaglandin,PG)同期发情处理母牛,发情检出率高于活动量数据检出结果(96.9% vs. 82.7%)。

相对于直肠温度监测,阴道温度采集设备可长时间监测母牛阴道温度变化。但是,使用阴道温度监测设备会造成母牛不适感,并且设备容易排出,供电以及维修问题也难以解决。因此,设计与研发更符合母牛阴道生理结构的设备外形,或采用其他检测阴道温度的方法,是未来亟待解决的问题。

2.3 瘤胃温度监测与发情鉴定

瘤胃温度也被用于母牛发情时体温监测[32, 43]。Andrade等[44]通过瘤胃丸连续监测母牛发情期间瘤胃温度变化,发现发情前1 d瘤胃温度为(39.30±0.05)℃,发情当天为(39.37±0.04)℃,发情后1 d温度为(39.17±0.05)℃,发情前1 d与后1 d的瘤胃平均温度差为0.13 ℃,发情当天与发情后1 d的平均温度差为0.20 ℃,检测到的温度变化比阴道温度监测结果小。Kim等[45]对比间情期与发情期间母牛瘤胃温度发现,发情期瘤胃平均温度升高0.13 ℃;发情期与间情期瘤胃最低温度无显著差异(34.60 ℃ vs 34.70 ℃,P>0.05),但发情期最高瘤胃温度(39.91 ℃)比休情期(39.67 ℃)高0.24 ℃(P=0.03)。Cooper-Prado等[46]发现,在发情后的0~8 h内,瘤胃平均温度((38.98±0.09)℃)高于发情前1 d((38.37±0.11)℃)和发情后1 d((38.30±0.09)℃)的相同时间段(P < 0.001)。一般认为,由于微生物发酵产热,瘤胃温度通常比其他部位高约0.5 ℃[47],而饮水会导致瘤胃温度较大波动[48-50]。或许,利用这种瞬时数据波动统计母牛发情期间的饮水次数变化,可能会提高牛只发情鉴定率。对瘤胃温度的深入研究或许能促进发情检出效率进一步提升。

2.4 眼部温度监测与发情鉴定

母牛发情时,可观察到兴奋不安,两眼充血现象[51],而充血则代表血流量增加,温度升高。研究发现,牛眼部与直肠及阴道温度呈中度相关(r=0.52,r=0.58)[52],意味着眼部温度或许可用以指示母牛CBT。眼睛不适合接触式体温测量,但可通过IRT进行监测[53-55]。Vicentini等[56]对57头同期发情母牛眼部(眼球区,泪小管区)进行IRT监测发现,发情前36~24 h,眼球区温度由38.10 ℃下降到37.40 ℃,泪小管区由39.10 ℃下降到37.60 ℃,温度明显下降(P<0.05);在发情前24~12 h,眼球区由37.40 ℃上升到38.20 ℃,泪小管区由37.60 ℃上升到38.70 ℃,温度明显上升(P<0.05);在发情前12 h到发情后12 h,中眼球区(38.20 ℃)和泪小管区(38.70 ℃)温度相对稳定,总体温度变化呈现出先下降后上升,随后保持平稳的趋势。王祯[57]通过红外热像仪采集10头自然发情泌乳牛眼部与外阴温度,分别利用Logistic模型与SVM模型建立母牛发情识别模型,结果表明:以眼部和外阴处最高温度为输入量,基于Logistic模型发情检出率为82.37%,基于SVM模型发情检出率为81.42%。虽然IRT对牛只无应激,但受场内环境、母牛体毛、设备所放位置等因素影响,且不能实现自动化和实时监测[32],对该技术的研发还有待完善。

2.5 耳部温度监测与发情鉴定

温度传感器还被集成在母牛耳标中进行发情监测。Stevenson[58]监测母牛发情期间耳表温度发现,发情前2 d((25.58±0.30)℃)、1 d((25.69±0.30)℃)以及发情后1 d((25.43±0.30)℃)均高于发情当天((25.00±0.30)℃),且差异极显著(P < 0.001),统计发现,发情前24 h,耳表温度降低了1.7%(P < 0.05),同时,发情当天低产奶牛(产奶量≤46 kg)耳表温度((25.20±0.50)℃)比高产奶牛(产奶量>46 kg)耳表温度((26.60±0.50)℃)低,但没有监测到妊娠及胎次与耳表温度的相关性。Randi等[15]使用热敏探头耳标监测44头同期发情母牛耳道温度发现,发情前2 d到发情后2 d期间呈现先升高后降低趋势,并且,在发情开始前48 h观察到最低温度(37.93 ℃),牛只发情持续时间(16.00±5.67) h,从发情开始到排卵平均时间间隔为(27.90±7.68) h。两个团队观察到不同温度变化规律,主要原因是耳部温度采集位置不同,Reynolds等[59]所采用的温度采集位于牛只耳郭中心位置[60],耳表温度与环境温度相关性较高(r=0.99)[58],而Randi等[15]则沿耳道向里贴近鼓膜处,相较于耳郭,耳鼓膜离体躯更近,受外环境影响更小,所以,检测结果也更接近躯体温度。母牛发情时,活动量升高,代谢旺盛,血液流动加快,导致温度升高,耳郭处温度下降,可能是耳廓两侧向外界的辐射以及与外界的传导、对流热交换较强,汗液蒸发使耳郭降温更多所致。进行耳部温度监测时,需要借助耳标将传感器固定在牛只耳部,佩戴时应进行消毒处理,防止伤口感染发炎,而且,耳部神经分布多而广,异物感是否影响母牛健康等问题,也应认真考虑,耳部作为母牛发情温度监测部位还要进行深入研究。

3 小结

不同躯体部位体温差异很大,发情监测结果同样差异很大,甚至耳部不同位置发情期间温度监测结果呈现相反的变化趋势。不同部位母牛发情期间温度监测结果差异,可能主要取决于生殖系统的生理变化,比如雌孕激素水平的变化以及躯体各部位器官功能状态的变化、生殖道的充血肿胀等;也受神经、免疫系统等不同程度的调节,躯体不同部位和器官的功能状态还因功能不同影响到血流速度和温度,各器官、部位的温度变化可能会表现出一定差异性,并影响到发情鉴定结果和效果。

就目前的母牛体温监测技术而言,直肠位置没有很好的连续温度监测手段,瘤胃内温度因牛只进食饮水有剧烈变化,眼睛处目前无法实现批量化监测,耳部温度因所选位置不同会有相反的监测结果。在母牛躯体的各个部位中,最直接,也是最有效反映牛只发情期间体温变化的部位是阴道。阴道在牛只发情期间可以明显反映出牛只体温的变化规律,并且可以批量化对牛群体温进行监测。但是该部位的特殊生理构造对接触式体温监测设备有着较高的要求,设备不但要小巧舒适不会对奶牛造成伤害,并且要安装牢固不掉出,而非接触式的温度监测方法中使用的设备成本较高,并且对操作人员有较高的技术要求,在我国的推广度不高。在今后的研究中如何对阴道温度更好的监测是一个值得研究的问题。

除了本文叙述的几个常见部位之外,还有团队对牛尾腹部[61]、后腿跟内侧的皮肤[23]、脖子[62]、腹部皮下[63]等位置进行了温度监测。但是,由于其所选位置因为设备固定难度较大,设备会给牛只带来较大的不适感,数据采集准确性较低等原因,这些监测部位并没有在后续推广开。随着传感器技术以及IRT的不断发展,母牛的蹄壳、背脊、腹部、肠道等部位在未来或许也可以作为指示牛只发情的温度变化来源。因此,需要深入研究不同部位体温变化机制与规律以及与母牛发情行为的相关性,为获得更高效自动化发情鉴定效果提供重要技术支撑,促进畜牧业向精准化、现代化、信息化、智能化发展。

参考文献
[1]
杜红玲. 奶牛场繁殖管理[J]. 吉林畜牧兽医, 2023, 44(4): 81-82.
DU H L. Reproduction management of dairy farms[J]. Jilin Animal Husbandry and Veterinary Medicine, 2023, 44(4): 81-82. (in Chinese)
[2]
ROELOFS J, LÓPEZ-GATIUS F, HUNTER R H F, et al. When is a cow in estrus?Clinical and practical aspects[J]. Theriogenology, 2010, 74(3): 327-344. DOI:10.1016/j.theriogenology.2010.02.016
[3]
MUNTHE-KAAS M, SVEBERG G, HOLMØY I H, et al. Pilot study investigating estrus length and estrus behavior in Norwegian Red cattle on a commercial dairy farm[J]. Front Vet Sci, 2023, 10: 1219001. DOI:10.3389/fvets.2023.1219001
[4]
MADUREIRA A M L, BURNETT T A, MARQUES J C S, et al. Occurrence and greater intensity of estrus in recipient lactating dairy cows improve pregnancy per embryo transfer[J]. J Dairy Sci, 2022, 105(1): 877-888. DOI:10.3168/jds.2021-20437
[5]
REITH S, HOY S. Review: behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle[J]. Animal, 2018, 12(2): 398-407. DOI:10.1017/S1751731117001975
[6]
MULFRISTIA D, SAPUTRA H, THASMI C N, et al. Determination of estrus duration based on cervical mucus characteristics in Aceh cattle using camera-equipped artificial insemination endoscope[J]. Ovozoa: J Anim Reprod, 2022, 11(2): 59-65. DOI:10.20473/ovz.v11i2.2022.59-65
[7]
KOTHANDARAMAN S, GNANASEKAR R, VARADHARAJAN A, et al. Comparative efficacy of different oestrous synchronization protocols on estrus induction response and conception rate in repeat breeder dairy cows[J]. Uttar Pradesh J Zool, 2023, 44(22): 109-116. DOI:10.56557/upjoz/2023/v44i223722
[8]
焦宏, 雷少斐. 2022年我国奶业成本与供需形势预测——保证生产者利益是中国奶业稳定的基石[EB/OL]. (2021-12-30)[2022-02-15]. http://www.farmer.com.cn/2021/12/30/99885647.html.
JIAO H, LEI S F. 2022 China′s dairy industry cost and supply and demand situation forecast-to ensure the interests of producers is the cornerstone of China′s dairy industry stability[EB/OL]. (2021-12-30)[2022-02-15]. http://www.farmer.com.cn/2021/12/30/99885647. html. (in Chinese)
[9]
中国奶业协会, 农业农村部奶及奶制品质量监督检验测试中心(北京). 2021中国奶业质量报告[M]. 北京: 中国农业科学技术出版社, 2021.
Dairy Association of China, Quality Supervision, Inspection and Testing Center for Milk and Dairy Products of the Ministry of Agriculture and Rural Affairs(Beijing). 2021 China dairy quality report[M]. Beijing: China Agricultural Science and Technology Press, 2021. (in Chinese)
[10]
王政, 宋怀波, 王云飞, 等. 奶牛运动行为智能监测研究进展与技术趋势[J]. 智慧农业(中英文), 2022, 4(2): 36-52.
WANG Z, SONG H B, WANG Y F, et al. Research progress and technology trend of intelligent morning of dairy cow motion behavior[J]. Smart Agriculture, 2022, 4(2): 36-52. (in Chinese)
[11]
杨亮, 张帆, 王辉, 等. 北京地区家畜养殖数字化现状[J]. 中国乳业, 2023(1): 35-40.
YANG L, ZHANG F, WANG H, et al. Digital status of livestock breeding in Beijing[J]. China Dairy, 2023(1): 35-40. (in Chinese)
[12]
GIORDANO J O, SITKO E M, RIAL C, et al. Symposium review: use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows[J]. J Dairy Sci, 2022, 105(5): 4669-4678. DOI:10.3168/jds.2021-21476
[13]
BORCHERS M R, CHANG Y M, TSAI I C, et al. A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors[J]. J Dairy Sci, 2016, 99(9): 7458-7466. DOI:10.3168/jds.2015-10843
[14]
CROCIATI M, SYLLA L, DE VINCENZI A, et al. How to predict parturition in cattle?A literature review of automatic devices and technologies for remote monitoring and calving prediction[J]. Animals, 2022, 12(3): 405. DOI:10.3390/ani12030405
[15]
RANDI F, MCDONALD M, DUFFY P, et al. The relationship between external auditory canal temperature and onset of estrus and ovulation in beef heifers[J]. Theriogenology, 2018, 110: 175-181. DOI:10.1016/j.theriogenology.2018.01.001
[16]
TSOUSIS G, BOSCOS C, PRAXITELOUS A. The negative impact of lameness on dairy cow reproduction[J]. Reprod Domest Anim, 2022, 57(S4): 33-39. DOI:10.1111/rda.14210
[17]
HEMALATHA R J, SONASHREE S P, THAMIZHVANI T R, et al. Detection of estrus in bovine using machine learning[C]// 2021 Seventh International Conference on Bio Signals, Images, and Instrumentation. Chennai: IEEE, 2021: 1-5.
[18]
李小俊, 王振玲, 陈晓丽, 等. 奶牛体温变化规律及繁殖应用研究进展[J]. 畜牧兽医学报, 2016, 47(12): 2331-2341.
LI X J, WANG Z L, CHEN X L, et al. Study progress on the rule of body temperature and its application in reproduction of dairy cattle[J]. Acta Veterinaria et Zootechnica Sinica, 2016, 47(12): 2331-2341. DOI:10.11843/j.issn.0366-6964.2016.12.002 (in Chinese)
[19]
WANG S L, ZHANG H L, TIAN H Z, et al. Alterations in vaginal temperature during the estrous cycle in dairy cows detected by a new intravaginal device-a pilot study[J]. Trop Anim Health Prod, 2020, 52(5): 2265-2271. DOI:10.1007/s11250-020-02199-5
[20]
MORITA Y, OZAKI R, MUKAIYAMA A, et al. Establishment of long-term chronic recording technique of in vivo ovarian parenchymal temperature in Japanese Black cows[J]. J Reprod Dev, 2020, 66(3): 271-275. DOI:10.1262/jrd.2019-097
[21]
YILDIZ A K, ÖZGVVEN M M. Determination of estrus in cattle with artificial neural networks using mobility and environmental data[J]. J Agric Fac Gaziosmanpasa Univ, 2022, 39(1): 40-45. DOI:10.55507/gopzfd.1116155
[22]
李永锋, 王文生, 郭雷风, 等. 基于穿戴传感器的牛日常行为识别研究进展[J]. 家畜生态学报, 2022, 43(10): 1-9.
LI Y F, WANG W S, GUO L F, et al. Research progress of cattle behavior recognition based on wearable sensors[J]. Acta Ecologae Animalis Domastici, 2022, 43(10): 1-9. (in Chinese)
[23]
田富洋, 王冉冉, 刘莫尘, 等. 基于神经网络的奶牛发情行为辨识与预测研究[J]. 农业机械学报, 2013, 44(S1): 277-281.
TIAN F Y, WANG R R, LIU M C, et al. Oestrus detection and prediction in dairy cows based on neural networks[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(S1): 277-281. (in Chinese)
[24]
徐衍向, 张敬智, 兰玉彬, 等. 基于红外热成像和机器学习的作物早期病害识别研究进展[J]. 中国农机化学报, 2023, 44(5): 188-197.
XU Y X, ZHANG J Z, LAN Y B, et al. Research progress of early crop disease identification based on infrared thermal imaging and machine learning[J]. Journal of Chinese Agricultural Mechanization, 2023, 44(5): 188-197. (in Chinese)
[25]
BORAH S, SOREN S, PAME K, et al. Application of infrared thermography for animal health study[J]. Emer Life Sci Res, 2022, 8(1): 152-157.
[26]
LEI M C, FÉLIX L, CARDOSO R, et al. Non-invasive biomarkers in saliva and eye infrared thermography to assess the stress response of calves during transport[J]. Animals, 2023, 13(14): 2311. DOI:10.3390/ani13142311
[27]
康熙, 刘刚, 初梦苑, 等. 基于计算机视觉的奶牛生理参数监测与疾病诊断研究进展及挑战[J]. 智慧农业(中英文), 2022, 4(2): 1-18.
KANG X, LIU G, CHU M Y, et al. Advances and challenges in physiological parameters monitoring and diseases diagnosing of dairy cows based on computer vision[J]. Smart Agriculture, 2022, 4(2): 1-18. (in Chinese)
[28]
HELLEBRAND H J, BREHME U, BEUCHE H, et al. Application of thermal imaging for cattle management[C]//Proceedings of the 1st European Conference on Precision Livestock Farming. Berlin: Springer, 2003: 761-763.
[29]
TALUKDER S, KERRISK K L, INGENHOFF L, et al. Infrared technology for estrus detection and as a predictor of time of ovulation in dairy cows in a pasture-based system[J]. Theriogenology, 2014, 81(7): 925-935. DOI:10.1016/j.theriogenology.2014.01.009
[30]
MARQUEZ H J P, AMBROSE D J, SCHAEFER A L, et al. Evaluation of infrared thermography combined with behavioral biometrics for estrus detection in naturally cycling dairy cows[J]. Animal, 2021, 15(7): 100205. DOI:10.1016/j.animal.2021.100205
[31]
刘金琪. 红外热像仪在泌乳牛皮温现场检测中的应用[D]. 哈尔滨: 东北农业大学, 2016.
LIU J Q. Application of infrared thermography in the field detection of dairy cow′s skin temperature[D]. Harbin: Northeast Agricultural University, 2016. (in Chinese)
[32]
张磊, 董茹月, 侯宇, 等. 奶牛体温评价指标及测定方法研究进展[J]. 动物营养学报, 2020, 32(2): 548-557.
ZHANG L, DONG R Y, HOU Y, et al. Research progress on evaluation indices and measurements of body temperature in dairy cows[J]. Chinese Journal of Animal Nutrition, 2020, 32(2): 548-557. (in Chinese)
[33]
葛利江, 吴志杰, 陈建鳌, 等. 关于奶牛发情周期及围产期体温变化规律的研究[J]. 黑龙江畜牧兽医, 1995(11): 3-5.
GE L J, WU Z J, CHEN J A, et al. Variations in body temperature in dairy cows druing the estrous cycle and peri parturient period[J]. Heilongjiang Journal of Animal Science and Veterinary Medicine, 1995(11): 3-5. (in Chinese)
[34]
PICCIONE G, CAOLA G, REFINETTI R. Daily and estrous rhythmicity of body temperature in domestic cattle[J]. BMC Physiol, 2003, 3: 7. DOI:10.1186/1472-6793-3-7
[35]
LILES H L, SCHNEIDER L G, POHLER K G, et al. Positive relationship of rectal temperature at fixed timed artificial insemination on pregnancy outcomes in beef cattle[J]. J Anim Sci, 2022, 100(7): skac100. DOI:10.1093/jas/skac100
[36]
REUTER R R, CARROLL J A, HULBERT L E, et al. Technical note: development of a self-contained, indwelling rectal temperature probe for cattle research[J]. J Anim Sci, 2010, 88(10): 3291-3295. DOI:10.2527/jas.2010-3093
[37]
SUTHAR V, BURFEIND O, MAEDER B, et al. Agreement between rectal and vaginal temperature measured with temperature loggers in dairy cows[J]. J Dairy Res, 2013, 80(2): 240-245. DOI:10.1017/S0022029913000071
[38]
李淦. 炎热气候下奶牛直肠温度、呼吸频率以及产奶量预测模型的建立[D]. 北京: 中国农业科学院, 2020.
LI G. Predicting rectal temperature, respiration rate and milk yield in dairy cows under hot climate[D]. Beijing: Chinese Academy of Agricultural Sciences, 2020. (in Chinese)
[39]
SAKATANI M, TAKAHASHI M, TAKENOUCHI N. The efficiency of vaginal temperature measurement for detection of estrus in Japanese Black cows[J]. J Reprod Dev, 2016, 62(2): 201-207. DOI:10.1262/jrd.2015-095
[40]
GAVAN C, RIZA M. Evaluation of vaginal temperature measurements versus walking activity as tool for detection of estrus in dairy cows[J]. Anim Vet Sci, 2022, 10(3): 73-77.
[41]
李蓝祁. 奶牛繁殖周期体温与活动量自动检测及变化规律研究[D]. 长沙: 湖南农业大学, 2018.
LI L Q. Study on automatic detection and variation of body temperature and activity in dairy cows during reproductive cycle[D]. Changsha: Hunan Agricultural University, 2018. (in Chinese)
[42]
HIGAKI S, MIURA R, SUDA T, et al. Estrous detection by continuous measurements of vaginal temperature and conductivity with supervised machine learning in cattle[J]. Theriogenology, 2019, 123: 90-99. DOI:10.1016/j.theriogenology.2018.09.038
[43]
LEE M, SEO S. Wearable wireless biosensor technology for monitoring cattle: a review[J]. Animals, 2021, 11(10): 2779. DOI:10.3390/ani11102779
[44]
ANDRADE V V, BERNARDES P A, VICENTINI R R, et al. Estrus prediction models for dairy Gyr heifers[J]. Animals, 2021, 11(11): 3103. DOI:10.3390/ani11113103
[45]
KIM J Y, LEE J S, JO Y H, et al. Measuring the effects of estrus on rumen temperature and environment, behavior and physiological attributes in Korean Native breeding cattle[J]. J Anim Sci Technol, 2023, 65(3): 579-587. DOI:10.5187/jast.2022.e117
[46]
COOPER-PRADO M J, LONG N M, WRIGHT E C, et al. Relationship of ruminal temperature with parturition and estrus of beef cows[J]. J Anim Sci, 2011, 89(4): 1020-1027. DOI:10.2527/jas.2010-3434
[47]
BEWLEY J M, EINSTEIN M E, GROTT M W, et al. Comparison of reticular and rectal core body temperatures in lactating dairy cows[J]. J Dairy Sci, 2008, 91(12): 4661-4672. DOI:10.3168/jds.2007-0835
[48]
AMMER S, LAMBERTZ C, GAULY M. Comparison of different measuring methods for body temperature in lactating cows under different climatic conditions[J]. J Dairy Res, 2016, 83(2): 165-172.
[49]
LUKAS J M, RENEAU J K, LINN J G. Water intake and dry matter intake changes as a feeding management tool and indicator of health and estrus status in dairy cows[J]. J Dairy Sci, 2008, 91(9): 3385-3394.
[50]
CAIRO F C, PEREIRA L G R, CAMPOS M M, et al. Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers[J]. Comput Electron Agric, 2020, 179: 105855.
[51]
沈莉. 母牛发情的鉴定方法[J]. 新农村, 2013(4): 31.
SHEN L. Method for identification of cow estrus[J]. New Countryside, 2013(4): 31. (in Chinese)
[52]
GEORGE W D, GODFREY R W, KETRING R C, et al. Relationship among eye and muzzle temperatures measured using digital infrared thermal imaging and vaginal and rectal temperatures in hair sheep and cattle[J]. J Anim Sci, 2014, 92(11): 4949-4955.
[53]
CHEN W A, HSU J T, LIN T T. An automated thermal imaging system based on deep learning for dairy cow eye temperature measurement[C]//2022 ASABE Annual International Meeting. Houston: American Society of Agricultural and Biological Engineers, 2022: 1.
[54]
WANG Y C, KANG X, CHU M Y, et al. Deep learning-based automatic dairy cow ocular surface temperature detection from thermal images[J]. Comput Electron Agric, 2022, 202: 107429.
[55]
WANG Z, WANG S, WANG C G, et al. A non-contact cow estrus monitoring method based on the thermal infrared images of cows[J]. Agriculture, 2023, 13(2): 385.
[56]
VICENTINI R R, MONTANHOLI Y R, VERONEZE R, et al. Infrared thermography reveals surface body temperature changes during proestrus and estrus reproductive phases in Gyr heifers (Bos taurus indicus)[J]. J Therm Biol, 2020, 92: 102662.
[57]
王祯. 基于温度分布特征的奶牛发情识别关键技术的研究[D]. 呼和浩特: 内蒙古农业大学, 2022.
WANG Z. Research on the key technology of cow estrus based on temperature distribution characteristics[D]. Hohhot: Inner Mongolia Agricultural University, 2022. (in Chinese)
[58]
STEVENSON J S. Daily activity measures and milk yield immediately before and after a fertile estrus and during the period of expected return to estrus after insemination in dairy cows[J]. J Dairy Sci, 2021, 104(10): 11277-11290.
[59]
REYNOLDS M A, BORCHERS M R, DAVIDSON J A, et al. Technical note: an evaluation of technology-recorded rumination and feeding behaviors in dairy heifers[J]. J Dairy Sci, 2019, 102(7): 6555-6558.
[60]
FAN B W, BRYANT R H, GREER A W. Automatically identifying sickness behavior in grazing lambs with an acceleration sensor[J]. Animals, 2023, 13(13): 2086.
[61]
MIURA R, YOSHIOKA K, MIYAMOTO T, et al. Estrous detection by monitoring ventral tail base surface temperature using a wearable wireless sensor in cattle[J]. Anim Reprod Sci, 2017, 180: 50-57.
[62]
CHEN P. Dairy cow health monitoring system based on NB-IoT communication[C]//International Conference on Electronic Engineering and Informatics. Nanjing: IEEE, 2019: 393-396.
[63]
IWASAKI W, ISHIDA S, KONDO D, et al. Monitoring of the core body temperature of cows using implantable wireless thermometers[J]. Comput Electron Agric, 2019, 163: 104849.

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