How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction.

Martina Crociati,Giuseppe Stradaioli,Lakamy Sylla,Maurizio Monaci,Arianna De Vincenzi

doi:10.3390/ani12030405

Martina Crociati, Giuseppe Stradaioli + Show 3 more

Open Access

https://doi.org/10.3390/ani12030405

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Abstract

Simple SummaryCattle farming is progressively facing an increase in the number of animals that farmers must care for, resulting in decreasing time for observation of the single cow. A large amount of the scientific literature has been published concerning remote automatic devices and machine learning technologies for continuous monitoring of animal behavior and health status, including sensors for calving prediction This review summarizes the current status of the art concerning available automatic devices for the identification of the beginning of calving.Cattle farming is facing an increase in number of animals that farmers must care for, together with decreasing time for observation of the single animal. Remote monitoring systems are needed in order to optimize workload and animal welfare. Where the presence of personnel is constant, for example in dairy farms with great number of lactating cows or with three milking/day, calving monitoring systems which send alerts during the prodromal stage of labor (stage I) could be beneficial. On the contrary, where the presence of farm personnel is not guaranteed, for example in smaller farms, systems which alert at the beginning of labor (stage II) could be preferred. In this case, time spent observing periparturient animals is reduced. The reliability of each calving alarm should also be considered: automatic sensors for body temperature and activity are characterized by a time interval of 6–12 h between the alarm and calving. Promising results have been shown by devices which could be placed within the vaginal canal, thus identifying the beginning of fetal expulsion and optimizing the timing of calving assistance. However, some cases of non-optimal local tolerability and cow welfare issues are reported. Future research should be aimed to improve Sensitivity (Se), Specificity (Sp) and Positive Predictive Value (PPV) of calving alert devices in order to decrease the number of false positive alarms and focusing on easy-to-apply, re-usable and well tolerated products.

Highlights

Good herd management is one of the major contributors to optimized reproductive performance and farm net return [1,2]
Dystocia is a great concern in dairy cattle, with an incidence ranging from 10.7 to 51.2% in USA, and from 2 to 22% in Europe
Increased workload for calving monitoring and newborn calf care could be perceived by farmers as time-consuming and expensive, but partial budget estimation of the effect of calving monitoring and assistance confirmed that a 100-lactating dairy herd could improve the net return from 37 to 90 €/cow/year

Summary

Introduction

Good herd management is one of the major contributors to optimized reproductive performance and farm net return [1,2]. Calving monitoring and assistance represent a weak point worldwide; sometimes neglected, parturition is a crucial event for both the dam and the newborn. Prolonged or difficult calving (dystocia) and untimed (both late and early) assistance can compromise welfare, fertility and milk production of the dam, together with survival, growth and future performance of the calf [3,4,5,6,7,8]. Dystocia is a great concern in dairy cattle, with an incidence ranging from 10.7 to 51.2% in USA, and from 2 to 22% in Europe. The variation can be associated with pa2roifty, breed, sire and sex of the calf [9,10]. The incidence of difficult calving is usually lower and ranges from 3 to 7.7% [11,12]. Iins cshyastlleemnsgiwngh.erTehneamtuarjaolrbitryeeodfifnagrmissurseeldy oornwsohfetnwtahre dtoatceaolcfutlhaetelatshteinesxepmecinteadtiodnatwe aosf ncaoltvrinegcobradseedd, othnethdeatdeayofocfatlhveinlagstciannseomnliynabtieonp,rebsuutmleendgtwh iothf gaepsptartoioxnimvatriioens. oInf 1sy0sdteamys [w2h2–e2re4]n. atural breeding is used or when the date of the last insemination was not recorded, the dTahteisorfecvailevwingsucmanmoanrliyzebsethpereasvuamileadblwe imthetahpopdrso,xiinmclautidoinngof“1w0edaaraybsl[e2”2s–e2n4s].ors, dedicatedThailsgroervitihewmssuamndmamriazcehsinthee-leaavranilianbgletemchetnhooldogs,ieins cflourdicnaglv“iwngeaprraebdleic”tisoenn.soFrisg,udred1ischatoewdsalvgaorriiotuhms psoasnsdibmiliaticehsinfoe-rlesaernnsionrgs tpelcahcneomloegnitesonfotrhcealcvaitntlge’psrbedoidcyti,otno.gFeitghuerrew1isthhotwhes mvaariinoudsepviocsessibaivliatiielasbfloerfsreonmsotrhsepilnadcuemstreyn.t Toanbtlehse 1c–a5ttlseh’sowboddeyv, itcoegsetchuerrrewntitlyh athveaimlaabilne fdoerviimcems ainveaniltabclaelvfrionmg dtheeteicntdiouns,trwy.itThabrleelsat1i–v5e sdheoswcrdipetvioicness ocuf rtriemnetlyinatveravilaalbloef fdoreliimvemryipnreendticcatilovnin, gpedrefotercmtiaonnc,ewainthd rmelaantuivfeacdtuesrcerripintfioornms aotfiotinmwe hinetneravvaaliloafbdlee.livery prediction, perfoOrmnlaynpceroadnudcmtsaannudfapctruorteortyinpfeosrmdeastciorinbewdhienn tahveaislcaibelnet.ific, peer-reviewed literature wereOconnlysidperoreddu.cts and prototypes described in the scientific, peer-reviewed literature were considered

Wearable Sensors for Automatic Monitoring

Devices Which Identify the Stage II of Labor

Vulvar Magnetic Sensors

Intravaginal Devices

Findings

Conclusions