izpis_h1_title_alt

Uporaba senzorjev kot podpora menedžmentu v čredi krav molznic
ID Kacin, Anja (Author), ID Klopčič, Marija (Mentor) More about this mentor... This link opens in a new window

.pdfPDF - Presentation file, Download (5,61 MB)
MD5: 4CEDC85376DB0610D4C318921E041065

Abstract
Zanimanje za uporabo sodobne senzorske tehnologije pri reji krav molznic je vedno večje, predvsem zaradi povečevanja števila živali v posamezni čredi. Poleg pedometrov in ovratnic s senzorji, na tržišče prihajajo tudi ušesne značke s senzorji. Eno takih smo preizkusili tudi v naši raziskavi v okviru mednarodnega projekta 2 ORG-COWS z namenom potrditi uporabnost senzorske tehnologije pri izboljšanju upravljanja v čredi krav molznic ter pravočasnem ukrepanju ob predvidenih in nepredvidenih dogodkih v čredi. Podatke o obnašanju krav molznic s pomočjo Agis senzorjev smo zbirali dve leti pri 74-tih kravah molznicah treh različnih pasem (rjava, črno bela, lisasta) na dveh visokogorskih kmetijah v Idrijskih Krnicah. Na osnovi velikega števila podatkov (N=24.241 dni) zbranih s pomočjo teh senzorjev, smo ugotovili, da so živali v povprečju počivale 7 ur, za prežvekovanje so porabile 7,5 ur, za zauživanje obroka 5,5 ur, za gibanje 2 uri in za povečano aktivnost (obnašanje ob pojatvi) 2 uri. Do sprememb v obnašanju pride ob različnih dogodkih, kot so telitev, pojatev ali pojav bolezni oz. poškodb. Ob telitvi krave manj časa prežvekujejo in so bolj aktivne. V času pojatve so krave bolj aktivne, manj časa pa porabijo za zauživanje krme in prežvekovanje. Uporabnosti senzorske tehnologije za pravočasno prepoznavanje bolezenskega stanja živali nismo mogli potrditi, predvsem zaradi majhnega števila živali, ki so v tem času zbolele. S statističnim modelom, v katerega smo vključili vpliv rejca, pasme, zaporedne laktacije, stadija laktacije in načina reje (paša/hlev) smo pojasnili med 9,21 in 15,48 % variabilnosti za posamezne vzorce obnašanja. Razlike med razredi pri vplivih so bile statistično značilne (p<0,0001).

Language:Slovenian
Keywords:govedo, krave molznice, menedžment, senzorji
Work type:Master's thesis/paper
Organization:BF - Biotechnical Faculty
Year:2019
PID:20.500.12556/RUL-111555 This link opens in a new window
COBISS.SI-ID:4305544 This link opens in a new window
Publication date in RUL:03.10.2019
Views:1169
Downloads:176
Metadata:XML DC-XML DC-RDF
:
Copy citation
Share:Bookmark and Share

Secondary language

Language:English
Title:Use of sensors as support to management in dairy cows herd
Abstract:
The interest in use of modern sensor technology in dairy cows is rising, mainly due to the larger number of animals in each herd. In addition to pedometers and collars with sensors, ear tags with sensors are appearing on the market. In our research carrying out within the international project 2-ORG-COWS we tested one of these sensors with the aim to confirm the usability of sensor technology in improving the management of dairy herds and timely respond to foreseen and unforeseen events in the herd. Data on the behaviour of dairy cows were collected for two years at 74 dairy cows of three different breeds (Brown Swiss, Holstein and Simmental) at two mountain farms in Idrijske Krnice using Agis sensors. Based on the big number of data (N=24,241 days) collected with these sensors, we found out that cows in average spent 7 hours to rest, 7.5 hours for rumination, 5.5 hours for eating, 2 hours for activity and 2 hours for high activity ('in heat' behaviour). Behaviour changes occur at different events, such as calving, estrus, or the appearance of disease or injury. Before the time of calving cows ruminate less and are more active. Cows are more active during estrus and spend less time for feeding and rumination. The usability of sensor technology for the timely detection of the disease in animals could not be confirmed, mainly due to the small number of animals that became ill during this time. With the use of the statistical model, which included the effect of the breeder, breed, lactation parity, stage of lactation and outdoor/indoor system (pasture/stable), we explained between 9.21% and 15.48% of the variability for the individual behaviours. The differences between classes of different effects were statistically significant (p<0.0001).

Keywords:cattle, dairy cows, management, sensors

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back