KaydedilenlerDerginizi ekleyin

Dergiler / Turkish Journal of Veterinary and Animal Sciences / 2017 / Cilt: 41 - Sayı: 4

Detection of high levels of somatic cells in milk on farms equipped with an automatic milking system by decision trees technique

Beata SITKOWSKA
Dariusz PIWCZYŃSKI
Joanna AERTS
Magdalena KOLENDA
Serkan ÖZKAYA
Sayfa
532–540
Yayın Tarihi
2017-08-21
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It has been shown that in order to keep somatic cell count low in automatic milking system herds a farmer should pay particular attention to the milking speed.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The purpose of the study was to use decision trees to predict increased levels of somatic cells in cow's milk.
📊
Temel Bulgular & Veri:The material for the study comprised data collected in 2012-2014 from five farms in Poland equipped with an automatic milking system.
💡
Sonuç & Katkı:It has been shown that in order to keep somatic cell count low in automatic milking system herds a farmer should pay particular attention to the milking speed.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2012-2014 Verileri
⚙️Değişkenler:Milk · cattle · automatic milking system

Abstract

The purpose of the study was to use decision trees to predict increased levels of somatic cells in cow's milk. The material for the study comprised data collected in 2012-2014 from five farms in Poland equipped with an automatic milking system. Data on 803 Polish Holstein-Friesian cows were collected. In order to predict somatic cell count data mining techniques were used to build a graphical model of a decision tree. This study found that the most important factors to anticipate an elevated somatic cell count in cow's milk are milk conductivity, lactation stage, and lactation (primiparous and multiparous cow groups), as well as milking speed and rumination time. An increase in these parameters was also associated with a higher percentage of samples with an elevated somatic cell count. It has been shown that in order to keep somatic cell count low in automatic milking system herds a farmer should pay particular attention to the milking speed.

🔖

Bu Makaleye Atıf Yap

SITKOWSKA, B., PIWCZYŃSKI, D., AERTS, J., KOLENDA, M., ÖZKAYA, S. (2017). Detection of high levels of somatic cells in milk on farms equipped with an automatic milking system by decision trees technique. Turkish Journal of Veterinary and Animal Sciences, Cilt: 41 - Sayı: 4, 532–540. https://ajindex.com/articles/121280

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (9)

  1. Jacobs JA, Siegford JM. Invited review: the impact of automatic milking systems on dairy cow management, behaviour, health, and welfare. J Dairy Sci 2012; 95: 2227-2247.
  2. Sun Z, Samarasinghe S, Jago J. Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks. J Dairy Res 2010; 77: 168-175.
  3. Hovinen M, Pyörälä S. Invited review: udder health of dairy cows in automatic milking. J Dairy Sci 2011; 94: 547-562. de Mol RM, Ouweltjes W. Detection model for mastitis in cows milked in an automatic milking system. Prev Vet Med 2001; 49: 71-82. Pyörälä S. Indicators of inflammation in the diagnosis of mastitis. Vet Res 2003; 34: 565-578.
  4. Hogeveen H, Kamphuis C, Steeneveld W, Mollenhorst H. Sensors and clinical mastitis—the quest for the perfect alert. Sensors-Basel 2010; 10: 7991-8009.
  5. Kamphuis C, Mollenhorst H, Heesterbeek JA, Hogeveen H. Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. J Dairy Sci 2010; 93: 3616-3627.
  6. Kamphuis C, Sherlock R, Jago J, Mein G, Hogeveen H. Automatic detection of clinical mastitis is improved by in-line monitoring of somatic cell count. J Dairy Sci 2008; 91: 4560- 4570. Špakauskas V, Klimienė I, Matusevičius A. A comparison of indirect methods for diagnosis of subclinical mastitis in lactating dairy cows. Vet Arhiv 2006; 76: 101-109. Piwczyński D, Nogalski Z, Sitkowska B. Statistical modelling of calving ease and stillbirths in dairy cattle using the classification tree technique. Livest Sci 2013; 154: 19-27. Sun Z. Application of artificial neural networks in early detection of mastitis from improved data collected on-line by robotic milking stations. MSc, Lincoln University, New Zealand, 2008. Antanaitis R, Žilaitis V, Juozaitienė V, Palubinskas G, Kučinskas A, Sederevičius A, Beliavska-Aleksiejūnė D. Efficient diagnostics and treatment of bovine mastitis according to herd management parameters. Vet Med Zoot 2015; 69: 3-10.
  7. Mrode RA, Swanson GJT. Estimation of genetic parameters for somatic cell count in the first three lactations using random regression. Livest Prod Sci 2003; 79: 239-247.
  8. Hammer JF, Morton JM, Kerrisk KL. Quarter-milking-, quarter-, udder- and lactation-level risk factors and indicators for clinical mastitis during lactation in pasture-fed dairy cows managed in an automatic milking system. Aust Vet J 2012; 90: 167-174.
  9. Mollenhorst H, Hidayat MM, van den Broek J, Neijenhuis F, Hogeveen H. T