Smart sensors in Thai dairy reproduction: A case study

Author:

Kaewbang Jirayus1ORCID,Lohanawakul Jidapa2ORCID,Ketnuam Napat2ORCID,Prapakornmano Kachapas2ORCID,Khamta Pongsanan2ORCID,Raza Aqeel3ORCID,Swangchan-Uthai Theerawat4ORCID,Makararpong Davids5ORCID,Inchaisri Chaidate2ORCID

Affiliation:

1. Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand; Chulalongkorn Animal Hospital, Faculty of Veterinary Science, Chulalongkorn University, 73000 Nakhonpathom Province, Thailand.

2. Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand.

3. International Graduate Program of Veterinary Science and Technology, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand.

4. CU-Animal Fertility Research Unit, Department of Veterinary Obstetrics and Gynecology, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand.

5. Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand; Senovate AI Co., Ltd., 10240 Bangkok, Thailand.

Abstract

Background and Aim: Movement activity sensors are known for their potential to boost the reproductive performance of dairy cows. This study evaluated the effectiveness of these sensors on three Thai dairy farms (MK, NF, and CC), each using different sensor brands. We focused on reproductive performance at these farms and expanded our evaluation to include farmer satisfaction with sensor technology on five farms (MK, NF, CC, AP, and IP), allowing for a thorough analysis of both operational outcomes and user feedback. Materials and Methods: A total of 298 lactation records and interviewing five experienced farm owners with over a year of sensor usage were our research methods. To measure the effect on the first service timing and post-parturition pregnancy rates, Cox regression models were utilized for sensor usage. Results: Biosensors’ implementation enhanced data precision while quickening the first service within 100 days and pregnancy within 200 days. The MK and NF farms showed significant progress. Within 100 and 200 days post-implementation, the overall improvement was 30%–34% in the first service rate and 39%–67% in the conception rate across all assessed farms. Farmers acknowledged improved reproductive performance from the sensors, overcoming language barriers. Conclusion: The study highlighted the advantages of using movement activity sensors in enhancing both cattle reproductive success and farmers’ satisfaction on Thai dairy farms. These sensors led to more accurate management decisions, increasing overall farm productivity. Keywords: dairy cattle, movement activity sensors, reproductive performance.

Funder

Chulalongkorn University

Publisher

Veterinary World

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