22/02/2023
Pig farming is a demanding activity that requires rigorous monitoring of breeding conditions, animal health, and feed consumption. However, manual data entry can be time-consuming and prone to errors, which can negatively impact overall productivity and profitability.
Pig'Up can help streamline and automate pig farming operations. By using RFID technology and Body condition analysis, it provides farmers with a range of benefits, including:
🐷 Time savings through instant data exchange
🐷 Elimination of double animal data entries
🐷 Increased efficiency with smartphone-enabled RFID readings
🐷 Better herd management with body condition analysis
Pig'Up is compatible with a variety of automated feeding systems, including Asserva, Skiold, Schauer, and Nedap. To activate the option, farmers simply need to contact their software support service and select the appropriate system. They can then configure the exchange according to their needs.
Farmers can easily import transponder numbers and assign them to unassociated sows. They can also export data on newly pregnant sows to their DAC system, including a filter for specific sow groups, and create an import file to manually integrate with the DAC system. For NEDAP maternity DACs, Pig'Up automatically exports new farrowing dates once a day.
Farmers can analyze the body condition of their pigs by measuring parameters such as muscle thickness, fat thickness, score, sow weight, and up to five body condition measures (entry, insemination, ultrasound, farrowing, weaning). They can then graphically analyze the pigs' body condition, view the distribution of body condition by class and parity rank, track farrowing performance based on body condition, and monitor changes in body condition over time. Additionally, Pig'Up allows farmers to assign sows to feed system groups based on their body condition values and identify sows that may need to be culled.
By leveraging modern technology pig farmers can gain valuable insights into their herd's health and productivity while reducing manual labor and errors.