Enhancing incubation results through analytics

In this podcast, aviNews International talked with Dr. Edgar Oviedo about his article, “Using predictive analytics to improve hatchery performance,” published in the magazine. Dr. Oviedo is the technical director of aviNews International, a professor at the Prestage Department of Poultry Science at North Carolina State University, and a poultry consultant in more than 40 countries. Hatcheries have pivotal roles in poultry production systems. The data generated in this process can be better utilized with predictive analytics to address common issues that affect hatchability and hatchling quality by supporting planning, preventive maintenance programs, and personnel interventions. CURRENT STATUS OF DATA MANAGEMENT IN HATCHERIES There is a significant variation in the technological level of hatcheries around the world. Despite these differences, the hatchery is one of the poultry chain segments with more control and data generation. Hatchery data comes from monitoring egg and supply inventories, equipment operation, breeder reproductive performance, and incubation outcomes. Most frequent hatchery data includes: Egg flows, fertility, hatchability Cause of embryo mortality obtained in egg breakouts Hatchling output Quality grades And records of the average machine and room temperature Humidity, and pressures The digitalization of hand-written records is still tricky in many hatcheries, and typing errors are still common. Data quality always requires close attention. The preferred method of data warehousing is Excel files with a single table or multiple tables or spreadsheets. Data should always be organized in continuous columns and rows. Unfortunately, spaces are frequently left intentionally to add notes, complicating the data analysis. The following questions were discussed during the podcast: Where does the data come from? What should the data include? How should the data be collected? It means collecting by hand and later typing it into the program, or it is better to do everything on the program. Once the information is collected, what is the next step? How do people identify the kind of analysis to do? What are neural networks and their benefits? To read the full article, click here