student profile: Mr Juan Molfino


Thesis work

Thesis title: Addressing Inefficiencies in pasture-based automatic milking systems.

Supervisors: Yani GARCIA , Kendra KERRISK

Thesis abstract:

The adoption of Automatic Milking Systems (AMS) in Australia continues to increase. In 2015 there are 34 farms operating and another 7 in an installation phase. Most of them (97%) are pasture-based and operate with voluntary cow traffic system (i.e. cows bring themselves to the dairy, get milked and walk back to pasture largely without human assistance). Cow movement in voluntary traffic is affected by many factors including (but not limited to) cow factors (e.g. breed, age, stage of lactation, production level), management factors (e.g. timing, placement and distribution of feed) and environmental factors (e.g. climatic conditions). In pasture based AMS operating with voluntary traffic there are different types of inefficiencies at both cow and herd level. Inefficiencies provide an opportunity to increase the whole farm system efficiency and productivity by identifying them, evaluating their impact and finding alternatives ways to manage them in the most efficient way.
On the first stage of the project I am focusing on Inefficiencies at individual cow level (based on cow performance) by developing a methodology to easily and effectively categorize efficient and inefficient individual cows through the analyses of large datasets captured on commercial AMS farms. My research will combine the use of ex-post data to identify these inefficiencies within a range of AMS farms with ex ante modeling analysis to predict impact on the system. Field research will be carried out focusing on trying to identify and understand any new challenges arising specifically through social interactions and cow behaviour through the herd dynamics and its impact on whole system performance and management routines. On a second stage the main focus will be on identifying Inefficiencies at herd level (e.g. how to manage cow traffic during under-utilization periods) in  order to understand them and find the best way to address them to lift efficiency and increase productivity of the whole system. 

Note: This profile is for a student at the University of Sydney. Views presented here are not necessarily those of the University.