Like other industries, it’s absolutely essential that all dairy farms have a strategic plan in place. But to gauge how well an automated milking operation is running, I recommend using some specific key performance indicators (KPIs).
KPIs can be used to help you achieve your goals by setting targets for the short term, mid term and long term. They need to be realistic considering the unique circumstances of your dairy.
Robotic milking KPIs should be established at three management levels: the herd, sub-systems and the milking procedure.
Standard KPIs like days in milk, pregnancy rate, days open, somatic cell count, etc., are very well defi ned and familiar to our industry. They should be used in addition to the following:
The capacity of a single-box robotic milking station is determined by milk frequency (x) number of cows to be milked. Generally, the higher number of cows, the lower the milk frequency and vice versa. Th is is just a matter of strategy and effi ciency. For example, a producer can decide if he or she wants to milk 60 cows 3X or 90 cows 2X. At the end of the day, the producer can achieve 180 milkings to spend according to his or her own operational circumstances and goals.
Volume of milk or butterfat harvested per robot per day
This is directly aff ected by two variables: number of cows to be milked per station per day and individual milk production per cow. Feeding strategies such as pTMR (TMR at the feedbunk plus concentrate at the VMS), grazing, organic milk production, freestalls, bedpacks and all possible combinations thereof, will also impact the outcome. For example:
Freestalls and pTMR: 60 cows per robot multiplied by 95 lbs per cow per day = 5,700 lbs per robot per day
Grazing, freestalls and pTMR: 80 cows per robot multiplied by 70 lbs per cow per day = 5,600 lbs per robot per day
When dairies harvest below 4,000 lbs per robot per day under open market conditions – like in the U.S. – it is necessary to evaluate and adjust; however, countries under the quota system may have to limit their performance per milk station due to growth restrictions, which is, of course, unrelated to dairy management practices.
Generally, most sub-systems follow standard industry principles. Major areas of difference are:
What should be measured here is the percentage of concentrate consumed per cow per day (at the robot or external feed stations). Feed consumption can fluctuate 80 to 100 percent. If lower, cow health can be compromised or system confi guration should be checked; if higher, it would suggest cows are achieving the nutritional component but missing the motivational side. Just like a salesperson working on commission, cows need to be motivated to visit the milking station and work hard every day – keeping the balance between feeding value, motivation and rumen health.
Somatic cell and bacteria counts are a must in all programs. MDI (mastitis detection index) is an excellent tool to manage daily routines. Your herd’s MDI = milking interval (x) conductivity (x) blood. Target MDI is less than 1.2. If higher than 1.4, check the following:
Milking interval: Less than 12 hours for all lactating cows
Conductivity: Below 7 (mS/cm) Blood: 0
If one or more is elevated, take action according to your standard operating procedures.
The milking procedure
From an operational standpoint, the main difference between robots and conventional milking parlors is, of course, the milking procedure. Robotic milking is all about time – and idle seconds are a main source of opportunity to harvest more milk per station per day.
Figure 1: Budget of time/robot/day
To evaluate milking efficiency, we have to look at the robot’s activity in a 24-hour period:
The amount of time the robot is only milking cows. It allows us to evaluate cow traffic to the station and how feeding strategies, cow comfort and cow training protocols are influencing cow motivation. A good target is at least 20 to 21 hours spent milking.
Depending on the manufacturer, this is typically between one to 1.5 hours daily to keep the robot clean.
This is the time the robot is empty and/or waiting for another cow to visit. These two to three hours should be considered as potential milking time.
The robot will refuse a cow if she does not have milking permissions. The goal should be zero refusals, which can easily be achieved by adding a pre-selection gate.
To evaluate the robot’s performance, we have to look at:
This is defined as from when a cow enters the station to when she exits – including time spent prepping, milking and post-spraying. A good goal is less than seven minutes per milking.
The relationship between stall time and milking time is defined as:
Total milkings available per robot per day:
A good target is 180 available milkings.
This is how it works:
One day: 24 hours or 1,440 minutes
Milking time: 21 hours or 1,260 minutes
Average stall time: 7 min
Total milkings available per robot: 1,260 min divided by 7 min per milking = 180 milkings per robot per day
Other options to evaluate milking efficiency are:
Production per milking:
Over 30 lbs
Milk flow rate = Production per milking (÷) stall time.
Above 4 lbs per minute is ideal but, in general, over 3.5 lbs per minute is considered acceptable.
Robotic milking is a highly interactive system and capacity will be defi ned by maximizing the number of milkings per station per day.
Optimizing your robotic milking system requires numerous considerations which must be aligned with your facility, management philosophies and, of course, your cows. But when aiming for improvement, you have to consistently measure your performance. It’s the only way to reach your goals and know you’re doing better.
Table 1: Herd performance data from three existing farms milking robotically
* Milk frequency is defi ned by a herd average. There will be cows above and below, depending on stage of lactation, cow characteristics and management strategies.
** Under normal conditions free-fl ow barns can handle up to 60 cows per robot regardless of the production system – more if using selection gates for guided-cow traffic.
This article was previously published in Progressive Dairyman