NZ619364B2 - A Method and System for Determining Milk Characteristics for Individual Animals in a Herd - Google Patents
A Method and System for Determining Milk Characteristics for Individual Animals in a Herd Download PDFInfo
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- NZ619364B2 NZ619364B2 NZ619364A NZ61936413A NZ619364B2 NZ 619364 B2 NZ619364 B2 NZ 619364B2 NZ 619364 A NZ619364 A NZ 619364A NZ 61936413 A NZ61936413 A NZ 61936413A NZ 619364 B2 NZ619364 B2 NZ 619364B2
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- milk
- milking
- individual
- animals
- animal
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- 210000004080 Milk Anatomy 0.000 title claims abstract description 337
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- 235000013365 dairy product Nutrition 0.000 claims description 40
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- 239000005018 casein Substances 0.000 claims description 2
- 235000021240 caseins Nutrition 0.000 claims description 2
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- GUBGYTABKSRVRQ-UUNJERMWSA-N Lactose Natural products O([C@@H]1[C@H](O)[C@H](O)[C@H](O)O[C@@H]1CO)[C@H]1[C@@H](O)[C@@H](O)[C@H](O)[C@H](CO)O1 GUBGYTABKSRVRQ-UUNJERMWSA-N 0.000 claims 1
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- 239000008101 lactose Substances 0.000 claims 1
- 238000000034 method Methods 0.000 description 21
- 210000002445 Nipples Anatomy 0.000 description 16
- 239000003925 fat Substances 0.000 description 15
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- 238000007796 conventional method Methods 0.000 description 6
- 230000006651 lactation Effects 0.000 description 6
- 230000001488 breeding Effects 0.000 description 5
- 208000004396 Mastitis Diseases 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 235000021243 milk fat Nutrition 0.000 description 4
- 210000000481 Breast Anatomy 0.000 description 3
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- 230000001771 impaired Effects 0.000 description 1
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- 150000002576 ketones Chemical class 0.000 description 1
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Abstract
Disclosed is a system of determining milk characteristics for individual animals within a cohort of milking animals. The system is for use in a milking shed (1) that includes a milk vat (8) and a plurality of milking stalls (2) such that milk from each stall (2) flows into a common milk line (6) to the milk vat (8). The system includes a processor (10), and at least one milk characteristic sensor (7) positioned in the common milk line (6) to receive milk collected from the cohort of milking animals, and configured to output at least one signal indicative of at least one milk characteristic of the milk. The processor (10) records individual identification of individual animals present at the milking shed for a milking event and records the timing of the milking events of individual animals. The signal from the milk characteristic sensor (7) is received and a milk characteristic of milk collected from the cohort of milking animals is determined. The processor (10) then correlates the identification of an individual cow with the timing of the milking event to develop a profile of one or more milk characteristics for the individual animal. the milk vat (8). The system includes a processor (10), and at least one milk characteristic sensor (7) positioned in the common milk line (6) to receive milk collected from the cohort of milking animals, and configured to output at least one signal indicative of at least one milk characteristic of the milk. The processor (10) records individual identification of individual animals present at the milking shed for a milking event and records the timing of the milking events of individual animals. The signal from the milk characteristic sensor (7) is received and a milk characteristic of milk collected from the cohort of milking animals is determined. The processor (10) then correlates the identification of an individual cow with the timing of the milking event to develop a profile of one or more milk characteristics for the individual animal.
Description
James & Wells ref: 133523/47
A METHOD AND SYSTEM FOR DETERMINING MILK CHARACTERISTICS FOR
INDIVIDUAL ANIMALS IN A HERD
TECHNICAL FIELD
The invention relates to a method and system for determining milking characteristics of
individual dairy animals within a herd of dairy animals. The invention has particular
application to estimating the milk yield and/or other milking characteristics of specific
cows of a herd using measurements of the total milk collected from the herd.
BACKGROUND ART
Milk yield, the quantity of milk obtained from an animal within a specified time frame, is
important information for a dairy farmer. It can form the basis for decisions pertaining to
management of a dairy herd or selected animals within the herd. Such decisions can
extend to when animals may be culled (usually the lower producing animals) or dried off
(allowing diversion of high quality feed, which contributes to good milk yield, to animals
still producing good quantities of milk).
These decisions help to optimise the milking performance of the herd and increase the
subsequent economic return.
Some aspects of an animal’s condition can be assessed by analysing the quantity
and/or quality of the milk from the animal. In some instances, changes in condition of
an animal are manifested in the milk before there is any readily observable physical
manifestation of the change.
Therefore, measurement of milk yield and other milk characteristics can be an important
aspect of dairy farm management.
On many farms, it is common to use milking meters in association with the milk lines of
James & Wells ref: 133523/47
a milking shed. When used in conjunction with identification tags associated with the
cow, the milk yield, and optionally, other characteristics of milk of a specific cow can
then be measured.
For accurate measurement of milk yield it is preferable that each stall of the milking
shed be provided with an inline milk meter, and this is often the case with new and
modern milking sheds.
Thus, the milking performance of an animal can effectively be measured in real time or
within a matter of hours. This allows early detection of any changes in milking
performance before those changes are manifested in physical cues such as variances in
the behaviour of the animal.
However, many older milking sheds, which represent the majority of dairy farms in New
Zealand, have limited numbers of milk meters. To retrofit existing milking sheds with the
necessary milk meters can incur significant expense. For example, some milk meters,
such as those based on technology described in New Zealand Patent Nos. 253311 and
255600 can cost in excess of NZ$1,000.
Therefore to retrofit a milking shed with 60 stalls with a meter for each stall will require
around NZ$60,000 of milking measurement equipment. Extra costs will be incurred if
additional meters measuring somatic cell counts (SCC), the presence of which can be
indicative of mastitis in an animal, are integrated into the milking shed. These meters
will often include Near Infrared (NIR) based sensors to estimate SCC, protein and fats
and can cost in excess of NZ$3,000 (at current market rates).
For dairy farmers, and particularly those with smaller farms, this can represent
significant financial investment and cost.
On smaller dairying operations a compromise must be found between the use of milk
meters and the economic investment in installing the milk meters. This may result in the
James & Wells ref: 133523/47
use of a reduced number of milk meters.
However, this may make it difficult for the dairy farmer to make management decisions
relating to the dairy herd based on data that is not necessarily reflective of the
performance of individual animals at any given time. This can make it difficult to
optimise dairy production.
For example, some animals in the herd may produce better quality milk than others.
Milking sheds equipped with individual milk meters may detect these animals, allowing
the dairy farmer to separate them out into a separate milking cohort or for breeding
stock.
This may be difficult to achieve if milk meters were limited in number. Depending on the
number of meters present, it may take several milking events to determine the
performance of individual animals, if at all, of a herd based on the collected milk. The
dairy farmer may have to rely on physical cues based on observations of an animal or
group of animals for decisions relating to management of those animals. However, the
physical cues may be preceded by undetected changes in milking performance of the
animal.
In many smaller dairying operations, the farmer or a herd tester may take regular
samples of milk from each animal for testing. This process, referred to as herd testing,
can provide useful information about specific characteristics of the milk of each animal,
such as percentage of fat and so forth. This can be of assistance in dairy herd
management without having to resort to identifying any physical manifestations of
impaired milking performance.
However, this sampling process can be time consuming and can also be expensive as it
involves using external contractors, such as Livestock Improvement Corporation
(www.lic.co.nz), to provide the necessary equipment, collect the required milk samples
James & Wells ref: 133523/47
and conduct the testing.
On average in New Zealand, LIC conducts four tests on each herd per lactation period
(eight months on average). Thus, in no way can this testing regime be considered a
“real time” process as is the case with dairy sheds having stalls using inline milk meters.
Thus, a change in the milking performance of an animal may not be detected for several
weeks. Furthermore, the use of herd testing in New Zealand has declined over the past
years.
It is an object of the present invention to address the foregoing problems or at least to
provide the public with a useful choice.
Further aspects and advantages of the present invention will become apparent from the
ensuing description which is given by way of example only.
All references, including any patents or patent applications cited in this specification are
hereby incorporated by reference. No admission is made that any reference constitutes
prior art. The discussion of the references states what their authors assert, and the
applicants reserve the right to challenge the accuracy and pertinency of the cited
documents. It will be clearly understood that, although a number of prior art publications
are referred to herein, this reference does not constitute an admission that any of these
documents form part of the common general knowledge in the art, in New Zealand or in
any other country.
Throughout this specification, the word "comprise", or variations thereof such as
"comprises" or "comprising", will be understood to imply the inclusion of a stated
element, integer or step, or group of elements integers or steps, but not the exclusion of
any other element, integer or step, or group of elements, integers or steps.
James & Wells ref: 133523/47
DISCLOSURE OF THE INVENTION
According to one aspect of the present invention there is provided a method of
determining milk characteristics for individual animals within a cohort of milking animals,
the method characterised by the steps of:
a) recording individual identification of animals present at a milking shed for a
milking event;
b) recording the timing of the milking event for individual animals;
c) measuring at least one milk characteristic of milk collected from the cohort of
milking animals;
d) correlating the identification of an individual animal with the timing of the milking
event to develop a profile of one or more milk characteristics for the individual
animal.
According to another aspect of the present invention, there is provided a method of
determining milk characteristics for individual animals within a cohort of milking animals
substantially as described above, the method including the additional step of:
e) repeating steps a) to d) to develop a profile of milk characteristics for the
individual animal over a period of time.
According to another aspect of the present invention there is provided a system of
determining milk characteristics for individual animals within a cohort of milking animals,
wherein the system is for use in a milking shed, wherein the milking shed includes a milk
vat, a plurality of milking stalls, wherein milk from each stall flows into a common milk
line to the milk vat, wherein the system includes
a processor, and
James & Wells ref: 133523/47
at least one milk characteristic sensor positioned to receive milk collected from the
cohort of milking animals, and configured to output at least one signal indicative of at
least one milk characteristic of the milk,
the system characterised in that
the processor is configured to:
a) record individual identification of animals present at the milking shed for a milking
event;
b) record the timing of the milking event for individual animals;
c) receive the signal from the milk characteristic sensor and determine a milk
characteristic of milk collected from the cohort of milking animals;
d) correlate the identification of an individual animal with the timing of the milking
event to develop a profile of one or more milk characteristics for the individual
animal.
According to another aspect of the present invention, there is provided a system of
determining milk characteristics for individual animals within a cohort of milking animals
substantially as described above, wherein the processor is configured to
e) repeat steps a) to d) to develop a profile of milk characteristics for the individual
animal over a period of time.
According to another aspect of the present invention there is provided an article of
manufacture having computer storage medium storing computer readable program code
executable by a computer to implement a method for determining milk characteristics for
individual animals within a cohort of animals, the code including:
James & Wells ref: 133523/47
computer readable program code recording individual identification of animals
present at a milking shed for a milking event;
computer readable program code recording the timing of the milking event for
individual animals;
computer readable program code receiving an indication of at least one milk
characteristic of milk collected from the cohort of milking animals; and
computer readable program code correlating the identification of an individual
animal with the timing of the milking event to develop a profile of one or more
milk characteristics for the individual animal.
The inventive method and system has particular application for determination of
characteristics of milk from a selected animal from a group of animals, particularly, but
not limited to, milk yield, based on measurement of an ensemble of milk production from
a group of animals, such as a dairy herd or cohort of dairy cows.
Estimated characteristics may also include percentage of fat, percentage of casein,
somatic cell count and so forth, of individual cows. This may be particularly
advantageous for smaller farms, which for economic reasons, may be limited in the
number of meters available for measurement of milk characteristics.
The milk characteristics can be an indicator of the phenotype of the animal, and any
changes in the phenotype or milk quality of the animal over time. From this information,
decisions can be made with respect to management of the animal or the group of
animals. This may result in the culling of low producing animals from the group or
selecting high producing animals (which is a valued phenotype) for breeding stock. The
determined characteristics may also be indicative of the health of the animal, and thus is
useful for detecting when the animal requires appropriate veterinary care.
James & Wells ref: 133523/47
Milk characteristic should be understood to be a property or properties of the milk of the
specific animal. The selected characteristic ideally should be one which in a normal and
healthy animal is relatively stable but which has some degree of variability to allow
detection of the characteristic against the milk of the other animals in the cohort.
The characteristic may be indicative of a particular phenotype of the animal, for example
the quantity of milk produced. For example, the milk characteristic may be total milk
collected from the animal, i.e. milk yield. Animals that have a high milk yield relative to
other animals have value as potential breeding stock.
The milk characteristics of a specific animal may also be indicative of its health. For
example, an increase in somatic cell count can indicate the presence of mastitis, a
bacterial infection of the udder, which may require veterinary intervention. While not
intended to be limiting, other characteristics of the milk can include milk metabolites,
percentage fat, percentage protein, electrical conductivity and so forth.
The characteristic may be indicative of a particular property of the milk produced by the
animal, for example, its quality. Measures of percentage fat and percentage protein of
the milk are useful in this regard, and again animals that have high quality milk may
have value as potential breeding stock. Milk quality can be dependent on external
factors as well, such as the time of season and type of feed given to the animal. This
has implications for the management of the cohort or the individual animal. Persons
skilled in the art will readily recognise what milk characteristics of an individual animal
can be assessed with the present invention for the purpose of appropriate management
of that animal and/or the cohort.
The milk characteristic should be one which can be measured in real time. It will be
appreciated that separating out collected milk for later analysis for specific parameters
will make it difficult to correlate that milk with a specific animal. Some characteristics of
milk, for example the presence of ketones, are not able to be measured in real time with
James & Wells ref: 133523/47
existing technology. However, once this can be achieved, then these characteristics
may also be investigated with the present invention.
The system should be understood to include a processor which is configured to
implement the inventive method. This may be a central processing station in the milking
shed, or a laptop computer which integrates and is communicative with the hardware in
the milking.
The animal should be understood to be an animal valued for its milk output. In preferred
embodiments of the present invention, the animal is a dairy cow and shall be referred to
as such throughout the remainder of this specification.
However, this is not meant to be limiting and the present invention may be used to
estimate milk characteristics for other animal species valued for its milk output. For
example, the present invention may be used for sheep, the milk of which is valued for
use in production of dairy products such as cheese.
It will be understood that the present invention is for use on a dairy farm, on which one
or more cohorts of cows are milked in a milking shed.
It will be understood that the present method and system is for use in a milking shed.
Such sheds have a plurality of milking stalls, with each stall having a milk line draining a
milk claw that typically includes four teat cups (one for each teat of the cow). The milk
lines of each stall, which may be referred to as a long milk line, lead to a common milk
line feeding a milk vat, in which the milk from the herd is collected.
The long milk line, milk claw, and the teat cups of each milking stall shall now be
referred to as the milking plant. However, this is not meant to be limiting. The milking
plant may also include equipment such as automatic teat cup removers, automated teat
sprayers and so forth.
James & Wells ref: 133523/47
Milking sheds can take a number of configurations, but the most common are rotary
sheds and “herringbone” sheds.
A rotary shed includes a rotating platform around the circumference of which is a trench
(or in many instances, the platform is elevated from the ground). The milking stalls are
mounted on the platform, with the rear of the cow facing outwards. The teat cups can
then be placed onto and removed from the udder during milking as the platform slowly
rotates. Cows enter the platform one at a time as a stall becomes available. Each stall
has its own milking plant.
Herringbone sheds are those in which the milking stalls are paired either side of a
trench. The dairy farmer moves along the trench to attach and detach the teat cups to
the cows as required. Usually, the cows are introduced to the shed in groups
corresponding to the number of stalls in the shed. While not all herringbone sheds may
have stalls for individual animals, they do have individual milking plant.
The present invention is readily applicable to milking sheds of both configurations.
A cohort should be understood to mean a group of cows. This may be a herd of cows,
or a subgroup of the herd.
The milking of each cow of the cohort should be understood to be a milking event for
that cow. Depending on the management of the dairy farm, the milking of the cohort of
cows may occur once or twice a day. Therefore, in some embodiments of the present
invention, the milking event may occur once a day and in others, twice a day.
The milking event has a start time and a finish time. In some embodiments of the
present invention, the start of the milking event is when the cow enters the milking shed,
and the end is when the cow exits the milking shed. However, as persons skilled in the
art will appreciate, there can be a time lag between the cow’s entry into the shed and
when milk begins to be collected from the udder. There may also be a lag between the
James & Wells ref: 133523/47
end of the milking event and the subsequent exit of the cow from the shed.
In preferred embodiments of the present invention, the milking event starts upon
application of the teat cups to the teat and ends when the teat cups are removed. This
is a more accurate determination of the timing of the milking event.
In these embodiments, the milking plant may include sensors to determine when they
are on and off the teats of the animal. These sensors may be associated with the teat
cups and thus send a signal when the cups are applied and removed from the animal.
Alternatively, the sensors may be in the claw or long milk line, and send a signal upon
detection of the presence and absence of milk. In some milking sheds, such sensors
are commonplace.
Alternatively, the sensors may be associated with other equipment within the milking
plant. For example, the sensors may be associated with automatic teat cup removers,
and the timing of the milking event determined by entry of the animal in the milking shed
or milking stall and removal of the teat cups. Persons skilled in the art will readily
appreciate other ways in which the duration of the milking event may be determined.
It will be understood that the processor is in communication with the sensors, either
directly or wirelessly.
The present invention requires each cow have an individual identifier. This will be a
number or similar identifying means that is specifically associated with that animal. In its
simplest form, the individual identifier may be a marked tag attached to the ear of the
cow.
For example, the individual identifier may be an ear tag on which is notated a number
unique to the animal provided with the ear tag.
Preferably, the individual identifier is an electronic identifier (EID) which is unique to the
James & Wells ref: 133523/47
animal. The EID may take a variety of forms depending of the requirements of the user.
For example, battery powered transponders may be used as an EID. These may be
integrated into an ear tag, bolus or subcutaneous implant.
In particularly preferred embodiments of the present invention, the individual identifier is
a Radio Frequency Identifier (RFID) ear tag. These are transponders that do not require
batteries. RFID ear tags are mandatory in the dairying industry of New Zealand, which
has to comply with the National Animal Identification and Tracing (NAIT) scheme.
Suitable RFID ear tags are readily available and include those manufactured by Allflex®
New Zealand Limited (http://www.allflex.co.nz).
Reference shall now be made throughout the remainder of the specification to the
individual identifier being a RFID ear tag. However, as should be appreciated this is not
meant to be limiting and other forms of the individual identifier are envisaged.
The present invention requires the system to have a recording device at the milking
shed. The recording device is configured to record the individual identifier, i.e. the
individual animals when they are in the milking shed, or using a milking plant in the
milking shed or within a stall of the milking shed.
In the particularly preferred embodiment of the invention, the RFID of the ear tag is
configured to communicate wirelessly or otherwise with a recording device in the form of
a RFID reader. This simplifies the data collection process.
However, the use of an RFID reader as the recording device is not meant to be limiting
as it should be appreciated that the nature of the recording device may depend on the
type of EID that is to be used.
The recording device may also time the start and cessation of the milking event.
In some embodiments of the present invention, the recording device is associated with
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the entry and exit points of the milking shed. This allows determination of when
individual cows enter and leave the milking shed. For example, the recording device
may be an Allflex® panel reader integrated into the races leading into and out of the
dairy shed. Therefore it will be appreciated in this embodiment of the invention, the
RFID reader not only records the RFID ear tag, but also the presence of the animal, to
mark the start of the milking event.
The end of the milking event may be determined by the RFID reader reading the ear tag
as the animal leaves the milking shed.
This may be particularly suitable for herring-bone milking sheds, which may not be
provided with individual milking stalls.
In some embodiments in which the invention is used in a rotary milking shed, the RFID
reader may only be associated with the entry race. If no RFID reader is associated with
the exit race, the exit of the animal may be determined by the position of the rotary
platform at any given moment. The position of the platform is known and therefore it is
relatively easy to determine when the animal reaches the exit point and thus determine
the length of the milking event if the entry time is known.
However, as discussed above, there may still be a lag between the cows entering (or
exiting) the dairy shed and the milking event actually commencing (or finishing, as the
case may be). For example, in a rotary platform, a cow may be fully milked well before
its stall has reached the exit point of the rotary platform. This may affect how quickly the
invention can arrive at an accurate profile for an individual animal.
In preferred embodiments, the recording device is a RFID reader linked to a data logger.
The presence or absence of the RFID ear tag of the animal is noted by the RFID reader
and the data provided to the data logger. The data logger may also be configured to
receive signals indicative of the start or end of the milking event.
James & Wells ref: 133523/47
It will be understood that the recording device and/or data logger is linked or otherwise
communicative with the processor of the present system.
In preferred embodiments of the present invention, the recording device or data logger
is also associated or otherwise communicative with the sensors of the milking plant of
each stall. This provides a much more accurate determination of the onset and
cessation of the milking event.
The method requires the recordal of the start and end of the milking event for each
specific cow, along with determination of total milk flow for the cohort. Therefore, a
sensor to measure the rate of milk flow into the vat of the milking shed is needed.
The sensor that measures the rate of milk flow is communicative or otherwise linked with
the processor of the present system. It will be appreciated that the processor is
configured to correlate milk data, collected by the sensor, together with the timing of the
milking event (from the recording device) and the identity of the animal being milked
(from the RFID ear tag).
In preferred embodiments of the present invention, the sensor is a milk flow meter and
shall be referred to as such throughout the remainder of this specification.
However, this is not meant to be limiting and the method may be used with other or
additional sensors of interest which can measure milk or its characteristics in real time.
For example, the sensor may measure the somatic cell count (SCC), or indicators
thereof such as conductivity.
As persons skilled in the art will appreciate, SCC may be used as a measure of mastitis.
Incorporating a SCC sensor and a milk flow sensor in the common milk line or at or
proximate the milk vat may allow for more accurate detection of mastitis in individual
animals without the need for SCC sensors in the milking plant of each stall. This can
represent significant cost savings for the dairy farmer.
James & Wells ref: 133523/47
The example described above is not meant to be limiting. Persons skilled in the art will
appreciate other characteristics of the milk aside from SCC may be estimated with the
present invention, depending on the types of sensors employed in the milking shed.
Essentially, the invention can be any characteristic of milk able to be measured in real
time by an appropriate sensor and which may be indicative of a particular phenotype of
the specific animal (for example, its milk yield although this is not meant to be limiting).
In preferred embodiments of the present invention, the milk flow sensor is positioned at
the milk vat, or in the common milk line preceding the milk vat.
The sensor measures the milk flow and/or other properties of the milk that is collected
from the cohort. From this, a profile for the total milk can be established.
Following the completion of the milking event, the data relating to the identification of
individual animals and the timing and duration of the milking event is transmitted, or
otherwise collected from, the recorder and/or data logger along with the total milk
information from the sensor for processing. Processing may be conducted using any
suitable processing device such as the central processing station of the milking shed or
on a laptop or desktop computer. The data is then run through an algorithm.
The algorithm separates data associated with the individual animal from the total milk
information to determine a milking profile for each animal. This profile may include milk
yield, SCC, percentage of fat, percentage of proteins and so forth.
The algorithm may include additional data derived from alternative sources of
information relating to the animal or the cohort. For example, the algorithm may take
into account the age of the animal or its weight.
It is envisaged that by collecting data from successive milking events, a more accurate
profile of a specific animal can be built in comparison with a single event in isolation.
James & Wells ref: 133523/47
In use, the cow (cow ‘A’) enters the milking shed and is identified, using the RFID ear
tag reader, by its unique identification number and has teat cups attached to its teats.
Upon the start of the milking event, the recording device notes that cow ‘A’ has
commenced milking. At the same time, the sensor associated with the common milk
line or milk vat measures the milk from the cohort.
The data collected from the recording device or data logger may be sent in real time to a
computer processor or the like. Alternatively, the data is stored and retrieved following
the end of the milking.
Each cow produces milk having a particular milk profile. The recording of the onset and
cessation of the milking event for specific animals means that the characteristics of the
total milk flowing through the common milk line and/or entering the vat at any one time
can be associated with the specific animals being milked at that time.
Each milking event is different; a specific cow may not be milked at the same time as
the previous milking event. However, as the milk flowing through the common milk line
and/or entering the vat at any one time can still be associated with the specific animals
being milked at that time, over time, the milk profile of a particular animal can be
identified as being distinct from other animals in the cohort.
Thus, by application of an appropriate algorithm to the collected data, the milk
characteristics of a specific animal, such as milk yield, can be determined with a
reasonable degree of accuracy. A profile of the animal can be established, and over
time, any variations to that profile can be detected.
This provides a useful management tool for the dairy farmer. Informed decisions
relating to the culling, breeding or veterinary care of specific animals in the herd can be
made. Additionally, decisions relating to improving milk quality can also be made.
James & Wells ref: 133523/47
Certain characteristics of milk are useful for determining quality of the milk derived from
the animal. Any changes in these characteristics may be related to external factors
rather than as a direct result of the animal’s physiology. For example, a change in feed
may affect the quality of milk.
The present invention allows a dairy farmer to correlate a decrease or increase in a milk
characteristic that is reflective of the milk quality (or a change in the phenotype of the
animal) with a change in a particular external factor (such as a change in feed).
Although the present invention may utilise only one milk flow meter, it is not beyond the
scope of the present invention that additional milk flow meters be employed along the
common milk line.
Doing so may improve the accuracy of the estimate and potentially within a shorter time
period as there would be a stronger correlation between the onset of a milking event and
a specific animal within the cohort.
For example, a herringbone milking shed may be configured with a milk line for each
side of the shed. Such an arrangement may allow the use of two appropriately
positioned milk meters, one for each line.
Having more than one milk meter along the common milk line may be useful for meter
calibration purposes but is not essential to the present invention.
Another way of improving the accuracy of the estimate is by milking twice a day, which
provides effectively two opportunities to gather data for implementation of the method.
In some countries, two milking events per day is common practice in management of the
dairy farm.
The time between successive milking events, which is on average 24 hours for animals
milked once a day or between 8 to 14 hours for animals milked twice daily, can be used
James & Wells ref: 133523/47
as a covariate to improve the way in which the profile of a specific animal is developed.
Other useful covariates may be the live weight and/or age of the specific animal or
information associated with previous seasons.
However, it should be appreciated that, depending on the milk characteristic being
determined, more (or less) time may be required to establish a reasonably accurate
profile for the animal.
Embodiments of the present invention may be implemented as instructions (for example,
procedures, functions, and so on) that perform the functions described. It should be
appreciated that the present invention is not described with reference to any particular
programming languages, and that a variety of programming languages could be used to
implement the present invention. Firmware and/or software codes may be stored in a
memory, or embodied in any other processor readable medium, and executed by a
processor or processors. The memory may be implemented within the processor or
external to the processor.
A general purpose processor may be a microprocessor, but in the alternative, the
processor may be any processor, controller, microcontroller, or state machine. A
processor may also be implemented as a combination of computing devices, for
example, a combination of a digital signal processor (DSP) and a microprocessor, a
plurality of microprocessors, one or more microprocessors in conjunction with a DSP
core, or any other such configuration. The processors may function in conjunction with
servers and network connections as known in the art.
The steps of a method, process, or algorithm described in connection with the present
invention may be embodied directly in hardware, in a software module executed by a
processor, or in a combination of the two. The various steps or acts in a method or
process may be performed in the order shown, or may be performed in another order.
Additionally, one or more process or method steps may be omitted or one or more
James & Wells ref: 133523/47
process or method steps may be added to the methods and processes. An additional
step, block, or action may be added in the beginning, end, or intervening existing
elements of the methods and processes.
Embodiments of the present invention offer one or more of the following advantages:
a cost effective method of determining milk characteristics, such as milk yield, of
a specific dairy cow by measuring the total milk of the milking cohort and
correlating this with timing of the milking event of the specific animal;
provides measurement of milk characteristics using relatively few milk meters
and achieving an accuracy comparable to dairy shed systems having inline milk
meters;
is readily adapted into existing dairy sheds in a cost effective manner due to the
potential for a reduced number of expensive milk sensing equipment;
a tool to optimise dairy herd performance on farms lacking inline milk meters;
at the very least, offers the public a useful choice.
BRIEF DESCRIPTION OF DRAWINGS
Further aspects of the present invention will become apparent from the following
description which is given by way of example only and with reference to the
accompanying drawings in which:
Figure 1 is a schematic of the system of the present invention;
Figure 2 is a graph of the measured total milk flow into a milk vat representing a
milking event for a cohort of 923 dairy cows being milked in a rotary shed;
Figure 3 is a graph of estimated and measured morning milk yield for a specific
James & Wells ref: 133523/47
cow of the cohort of Figure 2 over 100 days;
Figures 4 and 5 are graphs of predicted milk yields calculated using the present
invention and conventional methods compared against actual measured
milk yields;
Figure 6 is a flow chart of the step-by-step process for estimating milk yield of an
individual animal from a bulk milk flow;
Figures 7 and 8 are a graph of predicted milk electrical conductivity calculated using the
present invention and conventional methods compared against actual
measured milk electrical conductivity;
Figure 9 is a graph of estimated and measured milk electrical conductivity for a
specific cow of the cohort of Figure 2 over 100 days;
Figure 10 is a flow chart of the step-by-step process for estimating a milk
composition of interest for the milk from an individual animal from a bulk
milk flow;
Figure 11 is a graph of the percentage fat in the milk flow into a milk vat,
representing a milking event for a cohort of 347 dairy cows being milked
in a rotary shed;
Figure 12 is a graph of estimated and measured percentage fat in the milk of a
specific cow over 100 days; and
Figures 13 and 14 are a graphs of predicted percentage milk fat calculated using the
present invention and conventional methods compared against actual
measured percentage milk fat.
James & Wells ref: 133523/47
BEST MODES FOR CARRYING OUT THE INVENTION
The system of the present invention is illustrated as a schematic in Figure 1, and depicts
a herring bone milking shed (1).
The milking shed includes a plurality of milking stalls (2), each stall including a RFID
reader (3) to wirelessly (4) read the RFID (not shown) integrated into the ear tag (not
shown), which identifies each animal (not shown).
The RFID reader (3) is linked (3a) to a data logger (5) to record the identification of each
animal (not shown).
The milking stalls (2) are linked (2a) to a common milk line (6). The stalls have a milking
plant that includes cup sensors (7) which are linked (7a) to the data logger (5) to note
the commencement and cessation of the milking event.
The milking shed also includes a milk vat (8) in which the milk from each stall (2)
collects. A milk meter or sensor(s) (9) is integrated into the common milk line (6)
preceding the milk vat to measure the quantity of milk or a characteristic of interest of
the milk flowing through the common milk line (i.e. from the cohort of animals being
milked). A person skilled in the art will readily identify an appropriate meter or sensor for
the task, depending on the characteristic of interest.
The data logger (5) and meter or sensor(s) (9) are in communication (5a, 9a) with a
processor (10), which may be a computer used to monitor and control elements of the
milking system.
The system may be readily modified by a skilled addressee for implementation in a
rotary shed.
Aspects of the method shall now be described with reference to the following graphs
(Figures 2 to 5, 7, and 8), which is based upon data collected from a herd of 923 dairy
James & Wells ref: 133523/47
cows being milked in a rotary shed of 60 stalls at Tokanui, New Zealand over a four
month period, and simulations performed on same to test the method.
Figure 2 is a graph showing a simulation of the measured total milk flow (kilograms per
minute) in the common milk line leading to the milk vat, representing a milking event of
180 minutes for a cohort of dairy cows being milked in a rotary shed.
It will be observed that there is considerable variation in the milk flow, which is indicative
of the start and cessation of the milking event for specific cows. By noting the time at
which specific cows start and stop milking over a series of milking events it can be
possible to develop a profile of each animal in the cohort.
From the collected data, application of an algorithm then allows the dairy farmer to
estimate the milk yield for that animal. The algorithm shall now be discussed in detail.
The rate of milk production (y ) (litres/minute) of the individual animal (the i cow) as a
function of time (t) (minutes) can be described by Equation 1:
0, t 0
a , 0 t t
i 1i
y (t;a ,t ,t ) a y (t;t ,t )
(t t )
i i 1i 2i i i 1i 2i
a 1 , t t t
i 1i 2i
(t t )
2i 1i
0, t t
2i
where a is the rate of milk production, t is the milking duration and t is time for
i 2i 1i
maximum milk production. The milk yield is Y a t t litres. The expected value of
i i 2i 1i
t is known to be E[t ] 0.38t .
1i 1i 2i
If there are N cows milked daily with each cow beginning milking at time then the total
milk flow (litres/minute), as a function of the milk flow for individual animals, the common
milk line is determined by Equation 2:
James & Wells ref: 133523/47
b(t) y (t T ;a ,t ,t )
i i i 1i 2i
i 1
A necessary input into the algorithm is the total milk flow. This may be calculated using
Equation 3. Assuming the total milk flow is recorded periodically (for example, Δt=5
seconds; this is not meant to be limiting and other time intervals may be used) at times
r j t , j=1..M, then Equation 3 is:
b b(r ) a y (r T ;t ,t )
j j i i j i 1i 2i
i 1
This can be expressed as a matrix system, in which the rows of the matrix A are the
times the bulk flow is measured while the columns of matrix A are the individual animals.
The matrix system is defined by Equation 4:
Aa b
whereb b b ... b are the total flow measurements, a a a ... a are
1 2 M 1 2 M
the unknown individual yields per cow, and the (j, i) element of the matrixA is
y (r T ;t ,t ), which is determined from the recording start and finish time of milking
i j i 1i 2i
for each cow (and it is assumed that t 0.38t ).
1i 2i
It should be noted that T (the time of commencement of milking for an individual animal)
and t (the length of the milking event for the individual animal) are two further inputs to
the algorithm.
Therefore, a matrix system can be constructed for any one day (e.g. the m day) of
milking using Equation 5, which is as follows:
A a b
James & Wells ref: 133523/47
This assumes that the order that the cows are milked is different each day, which has
been verified.
A block matrix system over multiple days can then be constructed using Equation 6, as
follows:
m n m n
A a b
m n m 1 m n m 1
where A and b .
... ...
m n m n
Equation 6 can be readily solved by weighted least squares, where the weights relate to
the accuracy of flow measurement (simulations were conducted with n=3). The solution
to this is Equation 7, which is:
m n 1 m n m n 1 m n
a ˆ A V A A V b
m m m m
where V is the measurement variance-covariance matrix for the total flow. This can
then be used to calculate an estimate of the milk yield, in litres, using Equation 8:
Y a t t
i i 2i 1i
The outputs of the algorithm in this instance are values a , and for milk yield for
individual cows, Y .
The variance-covariance matrix for the estimated parameter a may be calculated using
Equation 9:
a m n 1 m n
M A V A
where the variance in a is M .
i ii
James & Wells ref: 133523/47
Changes in the milk yield of individual cows over a milking season can then be tracked
using a Bayesian filter. In the simplest case, a Kalman filter can be employed using
a ,M , an initial prior estimate of milk yield (assumed to 10 ± 3 litres in simulations) and
an estimate of the between day variance in milk yield (known to have a coefficient of
variation of around 15%). A state transition model can also be incorporated in the
Kalman filter to describe the milk lactation curve (expected change in milk yield between
days over a year).
Simulated total flow is calculated by Equation 10 as follows:
b(t) (t)b(t)
where represents the amplitude of the perturbations; and (t) denotes normal white
noise, which is a Gaussian distributed process with zero mean and independent
increments. For simulations =0.07, i.e. the assumption for errors in the milk flow
meter measurement is 7%.
Referring back to Figure 2, it should be noted that there is a drop in milk flow from 60
minutes to 80 minutes and 140 minutes to 150 minutes. This is indicative of the change
over in the milking shed as one group of cows exit the shed and another group enters.
Although not essential, by careful management of each group, i.e. keeping each group
separated for a period of days, the accuracy of the method can be improved over a
shorter period of time. It should be noted that an error of 7% is assumed for the milk
meter.
Figure 3 is a graph comparing the estimate and measured morning milk yield (in
kilograms) of a particular animal (cow 1) of the Tokanui herd over a 100 day period
beginning 1 January 2011.
It will be seen that over time the estimated yield (indicated by line) tracks the measured
James & Wells ref: 133523/47
yield (indicated by x). This assumes an initial morning milk yield of 10±3kg on day 0 of
the trial. Error bars denote standard errors for the estimate.
Figure 4 shows the relationship between total milk yield (measured in kilograms per
cow) over the four month period predicted using the present method (y axis) against the
measured milk yield (x axis, also in kilograms per cow) from the Tokanui herd. The
correlation between the predicted milk yield and the measured yield data was r=0.91.
Figure 5 shows the relationship between milk yield estimated using conventional
methods (in kilograms, horizontal axis) against the measured milk yield (in kilograms,
vertical axis) from the Tokanui herd.
The correlation between the milk yield predicted using standard Livestock Improvement
Corporation (LIC) herd testing (www.lic.co.nz/lic_Herd_Testing.cfm), based on an
average of four tests for each animal in the Tokanui herd during a lactation period of
120 days, and the measured yield data across the same lactation period is r=0.88. This
assumes that the Tokanui milk meters and herd test milk meters are of comparable
accuracy.
The method (600) that tracks the change in milk yield of individual animals over time
may also be exemplified in a basic form by the flow chart illustrated in Figure 6.
Prior to starting the process, in a preliminary step (601) m is set with the value 1 (day 1)
and n, the number multiple days, is selected (in the described simulations, n=3). The
first step (602) is to obtain an initial prior estimate of milk yield for each cow based on
weight, age and previous performance.
The second step (603) is to retrieve the individual identifier associated with the animal,
the time of start of milking for the animal (T ), the duration of the milking event (t ), and
i 2i
the bulk milk flow (b (t)) for each cow milked on days m, m+1,…, m+n after the start of
James & Wells ref: 133523/47
milking.
In the third step (604), the milk yield for each animal, and the variance thereof, may then
be estimated using Equations 1 to 9 as discussed above. The measurement variance-
covariance matrix V is specified by the accuracy of the bulk milk flow meter.
To track the change in the milk yield of individual animals over these m days (based on
an estimate of the between day variance in milk yield and the expected milk lactation
curve), a Kalman/Bayesian filter may be used in the next step (605). The process is
then repeated by setting m as m+n+1 in step 606 and performing steps 603 through to
605.
Thus, the present invention offers more accurate estimates for milk yield relative to
conventional methods for estimating milk yields. However, the use of the invention is not
limited to estimation of milk yields.
Figure 7 is a graph showing the relationship between milk electrical conductivity (EC)
predicted using the method of the present invention (horizontal axis) against the
measured average milk electrical conductivity (vertical axis), again demonstrated using
a herd from Tokanui of 923 cows. This is based on EC measured in the common milk
line. In this instance, the correlation between measured electrical conductivity and
predicted milk conductivity was r=0.77.
This is comparable to the relationship (r=0.83) between electrical conductivity per cow
estimated using herd testing (horizontal axis) against measured electrical conductivity
(vertical axis), as depicted in Figure 8, over the same four month period for the same
Tokanui herd.
This is based on morning herd tests carried out on day 0, 60 and 120 and also assumes
that the meters used on the dairy farm and by the herd testers are of comparable
accuracy.
James & Wells ref: 133523/47
Figure 9 shows estimated milk electrical conductivity (indicated by line) versus
measured milk electrical conductivity (indicated by x) for one cow of the Tokanui herd
over a 120 day period. It will be seen that the standard error bars are at their greatest
for the early milking events but gradually decrease as the estimates of electrical
conductivity becomes more accurate.
Simulations have also been performed to test the method for assessing other properties
of milk, on data collected from a herd of 347 dairy cows being milked in a rotary shed of
34 stalls at LIC’s Innovation Farm in New Zealand over a four month period beginning 4
July 2013.
Milk composition can be treated similarly where it is assumed that the individual milk
yields are measured (T ,a ,t ,t ) for each cow but the milk composition ( f ) of
i i 1i 2i i
individual cows is not measured. In this case the measured composition of the total flow
by the bulk composition sensor is expressed as the linear system (Equation 11):
y (t T ;a ,t ,t )
i i i 1i 2i
c(t) f
b(t)
i 1
In this instance, the inputs are T , t , a , t and c(t). These can be measured by milk
i 1i i 2i
meters or estimated using one of Equations 1 to 9 as previously discussed.
The procedure outlined with respect to Equations 4 to 9, as discussed above with
respect to milk yield, can then be used to solve for the milk composition ( f ) of individual
animals in Equation 11.
The simulated milk composition of the total flow is calculated using Equation 12 as:
c(t) (t)c(t)
James & Wells ref: 133523/47
where: represents the amplitude of the perturbations; and (t) denotes normal white
noise, which is a Gaussian distributed process with zero mean and independent
increments. For simulations, =0.025 (i.e. the milk composition measurement error is
2.5%).
The step-by-step process (1000) for estimation of milk composition, as exemplified by
the basic flow chart of Figure 10, is similar to that used for estimation of milk yield (as
described with respect to Figure 6).
Prior to starting the process in preliminary step 1001, m is set with the value 1 (day 1)
and n, the number of multiple days is selected (as previously, n=3). The first step
(1002) is to obtain an initial prior estimate of a particular characteristic of interest for the
milk of each cow based on weight, age and previous performance.
The second step (1003) is to retrieve the individual identifier associated with the animal,
the time of start of milking for the animal (T ), the duration of the milking event (t ), and
i 2i
the rate of milk production (a ) for each cow milked on days n, n+1,…, n+m after the
start of milking. This may be measured directly, using the appropriate individual milk
metering equipment or estimated using the bulk milk algorithm (Equations 2 to 9).
In the next step 1004, the milk composition for each animal, and the variance thereof,
may then be estimated using Equation 11 as defined above. The accuracy of the
metering equipment measuring the composition of interest will determine the
measurement variance-covariance matrix V.
A Kalman/Bayesian filter is then used in the fourth step 1005 to monitor any changes in
the milk composition of individual cows over the number of days. This is based on an
estimate of the between day variance in milk composition and the expected changes in
milk composition during lactation. The process is then repeated by setting m as m+n+1
in step 1006 and performing steps 1003 through to 1005.
James & Wells ref: 133523/47
It will therefore be appreciated that the present invention offers more accurate estimates
for milk composition relative to conventional methods for estimating milk composition.
Figure 11 is a graph showing the simulated measured percentage of fat in the milk flow
into the milk vat for a specific milking event for the LIC herd (based on a 2.5% sensor
and recorded milking times, % fat and yield per cow). As with Figure 2, there is
considerable variation in the milk flow, including a disruption about 118 minutes into the
milking event. These variations are indicative of the start and cessation of the milking
event for specific cows. Noting the time at which specific cows start and stop milking
over a series of milking events and measuring of percentage fat at any given time can
lead to the development of a profile of the milk of each animal in the cohort.
Turning to Figure 12, this is a graph for percentage fat in the milk of one animal (cow 1)
of the LIC herd (which numbered 347 animals), measured (indicated by x) and
estimated (indicated by line) with the present invention, over a milking period of 120
days beginning 4 July 2013. For the first 20 days, cow 1 was not milked at all. Upon the
first milking event the error bars, denoting standard errors, increase but then become
reduced over time. This is based on an initial guess of 4.8±1.1 % fat on day 0.
There is a greater correlation (r=0.70) between the percentage milk fat per cow
predicted (horizontal axis) with the present invention (based on % fat in the common
milk line) and measured percentage milk fat per cow (vertical axis) for the 347 animal
herd over the 120 day period (Figure 13) than the percentage fat per cow estimated
(horizontal axis) using the two-monthly LIC herd testing for the same herd, which is
r=0.58 (Figure 14). This assumes that the metering equipment used on-site and herd
test meters are of comparable accuracy.
Aspects of the present invention have been described by way of example only and it
should be appreciated that modifications and additions may be made thereto without
departing from the scope thereof as defined in the appended claims.
James & Wells ref: 133523/47
Claims (30)
1. A method of using a system of determining milk characteristics for individual animals within a cohort of milking animals, wherein the system is for use in a milking shed, wherein the milking shed includes a milk vat, a plurality of milking stalls, wherein milk from each stall flows into a common milk line to the milk vat, the system including: a processor, and at least one milk characteristic sensor positioned in the common milk line and configured to output a signal indicative of at least one milk characteristic of the milk, the method characterised by the steps of: a) recording individual identification of animals present at a milking shed for a milking event; b) recording the timing of the milking event of individual animals; c) measuring at least one milk characteristic of milk collected from the cohort of milking animals; d) correlating the identification of an individual cow with the timing of the milking event to develop a profile of one or more milk characteristics for the individual animal.
2. The method of claim 1, wherein the method includes the additional step of: e) repeating steps a) to d) to develop a profile of milk characteristics for the individual animal over a period of time.
3. The method of either claims 1 or 2 wherein the animals are assigned an individual identifier.
4. The method of claim 3 wherein the individual identifier is a RFID ear tag.
5. The method of any one of claims 1 to 4 wherein the milking event is determined as being the length of time between entry and exit of the individual animal from the milking shed.
6. The method of any one of claims 1 to 4 wherein the milking event is determined as being the length of time between entry and exit of the individual animal from a milking stall of the milking shed.
7. The method of any one of claims 1 to 4 wherein the milking event is determined as being the length of time between application and removal of teat cups from the individual animal.
8. The method of any one of claims 1 to 7 wherein the milk characteristic is milk yield.
9. The method of any one of claims 1 to 7 wherein the milk characteristic is milk electrical conductivity.
10. The method of any one of claims 1 to 7 wherein the milk characteristic is indicative of the composition or quality of the milk of the individual animal.
11. The method of claim 10, wherein the milk characteristic is selected from one or more of the following: percentage of fat, percentage of lactose, percentage of casein, or percentage of protein.
12. The method of any one of claims 1 to 7 wherein the milk characteristic is indicative of the health of the individual animal.
13. The method of claim 12 wherein the milk characteristic is selected from one or more of the following: metabolites or somatic cell count.
14. The method of any one of claims 1 to 13 wherein the animals are dairy cows.
15. The method of any one of claims 1 to 14 wherein the cohort of milking animals is a dairy herd.
16. A system of determining milk characteristics for individual animals within a cohort of milking animals, wherein the system is for use in a milking shed, wherein the milking shed includes a milk vat, a plurality of milking stalls, wherein milk from each stall flows into a common milk line to the milk vat, the system including: a processor, and at least one milk characteristic sensor positioned in the common milk line to receive milk collected from the cohort of milking animals, and configured to output at least one signal indicative of at least one milk characteristic of the milk, the system characterised in that the processor is configured to: a) record individual identification of individual animals present at the milking shed for a milking event; b) record the timing of the milking events of individual animals; c) receive the signal from the milk characteristic sensor and determine a milk characteristic of milk collected from the cohort of milking animals; d) correlate the identification of an individual cow with the timing of the milking event to develop a profile of one or more milk characteristics for the individual animal.
17. The system as claimed in claim 16, wherein the processor is configured to: e) repeat steps a) to d) to develop a profile of milk characteristics for the individual animal over a period of time.
18. The system as claimed in either claim 16 or claim 17 wherein the processor includes a recording device.
19. The system as claimed in claim 18 wherein the recording device is a RFID ear tag reader linked to a data logger.
20. The system as claimed in either claim 18 or claim 19 wherein the recording device is associated with entry and exit points of the milking shed.
21. The system as claimed in either claim 18 or claim 19 wherein the recording device is associated with the milking plant.
22. The system as claimed in any one of claims 16 to 21 wherein the system includes an individual identifier for the animals.
23. The system as claimed in claim 22 wherein the individual identifier is a RFID ear tag.
24. The system as claimed in any one of claims 16 to 23 wherein the milk characteristic sensor is a milk flow meter.
25. The system as claimed in any one of claims 16 to 24 wherein the milk characteristic sensor is configured to sense a property of the milk that is indicative of composition or quality of the milk of the individual animal.
26. The system as claimed in any one of claims 16 to 24 wherein the milk characteristic sensor is configured to sense a property of the milk that is indicative of the animal’s health.
27. The system as claimed in claim 26 wherein the milk characteristic sensor is a somatic cell count sensor.
28. An article of manufacture having computer storage medium storing computer readable program code executable by a computer to implement the method as claimed in claim 1, the code including: computer readable program code recording individual identification of animals present at a milking shed for a milking event, wherein the milking shed includes a milk vat, a plurality of milking stalls, wherein milk from each stall flows into a common milk line to the milk vat; computer readable program code recording the timing of the milking event for individual animals; computer readable program code receiving an indication from at least one milk characteristic sensor positioned in the common milk line of at least one milk characteristic of milk collected from the cohort of milking animals; and computer readable program code correlating the identification of an individual animal with the timing of the milking event to develop a profile of one or more milk characteristics for the individual animal.
29. A method of determining milk characteristics for individual animals within a cohort of milking animals substantially as herein described in the Best Modes and with reference to the accompanying figures.
30. A system of determining milk characteristics for individual animals within a cohort of milking animals substantially as herein described in the Best Modes and with reference to the accompanying figures.
Publications (1)
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