NZ623007B2 - Method and device for determining greenhouse gas emission from a ruminant - Google Patents
Method and device for determining greenhouse gas emission from a ruminant Download PDFInfo
- Publication number
- NZ623007B2 NZ623007B2 NZ623007A NZ62300712A NZ623007B2 NZ 623007 B2 NZ623007 B2 NZ 623007B2 NZ 623007 A NZ623007 A NZ 623007A NZ 62300712 A NZ62300712 A NZ 62300712A NZ 623007 B2 NZ623007 B2 NZ 623007B2
- Authority
- NZ
- New Zealand
- Prior art keywords
- eating
- ruminant
- greenhouse gas
- moments
- determining
- Prior art date
Links
- 239000005431 greenhouse gas Substances 0.000 title claims abstract description 70
- 241000282849 Ruminantia Species 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 48
- 241001465754 Metazoa Species 0.000 claims description 55
- 230000006870 function Effects 0.000 claims description 51
- 230000022676 rumination Effects 0.000 claims description 20
- 208000015212 rumination disease Diseases 0.000 claims description 20
- 238000005259 measurement Methods 0.000 claims description 14
- 239000007789 gas Substances 0.000 claims description 12
- 239000012141 concentrate Substances 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 8
- 244000144980 herd Species 0.000 claims description 6
- 235000013325 dietary fiber Nutrition 0.000 claims description 5
- 244000025254 Cannabis sativa Species 0.000 claims description 4
- 230000020595 eating behavior Effects 0.000 claims description 4
- 240000008042 Zea mays Species 0.000 claims description 2
- 235000016383 Zea mays subsp huehuetenangensis Nutrition 0.000 claims description 2
- 235000002017 Zea mays subsp mays Nutrition 0.000 claims description 2
- 230000001055 chewing effect Effects 0.000 claims description 2
- 235000009973 maize Nutrition 0.000 claims description 2
- 230000000284 resting effect Effects 0.000 claims description 2
- 239000004460 silage Substances 0.000 claims description 2
- XOOUIPVCVHRTMJ-UHFFFAOYSA-L zinc stearate Chemical compound [Zn+2].CCCCCCCCCCCCCCCCCC([O-])=O.CCCCCCCCCCCCCCCCCC([O-])=O XOOUIPVCVHRTMJ-UHFFFAOYSA-L 0.000 claims 1
- 241000283690 Bos taurus Species 0.000 description 19
- 230000000694 effects Effects 0.000 description 14
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 12
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 8
- 235000013365 dairy product Nutrition 0.000 description 7
- 229910002092 carbon dioxide Inorganic materials 0.000 description 4
- 239000001569 carbon dioxide Substances 0.000 description 4
- 230000029087 digestion Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 235000005911 diet Nutrition 0.000 description 2
- 230000037213 diet Effects 0.000 description 2
- 150000002500 ions Chemical class 0.000 description 2
- 241000282465 Canis Species 0.000 description 1
- 241000283707 Capra Species 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 229910001423 beryllium ion Inorganic materials 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 235000014113 dietary fatty acids Nutrition 0.000 description 1
- 229930195729 fatty acid Natural products 0.000 description 1
- 239000000194 fatty acid Substances 0.000 description 1
- 150000004665 fatty acids Chemical class 0.000 description 1
- 210000003608 fece Anatomy 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 210000001035 gastrointestinal tract Anatomy 0.000 description 1
- 230000008571 general function Effects 0.000 description 1
- 230000007614 genetic variation Effects 0.000 description 1
- 230000006651 lactation Effects 0.000 description 1
- 239000010871 livestock manure Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000007620 mathematical function Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 235000013336 milk Nutrition 0.000 description 1
- 239000008267 milk Substances 0.000 description 1
- 210000004080 milk Anatomy 0.000 description 1
- 238000009304 pastoral farming Methods 0.000 description 1
- 239000000700 radioactive tracer Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K29/00—Other apparatus for animal husbandry
- A01K29/005—Monitoring or measuring activity, e.g. detecting heat or mating
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/483—Physical analysis of biological material
- G01N33/497—Physical analysis of biological material of gaseous biological material, e.g. breath
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/40—Data acquisition and logging
-
- G06F19/00—
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C11/00—Arrangements, systems or apparatus for checking, e.g. the occurrence of a condition, not provided for elsewhere
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W90/00—Enabling technologies or technologies with a potential or indirect contribution to greenhouse gas [GHG] emissions mitigation
Abstract
method for estimating a greenhouse gas emission from a ruminant (6) in a loose housing environment (1), and in a predetermined time period from T0 to Tdesired, characterized in that the method comprises: - determining a model emission rate function EM(t) for the ruminant, - determining the moments of eating feed by the ruminant, at least during the predetermined time period, as a series of points in time {T1, T2, T3,..., Tn}, - constructing the estimated real emission rate function ER(t) on the basis of the model emission rate function and the moments of eating feed, and - integrating ER(t) from T0 to Tdesired. of eating feed by the ruminant, at least during the predetermined time period, as a series of points in time {T1, T2, T3,..., Tn}, - constructing the estimated real emission rate function ER(t) on the basis of the model emission rate function and the moments of eating feed, and - integrating ER(t) from T0 to Tdesired.
Description
Method and device for determining greenhouse gas emission from a ruminant
The invention relates to a method for estimating a greenhouse gas
emission from a ruminant in a loose housing environment, and in a predetermined
time period from T0 to Tdesired, and to a greenhouse gas emission estimation
device for estimating a greenhouse gas on from a ruminant, according to
said method.
Enhanced greenhouse gas (GHG) emissions are deemed to be a
cause of an enhanced greenhouse effect. Emissions by dairy s are believed
to contribute a large part of the enhanced greenhouse gas emissions, not only via
manure but also, and ed mainly, via tion of methane. Therefore, it is
important to know the emission of such ouse gases in dairy animals. For
knowing the emissions is a necessary first step of controlling and reducing such
emissions.
In the art, a lot of research has been done to try and determine the
GHG on of individual dairy animals in a controlled environment, such as
completely sealed cabins, in which the complete emission of the animal could be
measured and analyzed. However, in a normal dairy environment, useful models
are scarce and mainly ct such as through fatty acid profiles from milk.
It is an object of the present invention to provide a useful model for
the determination of greenhouse gas emissions of dairy animals in their normal
environment, in particular in a loose housing environment.
Alternatively, it is an object of the invention to provide the public with
at least a useful choice.
The invention provides a solution for either of these objects with the
method of claim 1, that is characterized in that the method comprises: determining
a model emission rate function EM(t) for the ruminant, ining the moments of
eating feed by the ruminant, at least during the predetermined time period, as a
series of points in time {T1, T2, T3, ..., Tn}, constructing the estimated real
emission rate function ER(t) on the basis of the model emission rate function and
the moments of eating feed, and integrating ER(t) from T0 to ed. In this way,
the various eating moments are taken into account as regards their effect on the
on. The invention's insight is to apply a fresh "start", each time that the
animal eats, and to determine the total as the sum over a consecutive series of
[Followed by page 1a]
such fresh starts. The inventors' work indicates that a useful approximation of total
on can be obtained with such a model. The inventors have found that each
moment
[Followed by page 2]
[Followed by page 2]
of eating starts a new "cycle" of GHG emissions, which has an effect on total
emissions in a certain time period. For example, in case there are more eating
moments in that time period, there is very likely also an increased output of GHG,
and vice versa.
It is noted, that the model emission rate function should be taken to have a peak
value some time after the eating moment. This reflects the fact that, when new
feed is eaten, the digestion that is to produce GHG will need some time to do so.
This will work to a peak, after which the gradually decreasing amount of feed still
to be ed will cause a decrease of the emitted GHG.
Advantageous or otherwise l ments are mentioned in
the dependent claims.
In embodiments, the function ER(t) is constructed as
ER(t) = EM(t - T1 - C1) for T1St<T2, EM(t - T2 - C2) for T25t<T3, EM(t - Tn-1)
for Tn-1St<Tn, and EM(t - Tn - On) for TnSt, n Cl, CZ Cn are constants.
In this way, after each eating moment, a similar shape of the emission curve is
constructed, as the emission mechanism, stemming from the particular animal,
does not change itself. One could also say that n each set of subsequent
eating moments, a new emission curve is set up, but in each case based on the
same basic curve shape. The nts serve to adapt the curve parts to specific
circumstances or approximations, as will be ned further below.
In a particular embodiment, all constants are zero. In this
approximation, each previous curve is simply cut off at a new eating moment, and
the curve starts anew from zero emission at that new eating moment. This is a
very simple imation, and easily implemented mathematically. Still, however,
especially since in practice most curves will show a peak emission very shortly
after the eating moment, this is already a useful approximation.
In other embodiments, all constants 01 Cn are determined such
that the function ER(t) is a continuous function. This holds in particular for the
eating moments, i.e. the transition points. In practice, the emission function will be
a continuous function, just like any other physical quantity. Therefore, an
imation that will take this into account can be a better approximation. In this
embodiment, a us curve part will have a certain value at a certain time after
its corresponding starting eating moment. In this ment, it is assumed that
the curve part for the subsequent interval, i.e. after the following eating moment,
will start at that same value. Note that this still leaves two ilities, one before
the peak value, and one after the peak value. It is assumed that the value before
the peak is taken, g to a corresponding value for the corresponding
constants C1, CZ,
The above shows just a number of possible embodiments of the
general idea of the invention. Others could be to simply add a new curve to the
existing one. In other words, all emission curves started before a new eating
moment are allowed to go on indefinitely, while the value for a new curve, starting
at the new eating moment, is added for each time after that new eating .
Therefore, in an additional embodiment, the function ER(t) is constructed as
ER(t):ZEM(t—Ti) for all eating moments T1, Tn relevant for the desired
1'21
period of time T0 - Tdesired. Note that this encompasses the case that even T1 is
after TO, leaving the first time part from T0 to T1 open to some uncertainty. It also
encompasses the case of one or more eating moments T1, T2, before TO. The
relevant time frame to be taken into account before TO depends on how quickly
the function EM(t) falls to zero. If that is a period Tfall, then ably all eating
moments up to Tfall before TO should also be taken into account, as the first
eating s T1, T2, However, if that time is only a small part of the total
period of time T0 - Tdesired, it may safely be ignored.
Note that the step of integrating is deemed to encompass all
mathematical equivalents thereof, among others the approximation of summing a
number of points times the width of the relevant intervals et cetera. Also note that
it is necessary for the most precise tion, to determine all moments of eating.
However, it is possible to miss one or more moments, allowing for a less precise
approximation. Also, an estimate between T0 and T1 for TO<T1 is not possible,
and is left out. In such a case, some (more general) approximation for the first
interval should be made, or the time and eating moments should be selected such
that this situation does not occur. Note that it is also possible in the present model
to have more than one measurement of GHG emissions between two eating
s. Such apparently additional measurements then serve to make the
model more precise.
In particular embodiments, the feed is ration, ning roughage
such as grass, hay, silage, maize, and optionally containing concentrates. Herein,
the concept of "ration" is deemed to se the so-called TMR, or total mixed
ration, as well as PMR, or partial mixed ration. All these rations se
roughage, while some also comprise concentrates. It is believed by the inventors
that these non-"concentrates only" feeds provide the main GHG emissions.
Concentrates only feeds, such as given in for e milking parlours or
ted concentrate feeding parlours, are to be excluded. It is believed that
"concentrates only" feeds will have an effect on the total GHG emissions, but what
that effect is, is not yet known.
In ning the invention above, a part comprises determining a model emission
rate function for the animal. In embodiments, determining the model emission rate
function comprises measuring a greenhouse gas on rate at a plurality of
measurement points in time TMO, ,TMn, providing tive values EMO, ,
EMn, establishing for each of said measurement points in time TMO, TMn the
most recent feeding moment TFO, TFn before the respective measurement
point in time, and determining the function ER(t) by fitting a mathematical curve
that fits h the sets of values ((TMO-TFO), EMO), ln
, ((TMn-TFn), EMn).
itself, the mathematical technique of fitting a curve to s points is well-known.
However, according to the present invention, it is understood that all measured
points can be related to a single curve, the (model) emission rate function. As said
above, this is based on the insight that, at least on average, the GHG mission for a
dairy animal will always show the same our after eating. Therefore, it does
not matter when the GHG emission (rate) is measured, as long as it is measured.
As a remark to be made here, the inventors believe that the actual amount of feed
consumed may have an influence on the production of GHG. However, to the
inventors' knowledge, there is not known a clear description of any mathematical
connection between amount of feed and GHG emissions.
In an important further development of the invention, the method
further comprises repeating the steps of - measuring at an additional point in time
Tadd said greenhouse gas emission rate EMadd, and - redetermine the function by
adapting and refitting the mathematical curve h one of the last X sets of
values of TM and EM, wherein X is a predetermined number, and all sets of values
of TM and EM. The former case represents something of a rolling e
emission rate function, while the latter represents a more general average
function. With this embodiment, it is possible to update the emission rate function
to new . This not only allows an improved accuracy, but also allows dynamic
adaptation of the function/the curve to changes in the , such as might follow
from development of the body of the animal, e.g. maturing or its lactation cycle, or
a change in diet, seasonal changes and so on.
In a particular embodiment, in the step of redetermining the on,
one or more ermined constraints are applied, comprising predetermined
rules with respect to the relative and/or absolute change of one or more
coefficients used in the function. In this embodiment, care is taken that the
function, i.e. the model emission rate function EM(t), does not change too wildly. A
particular approach to applying such constraints is the so-called DLM method, or
dynamic linear modeling. For an e, reference is made to EP2154952,
relating to a DLM model for feeding dairy animals. More in particular, this example
is based on Bayesian rules for adapting the model. Such Bayesian rules may also
advantageously be applied as the constraints to be applied in the model of the
present invention.
In the present invention, use is made of a standard curve for
emission EM(t) for each . This is a first approximation. In a particular further
development, the method is refined in that the mathematical curve is selected from
a set of standard curves depending on the type or race of animal, and wherein the
fitting comprises multiplying the curve by a constant. This allows the following
advantage. Although it is assumed that the basic shape of the emission function
EM(t) is basically the same for all animals at least of a n type or race, there
may be ions within such a type or race of animals. For example within the
race Holstein-Friesian, there may be a small or extra large animal. This might
simply be the result of age, or of genetic variation in the final ions.
heless, this embodiment assumes that the basic curve may then be scaled
up or down by a nt. This constant is to be determined from a number of
measurements. For example, the first 3 - 10 measurements are used to determine
the constant (factor) for scaling the standard curve for Holstein-Friesian cows,
selected from the set of standard curves. This can be done by fitting those
ements to the rd curve, with the scaling factor is the variable for
fitting. The resulting fitting factor, applied to the standard curve, then gives the
"adapted standard curve" for that specific animal, which will lead to more
accuracy. Of course, other ways of fitting to the standard curve are not excluded,
such as a shorter or longer time delay between eating moment and peak of the
emission curve.
One of the steps in the method is determining the moments of eating
of the feed. In embodiments, determining the s of eating feed ses
determining the moments when at least one of the ing conditions is met: new
ration, excluding concentrates only feed, is provided to the nt; feed is
displaced towards a feed fence at which the ruminant is allowed to feed; the
ruminant is provided access to a pasture. Herein, the choice is made that the
eating moments are the starting s. It may also be argued that the
moments of eating should be the average of the period of time during which the
condition is met, or a predetermined time after the first moment the condition is
met. Although the ors made a choice for the first moment, this is done more
out of practicality than for other specific reasons. For GHG emissions do not start
to increase at the very moment of the ng to eat, as they at least require
digestion down the digestive tract. It should be noted, therefore, that the
abovementioned alternative moments are also deemed to be within the scope of
the present invention.
In particular embodiments, determining the moments of eating
comprises identifying the ruminant at a feeding place and determining that said
ruminant is eating, by means of a eating sensor. In some barn layouts, there are
specific individual feeding stations for feeding ration. As these provide dually
adapted s to the animals, they are ideally suited to provide specific
information on eating moments. By the way, such individual feeding stations are
also ideal to determine GHG emission, since there is no interference from other
animals. Note that the emission is deemed to be caused by us eating. All
this will be ned further below.
In some embodiments, the eating sensor comprises a camera with
image processing software for recognizing eating behaviour, a feed weight sensor
arranged to determine a weight change in a feeding device, and/or a microphone
with sound processing software for recognizing eating behaviour. These have
proved useful ways of determining when an animal eats. For example, if the image
processing software ishes that the animal repeatedly puts its mouth where
the feed is, it is safe to assume that it is eating. If the, commonly known, feed
weight sensor establishes a change in the feed weight, the animal must have
eaten. As to the microphone with sound processing software, reference is made to
EP1301068, which discloses a sensor able to distinguish between ruminating and
eating. Thereby, eating actions may be established.
In embodiments, determining the moments of eating comprises
determining when the ruminant produces eating sounds by means of a sound
sensor, in particular provided on or near the neck and/or mouth, that is ed to
detect sounds produced by the ruminant when moving its mouth in particular for
chewing. This is a at more particular ment as mentioned before.
Again, reference is made to EP1301068 for more particulars. Also, as already
mentioned above, when determining the moments of eating, rumination sounds
are to be excluded, as these are not believed to have a major influence on GHG
ons.
In particular, it is possible to include detecting whether the ruminant
is standing up or lying down, and neglecting moments/periods with sounds
produced when lying down, more in particular detecting whether the head of the
ruminant is d to the ground, and neglecting moments/periods with sounds
ed while the head is not lowered during at least a predetermined time. All
this serves to select the s of actual eating, and leaving out other activities.
Herein, it is assumed that it is relatively rare that the animal ruminates while
standing up, and to further exclude this situation, which is not impossible, it is
assumed that the combination of standing up, ruminating and the head s
the ground is really to be ted. In other words, if the animal es eating
sounds while standing up and the head is down, this is deemed definitely eating.
In other embodiments, the method comprises that, when determining
the moments of eating, only rumination sounds are detected, and the moments of
eating are determined to be the moments of a local minimum in rumination
. Again, the rumination sensor as disclosed in e.g. EP1301068 may be
used to determine periods when the animal ruminates. It was found a good
approximation of the eating moments to take the time of minimum rumination
activity, inbetween periods of rumination. This embodiment is not applicable in
loose housing systems, but in particular also in systems where, at least during part
of the day, but also during longer periods before returning to the housing, the cows
graze on pastures. On pastures, it is more difficult to determine exact eating
moments. However, by using a rumination sensor, it is still le to determine
the moments by the method just described. Note that such rumination data may be
sent to a central computer, by Bluetooth(tm) or other transmitting devices.
In advantageous embodiments, the method comprises:
- providing a herd having a plurality of ruminants
- providing one or more ruminant ID devices, at least at one or more
ons for feeding the ruminants and at one or more positions for measuring a
greenhouse gas emission rate and arranged to identify the ruminant and provide
an ID signal
- performing the method for each of the ruminants in the herd, and
- providing a total greenhouse gas on in the ermined time period
as the sum of the greenhouse gas emissions from each of the ruminants.
With this embodiment, the GHG emissions may be determined for a whole herd,
instead of just one animal. It is understood that for each animal it is possible to
take the same standard curve EM(t) for constructing the individual curve ER(t), but
it is of course also possible, and more te, to use individualized curves as
described further above. By thus knowing the total GHG emissions of the herd,
le effects of diet or othenNise may be studied. This knowledge may be used
to see if it is possible to reduce the total GHG emissions.
In the present method, the animal can be any nt animal, such
as oes, goats, and so on. In particular however, the ruminant is a cow.
Furthermore, the greenhouse gas may be any gas that is emitted by the animal
that has an effect on the enhanced greenhouse effect, such as carbon dioxide and
methane. In particular, however, the GHG in the present method is methane, as
this is not only a gas with a much bigger relative effect on the enhanced
greenhouse , with a global warming potential of 25 times that of carbon
e, over a period of 100 years, but it is rmore more ly related to
digestion, thereby providing more direct information thereabout. Contrarily, carbon
dioxide is also, and maybe predominantly, related to burning bodily fuel. Thus any
information from carbon dioxide relating to digestion should be filtered out, which
is not an easy step.
The invention also relates to a greenhouse gas emission tion
device for estimating a greenhouse gas emission from a ruminant, according to a
method of any of the preceding claims, the device comprising:
- at least one greenhouse gas emission rate sensor, arranged to provide a
greenhouse gas emission rate signal
- a clock device arranged to provide a time signal each time when said
ruminant eats and each time when the at least one greenhouse gas emission rate
sensor measures a greenhouse gas emission rate signal
- a control unit arranged to apply the method of an y preceding claim, and on
the basis of the measured time signals and greenhouse gas emission rate signals.
This device s the method of the present invention, with a principal advantage
of supplying useful ation on (total) emission of GHG in a simple way.
In embodiments, each greenhouse gas emission rate sensor
comprises or is provided together with a nt ID device arranged to establish
the ID of a nt upon visiting the sensor. This is useful for coupling the
measured emission to a particular animal, especially in an environment where
there are more animals.
Advantageously, the device ses a feeding unit with an animal
ID station arranged to identify the ruminant upon g, the control unit being
arranged to s the corresponding time signal together with the animal ID.
Herein, the ID station identifies the animal and couples a clock signal, i.e. eating
moment signal, to the ID. Note that a similar signal may be ed otherwise,
such as with a feed fence with an ID station, or a feed fence with a feed pusher
having a control with a clock. Herein, when the feed pusher pushes feed towards
the feed fence, according to e.g. a programmed route, it appears that the animals
start eating anew. Thus, an eating moment is deemed present upon the feed
pusher pushing feed.
In embodiments, there is provided a greenhouse gas emission
sensor with an animal ID station at one or more of a g place, at a feeding
place, such as a feeding box or feeding fence, and/or at a resting place. For the
present invention, there needs to be at least one GHG . Preferably, such
sensor is positioned in a way in which an animal is more or less separated from
the others. In this way, emissions from other animals will not, or to an acceptable
, interfere with the emission to be ed. Advantageously, a milking box
and/or feeding box comprise such a GHG emission sensor. Having multiple
sensors spread throughout the housing may be advantageous, as this may
provide a larger set of measurements during the relevant time period.
[Followed by page 9a]
Unless the context clearly requires otherwise, throughout the
description and claims the terms “comprise”, “comprising” and the like are to be
construed in an inclusive sense, as opposed to an exclusive or tive sense.
That is, in the sense of “including, but not limited to”.
[Followed by page 10]
[Followed by page 10]
Further aspects, embodiments and ages of the invention will
be apparent from the detailed description below of particular embodiments and the
drawings thereof, in which:
Figure 1 is a very diagrammatic drawing, not quite in perspective, of
a loose-housing system ing to the invention
Figure 2 is graph for determining the basic function EM(t) based on
measurements
Figure 3 is a graph for ining an individual function EM(t) for a
specific animal, again based on measurements; and
Figure 4 is a graph showing a ucted function ER(t), according
to the ion.
Figure 1 is a very diagrammatic drawing of a system 1 with a
housing 2 with a stable 3 and optional pasture 4 with gates 5 there between. Cows
are denoted by 6.
Reference numeral 7 indicates a milking robot with a gas sensor 8
and an ID device 9, all connected to a computer 10, here shown to comprise a
transceiver 11. Afeeding box 12 is shown, with gates 13, a feed trough 14, as well
as a gas sensor 8 and ID device 9.
A feed fence is indicated at 15, with feed 16 being pushed by a feed
pusher 17, also having a transceiver 18. Cubicles are indicated by 19, in one of
them a cow with an ID tag 20.
At the pasture, a cow is grazing, wearing a rumination sensor 21 and
a height sensor 22.
In the housing, an important part is the milking robot 7. Here, the
animals are milked and optionally fed concentrates. When visiting the milking
robot, the animals are identified with the ID device 9. Additionally, the milking robot
comprises a GHG sensor 8, in particular a methane , arranged to measure
(greenhouse) gas emission from an animal in the g robot 7. For examples of
such gas sensors, which are known per se, nce is made to e.g. document
/0192213, in particular e.g. paragraph 0018. Also, as is described in this
document, it is possible to monitor the ratio of carbon dioxide and methane in
order to obtain an indication for the absolute emission of methane. Note that it is
seen as an advantage when no bolus is used to determine GHG emission, such
as those using slow release of SF6 as a tracer gas. No bolus means a more
natural behavior.
A rly important part may be played by the feeding box 12. This
may similarly comprise an animal ID , as well as a gas sensor 8 as
described above. In the feeding box 12, roughage may be provided to an animal
when visiting and identified by the ID device 9. Not only does this serve to offer the
best ration available for the animal, but it also ensures that the eating moment is
recorded with high cy. This may be achieved through e.g. a weight sensor
(not shown) for the , that registers eating moments when the feed weight
diminishes. Simultaneously, GHG emissions are measured by the sensor 8, and
combined with a timing signal.
It is noted that the computer 10, as well as each ID device 9, will
have a clock device for recording moments in time.
The ation from both the milking robot 7 and the feeding box 12
are sent to a central computer 10, where it is processed according to the invention,
to be explained in connection with Figures 2-4.
In addition, there are provided cubicles 19 for cows 6 to lie down and
ruminate. Shown here is only one cow 6 in a cubicle 19. The cow carries an ID tag
, with which she is identified by the ID device 9. A local gas sensor 8 again
serves to determine a GHG emission from that cow. It is remarked here that it is
only al to provide so many gas sensors 8, as in principle one would do.
The feed fence 15 shown in the Figure 1 serves for the cows 6 to get
feed 16, in particular ration, such as hay or the like. There may be added some
concentrates. Also shown is a feed pusher 17, such as the Lely Juno(tm), that is
arranged to push the feed 16 towards the feed fence 15, Le. towards the cows 6. It
is remarked in practice that the pushing of the feed 16 is a trigger for the cows to
come to the fence 15 and eat. Therefore, if the fee pusher 17 pushes, this is
considered an eating moment, for all practical purposes. The feed pusher,
programmed to follow certain routes and during certain times of day, will send a
clock or timing signal to the central computer 10 via their respective transceivers
18 and 11, such as a Bluetooth(tm) connection or the like. The central computer
will handle that signal from the feed pusher as an eating moment.
There is also shown a pasture 4, with gates 5. If these gates are
tic, their opening, combined with a time , may be considered an
eating moment, as the cows being presented with fresh grass may for all practical
purposes be considered the start of eating said grass. Alternatively or additionally,
the cows 6 may be provided with a rumination sensor 21. If this sensor 21 is
arranged to discern eating sounds and rumination sounds, as disclosed in
EP1301068, then sending a signal whenever there is an eating moment suffices.
However, even in cases when only rumination sounds are detected, an eating
moment may be inferred, in particular by the central er to which the signals
may be sent, e.g. by Bluetooth(tm). For the computer may infer the periods of time
when there is minimal rumination activity, in between periods with high rumination
activity. In practice, the moments of minimal rumination activity can be taken to be
the eating moments. These are also stored by the er 10. Moreover, also
shown is a height sensor 22, arranged to determine whether the cow 6 is standing.
In combination with the rumination sensor 21, this may well distinguish between
eating and ruminating, as a cow will hardly ruminate when standing up, in
particular if the height sensor 22 and/or the rumination sensor 21 is onally
able to tell whether the cow has her head towards the ground.
Thereby, the total system 1 can ine the moments when an
animal eats, either at the roughage trough 14 or at the feed fence 15, and even in
the pasture 4. It furthermore establishes the GHG emissions of an animal (ID
controlled) at various moments in the milking robot 7, and/or the feeding box 12
and/or in the es 19.
With the help of the above data, the system determines a scatter plot
of GHG emissions versus time after an eating moment, in Figure 2. Note that this
figure s either to the data for one animal, or (less preferably) for one type of
animal. Next, on the basis of a selected basic model function, a mathematical
curve is fitted through the data (the dashed line). As a basic model function, one
could take e.g. EM(t) = P(t)-exp(-t) wherein P(t) is a polynomic function of a
desired order, such as a first or second order. A concrete example would thus be
EM(t) = 4-t"2-exp(-t). In any case, it should be a function beginning at zero, having
a m, and then slowly falling off to zero again, to reflect the natural course
of normal GHG emissions.
In an optional step, relating to Figure 3, this basic function EM(t),
which holds for example for a certain race of cows, such as Jersey or Holstein,
may be zed for an individual animal. Suppose that the general function EM(t)
has been established for an average animal of that type. This is shown in the
Figure 3 as the solid line, peaking at a value X (relative units). Now, for a particular
animal, a number of emission values have been established, shown as the five
circles. Then, a curve is fitted, that has the same shape, but is multiplied by a fixed
nt F. This individualized curve is shown as the dashed line, which thus
peaks at a value F-X. It is of course possible to use different fitting techniques to
establish the individual curve, but this suits fine. Note that F is shown to have a
value smaller than 1. It may of course also be larger than 1 for other s.
Then, in Figure 4, this function EM(t), optionally individualized, is
plotted for various time intervals, each starting and ending with subsequent eating
moments, such that a continuous mathematical function ER(t) is obtained.
Thereto, from a first eating moment T1, one arbitrarily starts from zero or a zero
measurement, and the function EM(t) is plotted from t = T1. This is cut off at t =
T2. At such time T2, the function for the first al will have a certain value V1.
Next, another function (again) EM(t) is plotted, now from the time T = T2 and
beginning at the value V1. Thereto, the function EM(t - T2) has been shifted over a
constant C2 to reflect the starting at a ro value. This comes down to the
constant mentioned in claim 4. This next function is cut off at T3, where the whole
procedure is repeated, and so on, until the point in time "Tdesired" has been
reached. The resulting function ER(t) can now be integrated over the desired
period TO through Tdesired. Obviously, this will often be a 24 hour period, but it
can be any desired period, as long as it is likely that sufficient data points can be
gathered in the time in between.
Note that for the construction method for ER(t) it follows that there is
always the same maximum value X. This ly unnatural behavior is not present
in adapted construction methods, still according to the invention. For e, it is
possible to not cut off the function EM(t - T1) after T2, but let it continue, and
simply add another function (EM(t - T2) on top of it from T2, and so on. Then the
peaks will be higher, up to a maximum, when the first on EM(t - T1) has
fallen to zero sufficiently. Furthermore, the invention also asses a c
(re)determination of EM(t) when new data are added. This may also ensure that,
according to the method bed for Figure 3, the peak height may be adapted
to these new data, providing for more accuracy.
Claims (23)
1. A method for estimating a greenhouse gas emission from a ruminant in a loose housing environment, and in a predetermined time period from T0 to 5 Tdesired, wherein the method comprises: - determining a model emission rate function EM(t) for the ruminant - determining the moments of eating feed by the rum inant, at least during the predetermined time , as a series of points in time {T1, T2, T3, ..., Tn} - constructing the ted real emission rate fun ction ER(t) on the basis of 10 the model emission rate on and the moments of eating feed, and - integrating ER(t) from T0 to ed.
2. The method of claim 1, n the function ER(t) is constructed as ER(t) = EM(t - T1 - C1) for T1≤t<T2, EM(t - T2 - C2) for T2≤t<T3, ..., EM(t - Tn-1 - Cn-1) for Tn-1≤t<Tn, and EM(t - Tn - Cn) for Tn ≤t, wherein C1, C2, ..., Cn are 15 constants.
3. The method of claim 2, wherein all constants are zero.
4. The method of claim 2, wherein all constants C1 ... Cn are determined such that the on ER(t) is a continuous function.
5. The method of claim 1, n the function ER(t) is constructed as 20 ER t )( = ∑ EM (t - Ti ) for all eating moments T1, ..., Tn relevant for the desired period of time T0 - Tdesired.
6 The method of any one of the preceding claims, wherein the feed is ration, containing roughage such as grass, hay, silage, maize, and optionally containing concentrates. 25
7. The method of any one of the preceding claims, wherein determining the model emission rate function comprises: - measuring a greenhouse gas emission rate at a plu rality of measurement points in time TM0, ... ,TMn, providing respective values EM0, ... , EMn - establishing for each of said measurement points in time TM0, ... , TMn the 30 most recent feeding moment TF0, ... , TFn before th e respective measurement point in time, and - determining the function ER(t) by fitting a mathe matical curve that fits through the sets of values TF0), EM0), ... , ((TMn-TFn), EMn).
8. The method of claim 7, further comprising repeating the steps of - measuring at an additional point in time Tadd sai d greenhouse gas emission rate EMadd, and - rmine the function by adapting and refittin g the mathematical curve 5 through one of the last X sets of values of TM and EM, wherein X is a predetermined number, and all sets of values of TM and EM.
9. The method of claim 8, wherein, in the step of redetermining the function, one or more predetermined constraints are applied, comprising predetermined rules with respect to the relative and/or te change of one or 10 more coefficients used in the function.
10. The method according to any one of claims 7 to 9, wherein the mathematical curve is selected from a set of standard curves depending on the type or race of animal, and wherein the fitting comprises multiplying the curve by a constant (F). 15
11. The method of any one of the ing claims, wherein determining the moments of eating feed comprises ining the s when at least one of the following conditions is met: - new ration is provided to the ruminant - feed is displaced s a feed fence at which t he nt is allowed to 20 feed - the ruminant is provided access to a pasture.
12. The method of any one of the preceding claims, wherein determining the moments of eating comprises identifying the ruminant at a feeding place and determining that said ruminant is eating, by means of a eating . 25
13. The method of claim 12, wherein the eating sensor comprises a camera with image processing software for recognizing eating behaviour, a feed weight sensor arranged to determine a weight change in a feeding device, and/or a microphone with sound processing software for izing eating behaviour.
14. The method of any one of the preceding claims, wherein determining 30 the moments of eating comprises determining when the ruminant produces eating sounds by means of a sound sensor, in particular provided on or near the neck and/or mouth, that is designed to detect sounds produced by the nt when moving its mouth in particular for chewing.
15. The method of claim 14, wherein, when determining the moments of eating, rumination sounds are ed.
16. The method of claim 14, wherein, when determining the moments of eating, only rumination sounds are detected, and the moments of eating are 5 determined to be the moments of a local minimum in rumination sounds.
17. The method of any one of the preceding , comprising: - providing a herd having a ity of ruminants - providing one or more ruminant ID devices, at lea st at one or more positions for feeding the ruminants and at one or more positions for measuring a 10 greenhouse gas on rate and arranged to fy the ruminant and provide an ID signal - performing the method for each of the ruminants i n the herd, and - providing a total ouse gas emission in the predetermined time period as the sum of the greenhouse gas ons from each of the ruminants. 15
18. A greenhouse gas emission estimation device for estimating a greenhouse gas emission from a ruminant, according to a method of any one of the preceding claims, the device comprising: - at least one greenhouse gas emission rate sensor, arranged to provide a greenhouse gas emission rate signal 20 - a clock device arranged to provide a time signal each time when said ruminant eats and each time when the at least one greenhouse gas emission rate sensor measures a greenhouse gas emission rate signal - a control unit ed to apply the method of an y one of the preceding claims, and on the basis of the measured time signals and greenhouse gas 25 emission rate signals.
19. The device of claim 18, wherein each greenhouse gas on rate sensor comprises or is provided together with a ruminant ID device arranged to establish the ID of a ruminant upon visiting the sensor.
20. The device of claim 18 or 19, comprising a feeding unit with an 30 animal ID station arranged to identify the ruminant upon feeding, the control unit arranged to process the corresponding time signal together with the animal ID.
21. The device of claim 20, n there is provided a greenhouse gas emission sensor with an animal ID station at one or more of a milking place, at a feeding place, such as a feeding box or feeding fence, and/or at a resting place.
22. A method for estimating a greenhouse gas on from a ruminant substantially as herein described with reference to any one of the embodiments as illustrated in the accompanying drawings.
23. A greenhouse gas emission estimation device substantially as herein 5 described with nce to any one of the embodiments as illustrated in the accompanying drawings. WO 62404
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
NL2007645A NL2007645C2 (en) | 2011-10-24 | 2011-10-24 | Method and device for determining greenhouse gas emission from a ruminant. |
NL2007645 | 2011-10-24 | ||
PCT/NL2012/050641 WO2013062404A1 (en) | 2011-10-24 | 2012-09-13 | Method and device for determining greenhouse gas emission from a ruminant |
Publications (2)
Publication Number | Publication Date |
---|---|
NZ623007A NZ623007A (en) | 2015-11-27 |
NZ623007B2 true NZ623007B2 (en) | 2016-03-01 |
Family
ID=
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ruuska et al. | Validation of a pressure sensor-based system for measuring eating, rumination and drinking behaviour of dairy cattle | |
Ambriz-Vilchis et al. | Comparison of rumination activity measured using rumination collars against direct visual observations and analysis of video recordings of dairy cows in commercial farm environments | |
Schirmann et al. | Validation of a system for monitoring rumination in dairy cows | |
Pahl et al. | Feeding characteristics and rumination time of dairy cows around estrus | |
US10761107B2 (en) | Apparatus and method for detecting disease in dairy animals | |
Mattachini et al. | Methodology for quantifying the behavioral activity of dairy cows in freestall barns | |
Byskov et al. | Genetic parameters of rumination time and feed efficiency traits in primiparous Holstein cows under research and commercial conditions | |
EP0824309B1 (en) | A method of treating animals, in particular feeding same | |
Falk et al. | A comparison of reticular and ruminal pH monitored continuously with 2 measurement systems at different weeks of early lactation | |
Paudyal | Using rumination time to manage health and reproduction in dairy cattle: a review | |
Pérez-Ramírez et al. | Restricting daily time at pasture at low and high pasture allowance: Effects on pasture intake and behavioral adaptation of lactating dairy cows | |
Keil et al. | The development of intersucking in dairy calves around weaning | |
US9250226B2 (en) | Method and device for determining greenhouse gas emission from a ruminant | |
Winter et al. | Behaviour associated with feeding and milking of early lactation cows housed in an experimental automatic milking system | |
Grodkowski et al. | Comparison of different applications of automatic herd control systems on dairy farms–a review | |
Halli et al. | Investigations on automatically measured feed intake amount in dairy cows during the oestrus period | |
Blomberg | Automatic registration of dairy cows grazing behaviour on pasture | |
Lyons et al. | Animal behavior and pasture depletion in a pasture-based automatic milking system | |
NZ623007B2 (en) | Method and device for determining greenhouse gas emission from a ruminant | |
Holzhauer et al. | A proposed structural approach to improve cow-claw health on Dutch dairy farms | |
Büchel | Sensor-based control of chewing and rumination behavior of dairy cows | |
Churakov et al. | Proposed methods for estimating loss of saleable milk in a cow-calf contact system with automatic milking | |
MATSUI et al. | Automatic determination of grazing and feeding behavior of horse in paddock and stall | |
Danielsson | The effect of social rank on milking and feeding behaviour in automatic milking system for dairy cows | |
Perdana-Decker et al. | On-farm evaluation of models to predict herbage intake of dairy cows grazing temperate semi-natural grasslands |