METHODS AND APPARATUS FOR LIVESTOCK REARING

Abstract

Apparatus for rearing livestock comprising a livestock enclosure, a plurality of sensors, and a plurality of output devices, and methods of operating the same. Methods of determining measurements of animal welfare by processing vocalisations by chicks, especially by detecting the spectral entropy of animal vocalisations. The relationship between chick distress calling in early life and outcome.

Claims

1. A method of determining one or more welfare parameters of animals, the method comprising providing: a livestock enclosure; and a plurality of sensors arranged to make measurements in the livestock enclosure; the method further comprising determining one or more welfare parameters from the measurements made by the plurality of sensors.

2. A method according to claim 1, wherein the method comprises outputting a determined welfare parameter.

3. A method according to claim 2, wherein the method comprises measuring one or more welfare parameters of the animals using the one or more sensors.

4. A method according to any one preceding claim, wherein the plurality of sensors comprises one or more audio sensors to detect sounds made by individual animals or groups of animals and the method comprises determining one or more stress parameters by calculating the spectral entropy of sound measured by one or more audio sensors.

5. A method according to any one preceding claim, comprising calculating a parameter related to the number of distress calls from the one or more animals, optionally calculating parameters concerning each of acute distress calls and chronic distress calls.

6. A method according to any one preceding claim, wherein the welfare parameters (e.g. stress parameters) of the animals are calculated taking into account previous measurements of sounds made by the same or different animals using the sensors, for example measurements of the frequency of distress calls at an earlier point in the life of the animals.

7. A method according to any one preceding claim, comprising detecting changes in the volume and pitch of vocalisations made by the animals, to calculate welfare parameters (e.g. stress, activity or health parameters) amongst the animals in the livestock enclosure, optionally comprise processing the frequency spectrum of sounds measured by one or more audio sensors and calculating one or more of: a kurtosis parameter, a skewness parameter, an acoustic complexity parameter, a mean frequency, or standard deviation of the frequency of the acoustic signal from one or more audio sensors.

8. A method according to any one preceding claim comprising the step of processing data from one or more thermal imaging sensors to determine the comb temperature of chickens and relating this to a welfare parameter of the chickens.

9. A method according to any one preceding claim comprising processing data from one or more optical sensors to determine one or more livestock distribution parameters in the livestock enclosure and using the one or more livestock distribution parameters when determining one or more welfare parameters.

10. A method according to any one preceding claim comprising processing measurements from the one or more sensors taking into account the age of animals in the enclosure, or relative to a reference time, for example the start of rearing a batch of animals in the livestock enclosure.

11. A method according to any one preceding claim comprising determining welfare parameters, e.g. stress parameters, early in the life of animals, using measurements made by the sensors, and taking this into account in subsequent control of the output devices and/or when processing subsequent measurements made by the sensors to determine welfare parameters.

12. A method according to any one preceding claim, wherein the animals are chickens and the method may comprise detecting levels of stress in the animals in the livestock enclosure during their third, fourth or fifth day since hatching or placement in the livestock enclosure.

13. A method according to any one preceding claim, wherein one or more welfare parameters are measured during the rearing of a first group of animals in the livestock enclosure and environmental parameters, including the variation with time of environmental parameters, are varied during the subsequent rearing of a second group of animals in the same or another livestock enclosure.

14. A method of regulating livestock rearing apparatus, the method comprising providing: a livestock enclosure, a plurality of sensors arranged to make measurements in the livestock enclosure; and a plurality of output devices which control environmental parameters within the livestock enclosure; the method further comprising determining one or more welfare parameters from the measurements made by the plurality of sensors, by the method of any one preceding claim, and controlling the output from the plurality of output devices in dependence on the determined one or more welfare parameters.

15. A method according to claim 14, wherein in response to the measurements made using the one or more sensors, the output from the one or more output devices is regulated, and varied over time.

16. A method according to claim 14 or claim 15, wherein at least some of the output devices are lights and the outputs which are changed include the wavelength and/or intensity of light output by the lights.

17. A method according to any one of claims 14 to 16, wherein one or more of the output devices is a sound generator, such as a loudspeaker, configured to direct sound at animals within the enclosure, wherein one or more sound generators are controlled to output animal vocalisations indicative of animal pleasure or distress.

18. A method according to claim 17, wherein the animal vocalisations are generated when the animals are at predetermined ages or in response to measured welfare parameters, to regulate the welfare of animals in the animal enclosure.

19. A method according to any one of claims 14 to 18, wherein animal vocalisations recorded by the one or more audio sensors are processed and used to regulate the output of animal vocalisations in real time or to a subsequent group of animals.

20. A method according to any one of claims 14 to 19, wherein machine learning is employed to correlate welfare parameters determined from measurements from the one or more sensors, and interventions, which may be carried out by varying the output of the one or more output devices, or by stockpeople in response to data output through a user interface, to thereby optimise animal welfare and/or growth during the rearing of an individual group of animals, or during future rearing of a new group of animals in the same or another livestock enclosure.

21. A method according to any one of claims 14 to 20, wherein the method comprise analysing weight gain of one or more of the animals in the livestock enclosure, for example a measurement of the weight of the animals at the end of a growing cycle, and taking this into account when determining environmental parameters, or the relationship between outputs through the output devices in response to measurement using the sensors during subsequent growing cycles.

22. A method according to any one of claims 14 to 21, comprising regulating the output of the one or more output devices to reduce stress on the animals within the animal enclosure, e.g. responsive to a calculated welfare parameter (e.g. a stress parameter, such as frequency of distress calls) meeting one or more criteria.

23. A method according to any one of claims 14 to 22, comprising regulating the frequency spectrum of light output through one or more lights or to change the temperature in the animal enclosure to reduce animal stress, responsive to measurements of one or more animal stress parameters made using the sensors.

24. A method according to claim 23, comprising increasing blue light levels, or the ratio of blue light to red and green light to reduce stress responsive to measurements of one or more animal stress parameters made using the sensors.

25. A method according to any one preceding claim, comprising predicting next-day mortality amongst the animals in the livestock enclosure and/or correlating preventative actions and their subsequent effect on next-day mortality.

26. A method according to any one preceding claim wherein the animals are chickens.

27. Livestock rearing apparatus comprising: a livestock enclosure; a controller; a plurality of sensors arranged to make measurements in the livestock enclosure; wherein the sensors are in electronic communication with the controller, and wherein the controller is configured to calculate one or more welfare parameters of the animals using measurements made by the one or more sensors by the method of any one preceding claim.

28. Livestock rearing apparatus comprising: a livestock enclosure; a controller; a plurality of sensors arranged to make measurements in the livestock enclosure; a plurality of output devices arranged to control environmental parameters within the livestock enclosure; wherein the sensors and output devices are in electronic communication with the controller and the controller is configured to control the output from the plurality of output devices in dependence on the measurements made by the plurality of sensors.

29. Livestock rearing apparatus according to claim 28, wherein the controller comprises a local controller in electronic communication with the plurality of output devices and the plurality of sensors.

30. Livestock rearing apparatus according to claim 28 or claim 29, wherein the controller is configured to process measurements of the animals made by the one or more sensors to determine welfare (e.g. stress) parameters of the animals.

31. Livestock rearing apparatus according to claim 30, wherein the plurality of sensors comprises one or more audio sensors and the controller is configured to process audio signals from the one or more audio sensors to detect sounds made by individual animals or groups of animals to determine one or more welfare parameters of animals in the enclosure.

32. Livestock rearing apparatus according to claim 31, wherein the one or more audio sensors are located between 0.5 and 1.5 m above the floor of the animal enclosure and spaced between drinkers and feeders.

33. Livestock rearing apparatus according to any one of claims 27 to 32 wherein the one or more sensors comprise thermal imaging camera and data from the thermal imaging sensors is processed to determine the temperature of the comb of one or more chickens and so to calculate a welfare parameter of the chickens.

34. A data storage medium storing computer program instructions which when executed by the controller of livestock rearing apparatus according to any one of claims 27 to 33, cause the livestock rearing apparatus to carry out the method of any of claims 1 to 26.

Description

DESCRIPTION OF THE DRAWINGS

[0066] An example embodiment of the present invention will now be illustrated with reference to the following Figures in which:

[0067] FIG. 1 is a schematic perspective view of a livestock enclosure;

[0068] FIG. 2 is a schematic diagram of a local controller;

[0069] FIG. 3 is a flow chart of an operating procedure;

[0070] FIG. 4 is a flow chart of an operating procedure;

[0071] FIG. 5 is a graph showing a relationship between spectral entropy and manual count of distress calls per minute for different ages;

[0072] FIG. 6 is a graph showing a correlation between spectral entropy and recording from the front and rear of an enclosure;

[0073] FIG. 7 is a graph showing a relationship between spectral entropy and weight gain;

[0074] FIG. 8 is a graph showing a relationship between spectral entropy and mortality;

[0075] FIG. 9 is a graph showing a relationship between spectral entropy and flock weight;

[0076] FIG. 10 is a graph showing a relationship between spectral entropy and flock mortality;

[0077] FIG. 11 is a graph showing a relationship between change in distress call rates over time in isolation;

[0078] FIG. 12 is a graph showing a relationship between change in comb temperature over time in isolation;

[0079] FIG. 13 is a graph showing a relationship between time spent in contact with foraging trays when positioned in specific locations.

DETAILED DESCRIPTION OF AN EXAMPLE EMBODIMENT

[0080] FIG. 1 is a schematic diagram of livestock rearing apparatus 1 for rearing animals 2, in this example chickens. The apparatus includes a livestock enclosure, in this case a shed 4 having a floor 6, walls 8 and roof 10 on which are located a plurality of individually controllable light sources 12. These light sources are inductively coupled to and hanging from wires 14 which are strung along the length of the animal enclosure. They may be individually added to, removed from, and slid along the wires. The wires are typically twisted pairs, which conduct an AC current to power the light sources.

[0081] In addition, a plurality of microphones 16 are suspended within the enclosure, about 75 cm above the floor of the enclosure. They are used to record sounds from the animals during operation. These microphones are inductively coupled to and hanging from wires 14 which are strung along the length of the animal enclosure (not shown in FIG. 1). Thermal imaging cameras 18 are also provided to view a region of the enclosure and other environmental sensors 20, such as gas sensors, humidity sensors, temperature sensors, light sensors are also present. These cameras are inductively coupled to wires 14 which are strung along the length of the animal enclosure (not shown in FIG. 1). There are also a number of output devices, other than the lights, which can be used to regulate other aspects of the environment of the animal enclosure, for example heaters 24, and air conditioning units 26, and so forth.

[0082] The livestock rearing apparatus is controlled by a controller, which comprises a local controller 28 and a remote controller 29, to which the local controller periodically connects through the Internet 27. The local controller 28 comprises a processor 30 which has a clock 32. There is also memory 34, storing computer program data 36, measurement data 38 (e.g. reading from sensors) and operating parameter data 40, which is used to control the various outputs. The remote controller 29 may correspond although typically it is a more power computer which processes data from and which may in part control a plurality of different apparatuses.

[0083] In some embodiments, each of the plurality of microphones 16 comprise a micro-controller and data storage. The data storage stores computer program code which, when run by the micro-controller, causes the micro-controller to process audio data received at the microphone. That is, the micro-controller of each microphone filters and processes the audio data—i.e. before data is transmitted to the local controller 28 (or to the remote controller 29). The processing carried out within the microphones may include filtering and calculation of spectral entropy, for example.

[0084] In some embodiments, the apparatus is controlled entirely by a remote controller, e.g. a server located in the cloud, or entirely by a local controller, but it is convenient to provide a local controller which receives signals from sensors and regulates the various output devices, and which periodically provides data to or receives instructions from a remote controller (e.g. internet server). The remote server may also provide central recording, reporting, machine learning and so forth.

[0085] With reference to FIG. 2, the controller receives data from the various sensors, including the microphones 16, thermal imaging cameras 18, and other environmental sensors 20. In turn, the controller controls a number of output devices in real time, including the various lights 12, heaters 24, air conditioning unit 26, possibly loudspeakers 22, and so forth. The controller is also operable to output data to a user interface 42, to communicate with a stock person. The user interface 42, may be a display screen, which may be local to the animal rearing apparatus, or may be remote. For example, the controller may provide a web portal, or provide data to a mobile phone app, through which a stock person may receive data concerning the performance of the animal rearing apparatus, or the status of the animals in the animal rearing apparatus, the user interface may transmit text messages or emails or other alerts to a user during operation.

[0086] The controller also stores measurement 38, and data calculated from those measurements, such as measurements of animal welfare parameters, for current or future use.

[0087] With reference to FIG. 3, during operation, while animals are reared in the animal enclosure, measurements are made 100, using the various sensors. As described below these measurements may include measurements of audio signals indicative of distress, or other sounds are vocalisations made by animals, as well as environmental parameters and so forth. Data may also be input manually by a stock person and stored. When a group of animals are reared in the enclosure, their age or the time at which the program starts is recorded, which enables environmental properties to be varied with the age of the animals, and also enables measured data to be correlated with the age of the animals.

[0088] The measured data is then analysed 102, typically in real time, by the controller, which then controls the output devices, to vary 104 the current environment within the animal enclosure. For example, the controller may change the temperature, or humidity, or the intensity or frequency spectrum of the lighting within the animal enclosure, or within individual parts of the animal enclosure, responsive to the measurements which are made. Thus, factors affecting animal welfare may be automatically optimised. Typically, the environment is also controlled according to a program, taking into account the age of the animals (e.g. the time since a group of animals, e.g. a flock of chickens, were introduced into the animal enclosure), and other parameters such as the time of day. For example, lighting levels may be varied to give a day/night cycle, the properties of which may change with time as the animals age. Furthermore, changes are made to the environment and the program by which it is varied responsive to the measurements which have been made.

[0089] In addition, the controller may output information 106 to a stock person who may, at their discretion, vary the environment 108 within the animal enclosure, for example by making changes to fixtures or fitments, or the number of animals, which cannot be made automatically by the controller, using the one or more output devices which are available to it. It is also possible that a stock person will be asked to make certain checks, for example to check the health of the animals within the enclosure, and the stock person may feed data back, for to the controller, for example, measurements of animal health or other observations and which they make, at the prompt of the controller.

[0090] In addition, as well as varying the immediate environment, the controller may determine future changes in the environment, for example changes which will occur during the later feeding cycle, or night cycle, or the following day, or during the rearing of a further group of animals in the future.

[0091] The controller also monitors parameters indicative of the performance of the animal rearing apparatus, for example measurements of the weight or health of the livestock, mortality rates, or the response of animals in the animal enclosure to the changes which are made to the environment by the output devices, or by a stock person, typically responsive to a signal given to the stock person. This data is later used to output relevant data, such as key performance indicators, and also to modify a future programme to be followed by the controller when raising a later group of animals in the animal enclosure.

[0092] Modifications to the future programme may include changes in environmental factors (temperature, lighting spectrum and intensity, food and drink amount), including changes to the program which determines environmental parameters change with time, for example varying a day/night cycle, or increasing or decreasing or changing the wavelength distribution and of lighting at different times, for example times during the lifespan of the animals in the animal enclosure.

[0093] Modifications to the future programme, may also be modifications to the way in which output devices are controlled in real time in response to measurements made using the various sensors. Machine learning algorithms may be employed to determine optimum operating parameters, taking into account measurements, and results from the individual animal enclosure, and also many other animal enclosures over time.

[0094] FIG. 4 is an example implementation of the operation. An audio signal 200 from a microphone is received, typically at the controller. Samples of audio signals are taken within 4 hours of arrival of chicks into the livestock enclosure, typically between the first and fourth hour of arrival, and regularly repeated, typically with a minimum interval of 4 hours between samples. It may be that samples are not taken during periods which would otherwise trigger calls, such as feeding time or the presence of a human in the livestock enclosure. In this example, the audio signal 200 is emanated from a chick or a group of chicks (which may, for example, include as many as several thousands of chicks). The received audio signal is filtered 202, e.g. by passing through one or more digital filters, to isolate a frequency range (for example, typically 2.75 kHz to 5 kHz for early life chicks), and the spectral entropy of the filtered signal is calculated 204. This is a parameter which indicates the number of distress calls per unit time within the livestock enclosure. It may be that filtering the received audio signal includes using short-time Fourier transform (e.g. with a 512 sample, non-overlapping Hanning window). It may be that the parameters calculated from the audio signal also, or alternatively, comprise one or more of the mean, median, standard deviation, standard error, dominant frequency, upper quartile, lower quartile, interquartile range, centroid, skewness, or kurtosis of the audio signal. Next day mortality is predicted 206 based on the number of distress calls per unit time indicated by (and calculated from) the spectral entropy (and optionally, or alternatively, any other calculated parameters). For example, next day mortality may be more likely if there are more distressed signals within the livestock enclosure. Additionally, or alternatively, the calculated parameter may be correlated with previously measured data (e.g. data from a remote controller) to predict next day mortality. For example, the calculated parameter may be sent to a remote controller for correlation, or the filtered audio signal is sent to a remote controller to calculate distress calls and correlate to predict next day mortality. Preventative action 208 may be taken if the next day mortality prediction is high. This action may be determined and/or performed automatically, e.g. based on previous actions, or it may be determined and/or performed by a stock person. The preferable preventative action is to change the lighting frequency, by increasing the blue light levels in the range 440 nm to 475 nm (optimally 455 nm) to reduce stress in the birds. Alternative, or additional, preventative actions include altering the temperature or humidity of the livestock enclosure, allowing longer time for feeding or for access to water, playing calming sounds via loudspeakers and/or any other action to reduce stress in the birds.

[0095] When the flock has reached the end of their early life, e.g. the fourth, fifth or sixth day of placement, the spectral entropy of filtered audio signals (which is again indicative of mean distress call levels per minute) is measured 210. This is correlated with measured weight gain 212 to predict productivity 214 of the flock. Key performance indicators (KPI) are calculated 216 and to be used as benchmarks for future early life flocks. For example, certain preventative actions may be more efficient for specific next day mortality predictions and/or for flock weight gain. It may be that the KPI benchmark is used when determine whether to take preventative action at step 208, and/or what action to take, and whether the preventative action is to be performed automatically or by a stock person.

[0096] FIG. 5 demonstrates the relationship between spectral entropy, extracted from high-pass filtered data, and manual count of distress calls per minute for different ages of chickens (in days). Shaded areas denote confidence intervals.

[0097] FIG. 6 is a correlation between spectral entropy extracted from high-pass filtered data from time matched 1 min recordings from the front and rear of the house (i.e. livestock enclosure). It may reflect spatial variation in stress exposure, distressed birds and/or acoustic attributes of the house. We have found that different areas of a livestock enclosure may give different results depending for example on performance of the acoustic sensors, the age of the chicks at different locations and acoustic properties of the livestock enclosure. The methods may comprise controlling for location specific variations.

[0098] FIG. 7 is an age-specific relationship between spectral entropy extracted from high-pass filtered data and weight gain (logarithmic) in the next day. On days 1-3, spectral entropy became increasingly negatively correlated with weight gain into the next day, i.e. flocks which distress called most then had highest weight gain. However, this relationship was reversed on day 4, when continued high rates of distress calling were associated with lower weight gain. It is therefore advantageous to determine welfare parameters taking into account the age of the livestock.

[0099] FIG. 8 is an age-specific relationship between spectral entropy extracted from high-pass filtered data and mortality (logarithmic) in the next day. Shaded areas denote confidence intervals. Across days 1-4, spectral entropy became increasingly negatively correlated with mortality in the next day, i.e. flocks which distress called most had highest mortality.

[0100] FIG. 9 is a relationship between spectral entropy extracted from high-pass filtered data at 4 days from placement and flock weight at 32 days. In stepwise backward regressions of 32-day weight, the sole predictor was day 4 spectral entropy (slope 2.27±0.98, t=2.31, p=0.044): high rates of distress calling on day 4 predicted low weights at day 32.

[0101] FIG. 10 is a relationship between spectral entropy extracted from high-pass filtered data at 4 days from placement and flock mortality (percentage) by 32 days. Shaded areas denote confidence intervals. In stepwise backward regressions of 32-day % Flock mortality, the sole predictor was again day 4 spectral entropy (slope −60.21±24.21, t=−2.49, p=0.032): high rates of distress calling on day 4 predicted high % flock mortality by day 32.

[0102] FIG. 11 is a relationship between the change in distress call rates over 10 minutes in isolation. The relationship is shown for different audio playback treatments: a control 1110, a loud control 1120, anxiety-like playback 1130, and depression-like playback 1140. Error bars indicate 1 standard error of the mean per 30 second bin.

[0103] FIG. 12 is a relationship between the change in comb temperature over 10 minutes in isolation. The relationship is shown for different audio playback treatments: a control 1210, a loud control 1220, anxiety-like playback 1230, and depression-like playback 1240. Error bars indicate 1 standard error of the mean per 30 second bin. A greater comb temperature increase was indicative of a stronger acute stress response to isolation. That is, an increased comb temperature is indicative of a chick having increased stress.

[0104] FIG. 13 is a relationship between time of 1 minute spent in contact with foraging trays when positioned in each of 5 equidistant locations from a known rewarding location (1) to a known unrewarding location (5). The relationship is shown for different audio playback treatments: a control 1310, a loud control 1320, anxiety-like playback 1330, and depression-like playback 1340. Error bars show 1 standard error of the mean per location.

[0105] We have found it advantageous to measure the level of distress calling in chicks, and to control the environment in which they are reared responsive thereto. Chicks emit a repetitive high energy distress call when they are stressed. This is especially prevalent early in the life of chickens. We have found that it is especially important to monitor the levels of distress calling when chicks are between for example about 3 to 7 days of age, and the welfare at this stage, as evidenced by distress calling, affects their growth rate and emotional state at commercial slaughter age.

[0106] It is possible to detect individual distress calls from individual animals, but we have found it advantageous to monitor the sounds made by a plurality of animals around microphones. Microphones are spaced apart throughout the livestock enclosure, between feeders and drinkers. Their sensitivity is selected to detect animal vocalisations within a radius of about 10 to 20 m. Sounds is recorded periodically, e.g. 1 minute of sound is recorded every 10 minutes. Windows of sounds, typically 10 to 120 s are records and processed. Recorded sounds are filtered to extract sound in a wavelength range, e.g. 2.75 to 5 kHz.

[0107] Stress parameters can be calculated in several ways. Firstly, we have found it advantageous to calculate the spectral entropy of the filtered recorded sounds. We have found that this correlates with chick distress calling and is predictive of chick growth and welfare.

[0108] In addition, we have processed measured sound into the frequency domain with Fast Fourier Transform and analysed skewness, kurtosis, fundamental frequency and power in fundamental frequency. We have found that varying distress is associated with changes in these parameters. A decrease in skewness may indicate greater stress; a decrease in kurtosis may indicate greater stress; and increase in the fundamental frequency of the sound may indicate greater stress; an increase in power at the fundamental frequency may indicate greater stress.

[0109] We measure spectral entropy, or another measure of distress calling, particularly during the fourth day of life, or fifth day of life (or fourth day from placement, which may be one day from hatching) of chicks. We have found that this correlates well with the ultimate growth, health and wellness of chicks at the time of slaughter.

[0110] We have found that early life distress calls and other calls indicative of emotions are contagious amongst chickens. Chickens which are distressed early in life (e.g. around day 4) may continue to generate high levels of distress calls throughout life.

[0111] This has two implications. Firstly, welfare parameters can be calibrated taking into account measurements of distress calling (e.g. the spectral entropy of measured sound) earlier in the life of the animals, thereby improving the accurate of estimates of welfare parameters.

[0112] Secondly, the controller may use the loudspeakers to play sounds to the animals, particularly sounds associated with pleasure, although potentially distress calls, to reduce the spread of contagious emotions and/or to improve animal wellbeing and/or growth rate.

REFERENCES

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