Determining reproductive status

Abstract

A device, system and method to determine or forecast one or more reproductive events of an animal, such as a forecasted future heat event, the probability of the animal being pregnant currently, or the probability of the animal being pregnant at a future forecasted heat event. Where the device, system and method determine future heat events, cycling status or pregnancy status based on historical information from non-target group animals and current information from target group animals. Examples using Bayesian modelling and machine learning are described.

Description

[0001]DETERMINING REPRODUCTIVE STATUS Field The present disclosure pertains to the field of livestock management. In some cases the disclosure relates to methods for determining when an animal will have a heat or is or will be cycling. In some cases the disclosure relates to herd management based on animal pregnancy status. More particularly, the disclosure relates to methods for determining when an animal of a target group will have a heat, whether the animal is pregnant, and / or whether the animal will be pregnant given a proposed insemination date, based on data obtained from a non-target group of animals. BACKGROUND OF THE INVENTION Effective management of reproductive cycles in a group of animals, such as cows, (also referred to as a mob or herd) is crucial for optimising breeding and overall group health. Cycling refers to a female animal regularly having heats or estrus (the terms heat and estrus being used interchangeably). A heat typically indicates the start of a cycle. Animals are typically inseminated during a heat. Therefore the cycling status of a female animal during a time period indicates whether an animal has the potential to become pregnant if mated or artificially inseminated during the time period. For a female bovine, such as a dairy cow, it would be considered cycling if the cow’s last heat was approximately 26 days ago or less. This is around the time between recurring heats for 99 percent of cycles during cycling. The average cycle length for a cow is about 21 days. Typically, non-pregnant cows will cycle every 16-26 days and a heat will last on average between 14 and 15 hours but can last from 2 to 30 hours. The cycling percentage (also referred to as ‘cycling rate’) of a group of animals refers to the percentage or proportion of animals within the group that are currently cycling. Typically farmers or livestock managers determine the cycling percentage of a group of animals through observation and recording of what animals are in heat, and what animals are not in heat. The mating period is a specific time period during which animals are mated or inseminated to optimise the chances of successful pregnancies. A mating period will start on a mating date, also known as the insemination date, or the planned or projected start of mating (PSM). A mating period is typically six to nine or six to twelve weeks long. To encourage greater pregnancy rates the mating period should coincide with the highest cycling percentage (60 - 100%, for example) of the group of cows. If the cycling percentage is not high enough, the mating date may be changed to a date when the cycling percentage is higher. Typically, a mating period is established based on the expected cycling patterns of the group. The length of the mating period can be extended to ensure that a significant proportion of cows are bred during their fertile period, or a significant proportion of cows get pregnant. Livestock managers have developed methods to determine the cycling status of female animals, without medical tests. These methods require detecting if the animal is in heat. Methods of detecting heat include visual observation of behavioural signs. Example visual behaviours include increased activity, restlessness, mounting behaviour, and the willingness of cows to be mounted by others, among other signs. Additionally, farmers often employ heat detection aids like tail paint or chalk, heat detection patches, and electronic devices such as pedometers or accelerometers to assist in identifying cows in heat. These aids help automate and enhance the accuracy of heat detection, contributing to effective breeding management practices. In some cases livestock managers have developed methods to control or adjust the heat event dates or cycling status of a group of animals. For example, farm system changes can be adjusted to get more cows cycling. Example system changes include adjusting feed, supplements, or administering hormones. However, these methods require estimation, or physical observation of the group of animals. Many of the relevant variables change over time. Typically, each cow in the group may have a slightly different cycle length and / or timing. Individual variations in hormonal levels and environmental factors may influence when cows come into heat. Environmental and management factors such as nutrition, health, climate conditions, and management practices may impact the cycling status of an animal. For example, cows in better health and with proper nutrition may be more likely to cycle regularly. Cycling percentages may also vary seasonally. In some regions, cows may exhibit stronger heat signs during certain times of the year. After the mating period the livestock manager may determine a pregnancy percentage for the group of animals. The pregnancy percentage (also referred to as in-calf percentage, for cows) is the percentage of cows in the group that are pregnant (in-calf for cows). A high pregnancy percentage indicates that a significant portion of the cows in the group are successfully conceiving and will likely give birth to calves. Therefore, a high pregnancy percentage is essential for maintaining and increasing the group of animals (or dairy herd) and an important factor for the livestock manager. A particular animal, such as a cow, may have a pregnancy status. The pregnancy status refers to the likelihood or probability that a particular cow is pregnant. It is advantageous for as many of the animals in a group to be pregnant at the same time. Livestock managers and veterinarians use various methods to determine if a cow is pregnant. The method used may depend on the pregnancy stage and available tools. All methods require a physical medical test, such as a visual or medical observation. Livestock managers often enlist veterinarians to conduct pregnancy tests six weeks into the mating period. A common indicator of how a breeding program is going is how many animals are pregnant at some point within the mating period. For example it may be within six weeks for dairy cows. Six weeks is used because it represents two oestrus cycles for a cow, and / or the halfway point through a twelve-week mating period. The six-week in-calf rate (6WICR) is a key performance indicator in dairy farming that measures the percentage of cows in a herd that become pregnant within the first six weeks of the mating period. To calculate the 6WICR, farmers track the number of cows that become pregnant within the first six weeks of the mating period and divide this by the total number of cows eligible for breeding. This percentage helps farmers assess and improve their reproductive management strategies, ensuring optimal productivity and health of the herd. The cycling percentage and pregnancy percentage are important factors for livestock managers. However, as described they can be difficult to predict, require visual or physical examination and vary considerably between animals or mobs. OBJECT OF THE INVENTION It is an object of the present disclosure to provide a system to predict a variable relating to the cycling percentage that overcomes or at least partially ameliorates some of the abovementioned disadvantages or which at least provides the public with a useful choice. SUMMARY OF THE INVENTION Other aspects of the invention may become apparent from the following description which is given by way of example only and with reference to the accompanying drawings. In a first aspect the disclosure broadly relates to a method of determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the method comprising the steps of: obtaining historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates comprising dates of prior heat events and / or birthing events for each animal of the non-target group of animals; and secondary event dates comprising dates of secondary heat events following the primary event dates; determining, based at least in part on the historical heat data, a prior distribution of the time between the primary event dates and the secondary event dates; obtaining target heat data from the target group of animals, the target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date; updating the prior distribution based on the target heat data; obtaining a primary event date for each of the at least one female animal; and determining, using the primary event date for each of the at least one female animal and the updated prior distribution, a future heat event of the at least one female animal. In some cases, the step of determining a prior distribution comprises a first prior distribution for secondary event dates and primary event dates of prior heat events and a second prior distribution for secondary event dates and primary event dates of a birthing event. In some cases, the at least one female animal comprises a group of female animals, optionally the group of female animals located in a specific area. In some cases, the specific area comprises a farm, or the specific area is within a distance from a predetermined location. In some cases, comprising determining, based on the future heat event for each of the group of female animals, a distribution of future heat events for the group of female animals. In some cases, the future heat event comprises a date by which a percentage of the group of female animals have been, or will be, in heat. In some cases, the future heat event comprises any one or more of: a distribution of predicted dates, and a probability of the at least one female animal having a heat event on a particular date. In some cases, comprising the step of inseminating the at least one female animal based, at least in part, on the determined future heat event, optionally comprising selecting a date of insemination based on the determined future heat event. In some cases, comprising one or more of the steps of: outputting the determined date of the future heat event, and moving the at least one female animal based, at least in part, on the future heat event. In some cases, comprising the step of obtaining the primary event date of the at least one female animal from a device attached to the at least one female animal. In some cases, comprising the step of determining a cycling status from the future heat event. In some cases, the non-target group is selected based on one or more characteristics. In some cases, comprising the step of outputting one or more control signals, optionally wherein the one or more control signals are configured to control one or more of: a drafting gate operable to draft the at least one female animal to different locations; a wearable device operable to display indicia on the at least one female animal, such as an LED light; a wearable device configured to apply stimuli to the at least one female animal to guide the at least one female animal in operation; and a display device comprising a display operable to be read by a user. In some cases, comprising the step of performing one or more actions based on the future heat event, the one or more actions comprising any one or more of: an alert; adjusting a feed parameter; optionally comprising a composition, energy and / or quantity of feed given to the at least one female animal or a feeding schedule; providing nutritional supplements or hormones to the at least one female animal; adjusting a timing of a reproductive event, such as the timing of insemination of the at least one female animal; and geographically moving the at least one female animal. In a second aspect, the disclosure broadly consists of a method of determining an in-calf probability of at least one female animal, the method comprising: determining the date of one or more future heat events according to the methods described; obtaining historical insemination event data from a non-target group of animals and determining a prior distribution between insemination date and pregnancy status; obtaining target insemination event data of the target group of animals and updating the prior distribution based on the current target insemination event data; obtaining a planned insemination date for the at least one female animal, and determining, based on the determined date of one or more future heat events and updated prior distribution and the planned insemination date, a predicted in-calf probability. In a third aspect, the disclosure broadly consists of a system for determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the system comprising a processor configured to: obtain historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates, comprising dates of prior heat events and / or birthing events for each of the non-target group of animals, and secondary event dates comprising dates of a secondary heat events following the primary event dates; determine, based at least in part on the historical heat data, a prior distribution of the time between the primary event dates and the secondary event dates; obtain target heat data from the target group of animals, the target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date; update the prior distribution based on the target heat data; obtain a primary event date for each of the at least one female animals, and determine, using the primary event date for each of the least one female animals, and the updated prior distribution, a future heat event of the at least one female animal. In some cases, comprising at least one wearable device configured to be worn by the at least one female animal and / or at least one of the non-target group of animals, wherein the at least one wearable device is configured to detect one or more of the primary event dates, secondary event dates of the at least one female animal and / or at least one of the non-target group of animals. In some cases, the at least one wearable device is configured to detect a birth event and / or insemination event of the at least one female animal and / or at least one of the non-target group of animals. In some cases, the device processor is configured to receive one or more signals from the system processor, wherein the received signals comprise a guidance command based, at least in part, on the future heat event. In a fourth aspect, the disclosure broadly consists of a method of determining an in-calf status of at least one female animal forming part of a target group of animals, the method comprising the steps of: obtaining historical insemination event data from a non-target group of animals, the historical insemination event data comprising insemination dates and pregnancy status in a time period after the insemination dates; determining, based at least in part on the historical insemination event data, a prior distribution of the time between insemination dates and pregnancy status; obtaining target insemination data from the target group of animals, the target insemination data comprising insemination event date(s), and pregnancy status date(s) for each animal in the target group of animals having pregnancy status data; updating the prior distribution based on the target insemination data; obtaining an insemination date for the at least one female animal; and determining, based on the updated prior distribution, and the insemination date, an in-calf status of the at least one female animal. In a fifth aspect, the disclosure broadly consists of a method of determining an in-calf probability of at least one female animal, the method comprising: obtaining historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates comprising dates of prior heat events and / or birthing events for each animal of the non-target group of animals; and secondary event dates comprising dates of secondary heat events following the primary event dates; determining, based at least in part on the historical heat data, a prior distribution of the time between the primary event dates and the secondary event dates; obtaining target heat data from the target group of animals, the target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date; updating the prior distribution based on the target heat data; obtaining a primary event date for each of the at least one female animal; determining, using the primary event date for each of the at least one female animal and the updated prior distribution, a future heat event of the at least one female animal; determining the date of one or more future heat events according to the methods described; obtaining historical insemination event data from a non-target group of animals and determining a prior distribution between insemination date and pregnancy status; obtaining target insemination event data of the target group of animals and updating the prior distribution based on the target insemination event data; obtaining a planned insemination date for the at least one female animal, and determining, based on the determined date of one or more future heat events and updated prior distribution and the planned insemination date, a predicted in-calf probability. In a sixth aspect the disclosure broadly relates to a method of determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the method comprising the steps of: obtaining historical heat data from a non-target group of animals, the historical heat data comprising: determining, based at least in part on historical heat data, a prior distribution of the time between the primary event dates and the secondary event dates, the historical heat data comprising: primary event dates comprising dates of prior heat events and / or birthing events for each animal of the non-target group of animals; and secondary event dates comprising dates of secondary heat events following the primary event dates; updating the prior distribution based on target heat data from the target group of animals, the target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date;; obtaining a primary event date for each of the at least one female animal; and determining, using the primary event date for each of the at least one female animal and the updated prior distribution, a future heat event of the at least one female animal. In a seventh aspect the disclosure broadly relates to a system for determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the system comprising a processor configured to: determine, based at least in part on historical heat data comprising primary event dates, comprising dates of prior heat events and / or birthing events for each of the non-target group of animals, and secondary event dates comprising dates of a secondary heat events following the primary event dates, a prior distribution of the time between the primary event dates and the secondary event dates; update the prior distribution based on target heat data from the target group of animals, the target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date; obtain a primary event date for each of the at least one female animals, and determine, using the primary event date for each of the least one female animals, and the updated prior distribution, a future heat event of the at least one female animal. In an eight aspect the disclosure broadly relates to a method of determining an in-calf status of at least one female animal forming part of a target group of animals, the method comprising the steps of: determining, based at least in part on historical insemination event data comprising insemination dates and pregnancy status in a time period after the insemination dates, a prior distribution of the time between insemination dates and pregnancy status; updating the prior distribution based on target insemination data, the target insemination data comprising insemination event date(s), and pregnancy status date(s) for each animal in the target group of animals having pregnancy status data;; obtaining an insemination date for the at least one female animal; and determining, based on the updated prior distribution, and the insemination date, an in-calf status of the at least one female animal. In a ninth aspect the disclosure broadly relates to a method of determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the method comprising the steps of: obtaining historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates, the primary event dates comprising dates of prior heat events and / or birthing events for each of the non-target group of animals; and secondary event dates, the secondary event dates each comprising the date of a heat event following the primary event dates; obtaining a primary event date for each of the at least one female animal, and determining, using the primary event date and the historical heat data, a future heat event of the at least one female animal. In some cases, the method comprising the step of: determining, based at least in part on the historical heat data, a prior distribution of the time between the primary event date and the secondary event date; wherein the future heat event date is determined using the primary event date and the historical heat data. In some cases, the prior distribution is determined based on a distribution of secondary heat events following primary heat events. In some cases, the prior distribution is determined based on a distribution of predicted time between primary events and secondary events. In some cases, the distribution of predicted time between primary and secondary events comprises a distribution of dates for a first heat after a birthing event, and / or a distribution of dates for a next heat after a previous heat. In some cases, determining a future heat event of the at least one female animal uses a machine learning algorithm trained on the historical heat data to determine, based on the primary event date of the at least one female animal, the future heat event. In some cases, the method comprising the step of: obtaining historical target heat data from the target group of animals, the historical target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date. In some cases, the method comprising the step of: determining a current distribution of the target group of animals, based on the historical target heat data primary event dates, the current distribution showing the distribution of a current or a next heat event. In some cases, primary event dates of prior heat events and primary event dates of a birthing event are distinguished from one another. In some cases, the at least one female animal comprises a group of female animals, the group of female animals located in a specific area. In some cases, the specific area comprises a farm, or is within a distance from a location. In some cases, the method comprising determining, based on the future heat event for each of the group of female animals, a predicted distribution of secondary events for the group of female animals. In some cases, the future heat event comprises a date by which a percentage of the group of female animals have been, or will be, in heat. In some cases, the method comprising the step of outputting a date by which a percentage of the group of female animals have been, or will be, in heat. In some cases, a cycling percentage is determined by a sum of the number of animals in the group of female animals cycling, divided by the total number of animals in the group of female animals. In some cases, the future heat event comprises a distribution of predicted dates. In some cases, the future heat event comprises a probability of the at least one female animal having a heat event on a particular date. In some cases, the method comprising the step of inseminating the at least one female animal based, at least in part, on the future heat event. In some cases, the method comprising the step of outputting the determined date of the future heat event. In some cases, the method comprising the step of moving the at least one female animal based, at least in part, on the future heat event. In some cases, the method comprises the step of obtaining one or more of the prior heat events from a device attached to the at least one female animal. In some cases, the method comprises the step of determining a cycling status from the future heat event. In some cases, cycling status is determined based on the future heat event falling within a cycling time period. In some cases, the cycling time period is a period of time after a heat event. In some cases, the at least one female animal is a cow. In some cases, the non-target group is selected based on one or more characteristics. In some cases, the method comprises the step of outputting one or more control signals. In some cases, the control signals are based on one or more of: the future heat event, cycling status; or a derivative thereof. In some cases, the one or more control signals are configured to control one or more of: a drafting gate operable to draft the at least one female animal to different locations; a wearable device operable to display indicia on the at least one female animal, such as an LED light; a wearable device configured to apply stimuli to the at least one female animal to guide the at least one female animal in operation; and a display device comprising a display operable to be read by a user. In some cases, the method comprises outputting the future heat event. In some cases, the method comprises outputting the future heat event based on one or more parameters. In some cases, the one or more parameters comprise a threshold, date or status. In some cases, the method comprises the step of performing one or more actions based on the future heat event, the one or more actions comprising any one or more of: an alert; adjusting a feed parameter; optionally comprising a composition, energy and / or quantity of feed given to the at least one female animal or a feeding schedule; providing nutritional supplements or hormones to the at least one female animal; adjusting a timing of a reproductive event, such as the timing of insemination of the at least one female animal; and geographically moving the at least one female animal. In some cases, geographically moving the at least one female animal is performed by a wearable device configured to apply stimuli to the at least one female animal to guide the at least one female animal in operation. In some cases, the method comprises the step of updating the future heat event determination following completion of the one or more actions. In some cases the aspect is applied to a method of determining a cycling status of the at least one female animal. In some cases, the above features are applied to any one of the other aspects. In a tenth aspect, the disclosure broadly consists of a method of determining a pregnancy status of at least one female animal, the method comprising: determining the date of one or more future heat events according to the method of the first aspect; obtaining historical insemination event data; obtaining a predefined date for the at least one female animal, determining, based on the future heat event and the historical insemination event data, a predicted in-calf probability. In some cases, the predicted in-calf probability is determined based on the one or more future heat events and the prior distribution of mating. In some cases, the historical insemination event data comprises an artificial insemination date. In some cases, the predefined date is a proposed date to inseminate the at least one female animal. In some cases, any one of the additional features of the previous aspects are also combined with the tenth aspect. In an eleventh aspect, the disclosure broadly consists of a method of determining a pregnancy status of at least one female animal forming part of a target group of animals, the method comprising the steps of: obtaining historical insemination event data from a non-target group of animals, the historical insemination event date comprising insemination dates and pregnancy status in a time period after the insemination dates; obtaining a planned insemination date for the at least one female animal, determining, based on the historical insemination event data, and the planned insemination date, a future or predicted pregnancy status of the at least one female animal. In some cases, any one of the additional features of the previous aspects are also combined with the eleventh aspect. In some cases, the method comprising the step of: determining, based at least in part on the historical insemination data, a prior distribution of the time between the insemination date and the pregnancy status; wherein the pregnancy status is determined using the planned insemination date and the historical insemination event data. In some cases, the prior distribution is determined based on a distribution of pregnancy status following insemination events. In some cases, the prior distribution is determined based on a distribution of predicted time between pregnancy status and insemination event dates. In some cases, the distribution of predicted time between pregnancy status and insemination event dates comprises a distribution of dates for a pregnancy after an insemination event In some cases, determining a pregnancy status of the at least one female animal uses a machine learning algorithm trained on the historical insemination event data to determine, based on the planned insemination date of the at least one female animal, a future pregnancy status. In some cases, the method comprising the step of: obtaining historical insemination data from the target group of animals, the historical insemination data comprising insemination event date(s), and pregnancy status date(s) for each animal in the target group of animals having pregnancy status data. In some cases, the method comprising the step of: determining a current distribution of the target group of animals, based on the historical insemination event dates, the current distribution showing the distribution of a current or a next pregnancy status. In some cases, prior artificial insemination dates and prior mating dates are distinguished from one another. In some cases, the at least one female animal comprises a group of female animals, the group of female animals located in a specific area. In some cases, the specific area comprises a farm, or is within a distance from a location. In some cases, the method comprising determining, based on the future pregnancy status for each of the group of female animals, a predicted distribution of future pregnancy status for the group of female animals. In some cases, the future heat event comprises a date by which a percentage of the group of female animals have been, or will be, pregnant. In some cases, the method comprising the step of outputting a date by which a percentage of the group of female animals have been, or will be, pregnant. In some cases, a pregnancy rate is determined by a sum of the number of animals in the group of female animals who are pregnant, divided by the total number of animals in the group of female animals. In some cases, the future pregnancy status comprises a distribution of predicted pregnancy status. In some cases, the future heat event comprises a probability of the pregnancy status of the at least one female animal. In some cases, the method comprising the step of inseminating the at least one female animal based, at least in part, on the future pregnancy status. In some cases, the method comprising the step of outputting the determined date of the future pregnancy status. In some cases, the method comprising the step of moving the at least one female animal based, at least in part, on the future pregnancy status. In some cases, the method comprises the step of obtaining the historic insemination data from a device attached to the at least one female animal. In some cases, the at least one female animal is a cow. In some cases, the non-target group is selected based on one or more characteristics. In some cases, the method comprises the step of outputting one or more control signals. In some cases, the control signals are based on one or more of: the future heat event, cycling status; or a derivative thereof. In some cases, the one or more control signals are configured to control one or more of: a drafting gate operable to draft the at least one female animal to different locations; a wearable device operable to display indicia on the at least one female animal, such as an LED light; a wearable device configured to apply stimuli to the at least one female animal to guide the at least one female animal in operation; and a display device comprising a display operable to be read by a user. In some cases, the method comprises outputting the future pregnancy status. In some cases, the method comprises outputting the future pregnancy status based on one or more parameters. In some cases, the one or more parameters comprise a threshold, date or status. In some cases, the method comprises the step of performing one or more actions based on the future pregnancy status, the one or more actions comprising any one or more of: an alert; adjusting a feed parameter; optionally comprising a composition, energy and / or quantity of feed given to the at least one female animal or a feeding schedule; providing nutritional supplements or hormones to the at least one female animal; adjusting a timing of a reproductive event, such as the timing of insemination of the at least one female animal; geographically moving the at least one female animal. In some cases, geographically moving the at least one female animal is performed by a wearable device configured to apply stimuli to the at least one female animal to guide the at least one female animal in operation. In some cases, the method comprises the step of updating the future pregnancy status determination following completion of the one or more actions. In some cases the aspect is applied to a method of determining a cycling status of the at least one female animal. In a twelfth aspect, the disclosure broadly consists of a system configured to implement the method(s) as described in the examples above. In a thirteenth aspect, the disclosure broadly consists of a computer implemented method as described in the examples above. In a fourteenth aspect, the disclosure broadly consists of a system for determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the system comprising a processor configured to: obtain historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates, the primary event dates comprising dates of prior heat events and / or birthing events for each of the non-target group of animals, and secondary event dates, the secondary event dates each comprising the date of a heat event following the primary event dates; obtain a primary event date for each of the at least one female animal, and determine, using the primary event date and the historical heat data, the future heat event of the at least one female animal. In some cases, the processor is configured to: receive a signal request from a user device; determine the future heat event based on a request from the user; and send the determined future heat event to the user device. In some cases, the system comprises at least one wearable device configured to be worn by the at least one female animal and / or at least one of the non-target group of animals. In some cases, the at least one wearable device is configured to detect one or more of the primary event dates, secondary event dates of the at least one female animal and / or at least one of the non-target group of animals. In some cases, the at least one wearable device is configured to detect a birth event and / or insemination event of the at least one female animal and / or at least one of the non-target group of animals. In some cases, the processor is configured to retrieve signals from the at least one wearable device. In some cases, the processor is configured to transmit signals to the at least one wearable device. In some cases, the wearable device comprises: a device processor, one or more movement sensors, and one or more stimulus devices, wherein the device processor is configured to control the one or more movement sensors and one or more stimulus devices. In some cases, the device processor is configured to communicate with the system processor. In some cases, the device processor is configured to receive one or more signals from the system processor. In some cases, the received signals comprises a guidance command based, at least in part, on the future heat event. In some cases, the device processor is configured to transmit one or more signals to the system processor. In some cases, the transmitted signal comprises one or more movement signals, the movement signals indicative of one or more of a heat event, a birth event and an insemination event. In some cases, the processor is configured to compare the determined heat event to a threshold value. In some cases, the processor is configured to transmit a signal to one or more of: a user device, a display, and a wearable device, based on the heat event and the threshold value. In some cases, the signal comprises a guidance command. In some cases, the guidance command comprises one or more of: guiding the at least one female animal to a location; holding the at least one female animal at a location; or instructions for the device processor to control the stimulus device to separate the at least one female animals from their target group of animals, based on the heat event and threshold value. In some cases the system comprises any one of more of the features of, or is configured to perform one or more of the steps of the other aspects. In a fifteenth aspect, the disclosure broadly consists of a system for determining a pregnancy status of at least one female animal, forming part of a target group of animals, the system comprising a processor configured to: determine the date of one or more heat events according to the method of any of the above aspects; obtain historical insemination event data; obtain a predefined date for the at least one female animal, determine, based on the future heat event and the historical insemination event data, a predicted in-calf probability. In some cases the system comprises any one of more of the features of, or is configured to perform one or more of the steps of the other aspects. In a sixteenth aspect, the disclosure broadly consists of a system for forecasting a next heat event of a female animal forming part of a target group of animals utilising data from a non-target group of like animals, the system comprising a controller configured to perform the steps of: - receiving or determining for each animal of the target group and non-target group which have had two reproductive events, the time of a prior event and a next event; and - determining for each animal of the target group and non-target group which have had two reproductive events, - a prior distribution of the non-target group of time between the prior event and the next next event; and - a current distribution of the target group of time between the prior event and the next event; - receiving or determining for the animal of the target group which has only had a prior event, the time of the prior event; and - calculating a posterior distribution of time between the prior event and a forecasted next heat event of the animal within the target group based on the prior distribution and current distribution. In some cases the system comprises any one of more of the features of, or is configured to perform one or more of the steps of the other aspects. In a seventeeth aspect, the disclosure broadly consists of a system for predicting the pregnancy status an animal of a target group, which has been inseminated and has an unknown pregnancy status, utilising reproductive data of a non-target group which have also been inseminated, the system comprising a controller configured to perform the steps of: receiving or determining the reproductive data for each animal of the non-target group and target group, the reproductive data comprising: an insemination date; and a pregnancy status relating to the insemination determining from the reproductive data for each animal of the target group and non-target group: a prior probability distribution of pregnancy status for each animal of the non-target group; and a current probability distribution of pregnancy status for each animal of the target group; and determining a posterior probability of the pregnancy status of the animal based on the prior probability and current probability. In some cases the system comprises any one of more of the features of, or is configured to perform one or more of the steps of the other aspects. In a tenth aspect, the disclosure broadly consists of a method of forecasting the pregnancy of an animal to be inseminated at a proposed insemination date, based on a forecasted cycling status, comprising a. determining for each animal of the target group and non-target group which have had two reproductive events, the time of a prior event and a next event, and b. determining for each animal of the target group and non-target group which have had two reproductive events, i. a prior distribution of the non-target group of time between the prior event and the next event; and ii. a current distribution of the target group of time between the prior event and the next event; c. receiving or determining for the animal of the target group which have had only a prior event, the time of the prior event; and d. calculating a cycling status posterior distribution of time between the prior event and a next event of the animal within the target group based on the prior distribution and current distribution; e. receiving or determining the reproductive data for each animal of the non-target group and target group, the reproductive data comprising: i. an insemination date; and ii. a pregnancy status relating to the insemination f. determining from the reproductive data for each animal of the target group and non- target group: i. a prior probability distribution of pregnancy status for each animal of the non- target group; and ii. a current probability distribution of pregnancy status for each animal of the target group; and g. determining a pregnancy status posterior probability of the pregnancy status of the animal based on the pregnancy status prior probability and current probability; and h. receiving a proposed insemination date for the animal to be inseminated: i. forecasting the pregnancy status of the animal to be inseminated across a time period after the start of the proposed insemination time period utilising the i. cycling status posterior distribution; ii. pregnancy status posterior distribution; and iii. proposed insemination time period. In some cases the system comprises any one of more of the features of, or is configured to perform one or more of the steps of the other aspects. In an eighteenth aspect, the disclosure broadly consists of a method of determining a probability of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the method comprising the steps of: obtaining historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates, the primary event dates comprising dates of prior heat events and / or birthing events for each of the non-target group of animals, and secondary event dates, the secondary event dates each comprising the date of a heat event following the primary event dates; obtaining a primary event date for each of the at least one female animal, and determining, using the primary event date and the historical heat data, a probability of the at least one female animal having a heat event on the predetermined date.. In some cases the system comprises any one of more of the features of, or is configured to perform one or more of the steps of the other aspects. In this specification where reference has been made to patent specifications, other external documents, or other sources of information, this is generally for the purpose of providing a context for discussing the features of the invention. Unless specifically stated otherwise, a reference to such external documents is not to be construed as an admission that such documents, or such sources of information, in any jurisdiction, are prior art, or form part of the common general knowledge in the art. It is also to be understood that the specific devices illustrated in the attached drawings and described in the following description are simply exemplary embodiments of the invention. Hence, specific dimensions and other physical characteristics related to the embodiments disclosed herein are not to be considered as limiting. It is acknowledged that the term ‘‘comprise’’ may, under varying jurisdictions, be attributed with either an exclusive or an inclusive meaning. For the purpose of this specification, and unless otherwise noted, the term ‘comprise’ shall have an inclusive meaning, allowing for inclusion of not only the listed components or elements, but also other non-specified components or elements. The terms ‘comprises’ or ’comprised’ or ‘comprising’ have a similar meaning when used in relation to the system or to one or more steps in a method or process. For the purposes of this patent, the term “in-calf” means that an animal, especially a bovine animal, is pregnant or carrying a foetus or foetuses in its uterus. The term “calving” means the process of giving birth to a calf or calves by an animal, especially a bovine animal. The term “calved” means that an animal, especially a bovine animal, has given birth to a calf or calves. These terms are used to describe one embodiment of the invention that relates to bovine animals, (or cows, elephants and deer) and their specific language, but they could be used for any animal that reproduces by giving birth to live offspring. Different domestic animals have different names for their young and their reproductive processes, and these are herein included within the scope of the invention. For example, horses do not give birth to calves, but to foals, and the process is called foaling. Similarly, sheep give birth to lambs; goats give birth to kids; pigs give birth to piglets and the process is called farrowing. Broader terms may be used herein, or used interchangeably with animal specific terms, for example, the term parturition, giving birth, or birthing may be used for calving, or calved, or vice versa. The term pregnant, may be used for in-calf. These terms may herein be used interchangeably in this specification and relate to all animals, regardless of the occasional cow specific nature of the terms. As used hereinbefore and hereinafter, the term “and / or” means “and” or “or”, or both. As used hereinbefore and hereinafter, “(s)” following a noun means the plural and / or singular forms of the noun. When used in the claims and unless stated otherwise, the word ‘for’ is to be interpreted to mean only ‘suitable for’, and not for example, specifically ‘adapted’ or ’configured’ for the purpose that is stated. For the purpose of this specification, where method steps are described in sequence, the sequence does not necessarily mean that the steps are to be chronologically ordered in that sequence, unless there is no other logical manner of interpreting the sequence. The entire disclosures of all applications, patents and publications, cited above and below, if any, are hereby incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS Preferred embodiments of the invention will be described by way of example only and with reference to the drawings, in which: Figure 1: shows a schematic of the possible communications network between a device on an animal and the user or controller. Figure 2: shows a chart of an animal’s reproductive cycle. Figure 3: shows a flow chart of the determination of a reproductive cycle event. Figure 4: shows an example distribution of a first heat after a birth event. Figure 5: shows an example distribution of time between heat events. Figure 6: shows an example distribution of a probability of being in-calf after mating. Figure 7: shows an example distribution of being in-calf with changing events. Figure 8: shows an example distribution of being in-calf with changing event occurrence. Figure 9: shows an example distribution of being in-calf with changing event occurrence. Figure 10: shows an example of updating distributions with changing event occurrence. Figure 11: shows a flow chart of the outputs used to adjust the reproductive cycles. Figure 12: shows the components and communications of an animal device. Figure 13: shows a display of a forecast herd cycling percentage. DETAILED DESCRIPTION With reference to the above drawings, in which similar features are generally indicated by similar numerals, Figure 1 illustrates a system 2. The system may be referred to as an animal system. In some cases, the system 2 is adapted to receive animal guidance commands and / or record animal movement data to allow determination of animal behaviour, such as cycling and / or birthing (aka calving for cows). The system 2 may be used to implement, or at least provide data too and / or implements of the described methods. The described methods relate to determination of heat events and / or birth or pregnancy events. The methods use historical data to determine the future events. The historical data may be detected by a sensor, observation, or other device, or they may be observed and recorded by a user. There are systems and devices known in the art that are capable of detecting heat and / or birthing events. An example device 400, shown in Figures 1 and 2, utilises technology by the company HALTER® and is further described in patent publications WO2019180624A1, included herein by reference, and WO2019180623A1, included herein by reference. Devices, such as device 400, can analyse the movement data to determine when a birthing and / or heat event has occurred. For example, WO2017211473A1, included herein by reference, discloses a method and apparatus for providing an indication of the oncoming parturition, i.e. calving, in livestock. In one example, system 2, as shown in Figure 1, comprises a wearable device 400 or housing configured to be worn by an animal 10. Such an animal 10 may be any of dogs, pets, dairy cows, beef animals, bovine, goat, bos, Bos taurus, bison, sheep, bull, lama or any other female animal that is desired to have a future heat event, cycling status and / or in pregnancy status determined. In this specification, the wearable device 400 is implemented as a collar, e.g. for placement around the neck of an animal 10. Many placements and appropriate implementations are possible and the most suitable location will be dependent on the particular animal and the environment in which the animal 10 and wearable device 400 are used. The device 400 may comprise or communicate with a secondary device 700 or secondary housing. The secondary device is also associated with or attached to the animal 10. The secondary device 700 optionally has some or all capabilities of the device 400, or shares capabilities with the device 400. The secondary device 700 may be located remote from the first device 400, such as a tail- mounted device 700 specifically for detecting birthing or a birth event. The device 400 is described in more detail towards the end of the description. The animal 10 may form part of a target group which may form all or part of a mob or herd - where one or more animals 10 in the target group may wear a device 400. Preferably all or substantially all the animals wear a device 400. A farm may be made up of a herd of animals. The herd may be broken down into multiple mobs which are retained together in a paddock. A group may describe a mob, a herd, multiple mobs, or even multiple farms of animals. There may be multiple farms, each with multiple groups from which data can be detected and collected. The target group of animals may be any collective group of animals, which may or may not be contained together in the same area. Herein, the term cow may be used interchangeably with animal, however particular timings, dates and animal markers described herein may relate to cows. These timings, dates and animal markers could be updated for a different animal. As shown in Figure 1, system 2 may optionally communicate with, or comprise, a system comprising a backend 500 and front end 200. The backend 500, in one example, is configured to run a computer program to receive data from the front end 200, a user 202, user device 201, or a device 40. The backend 500 may implement one or more methods to predict variables relating to a heat event, the cycling status and / or in pregnancy status (aka in-calf status for animals which calve) of an animal 10 or group of animals. The disclosed methods may be operated by a processor(s). The processor(s) may be located in the front end 200, backend 500 and / or on devices 400 and / or 700. In some cases, the processor (or controller) may be separate from system 2 and communicate with one or more processors or devices of system 2. The disclosed systems and methods may be used to determine, predict or estimate a heat event. The heat may be the cycling of an animal 10. In some cases, the heat event may be a particular portion of the cycling, such as a heat or estrus. In some cases, a combined figure for a group of animals, such as a cycling percentage may be determined. Livestock management can be improved by determination of heat events so as to improve pregnancy rates in animal 10, or group of animals. Livestock managers may refer to the reproductive cycles of their animals using a variety of terms. For example: ● a cycling animal is one that is reproductively active and capable of conceiving; ● cycled may be used to describe an animal that has experienced its first heat cycle after birthing; and ● a first heat refers to the initial estrus or heat that a female animal goes through after giving birth (calving). It therefore marks the beginning of the animal's reproductive cycle after birthing. If an animal 10 has had their first heat, and is continuing to cycle, their cycling status is ‘cycling’. If the animal 10 has not had a heat, and / or is not cycling, the cycling status is ‘not cycling’. Some examples in this disclosure determine an animal’s first heat event time or their cycling status. A livestock manager would prefer, by an insemination date or mating period, the animal 10 to have had their first heat and hence be cycling, and be ready to be inseminated at some time within the mating period. This is because the animal 10 is likely to have a heat within the mating period if it is cycling. In some cases, the device 400 is configured to detect cycling, or portions of cycling. For example, heat events may be detected by movement sensors. In some cases, portions of the cycle are detected by medical tests performed by veterinarians. Heat events detected by the device 400 may be used to determine if an animal 10 is, or will be, cycling. In some cases, other techniques may be used to determine cycling, such as those used by veterinarians to determine cycling without detecting heat. Heat events may be used to determine cycling. Determining a heat event should be understood as related to, or a part of, cycling status determination, as cycling status can be determined from the heat event. A future heat event refers to a heat event that has not yet occurred. Livestock managers may use predicted heat events, or other predicted information. to manage their animals. In some cases, a heat event is determined as a date. A date may be a particular date, or may represent an expected date. An expected date may be the date of highest probability of the heat event, with dates either side having lower (but greater than zero) probability. In other cases, a distribution is provided showing the probability of a heat event across one or more dates. This use of date is because the forecast heat event will have a bound or level of accuracy that may be greater than a day. This accuracy may be due to variability between animals and / or the difficulties in determining heat events. In some cases, forecast heat events will be confirmed by direct measurement or sensing of the animal 10 to determine when to inseminate the animal. A determined heat event helps simplify or direct this testing. It can also be used to change inputs to the animal 10 to change the forecast. This can ensure a group of animals is having heat events at substantially the same time. Natural variation, such as the calving spread of a previous season can affect conception rates or heat timings, meaning monitoring is advantageous every year. A heat event may be the first, next, or prior heat event from a birth event. A birth event may be the actual birth event date, or an expected birth date. A birth event may be last season’s birth event, or this season’s birth event - where the pregnancy status is being predicted. A pregnancy and / or birth event will alter an animal’s cycling pattern. In particular, the date of a heat event following a birth event may be different than following a previous cycle. Therefore, the method may distinguish between birth events and prior heat events in the data. Herein the term forecast or projection is used to describe a determination of a status or event, such as a heat event, in the future. For example, the heat or estrus of an animal 10 at a future time. A forecast refers to a calculation that uses data from previous events (e.g., of other animals), combined with recent trends to determine a future event outcome. A prediction may be described as an actual act of indicating that something will happen in the future, or a determination of something that is occurring in the present, such as predicting if an animal 10 is currently pregnant. In some cases, the method determines a date. However, this date may be understood as a predicted date, an approximate time period, or a distribution of probabilities of a date, unless a specific date is determined. For example, a date of 11 January may be understood to refer to a distribution of days including 11 January, each day having a probability of being the heat event. This date distribution may be used to express a tolerance or variability of the determination. The term “date” shall be construed broadly to include any representation of a point or period of time, such as a timestamp, a calendar date, a day of the week, an hour, a minute, a second, a millisecond, a nanosecond, or any other unit or format of time measurement. The term “date” is not limited by any specific convention, standard, protocol, or system of time representation or measurement. For example, a birthdate may be the 1st of January, or 12pm on 1 January, in the 12th hour of the year, or 12:00:00:00, Monday of the 1st week of January, the 1st week of the year, or 35 days before the first heat event. Data sets Methods of determining heat events for a target group of animals are disclosed based on, at least, a non-target group of animals. The terms "target group" and "non-target group" refer to groups of animals. The animals may be within a herd, on farm, or amongst multiple farms or even multiple states, regions or countries. The non-target group refers to animals for which determinations are not being made. The target group refers to animals for which determinations are being made. The method uses historical heat data from the non-target group to determine heat events of the target group. Historical heat data refers to collected data of previous heat events and birth events of the non-target group. The historical heat data may be updated by the current heat data of the target group (e.g., target heat data) of animals. For example, if animals in the target group of animals have previous heat events and / or birth events this data may be used to update the historical heat data. This updating may adjust the historical heat data to account for variations between the non-target animals and the target animals. The target heat data may comprise observations from a period (such as the current year), or from one or more previous years. The selection of the target heat data period may be determined dependent on changes in the target group over time. A stable target group may be use multiple previous years of data, whereas a more variable target group may only use the current year. The target heat data may include new data, or observations as they occur. For example, the animals may be monitored, and if heat events are detected these may be added to the target heat data, and used to update the prior distribution. The non-target group may have a number of characteristics. The characteristics may correspond to (for example being the same as, or within a predetermined range of) characteristics of the target group. This may improve the determination of the target group heat events because the non-target group is similar to the target group. The characteristics may be based on location. For example, the non-target group may be in the same mob as the target group, i.e. they physically graze the same location. For example, the non-target group may graze on similar land (e.g. pasture) as the non-target group. For example, the non-target group may be located within a predetermined distance of the target group. The non-target group may be in the same mob, farm or region as the target group. The characteristics may be based on the type of animal. For example, the non-target group may be the same breed as the target group, or a related breed. The characteristics may be based on a feature of the animal. For example, the non-target group may be the same or similar age as animals in the target group, or a similar size to the animals in the target group, or have a similar number of birth events. The characteristics may be based on the genetics, breed or species of the animal. For example, the non-target may share the same genetics, or be the same or similar breed or species of animal as the target group. The characteristics may be based on the ownership of the animal. For example, the non-target group may be owned by the same person or company as the target group. The characteristics may be temporal. For example, the non-target group may comprise animals with heat data within the last twelve months, or twenty four months, or the previous season. The characteristics may be based on the farm system, feed input, or forage input. For example the non-target group may eat the same type of pasture (such as rye grass) or eat the same amount of grain, or the same feed ‘system level’ as the target group. The non-target group may be a combination of non-target animals with different characteristics. For example, the non-target group could include groups of animals with a first characteristic and groups of animals with a second characteristic. For example, the groups may be selected on location and / or breed. For example, the groups may be selected based on location and / or heat data within the past season. Further representative examples of non-target characteristics include: animals from the same mob, last season; spring calving cows in the same mob; two year old cattle on the same farm across the current and previous seasons; an age group of cattle across the mob, farm, or region that were selected to calve at a certain time of year, across the years (for example, three year old spring calvers from the same region, over the last 4 years). In some cases, the non-target group may comprise tens, hundreds, thousands, tens of thousands, or hundreds of thousands of animals across multiple farms, and / or across time. The non-target group may be all animals for which data is available. This may include prior data from animals in the target group. For example, where the target group includes data from previous seasons. In the examples where animals wear the device 400, the non-target group may include all animals wearing the devices 400 which have suitable data. Groups with different characteristics may be treated differently by the system. For example, the historical data of different groups may be weighted differently. In some cases, this weighting may create different distributions used to determine the heat events. For example, non-target group animals in the same mob, or the same farm, as the target group animals may be weighted more heavily. This is because they may be expected to be more representative of the target group animals compared to non-target group animals in a different mob, farm, or region. Multiple weightings, or combinations of weightings may be used. For example, a non-target group in the same mob as the target group may be weighted favourably over a non-target group on the same farm, and the same farm may be weighted favourably over the same region, and the same region may be weighted favourably over different regions. The animals 10 in the non-target group have had at least two events. Two events provide th dates of, or an interval between, the first (primary event) and the second event (secondary event). The events may comprise a birth event and a heat event, or two heat events. Figure 2 shows a timeline of the reproductive cycle of an animal. In some cases, the non-target group for determining a heat event may comprise animals that have already given birth (birth event 150, or last season’s calving) and subsequently had a first heat event 151 after birthing, or had a first heat event 151 (a prior heat) and a second heat event 152 (a next heat). If a pregnancy is being determined the non-target group may comprise animals that have already given birth (this season’s birthing) following a prior heat event. As two events (at least - more events may be known, including previous years) are present for each non- target animal the method distinguishes primary events and secondary events. Where multiple sets of two events are known these may each be used to identify primary and secondary events, including where they overlap (e.g. birth event 150 and first event 151 and first event 151 and second event 152). The primary events refer to first events (either a birth 150 or heat event 151, 152) and the secondary events refer to the events directly following the first events. For example, a secondary event is the first heat event 151 after a birth primary event 150, or a second heat event 152 after a first heat event 151, or a third heat event where the second heat 152 event is the primary event. Although preferably available as specific dates, date ranges or estimates may be used, if required. The target group may be any group of animals which are to have a prediction for, e.g., the animals which have had only one reproductive event, or which are being monitored for a next heat event. In one example, the target group is a group of female animals which include animals that have given birth but have not yet experienced their first heat (estrus), or have started cycling, after birthing. The target group may be a mob or an entire farm herd, and hence the non-target group may be animals on other farms, or at least not within the target group. Where prior data of the target group is available, or becomes available this can be added to the non-target data. In one example, a method is disclosed of determining a future heat event of the target animals. Figure 3 shows diagrammatically how the method uses the historical heat data 220 from the non-target group of animals (and optionally historical heat data 221 from the target group of animals) to determine a secondary (future) heat event 225 from a primary heat event 224. As an intermediate step the method may create a trained model 223 of secondary heat events based on primary heat events. The model 223 may be trained by dividing the historical data 220, 221 into training and validation data sets. The division may be based on the selection characteristics 222. For example, the training data set may use the non- target animal data 220 and the validation set may use the target animal historical data 221. In some cases, the historical data 220, 221 may be selected 222 or filtered based on characteristics of the animals. The historical heat data 220, 221 includes the dates, days or distribution of days between primary and secondary event dates. In some cases, a distribution is formed from the non-target historical data and is updated with the target animal historical data. This means that the days from birthing to a first heat, or between heats, is used to inform determinations about the target group. A corresponding approach is disclosed for determining pregnancies or birthings. The non-target group historical heat data provides cycling and / or pregnancy information. This historical heat data can therefore serve as a reference for determining or forecasting when animals in the target group are likely to have their first heat or start cycling. In the corresponding case for pregnancies the historical heat data can serve as a reference for when animals that have been inseminated are predicted to be pregnant. Surprisingly, the historical heat data allows an accurate determination of an expected heat event based on an earlier heat event in the target animal (e.g., a secondary heat event). This means that the use of historical data with primary event dates and secondary event dates, and target group primary event dates allows accurate determination of secondary event dates (future heat events) for the target group. Advantageously, this can be updated autonomously and frequently, allowing updated monitoring of the future heat events, or correspondingly future reproductive states (heat events, pregnancies or cycling). The model can be updated by addition of target animal data, for example as heat events occur in the target animals. Similarly, the historical data allows accurate determination of pregnancy status. Historical data 220, 221 may comprise one or more of: birthing event data; heat event data, and insemination data. In some cases, historical data 220, 221 is, or has been, automatically captured sensors. The sensors may be part of system 2 or device 400, as shown in Figure 1. Alternatively, the data 220, 221 may be gathered and uploaded to a processor or storage device. For example, by the livestock manager. Heat data (historical or current) may relate to cycling, heat events, a first heat event 151 (after birth event 150), a second heat event 152, or subsequent heat event. In some cases, the heat events 151, 152 may be determined based on cycling data or insemination data. For example, if a cycle is identified the start of the cycle may be determined as a heat event. Similarly if a pregnancy 161 is detected after an insemination 160, a heat event may be determined based on the insemination date 170. Heat events 151, 152 may be described relative to one another. For example, a heat event may be described as the last heat and the heat before that, a prior heat. It will be understood that this refers to an immediately prior heat event (i.e. without an intermediate heat event occurring between the two). Insemination event data, or pregnancy data, refers to any information related to an animal giving birth. Insemination event data may comprise data relating to the insemination and pregnancy or birth following insemination. This may include: an actual date of birth following the pregnancy or an expected birthdate. The actual birthdate may, for example, be determined by: a sensor that detects the signs of birthing (including one or more of changes in temperature, movement, or behaviour of the animal); observing the animal visually or remotely; or otherwise. Device 400 may be used. An expected birthdate (of last season where an actual birthdate is unknown) may, for example, be determined by a prediction or an estimate based on known data (for example, insemination date, which is the date when the animal was artificially bred, or the expected insemination date, which is the date when the animal was planned to be artificially bred, or from an aged pregnancy test which confirms pregnancy). This may be input manually or from a device 400. The method may characterise the data dependent on if an actual or estimated date is used. The insemination data or pregnancy data may also include the insemination date (mating date). This may be an actual date at which insemination took place, or an estimated date of insemination. For example, it may be a projected start of mating (PSM) date (when the farmer or the veterinarian plans to begin the artificial breeding (AB) program for the herd). Alternatively, it may reflect a mating period 180 in which a bull is available for mating. The insemination data may be entered manually, by a user input or by a barcode scanner or a RFID reader or similar. The livestock manager may plan to have a desired insemination date or birthday for each season, and select an insemination date or mating period based on the desired dates. The disclosed methods can be used to increase the proportion of a mob in estrus at the desired insemination date. As with heat data the insemination event data may be divided into historical insemination event data, which is insemination event data from the non-target group of animals, and target insemination event data, which is insemination event data from the target group of animals. The target insemination event data may include observations of the target group. The observations may be from the current year and / or one or more previous years. In some cases, the method is configured to filter out one or more values from the historical data. These may comprise outliers and / or erroneous events. Possibly erroneous events include silent heats or false heat events. In some cases, the method is configured to receive all data from a larger set of animals (for example, all animals where data is available) and select only a relevant subset for the historical data. This may be based on the described characteristics. Figure 3 shows a broad outline of the disclosed method. However, a variety of models 223, or the specific determination methods for the determination of heat events or pregnancy, may be used. Herein a Bayesian modelling approach is explained in detail. However, this is not the only model which may be used. For example, a machine learning method may be trained and / or validated on the historical data 220, 221. The machine learning model may distinguish between the non-target animal heat data and the target animal heat data. Representative examples of machine learning methods include neural networks (including variations such as deep neural networks), decision trees, support vector networks (SVNs), artificial intelligence, expert systems, evolutionary algorithms. The weights or model of the machine learning methods would depend on the historical data 220, 221. In one example the machine learning method uses historical data as described. However, additional data, or additional data labels may be added. For example, reproductive events could be labelled for inputs into a neural network. Additional types of labelled data may be used. Some examples include: farm type; animal species; feed type; region; weather; animal characteristics; animal history; animal health history; farm business spend. The use of this data may enable the neural net (or other machine learning method) to identify trends in the data to improve determinations. The machine learning methods may use training methods or model construction techniques based on this data. The training or models may be tailored towards determination for a group of animals, or may be tailored towards individual animals. In one example, the machine learning algorithm would use the non-target and target data described herein and be trained to make one or more of the described predictions. Distributions In these described examples, the forecasts and predictions are determined by using Bayesian modelling. Bayesian modelling operates on the principle of updating beliefs or probability distributions as new data becomes available. Starting with an initial belief, known as the prior distribution, Bayesian modelling updates the prior distribution, using observed data, to obtain a posterior distribution. The posterior distribution represents the refined belief or prediction based on the available data. A prior and current distribution are determined to determine a forecast, or ‘posterior distribution’. The use of Bayesian modelling allows the use of distributions to determine future heat events (or reproductive status) from historical data and primary events of target animals. The historical data is used to determine a prior distribution of the primary and secondary dates. The prior distribution is updated based on observed data (aka an updated prior distribution or current distribution) to obtain a posterior distribution. This posterior distribution may then be used to determine future heat events of the target animals based on their primary event dates. The prior distribution and / or observed data may be based on the days or spacing of the primary and secondary events of the non-target animals. Where measured data is not available one or more of the dates may be predicted based on known events (e.g. birth events). In some cases, the distribution does not provide a single number of days, or length of period, but instead a distribution of one or more numbers of days, each optionally associated with a probability. This can provide flexibility to the method to account for variations between animals. Once the prior distribution has been obtained (by measurement of prediction), the posterior distribution can be determined by combining the prior distribution and the observed data of r the non-target animals. Bayesian modelling has several advantages. Because Bayesian modelling combines prior knowledge (historical data 220, 221) with observed data (target data 221), the determinations of heat events or pregnancy become more robust and less susceptible to fluctuations or anomalies in the data from the target group alone. The Bayesian model adapts over time as new data becomes available. For example, as heat events occur in the target group, providing additional target animal historical data. This allows for continuous improvement in the determination, particularly where large data sets are available. This example introduces three distributions (D1, D2, D3). The distributions are estimates of the current distribution for a target group of animals. The distributions are calculated as Bayesian posteriors, calculated from the prior distribution 220 of the non-target group and the current observation of the target group (i.e., the target event data). The prior distribution in one example represents the time between reproductive events of the non-target group based on the historical data 220 (a second example will represent the probability an animal is pregnant following an insemination). The method uses this estimate of time between reproductive events to determine an animal’s next heat event (or equivalently cycling status). The examples are provided for a group of cows, although it may be applied to other animals or for an individual cow. The prior distribution uses data (measured or otherwise obtained) of two previous reproductive events. This may be the time between subsequent reproductive events. For example, from birthing to the first heat event, or between successive heat events. Typically this requires both the birthdate and the heat date, or two heat dates to be known. Because of differences between birth and a typical heat the method may differentiate between these (or other) reproductive events. This can improve the accuracy of the method. The prior distribution is updated based on the heat data of the target group. This improves accuracy by incorporating knowledge on the target group and may allow updates as new target group heat data becomes available. For example, the target group may be monitored for heat events during the season (e.g. by devices 400) and the prior distribution updated based on any new heat events. A first distribution (a heat distribution) may be used to estimate the date of, or time to a heat event 151, 152. If an animal has had at least one heat event after last season’s birthing, it has cycled. In this example we create a distribution, D1, of the probability of a next heat event occurring in ≤ n days since a birthing event (prior reproductive event) is: D1 = P(animal has a next heat event | n days since last season’s birthing event) In this example, the probability distribution is built as a non-parametric distribution. However, alternative distributions are possible. For a given number of days, n ∈[0... ∞], and a specified group of cows: Equation 1: Figure 4 shows an example plot of D1. Figure 4 shows the probability of having cycled (a first heat) since calving against the number of days since calving. Figure 4 shows the prior distribution (the distribution based on the non-target animals). As expected, the probability of having cycled increases until substantially all the cows have cycled, with a peak rate being at or near 21 days. However, the target group of animals may differ in one or more characteristics from the non-target group. This may, after observation of at least some heats in the target animals, lead to the posterior distribution shown in Figure 4. The posterior distribution in one example is an updated prior distribution based on the actual heat events of the target group of animals. In other examples, the posterior distribution results from any combination of the target event data (e.g. current observations of or events of the target group) and the prior distribution from the non-target group. The appropriate combination could be determined by Bayesian updating, machine learning, etc. This allows updating of the distribution to more accurately represent the target group. In some cases, the posterior distribution is used as the prior distribution for determination of the next season, for example. In this way the method can improve in accuracy over time. In some cases the distribution is used to determine further detail of the heat events. For example the D1 distribution can be used with different parameters to calculate the probability of a cow having a heat on a particular day given she calved c days ago and has not yet cycled: Equation 2: Further combinations of distributions may be formed, if helpful to determine heats, cycling status and / or pregnancy. A second distribution may be used to estimate the time to a heat event 152 after a previous heat event 151. The distribution assumes that the animal is not pregnant. In other cases, individual distributions could be created for each heat event (instead of the described cumulative distribution). The distribution D2 is the probability distribution of an animal having exactly x heats in the m days since the animal's last heat event. Because m represents the number of days since a heat event if m is negative, D2 = 1 because a negative m implies a heat event is known to occur in n days. The distribution is expressed as: D2 = P(animal has x heats | n days since last heat event)The probability distribution may be built as a non-parametric distribution. In this example, For a given x∈[0...∞], and n ∈[0...∞]:Equation 3: In some cases the distribution uses a parameter f to distinguish between the first heat since a birth event (f = 1) and a following heat (f = 0), and that the last heat event was the first heat event since a birth event. This forms two related distributions dependent on the type of primary event. Figure 5, shows an example plot of D2. In this case no difference is made between birth events and following heats. However, these may be treated separately and / or the D2 distribution may be summed across f = 0 and f = 1. Figure 5 shows the probability of having 1, 2 or 3 heats within the time range. As might be expected the heats occur approximately every 21 days. However, this is at the group level. For individual animals the particular characteristics of the animal (such as number of heats, previous time between heats, age, type, movement or other characteristics) may be used to change the prediction. In some cases, the characteristics used are that of the reproductive event, meaning that the determination of a heat event will be the same as other animals in the target group with the same date for their reproductive event. In some cases, such as for insemination, a livestock manager is interested in heat events. However, in some cases the pregnancy status of the animal is also, or primarily of interest. A third distribution, D3, may be used to estimate the probability an animal is pregnant 161 after an insemination event 170 during a mating period 180. This distribution may be used in combination with heat event determination, or independently. For example, in one example, the pregnancy status may only be predicted for animals after a known heat. In another example, the pregnancy status is predicted for animals after a predicted heat in the future. An insemination event may refer to a particular mating date (which may be known due to artificial insemination), or to a mating period when mating was expected to occur. The distribution represents that when an animal is mated (inseminated), she has a chance of getting pregnant (in-calf) to that insemination of between 30% - 80% depending on various factors. The conception rate may vary. For example some herds may have a conception rate of 40 to 65%. This rate may be modelled in the method, or a static rate (for example 55%) may be used. In some cases, the method may be updated based on outcomes of the target animals. For example, after a predefined period (such as 21 days) animals which do not have a heat event may be in-calf. The probability distribution of an animal (e.g., a cow) being pregnant (e.g., in-calf, or a pregnant / positive pregnancy status) in the days since mating, given insemination (e.g., mating date) was n days ago and the animal has not had a subsequent heat event is defined as: D3 = P(animal is pregnant | insemination was n days ago) In this example D3 is built in two steps, for both the prior distribution and the current distribution / observation (the posterior distribution being the prior distribution updated based on the current distribution / observation) for the target group. D3 uses two data points, an insemination date 170 and a pregnancy status. The pregnancy status may be determined from the insemination date, and another variable which relates to whether an animal is pregnant. One animal may have multiple inseminations and thus insemination dates. However the conception date is the date which caused pregnancy (e.g. the insemination directly preceding the pregnancy). Three examples are provided of measurements or variables which may be used along with the insemination date to determine the conception date: 1. Birthing date. If an animal has a birthing date (of an offspring of the animal), the conception date is likely the closest insemination date to her gestation period before the birthing date. For a cow, the conception date will likely be the insemination date closest to ~283 days, which is a cow’s gestation period, before the birth date. 2. Pregnancy test. If the animal has a positive pregnancy test result, her conception date is the closest insemination date to the estimated conception date. For example, an agglutination pregnancy test for animals, or commonly known as the Agerd pregnancy test, is a diagnostic method used to determine if a female animal, such as a cow, is pregnant. The pregnancy test may provide an estimated conception date. In some embodiments, the estimated conception date is used without the insemination date. 3. Not having a heat. E.g., non-returned, stopping cycling, or becoming cycled. If the animal has not had another heat following the animal’s latest insemination, then the most recent insemination date from detecting a cycled status is considered to be the conception date. The historical data may be labelled based on the above measurements, or reviewed to ensure suitable pregnancy status and / or insemination dates are included. Once the insemination dates have been labelled, i.e., determined which insemination dates are conception dates, then the prior and current distributions can be created as a non-parametric distribution for a number of days, n ∈ [0,∞]: Equation 4: As shown in Figure 6 the prior distribution shows there is initially an expected portion of animals in-calf. As the number of days increases several of the animals will have a heat event, so the proportion of the animals in-calf will increase until all the remaining animals must be in-calf based on the insemination. The distribution only shows the animals which have not had a heat. An alternative example would look at the percentage across all animals and / or include animals which are pregnant and still experiencing heats. A posterior distribution is shown which updates the prior distribution based on the data or observations of the target animals. The posterior distribution of Figure 6 shows, for the target group of animals, that the probability of being in-calf was somewhat higher than expected. As explained above, this distribution could be used to determine improved results (by using the posterior distribution, or weighting a future distribution to follow the posterior distribution more closely). Determination of heat events The method may determine a group of animals at once, providing an expected distribution for all the animals, or it may determine individual animals and form the group by the summary of all of the group individuals. In the examples below the determination is made for each animal in the target group of animals. The group percentage can be calculated by adding the individual probabilities across all animals in the group and dividing by the size of the group, or otherwise summarised, if required. In one example the probabilities are summarised by the processor constructing probability intervals, and determining the date of one or more confidence intervals. For example, the date at which there is a 95% confidence that a predetermined percentage of the group has cycled or is pregnant. In a first example it can be determined if an animal has already cycled. A projected cycling percentage may be calculated using D1, or other method based on last season’s birthing and / or expected birthdates, or a measured heat event. A cycling probability at day n (in the future) for a certain animal may depend on whether the animal has cycled since birthing, has given birth, or is expected to give birth. The probability of an animal having cycled if the animal: 1. has cycled since birthing last season: P(cycled by day n) = 1 2. has given birth (calved) last season at day c: P(cycles on day n) = (^^1(^^ − ^^) − ^^1(^^) ) / (1 − ^^1(^^))3. is expected to birth (expected to calve) last season at day e: P(cycled by day n) = D1(n − max(0, e)) In a second example, it can be determined if an animal is still cycling (will have a second heat event) after a first heat event. In this example the cycling probability is calculated using D1 and D2. In a similar way to the above process, it estimates the probability that the animal continues to cycle after the animal's first heat. The probability of an animal having a heat on a certain day (n) after birth (if no heat event has yet occurred since birth) can be calculated by summing, for each day (p) between birth (c) and day n, and for each possible number of heats, x, between day p and day n, the probability that the cow has her first heat on day p, multiplied by the probability that the animal has exactly x heats in n-p days. This probability can be expressed as Equation 5: Equation 5 Once a first heat has been detected, the probability of having a subsequent heat can be more accurately tracked. The probability of the animal having a heat event on day n, given they had a detected heat event at day h, can be expressed as: Equation 6 The method allows determination of a single animal’s heat event. However, these can be extrapolated to determinations for groups of animals (e.g., the target group). This allows the livestock manager to determine if a sufficient percentage of animals are cycling to make insemination practical and / or to encourage non-cycling animals to begin cycling. A livestock manager may want to change other aspects of the farming system that will affect the cycling status of animals. For example, changes may be made to animals, or target groups, which are forecasted as having a low cycling status at predetermined date, for example, the projected start of mating date. In some cases, the determination of a heat event may be checked or confirmed on the determined date before insemination takes place. This helps reduce the effects of any errors. In one example, the method compares the determined percentage of animals cycling (or having a heat event at a particular day) to a predefined threshold. The method may determine if the percentage of the target group having a heat event is below a predefined cycling status threshold. If so, the system may output a notification and / or perform an action, for example by a control signal. The output may be determined based on a parameter, such as the date of the heat event, or the cycling percentage. The notification may be configured to alert the livestock manager or other user via a graphical interface on the front end of system 2. A control signal may instruct device 400 to perform an action. The action may use an animal guidance system on device 400. The animal guidance system may be instructed to guide animals (for example with cycling status below the threshold) to a different location or geographical area compared to other animals of the target group (for example with cycling status above the threshold). Other movement actions may be used. For example, the control signal may instruct the device to control a drafting gate, to separate the animals or groups that have and haven’t met the predefined cycling status threshold. In further examples the control signal may be used to instruct other farm equipment or machinery. The notification or control signal may be activated at a predefined time. The predefined time may be relative to a farm event, such as the planned start of mating (PSM). For example, five weeks before the Planned Start of Mating (PSM). The livestock manager may use this time period to provide sufficient time before the event to increase the chances of cycling. For example, the livestock manager (or the device 400 or system 2) may separate the animals with a low probability of cycling at PSM from the remaining target group. These animals may have, for example, their feed or environment changed, or medication or treatment applied to them to increase their chances of cycling by the PSM. The method may be used to determine a date of a heat event. However, there are many different determinations of the heat event which may be used. For example, the heat event may refer to any of the following, in some cases (each relating to a date when an animal or percent of a group may be cycling): I. Probability of an animal of the target group having had a first heat by a predefined date. Knowing the probability that an animal in the target group has experienced a first heat cycle by a predefined date may be helpful to the livestock manager. The predefined date may be a mating date, a start of the mating period, an insemination date or PSM). This probability livestock managers to estimate the probability of an animal being reproductively active within a certain timeframe. For example, if a livestock manager aims to maximise the chances of early pregnancies, knowing the probability of animals being in heat by a particular date helps in optimising breeding practices leading up to the particular date. For example, a livestock manager may want to introduce an intervention. An intervention may comprise one or more of adjusting feed, administering hormones, separating animals into different mobs, or other physical or medical actions). The intervention may be performed on animals which have less than a predefined percentage chance of cycling before the predefined date. The predefined percentage may be less than a 40% chance of cycling by the predefined date. II. A distribution of dates (or days from the birth event) of the first heat event. The distribution may represent a probability of an animal having a heat event on one or more days. In a group of animals (e.g. the target group) the range of days shows the variation of expected heat events for the group. If the range is too large an action may be applied to narrow the range. A distribution may provide additional information over a specified date because it shows the most likely date, as well as the likelihood of variation from that date. III. A date. For example, a date by which a predefined percentage of the target group will have had a first heat, or by which the probability of the animal having had a first heat is above a predefined threshold. This may be useful as the livestock manager can determine a required percentage of animals to be cycling before insemination. IV. Percentage of the target group that will have a probability over a predefined threshold that will have a first heat by a predefined date. This prediction quantifies the proportion of animals in the target group that are expected to have experienced their first heat cycle, or are cycling, by a specified date. Example methods will be described with reference to the percentage of the target group that is cycling at each date or time. However, it will be understood that the above determinations or outputs could be similarly used. Pregnancy Status Once a heat event has been determined the livestock manager may inseminate the animal. The livestock manager is then concerned about the pregnancy status of the animal. For example, the livestock manager may want to determine if the animal needs to be inseminated again, or requires action to improve pregnancy outputs. The probability of an animal being pregnant (a positive pregnancy status, known as ‘in-calf’ for cows) can be calculated using the D3 distribution. This uses the insemination date (s) of the animal: 1. If an animal has not been inseminated: P(currently pregnant) = 0 2. If an animal has been inseminated, with an insemination date of s: P(currently pregnant) = D3(−s) If the heat event of the animal is not known at insemination, or if the determination is being made before insemination, the pregnancy status can be determined based on a combination of the distributions of heat events and pregnancy status. To determine a probability of an animal being pregnant 161 n days in the future, given a proposed insemination date 160 m, or planned start of mating date (PSM) z, the variables x and y model the different combinations of cycles before and after the insemination date 160. These variables help consider the cycles (or opportunities to get pregnant). x is the number of cycles the animal has before z, and y is the total cycles the animal has before day n. This means that y − x is the number of heats which get mated because this removes the cycles between insemination and the current day n. Alternative calculations of the distributions may also be used. Using the D2 and D3 distributions we can express the probabilities. However, the variables input into the distribution may be modified to suit the example described. For example: ● D2(x, m − h) is the probability that the animal has x heats before the insemination date 160 m, in the m − p days between the insemination date 160 and the animal’s last known heat h; ● D2(y, n − h) is the probability that the animal has y heats in the n − h days between a target date n and the animal’s last known heat h; ● D3(0) is the probability that the animal conceives to an insemination, because it is the distribution on the day of insemination; ● F1(y) is the probability of being in-calf after y matings (inseminations), where F1(0) = D3(0), and F1(y) is given by calculating the probability of being pregnant after the next mating. Equation 7: ● F2(h, n | z) is the probability of a cow being in-calf on day n, given she had her first heat on day h and the PSM is on day z. This the probability that the cow has a certain number of heats before (x) and after (y-x) PSM (z), multiplied by the probability of becoming pregnant at each possible insemination event, multiplied by the the probability of any previous matings have been successful: Equation 8: Given the above, the determined probability of an animal being pregnant at day n can be calculated in several ways, depending on the point of the reproductive cycle and / or the information available. For example: ● If an animal was inseminated at day m, and assuming the animal will continue to be inseminated until the end of mating period 180. This is the sum of the animal not already being pregnant at insemination and the chance of the animal being in heat and successfully inseminated at each of the following heats: Equation 9: Figure 7 shows an example plot of this probability distribution. It shows a series of steps at each heat event where further mating takes place. ● If an animal has had a heat at day h since last season’s birthing. This is the F2 probability calculated from the animal’s next heat (estimated to be 21 days from her most recent heat h): Equation 10: Figure 8 shows an example plot of this probability distribution. Again a series of steps are shown for each heat event where mating takes place. However, there is greater uncertainty about the first heat event date. ● If an animal has not had a heat since this last season’s birthing, and given birth last season at day c. This modifies the previous calculation by including the probability of the cow beginning to cycle on day h (i.e. having a first post-birth heat event) with distribution D1: Equation 11: Figure 9 shows an example plot of this probability distribution. This shows the greater variability in the first heat event depending on the time from calving date to the first heat event. In some cases this can be controlled by, for example, medical intervention. ● If an animal has not yet given birth last season, and is expected to birth last season at day e in the future. This modifies the previous equation by have a predicted date of birth, instead of a known state of birth: Equation 12: In each of these examples the date, n, may be varied based on the livestock managers requirements. In one example, n is between n = 1 (where today may be day 0) to a future date that is likely to be close, at, or past a date where the animal is expected to be pregnant. For example, the future date may be 15 weeks in the future, if today is 21 days before insemination. Advantageously, this method allows a determination for an individual cow of a likely heat event or pregnancy outcome well in advance of a verification. This determination can be used to take or instruct an action to improve the determined outcome. Moreover, the individual cow predictions can be applied across a group of animals (such as a herd) to determine expected or ideal heat events, insemination dates or pregnancy rates. As data or observations from the target group become available the determination can be updated. In some cases devices, such as devices 400, can be used to autonomously collect the data and / or apply actions to implement control strategies based on the determined reproductive events. Use of autonomous collection, for example by the animal worn devices, allows the method to be updated in real time (for example by updating to a posterior distribution during a season based on early season performance). In some cases a graphical output is used. For example a chart or graph of an expected output may be outputted to a display. For example Figure 10 shows the different distributions which may be determined based on the timing of the request to determine if a heat event has occurred. As shown in the process at the top of Figure 10 if a cow has cycled since calving then it is known to be cycling. However, if we know when the cow has calved but there has not been a first heat event post calving a first distribution can be used (shown in the middle of FIgure 10). However, if we are pre-calving we need to first determine an expected calving data, and then apply the calculation of the expected heat event date following the determined calving. This produces the bottom plot shown in Figure 10. Outputs The method may use or apply the output in one or more ways. These may comprise an output or notification delivered to the livestock manager, one or more control actions, or one or more physical In one example the method includes providing one or more outputs via a user interface 201. The outputs may comprise one or more audio or visual notifications. The outputs may be configured to optimise cycling or pregnancy percentages. The output may be determined based on one or more parameters, such as a threshold, date or statues. For example, the output may be outputted if the date differs from a predetermined date, or if the number of cycling animals is above or below a threshold. The comparison between a determined heat event (or pregnancy status) and the threshold may be used to decide if, or how, to perform an action. For example, whether to report the output to a user and / or to send a signal to a device 400. The output may comprise an adjustment made to the animal or environment of the animal. Outputs may comprise one or more selected from the following: altering the composition, quantity, type, and / or energy of feed, e.g. feed parameter, given to one or more animals in the target group; changing the milking frequency (e.g. from twice a day to once a day); providing nutritional supplements to one or more animals in the target group; changing the feeding schedule of one or more animals; and / or administering medications such as hormones to one or more animals. Outputs may be applied to either or both of animals exceeding a predefined heat event date or preceding a predefined heat event date. For example the outputs may be applied to one or more animals in the target group that have been determined to have a low probability of cycling by the planned start of mating, or not pregnant six weeks into the mating period. For example the outputs may be applied to animals which have a higher probability of having a next heat or being pregnant by a desired date. One example would be medications or supplemental feed being administered to animals which are going to be pregnant. In a further output an animal may receive medicaments (e.g. a hormone shot) if a heat has not been detected at a predefined period after a birth event or prior heat. By these outputs the method can improve cycling percentages and therefore overall reproductive performance. For example, the animals in a group may be controlled to have heat events over a shorter period of time. A level or rate of action or output may be adjusted depending on the difference between a determined heat event and / or pregnancy and a desired heat event and / or pregnancy. The threshold or levels may be predetermined or adjusted for in the method. For example, if an animal is within a range of low probability percentage for a heat event (i.e., within 20-50% chance of cycling) by the mating date, then a recommendation for hormones may be given. If an animal is within a higher range (e.g., 50-70%) then a recommendation for feed adjustment may be given. If over a predefined probability percent (e.g., over 70%), no insight or alert or recommendation may be given. Figure 11 shows an example application of one or more outputs. A target group of animals is determined 320. The livestock manager may identify a desired insemination or pregnancy date 321. The method may then determine 322 a reproductive event, such as a heat event or pregnancy outcome based on that insemination date and the determined reproductive cycles of the animals. The method can then determine 323 if the determined date or distribution meets one or more thresholds or criteria. If the thresholds or criteria are not met the method may apply one or more output actions 324. The output actions are configured to adjust the heat event or pregnancy status 322. As the system, or at least heat events, can be determined autonomously, the desired outcomes can be regularly or constantly monitored 325 throughout the season. In one example the livestock manager may manage the predicted six week in-calf rate (6WICR) of a target group of cows. The method is configured to determine which cows will be in-calf six weeks after the predefined insemination date. The method may further determine the percentage of the target group that will be pregnant (or at least have a probability of pregnancy above a threshold) by the six-week date. In this example the method is further configured to display, for example on a user device, the determined six week in-calf rate. In some cases, the output or display may also include further animals. For example, herd or mob level data may be shown (which may include non-target group animals) because the 6WICR is typically a mob level metric, instead of for part of a mob. In a further example the method can directly, or based on input from the livestock manager, configure (e.g. by a control signal or other instruction) a device 400 to perform an action, as described previously. In another example, the determination of a next heat event may be graphically displayed (at cow, target group or mob level for example) over time. In a further example the method can directly, or based on input from the livestock manager, configure (e.g. by a control signal or other instruction) a system (such as system 2) to send a control signal to device 400 to perform an action, as described previously. For example, the control signal may instruct an action, or may indicate a determined heat event or pregnancy and the device 400 may decide on a suitable action. In some cases, the method controls a drafting gate directly to separate the animal from other animals. Alternatively, the device 400 may directly communicate with the drafting gate via wireless communication. The operation of the drafting gate (and therefore the location of the animal) may be controlled based on the heat event of pregnancy determination. In some cases, the target group of animals may be separated into multiple sub-groups dependent on their determined reproduction cycle status. In some cases, the output or control signal is configured to display indicia on the animal, such as an LED light on device 400. The device 400 may also apply stimuli to the animal to guide the animal to a location. A livestock manager may use the LED lights to manually separate lit animals from the non-lit animals. In some cases, the output or control signal comprises a guidance command. The guidance command may relate to separating animals based on reproductive cycle status. For example, by guiding them to different locations. The guidance command may control a virtual fence controlled by device 400. For example, in a virtual break, the animals with a low probability have a larger break, or can access more grass, compared to higher probability animals. In some embodiments the system comprises outputting the determined date of the future heat event to one or more user interfaces. The output may comprise the date and an identifier for the at least one female animal. In some embodiments the system is operatively coupled to a virtual fencing device 400 configured to influence movement of the animal. The system may comprise issuing guidance commands based, at least in part, on the future heat event to guide the at least one female animal to a target location. In certain embodiments the foregoing occurs automatically without human intervention. A policy engine comprises rules that, when a future heat event is determined and meets one or more criteria, automatically schedules and issues corresponding guidance commands to the device and hence animal. In some embodiments movement occurs automatically from control signals generated by the processor. The system classifies animals based on the determined future heat event, cycling status, and / or in-calf status and issues guidance commands to a wearable virtual fencing device 400 configured to apply stimulus outputs to the animal to guide the animal to a location. The commands may comprise geospatial definitions and time windows so that animals with different predictions or reproductive statuses can be guided automatically to specific area. For example, animals predicted as non-cycling are guided to a first area for intervention or alternative management, while animals that are cycling or forecast to cycle are guided to a second area suitable for insemination workflows, or left in their current area. The method may be configured to continuously monitor the reproductive status, heat event or pregnancy of the animal(s). This continuous monitoring allows updates based on specific performance of target animals. The continuous monitoring also allows adaption and / or outputs or output actions to improve performance. The monitoring may also be used to improve the following season results. Figure 13 shows a possible display of the predicted cycling percentage of a target group of animals. A herd cycling percentage 131 is shown from the previous year. A current herd cycling percentage 133 is shown for the current year. The measured, or known, values are shown in dashed line. The future cycling percentage 135 is shown in dotted line. The described systems and methods may be used to determine the future cycling percentage based on the current cycling percentage 133. The determination uses prior data. The prior data may comprise prior data from the same herd (i.e. as used to generate the head cycling percentage 131), but preferably also or alternatively uses prior data from a larger set of herds. The cycling percentages 131, 133, 135 are plotted relative to a planned start of mating 132. However, a livestock manager may, on review of the date, adjust the planned start of mating to improve the herd cycling percentage during mating. Alternatively, the livestock manager may take one or more actions to increase the cycling percentage. The cycling percentage may be determined by combining the determined future heat event date for each animal and determining a percentage expected to be cycling based on the future heat event date. The described system and methods allow the future cycling percentage 135 to be adjusted depending on the current cycling percentage. In prior systems a future cycling percentage may have been expected to replicate the prior cycling percentage 131. However, the described methods adjust the determined future cycling percentage 135 based on the current cycling percentage 133. The use of distributions of the prior, current and determined cycling percentages (as determined based on heat event dates) provides increased accuracy in this determination, allowing farmers to improve the timing of pregnancy events – for example by moving the planned start of mating 132. Returning to Figure 1 a system 2 for monitoring animals 10 is shown. Each animal 10 in the herd may wear a device 400. The device 400 can be configured to collect movement data from the animal 10 it is associated with. Figure 12 shows detail of components within the device 400. The device 400 is equipped with one or more sensors. The device 400 can be configured to measure one or more parameters to be used by the method (for example to determine a heat, or calving). The device 400 can be configured to apply one or more outputs based on a determination of the method. The sensors may be referred to as a sensor package 440. The sensor package 440 is configured to record an animal's movement patterns. In some cases, the sensors in the sensor package 440 detect and record data related to the animal's activity. This activity may include movement and / or position and / or physiological data. The device 400 comprises a controller 470. The controller may be configured to manage and control the overall functionality of the device, such as receiving data from the sensor package 440 or sensors, providing outputs, such as with stimulus device 460 and sending the data off-device; communicating with communication package 410 and other controls, inputs or outputs of the device 400. Transmitting the data may use a communication package 410. As the animals 10 move about their environment, the associated devices 400 may be configured to continuously collect data on the animal’s movements. This movement data may include information relating to estrus (heat events), cycling, and / or birthing events. For example, the device 400 may be configured to identify one or more these events based on changes in movement of the animals 10. Multiple groups (herds or mobs) of animals 10 are monitored. These multiple groups may be across a single farm, or multiple farms, or across areas with all or common properties. The data collected from all the animals 10 across multiple groups may be collated. The collated (or raw) data may be stored in the backend 500. This may comprise a server and / or database or similar. The backend 500 may serve as a repository for all the movement data gathered from the individual animal devices 400. Storing, or being able to obtain this data centrally, allows a user to aggregate and / or organise the data. The data can be made accessible for further analysis. A user 202 may have the ability to input additional data to the collected data. A user interface 201 may be used. The user interface may be a mobile device or similar front end 200 device. Input data may include information related to each animal's 10 reproductive status. For example, whether an animal is cycling, when an animal has given birth, or details about a heat event, such as the first heat observed. The user 202 may use a software application (such as a mobile application) on a mobile device or computing device to input the device. A transmission network (wired and / or wireless), including the internet, may be used to transmit inputs from or between the front end 200 and the back end 500. The back end 500 may comprise software applications, and any processors or server utilities. In some cases, the backend 500 may comprise any devices that communicate with the device 400, that is not on the device end. For example, the backend 500 may include intermediate devices or processors located between the animal device 400 and a remote processor 520 such as a server. However, in most applications, the backend represents a computing device that is immobile and / or remote from the animals 10. In some cases, the remote processor 520, such as a server and / or a computer and / or a user’s 202 user interface 201 or device supporting the user interface may, in some examples, be referred to as a first or primary transmission device. The primary transmission device may operate a first transmission protocol to communicate with the animal device 400. In one example the user inputs the heat event dates into the user interface, to be then received by the back end 500. The collected data (for example from one or both of the devices 400 and user input) may be incorporated in a model. The model may comprise any one or more of the predictive models discussed earlier, or used for alternative modelling purposes. The historical data may be determined from the collected data (e.g., including movement data). In some cases, the historical data is directly based on recorded birth dates and first heat events and / or cycling dates. A combination of data sources may be used. Alternatively, the historical data may comprise a distribution of dates. For example, dates from birth events to first heat events for one or more groups of animals. The collected data may be used to update the model based on the data or observations of the target group. For example, the model may be updated when the devices detect heat events. The wearable animal device 400 is configured to communicate through one or more transmitters or transmission means. Transmission means may include one or more of GPS satellites 610, base stations 620, short-range communications devices, internet 640, and one or more cell towers 630 or local computers or servers 650. In some cases, the animal device 400 connected directly to a user device 201. These may use the transmissions means above, including one or more short-range communication signals such as Bluetooth™ or WIFI™. The device 400 may communicate directly with the front end 200 or via the backend 500 to the front end 200. The data from the device 400 (e.g. a collar on the animal 10) may be sent to a remote processor 510 as part of the back end 500, and can also be accessed by a processor of a computing device, such as a PC 520, via a communication means or network or connection such as the Internet 640. In one example, the device 400 comprises a sensor package 440. The sensor package comprises one or more sensors. For example, the sensor package may comprise a position sensing system, or interface with a position sensing system. The position sensing system operates to provide animal position data. The position sensing system further operates to provide a reference to any one or more locations. The position sensing system may provide a relative frame of reference to the animal position data and the one or more locations. In some cases, the position sensing system comprises at least a movement sensor (such as an IMU) and optionally a location sensor (such as GPS). In one example, the position data comprises data derived from an animal location sensor and / or an animal position sensor. In one example, the position data comprises one or more of animal location data, animal heading data, animal speed data, and animal angular position data. One or more of which may be described herein as movement data. In some cases, animal device 400 comprises a communications package 410 comprising a communications device or is a radio transceiver or uses a radio signal to send sensor data to the back end of front end. The communications device 410 or communication package. The communication device 410 is configured to communicate to at least a back end 500 using a communication channel 600. The communications package may allow communications and / or signals between the device 400 and a system processor performing the method. The communication may be two-way. The system processor may determine heat events, pregnancy events, insemination events and / or birth events from the device 400. The device 400 may receive instructions from the system processor. The instructions may comprise control (guidance) and or requests for information. The device 400 and / or the system processor may directly or indirectly communicate with the user device or front end 200. Where in the foregoing description reference has been made to elements or integers having known equivalents, then such equivalents are included as if they were individually set forth. Although the disclosure has been described by way of example and with reference to particular examples, it is to be understood that modifications and / or improvements may be made without departing from the scope or spirit of the invention. Examples may be implemented by hardware, software, firmware, middleware, microcode, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine-readable medium such as a storage medium or other storage(s). A processor may perform the necessary tasks. A code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc. In the foregoing, a storage medium may represent one or more devices for storing data, including read- only memory (ROM), random access memory (RAM), magnetic disk storage mediums, optical storage mediums, flash memory devices and / or other machine readable mediums for storing information. The terms "machine readable medium" and "computer readable medium" include, but are not limited to portable or fixed storage devices, optical storage devices, and / or various other mediums capable of storing, containing or carrying instruction(s) and / or data, including non-transitory mediums. The various illustrative logical blocks, processors, modules, circuits, elements, and / or components described in connection with the examples disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic component, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, circuit, and / or state machine. A processor may also be implemented as a combination of computing components, e.g., a combination of a DSP and a microprocessor, a number of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The methods or algorithms described in connection with the examples disclosed herein may be embodied directly in hardware, in a software module executable by a processor, or in a combination of both, in the form of processing unit, programming instructions, or other directions, and may be contained in a single device or distributed across multiple devices. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD- ROM, or any other form of storage medium known in the art. A storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. One or more of the components and functions illustrated in the figures may be rearranged and / or combined into a single component or embodied in several components without departing from the present disclosure. Additional elements or components may also be added without departing from the present disclosure. Additionally, the features described herein may be implemented in software, firmware, hardware, and / or any combination thereof. In its various aspects, the present disclosure can be embodied in a computer-implemented process, a machine (such as an electronic device, or a general purpose computer or other device that provides a platform on which computer programs can be executed), processes performed by these machines, or an article of manufacture. Such articles can include a computer program product or digital information product in which a computer readable storage medium containing computer program instructions or computer readable data stored thereon, and processes and machines that create and use these articles of manufacture.

Claims

CLAIMS 1. A method of determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the method comprising the steps of: obtaining historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates comprising dates of prior heat events and / or birthing events for each animal of the non-target group of animals; and secondary event dates comprising dates of secondary heat events following the primary event dates; determining, based at least in part on the historical heat data, a prior distribution of the time between the primary event dates and the secondary event dates; obtaining target heat data from the target group of animals, the target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date; updating the prior distribution based on the target heat data; obtaining a primary event date for each of the at least one female animal; and determining, using the primary event date for each of the at least one female animal and the updated prior distribution, a future heat event of the at least one female animal.

2. The method of claim 1, wherein the step of determining a prior distribution comprises a first prior distribution for secondary event dates and primary event dates of prior heat events and a second prior distribution for secondary event dates and primary event dates of a birthing event.

3. The method of claim 1 or 2, wherein the at least one female animal comprises a group of female animals, optionally the group of female animals located in a specific area.

4. The method of claim 3, wherein the specific area comprises a farm, or the specific area is within a distance from a predetermined location.

5. The method of any one of claims 3 or 4, comprising determining, based on the future heat event for each of the group of female animals, a distribution of future heat events for the group of female animals.

6. The method of any one of claims 3 to 5, wherein the future heat event comprises a date by which a percentage of the group of female animals have been, or will be, in heat.

7. The method of any one of claims 1 to 6, wherein the future heat event comprises any one or more of: a distribution of predicted dates, anda probability of the at least one female animal having a heat event on a particular date.

8. The method of any one of claims 1 to 7, comprising the step of inseminating the at least one female animal based, at least in part, on the determined future heat event, optionally comprising selecting a date of insemination based on the determined future heat event.

9. The method of any one of claims 1 to 8, comprising one or more of the steps of: outputting the determined date of the future heat event, and moving the at least one female animal based, at least in part, on the future heat event.

10. The method of any one of claims 1 to 9, comprising the step of obtaining the primary event date of the at least one female animal from a device attached to the at least one female animal.

11. The method of any one of claims 1 to 10, comprising the step of determining a cycling status from the future heat event.

12. The method of any one of claims 1 to 11, wherein the non-target group is selected based on one or more characteristics.

13. The method of any one of claims 1 to 12, comprising the step of outputting one or more control signals, optionally wherein the one or more control signals are configured to control one or more of: a drafting gate operable to draft the at least one female animal to different locations; a wearable device operable to display indicia on the at least one female animal, such as an LED light; a wearable device configured to apply stimuli to the at least one female animal to guide the at least one female animal in operation; and a display device comprising a display operable to be read by a user.

14. The method of any one of claims 1 to 13, comprising the step of performing one or more actions based on the future heat event, the one or more actions comprising any one or more of: an alert; adjusting a feed parameter; optionally comprising a composition, energy and / or quantity of feed given to the at least one female animal or a feeding schedule; providing nutritional supplements or hormones to the at least one female animal; adjusting a timing of a reproductive event, such as the timing of insemination of the at least one female animal; and geographically moving the at least one female animal.

15. A method of determining an in-calf probability of at least one female animal, the method comprising: determining the date of one or more future heat events according to the method of claims 1 toobtaining historical insemination event data from a non-target group of animals and determining a prior distribution between insemination date and pregnancy status; obtaining target insemination event data of the target group of animals and updating the prior distribution based on the target insemination event data; obtaining a planned insemination date for the at least one female animal, and determining, based on the determined date of one or more future heat events and updated prior distribution and the planned insemination date, a predicted in-calf probability.

16. A system for determining a future heat event of at least one female animal, forming part of a target group of animals, having a heat event on a predetermined date; the system comprising a processor configured to: obtain historical heat data from a non-target group of animals, the historical heat data comprising: primary event dates, comprising dates of prior heat events and / or birthing events for each of the non-target group of animals, and secondary event dates comprising dates of a secondary heat events following the primary event dates; determine, based at least in part on the historical heat data, a prior distribution of the time between the primary event dates and the secondary event dates; obtain target heat data from the target group of animals, the target heat data comprising primary event date(s), and secondary event date(s) for each animal in the target group of animals having a secondary event date; update the prior distribution based on the target heat data; obtain a primary event date for each of the at least one female animals, and determine, using the primary event date for each of the least one female animals, and the updated prior distribution, a future heat event of the at least one female animal.

17. The system of claim 16, comprising at least one wearable device configured to be worn by the at least one female animal and / or at least one of the non-target group of animals, wherein the at least one wearable device is configured to detect one or more of the primary event dates, secondary event dates of the at least one female animal and / or at least one of the non-target group of animals.

18. The system of claim 17, wherein the at least one wearable device is configured to detect a birth event and / or insemination event of the at least one female animal and / or at least one of the non-target group of animals.

19. The system of claim 18, wherein the device processor is configured to receive one or more signals from the system processor, wherein the received signals comprise a guidance command based, at least in part, on the future heat event.

20. A method of determining an in-calf status of at least one female animal forming part of a target group of animals, the method comprising the steps of: obtaining historical insemination event data from a non-target group of animals, the historical insemination event data comprising insemination dates and pregnancy status in a time period after the insemination dates; determining, based at least in part on the historical insemination event data, a prior distribution of the time between insemination dates and pregnancy status; obtaining target insemination data from the target group of animals, the target insemination data comprising insemination event date(s), and pregnancy status date(s) for each animal in the target group of animals having pregnancy status data; updating the prior distribution based on the target insemination data; obtaining an insemination date for the at least one female animal; and determining, based on the updated prior distribution, and the insemination date, an in-calf status of the at least one female animal.

Images