Quantified water intake estimation and closed loop herd management using reticular bolus sensor fusion

Abstract

Systems and methods for quantified water intake estimation and closed loop management of ruminants are disclosed. An ingestible reticular bolus transmits temperature data. A water source sensor measures water temperature and an ambient module provides THI. A processor detects drinking events from temperature slopes and amplitudes, applies a non-linear heat exchange model parameterized by measured water and ambient conditions to estimate per event volume, and accumulates daily intake. Control signals are generated to cooling, water, or feeding actuators according to hydration status and THI. Optional fusion with bolus accelerometry and near muzzle gas sensing estimates methane emissions per animal. The system integrates feed sensors and reproduction analytics, supports portable gateways for pasture, and provides traceability.

Description

QUANTIFIED WATER INTAKE ESTIMATION AND CLOSED LOOP HERD MANAGEMENT USING RETICULAR BOLUS SENSOR FUSIONCross Reference to Related Applications

[0001] This application claims the benefit of, and priority to U.S. Provisional Application 63 / 715,470 which was filed on November 01, 2024 and entitled "LIVESTOCK MONITORING SYSTEMS", the entirety of which is incorporated by reference herein.Background

[0002] This invention relates to precision livestock systems for ruminants (e.g., cattle, sheep, goats) employing ingestible bolus sensors integrated with farm loT sensors and actuators to manage hydration, thermal stress, feeding, reproduction, emissions and other factors.

[0003] Reticular boluses can monitor internal temperature and activity; drink events are detectable as transient drops in reticular temperature. Commercial systems and studies report drinking events and water intake indices, but robust per event volume estimation is underdeveloped, and existing platforms seldom close the loop by actuating farm systems according to quantified intake with environmental context. Other farm loT systems are similarly underdeveloped in the literature.

[0004] There is a desire for a reticular bolus sensor for improved herd management.Summary

[0005] The present disclosure discloses a system for the management and monitoring of livestock and animals generally. In conjunction with a smart bolus, the system may be enabled to calculate animal water intake, estimate heat stress, actuate physical systems based on data or calculations, measure and control feed amounts, estimate milk quality and production and create milk / feed efficiency measures, measure bovine greenhouse gas emissions, compute and log a resting / activity index for an animal, measure or estimate and log blood sugar measurements for an animal, track specific animals with a CowID, integrate with other loT systems on a farm and take measurements and actions to optimize selection of animals for provision of semen. Integration of various sensors, farm loT technologies, Al / machine learning, enhanced data analytics and blockchain systems is discussed. Genetic optimization, pharmaceutical tracking, disease prediction and nutrient tracking are discussed.Brief Description of the Drawings

[0006] FIG. 1 is an overview system diagram of a farm system, showing the bolus, sensors, actuators, server and other components.

[0007] FIG. 2 is a system block diagram showing the components within a bolus.

[0008] FIG. 3 is a sample packet / event flow diagram, showing communication from a bolus to a server and other network elements.

[0009] FIG. 4 is a flowchart showing a sample embodiment of a drinking-event detection system.

[0010] FIG. 5 is a flowchart showing a sample embodiment of a drinking volume estimation system.

[0011] FIG. 6 is a flowchart showing a sample embodiment of the calibration procedure for the drinking volume estimation system.

[0012] FIG. 7 is a flowchart showing a sample embodiment of the workflow for calculating and displaying drinking events.

[0013] FIG. 8 is a component block diagram showing Ul elements and information presented on the system operations dashboard.

[0014] FIG. 9 is a flowchart showing a sample embodiment of the system for activating actuators and logging results based on calculated values related to heat stress.

[0015] FIG. 10 is a flowchart showing a sample embodiment of the system for activating actuators based on feed mass.

[0016] FIG. 11 is a sample event flow and timing diagram for an embodiment of a greenhouse gas quantification system.

[0017] FIG. 12 is an overview diagram of a data relay system using portable gateways.

[0018] FIG. 13 is a flowchart of a sample workflow for a semen analysis system.

[0019] FIG. 14 is a flowchart of a sample workflow for a machine learning system for improving on some algorithms (such as drink event detection).

[0020] FIG. 15 is a flowchart of a sample workflow for semen and identity tracking.

[0021] FIG. 16 is a flowchart of a sample workflow for transportation-related risk tracking and mitigation.

[0022] FIG. 17 is a system overview diagram of a sample system for calculating and presenting carbon intensity.

[0023] FIG. 18 is a system overview diagram of a sample system for creating performance and sustainability scorecards from animal-level metrics.

[0024] FIG. 19 is a flowchart of a sample workflow for an ROI system.

[0025] FIG. 20 is a flowchart of one embodiment of a Cow Stress Index pipeline.

[0026] FIG. 21 is a flowchart of another embodiment of a Cow Stress Index pipeline.Detailed DescriptionBolus device and data collection

[0027] According to the disclosure, disclosed herein is an ingestible device for herd management. The ingestible device is a sealed reticular bolus 100 that collects the physiological and motion data required for water-intake estimation and health analytics. The housing 105 defines a fluid-tight cavity that protects the electronics and presents a smooth exterior surfacesuitable for long-term residence in the reticulum. A dense internal weight 110 ensures that the device settles into a repeatable orientation so that the thermal coupling between the distal end of the capsule and the surrounding fluid remains consistent. The power system 115 combines a primary lithium battery 117 for energy storage with a supercapacitor 116 that supplies the peak current required during radio transmissions. The microcontroller operates as the data processing unit 120, with memory 125, and connects to a magnetically actuated activation receiver 140.

[0028] A technician places the device in a deep-sleep state for storage and transport and then enables the device with a handheld magnet held near the activation receiver 140 immediately before administration. The printed circuit board locates a calibrated temperature sensor 130 near a thin-wall thermal window so that the measured reticular temperature follows ingested water temperature with minimal delay. A three-axis accelerometer 135 measures motion and vibration so that the system can distinguish drink events (a time window where the reticular temperature decreases faster than a slope threshold, followed by a recovery which may be exponential) from artifacts and can derive behavioral features such as rumination. A sub-gigahertz narrowband radio 160 and a matched antenna 165 provide a reliable low-power uplink to farm gateways 350.

[0029] FIG. 2 shows the internal arrangement of these bolus subassemblies. FIG. 1 shows how the bolus cooperates with the rest of the system. Alternate embodiments and differences from this embodiment are discussed in PCT application CA2022051434, entitled "SYSTEMS AND METHODS OF LIVESTOCK MANAGEMENT" which was filed on Sep 27, 2022 and is incorporated by reference in its entirety.

[0030] The firmware uses a deterministic state machine so that the device behaves predictably in the field. The device remains in a shelf state until it senses a valid magnetic activation. After activation the device performs a join sequence with the nearest gateway and then enters a normal measurement state. During normal measurement the device samples reticular temperature and accelerometer statistics at a baseline interval between thirty and one hundred twenty seconds. When a slope detector identifies the onset of a drink event the device increases its sampling rate to a burst interval between five and fifteen seconds. The device remains in this burst mode for three to six minutes so that the entire temperature drop and a portion of the recovery are captured. The device then constructs and transmits a compact data packet. The packet contains a device identifier, an assigned animal identifier, a thirty-two bit device time stamp, an operating mode byte, a reticular temperature value encoded in centigrade hundredths, one or more accelerometer features such as variance or root-mean-square magnitude, a battery voltage reading, and status flags.

[0031] The packet ends with a cyclic redundancy check so that the gateway can detect transmission errors. FIG. 3 illustrates the flow of these packets from the bolus through the gateway to the ingest service. Alternate embodiments and differences from this embodiment are also discussed in PCT application CA2022051434, entitled "SYSTEMS AND METHODS OF LIVESTOCK MANAGEMENT" which was filed on Sep 27, 2022 and is incorporated by reference in its entirety.

[0032] Clock drift does not compromise analysis because the server records the difference between the device time and the gateway receipt time and maintains a per-device drift model. The firmware conserves energy by sleeping between samples, by using interrupt driven wake-ups, and by relying on the supercapacitor for short radio bursts. If the battery voltage indicates a low-energy condition the firmware postpones high-rate sampling and marks the event so that the server can widen uncertainty intervals. The onboarding workflow binds the device identifier to an animal identity by scanning aprinted code before administration and confirming the association during the first successful join. A factory test mode verifies sensor calibration and radio output. A field diagnostic mode can be invoked by a specific magnet tap pattern so that the device emits an immediate status packet. These design choices produce temperature curves with the resolution and accuracy required by the estimators and controllers detailed earlier, and they do so with a power budget that supports long operating life.Water Temperature Node External Sensor

[0033] According to the disclosure, accurate volume estimation requires knowledge of the water temperature immediately before ingestion and knowledge of the ambient thermal load during and after the event. When livestock drink water, the temperature measured by an electronic bolus will drop by an amount, which amount increases with increased water ingestion. As this is an issue of heat exchange and temperature differentials, this change in bolus temperature is a non-linear function of the amount of water ingested.

[0034] Factors which allow more accurate calculation of water intake include the temperature of the water just before ingestion and the ambient air / room temperature in the vicinity of the livestock. Using these values, along with a known bolus temperature curve (change in bolus temperature when a certain quantity of water at a given temperature is ingested), the amount of water ingested may be closely estimated. In one aspect, the system may retrieve information from livestock to estimate each animal's water consumption. The system may track this water consumption estimate over time to estimate the health of a given animal at any given time, as water consumption increased or decreased from a baseline value may indicate illness in an animal. Any or all of this information may be logged for later reporting and retrieval and may be presented to a user on a computer, phone or other user interface.

[0035] In one embodiment, the system therefore equips each water source with a small water-temperature measurement node 310. In one embodiment, the installer positions the probe one to three centimeters below the water surface and avoids the inflow jet so that the reading reflects the bulk water that the animal will drink. In one embodiment, the node samples once per minute during normal operation and can switch to a fifteen-second cadence during heat alerts. Each sample becomes a packet that carries a unique source identifier, a time stamp, the water temperature encoded in centigrade hundredths, and a received signal strength indicator that aids proximity inference.

[0036] In a further embodiment, each pen receives an ambient node 320 that measures air temperature and relative humidity at cow head height and computes a standard temperature-humidity index. The ambient node transmits at the same cadence as the water node and also supports a higher rate during heat stress. Feed availability is instrumented either with load-cell plates or with a fixed camera. Load-cell plates measure mass at two hertz and the node reports a five-minute median value that suppresses short pushes. The camera method estimates feed area at one frame per second and converts area into kilograms using a linear model that is calibrated with weekly weigh-backs for each ration. An optional near-muzzle methane node is mounted near waterers or high-traffic lanes and samples when the server predicts that a tagged animal is present during an eructation window. FIG. 1 shows how these nodes and their gateways fit into the overall farm topology.

[0037] In a further embodiment, all nodes uplink their readings to a fixed gateway 350 that forwards data to the server over a secure internet connection. The ingest service writes the data into normalized time-series tables that are partitioned by animal and by pen. A spatial mapping table links each source identifier to a physical location so that the estimator 420 can select the nearest and most temporally relevant water and ambient readings for each event. The server stores both raw and derived values and maintains a calibration history for each sensor. The maintenance plan includes weekly water probe checks against a reference thermometer with stored offsets, monthly two-point humidity calibration for ambient nodes, load-cell gain and offset checks with known weights, and weekly updates of the camera conversion coefficients through weigh-back audits. Methane nodes receive quarterly zero and span checks. The system implements fallbacks that maintain estimation continuity when data are missing. If the server cannot find a water temperature reading within ten minutes of an event then it substitutes the median water temperature for that pen at that time of day and sets a missing-sensor flag that is later used to widen volume uncertainty. If multiple water points serve a pen then the estimator 420 selects the source whose measurements align best with the event window. If the cows are on pasture then the server uses the last known relay position to associate animals with the nearest water point. These data flows are depicted in FIG. 3, which shows the ingest service, the time-series database, and the estimator that consumes the fused streams.

[0038] In a further embodiment, one sensor per water source (trough, bowl, robotic cup) is installed. The sensing element (e.g., encapsulated temperature probe) is placed preferably 1-3 cm below the water surface but away from direct inflow jets to avoid transient bias. In one embodiment, the sampling cadence may be 60 s standard, or 15 s during high heat alerts to refine intake volume estimates. Each node broadcasts Tw packets {nodelD, ts, Tw_centi, sourcelD, rssi} via LoRa to the farm gateway 350. The node is physically mounted to the trough wall with a stainless bracket and potted cable gland to prevent moisture ingress.THI Node External Sensor

[0039] According to the disclosure, the electronic bolus can take temperature measurements at regular or irregular intervals. By correlating these readings with the time of day, a baseline measurement of healthy temperature for a given time of day may be estimated. If the temperature reading deviates significantly upward from this baseline, this may indicate that the animal is experiencing heat stress. In one aspect, the system may track the temperature measurements of each animal to obtain an individualized baseline measurement of healthy temperature, and upon receiving a reading which is significantly higher than the calculated baseline temperature, the system may take an action such as requesting an additional reading for verification, increasing the frequency of readings, correlating the reading with other nearby animals' readings to provide a local or herd-wide analysis of temperature (ie an indication of whether just an individual animal has higher temperature or whether there may be a heat issue for some or all animals), logging the information about the temperature deviation, raising an alert through a user interface, causing an actuator 370 to move or change state, turning on additional air or water cooling systems, changing the frequency of water freshening, or any other action. In this situation, the system may log the measurements taken, an indication of the status derived from the measurements, and an indication of the action taken; any or all of these may be presented to the user on a user interface or through an alerting mechanism.

[0040] The temperature-humidity index (TH I), a measurement of thermal stress for livestock, may be measured or closely estimated. In one embodiment, a THI node may be placed one per pen (or every ~250 m2) at cow head height, shielded from direct spray but in airflow, with sensors to measure air temperature Ta, relative humidity RH, and optionally airflow v. THI may then be computed locally or server-side, with some or all of {Ta, RH, THI, v} stored with ts, the sampling time. In one embodiment, a sampling cadence of 60 s (increasing to 20 s during heat stress) may be used.Feed Presence / Consumption Sensors

[0041] According to the disclosure, the system may receive input from a sensor 330 monitoring the amount of feed in the feed alley. This may be a force sensor measuring the weight of a feed alley segment or feed receptacle, a camera aimed at the feed alley as part of a subsystem which interprets the image and calculates the amount of feed present in the alley, a photo sensor in the feed alley or receptacle whose beam is interrupted by feed when there is more than a certain measure of feed present, or some other sensor which senses the presence or amount of feed present in the alley or receptacle. In one aspect, the system may use this input to estimate, calculate or otherwise derive the amount of feed present in the feed alley or receptacle.

[0042] If this value is lower than a particular threshold, which threshold may vary by time of day, number of animals present, composition of the feed, health of the animals present or some other factor, the system may cause a feed pump, gate or other mechanism to operate to cause more feed to enter the feed alley or receptacle. In this situation, the system may log the measurements taken, an indication of the status derived from the measurements, and an indication of the action taken; any or all of these may be presented to the user on a user interface or through an alerting mechanism.

[0043] In one embodiment, load-cell plates may be situated under feed alley segments or under the bunk. These may be sampled every 30 s or some other interval. The values may be filtered with a 5-minute median to reject animal push forces. Feed remaining (mass measurement) per segment may thereby be easily calculated.

[0044] In one embodiment, a fixed camera may be installed overlooking the bunk. A segmentation model running at 1 fps (edge device) may estimate feed area / volume. The conversion of this to mass (preferably in kg) may be calibrated by a per-mix linear regression using a weekly manual weigh-back audit, or some other calibration process.

[0045] In these embodiments, a message containing at least some of {sourcelD, ts, feed_kg} may be sent to the server.

[0046] In one embodiment, the server may store time-series tables with cow-aligned partitions: bolus_reading(cowlD, ts, Tr, accel, vbat, flags, Tw_nearby, THI_nearby), water_temp(sourcelD, ts, Tw), ambient(sourcelD, ts, Ta, RH, THI), feed(sourcelD, ts, feed_kg), gas(sourcelD, ts, CH4_ppm). A spatial mapping table ties sourcelD to pen and water point, allowing selection of the closest Tw and THI for each cow at each time.

[0047] To calibrate and maintain the system, in one embodiment, Tw may be sampled with a reference thermometer at each trough and Tw_offset adjusted. For THI nodes, a monthly 2-point humidity calibration may be run. For feed load-cells, the system may be calibrated with known weights; for vision, the system may be audited with weigh-backs. All calibration history may be stored linked to sourcelD.

[0048] In one embodiment, the following failure mode may be implemented: If Tw is unavailable within ±10 min of a drink event, substitute pen median Tw with a penalty flag (later used to widen volume uncertainty). If two Tw sensors are within 2 m, fuse them by inverse-variance weighting. If the cow is out on pasture (no nearby Tw), use a portable gateway location to associate the nearest water source in the paddock.Drinking-event Detection and Non-linear Heat-Exchange Model

[0049] In one embodiment, the server converts the device readings into a uniformly sampled series and applies gentle smoothing that preserves slopes while removing spikes. A drink event begins when the short -window derivative of reticular temperature becomes more negative than a configured threshold and when the cumulative drop over the window exceeds a configured minimum. In one embodiment, the slope threshold lies between one half and one and one half degrees Celsius per minute and the minimum drop lies between one and one half and five degrees Celsius. The detector merges two candidates into a single event when the gap is shorter than ninety seconds, or another gap length. In one embodiment, the detector ends the event when the slope remains at or above minus one tenth of a degree Celsius per minute for at least a minute.

[0050] The accelerometer features reduce false positives by revealing confounding motion. The server rejects candidates that coincide with large body motion above two times gravity for more than three seconds. The server also rejects candidates that exhibit chewing-like vibration without the expected steep thermal decline. FIG. 4 illustrates how the detector identifies the start and end of the event window and how it preserves the shape of the thermal curve.

[0051] In one embodiment, the estimator models the reticular compartment as an effective thermal mass that exchanges heat with the colder ingested water during the event and exchanges heat with the ambient environment during recovery. During the event the rate of change of reticular temperature is proportional to the difference between the reticular temperature and the water temperature. After the event the rate of change is proportional to the difference between the reticular temperature and the ambient air temperature or a proxy derived from the ambient index. The recovery is well approximated by an exponential approach to a steady value.

[0052] The estimator therefore computes the integral over the event window of the reticular-minus-water temperature difference multiplied by the sampling interval. The estimator multiplies that integral by a calibration factor that encodes the effective heat capacity and geometry of the system. The estimator further adjusts the result using a function of the fitted recovery time constant, the animal's mass, and a proxy for rumen volume. The server fits the recovery time constant by regressing an exponential model to the post-event samples and requires the result to lie within a physiological range such as five to twenty-five minutes.

[0053] FIG. 5 presents an example of the exchange relationships in conceptual form. FIG. 6 shows an example calibration workflow that initializes constants from herd priors and refines them by comparing daily sums of estimated intake to pen water meter readings. This embodiment uses recursive least squares and limits the magnitude of weekly parameter changes so that the calibration remains stable. For each event the server propagates the uncertainty from sensor noise, from segmentation, and from any substitution of water temperature. The server discards events with implausible shapes such asa recovery that is faster or slower than a plausible bound. The final event record contains the start and end times, the estimated volume, the fitted recovery constant, the average water temperature and ambient index during the window, and flags that describe the data quality. FIG. 7 illustrates the overlay of raw samples, the smoothed curve, the exponential fit, and the selected water temperature trace for a single event.

[0054] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• Pre-processing: From the bolus stream, resample T_r to uniform At = 10 s using linear interpolation when the device is in EVENT_BURST; otherwise use the native cadence (30-120 s). Apply a robust smoother: 3-point median then 3-tap Savitzky-Golay (order 1) to reduce sensor noise while preserving slopes.• Slope and amplitude triggers: Maintain a rolling derivative estimate dT / dt using a 30-60 s window; mark candidate drink starts at index / where dT / dt[i] < -S_th and T_r[i] -T_r[i-w] < -AT_min with S_th e [0.5, 1.5] °C / min and AT_min e [1.5, 5.0] °C, tunable per farm / season. Merge adjacent candidates if separated by <90 s. Determine event end when dT / dt > -0.1 °C / min for >60 s. Store [tO, tl],• Confound rejection via accelerometry: Reject events coincident with chewing / rumination vibration patterns (characteristic 0.5-1.5 Hz bursts) without the expected rapid negative slope. Reject motion artifacts if accelerometer magnitude exceeds 2 g sustained for >3 s during the drop.• Selecting T_w and T_a / THI: For each event, select the closest water sensor by pen and distance; fetch T_w(t) trace over [tO-5 min, tl+10 min]. Similarly, obtain ambient data (T_a(t), THI(t)). If multiple water points are in range, pick the one with shortest time-offset to the event (i.e., cow was at that trough recently per gateway RSSI / co verage).• Heat-exchange model: Model the reticular environment near the bolus as a lumped thermal mass with effective heat capacity C_eq and two exchange coefficients: k_w during the event (liquid contact at T_w), and k_env otherwise (ambient effects). The continuous model:Discretize at cadence At, yielding:T'r.t+i = Tr,i - At kx(Tr i- Tx i) + rii, with x e {w, env}. The per-event volume V (liters) may be estimated from the temperature deficit integral relative to T_w scaled by parameters:y = a (Tr i— TWj ^t - f(r,M, Vrum') iGevent where T is the recovery constant fitted post-event (exponential T_r(t) = T_{r,°°} - (T_{r,°°} - T_{r, 1}) eA{-(t-t_l) / \ta u}), M is body mass, and V_rum is rumen volume proxy by breed / parity. The scalar a embeds C_eq and unit conversions.• Calibration: Initialize {k_w, k_env, a} from herd priors (breed / parity). Over the first week, solve a least-squares fit between daily IV and barn water meter usage apportioned probabilistically by trough visit timing (gateway RSS I time-window). Update coefficients per animal nightly with RLS (recursive least squares) and cap updates to ±10% / week to prevent drift.• Uncertainty and quality flags: Propagate uncertainty from sensor noise (o_T), missing T_w, and event segmentation. Report V ± u_V. Discard events with fitted T outside [5, 25] min, or with inconsistent drop shapes (e.g., plateau).• Outputs: For each event: {cowID, tO, tl, V, T, Tw_avg, THI, flags}; daily: IV, 7-day baseline, and hydration status computed as z-score zV = (IV - p_7) / o_7 with hysteresis to reduce false alerts.Heat-stress Analytics and Closed-loop Actuation

[0055] In one aspect, the system may use calculation, estimation or other analysis to derive a signal to actuate or control a physical mechanism such as a switch, pump, gate, lock or other mechanism. For example, an ambient temperature sensor may detect high ambient temperatures, and this may trigger a cooling system to turn on or increase its cooling, a water system to cool the water or increase water flow, or some other mechanism. In the absence of ambient temperature measurement, increased bolus temperatures from several livestock animals may be used as a proxy for increased ambient temperature measurement. In this situation, the system may log the measurements taken, an indication of the status derived from the measurements, and an indication of the action taken; any or all of these may be presented to the user on a user interface or through an alerting mechanism

[0056] In one aspect, the system quantifies heat stress by comparing each reading to a personalized circadian baseline and by combining that deviation with the ambient thermal load and the animal's hydration status.

[0057] In one embodiment, the server constructs a baseline by computing the median reticular temperature for each hour of the day over a rolling three-week window. The server computes a deviation by subtracting the baseline for the current hour from the current reading. The server retrieves the temperature-humidity index for the pen and computes a hydration status as a z-score of the current day's cumulative intake relative to the recent seven-day distribution. The server also computes daily rumination minutes so that it can recognize stable behavior. The system forms a heat-stress score that increases when the internal temperature is above baseline, when the ambient index exceeds a pen threshold, and when hydration is below normal. The score decreases when rumination minutes remain stable because stable rumination suggests that the animal is coping. The server smooths the score with an exponential average so that small fluctuations do not cause unstable control.

[0058] A dedicated controller 430 maps the pen-level score to device-safe actuator commands. The controller 430 increases fan speeds and mister duty cycles when the score rises and reduces them when the score falls. The controller honors hard safety limits so that misters do not operate in cool conditions, floors are allowed to dry, and electrical faults do not lead to unsafe operations. The controller supports scheduled overrides and veterinary overrides so that staff can hold settings during procedures or maintenance.

[0059] The server logs the inputs, the score, the command, and the subsequent response so that each action can be audited. Over time the system fits a pen-specific response model by comparing the change in score after a control action to the historical response for that pen. The fitted model adjusts the gains of the controller within safe bounds. FIG. 9 shows the flow from inputs to score to safety checks to setpoints and to the log. The dashboard tiles in FIG. 8 present the score, water intake, rumination, activity, and alerts so that staff can see the state of the pen at a glance.

[0060] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• Baselines: For each cow, maintain circadian baselines of T_r(t) computed as a rolling 21-day median by clock hour (e.g., 24 bins). Maintain environmental baselines for THI by pen• Heat-stress indicators: Compute (i) AT_circ = T_r(t) - baseline_Tr(hour); (ii) THI(t) from ambient node; (ill) hydration drift zV as outlined above; and (iv) rumination minutes per hour. Define HeatStressScore:H = w4• damp(ATcirc, 0,2.0) + w2• clamp(THI — THIth, 0,20) / 20 + w3• clamp(— zV , 0,3) / 3 — w4• rumination_z with typical weights {wl=0.4,w2=0.3,w3=0.2,w4=0.1}. TH l_th is farm-configurable (e.g., around the farm's chosen guideline). Smooth H with an EMA (time constant 1 h).• Control policy: Map pen-level aggregated H to actuator setpoints: fans (speed), misters (duty cycle), water pump (flow rate), and shade gates (open / close). Implement a PID-like controller: u(t) = Kp*(H - HO) + Ki * f( H - HO) dt with HO the comfort target (e.g., 0.2). Clamp outputs to safe ranges. Safety interlocks: (a) disable misters if Ta < T_min (e.g., 18 °C), (b) enforce dry-out periods to prevent slick floors, (c) lockout if electrical fault detected.• Scheduling & overrides: Allow scheduled profiles (night cooling boosts) and vet override for sick pens. Provide per-pen H hysteresis bands to prevent actuator chatter. The server generates machine-readable control messages ({penlD, actuatorlD, setpoint, ttl}) and uses a gateway to address local PLC / loT relays 360.Detection of systemic heat events: Compare herd median H with pen-level H; if herd H>0.8 for >1 h, escalate an all-farm alert (SMS / push), attach a dashboard card with IV trend and rumination suppression.Feedback and learning: Log actuation->response pairs: for each control change, store subsequent 2-h changes in H, IV, and milk yield (if integrated). Weekly, fit a simple pen-level response model (linear or tree) to adapt Kp / Ki gains by pen / season.• Fail-safe behavior: If sensors are down (no THI for 15 min), fall back to T_r-only triggers; if bolus density is insufficient (<30% of cows in pen), revert to ambient THI control curves; if comms to actuators fail, send repeated alerts and do not assume success.• Audit & compliance: For each event, keep a hash-chained log record with: inputs (H components), setpoint, operator overrides, and outcomes.Feed Control & Efficiency Analytics — Sensing, Logic, and KPIs

[0061] According to the disclosure, it is preferable, all else being equal, that a cow produce more milk, and that this milk be of as high quality as possible. An abundance of high quality milk produced by a cow means more revenue being generated by that cow. Through bolus temperature measurements taken during milking and preferably correlated with measurements of milk quantity produced (as measured externally during milking), the quality and quantity of milk produced by a cow may be estimated, calculated or otherwise derived.

[0062] In one aspect, the system may log the quantity and / or quality of milk produced in a milking session, and may aggregate this data with data from other milking sessions of the same animal, to ascertain overall production capacity of the animal. This information may be correlated with feed intake to generate a measure of milk production efficiency. This measure of efficiency may be logged or shown in an animal-specific report, or in a report on herd efficiency.

[0063] In one embodiment, the system maintains feed availability within planned limits and quantifies efficiency by integrating multiple data sources. When load-cell plates are present the system samples at two hertz and computes a five-minute median that suppresses transient force spikes. When a camera is present the system runs a segmentation model at one frame per second and converts the segmented area to kilograms using a per-ration regression that is updated weekly through weigh-back audits. The server defines a target profile for feed availability across the day. The profile reflects the number of animals, the ration plan, and management preferences. When measured availability falls below the target for longer than a configurable duration the system commands a gate to open or a feeder to start until the measured availability returns to the target. The commands have rate limits and timeouts so that overfeeding and undue mechanical wear are avoided. Every decision is recorded together with the sensor values and the result so that a manager can reconstruct the history.

[0064] The system estimates feed disappearance by comparing the measured mass immediately before and after a restocking event and by subtracting known waste. When the farm provides presence data from readers near the bunk the system apportions disappearance to animals using the timing and duration of presence so that each animal receives a probabilistic intake estimate. The system integrates milk yield and composition when available and computes a daily efficiency index for each animal. A useful choice divides milk kilograms by estimated feed kilograms and can include a water term if the farm wishes to reflect water-associated costs or constraints. The server flags animals and pens whose efficiencydeviates from their recent history. The server enforces calibration and quality checks for the sensors and disables automatic control if the correlation between vision estimates and weigh-backs falls below a threshold or if a camera fails. FIG. 10 presents the flow from sensing to mass estimation to comparison to actuation. FIG. 8 shows how the efficiency index appears alongside other key indicators.

[0065] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• Sensing options and installation: If using load-cell plates, situate N plates evenly under the bunk. Calibrate each using two known weights (e.g., 100 kg and 300 kg) and store coefficients {gain, offset}. Sample at 2 Hz, filter with 5-min median to suppress animal pushes and step loads; derive feed_kg per segment and sum to pen_feed_kg. If using camera vision, mount cameras 2.5-3 m above the bunk, angled 45°. Run an on-edge segmentation model producing feed area; convert to feed kg using per-mix calibration (see below). Note that the load-cell plate method and the camera method may be used together, with one providing backup for the other (in the event of system failure) or with the two results combined (through averaging or some other method).• Control loop: Define a target feed presence profile by time of day (e.g., >X kg between feedings), account for number of animals and ration. The controller monitors pen_feed_kg; if it drops below the time-varying threshold for >Y minutes, issue a command to open feed gate or start TMR auger until the target is restored. Provide rate limits (e.g., maximum 10 kg / min) and timeouts. Log all actions.• Intake estimation: Compute feed disappearance between restocking events: Afeed_kg = feed_kg(t_before) - feed_kg(t_after) after correcting for waste (edges / trough sweep logs). Optionally, estimate per-cow contributions with presence data (from IDs at waterers or UWB beacons) to distribute Afeed_kg probabilistically across animals in the pen.• Milk integration: If the farm has robotic milkers or parlors, ingest milk yield (kg / session) and quality (fat / protein) per animal.• Efficiency KPIs: Compute a daily Efficiency Index per cow:Milk_kgE = -Feed_kg,est + a • V where IV is daily water intake found above, and a is a scalar (e.g., liters-> kg water weight factor or a tunable penalty).Report pen medians and identify outliers (low E) for nutritionist review. Complement with health overlay (subclinical fever flags from T_r) to distinguish nutrition vs. illness.Calibration for vision method: For each ration, conduct weekly weigh-back audits: after the last feeding, manually weigh leftover feed at 3 random segments; regress vision area to kg; update model coefficients. Store R2and confidence intervals; if R2< 0.8, flag re-calibration.• Fault handling: If cameras fail (no frames >10 min) or R2deteriorates, suspend vision control and revert to load-cell if available; else, alert and disable automatic feeding.• Data fusion: Combine feed intake, IV, rumination minutes, and activity to compute a Feed Behavior Score (FBS) per cow. A drop in feed presence plus increasing IV and reduced rumination may suggest heat stress; feed control then coordinates with the cooling actuation aspect detailed elsewhere in this disclosure.GHG Quantification by Fusion

[0066] According to the disclosure, bovine methane emissions are a concern as they contribute to greenhouse gas (GHG) concentrations in the atmosphere. Most bovine GHG emissions are from exhalation or burping, while about 5% are from flatulence. In one aspect, the system comprises a sensor implanted on the animal or worn by the animal on its back or around its neck, which sensor measures GHG emissions, and is enabled to relay these measurements to a central system either through the bolus or through some other method. These measurements may be logged, correlated with other measurements, noted in a report or reported to the user through a user interface or some alerting mechanism.

[0067] In one embodiment, the platform estimates enteric methane emissions by aligning motion-based eructation windows with opportunistic near-muzzle gas concentration sampling. The accelerometer reveals primary and secondary reticular contractions as distinctive bursts in a narrow frequency band during periods of otherwise low motion. The server marks short windows where an eructation is likely. When a cow approaches a waterer or a lane where a gas node 340 is installed the server requests a burst of gas samples at two to five hertz for ten to twenty seconds.

[0068] The server aligns the recorded concentration with the windows and converts the concentration into a mass flow using a local airflow model that accounts for estimated air velocity and the sensor's effective sampling area. The server aggregates multiple bouts during the day and computes a daily mass of methane. Because dilution and airflow vary across bouts and days the server normalizes the estimate using water intake and simple descriptors of the thermal recovery after drinks. This normalization reduces day-to-day variability and improves repeatability. When gas nodes are not present the server computes a relative emission index by counting eructation windows and normalizing by feed and water intake.

[0069] The server treats this index as a trend metric rather than an absolute measurement. The system validates the fused estimate during field trials by comparing it to reference measurements for a subset of animals and stores a calibration factor for each farm. FIG. 11 shows the timing diagram in which eructation windows and gas sampling windows overlap and flow into a fused estimate that is expressed both as grams per day and as an intensity per liter of milk or per kilogram of gain.

[0070] In one embodiment, a near-muzzle gas node 340 may be employed. For GHG fusion, low-power CH4ppm node may be mounted near waterers or high-traffic lanes at some distance, such as 40-60 cm, above the floor, aimed at the animal's muzzle region. The sensor may sample on demand: the server notifies the node when a tagged animal is within N secondsof a detected eructation window (from bolus accelerometry). The packet may carry some or all of {nodelD, ts, CH4_ppm, temp, humidity, status}.

[0071] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• The objective is to estimate daily methane (g / day) per animal using on-animal cues (bolus accelerometry and temperature) optionally fused with near-muzzle CH4ppm samples at waterers / lanes.• Eructation window detection (bolus): From the accelerometer, compute magnitude and band-power in 0.2-2.5 Hz windows. Identify primary / secondary reticular contraction patterns (periodic bursts) and low-motion windows (<0.1 g RMS). Train a simple threshold logic (no ML necessary) to mark eructation windows (1-3 s bursts) during low gross movement periods.• Opportunistic gas sampling: Place a gas node near waterers. When (a) the server predicts the cow is at the waterer (based on recent IV event or gateway proximity), and (b) a succession of eructation windows is detected, instruct the node to acquire CH4ppm @ 2-5 Hz (or some other frequency) for 10-20 s (or some other time window). Capture temperature / humidity for normalization.• Fusion model: For a sampling bout, compute mean CH4ppm and burst rate R_e (eructations / min). Convert ppm to mass flux using a mixing model that accounts for airflow around the sensor (approximate ventilation velocity v from ambient node or a fixed default per installation) and effective sampling area A (~0.01-0.03 m2). The instant mass rate is m = K ■ ppm ■ v ■ A, with K a constant for unit conversion. Aggregate multiple bouts per day; weigh by sampling confidence (angle / proximity heuristics). To adjust for variable dilution across bouts and days, normalize by water intake IV and T_r patterns (as proxies for mouth humidity / temperature conditions affecting dispersion). Report CH4g / day with an uncertainty band (±lo) based on variability across bouts.• Fallback (no gas sensor): When gas nodes are absent, estimate relative emissions index from eructation counts N_e / day normalized by feed intake and IV; trend per animal day-over-day.• Calibration & validation: Perform a farm trial, preferably for one or more weeks. For a subset of cows, compare fused estimates with spot reference (e.g., a known chamber or portable analyzer when available). Fit a per-farm K and optionally a per-pen airspeed v. Store calibration in the metadata table alongside the gas node.• Edge cases: High wind reduces effective detection; clamp v within a plausible range; discard bouts where CH4ppm is flat at ambient. If IV is unusually high (very cold water), adjust dilution normalization.• Outputs & KPIs: Daily per-cow {CH4_g_day, uncertainty, bouts, R_e} and intensity metrics, e.g., g CH4 / L milk and g CH4 / L water. Visualize in the server Ul.

[0072] According to the disclosure, other aspects for the monitoring of GHG emissions are contemplated, such as:Direct Measurement: Sensors may be integrated within the bolus to measure methane production from the rumen directly. This data can provide insights into an animal's enteric fermentation and overall GHG emissions.• Milk Quality Indicators: The correlation between bolus data (e.g., temperature, rumination, rumen PH) and milk components (fat, protein, lactose) may be analyzed. Understanding these relationships can help optimize feed and breeding strategies for more efficient milk production with lower GHG emissions.• Predictive Analytics: Historical data may be used to predict changes in milk quality based on individual cow health and GHG emissions, allowing for more sustainable production practices.• Optimizing Feed Composition: Bolus data may be used to assess individual animal responses to different feed types, enabling farmers to optimize diets for better nutrient absorption and lower GHG emissions.• Reducing Waste: Sensors may be implemented that track feed intake and manure output to identify inefficient feed usage, leading to dietary adjustments that minimize waste and associated GHG emissions.• Air Quality Monitoring: Sensors to measure air quality in barns may be implemented, tracking ammonia, carbon dioxide, and other emissions. This data can inform ventilation and management practices to improve air quality and reduce GHG emissions.• Manure Management: Sensors may be added to monitor manure characteristics (e.g., moisture content, nutrient composition) to develop more effective manure management strategies, potentially reducing methane emissions from decomposition.• Comprehensive Sustainability Metrics: A dashboard that aggregates data from bolus systems, additional sensors, and milk quality measurements may be created to provide a holistic view of farm sustainability, including GHG emissions, feed efficiency, and milk production metrics.• Behavioral Insights: Behavioral data collected from the bolus system may be used to identify stress or health issues that could lead to inefficiencies and higher GHG emissions, enabling proactive management.Reproduction & Semen-Selection Scoring

[0073] In one aspect, the platform supports breeding decisions by turning continuous physiological and production data into a transparent selection score. Among other factors, it is preferable for breeders to breed cows which will have high milk production, high reproduction and lower susceptibility to (or effects from) illness.

[0074] In one embodiment, the server computes a health stability index from the variance of reticular temperature after removing drink cycles and after aligning with the animal's circadian baseline. The server computes hydration consistency as the median absolute deviation of daily water intake across a rolling week. The server computes daily rumination minutes from the accelerometer stream and computes a night-time activity balance that reflects rest versus motion. The server integrates milk yield and composition when available and computes a daily efficiency index as described in this disclosure. The server counts episodes that indicate illness and marks vaccination-related spikes so that known interventions do not unduly penalize an animal. The server may also log vaccination-related spikes separately, as an indication of vaccinationefficacy. The server computes a heat-response resilience measure based on how quickly the animal's heat-stress score declines after the cooling system activates.

[0075] In one embodiment, the server standardizes all metrics to z-scores within cohorts that share parity and stage so that comparisons are fair. The server combines the z-scores using weights agreed with the farm's management and veterinary team. The result is a single selection score that ranks animals and that includes an explanation showing the contribution of each component. The server enforces data sufficiency rules and suppresses scores for animals that lack adequate observations. The server produces versions of the score appropriate for lactating cows, for heifers, and for beef animals. The server exports the scores and features to the farm's breeding software and allows managers to correlate outcomes such as pregnancy checks with prior scores so that weights can be refined over time. FIG. 13 summarizes the path in one embodiment from features through standardization to a weighted score and a ranked shortlist.

[0076] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• The objective is to produce a Selection Score for breeding and semen decisions using health stability and performance signals computed from the core platform.• Signal set: For each cow over a rolling 90-day window, compute: o Health Stability Index (HSI): inverse of T_r variance from circadian baseline after removing drink cycles. o Hydration Consistency (HC): 7-day moving MAD (median absolute deviation) of daily IV; lower MAD is better. o Rumination Minutes (RM): daily minutes classified as rumination averaged over 7 days. o Activity Balance (AB): ratio of rest to active minutes during night hours. o Milk Productivity (MP): kg milk / day averaged over 14 days (if integrated). o Efficiency (E): from elsewhere in this disclosure (Milk per Feed+crWater). o Illness Flags (IF): count and severity of subclinical fever spikes (AT beyond baseline) and veterinary events. o Heat-response Resilience (HRR): slope of H vs. actuation improvements — cows whose H reduces faster after cooling have higher resilience. o Reproduction timing (if available): estrus detection corroborated by activity / rumination patterns.• Normalization: Convert each metric to z-scores within parity cohort to control for age / lactation stage. Cap extreme z at ±3.• Selection Score formula: A sample scoring formula, with sample weights, is shown here. The weights may be algorithmically, iteratively or experimentally determined.S = 0.18 HSIZ+ 0.12 HCZ+ 0.12 RMZ+ 0.10 ABZ+ 0.18 MPZ+ 0.15 Ez- 0.10 IFZ+ 0.05 HRRZThe weights may be adjusted with farm feedback. A higher S indicates a preferred breeding candidate.• Seminal bull valuation: For bulls (or semen straws analyzed through progeny performance), aggregate offspring S after normalizing by farm effects to produce a Bull Merit Index (BMI). Store lineage relationships and build a tree view to identify sires with consistently high S across environments.• Workflow: Weekly, recompute S for all animals; present a shortlist (top 10%) with explanations (Shapley-style or weight breakdown). Operators can pin or exclude animals (e.g., genetic diversity rules).• QA and fairness: Require minimum data sufficiency (e.g., at least 45 days with >70% data coverage). Backfill MP and E with pen-level medians if missing to avoid penalizing non-milking animals (e.g., heifers) — or compute S variants by class.• Export and integration: Export S to breeding software and semen inventory systems. Log decisions and outcomes (pregnancy check results) to close the loop and refine weights.Portable Gateways

[0077] In one aspect, the system relies on being able to communicate with the bolus even when the cows are pastured, or in remote pens, where the bolus may not be read by a conventional reader, or where there may not even be wireless network connectivity.

[0078] In one embodiment, the platform provides three relay types so that farms and ranches without full fixed coverage can still receive data with acceptable latency. An on-cow relay 380 integrates a low-power receiver (to communicate with the bolus), a microcontroller, a positioning module, and a wireless data connectivity module, to communicate with a network 350, such as Wifi, LoRA, WAN, or cellular or satellite uplink if there is no local wireless network coverage in the area. The relay wakes every thirty seconds (or some other interval) to listen for nearby device packets and forwards any packet that it receives. The relay batches packets so that it reduces backhaul overhead and so that short backhaul outages do not cause data loss.

[0079] A drone relay 382 may carry a lightweight concentrator and may follow a route that places the drone over water points and high-traffic areas on a schedule. The drone opens long receive windows while it loiters and forwards the stored packets when it regains strong backhaul coverage. The drone may manoeuvre close to a cow and communicate with the bolus directly, or may act as an intermediate relay between an on-cow relay and the wireless network 350.

[0080] An unmanned ground vehicle (UGV) robot relay 384 may patrol fence lines and service lanes and provides the same receive and forward behavior while also enabling visual inspection of waterers and gates. As with the drone, the ground robot may be enabled to manoeuvre close to a cow and communicate with the bolus directly, or may act as an intermediate relay between an on-cow relay and the wireless network 350.

[0081] The server 400 deduplicates packets received through multiple relays by using the device time stamp and the relay time stamp. The server keeps link quality statistics for each animal and asks relays to focus on animals that show hydration risk so that events are not missed. Each relay authenticates to the server with a client certificate and reports its health,location, and battery level. FIG. 1 shows the system overview, while FIG. 12 depicts one embodiment of these relay options and shows how the server may choose among them to maximize coverage and reliability.

[0082] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• On-cow collar gateway: Hardware: low-power LoRa transceiver (RX-only most of the time, TX on schedule), BLE (optional for sensor maintenance), microcontroller with GNSS (wake on schedule), and a cellular / SAT uplink or mesh backhaul to a fixed edge gateway. Power via rechargeable Li-ion with solar trickle or kinetic harvester. Duty cycle: RX windows every 30 s (2-3 s each); uplink every 5-15 min. Software: buffer bolus packets from nearby cows (RSSI filter), batch and forward; de-duplicate on the server. Ensure collar ID identifies as a relay, not a cow, to avoid mixing.• Drone relay: Multirotor with a LoRa concentrator and LTE / SAT backhaul, or bolus reader with LoRa or other backhaul. One sample flight plan would be to survey the pasture in sweeps every 1-3 h (weather / regs permitting). During loiter at waypoints above water points or salt licks, open RX window for 30-60 s to collect bolus bursts. Implement a store-and-forward buffer with timestamping for delayed delivery. Safety: geofences; auto-return on low battery. Routing: the drone tags packets with relaylD so the server can resolve duplicate deliveries from other relays.• Ground robot (UGV) relay: A small rover with a mast-mounted LoRa antenna follows predefined lanes (fencelines, water routes) once or twice per day. It serves as both coverage extender and maintenance scout (camera to inspect troughs).• Routing & prioritization: The server maintains a relay table and link-quality estimates per cow. For hydration-risk cows, instruct relays to increase RX frequency in their vicinity (on-cow) or adjust waypoints (drone / UGV). When multiple relays hear the same packet, accept the earliest arrival and use others for redundancy.• Edge cases & power: If on-cow relay battery low, throttle RX duty. If drone grounded (wind), increase on-cow RX and request UGV patrols via operator alert. Log relay health status (RSSI histograms, battery, GNSS fix).• Data structures: Relay packets may comprise some or all of {relaylD, cowID, dev_ts, rssi, snr, payload, relay_ts}. The server's ingest service may de-duplicate using {cowID, dev_ts} and merges rssi / snr for coverage maps.• Security: Preferably, use mutual TLS between relays and server. Provision relays with client certs distinct from bolus certs.Al, Machine Learning and Edge-Cloud Considerations

[0083] According to further embodiments of the disclosure, the platform may reduce latency and uplink volume by running Al classifiers on the bolus and by managing model training and deployment in the cloud 400. Al or machine learning systems may also be used to improve the interpretation of sensor data, enabling real-time decision-making support forfarmers. This could include predictive models for health issues or automated alerts for specific conditions, or help with herd management.

[0084] In one embodiment, the bolus may execute a small convolutional network that recognizes drink events from short temperature windows and recognizes rumination from accelerometer features. A compact decision tree flags fever spikes when the reticular temperature deviates from the animal's circadian baseline while short-term variance remains low. All models are quantized to eight-bit integers so that they use less memory and energy. The bolus maintains a circular buffer of raw samples. When the slope trigger fires the bolus samples at the higher burst rate and runs the local model. If the confidence exceeds a threshold such as eighty-five percent the bolus emits an event record. If the confidence lies between sixty and eighty-five percent the bolus stores a shadow window for later training and transmits only a compact summary when energy and connectivity allow.

[0085] In one embodiment, the cloud 400 trains candidate models from labeled windows that include confirmed events and corresponding water-temperature and ambient streams. The cloud selects a canary cohort (a small subgroup of devices selected to receive early or test data or code) and delivers a signed model package to those devices using a fragmentation and multicast update protocol supported by the gateways. Each device maintains two model slots so that it can atomically swap to a new model and roll back if validation fails.

[0086] The cloud monitors precision and recall using corroboration from external sensors and also monitors uplink bytes and battery trends. If the canary cohort improves on these metrics the cloud promotes the model to the rest of the fleet. If performance degrades or energy use rises the cloud demotes the model and instructs devices to roll back. The system records each promotion or rollback in the audit log. The user interface shows the model version running on each device and a summary of recent false positives and false negatives. FIG. 14 illustrates the life cycle in one embodiment, in which the device performs on-board inference, stores shadow windows, and receives over-the-air model updates while the cloud runs training, A-B testing, and rollback management.

[0087] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• Hardware / RTOS baseline: An appropriate MCU should be chosen for the bolus. One example would be an ARM Cortex-M4F / M33, with >256 KB RAM, >1 MB flash. Sensor set: reticular temperature and 3-axis accelerometer. Radio: LoRa / LoRaWAN. RTOS: FreeRTOS or Zephyr with two tasks: sense_infer_task (high priority) and uplink_task (low).• Example on-device models (TinyML): o Drinking event classifier. A 1-D CNN (two conv layers + max-pool + dense) on a 60-120 s temperature window (resampled to 10 Hz during bursts), plus a scalar feature for slope. Quantize to int8; footprint ~25-35 KB.o Fever spike detector. An exponentially weighted baseline + small decision tree (CART) that classifies "spike / normal / verify" using AT from circadian baseline and short-term variance; footprint ~4 KB. o Rumination classifier. A 1-D CNN on accelerometer magnitude band-power (0.5-1.5 Hz) windows; footprint ~32-40 KB.• Local inference pipeline: sense_infer_task samples at 1 Hz (baseline). A slope trigger arms EVENT_BURST (10 Hz) and loads the CNN. If confidence > 0 (e.g., 0.85), it emits a compact event record{tO, tl, type, conf, max_drop,T_hint} to RAM and posts to uplink_task. If confidence e [0.6, 0.85), store locally as "shadow" (no uplink now) for training.• Model packaging and update: Models are built and quantized (TFLite-Micro) in the cloud. Package: {model. tflm, labelmap.json, meta.yml} with semantic versioning (e.g., drink-v3.2.1). Deliver by FUOTA compatible with LoRaWAN Class C fragmentation (or sent via gateway Wi-Fi / LTE if available). Use signed packages (ED25519) and safe-swap partitions; keep previous champion for rollback. OTA TinyML and FUOTA over LoRaWAN are well-documented patterns; adopt standard safety checks and packetization.• Cloud training and selection: The cloud stores (i) shadow windows and (ii) labeled truth (from Ul audits and cross-sensors). Train candidate models and push to A / B: a random 10-20% fleet slice gets canary model, others keep champion. Compare precision / recall, confusion matrices, and battery impact and uplink bytes over 7-14 days. If canary wins (p < 0.05) promote to champion; else rollback with one click; maintain lineage. Edge A / B and rollback for ML at the edge are recognized as best practice in MLOps.• Monitoring: Devices report weekly model KPIs: hit counts, false-positive rates estimated from server side consistency (e.g., a "drink" with no water-temperature change nearby is suspicious), average CPU time per inference, and battery trend.• Failure modes: If model fails to load, revert to threshold-only logic and raise a diagnostic. If FUOTA incomplete, keep champion. If model drifts (drop in precision or spike in misses), auto-demote and pin prior version for N days.• Outputs to rest of system: Local event detection reduces uplink (send events, not raw), improves latency for actuation, and increases resilience during backhaul outages.Livestock Traceability

[0088] According to the disclosure, bolus systems can help track the entire lifecycle of cattle, including genetic background, health history, and pharmaceutical treatments. This information can enhance consumer confidence in product quality and safety.

[0089] In one aspect, the system binds semen genetics, calf identity, feedlot performance, and carcass metrics into a continuous record that survives ownership changes. During insemination 520 the dairy scans a semen lot 510 and records the event together with the dam identity. During calving the farm assigns a new animal identity 530 and binds it to an officialelectronic ear tag, with a uniquely assigned identifier, a CowlD. The farm may place a bolus so that physiological continuity begins early in life.

[0090] The system can store a cryptographic hash of a DNA sample so that a dispute can later be resolved without storing the raw DNA data. The presence of a life-long bolus (a bolus ingested very early in the animal's life, and remaining in place at least for the duration of the animal's life) may also be used for life-long identity and characteristic tracking. When the animal is sold or transferred the seller and buyer sign a digital ownership record that identifies both parties and the effective date. The platform records each transfer in a hash-chained log / ledger 540 so that the sequence is immutable.

[0091] During the feedlot phase 550 the platform ingests the same water-intake, rumination, and heat-stress signals already used in health analytics and computes daily intake, stress exposure, and treatment history. At the processor 560 the plant provides lot composition, hot carcass weight, marbling, and yield. The platform aligns the processor data with the upstream chain and produces value-back-propagation reports 470 that summarize performance by genetics cohort.

[0092] The platform enforces role-based access so that each actor sees only what it needs. FIG. 15 presents the graph of semen lot to insemination event to calf identity to ownership ledger 540 to feedlot phase to processor lot and finally to value reporting. As of 2024, USDA APHIS finalized a rule requiring electronically readable eartags as official ID for interstate movement of covered cattle and bison, 180 days after publication. This invention makes EID routine and simplifies linking animals to records.

[0093] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• The objective is to create a bolus-anchored chain of identity spanning Al service (semen) through the calf stage, through growth / finishing, through carcass quality, surviving ownership transfers and aligning with U.S. electronic ID rules and private certification programs.• Data model (core tables / objects): o Bull / SemenLot: {bulllD, breed, genomics, STgenetics / ABS / ... code, strawLotID, QC}. o InseminationEvent: {damCowID, strawLotID, techlD, date, location, successFlag}. o Calfidentity: {calfCowID, birthDate, damCowID, sireBulllD (from straw), EID, bolusDevID (if placed), DNA sample hash}. o OwnershipRecord: {entity I D, calfCowID, startDate, endDate, docURI} (transfer ledger). o FeedlotPhase: {entryDate, pen, ration plan, health interventions, 2V / day, E index}. o ProcessorLot: {plantID, lotID, arrivalDate, cattle list, hot carcass weight, marbling, yield grade, MSA / USDA grades, trim data}. o TraceLink (hash-chained): prev_hash, event, actorSig, timestamp.Workflow:1. At Al service, a strawLotID is scanned; InseminationEvent is signed by the dairy.2. At calving, assign CowID and (optionally) administer a mini-bolus for lifetime continuity; capture EID and DNA hash (saliva / hair).3. At sale / transfer, buyer signs OwnershipRecord; our ledger stores both signatures.4. At feedlot, ingest IV, T_r and rumination via our platform to compute efficiency (E) and health metrics.5. At processing, plant uploads lot and carcass metrics; our service back-propagates value to genetics and rearing cohorts.• Identity binding & privacy: The bolus identity (devID) binds to CowID at the dairy; EID tag binds at first movement. A privacy layer is kept: counterparties see only what they need (e.g., feedlot cannot read the dairy's full milk history). The underlying log is hash-chained, or a permissioned ledger (e.g., Hyperledger Fabric). Blockchain-oriented cattle traceability is one option; this design is implementable with a simpler hash-chained store if full blockchain is unnecessary.• Continuity guarantees: o Two anchors: (i) EID (regulatory), (ii) bolus CowID (continuous physiology stream). o DNA anchor optional to resolve disputes. o Event notarization: transport, treatments, ration changes, and lot formation at processor are signed and time-stamped. o APIs: REST endpoints for partners (genetics firm, calf raiser, feedlot, processor, retailer) with role-bound scopes.• KPIs & value: For beef-on-dairy calves, the chain enables premiums for verified genetics and performance; trade literature emphasizes market preference for traceable beef-on-dairy calves.Transport & feedlot stress detection

[0094] Transportation of livestock can involve numerous risks to the livestock, including heat stress and dehydration. In one aspect, the platform detects dehydration and heat stress during transport and receiving by combining device and environmental signals and by comparing them to explicit thresholds. During transit the absence of drink events indicates zero intake. When a sustained slow rise in reticular temperature is observed over three to six hours compared to the circadian baseline and when the cargo area temperature-humidity index remains elevated for more than an hour the system flags dehydration and heat risk.

[0095] The system alerts the dispatcher and recommends a rest stop in a shaded area when regulations and safety permit. After arrival the system expects one or more large drink events within the first four hours. When this rebound drink does not occur the system recommends setting up an electrolyte trough, increasing water flow, and checking access to clean water. When the pen environment is hot the system increases cooling duty and shade.

[0096] The system monitors bunk engagement with feed sensors 330 and presence information and flags poor starters for closer observation. It tracks time to first drink, day-one water intake, heat-stress score during the first three days, and treatment rates during the first two weeks. FIG. 16 shows the pipeline that begins with cargo sensors and bolus readings, continues through transit and receiving rules, and ends with alerts and recommended interventions.

[0097] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• The objective of this aspect is to detect dehydration and heat-stress during transport and receiving, when morbidity risk spikes, and deliver actionable playbooks (electrolytes, shade / cooling, bunk management).• Signals & hardware: o On-animal: bolus T_r; accelerometer for low-motion hypokinesia vs restlessness; drink events (none expected in-transit). o Transport ambient: a cargo node (T / RH) computes THI; optional vibration (truck dynamics). o At arrival: pen ambient (THI), water T_w, feed presence.• Transit detectors: o Dehydration risk when (i) IV = 0 during transit, (ii) T_r shows monotone slow rise against circadian baseline (> 0.3 °C over 3-6 h) and (ill) HRV proxy (accel variance) is depressed (rumination off). o Heat-risk when THI_cargo > 74 for > 1 h or T_r rise exceeds z-score +1.5 vs baseline. Evidence supports accelerometer systems for welfare assessment and heat-stress time-budgeting; we tailor that to bolus / THI fusion in transit and at feedlot intake.• Receiving detectors: o Rebound drink model: within 4 h post-unload, expect one or more large drink events (AT > 2.5 °C, T ~10- 20 min). Absence triggers an electrolyte / clean-water intervention card. Literature links feedlot arrival dehydration to early morbidity; electrolyte supplementation and hydration are recognized mitigations. o Feed engagement: combine feed presence (as detailed elsewhere in this disclosure) + cow presence at bunk (gateway coverage) to flag poor starters. o Heat-stress at finishing yards uses the H-score (as discussed elsewhere in this disclosure) but with pen-level overrides for fresh arrivals.• Playbooks & alerts: o Transit alert to dispatcher if THI_cargo spikes or sustained T_r climb detected; recommend rest stop in shade, if legal / feasible. o Receiving alert: (i) set up electrolyte trough, (ii) mister duty +20% for 2 h, (ill) shade access, (iv) observe for BRD signs at 24-72 h. NAHMS feedlot reports and veterinary reviews support focus on BRD in receiving; we add hydration-focused triggers.• Validation: For before / after cohorts: compare morbidity events within 14 days for lots with vs without hydration playbook triggers. KPIs would include time-to-first drink, IV day-1, day-3 average H, and treatment rates.Sustainability & certification (carbon intensity per liter of milk / kg beef)

[0098] In one aspect, the platform may convert animal-level measurements into product-level carbon intensity values that follow accepted life-cycle methods. In one embodiment, for milk the engine uses a cradle-to-farm-gate boundary and expresses output as kilograms of carbon-dioxide equivalent per kilogram of fat-protein corrected milk. For beef the engine typically uses a cradle-to-processor-gate boundary and expresses output as kilograms of carbon-dioxide equivalent per kilogram of carcass or liveweight as the program dictates.

[0099] The engine 460 aggregates the contributions from enteric methane, manure emissions, feed embedded emissions, and on-farm energy. It converts the daily animal-level methane estimates into carbon-dioxide equivalents using a selected global warming potential. It estimates manure methane and nitrous oxide using tiered factors that depend on climate and diet or using sensor inputs when available. It attributes feed emissions using supplier data when the supplier provides it or using default tables when it does not. It reads fuel and electricity data so that direct energy use is included.

[0100] It divides the sum of emissions by the quantity of product in the chosen functional unit. It tags each result with the system boundary, the allocation method, the version of emission factors, and the warming potential used. It can generate scenarios so that managers can test the effect of diet changes or mitigations such as methane inhibitors. FIG. 17 presents the computation pipeline for one embodiment from inputs to a method-tagged carbon-intensity output and shows how the result feeds into program reports.

[0101] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factors and the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• The objective of this aspect is to Compute product-level carbon intensity (Cl) using animal-level signals (CH4and efficiency E from elsewhere in this disclosure) and farm / processor activity data, aligning with accepted frameworks (ISO 14067, GHG Protocol Product Standard, FAO LEAP sector guidance, IDF dairy methods) for credible reporting toward customer programs (e.g., SDP, USRSB, retailer initiatives).• System boundaries (configurable): o Milk default: cradle-to-farm-gate (feed production + enteric + manure + energy). Functional unit: kg FPCM (fat-protein corrected milk) at farm gate, per IDF. ukidf.or o Beef default: cradle-to-processor-gate for fed cattle (cow-calf + Stocker or dairy-origin + feedlot + enteric + manure + energy). Follow LEAP large ruminant and beef guidance.• Calculation blocks:1. Enteric CH4: per animal (g / day). Upscale to CO2e with GWP factors; aggregate to product output (milk kg, liveweight / carcass kg).2. Manure: either Tier 2 factors by diet / climate or farm-integrated sensors if available; start with LEAP default tables.3. Feed embedded CO2e: farm- or supplier-specific data if available; else LEAP feed defaults.4. On-farm energy (Scope 1 / 2): fuel and electricity meter data.5. Allocation: dairy systems use biophysical allocation between milk and meat; IDF and LEAP explain standard choices.• Formulas (simplified):_. > ^enteric + ^manure + ^feed + ^energy milk“ kg FPCM ^enteric + ^manure + ^feed + ^energykg carcass (or LW)Where E_enteric leverages our bolus fusion, which improves resolution vs. herd-average factors. Efficiency E (milk per feed+water) informs feed attribution and scenario testing.• Governance & claims: The dashboard outputs are tagged by boundary, method, factors version (e.g., LEAP 2016, IDF 2015 / 2022 update, GWP100 AR6), supporting assurance per SDP and alignment with retailer programs (e.g., Walmart Project Gigaton collecting supplier emissions and avoided emissions).• Targets & FLAG: For corporate target-setting we map farm output to FLAG (SBTi) guidance and GHG Protocol agricultural accounting (corporate inventory and product standard).Auditable "Green Scorecards" for Processors / Retailers / ESG

[0102] According to the disclosure, the scorecard function produces machine-readable and human-readable records that summarize performance and sustainability at the animal, lot, and shipment levels. At the animal level the record includes water-intake stability, heat-stress exposure, rumination, treatment history, and emission intensity. At the lot level the record includes distributions of the same metrics and includes mortality, morbidity, and energy and water intensity. At the shipment level the record includes lot composition and processor outcomes such as hot carcass weight and marbling.

[0103] The server converts these measurements into a structured document that maps to the fields of common dairy and beef sustainability frameworks and that can be ingested by processor and retailer systems. The server signs each record digitally and embeds a pointer into the hash-chained log so that an auditor can verify provenance. The server emits both a JSON file for automated ingestion and a portable document for human review. Processors can configure incentive rules such as bonuses for carbon-intensity values below a benchmark or for zero welfare incidents. Retailers can import the records into their portals without manual transcription. FIG. 18 shows one embodiment of how animal metrics roll up to lots and shipments and how the mapping, signing, and export steps produce scorecards and incentive outputs that are ready for use.

[0104] In one embodiment, the following steps may be followed, and aspects implemented. It will be understood that the values and algorithms will be substituted or modified by those skilled in the art based on implementation factorsand the kinds of available systems. As well, it will be understood that other embodiments of the disclosure above may be derived:• The objective of this aspect is to produce tamper-evident sustainability scorecards aggregating from animal to lot to shipment, aligned with SDP (dairy) and USRSB (beef) frameworks and retailer reporting (e.g., Project Gigaton), suitable for incentives and verified disclosures.• Data roll-up: o Animal level: CH4 intensity (g CH4 / kg milk or / kg gain), H-score time > threshold, IV stability, treatments / antibiotics, transport-stress flags, E index. o Lot level: pen averages, distribution tails (5th / 95th), mortality / morbidity, water / energy intensity. o Shipment level: mix of lots + processor metrics (yield grade, marbling); values normalized to functional unit (kg FPCM or carcass kg).• Framework mapping: o USRSB indicators (water resources, land resources, air & GHG, animal welfare, efficiency & yield, employee / community): we auto-populate metrics + documentation links. o SDP (dairy processors <-> buyers): we generate the SDP Report fields and attach third-party verification documents as URIs.• Verification & audit: Each scorecard is a JSON + PDF with a digital signature and hash-chain pointer. It embeds method tags from elsewhere in this disclosure (e.g., boundary, allocation, factors version). Retailers asking for Scope-3 quantification can ingest the JSON and align to their portals (Project Gigaton).• Incentives & flags: Processors can set bonus rules (e.g., Cl below benchmark; transport-stress < X; animal welfare incidents = 0). Failing metrics trigger root-cause drill-downs (by lot or supplier).Cow ROI (Return on Investment) System

[0105] In one aspect of the invention, the Cow ROI function converts continuous sensing and farm records into a defensible, per-animal profitability measure that supports daily and strategic decisions.

[0106] In one embodiment, the function runs on the server as an analytics service identified here as the ROI engine 480. It ingests the same data streams described earlier in this disclosure, and it supplements them with production and accounting information that comes from robotic milking systems, milk analysis laboratories, any other milking data and milk component sources, feed mixers, any other feed data sources, purchase ledgers, and regional market feeds. The engine produces trailing profitability, forward profitability forecasts, and ranked decision lists. Each output is accompanied by an explanation that traces the numbers back to the underlying signals and assumptions, and each calculation is recorded in the hash-chained log 450 so that an auditor can reproduce the result. A sample embodiment is shown in FIG. 19. Note that not every factor discussed here for the ROI calculation need be implemented, and some aspects, such as feedback values, outputs and other aspects, may be omitted.

[0107] In one embodiment, the engine begins with revenue because it is the most familiar quantity to producers. For dairy animals the engine reads milk volumes from the robotic milking system or the parlor management system and aligns those volumes to animal identifiers and timestamps. It then joins component tests from the farm's analysis reports so that the fat percentage, the protein percentage, and other quality factors such as somatic cell count can influence payments. The engine multiplies the measured milk mass or weight (preferably in kilograms but can be in any mass or weight unit) by the current milk price and applies the component differentials that the cooperative or processor publishes. It applies deductions when quality penalties are present. It adds expected calf value when the animal has a confirmed pregnancy and discounts that value by the probability of survival that is derived from the herd's own historical records. It adds sustainability incentives when the farm participates in programs that pay for documented carbon-intensity reductions or animal-welfare performance, and it computes those incentives directly from the method-tagged outputs and signed scorecards described elsewhere in this disclosure. When a cull is scheduled, the engine includes the expected salvage value and places it on the day that the cull will occur.

[0108] The engine then constructs costs in a way that reflects how money is actually spent on a farm. It attributes feed cost by multiplying the animal's estimated intake (detailed elsewhere in this disclosure) by the composition of the ration that was actually fed on that day. It reads the ration recipe from the mixer's batch ticket or from the nutritionist's plan and it converts the recipe into costs by multiplying the kilograms of each ingredient by the ingredient's unit price. It uses the farm's purchase records and regional market data to assign current prices for corn, soybean meal, by-products, and minerals. It does not assume a single price for feed because geography and supply chains alter ingredient costs. It attributes water cost by multiplying the daily sum of water intake (calculated as detailed above in this disclosure) by the cost of water at that site. It attributes energy cost for fans and misters by tracking actuator on-time (taken from elsewhere in this disclosure) and multiplying by metered energy consumption and tariffs. It attributes labor and treatment cost through a health index that turns attention into dollars. The health index records the time to locate and move a cow, the veterinary time, the cost of pharmaceuticals, the withdrawal effects, and the loss of production during and after treatment. The principle that the best cow is the cow that requires no attention becomes a numeric quantity because an absence of attention lowers labor and treatment cost and raises net revenue.

[0109] The engine includes fertility because reproduction controls the lactation curve and the calving interval. It reads breeding and pregnancy records and assigns a fertility factor to each animal. The fertility factor influences revenue because it changes expected milk in future weeks. It also influences cost because failed services and extended days open consume semen, time, and feed without generating additional milk. The engine models the expected date of the next calving and dry-off and moves forecast milk and costs accordingly. It updates the fertility factor after each breeding result and pregnancy check, and it allows the user to choose whether to adopt conservative or aggressive assumptions for animals with limited history.

[0110] The engine accepts data from several sources because not every farm has every system connected. It accepts milk and component values either as automated feeds from a robot or as daily entries from an analysis report. It accepts ration composition either as a mixer download or as a manual entry. It accepts ingredient prices as live feeds froma purchasing system or as periodic manual updates. It accepts labor rates and veterinary fees as constants that the manager can change in the settings. It accepts energy tariffs either from a utility interface or as a schedule of rates. The engine labels each input with its provenance and quality so that uncertainty is visible to the user.

[0111] The engine computes daily gross revenue and daily allocated costs and subtracts the costs from the revenue to produce daily net revenue for each animal. It summarizes these values into seven-day and thirty-day trailing averages and reports confidence intervals that widen when inputs are imputed or stale. It then produces a forward forecast for thirty to ninety days by combining an autoregressive model for milk yield, expected ration costs, expected energy costs based on seasonal heat exposure, scheduled reproductive events, and the current fertility factor. It discounts the forecasted cash flows at a farm-selected rate to form a net present value for keeping the animal. It computes the difference between that value and the net present value of replacing the animal with a cohort-typical heifer. That difference is the cull-or-keep signal that the manager can act on.

[0112] The engine ranks animals by the profitability measures that the manager cares about. If the manager has one hundred cows and twenty heifers have matured, the engine produces a list that identifies the twenty least attractive animals to sell if capacity is fixed. It can rank by trailing net revenue per day when immediate cash flow is the priority. It can rank by forward net present value when strategic herd improvement is the priority. It shows a short explanation next to every rank that states which drivers contributed most to the position. It tells the user whether the rank was driven by high feed cost, low milk component value, high labor and treatment cost, poor fertility, or unfavorable incentives.

[0113] The engine learns as new animals enter the herd. When heifers begin lactation, the engine observes their actual milk yield, their intake, their health attention, and their fertility outcomes. It uses those observations to update the coefficients in the milk and cost models for that cohort. It reduces the uncertainty on forecasts for heifers that share breed and rearing conditions with the newly observed animals. It stores before-and-after snapshots of the coefficients so that the management team can see how the model has adapted. This feedback loop improves the accuracy of the rankings overtime and aligns the model with the reality of the farm's genetics, rations, and management.

[0114] The engine integrates with the user interface 440 and with the scorecard generator 470 so that profitability appears alongside health and sustainability. The interface presents ranked lists with filter and scenario tools. The manager can simulate moving a heat-sensitive cow to a cooler pen, changing a ration, treating a borderline case, breeding a high-score animal sooner, or selling a chronic low performer. The engine recomputes the forward profitability under the simulated change and reports the expected difference with an uncertainty band. The outputs are signed and archived so that the farm's advisors and processors can review them with confidence. The Cow ROI function makes the economics of individual animals explicit and connects them to the measurable drivers that the platform records every day.

[0115] In one embodiment, the Cow ROI engine 480 supports cross-herd benchmarking by converting all profitability drivers into standardized, cohort-aware metrics and then normalizing those metrics across connected herds. The engine expresses feed efficiency, water use, milk component yield, labor attention, treatment rate, and fertility performance as z-scores within parity and days-in-milk bands so that animals are compared only to biologically similar peers. The engine pools anonymized statistics across participating herds and fits a hierarchical model that separates within-farmvariation from between-farm baselines. The model corrects for ration type, climate zone, and housing style so that the benchmark reflects real management differences rather than environmental noise. The user interface 440 displays an animal's position within its farm distribution and within the multi-farm distribution, and it provides a narrative that states the practical meaning of the rank. The narrative explains how far the animal sits from the median of comparable animals and which drivers contribute most to that position.

[0116] The engine links genetics to economics by merging genomic predictors with observed efficiency and health performance. The engine ingests genomic estimated breeding values or predicted transmitting abilities when they are available, and it converts those values into marginal economic contributions using the farm's current prices and costs. The conversion uses the same revenue and cost models that drive the daily ROI, which makes the linkage explicit. The engine then forms a combined index that includes genomic value and observed feed efficiency from the aspect detailed above, observed health attention from the labor and treatment channel, and observed fertility performance from the breeding records. The index predicts the marginal profit that would be expected from daughters of a given sire under the farm's actual ration and price structure. The engine updates the weights in the index when new heifers calve and begin to generate real data, and it records the before and after weights in the hash-chained log 450 so that the change is transparent. This linkage allows selection decisions to rest on a single quantity that is expressed in currency units and grounded in the farm's real economics.

[0117] The engine quantifies uncertainty at the decision level so that a manager can act with appropriate confidence. The engine assigns probability distributions to the quantities that move most and that are not known with certainty on the decision day. These quantities include future milk price, component differentials, ingredient costs, energy tariffs, health event probabilities, fertility outcomes, and sensor-level measurement noise. The engine generates a large set of forward scenarios by sampling from these distributions and by simulating daily milk, intake, costs, and events under the current control policies. The engine computes a distribution of net present value for each animal under each contemplated action such as keeping, selling, breeding now, or breeding later. The engine reports the probability that one action dominates another by delivering a higher net present value. The engine also reports the expected regret, which is the expected loss that would occur if the less profitable action were chosen. The engine computes the expected value of perfect information for the most influential uncertain inputs by comparing the expected outcome under current uncertainty to the expected outcome under a hypothetical oracle that reveals the future value of a single input. When the expected value of perfect information for a variable is large, the engine recommends low-cost tests or short deferrals that would reduce that uncertainty. The recommendation includes the time window in which waiting is still rational and the level of confidence that would flip the decision. The user interface presents ranks with confidence bands and shows when a difference is statistically meaningful versus when two candidates are practically tied.

[0118] The engine supports herd-level optimization when farms face quotas and capacity limits. The optimization module receives a set of feasible actions and a set of constraints that describe the production system. The feasible actions include keeping or selling each animal, choosing a sire for the next service, moving animals between pens, and scheduling treatments and cooling setpoint changes. The constraints include the maximum number of stalls, the maximum number ofparlor turns per hour, the daily milk quota, the number of head that a pen can hold, the available hours of labor by shift, the maximum energy draw for cooling during peak tariff windows, and the available inventory of each ration ingredient. The module forms an objective that maximizes total herd profit over a planning horizon while applying penalties for risk and for large swings that would stress operations. The module uses the same revenue and cost functions that the ROI engine uses for individuals and sums them over animals and days. The module encodes withdrawal periods, welfare rules, and shipping dates as hard constraints so that the solution never recommends an action that would violate safety or compliance. The solver produces a set of recommended actions and the shadow prices of active constraints. The shadow prices explain which constraints are binding and how much profit would increase if a constraint were relaxed. The engine translates the recommendations into a sequenced worklist with responsible roles and timestamps and delivers it to the user interface 440. The engine repeats the optimization on a daily cycle and on demand when large shocks occur, such as a change in processor price schedules or a heat wave that materially alters energy costs and expected milk.

[0119] The engine integrates with external financial and enterprise systems so that the numbers used in ROI calculations and the numbers posted to ledgers match. The engine connects to accounting platforms through secure interfaces and pulls the chart of accounts, the trial balance, and recent journals. The engine maps general ledger accounts to economic drivers such as feed, energy, labor, veterinary, and overhead and stores the mapping as a farm-specific dictionary. The engine reconciles its calculated totals with ledger totals over a user-selected period and reports any differences with suggested corrections. The engine also interfaces with lenders and milk check portals so that milk receipts and loan payments can be imported automatically rather than typed by hand. When the farm requests it, the engine posts summary journals that allocate costs to cost centers at month end using the same drivers that the ROI engine used during the month. Each posting carries a reference to the hash-chained log 450 entries that justify the allocation so that the auditor can follow the trail from ledger back to sensors. The engine records the identity of the user who authorized the posting and the credentials used to access each external system. The credentials are stored using a secrets manager and are never written to plaintext logs.

[0120] The engine supports real-time, event-driven triggers so that profitable actions occur without delay. The ingest service 410 emits a stream of events that include per-event water intake, heat-stress score changes, rumination changes, treatment entries, breeding outcomes, and price and tariff updates. The engine listens to this stream and evaluates a set of rules that the user can configure. When a rule's conditions are met, the engine creates an action proposal with the expected net present value change and the confidence of improvement. Examples include an automated sell alert when an animal's forward net present value falls below zero by a margin that exceeds a configured threshold and the probability of dominance exceeds a configured confidence level. Another example is an automated breed alert when a high selectionscore animal is in an optimal fertility window and the expected marginal profit from early conception exceeds the cost of an additional service. A third example is a ration change suggestion when the price of a key ingredient moves sharply and a simulation shows that a different formulation would reduce feed cost per unit of milk without harming component yields. Each alert contains the action, the rationale, the expected economic impact, and the confidence interval. The alert routes to the user interface 440 and to any subscribed channels such as email or text messages. The engine requires explicit user approval for actions that change the herd state or incur costs, and it records every approval and rejection in the hash-chained log 450 with the full context. The engine throttles alerts so that the staff does not receive conflicting instructions during volatile periods. The throttle uses hysteresis and minimum action spacing to avoid churn. The result is a system that notices meaningful changes as they happen, translates those changes into clear financial terms, and proposes timely actions that a manager can accept with confidence.Cow Stress Index

[0121] The Cow Stress Index is a continuously computed measure that expresses the acute and chronic stress burden carried by a specific animal identified by its CowlD. The index is designed to be interpretable by managers and veterinarians and to be stable enough for trend analysis while remaining responsive to acute events. The index is calculated on the same server that processes water-intake estimation, heat-stress control, greenhouse-gas fusion, selection scoring, traceability, and return-on-investment analysis. The outputs of the Cow Stress Index are written to the hash-chained log so that every value can be reconstructed and defended. The index is tracked for the lifetime of the animal so that the complete history can be presented as a CarFax-like report during herd decisions and supply-chain transactions.

[0122] The engine that computes the Cow Stress Index consumes streams and records from several subsystems. The ingestible bolus supplies reticular temperature, accelerometer features, and event markers for drinking and rumination. The water nodes 310 and ambient nodes 320 supply the water temperature at the point of drinking and the temperaturehumidity index in each pen. The transport subsystem supplies cargo thermal conditions and trip durations when the animal is in transit, which are critical for receiving stress detection. The feed subsystem supplies ration composition, feed disappearance, and bunk presence patterns, and it identifies abrupt ration changes and periods of scarcity. The medical record supplies diagnoses, treatments, vaccination events, and withdrawal windows, and it records the time required to locate and handle the animal during care. The reproduction and traceability subsystems supply insemination attempts, pregnancy results, days open, ownership changes, and transfers between groups. The calf health workflow supplies birth weight, weaning weight, age of weaning, ultrasound lung measurements as a proxy for oxygen capacity, the age when rumination begins, and early activity patterns captured by the bolus or a collar sensor once the calf is large enough to carry instrumentation.

[0123] The engine converts these heterogeneous inputs into coherent physiological and management features. It computes thermal load by comparing the current reticular temperature to the individualized circadian baseline and by measuring the duration and amplitude of elevations that are not attributable to drinking. It computes dehydration load by comparing the current daily sum of water intake to the animal's seven-day distribution and by identifying multi-hour stretches with no drink events in hot conditions. It computes transport burden by counting trips, summing minutes spent in cargo thermal conditions that exceed safe thresholds, and including the time since the last trip. It computes social integration burden by measuring the frequency of pen changes, the fraction of sick animals in the destination group, and the change in bunk presence conflicts inferred from overlapping presence at the same feed position. It computes health burden by counting recent illnesses, by weighting treatments by invasiveness and withdrawal impact, and by discounting vaccination-related temperature spikes so that routine vaccines do not create false stress signals. It computes developmental burden for calves by considering early weaning, low birth weight, low weaning weight, delayed onset ofrumination, and lung ultrasound scores that indicate reduced capacity. It computes reproductive burden by counting failed services, by measuring days open against parity-matched expectations, and by quantifying the energy demand of late gestation during heat exposure.

[0124] The engine standardizes each feature against the relevant cohort so that parity, stage of lactation, and production class do not confound interpretation. It defines three windows of analysis because stress expresses itself on multiple time scales. The acute window covers the past twenty-four hours and captures events such as a long transport or a sudden heat episode. The subacute window covers seven days and captures integration into a new group, ration changes, and short illnesses. The chronic window covers thirty days and captures persistent thermal challenges, repeated treatments, and long periods of low water intake. Within each window the engine produces sub-indices for thermal stress, dehydration, transport, social integration, health, development, and reproduction. Each sub-index is scaled between zero and one by mapping cohort percentiles to a bounded range and by clipping extreme outliers after verification to avoid undue influence from faulty sensors.

[0125] The engine converts the sub-indices into a single Cow Stress Index value that ranges from zero to one hundred. A higher value indicates higher stress. The mapping from sub-indices to the final index uses weights that depend on the animal's life stage and production class. For a pre-weaned calf the development and health sub-indices carry more weight than the reproduction sub-index. For a lactating dairy cow the thermal, dehydration, health, and reproduction subindices carry more weight than the development sub-index. For a feedlot beef animal the transport, social integration, and dehydration sub-indices receive additional emphasis during the first week on feed. The weight set is stored as a versioned policy so that adjustments are auditable. The engine smooths the final index with a short exponential average so that natural variability does not cause alarm fatigue, and it publishes both the raw and smoothed values so that analysts can see the underlying movement.

[0126] The engine calibrates itself to the herd by anchoring the stress scale at two reference points. The tenth percentile of the chronic stress distribution over the past month is labeled as a calm anchor. The ninetieth percentile of the chronic stress distribution is labeled as a high anchor. The engine maps these anchors to fixed points on the zero to one hundred scale and maps intermediate values linearly with guardrails to prevent drift when the herd composition changes. The engine checks the calibration by measuring whether animals with high index values have higher rates of illness, lower milk persistence, or higher treatment counts in the following week. If the association weakens, the engine prompts a recalibration and logs the suggested change for review.

[0127] The engine handles missing and low-quality data in a transparent way. It assigns a quality score to every stream and imputes short gaps with cohort medians when gaps are unlikely to change the classification. It widens the confidence interval on the index when imputation is used. It does not compute sub-indices that depend on unavailable inputs, and it reports which components are missing so that a user can schedule maintenance or data entry. It uses redundant signals where possible so that a missing water-temperature reading does not halt dehydration assessment, because ambient conditions and reticular patterns still carry information. It drops aberrant features when a sensor fails quality checks and marks the event in the log.

[0128] The Cow Stress Index connects directly to interventions and to business logic. The heat-stress controller increases cooling duty and provides shade when the thermal sub-index rises beyond a threshold while ambient conditions are high. The grouping workflow delays moving a fragile calf into a large group when the development sub-index is elevated. The receiving playbook adds electrolytes and gentle handling when the transport sub-index spikes on arrival. The reproduction team gives extra monitoring to animals with high stress readings before breeding. The return-on-investment engine incorporates the stress index by increasing the expected labor and treatment costs for high-stress animals and by decreasing the expected milk forecast when stress is persistent. The selection scoring engine penalizes chronic high stress in its ranking because chronic stress predicts higher health costs and lower persistence. The traceability ledger stores the index and its drivers for each ownership segment so that a buyer can see the handling and environmental history that may affect future productivity and health. The scorecard generator includes the distribution of stress indices by lot so that processors and retailers can benchmark welfare outcomes in a verifiable way.

[0129] The engine learns from outcomes so that its predictions and alerts improve over time. It refines the mapping between sub-indices and risk by observing subsequent morbidity, reproduction outcomes, growth in beef settings, and milk persistence in dairy settings. It updates cohort models when a new heifer cohort enters production and replaces prior assumptions with observed responses. It reports the effect of each learning step so that managers understand how the weights and anchors have changed and why those changes were justified.

[0130] FIG. 20 depicts one embodiment of the Cow Stress Index pipeline. The figure shows how sensors, records, and context feed feature extraction. It shows how the engine forms sub-indices across acute, subacute, and chronic windows. It shows how the final index flows to controllers, the return-on-investment engine, the selection score, the traceability ledger, and the scorecard generator. The figure also shows how outcome feedback updates the mapping so that the system remains aligned with the realities of the herd.

[0131] In one embodiment, the Cow Stress Index may additionally include a fully specified sensor-fusion and machine learning implementation that converts heterogeneous data streams into calibrated sub-indices and a unified stress value. FIG. 21 shows one embodiment of the Cow Stress Index pipeline with these features. The implementation begins with feature extraction that is consistent across devices and production classes. The reticular temperature stream is transformed into three families of features that capture physiology and context. The first family describes departure from the animal's circadian baseline and includes the absolute elevation, the duration of the elevation, and the area under the elevation curve after removal of drinking events. The second family describes hydration dynamics and includes the interval since the last drink, the number of drink events in the last six hours, the cumulative daily volume relative to the animal's rolling distribution, and the shape of post-drink thermal recovery which is parameterized by the fitted recovery time constant detailed earlier. The third family captures motion and behavior and includes rumination minutes derived from the accelerometer, night-time activity ratios that represent rest balance, and a contraction periodicity score that distinguishes eructation and reticular cycles from non-specific movement.

[0132] The external sensing streams are converted into aligned contextual features. The water nodes 310 provide bulk water temperature at each source, and the system creates a water availability descriptor that reflects the presence ofcold water during high ambient load. The ambient nodes 320 provide the temperature-humidity index, and the system computes exceedance minutes above a pen-specific threshold and the rate of change of the index during the last hour. The cargo and movement records produce transport features that count trips, sum minutes spent above a safe cargo index, and measure the time since the last trip. The feed system produces ration features that indicate abrupt changes in ration composition, periods of feed scarcity, and increased competition at the bunk inferred from overlapping presence at the same position. The medical record is reduced into health features that weight treatments by invasiveness and withdrawal impact, track diagnoses, and estimate handling time for each episode. The reproduction record contributes features that include failed service counts, days open relative to parity expectations, and the late gestation window during heat exposure. Calf health adds features that include birth and weaning weights, age at weaning, lung ultrasound scores as a proxy for oxygen capacity, and the age at which rumination begins. Each feature is standardized within the relevant cohort defined by parity, stage, and production class so that comparisons remain fair across the herd.

[0133] The fusion layer combines these features with a model family that has been selected for accuracy, robustness to missing data, and explainability. The sub-indices for thermal stress, dehydration, transport, social integration, health, development, and reproduction are produced by a gradient-boosted decision tree ensemble that has been trained on labeled events and expert annotations. The labels include veterinarian-confirmed heat stress, dehydration episodes identified during receiving, integration challenges indicated by abnormal bunk conflict patterns, and medically significant illnesses recorded in the farm's system. The ensemble is configured to tolerate missing features through learned default splits so that the system continues to operate during partial outages. The ensemble outputs sub-index scores that range from zero to one and that are accompanied by feature importances for audit. The sub-indices are then combined into a single stress value using a weight vector that is optimized per life stage and production class. The optimization solves a constrained regression problem in which the predicted stress value is encouraged to align with downstream outcomes such as subsequent treatment within seven days, reduced milk persistence during the next week for dairy animals, and reduced average daily gain during the next week for beef animals. The constraints enforce nonnegativity for weights on sub-indices that represent pure burdens and bound the weights to prevent dominance by any one component. The weight vector is learned on historical data with cross validation and is versioned so that a change in herd composition or management can be tracked over time.

[0134] The predictive capability of the Cow Stress Index is explicit and measurable. The system includes a survival analysis model that estimates the hazard of a medically significant event during the next three to seven days as a function of the current acute and subacute stress values, the recent trajectory of those values, and the feature families that produced them. The model uses a proportional hazards framework with regularization so that it remains stable as herds change. The system also includes a regression model that forecasts loss in milk yield persistence for lactating dairy cows given the current stress and its recent trajectory. This regression uses a mixed-effects structure with animal-level random effects so that individual differences in lactation curve and resilience are represented without biasing the population estimate. The feedlot variant uses a similar regression to forecast reduced average daily gain when the stress value remains elevated during the first week on feed. Each forecast includes a confidence interval that widens when upstream inputs are imputed or of low quality. The system validates these forecasts by comparing them to observed outcomes and reports area under the receiveroperating characteristic for the hazard model and mean absolute error for the regression models at rolling intervals. The validation reports are logged and exposed so that managers and auditors can see whether the predictive claims remain supported by current data.

[0135] The platform provides spatial analysis features that reveal where and when stress consolidates across the physical operation. The farm's pens and paddocks are represented as polygons in a geospatial layer that is maintained with coordinates or survey drawings. Each animal is assigned to a pen through gate readers, pen assignment records, or probabilistic association to the gateway that received the last packet when explicit assignment is missing. The system constructs pen-level aggregates by computing the median, the interquartile range, and the ninety-fifth percentile of the stress value across animals present during a specified time window. It computes similar aggregates for each sub-index so that specific burdens can be visualized. The system renders choropleth maps in the user interface in which pen polygons are shaded according to the selected aggregate and time window. For operations that span multiple barns or large pastures, the system produces a grid overlay that partitions the map into cells and performs inverse distance weighting to interpolate sparse point locations when exact pen assignment is uncertain. The system supports time-lapse playback so that users can observe how stress hotspots move during the day and across weather changes. The system records the renders as vector tiles with numeric legends and writes a summary into the log so that the spatial analysis can be reproduced later. The spatial view directly integrates with the controller so that a user can click a hotspot and see suggested actuator and grouping changes for that location.

[0136] The learning component incorporates outcome feedback to refine the fusion, weights, and forecasts. The system accumulates outcome pairs consisting of current stress values and subsequent events such as morbidity, reproduction outcomes, growth changes, and milk persistence changes. The hazard and regression models are retrained on a schedule with a canary cohort so that the fleet is protected from regressions. The weight optimization is updated only when cross-validated improvements exceed a configured threshold and when the tradeoff between sensitivity and alert volume remains acceptable. The system keeps the prior model as a rollback option and records every promotion event with the measured gains in discriminative performance or error reduction and with the cost in energy or uplink volume, if any. The platform publishes a plain-language summary of each learning step so that managers can understand how stress metrics connect to observed outcomes and how those connections have evolved.

[0137] In one embodiment, a system comprising an ingestible bolus, external sensors, and a server is configured to compute, for each animal, a stress value that combines calibrated sub-indices for thermal, dehydration, transport, social integration, health, development, and reproduction burdens, and to predict a risk of medically significant events during the next seven days using a survival model that consumes the current stress value and its trajectory. This system is further configured to forecast yield loss for a lactating dairy animal during the next seven days using a regression model that uses the stress value and cohort covariates and to output a confidence interval for the forecast. These systems are further configured to render a geospatial map in which each pen polygon is shaded according to a pen-level aggregate of the stress value or a sub-index and to generate a control recommendation that targets the pen with a specified actuator change when the aggregate exceeds an adaptive threshold. These systems are further configured to write each prediction, forecast, andmap to a hash-chained log together with the features and model versions that produced them so that the results can be audited and reproduced.

[0138] FIG. 21 shows the full fusion, prediction, and spatial layers in a single view. The figure shows how feature extraction and cohort standardization feed the sub-index ensemble, how the optimized weight vector produces the final stress value, how survival and regression models produce risk and yield forecasts, and how the spatial mapper generates pen-level stress maps and hotspots. The figure also shows how outcome feedback updates the models and the weights with auditable promotions and rollbacks.Further Embodiments

[0139] According to the disclosure, the server-based system may present a user interface (Ul), through a computer system or a mobile device, either through a browser interface or a dedicated application, to allow for visualization and analysis of historical data trends, to predict health issues, reproduction cycles or nutritional needs. Al, machine learning or algorithmic systems may assist with these, may allow for automated alerts for specific conditions, and may assist with herd management.

[0140] Similar to the aspects disclosed above, biosensors such as heart rate, milk components, feed intake and others, and environmental monitoring of conditions such as air quality, temperature and humidity, may provide insights into metabolic health, disease detection or nutritional deficiencies, and may provide comprehensive farm management solutions. The systems above may easily be adapted to animals such as bison, sheep, goats, giraffes, or other livestock or animals.

[0141] Bolus systems can be enhanced to gather genomic data over time. This data could help in tracking specific traits related to health, productivity, and reproduction, allowing farmers to make informed breeding decisions. By integrating bolus data with genetic databases, farmers can select for desirable traits more effectively, ensuring healthier and more productive herds.

[0142] Boluses can be designed to monitor an animal's physiological responses to specific medications, enabling veterinarians to adjust dosages based on real-time data. This personalized approach can improve treatment efficacy and reduce the risk of adverse reactions. The bolus system could track vaccination responses, helping farmers and veterinarians monitor immunity levels and determine the need for booster shots.

[0143] Boluses can provide continuous data on reproductive health indicators (e.g., temperature, activity levels, hormone levels), which can be correlated with semen quality and fertility rates. This data can help optimize breeding programs.

[0144] Bolus systems can monitor biomarkers that indicate genetic predisposition to certain diseases. This information could be valuable for breeders to select against those predispositions. As well, by analyzing health data collected from the bolus, farmers can detect early signs of genetic disorders in cattle, allowing for timely intervention and management.

[0145] Integrating bolus data with genetic and pharmaceutical data platforms can provide a comprehensive view of cattle health and breeding. This ecosystem can facilitate better decision-making and enhance operational efficiency on farms. Utilizing machine learning algorithms to analyze data from boluses, genetics, and pharmaceuticals can lead to predictive models for breeding success, disease outbreaks, and treatment responses.

[0146] Bolus systems can help track the entire lifecycle of cattle, including genetic background, health history, and pharmaceutical treatments. This information can enhance consumer confidence in product quality and safety.

[0147] Collaborating with genetic and pharmaceutical companies can lead to the development of targeted bolus technologies that cater specifically to genetic or health needs, such as breed-specific health monitoring.

[0148] Bolus systems may be integrated with milking robots to monitor physiological parameters (e.g., temperature, rumen health) during the milking process, allowing for immediate adjustments based on cow health.

[0149] Data from bolus systems may be used to assess milk quality in real-time, ensuring only high-quality milk is collected and sent for processing.

[0150] Bolus data on body temperature and activity levels may be connected to cooling systems, triggering adjustments to ventilation and cooling protocols during heat stress events. This integration may also be used to optimize energy consumption in cooling systems based on real-time herd behavior and environmental conditions.

[0151] Bolus data may be linked with feed management systems to create personalized feeding plans based on individual health metrics, optimizing nutrient intake and minimizing waste. As well, automated feed adjustments may be implemented based on real-time data from bolus systems regarding rumen health and feed consumption patterns.

[0152] Bolus data may be integrated with manure management systems to assess nutrient levels in manure and adjust application rates for fertilizers based on real-time data. As well, bolus data on health and feeding may be used to correlate with manure characteristics, helping to manage GHG emissions from manure effectively.

[0153] A centralized platform may be developed that aggregates data from bolus systems, cow ID systems, milking robots, and other farm technologies, providing comprehensive insights into herd health and farm performance. As well, combined data sets may be utilized for predictive analytics, allowing farmers to forecast health issues, productivity, and even market trends.

[0154] loT technology may be used to connect bolus systems with other automated farm management systems, creating a fully integrated network for real-time decision-making. Remote access to bolus data may be enabled alongside other farm systems, allowing farmers to monitor herd health and operations from anywhere.

[0155] Bolus data may be integrated with health management systems to automatically alert veterinarians of any significant changes in an animal's health metrics, enabling quicker responses to health concerns. As well, a historical record of health data collected from boluses may be maintained, aiding in long-term health management and veterinary assessments.

[0156] According to the disclosure, a livestock management system is disclosed. The livestock management system comprises an ingestible bolus configured to transmit reticular temperature data over time, a water-source sensor configured to measure water temperature at a drinking location, an ambient climate sensor configured to provide temperature and humidity for THI and a data processor and a communication interface to deliver alerts to a user interface. The data processor is programmed to detect a drinking event from the temperature data by applying slope-and-amplitude criteria, estimate per-event water volume using a non-linear heat-exchange model parameterized by the measured water temperature and ambient conditions and calibrated per animal, accumulate daily intake and generate a hydration status; and generate a control signal to at least one actuator 370 selected from a fan, mister, water pump, gate, or feeder when the hydration status and THI satisfy a control condition.

[0157] According to the disclosure, the data processor of the system estimates per-event water volume by fitting an exponential recovery of reticular temperature to determine a per-animal recovery time constant and integrating the temperature deficit relative to the measured water temperature during the detected drinking window. The water source sensor of the system is installed at a trough or bowl and transmits time-stamped water temperature so that each drinking event is parameterized by the measured water temperature at the moment of ingress.

[0158] According to the disclosure, the non-linear heat-exchange model of the system is calibrated by recursive least-squares against a pen-level water meter with apportionment of meter totals to animals by detected drink windows. The data processor of the system computes hydration status as a z-score of daily water intake relative to a rolling, parity- matched cohort distribution and flags dehydration when the z-score is below a configurable threshold during elevated THI.

[0159] According to the disclosure, the control condition of the system requires both a hydration-risk state and a THI exceedance, and the control signal increases duty cycle of fans or misters preferentially in the pen containing the largest number of hydration-risk animals.

[0160] According to the disclosure, the system further comprises an ingest pipeline that extracts rumination minutes and night-to-day activity ratios from the bolus accelerometer and incorporates those features into hydration status confidence intervals. The ambient climate sensor network of the system produces pen-level THI maps and the data processor selects actuators in pens whose stress aggregates exceed adaptive thresholds.

[0161] According to the disclosure, the system comprising a feed-intake apportionment module that combines bunk-presence timing with pen-level disappearance to estimate per-animal feed intake when individual feeder hardware is unavailable. The feed efficiency is computed as energy-corrected milk divided by estimated dry-matter intake and is displayed with an uncertainty interval derived from apportionment variance.

[0162] According to the disclosure, the data processor of the system computes a Cow Stress Index comprised of sub-indices for thermal load, dehydration, transport, social integration, health, development, and reproduction, each standardized by cohort stage. A survival model predicts a risk of a medically significant event within seven days as a function of the Cow Stress Index and its recent trajectory and produces a confidence interval.

[0163] According to the disclosure, a mixed-effects regression predicts milk yield persistence during the next week conditioned on the Cow Stress Index and parity, and the controller delays elective pen moves when predicted yield loss exceeds a threshold.

[0164] According to the disclosure, the system further comprises a greenhouse-gas fusion module that requests short bursts from a near-muzzle methane sensor when the bolus accelerometer indicates low-motion windows containing eructation signatures and aligns gas peaks to those windows to estimate per-animal methane mass flow. The methane intensity of the system is computed by normalizing mass-flow estimates by individualized water-intake and recovery descriptors and the result is tagged with method metadata for audit.

[0165] According to the disclosure, the system further comprising a return-on-investment engine that allocates feed, water, energy, labor, treatment, capital, and program incentives to each animal using physically meaningful drivers and produces trailing and forward profitability with audit trails. The return-on-investment engine of the system simulates user-selected actions comprising cooling adjustments, ration changes, pen moves, breeding timing, treatment, or culling and outputs an expected change in net present value for each action.

[0166] According to the disclosure, the on-device classifiers running on the bolus perform preliminary event detection and the server executes A / B model selection with rollback based on fleet-level key performance indicators. The control signal is rate-limited and records the command with contextual features in a hash-chained log to provide a non- repudiable provenance of decisions.

[0167] According to the disclosure, the water source sensor and ambient sensor of the system are synchronized to Network Time Protocol and each event record stores offsets to ensure deterministic reconstruction of volume estimates. The data processor computes a pen-level stress choropleth by aggregating Cow Stress Index values and subindices over a selectable window and renders a time-lapse map for operator review.

[0168] According to the disclosure, hydration status of the system is adjusted using the fitted recovery time constant during late gestation so that animals in late gestation receive prioritized cooling at a given THI. The drinking event detection applies both a negative slope threshold and a minimum cumulative temperature drop over a contiguous window and merges adjacent windows separated by less than ninety seconds.

[0169] According to the disclosure, the processor of the system updates per-animal calibration parameters only when cross-validated improvements exceed a configured threshold and retains a rollback image of the prior parameters with version identifiers.

[0170] According to the disclosure, the system further comprises a traceability ledger that links semen lot, calf identity, ownership transfers, feedlot performance, and processor outcomes to the hydration status, Cow Stress Index, and greenhouse-gas intensity recorded for each segment.

[0171] According to the disclosure, a method of managing livestock is disclosed. The method comprises the steps of receiving reticular temperature and accelerometer data from an ingestible bolus, receiving water temperature ata drinking location, receiving ambient temperature and humidity, detecting drinking events using slope and amplitude criteria, estimating per-event water volume using a non-linear heat-exchange model parameterized by measured water and ambient conditions with per-animal calibration, accumulating daily intake, computing hydration status, and issuing control commands to at least one actuator when hydration status and THI satisfy a control condition.

[0172] According to the disclosure, a non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the processors to perform the aforementioned method and to generate audit records comprising event features, model versions, and control outputs.

[0173] According to the disclosure, a livestock emissions measurement system is disclosed. The livestock management system comprises an ingestible bolus with an accelerometer, a near-muzzle gas sensor, and a server. The server is configured to trigger gas-sensor bursts during bolus-identified eructation windows, align gas peaks to the windows, compute methane mass flow and normalize intensity by individualized intake and recovery descriptors.

[0174] According to the disclosure, a system for geospatial stress visualization is disclosed. The system comprises pen polygons, animal-to-pen assignment from gateway reception and records, a processor configured to compute pen-level aggregates of a Cow Stress Index and its sub-indices, and a Tenderer that outputs choropleth maps and hotspot suggestions with actuator recommendations.

[0175] According to the disclosure, a livestock profitability system is disclosed. The livestock profitability system comprises a return-on-investment engine configured to allocate costs and revenues per animal from sensor-derived drivers, forecast thirty- to ninety-day cash flows under alternative actions, compute net present value for each action, rank animals for culling or retention, and output signed reports.

[0176] Implementations disclosed herein provide systems, methods and apparatus for generating or augmenting training data sets for machine learning training. The functions described herein may be stored as one or more instructions on a processor-readable or computer-readable medium. The term "computer-readable medium" refers to any available medium that can be accessed by a computer or processor. By way of example, and not limitation, such a medium may comprise RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. It should be noted that a computer-readable medium may be tangible and non-transitory. As used herein, the term "code" may refer to software, instructions, code or data that is / are executable by a computing device or processor. A "module" can be considered as a processor executing computer-readable code.

[0177] A processor as described herein can be 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 device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor can be a microprocessor, but in the alternative, the processor can be a controller, or microcontroller, combinations of the same, or the like. A processor can also be implemented as acombination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor may also include primarily analog components. For example, any of the signal processing algorithms described herein may be implemented in analog circuitry. In some embodiments, a processor can be a graphics processing unit (GPU). The parallel processing capabilities of GPUs can reduce the amount of time for training and using neural networks (and other machine learning models) compared to central processing units (CPUs). In some embodiments, a processor can be an ASIC including dedicated machine learning circuitry custom-build for one or both of model training and model inference.

[0178] The disclosed or illustrated tasks can be distributed across multiple processors or computing devices of a computer system, including computing devices that are geographically distributed. The methods disclosed herein comprise one or more steps or actions for achieving the described method. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is required for proper operation of the method that is being described, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0179] Portions of this application, including the specification and / or claims, may have been drafted using Al- assisted tools under the direction and supervision of a human practitioner. All inventive contributions are attributable to the listed inventors.

[0180] As used herein, the term "plurality" denotes two or more. For example, a plurality of components indicates two or more components. The term "determining" encompasses a wide variety of actions and, therefore, "determining" can include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, "determining" can include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, "determining" can include resolving, selecting, choosing, establishing and the like.

[0181] The phrase "based on" does not mean "based only on," unless expressly specified otherwise. In other words, the phrase "based on" describes both "based only on" and "based at least on." While the foregoing written description of the system enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The system should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the system. Thus, the present disclosure is not intended to be limited to the implementations shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

ClaimsWhat is claimed:

1. A livestock management system comprising: an ingestible bolus configured to transmit reticular temperature data over time; a water-source sensor configured to measure water temperature at a drinking location; an ambient climate sensor configured to provide temperature and humidity for TH I; a communication interface to deliver alerts to a user interface; a data processor programmed to: detect a drinking event from the temperature data by applying slope-and-amplitude criteria; estimate per-event water volume using a non-linear heat-exchange model parameterized by the measured water temperature and ambient conditions and calibrated per animal; accumulate daily intake and generate a hydration status; and generate a control signal to at least one actuator selected from a fan, mister, water pump, gate, or feeder when the hydration status and THI satisfy a control condition.

2. The system of claim 1, wherein the data processor estimates per-event water volume by fitting an exponential recovery of reticular temperature to determine a per-animal recovery time constant and integrating the temperature deficit relative to the measured water temperature during the detected drinking window.

3. The system of claim 1, wherein the water source sensor is installed at a trough or bowl and transmits time-stamped water temperature so that each drinking event is parameterized by the measured water temperature at the moment of ingress.

4. The system of claim 2, wherein the non-linear heat-exchange model is calibrated by recursive least-squares against a pen-level water meter with apportionment of meter totals to animals by detected drink windows.

5. The system of claim 1, wherein the data processor computes hydration status as a z-score of daily water intake relative to a rolling, parity-matched cohort distribution and flags dehydration when the z-score is below a configurable threshold during elevated THI.

6. The system of claim 1, wherein the control condition requires both a hydration-risk state and a THI exceedance, and the control signal increases duty cycle of fans or misters preferentially in the pen containing the largest number of hydrationrisk animals.

7. The system of claim 1, further comprising an ingest pipeline that extracts rumination minutes and night-to-day activity ratios from the bolus accelerometer and incorporates those features into hydration status confidence intervals.

8. The system of claim 1, wherein the ambient climate sensor network produces pen-level THI maps and the data processor selects actuators in pens whose stress aggregates exceed adaptive thresholds.

9. The system of claim 1, further comprising a feed-intake apportionment module that combines bunk-presence timing with pen-level disappearance to estimate per-animal feed intake when individual feeder hardware is unavailable.

10. The system of claim 9, wherein feed efficiency is computed as energy-corrected milk divided by estimated dry-matter intake and is displayed with an uncertainty interval derived from apportionment variance.

11. The system of claim 1, wherein the data processor computes a Cow Stress Index comprised of sub-indices for thermal load, dehydration, transport, social integration, health, development, and reproduction, each standardized by cohort stage.

12. The system of claim 11, wherein a survival model predicts a risk of a medically significant event within seven days as a function of the Cow Stress Index and its recent trajectory and produces a confidence interval.

13. The system of claim 11, wherein a mixed-effects regression predicts milk yield persistence during the next week conditioned on the Cow Stress Index and parity, and the controller delays elective pen moves when predicted yield loss exceeds a threshold.

14. The system of claim 1, further comprising a greenhouse-gas fusion module that requests short bursts from a nearmuzzle methane sensor when the bolus accelerometer indicates low-motion windows containing eructation signatures and aligns gas peaks to those windows to estimate per-animal methane mass flow.

15. The system of claim 14, wherein methane intensity is computed by normalizing mass-flow estimates by individualized water-intake and recovery descriptors and the result is tagged with method metadata for audit.

16. The system of claim 1, further comprising a return-on-investment engine that allocates feed, water, energy, labor, treatment, capital, and program incentives to each animal using physically meaningful drivers and produces trailing and forward profitability with audit trails.

17. The system of claim 16, wherein the return-on-investment engine simulates user-selected actions comprising cooling adjustments, ration changes, pen moves, breeding timing, treatment, or culling and outputs an expected change in net present value for each action.

18. The system of claim 1, wherein on-device classifiers running on the bolus perform preliminary event detection and the server executes A / B model selection with rollback based on fleet-level key performance indicators.

19. The system of claim 1, wherein the control signal is rate-limited and records the command with contextual features in a hash-chained log to provide a non-repudiable provenance of decisions.

20. The system of claim 1, wherein the water source sensor and ambient sensor are synchronized to Network Time Protocol and each event record stores offsets to ensure deterministic reconstruction of volume estimates.

21. The system of claim 1, wherein the data processor computes a pen-level stress choropleth by aggregating Cow Stress Index values and sub-indices over a selectable window and renders a time-lapse map for operator review.

22. The system of claim 1, wherein hydration status is adjusted using the fitted recovery time constant during late gestation so that animals in late gestation receive prioritized cooling at a given THI.

23. The system of claim 1, wherein the drinking event detection applies both a negative slope threshold and a minimum cumulative temperature drop over a contiguous window and merges adjacent windows separated by less than ninety seconds.

24. The system of claim 1, wherein the processor updates per-animal calibration parameters only when cross-validated improvements exceed a configured threshold and retains a rollback image of the prior parameters with version identifiers.

25. The system of claim 1, further comprising a traceability ledger that links semen lot, calf identity, ownership transfers, feedlot performance, and processor outcomes to the hydration status, Cow Stress Index, and greenhouse-gas intensity recorded for each segment.

26. A method of managing livestock, comprising: receiving reticular temperature and accelerometer data from an ingestible bolus; receiving water temperature at a drinking location; receiving ambient temperature and humidity; detecting drinking events using slope and amplitude criteria; estimating per-event water volume using a non-linear heat-exchange model parameterized by measured water and ambient conditions with per-animal calibration; accumulating daily intake; computing hydration status; and issuing control commands to at least one actuator when hydration status and THI satisfy a control condition.

27. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the processors to perform the method of claim 26 and to generate audit records comprising event features, model versions, and control outputs.

28. A livestock emissions measurement system comprising: an ingestible bolus with an accelerometer; a near-muzzle gas sensor; anda server configured to: trigger gas-sensor bursts during bolus-identified eructation windows; align gas peaks to the windows: compute methane mass flow; and normalize intensity by individualized intake and recovery descriptors.

29. A system for geospatial stress visualization comprising pen polygons, animal-to-pen assignment from gateway reception and records, a processor configured to compute pen-level aggregates of a Cow Stress Index and its sub-indices, and a Tenderer that outputs choropleth maps and hotspot suggestions with actuator recommendations.

30. A livestock profitability system comprising the system of claim 1 and a return-on-investment engine configured to allocate costs and revenues per animal from sensor-derived drivers, forecast thirty- to ninety-day cash flows under alternative actions, compute net present value for each action, rank animals for culling or retention, and output signed reports.

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