What Is Poultry Feed Intelligence?

1. What Is Poultry Feed Intelligence?

Poultry Feed Intelligence is the use of flock-specific data, production performance, feed history, predictive analytics, and relevant context to support decisions about how much feed a poultry flock should receive as its conditions and performance change over time.

Instead of relying solely on a static or standardized feeding recommendation, Poultry Feed Intelligence evaluates what is happening with the actual flock as new data become available and transforms that information into a feed-quantity recommendation.

Where predictive capabilities are available, Poultry Feed Intelligence can also use historical and current flock data to estimate expected flock performance, adding a forward-looking layer to the feeding decision.

The objective is not simply to feed less.

The objective is to feed more intelligently by aligning feed quantity with the needs, performance, and expected trajectory of the individual flock.

For commercial layer farms, this can mean evaluating whether the flock should receive more feed, less feed, or the same amount as conditions change. Feed recommendations are commonly expressed in grams per bird per day (g/bird/day).

Poultry Feed Intelligence represents a specialized application of the broader concept of Poultry Decision Intelligence, where poultry farm data are transformed into recommendations, forecasts, alerts, benchmarks, predictions, and other intelligence that can support farmer decisions.

2. Why Does Poultry Farming Need Feed Intelligence?

Feed is one of the most important inputs in commercial poultry production and one of the largest components of production cost. Yet feeding decisions are frequently based on standardized recommendations designed for populations of birds under expected production conditions.

These references are extremely valuable. They provide scientifically developed feeding guidance based on factors such as breed, age, production stage, body weight, and expected performance.

But an individual flock exists in a specific environment. Its actual performance may differ from standardized expectations because of differences in:

flock age;
flock population;
egg production;
historical feed intake;
body weight;
health status;
environmental conditions;
weather;
farm location;
feed quality;
management practices; and
previous flock performance.

This creates an important decision problem. A standardized feeding reference can tell a farmer what a flock like this one would typically be expected to receive. It cannot automatically adapt itself to what is happening with this particular flock on this particular farm as the flock’s conditions and performance change.

Poultry Feed Intelligence adds that flock-specific analytical layer.

3. What Problem Does Poultry Feed Intelligence Solve?

The fundamental problem is the gap between generalized feeding guidance and the changing reality of an individual flock.

Consider two commercial layer flocks of the same breed and age.

A standardized feeding reference may recommend the same quantity of feed for both.

But the two flocks may have different:

egg production levels;
feed-consumption histories;
flock populations;
body weights;
environmental conditions;
production trajectories; and
farm-management conditions.

Should both automatically receive exactly the same quantity of feed simply because they are the same breed and age?

Poultry Feed Intelligence introduces another layer of information into that decision.

Given what is actually happening with this flock, how much feed should it receive now?

Where predictive intelligence is available, it can also consider:

Given the flock’s current trajectory, what performance should reasonably be expected next?

The answer may be higher than a reference quantity, lower than the reference quantity, or equal to it. The purpose is not to force feed consumption downward. The purpose is to make the feeding decision more responsive to the flock.

4. How Does Poultry Feed Intelligence Work?

Poultry Feed Intelligence operates as a recurring data-to-decision cycle:

Flock data → Context → Analysis → Prediction and Feed Intelligence → Feed recommendation → Farmer decision → Flock outcome → New data

The process begins with poultry farm records generated through normal poultry production. Relevant flock data are recorded and analyzed within the context of the individual flock. Where predictive models are used, historical and current flock information can also be analyzed to estimate how flock performance is likely to develop.

The resulting intelligence can then support a feed-quantity recommendation. The farmer decides how to use that recommendation.

Subsequent flock performance generates new information, which can then inform future analysis, predictions, and recommendations. This creates a feedback loop in which the flock’s own performance becomes part of the information used to support future feeding decisions.

5. What Data Can Poultry Feed Intelligence Analyze?

The exact data used depends on the system, the decision being made, and the information available.

Relevant data may include:

flock age;
flock population;
breed;
daily feed intake;
historical feed intake;
egg production;
hen-day egg production;
body weight;
historical flock performance;
mortality;
weather;
farm location;
environmental conditions; and
other relevant production variables.

Not every Feed Intelligence system must use every variable. The important distinction is that the feeding decision is informed by actual flock data and relevant context, rather than relying exclusively on a generalized reference.

Poultry Feed Intelligence vs. Breed Feeding Guidelines

Breed feeding guidelines and Poultry Feed Intelligence should not be viewed as competing approaches. Breed guidelines provide an important standardized scientific reference.

They help answer:

What should a flock like this typically receive?

Poultry Feed Intelligence adds a different question:

What should this particular flock receive given its actual performance and context?

Table 1. Breed Feeding Guidelines vs. Poultry Feed Intelligence

Breed Feeding Guideline	Poultry Feed Intelligence
Provides standardized feeding guidance	Provides flock-specific decision support
Commonly based on breed, age and production stage	Uses actual flock data and relevant context
Provides a reference quantity	Generates a context-specific feed recommendation
Designed for populations under expected conditions	Responds to the individual flock's recorded conditions
Changes according to the published feeding program	Can be re-evaluated as new flock data become available
Uses expected performance as a standardized reference	Can incorporate flock-specific predictive intelligence
Answers “What should a flock like this typically receive?”	Helps answer “What should this particular flock receive now?”

The limitation of a standardized reference is not that it is wrong. The limitation is that a standardized reference cannot observe and adapt itself to the individual flock on a particular farm as that flock’s conditions and performance change. Poultry Feed Intelligence therefore adds a flock-specific intelligence layer on top of the standardized reference.

Poultry Feed Intelligence vs. Poultry Feed Management

Feed management and Feed Intelligence perform different functions.

Poultry feed management may involve:

recording feed received;
tracking feed consumption;
monitoring inventory;
managing feed purchases;
recording feed transfers;
calculating feed costs; and
reviewing historical feed usage.

These are important management functions. But recording that a flock consumed 112 g/bird/day does not, by itself, answer whether 112 g/bird/day is an appropriate quantity for the flock tomorrow. That requires another analytical step.

Feed management records and manages feed. Poultry Feed Intelligence interprets relevant data to support a feeding decision.

A farm can therefore have excellent digital feed records without having Feed Intelligence. Likewise, a farm-management platform can provide the operational data foundation upon which Feed Intelligence is built.

Poultry Feed Intelligence vs. Poultry Feed Formulation

Poultry Feed Intelligence and poultry feed formulation address fundamentally different decisions.

Feed formulation asks:

What should the feed contain?

It focuses on factors such as:

ingredients;
nutrient requirements;
metabolizable energy;
crude protein;
amino acids;
calcium;
available phosphorus;
ingredient constraints;
ingredient costs; and
ration composition.

Poultry Feed Intelligence asks:

How much of the appropriate feed should this flock receive?

A feed may be nutritionally well formulated while the farm still needs to determine the appropriate daily quantity for a particular flock. The two capabilities are therefore separate but complementary.

Aviarai Smart Feed does not formulate poultry diets or determine ration nutrient composition. Aviarai’s AI-powered poultry feed formulation is a separate platform capability from Aviarai Smart Feed.

Poultry Feed Intelligence vs. Precision Feeding

Precision feeding and Poultry Feed Intelligence overlap, but they describe different aspects of the feeding process.

Precision feeding generally focuses on aligning feed or nutrient supply more closely with the requirements of animals or groups of animals.

Poultry Feed Intelligence focuses specifically on the analytical process that transforms flock data, production performance, feed history, predictive information, and relevant context into intelligence supporting the feed-quantity decision.

Put simply:

Precision Feeding → the management objective or approach

Poultry Feed Intelligence → the data-to-decision capability that can help enable that approach

A poultry operation can pursue precision feeding using established nutritional programs and management techniques without using an AI-driven Feed Intelligence system.

Poultry Feed Intelligence can add a dynamic analytical layer by using the individual flock’s recorded performance and, where available, predicted performance to support more precise feed-quantity decisions.

6. Can AI Help Determine How Much Feed Layers Should Receive?

Yes. Artificial intelligence and predictive analytics can be used to analyze relationships within flock data and generate decision-support recommendations about feed quantity.

Instead of considering only breed and age, an AI-powered system can evaluate multiple relevant variables simultaneously and identify patterns within historical and current flock performance.

For example, a system may analyze relationships among:

Feed intake + Egg production + Flock age + Population + Historical performance + Context

and use those relationships to support a feed recommendation for the individual flock.

AI can also introduce a forward-looking dimension into Feed Intelligence by predicting aspects of expected flock performance. That creates an important distinction between simply analyzing historical feed consumption and using predictive intelligence to support the next feeding decision.

From Feed Analysis to Predictive Feed Intelligence

Poultry Feed Intelligence can go beyond analyzing what has already happened. The use of machine learning to predict poultry production performance is already being investigated in peer-reviewed research. Ji et al. (2025)1 evaluated seven machine-learning models for predicting egg production rate and egg weight using commercial poultry data, including feed intake, age, water consumption and environmental conditions. Bumanis et al. (2023) 2 examined machine-learning approaches for forecasting hen egg production, including scenarios involving limited historical datasets.

These studies do not establish the appropriate feed quantity for a particular commercial layer flock. Rather, they demonstrate that multiple poultry-production variables and historical records can be integrated into predictive models of flock performance. Poultry Feed Intelligence extends that predictive principle into the feed-quantity decision: if a system can develop a flock-specific understanding of expected performance, that prediction can become another source of intelligence when evaluating how much feed the flock should receive.

Predictive models can use longitudinal flock data to estimate how flock performance is likely to develop based on the flock’s current state and historical production trajectory. For commercial layers, this can include predicting production outcomes such as hen-day egg production (HDEP).

This allows the intelligence system to consider not only:

What has happened with this flock?

but also:

Given the flock’s current trajectory, what performance should reasonably be expected next?

Predicted performance can therefore become another source of intelligence supporting the feed-quantity decision.

Historical flock data + Current flock state + Context → Predicted flock performance → Feed intelligence → Feed recommendation

Prediction does not eliminate uncertainty.

Predicted performance should be evaluated against subsequently recorded outcomes to determine how closely the model’s expectations align with actual flock performance.

When predictive models remain closely aligned with subsequently observed performance, they can provide a more flock-specific basis for decision support than generalized expectations alone.

The resulting feed recommendation is not necessarily lower than the standardized reference.

Depending on the data and analysis, the system may recommend:

increasing feed;
decreasing feed; or
maintaining the current quantity.

The farmer remains responsible for the final feeding decision.

7. Does Poultry Feed Intelligence Always Recommend Less Feed?

No.

A system designed simply to recommend less feed is not the same thing as a Feed Intelligence system.

The appropriate recommendation depends on the flock.

If the analysis indicates that the flock requires more feed, the recommendation may increase.
If the flock’s current quantity appears appropriate, the recommendation may remain unchanged.
If the available data support a lower quantity, the recommendation may decrease.

This distinction matters because feed reduction is not the objective.

The objective is a better-informed feed-quantity decision.

8. Can Poultry Feed Intelligence Help Reduce Unnecessary Feed Usage?

Potentially, yes.

If a flock is receiving more feed than its current conditions and performance indicate it needs, a flock-specific recommendation may identify an opportunity to reduce that unnecessary quantity.

For example, if a standardized reference indicates 115 g/bird/day but flock-specific analysis supports 112 g/bird/day, the 3 g/bird/day difference represents feed that would not be issued if the farmer follows the lower recommendation.

Across a large commercial flock, small daily differences can accumulate into meaningful quantities of feed.

However, this should not be interpreted to mean that Poultry Feed Intelligence completely eliminates every source of feed waste.

Feed can also be lost through:

spillage;
pests;
poor storage;
equipment problems;
handling practices;
feed deterioration; and
other operational causes.

Feed Intelligence addresses the feed-quantity decision, not every physical source of feed loss.

9. Does Poultry Feed Intelligence Require Sensors or IoT Hardware?

No.

Poultry Feed Intelligence does not inherently require sensors, cameras, IoT devices, automated feeders, or proprietary poultry-house hardware.

Commercial poultry farms already generate useful data through routine activities such as recording:

feed intake;
egg production;
mortality;
flock population;
body weight; and
other production information.

Those records can provide a foundation for Feed Intelligence.

Where sensors, environmental monitors, connected feeding equipment, or other Precision Livestock Farming technologies are available, their data may enrich the information available for analysis.

The intelligence layer is therefore not dependent on how the data were collected.

10. Does Poultry Feed Intelligence Replace Poultry Nutritionists?

No.

Poultry nutritionists determine nutrient requirements, formulate diets, evaluate ingredients, investigate nutritional problems, and provide expertise that a feed-quantity recommendation does not replace.

Feed Intelligence addresses a narrower decision:

Given an appropriate diet and the available information about this flock, how much feed should the flock receive?

Veterinary, nutritional, and farmer judgment remain important.

Poultry Feed Intelligence should be understood as decision support, not autonomous replacement of poultry professionals or farm management.

11. How Does Aviarai Apply Poultry Feed Intelligence?

Aviarai applies Poultry Feed Intelligence through Aviarai Smart Feed.

Aviarai Smart Feed is an AI-powered feed decision-intelligence capability within the Aviarai Poultry Decision Intelligence Platform.

It transforms commercial layer flock data into dynamic, flock-specific daily feed recommendations expressed in g/bird/day.

Smart Feed combines feed decision intelligence with predictive flock intelligence. Rather than evaluating feed quantity in isolation, Aviarai analyzes the flock’s production history and performance trajectory to help understand expected flock performance as part of the feeding decision.

This means Smart Feed is not simply a calculator that applies a fixed adjustment to a breed feeding table. Its intelligence is informed by learned relationships within longitudinal commercial poultry production data and the evolving performance of the individual flock.

Smart Feed evaluates relevant flock information and performance to determine whether the recommended feed quantity should:

increase;
decrease; or
remain unchanged.

Rather than treating the breed feeding guideline as the final answer, Aviarai uses it as an important reference while adding another layer of intelligence based on what is happening with the individual flock.

Poultry Decision Intelligence
↓
Poultry Feed Intelligence
↓
Aviarai Smart Feed
↓
Flock-specific predictive intelligence
↓
Flock-specific daily feed recommendation

Figure 1. Aviarai Poultry Feed Intelligence Hierarchy

Aviarai Smart Feed therefore represents a specific application of Poultry Decision Intelligence to the feed-quantity decision.

12. How Does Aviarai Smart Feed Work?

Aviarai Smart Feed applies Poultry Feed Intelligence through a recurring decision-intelligence cycle:

Farm data → Contextual analysis → Predictive intelligence → Smart Feed Recommendation → Farmer decision → Flock performance → New data

Before flock-specific Smart Feed recommendations begin, an eligible flock must:

be at least 18 weeks old;
have started laying; and
have at least seven consecutive days of required flock data.

Before these requirements are satisfied, Aviarai provides the applicable Global Feed Reference rather than a flock-specific Smart Feed Recommendation. Once eligible, the flock’s data can be used to generate dynamic daily recommendations. The recommendation can change as the flock changes. Subsequent production data also provide new information against which the system’s understanding of the flock can be evaluated.

13. What Does Field Evidence Tell Us About Poultry Feed Intelligence?

One way to evaluate predictive Feed Intelligence is to compare the system’s prediction of flock performance with what the flock subsequently produces.

This matters because a feed recommendation informed by an inaccurate understanding of expected flock performance may lead to a poor feeding decision. Conversely, a predictive model that remains closely aligned with subsequently observed flock performance provides stronger evidence that the intelligence supporting the feeding decision reflects the actual flock rather than generalized expectations alone.

An observed commercial layer flock provides an example. Aviarai has previously published field evidence from this commercial flock showing Smart Feed recommendations alongside subsequently recorded production outcomes. Over a 27-day observation period from July 31 through August 26, the flock’s actual recorded hen-day egg production (HDEP) was compared with:

Aviarai’s flock-specific projected HDEP; and
the applicable ISA Brown reference projected HDEP.

Table 2. Aviarai vs. ISA Brown Reference: 27-Day HDEP Prediction Performance

Metric	27-Day Observation
Average Actual HDEP	78.23%
Average Aviarai Projected HDEP	78.81%
Average ISA Brown Reference Projected HDEP	82.19%
Aviarai Mean Absolute Error	0.73 percentage points
ISA Brown Reference Mean Absolute Error	3.95 percentage points
Days Aviarai projection was closer to actual HDEP	27 of 27

Across these 27 observations, Aviarai’s flock-specific HDEP projection was closer to the flock’s actual recorded HDEP than the ISA Brown reference projection on every observed day.

The ISA Brown reference’s mean absolute error was approximately 5.4 times Aviarai’s mean absolute error during the observation period.

This does not establish that Aviarai caused the flock’s production performance.

It demonstrates that, in this observed flock and period, Aviarai’s flock-specific predictive intelligence tracked subsequently recorded production substantially more closely than the standardized reference projection.

That distinction is important because Poultry Feed Intelligence is not only concerned with what the flock has done historically. Predictive intelligence can help the system understand the flock’s expected performance trajectory as part of supporting the next feeding decision.

14. What Did Smart Feed Recommend During the Observation?

During the observation period, the Global Feed Reference was generally 115 g/bird/day.

Smart Feed recommendations varied according to the flock-specific analysis.

The recommendations included quantities below the Global Feed Reference during portions of the observation period and subsequently increased as the flock data changed.

Examples included:

112 g/bird/day
110 g/bird/day
109 g/bird/day
108 g/bird/day
111 g/bird/day
113 g/bird/day
and eventually 115 g/bird/day

This illustrates an important property of Feed Intelligence:

The recommendation is dynamic rather than permanently optimized toward the lowest possible feed quantity.

On August 22, Smart Feed returned to 115 g/bird/day, matching the Global Feed Reference.

That is important because it demonstrates that the system was capable of removing the difference between its flock-specific recommendation and the standardized reference when the flock data supported doing so.

15. What About Feed-Cost Savings?

When Smart Feed recommended less than the 115 g/bird/day Global Feed Reference, the difference represented feed that would not be issued if the flock was fed according to the Smart Feed recommendation. Across the observed flock, the calculated cumulative feed-cost difference relative to feeding the Global Feed Reference quantity reached:

₦1,247,593.57 by August 21.

On August 22, Smart Feed increased its recommendation to 115 g/bird/day, matching the Global Feed Reference.

From that point through the end of the observation period, the calculated cumulative savings remained ₦1,247,593.57 because there was no longer a difference between the Smart Feed Recommendation and the Global Feed Reference.

This is an important distinction.

Smart Feed did not continue recommending less feed merely to make the calculated savings figure increase. When the flock-specific recommendation returned to the reference quantity, additional calculated feed-cost savings from the difference in recommended feed quantity stopped accumulating.

The objective of Feed Intelligence is therefore not maximum feed reduction or maximum calculated savings.

The objective is to support an appropriate feed-quantity decision for the flock based on the information available.

16. Did Lower Feed Recommendations Cause the Recorded Production Performance?

The observation does not establish causation.

This was not a randomized treatment-and-control experiment designed to isolate the causal effect of Smart Feed recommendations on egg production.

The flock’s recorded production could have been influenced by numerous factors, including:

genetics;
flock age;
nutrition;
environmental conditions;
health;
management;
housing;
weather; and
other variables.

The evidence therefore should not be interpreted as:

Lower feed caused the observed production outcome.

Rather, it demonstrates that during the observed period:

Smart Feed generated flock-specific feed recommendations;
those recommendations sometimes differed from the standardized Global Feed Reference;
Aviarai generated flock-specific production projections;
those projections were compared with subsequently recorded HDEP;
Aviarai’s projection was closer to actual HDEP than the ISA Brown reference projection on all 27 observed days; and
calculated feed-cost differences accumulated when Smart Feed recommended below the Global Feed Reference and stopped accumulating when the recommendations became equal.

This is observational field evidence, not proof of causation. Importantly, prediction accuracy and causation are different questions. The observation provides evidence about how closely Aviarai’s prediction aligned with subsequent flock performance. It does not establish that Smart Feed caused that performance.

Table 3. Poultry Feed Intelligence at a Glance

Question	Answer
What is Poultry Feed Intelligence?	Flock-specific and potentially predictive analysis that supports decisions about how much feed a poultry flock should receive
Primary decision	How much feed should this flock receive?
Typical output	Feed-quantity recommendation
Common unit for commercial layers	g/bird/day
Uses flock-specific data?	Yes
Can it use predictive analytics?	Yes
Can predicted flock performance inform the decision?	Yes
Can recommendations change?	Yes
Must recommendations always be lower?	No
Can recommendations increase?	Yes
Can recommendations equal a breed/reference quantity?	Yes
Does it replace breed feeding guidelines?	No
Does it formulate poultry diets?	No
Is feed formulation the same thing?	No, it is a separate but complementary capability
Is inventory tracking Feed Intelligence?	Not by itself
Does it inherently require sensors or IoT?	No
Does it replace nutritionists or veterinarians?	No
Is it a form of Poultry Decision Intelligence?	Yes
Aviarai implementation	Aviarai Smart Feed

Frequently Asked Questions About Poultry Feed Intelligence

What is Poultry Feed Intelligence?

Is Poultry Feed Intelligence the same as feed formulation?

No. Feed formulation determines what the feed should contain. Poultry Feed Intelligence supports the decision about how much of the appropriate feed a flock should receive.

Is Poultry Feed Intelligence the same as feed management?

No. Feed management records and manages feed usage, purchases, inventory, and related operations. Feed Intelligence analyzes relevant information to support a feeding decision.

Does Poultry Feed Intelligence replace breed feeding guidelines?

No. Breed guidelines remain valuable standardized references. Feed Intelligence adds flock-specific analysis based on actual performance and context.

Can Poultry Feed Intelligence use predictive analytics?

Yes. Predictive models can use historical and current flock data to estimate aspects of expected flock performance. That forward-looking intelligence can then support the feed-quantity decision.

Why does predicting egg production matter to Feed Intelligence?

For commercial layers, expected production is relevant to understanding the flock’s performance trajectory. A flock-specific prediction of HDEP can therefore provide additional intelligence for evaluating the flock and supporting feeding decisions.

Does Poultry Feed Intelligence always recommend feeding less?

No. A recommendation may increase, decrease, or remain unchanged depending on the flock’s data and performance.

Can Poultry Feed Intelligence help reduce unnecessary feed usage?

Yes, where flock-specific analysis indicates that the flock does not require the full quantity that would otherwise have been issued. It does not eliminate other forms of feed waste such as spillage, pests, storage losses, or equipment problems.

Can AI help determine how much feed layers should receive?

Yes. AI and predictive analytics can analyze multiple flock variables, historical relationships, production trajectories, and relevant context to generate decision-support intelligence about feed quantity. The farmer remains responsible for the final feeding decision.

Does Poultry Feed Intelligence require sensors?

No. Routine poultry farm records can provide the data foundation. Sensor and IoT data can potentially enrich the analysis where available.

Does Poultry Feed Intelligence replace poultry nutritionists?

No. Nutritionists address nutrient requirements, diet formulation, ingredient selection, and other nutritional issues. Feed Intelligence supports a different decision concerning feed quantity.

How does Aviarai apply Poultry Feed Intelligence?

Aviarai applies Poultry Feed Intelligence through Aviarai Smart Feed, which combines flock-specific data, predictive intelligence, and dynamic analysis to generate daily feed recommendations for commercial layer flocks.

Evidence and Research

Poultry Feed Intelligence sits at the intersection of poultry nutrition, production management, data analytics, predictive modeling, precision feeding, and decision-support systems.

Recent research in precision animal nutrition has highlighted the growing role of big data, artificial intelligence, machine learning, and predictive modeling in moving animal nutrition toward more dynamic, individualized, and data-driven approaches. Zhang et al. (2025), for example, describe the progression of animal-nutrition modeling from traditional empirical and static approaches toward increasingly dynamic models capable of supporting performance forecasting, feed-intake prediction, nutrient-requirement estimation, and precision feeding.3

Scientific research in poultry nutrition and precision livestock production provides important foundations for understanding feed requirements, flock performance, environmental effects, precision feeding, and the use of data in poultry management.

Aviarai also evaluates its own decision-intelligence capabilities using field observations and recorded commercial flock data. First-party field observations should be interpreted according to their methodology and limitations.

Where an observation is not a randomized controlled experiment, it should not be presented as proof that an Aviarai recommendation caused a particular production outcome.

Instead, such observations can help evaluate questions such as:

whether recommendations change as flock data change;
whether flock-specific predictions align with subsequently recorded outcomes;
how recommendations compare with standardized references;
how feed-quantity differences accumulate over time; and
whether the system responds when the flock’s performance or conditions change.

In the 27-day observation described on this page, Aviarai’s flock-specific HDEP projection had a mean absolute error of 0.73 percentage points, compared with 3.95 percentage points for the ISA Brown reference projection, and was closer to actual recorded HDEP on 27 of 27 observed days.

These results provide evidence of predictive alignment for this specific flock and observation period. They should not be interpreted as guaranteed prediction accuracy or production outcomes for other poultry farms or flocks.

From Feeding Tables to Predictive Feed Intelligence

Breed feeding tables remain an important part of commercial poultry production.

They provide farmers with standardized guidance grounded in poultry genetics, nutrition, and expected production performance.

Poultry Feed Intelligence does not make those references obsolete. It adds another layer. A poultry farm’s accumulating digital records can become part of the feeding decision itself. And with predictive intelligence, those records can potentially do more than describe what has already happened. They can help answer questions about where the flock appears to be heading next.

Instead of asking only:

What does the feeding table recommend?

the farmer can also ask:

What is my flock telling me?

and:

Based on its current trajectory, what should I expect from this flock next?

The feeding table remains valuable.

But it no longer has to be the end of the decision.

The flock’s history, current performance, and expected trajectory can become part of the intelligence used to determine how much it should be fed.

References

Ji, H., Xu, Y., & Teng, G. (2025). Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations. Poultry Science, 104(1), 104458. DOI: 10.1016/j.psj.2024.104458.
Bumanis N, Kviesis A, Paura L, Arhipova I, Adjutovs M. Hen Egg Production Forecasting: Capabilities of Machine Learning Models in Scenarios with Limited Data Sets. Applied Sciences. 2023; 13(13):7607. https://doi.org/10.3390/app13137607
Zhang, S., Lai, C., Zhao, J., & Wang, J. (2025). Big Data and AI-Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition. Advanced Science, 12(41), e07564. DOI: 10.1002/advs.202507564.