From Static to Dynamic: How AI is Transforming Feed Models

For decades, farmers have relied on mechanistic animal models to guide livestock nutrition. While these approaches provided structure and predictability, they lacked the flexibility to adapt to real-world changes on the farm. Today, advances in data science are opening the door to a new era: big data driven, adaptive feed models that continuously adjust diets based on real-time inputs such as animal health, animal performance and ingredient availability.

The Agriculture AI Gap

Big data and machine learning have been hailed as the next big revolution in agriculture, with cropping systems leading the way in adoption. Yet, progress in the livestock sector has been slower. A study by Liakos et al. (2018) highlighted this imbalance, showing that only 19% of published research applying machine learning in agriculture was focused on livestock production.

The Growing Demand for Precision

The urgency of advancing livestock nutrition is clear. Feed conversion efficiency is central to farm economics and environmental sustainability. As livestock farming has evolved from small-scale, community-based systems into intensive, industrial-scale operations, the demand for precision has grown. To meet this challenge, animal scientists developed mechanistic models. These models are mathematical tools that simulate animal biology through cause-and-effect relationships, and have long been essential for both understanding biological systems and helping farmers make informed decisions about how to feed and grow their animals.

The AI Challenge to Traditional Models

Now, however, mechanistic models are being challenged by machine learning and other data-driven methods. AI models excel at finding patterns in vast datasets and making accurate predictions, even in complex environments. This raises important questions: Do mechanistic models still have a role in the era of Big Data and AI? Or should they give way to fully automated, machine learning–based systems?

Emerging studies suggest that instead of choosing one over the other, the future lies in hybrid approaches that integrate the two (Ellis et al., 2020). Such integration promises sharper predictions and insights, but demands animal science modelers to adapt to new tools and technologies.

The Limitations of Static Feed Models

Mechanistic models have a long history of application in animal production, with objectives typically focused on optimally feeding and growing livestock. These predominantly nutritional models were created to mathematically express our cumulative biological knowledge of how animal systems function, particularly to understand and manipulate nutrient dynamics. Over successive generations, nutrition models have been revised, expanded, and supplemented with sub-models to address new concerns of their time — such as environmental sustainability (Lovarelli et al., 2020). This adaptability reflects the robust structural foundation of mechanistic models: because they are grounded in biological principles, they can be extended and refined rather than being rebuilt entirely for each new innovation.

Farmers and ration advisors use these mechanistic models in combination with farm-specific data, to run different scenarios and turn these predictions into practical ration advice tailored to the farmer's goals (e.g. cost savings, production, sustainability). However, these mechanistic models have some limitations in practice. First of all, they require a lot of manual input (animal type, stage of production, feed analysis, environment..) and the quality of the advice depends on the accuracy of the input data (feed analysis values as well as herd data) as well as the expertise of the user to understand both the biology and the limitations of the models. Another downside is that these models assume "average" conditions and don't always adapt quickly to real-time changes.

Given the push towards fully automated machine learning systems to interpret the new wave of on-farm big data the role of mechanistic models is occasionally questioned. Especially since machine learning-based systems can also be trained to predict outcomes traditionally modelled by mechanistic models, such as milk yield or feed conversion ratio.

The Rise of Dynamic, AI-Driven Ration Adjustments

How AI Models Differ

Data-driven models excel at identifying patterns within large, heterogeneous datasets, drawing from sources such as milk yield records, feed intake sensors, animal activity monitors, weather data, and market information. These methods allow for dynamic ration adjustments that respond to real-time farm conditions. For example, a machine learning system could detect a reduction in feed intake during a heat stress event and propose adjustments to the diet in order to maintain animal performance and health. Such capabilities offer a level of responsiveness that traditional mechanistic models, which are largely static in nature, cannot provide.

The Role of IoT Sensors

Expanding Data Collection Capabilities

The integration of Internet of Things (IoT) technologies within livestock production systems has significantly expanded the scope and applicability of data-driven models. IoT sensors provide continuous, high-resolution measurements of variables that are central to animal health, productivity, and environmental management. Examples include feed intake sensors, which monitor individual or group-level consumption; milk meters, which capture yield and composition at each milking; environmental sensors, which measure temperature, humidity, and air quality within housing systems and imaging technologies, which can assess body condition and growth.

Enabling Real-Time Responsiveness

These data streams form the primary input for machine learning and deep learning algorithms, which require large volumes of high-frequency data to identify patterns and generate reliable predictions. The availability of continuous IoT-derived data enables data-driven models to move beyond static assumptions, such as average feed intake or estimated animal weights, thereby improving both the accuracy and timeliness of predictions. Furthermore, IoT-enabled monitoring supports the transition from static ration recommendations to dynamic, adaptive nutrition strategies. For example, when sensors detect reduced intake during periods of heat stress, data-driven models can suggest diet adjustments (e.g., increased energy density or reduced fibre content) to mitigate performance losses.

Supporting Automated Decision-Making

Ultimately, the role of IoT sensors in data-driven models is to provide continuous, multidimensional datasets, in order to enable the development of automated tools capable of improving feed efficiency, reducing costs, and enhancing sustainability within livestock production systems.

Limitations of AI-Driven Models

The Black Box Problem

Nevertheless, data-driven models are not without limitations. Their predictive strength often comes at the expense of interpretability, as they may function as "black boxes" without providing insight into the underlying biological mechanisms.

Overfitting and Generalization Issues

Another limitation with machine learning/ big data model training is their tendency to over-train, where the machine learning model learns the 'noise' within the training data which leads to poor generalization capacity. To try and deal with these problems, several methods have been developed to show how machine learning models build up their understanding and guidelines can be applied to limit their over-training tendency.

The Continued Relevance of Mechanistic Models

For these limitations, mechanistic models remain highly relevant as of today. The future of ration advising is therefore unlikely to be characterized by a complete replacement of mechanistic models with machine learning systems, but rather by opportunities for integration between the two.

Synergies Between Both Approaches

Complementary Strengths

Each modelling approach has own limitations, and hybridization may offer solutions greater than the sum of their parts. Data-driven models can provide inputs to mechanistic models, and vice versa.

Solving Input Data Challenges

For example, a longstanding criticism of mechanistic models has been the difficulty of obtaining accurate inputs. With the rise of big data and widespread sensor use, collecting and managing animal and feed analysis data has become increasingly feasible. New feed analysis technologies, walk-over scales for frequent weight measurements, environmental sensors for temperature, humidity, and air quality now provide highly detailed, high-resolution data.

Precision Feeding Success Stories

For mechanistic models to leverage this wealth of data effectively, they must adapt to new data types and align with the time intervals of IoT measurements. Such integration has already been demonstrated in swine, where precision feeding systems use real-time bodyweight and feed intake data in combination with mechanistic models to optimize feed blends for individual animals (Pomar & Remus, 2019). Precision agriculture has greatly benefited from these technologies, allowing nutrient requirements to be tailored in real time based on bodyweight, milk production, or feed intake.

Untapped Integration Opportunities

What remains largely unexplored is the integration of other machine-learning–interpreted data, such as health and reproductive status, activity levels, omics data and environmental conditions, which could capture further differences between animals or farms. Conversely, mechanistic models can also inform data-driven models, providing a layer of "biological common sense" that enhances prediction and supports decision-making.

Conclusion

The field of livestock nutrition is shifting from static mechanistic feed models to dynamic, data-driven systems that continuously adjust outcomes based on real-time data from IoT sensors. Hybrid approaches that merge mechanistic models' biological insight with machine learning's predictive power are emerging as the most promising path forward, enabling more accurate, responsive, and farm-specific nutrition strategies. BESTMIX Software shares this vision and actively invests in research and innovation together with their customers, ensuring these new tools are grounded in both science and practical application. This integration empowers farmers to optimize feed conversion, improve animal performance, and reduce environmental impact, marking a new era of precision livestock management where efficiency, profitability, and sustainability go hand in hand.