Artificial Intelligence in the Agricultural Sector: Enhancing Animal Nutrition Through Smart Technologies

Artificial Intelligence in the Agricultural Sector: Enhancing Animal Nutrition Through Smart Technologies

Introduction

Artificial Intelligence (AI) is steadily redefining the agricultural landscape. Within animal nutrition—a vital pillar of livestock productivity—AI technologies are enabling data-driven decisions, predictive modelling, and precision feeding at unprecedented scale. As the industry shifts toward sustainability and operational efficiency, the adoption of AI, underpinned by international governance frameworks such as ISO/IEC 42001, is no longer optional—it is strategic.

The Promise of AI in Animal Nutrition

In the past, animal nutrition relied heavily on historical data, trial-and-error methods, and generalized feed formulas. Today, AI enables the creation of dynamic, personalised nutrition plans that adapt in real time to an animal's health, genetics, and environmental context. Here are the most transformative use cases:

Precision Feed Formulation AI models integrate data from various sources—feed composition, growth phases, environmental conditions, and genetic markers—to formulate highly specific diets. These diets not only improve animal health but reduce waste and feed costs.

Livestock Health and Growth Monitoring Using computer vision and sensor data, AI systems detect signs of malnutrition, behavioral changes, or disease risks. Predictive algorithms help farmers intervene early, improving animal welfare and reducing veterinary costs.

Supply Chain and Ingredient Optimization AI predicts ingredient shortages and price fluctuations using global commodity trends, weather patterns, and logistics data. This capability allows nutritionists to adjust formulations without compromising quality or profitability.

Sustainability and Environmental Footprint With increasing pressure to reduce emissions from livestock, AI-assisted feed formulations help lower methane output and nitrogen excretion. These sustainable practices align with ESG goals and evolving regulatory expectations.

Forecasting Demand and Production AI enables producers to accurately forecast feed demand based on herd size, animal performance, and future climatic conditions. This forecasting helps optimize procurement, storage, and delivery logistics.

AI-Powered Reference Framework for Animal Nutrition

To ensure scalable, ethical, and effective AI deployment, the following five-level reference model is proposed:

1. Strategic Governance and Ethics

• Establish an AI governance model aligned with ISO/IEC 42001.

• Implement ethical principles such as transparency, explainability, and human oversight.

• Define roles, responsibilities, and data stewardship protocols.

2. Data and Infrastructure Management

• Centralize and integrate farm data, feed mill operations, animal health records, and climate data.

• Deploy cloud or edge infrastructure for scalable processing.

• Ensure data quality, interoperability, and cybersecurity.

3. Use Case Development and Risk Evaluation

• Prioritize use cases by impact, feasibility, and ethical considerations.

• Validate models with domain experts (e.g., veterinarians, nutritionists).

• Evaluate model bias, robustness, and adaptability to local conditions.

4. Deployment and MLOps Integration

• Adopt Machine Learning Operations (MLOps) for model deployment, retraining, monitoring, and versioning.

• Integrate AI with ERP, CRM, and on-farm systems.

• Track model performance and feedback loops.

5. Continuous Improvement and Compliance

• Define key metrics: Feed Conversion Ratio (FCR), growth rates, cost-efficiency, and emissions reduction.

• Conduct regular audits and align with ISO/IEC 42001 compliance requirements.

• Refine models based on evolving data and field feedback.

ISO/IEC 42001: Building Trust in Agricultural AI

The ISO/IEC 42001:2023 standard provides a blueprint for managing AI systems responsibly. In the agricultural context, it ensures that algorithms used in animal nutrition are explainable, auditable, and aligned with international best practices. For agribusinesses, this means reduced risk, increased trust, and a clear path toward responsible innovation.

AI is no longer a distant concept for the agricultural sector—it is a catalyst for transformation. From formulating feed with surgical precision to forecasting market dynamics, AI’s potential is vast. By adopting a structured, standards-aligned approach, particularly one based on ISO/IEC 42001, the animal nutrition industry can enhance both productivity and sustainability—without compromising integrity.