Near Infrared (NIR) spectroscopy is a pivotal tool for feed millers, nutritionists, and livestock producers alike. By measuring the way feed ingredients absorb and reflect near-infrared light, the technology can predict the nutritional composition of raw materials and finished feeds within minutes. It’s a capability that has transformed how diets are formulated – reducing waste, saving costs and improving overall feed efficiency, which equates to better animal performance.
Yet as farming practices evolve, so too do the expectations placed on technology. Producers are realizing that they need faster, more reliable insights and systems that work even when connectivity is limited – a reasonable request when you consider that in the UK, around one in 10 farms still lacks consistent internet access. This highlights a key limitation of many traditional NIR systems, which depend on stable connectivity to operate at their best. To remain relevant to today’s production systems, NIR must continue to evolve.
This article explores how NIR has developed, the new demands of farmers and nutritionists, and the innovations shaping the next generation of feed analysis.
How Has NIR Evolved?
In the past, laboratory-based “wet chemistry” analysis for feed was commonly used but
proved slow and expensive. Results could take days to arrive, delaying decisions and risking productivity losses. The introduction of NIR instruments changed the game as it allowed producers to analyse samples in-house and act on the results the same day.
Over time, calibrations expanded to cover a broader range of nutrients, including fibre fractions, amino acids and phytic phosphorus. This allowed nutritionists to fine-tune rations with greater precision and monitor quality more consistently across sites. Next came portable and handheld devices which brought NIR directly onto the farm or feed mill floor, further reducing reliance on external laboratories.
The ability to generate accurate data quickly has helped underpin precision nutrition strategies, which aim to supply animals with exactly what they need – no more, no less – for ultimate efficiency, reducing feed costs and environmental impact in the process.
Speed, Reliability and Offline Access
While NIR has delivered significant gains, the modern farming landscape is creating new pressures. Farmers, nutritionists, and feed manufacturers increasingly expect their technology to deliver immediate, accurate results with minimal need for specialist knowledge. They also want systems that work seamlessly in remote or isolated locations where internet connections can be unstable or absent. The fact that one in 10 UK farms still lack reliable broadband coverage demonstrates the importance of offline functionality.
Downtime is another critical factor. In high-throughput operations, every hour counts, so devices and software must be resilient, with updates and maintenance scheduled to avoid disruption. Alongside this, producers want greater control over their data. They expect analysis results to be stored securely, easily exported into common formats such as Excel, and accessible across multiple sites or teams.
But meeting these expectations is not just about convenience. Inaccurate or delayed analysis can lead to suboptimal formulations, increased costs and greater variability in animal performance. For farmers operating on tight margins, that is a risk few can afford to take.
Advances in NIR: How Technology Is Responding?
Developments in hardware, software and connectivity are helping NIR systems keep pace with these demands. Modern platforms now feature cleaner displays, intuitive dashboards and built-in guidance, making them easier for new users to navigate. Enhanced storage and filtering options allow producers to manage large datasets more effectively, track trends over time and share insights across teams.
Recognizing that not all farms have reliable internet, some systems can now operate fully offline, uploading data automatically once a connection is restored. Next-generation NIR platforms are also being designed to handle new parameters and integrate with emerging technologies over the next decade. This kind of futureproofing reflects a wider shift in agriculture towards digital tools that are not just accurate but also accessible, resilient and adaptable.
Looking Beyond
The next leap forward for NIR will be in how data is interpreted. Machine learning algorithms can process vast amounts of spectral data to identify subtle patterns and predict outcomes with greater precision. This could enable automated alerts when ingredient quality shifts outside expected ranges, or real-time adjustments of feed formulations in response to new data. It could also allow benchmarking of performance across regions or production systems to uncover hidden efficiency gains.
As artificial intelligence becomes further embedded in agricultural technology, the focus will shift from producing insights to delivering actionable recommendations – telling the user not just what is happening, but what to do next.
For farmers and feed professionals, these advances promise faster, more reliable nutritional analysis and more informed feed and farm management strategies, even in remote areas. They offer greater confidence in diet formulations, improving feed efficiency and animal performance. They also provide better control over data, enabling long-term tracking and more informed decision-making. In practical terms, this means reduced reliance on external laboratories, fewer delays in responding to on-farm challenges and a more resilient, cost-effective, and sustainable feed supply chain.
A Shared Industry Challenge
With rising competition across the animal production industry, demand for nutrition strategies that deliver both efficiency and value is growing.
NIR spectroscopy has already transformed feed analysis, providing the speed and accuracy needed to enable targeted nutrition on a commercial scale. To keep delivering meaningful value it must continue to evolve in step with the needs of the user, ensuring accurate, immediate results with minimal downtime; offline functionality for remote locations; seamless integration with other data sources, and machine learning capabilities to move from analysis to decision support.
As production costs and environmental pressures mount, the ability to turn vast amounts of data into actionable insights will be critical. Shared datasets, open standards, and robust calibrations based on diverse global samples can help ensure NIR remains a valuable tool regardless of geography or production system.
The next generation of NIR systems – designed not just for today’s needs but for the challenges of tomorrow – will be central to supporting more efficient, resilient, and sustainable animal production.
by Simon Flanagan, NIR Services Manager, AB Vista and Mohy-El-Din Sherif, Applications Scientist at AB Vista
