Feed efficiency in pasture-based beef production: a data-driven approach

Jim McRobert, Carrs Billington Agriculture

1. Introduction – The Herd of the Future

Agriculture is undergoing a transformative period, driven by innovations in data analytics, biotechnology, and a growing imperative to address environmental sustainability. By 2035, beef production systems will rely heavily on real-time data, artificial intelligence (AI), and genetic advancements to optimise feed efficiency, reduce costs, and meet consumers’ evolving demands for transparency and environmental stewardship. This paper examines how data-driven approaches to feed efficiency in pasture-based beef production will shape the herd of the future and argues that integrating sensors, AI, and gene-editing techniques holds the key to maintaining a profitable, resilient, and sustainable beef industry.

1.1. Overview of Changing Consumer Demands

Modern consumers are increasingly concerned about the environmental impact of livestock production, animal welfare, and the nutritional quality of their food (McCarthy et al., 2015). By 2035, consumer expectations will likely include transparent supply chains, real-time environmental and welfare metrics, and personalised nutritional information. Such consumer-driven pressures necessitate a data-rich ecosystem wherein producers can document, verify, and communicate their production practices. As a result, the next decade will see a more robust synergy between technology providers, genetic researchers, and livestock producers to ensure that beef production aligns with these heightened expectations (Goldsmith et al., 2021).

1.2 Significance of Feed Efficiency

Feed efficiency – how well an animal converts feed into body mass – is pivotal to profitability and sustainability in beef production (Basarab et al., 2013). Because feed costs can account for up to 70% of total production expenses in beef operations, even small improvements can have substantial economic effects (Archer et al., 1999). Beyond economic implications, improving feed efficiency is also linked to reducing greenhouse gas emissions, particularly methane (CH4), a major contributor to agricultural emissions globally (IPCC, 2019). As consumer and regulatory pressures intensify, finding ways to enhance feed efficiency while maintaining animal welfare is a critical goal for the beef industry.

2. The Future of Feed Efficiency in Pasture-Based Beef Production

2.1 Real-Time Data Collection in Pasture Systems

Historically, feed intake measurement in pasture-based systems has been challenging. Precise data on grazing patterns, bite counts, and forage quality were difficult or expensive to obtain, often requiring labour-intensive methods (Rook & Penning, 1991). Recent innovations, however, have transformed this landscape. For example, CERES Tag represents a breakthrough in monitoring cattle in extensive grazing environments. These solar-powered ear tags use accelerometer and satellite technology to measure grass intake per bite, track animal movement, and transmit data in real time. Because they are entirely infrastructure-free, producers can bypass traditional logistical hurdles of installing base stations or towers on large-scale pastures (CERES Tag, 2023).

The significance of such technology extends beyond convenience. High-resolution data enable farmers to identify variation in individual animals’ grazing efficiency, detect early signs of health issues, and make informed decisions about pasture rotation. In practice, producers can receive automated alerts when certain animals are not meeting feed-intake targets, prompting quick interventions that improve welfare and productivity (Johnston et al., 2022). Additionally, the Pasture Feed Intake (PFI) feature in some tags estimates the volume of forage consumed and methane production, providing a more holistic view of the animal’s environmental footprint (CERES Tag, 2023).

2.2 CRISPR and Genetic Tools for Enhanced Efficiency

Parallel to sensor-based developments, advances in biotechnology, especially CRISPR gene editing, show immense promise for improving feed efficiency (Carlson et al., 2012). CRISPR allows precision editing of cattle genomes to enhance traits related to feed conversion ratios (FCR) and even reduce methane emissions by targeting genes associated with rumen microbial composition. Some researchers are investigating edits to gut microbes that convert hydrogen into methane, aiming instead to produce acetate that could be used more efficiently by the animal (Broucek, 2018).

Moreover, data gleaned from sensor technologies guide these genetic improvements by pinpointing the most efficient animals in a population. Genome-wide association studies (GWAS), cross-referenced with sensor-based performance data, enable highly precise selection for beneficial traits (Speidel et al., 2015). Instead of waiting multiple generations to confirm the heritability of complex traits, researchers and breeders can rapidly identify and propagate animals that demonstrate superior feed efficiency. This blend of real-time data and genome editing creates a virtuous cycle: sensors highlight top-performing cattle, CRISPR refines traits at the genetic level, and each subsequent generation shows measurable improvements in efficiency (Sharma et al., 2018).

2.3 Economic and Sustainability Implications

Numerous studies suggest that improving feed efficiency by just 10% can boost profits by over 40% (Archer et al., 1999; Basarab et al., 2013). Beyond direct monetary gains, such progress aligns with broader sustainability targets. In the UK, agriculture faces increasing pressure to reduce greenhouse gas emissions and adapt to more stringent environmental regulations. Data-driven feed efficiency strategies, combined with CRISPR-based genetic gains, can reduce methane production, land use, and resource inputs per kilogram of beef (IPCC, 2019). These improvements help position the beef sector as a credible, sustainable protein option, preserving public trust and market access.

3. The Future on the Plate – Evolving Consumer Demands

3.1 Increasing Transparency and Personalised Nutrition

By 2035, consumers are expected to place heightened importance on supply-chain transparency and environmental stewardship (McCarthy et al., 2015). Advances in data analytics will help meet these demands by providing real-time information on an animal’s life cycle, from birth to slaughter. Smart packaging, QR codes, and interactive smartphone apps could offer insights into where and how an animal was raised, the carbon footprint of the production, and even welfare indicators tracked through sensor data (Kubiriba et al., 2021).

Alongside this, personalised nutrition – driven by genetic testing, wearable devices, and AI-driven dietary recommendations – promises to shape the types of beef products in demand (Boyle et al., 2019). Health-conscious consumers may request beef from cattle raised on diets that yield specific fatty acid profiles. Such “designer beef” will only be feasible through precise data collection, ensuring animals receive diets optimised for both efficiency and nutrient composition (Smith et al., 2020).

3.2 Leveraging Consumer Data for Targeted Products

Consumer-facing data platforms will increasingly be fed back into production decisions. By tracking consumer preferences in real time – via grocery store purchases, online orders, and health apps – producers can adjust breeding strategies and feeding regimes to deliver niche products. Data from sensors like the CERES Tag on grazing times, daily weight gain, and methane outputs can be combined with consumer analytics to create targeted marketing strategies for premium, eco-friendly, or health-focused beef (Goldsmith et al., 2021). This two-way data exchange, from pasture to plate and back again, forms a feedback loop that aligns production with evolving consumer tastes.

4. Connecting Data and Biotechnology

4.1 A Holistic Data Ecosystem

The convergence of data analytics with biotechnology forms the core of future livestock management. Advanced sensor platforms collect data on feed intake, weight gain, and real-time grazing behaviour. This high-resolution data is integrated into centralised dashboards, where AI algorithms provide predictive analytics and immediate recommendations (Johnston et al., 2022). For instance, if sensor data indicates an uptick in parasite load or a decrease in feed intake efficiency, AI-driven alerts can guide farmers to alter feed composition, administer targeted treatments, or investigate changes in pasture quality.

4.2 Data-Driven Breeding and Microbiome Optimisation

Real-time data on feed intake and animal performance complements genetic selection programmes. Breeders can more swiftly identify top performers, focusing on traits like disease resistance, feed efficiency, and lowered methane emissions (Speidel et al., 2015). The microbiome also plays an increasingly pivotal role. Research into rumen microbiota is revealing how microbial communities impact nutrient absorption and emission profiles (Tapio et al., 2017). By editing microbial compositions – either genetically or through targeted probiotics – producers can enhance digestive efficiency and reduce environmental impacts. Essentially, the integrated data-biotech model offers a loop of constant improvement: data reveals opportunities for selection and microbiome manipulation, which then yields further performance data, fostering iterative gains over time.

5. Genetic Selection – Data-Driven Breeding

5.1 CRISPR-Focused Advancements

At the heart of next-generation livestock breeding lies CRISPR, enabling rapid genetic improvements. Rather than relying solely on traditional selective breeding, CRISPR allows targeted modifications to genes that control essential traits. Early applications have demonstrated the technology’s capacity for increasing muscle mass, improving disease resistance, and potentially reducing methane emissions (Carlson et al., 2012). This precision is critical given that broad genetic modifications risk unintended consequences, whereas CRISPR can specifically address defined gene loci.

5.2 Genome-Wide Association Studies (GWAS)

GWAS have emerged as powerful tools for correlating specific genomic regions with performance traits (Sharma et al., 2018). Coupling GWAS with real-time data from ear tags accelerates the identification of genetic markers linked to feed efficiency. For instance, the Agriculture and Horticulture Development Board (AHDB) reported that selecting cattle genetically predisposed to more efficient feed conversion ratios can cut daily liveweight gain costs by 10-15p/kg (AHDB, 2022). Farmers adopting these breeding strategies can thus realise sizeable feed cost savings, potentially amounting to thousands of pounds per year in large herds.

5.3 Balancing Efficiency with Other Traits

While improving feed efficiency is paramount, genetic selection must also balance other traits such as calving ease, growth rate, carcass quality, and resilience to climatic stressors. Data-driven approaches allow multi-trait selection indices that weigh the economic value of each characteristic (Berry & Crowley, 2013). Advanced data integration ensures that efforts to improve feed efficiency do not compromise essential qualities like meat tenderness or overall herd health.

6. Smart Sensors – Measuring Feed Intake

6.1 CERES Tag Innovations

A prime example of emerging sensor technology is CERES Tag, which combines a solar-powered battery system, direct-to-satellite communication, and advanced accelerometer features. The system’s infrastructure-free design reduces setup and maintenance costs, appealing to both small-scale and large-scale operations. Updated up to 12 times daily, the tags track grazing intervals, estimate feed intake, and measure methane production, bridging critical information gaps in pasture-based systems (CERES Tag, 2023).

6.2 Indoor Livestock Applications

While CERES Tag has gained attention primarily for outdoor applications, similar sensor-based solutions are transforming indoor beef systems. Integrated weigh scales and electronic identification devices provide continuous data on an animal’s feed consumption versus weight gain. AI algorithms overlay this data with environmental parameters – such as barn temperature or feed composition – to generate individualised feeding recommendations (McAloon et al., 2020). These real-time insights reduce overfeeding and wastage while improving uniformity in weight gain across the herd.

6.3 Predictive Analytics

Beyond immediate measurements, sensor data feeds into predictive models for disease outbreak likelihood, breeding success rates, and overall herd performance. If an emerging health issue is detected, farmers can intervene quickly to isolate animals or adjust treatments before an outbreak spreads (Lyons et al., 2020). By harnessing historical sensor data, producers can also anticipate fluctuations in pasture availability due to seasonal climate changes, optimising grazing rotations and input costs.

7. Implications for Farmers and the Industry

7.1 Farmer Profitability and Competitive Advantage

Adopting sensor and biotechnology tools yields multiple layers of benefit. Improvements in feed efficiency directly enhance profit margins by cutting down the largest single cost in beef operations. Additionally, data-backed evidence of sustainable practices – like lower methane emissions – can fetch premium prices in markets where consumers are willing to pay more for eco-conscious products (Goldsmith et al., 2021). This synergy between sustainability and profitability is a powerful driver for technology adoption.

7.2 Transparency as a Market Differentiator

Increasingly, transparency is becoming a market requirement. Retailers and consumers alike demand verifiable data on environmental footprint, animal welfare, and feed quality. For farmers, this level of transparency is both a challenge and an opportunity. By leveraging real-time sensor data, farmers can provide robust evidence of their practices, thereby building consumer trust and securing access to markets with stricter quality and sustainability standards (McCarthy et al., 2015).

7.3 Competition with Alternative Proteins

The rise of alternative proteins, including lab-grown meat and plant-based substitutes, poses a growing challenge. However, data-driven improvements can position traditional beef as a premium, sustainable choice. Demonstrable gains in feed efficiency and methane reduction weaken the primary criticisms levelled at livestock production. Indeed, producers who can showcase superior environmental performance, traceability, and welfare standards may stand out even in a saturated protein market (Roosen et al., 2020).

8. Biotechnology in Action

8.1 CRISPR-Based Disease Resistance

Beyond feed efficiency, CRISPR technology has shown promise in conferring disease resistance – for instance, targeting genes related to bovine respiratory disease or mastitis (Carlson et al., 2012). Disease resilience translates directly to economic gains via fewer veterinary expenses, lower mortality rates, and improved overall herd performance. Combined with sensor data that detects early signs of illness, CRISPR-enabled resistance can further reduce the need for broad-spectrum antibiotics, addressing consumer and regulatory concerns about antibiotic resistance.

8.2 Microbiome Management

Ongoing research highlights the importance of the rumen microbiome in shaping feed conversion efficiency and methane output. Certain microbial strains are more efficient at digesting fibres and can reduce methane production by shifting fermentation pathways (Tapio et al., 2017). Biotechnology also includes precision probiotics, which seed the rumen with beneficial strains, and targeted antimicrobial peptides that selectively remove undesirable microbes. As these approaches scale up, they can be tailored to the genetic background of the animal, thereby maximising synergy between genome and microbiome (Sharma et al., 2018).

8.3 Precision Vaccines

Conventional vaccination schedules seldom account for the specific pathogen load or genetic vulnerabilities of each farm. In the future, genomic surveillance combined with big data analytics will enable precision vaccines. Vaccines can be developed or updated rapidly to target emerging or region-specific strains. By reducing disease outbreaks, farmers improve animal welfare, reduce economic losses, and bolster consumer confidence (Chapman & Hill, 2012). Ultimately, precision vaccines align with the overarching theme of data-driven, tailor-made interventions.

9. Roadmap to 2035 – A Non-Linear Path

9.1 Phased Technological Adoption

Transitioning to data-driven, biotech-enabled beef production will likely be non-linear, characterised by “leapfrog” moments in CRISPR applications and AI breakthroughs. Early adopters who demonstrate significant gains in feed efficiency will encourage widespread uptake, creating a cascading effect. Once new sensor models or genetic editing strategies prove successful in commercial herds, more conservative producers will join the wave to remain competitive (Rogers, 2003).

9.2 Regulatory and Ethical Considerations

Regulations around gene editing, data privacy, and environmental standards will shape the adoption curve. The European Union, for instance, imposes stringent rules on gene editing in livestock, and producers must navigate these frameworks to avoid trade restrictions. Likewise, data privacy legislation demands that farms protect and anonymise data collected from sensor technologies (Kubiriba et al., 2021). Thus, the timeline for achieving a fully integrated system by 2035 may fluctuate based on policymaking and ethical debates.

9.3 Climate Change Adaptation

Climate change may accelerate the adoption of advanced technologies. As extreme weather events and shifting precipitation patterns threaten pasture availability, real-time data becomes even more critical (IPCC, 2019). Genetic lines that tolerate heat or disease pressures associated with a changing climate will become more valuable. CRISPR and sensor technologies thus serve not just as efficiency tools but also as vital resilience strategies.

10. Call to Action – Dream, Build, Lead

10.1 Farmers: Embrace Innovation and Collaboration

Farmers are central to driving change. Small-scale pilots with new ear-tag sensors or CRISPR-based breeding can offer insights and refine best practices. Collaboration with researchers is essential to customise these technologies, ensuring that solutions address real-world challenges while remaining practical and cost-effective (Lyons et al., 2020). As technology prices decrease, barriers to entry will lessen, making widespread adoption more feasible.

10.2 Researchers: Prioritise Scalability and Affordability

Academics and industry scientists must focus on developing scalable, affordable solutions (Sharma et al., 2018). This involves refining genetic tools to reduce off-target effects, optimising microbial interventions, and making sensors more robust in challenging environments. By keeping smallholders in mind, researchers can help avoid a digital divide that leaves behind critical segments of the agricultural community.

10.3 Industry Leaders: Fund Innovation and Ensure Inclusion

Industry stakeholders – such as large processors, retailers, and feed companies – hold considerable influence by funding development projects and setting industry-wide standards. Offering incentives for data-sharing, building robust networks for digital innovation, and supporting training programmes can accelerate adoption across the supply chain. Ensuring that small and medium-scale producers are included in this transformation helps the entire sector remain resilient and competitive (Goldsmith et al., 2021).

10.4 Policymakers: Align Regulations with Technological Potential

A supportive regulatory environment is crucial. Clear guidelines on gene editing, data protection, and sustainability metrics can foster responsible innovation without stifling progress (Carlson et al., 2012). Policymakers must engage with scientists and industry representatives, balancing ethical concerns with the need for more productive and sustainable agriculture.

11. Conclusion

By 2035, the landscape of pasture-based beef production will be markedly altered by data-driven feed efficiency strategies. Sensors such as the CERES Tag will provide unprecedented insights into individual animal behaviour and grazing intake, while CRISPR gene editing and microbiome manipulations will enable precise genetic and microbial optimisations. These advancements will not occur in isolation; rather, they will feed into each other, forming a continuous cycle of improvement. On-farm data guides genetic selection, which in turn leads to healthier, more efficient animals, generating more robust data for future refinement.

This integrated approach has profound implications for farmers’ profitability, environmental impact, and competitiveness in a market increasingly occupied by alternative proteins. The ability to document and disclose sustainable practices will be a critical differentiator, meeting rising consumer expectations for transparency and ecological responsibility.

Achieving this future requires concerted efforts from farmers, researchers, industry leaders, and policymakers. By embracing innovation in data analytics and biotechnology, the global beef industry can navigate evolving challenges – climate change, consumer scrutiny, and economic volatility – ultimately securing a resilient food system for generations to come.

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Thank you for your attention to this paper on feed efficiency in pasture-based beef production. The integration of real-time data, innovative sensor technology, and breakthrough biotechnologies like CRISPR stands to redefine how producers optimise feed utilisation, enhance profitability, and bolster sustainability in an evolving global marketplace.