BEYOND THE HYPE: CONSEQUENCES OF TECH DRIVEN CHANGE IN ANIMAL HEALTH

BEYOND THE HYPE: CONSEQUENCES OF TECH DRIVEN CHANGE IN ANIMAL HEALTH

¹Dr.Sanober Rasool and ²Dr. Burhan Nabi

(Assistant Professor (¹VAHEE,²VMD) , KCVAS, Amritsar

 Technological innovation has become the driving force transforming India’s livestock sector. Tools such as artificial intelligence, machine learning, IoT (Internet of Things) devices, digital diagnostics, and tele-veterinary services are being hailed as game-changers for animal health and productivity. These advancements pledge earlier disease detection, lower mortality rates, better animal welfare, and improved livelihoods for farmers. However, beneath the surface of these benefits lie serious challenges, unintended effects, and risks that demand careful examination.

Technological Promises and Initial Success

Already, India is witnessing early gains from advanced technologies. For instance, IIT-Allahabad has developed an AI/ML-based “smart dairy monitoring system” that tracks cattle behaviour (such as walking, feeding, posture, and social interactions) via video surveillance to spot early warning signs of diseases like mastitis, lumpy skin disease, and ketosis – notifying farmers if something unusual is detected. In the state of Jammu & Kashmir, SKUAST-K launched tele-health services during the COVID-19 lockdown, providing diagnostics and veterinary counselling through WhatsApp, phone calls, SMS, and images/videos; especially helpful for dairy farmers. More recently, academic research (like an “Explainable AI approach for Monitoring Animal Health”) has employed accelerometer sensors along with machine learning to monitor cattle behaviour, with tools like SHAP used to make these models more transparent—highlighting which features lead to what conclusions.

Consequences, Challenges & Risks

1. Unequal Access, High Cost & Digital Divide: These technologies often require substantial investments: sensors, software, connectivity, upkeep. For small or marginal farmers, many of whom operate on thin margins, such costs may be prohibitive. Moreover, areas with weak infrastructure—unstable internet or electricity, difficult terrain—may not be able to benefit fully. In remote or hilly regions like parts of Kashmir, network coverage is often patchy, reducing the reliability of data-driven tools.
2. Reliability, Data Quality & Diagnostic Risks:Artificial intelligence and machine learning models are only as good as their training data. If data are limited, biased toward particular breeds, or collected in only certain environmental settings, models may misclassify or fail to recognize disease early. False positives cause unnecessary treatments or expense; false negatives may delay critical care. Also, many veterinary clinics especially in rural or remote zones lack diagnostic labs, reliable test kits, or equipment (like PCR labs), undermining the usefulness of technology-based guidance.
3. Ethical, Welfare & Regulatory Issues: Automation or algorithmic decisions might overlook qualitative aspects of animal wellbeing stress, behaviour, comfort that are harder to measure. In pursuit of higher productivity, animals might be pushed beyond safe thresholds. Additionally, regulatory oversight is often lacking there may be no clear norms for AI tools, telemedicine devices, or liability in case of errors. Standards for device certification and ethical guidelines in many cases are still evolving or absent.
4. Privacy, Data Security & Trust Concerns: As digital tools proliferate, more animal health data is being captured—health, behaviour, movement, breeding history. Who owns this data? How securely is it stored? What prevents misuse or breach? If farmers don’t trust the systems, or fear their data might be used against them, they may withhold participation or supply only partial information, reducing the potential impact of the technologies.
5. Training, Skills & Dependence: Advanced tools don’t replace the need for veterinarians, para-vets, and farmers to have observational skill and local knowledge. Many users may lack technical literacy to interpret sensor outputs, maintain devices, or adapt models to their local breeds or climate. Over-dependence on automated systems may erode traditional hands-on expertise.
6. Sustainability & Environmental Costs: Efficiency gains are promising, but there can be hidden costs: energy consumption (especially for cloud services, continuous data transmission), device manufacture/disposal, sourcing of replacement parts. Also, pushing for high yields without ensuring sustainable feed/fodder availability, breed resilience, or climate adaptability may leave the system vulnerable especially in the face of changing climate patterns.
7. Staff and Institutional Constraint: Even when the technology is available, actual implementation often falters due to insufficient manpower, gaps in technical support institutions or veterinary services. Maintenance of devices, troubleshooting, and scaling up require staff, infrastructure, and institutional coordination that many regions lack.

Moving Forward: How to Mitigate the Risks

• Governments should make access more affordable, especially for small and marginal farmers, through subsidies, collective service models, or shared facilities.
• Improve rural infrastructure reliable electricity, network connectivity, diagnostic labs, cold chains foundational support without which high-tech tools cannot deliver.
• Validate AI, diagnostic, and monitoring tools under local conditions testing for breed, climate, terrain compatibility; measure error rates; assess welfare impacts.
• Establish clear regulation, standard protocols, ethical guidelines device certification, liability norms, data governance, animal welfare standards.
• Focus on capacity building training farmers, para-vets, veterinary staff to use, maintain, and interpret technology.
• Incorporate traditional local knowledge and practices; integrate indigenous breeds adapted to local stressors, not replace them outright.

While technology-driven change in animal health in India holds enormous promise, it also carries significant risks. Without conscious efforts, the gap between ambitious innovation and everyday reality could grow. If not managed carefully, the consequences could include increased inequality, compromised welfare, loss of trust, and environmental or financial instability. For tech to deliver on its promise not just in theory but in the lived experience of millions India must embrace innovation with responsibility, inclusivity, pragmatism, and ethical oversight.

Please follow and like us: