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Introduction

India has over 535 million livestock, and for many rural families, a few animals are their only productive asset. Through the Bharat Pashudhan platform under National Digital Livestock Mission, digital livestock IDs link health, insurance, and trade records, enabling better prices and market access turning livestock data into poverty-reducing income.

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How Digital Livestock Identities Reduce Poverty

• Improved animal health & productivity: Digital ID tracks vaccination, treatment, breeding cycle thus reducing disease losses and improving milk yield.
  Eg: NDLM digitally records vaccinations for FMD and brucellosis, preventing income loss from outbreaks.
• Market access & better price discovery: Traceability builds buyer trust and enables premium pricing for high-quality products.
  Eg: Traceability features are helping certify products like cow ghee and pashmina for export.
• Faster access to credit and insurance: Digital records reduce paperwork, making animals a recognisable asset for banks.
  Eg: Every tagged animal is linked to insurance and credit, enabling quicker claim settlements.
• Integration with digital platforms for trade: Farmers can buy/sell animals on the Bharat Pashudhan platform, reducing dependence on middlemen.
  Eg: >9crore livestock owners already conduct transactions on the platform.
• Supports poorest households with small ruminants: Includes goats and sheep, crucial for landless and marginal farmers.
  Eg: India’s large goat and sheep population is being digitally registered, securing income for the poorest families.

Challenges In Using Digital Livestock Identities

• Digital literacy gap among para-vets and farmers: Inadequate training may turn the platform into data entry rather than empowerment.
  Eg: Para-vets lack digital skills, risking poor-quality data.
• Risk of platform becoming record-keeping, not decision-making: Data may be collected without analysis for planning.
  Eg: Value depends on turning the dataset into disease forecasting and policy decisions.
• Institutional limitations: Slow administrative processes delay adoption, as the platform’s effectiveness depends on adequately trained personnel at the ground level.
• Unequal digital access in remote areas: Connectivity gaps hinder real-time updates and usage, especially in remote areas where small ruminant owners lack reliable digital infrastructure
• Behavioral resistance to change: Farmers accustomed to traditional practices may hesitate to adopt digital tagging.

Conclusion

To convert digital livestock identities into lasting poverty reduction, India must focus on field-level training, seamless insurance–credit linkage, and real-time disease analytics. Strengthening digital access, improving para-vet capacity, and ensuring data is actively used for decision-making will transform livestock IDs from record-keeping to rural value creation.

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