Sustainability incentives and dairy processors’ considerations : White Paper
Abstract
In the Low Carbon Dairy Public-Private Partnership (PPP), the feasibility and impact of reducing the carbon footprint of milk by 50% by 2030 is assessed. Achieving this target requires not only implementing effective greenhouse gas (GHG) reduction measures on farms, but also ensuring farmers are incentivised to adopt them. While dairy processors worldwide are increasingly offering various sustainability incentives—ranging from technical support to price premiums—a clear overview of these incentive types and the considerations involved in their design is lacking. Therefore, this study aims to identify the types of incentives dairy processors can use and examine factors influencing its design and implementation.The study found a wide array of sustainability incentives, which can be defined as tools, mechanisms or strategies that a dairy processor (i.e. a company that buys raw milk from farmers and processes it into dairy products) uses to encourage farmers to produce milk more sustainably. Four main types of incentives are identified: assistance on farm, direct support payments, price premiums and voluntary credit markets. Moreover, in deciding how to design incentives, dairy processors balance seven key considerations: sustainability impacts, social pressure, politics, market development, supply chain position, available resources and preferences of the target group.This whitepaper concludes with a practical checklist for the design of incentives with important questions to consider for dairy processors. Eight aspects are included: strategic alignment, farmer engagement & feasibility, measurement & verification, economic impacts, market & consumer value, commercialisation, policy & regulatory context, scheme design features.
Fields of Study & Subjects
Sustainable Agricultural Systems Analysis
Agriculture Sustainability and Environmental Impact
Agricultural Economics and Policy
Publication details
- Journal / source
- Socio-Environmental Systems Modeling
- Publication date
- 1 January 2026
- Language
- EN