GLOBAL COFFEEPLATFORM

The Vietnamese coffee sector has engaged with local government, trade, producers and GCP since 2017 in order to gather sustainability information and baselines on coffee regions. The aim is to inform local partners’ agendas for improvement and interventions. 

But the benefits extend beyond the crops. The data will also help improve the National Sustainability Curriculum (NSC) and the necessary capacity building, will assist with the monitoring of NSC practice adoption, and help identify opportunities to better support smallholder producers.

In 2018, data on around 8,800 coffee-producing households in Di Linh district (Lam Dong province) was gathered using GCP Progress (Farm Code System in Vietnam context). This resulted in five key learnings and directly led to several projects being developed and rolled out to address the issues found.

In 2019 and early 2020, another 5,000 farmers have been surveyed in three communes of Krong Nang district (Dak Lak province). The information gathered will provide a baseline for implementing the continuous improvement approach towards a sustainable coffee region.

In addition, in these two impact areas of Di Linh and Krong Nang districts, local authorities and traders are implementing various programs aiming to make 100% of the coffee produced sustainable and to help increase farmer income.

To address the previous learnings on the need for close alignment and shared understanding, the active participation of extension services and local authorities in organizing and monitoring the process played a key role as well as the contribution by traders on site.

The improving data quality directly affects the quality of decisions and so the value of the improvement activities for the local coffee sector.

The learnings from the last two years’ monitoring activities show that close participation and buy-in from local sector players and government can drive sustainability improvement of coffee regions at scale. The demand for investment, local ownership and alignment may appear high in the beginning, but it leads to cost and time savings ensuring data credibility and decision-making based on shared understanding and information.