Putting all cocoa farms and changes in farmland on the map in Ghana
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Mondelēz International Cocoa Life aims to have 100% of its member farms mapped for deforestation assessments and compliance with its transparency standards.
Farmer data in Ghana is difficult to collect and consolidate due to the sheer number of farmers working across the country, often in remote areas with low network coverage. As a result, Cocoa Life, Mondelēz International’s cocoa sustainability program, had received flawed and hard-to-use data from other suppliers in the past.
Mondelēz chooses us again because of the assurance of receiving compliant, high-quality data.
Mondelez Cocoa Life chooses Meridia as their go-to party for reliable smallholder land mapping in their program. We have so far completed two large mapping projects for Mondelez.
In 2019, we mapped all 47,000 farms in just eleven months. In 2022, we returned to map all new farmland under the Cocoa Life program across all twelve unions, making up about 16,500 farms. Mondelēz chooses us again because of the assurance of receiving compliant, high-quality data.
The quality of the maps Meridia produced is very good. It’s one of the clearest data sets I have seen, and I have touched upon many organisations in my career.
We engage with local communities and adapt our in-house technology to fit local circumstances
For all our mapping activities, we engage with local communities, adapt our in-house technology to fit local circumstances, and set up a local field team to work from parcel to parcel. We review collected data along the field data collection to enable feedback loops between data review and data collection.
Returning to the same areas for a second mapping project to capture new farmland and changes in farmland proposed some challenges, for which we found fitting solutions.
Identifying new farmers in the communities
with only a list including all farmers, of which most were already mapped. We solved this by deeply engaging with the Mondelēz Cocoa Life field network (unions and cooperatives). We were able to develop a method allowing a high identification rate.
Ensuring the capture of changes to already mapped farms
Farm sizes change over time, for example, when neighbouring farmland is accessed. The challenge was to identify the changes in the field and develop a technological approach allowing cost-effective mapping. We developed technical features for our data collection, processing technology and effective farmer sensitisation, allowing us to map farm size changes and attribute these to the already mapped farm.
Speed of mapping
As there were fewer farms per community to map, which were spread wider apart, our field team created efficient mapping schedules to work at the highest speed possible.