Analysis showing the change in vegetative coverfrom 2017 to 2022

Benefits of the Data Powered Positive Deviance Method

Adaptation measures in rural agrarian areas rely on behavioral changes within communities, utilizing collective wisdom and lived experiences for sustainable outcomes and social acceptance.

Community-identified adaptation measures are sustainable since it capitalizes on local knowledge, resources, and local dynamics and governance for implementation with less reliance on aid, external expertise, and government support. Implementation of the defined measures enhances the community’s shared responsibility for resilience and a belief in their ability to succeed.

To identify community-led approaches the novel method of data-powered positive deviance was developed in 2019. It approaches the subject by asking: What can we learn from outliers? What information can we get from people who do things differently?

The Positive Deviance Collaborative defines positive deviance as “based on the observation that in every community there are certain individuals or groups whose uncommon behaviours and strategies enable them to find better solutions to problems than their peers — while having access to the same resources and facing similar or worse challenges.”

In our case we wanted to investigate how communities manage tree covers, trying to identify those who  preserve or increase their tree covers in the community – contrary to common observations

In a training organized through SNRD Africa, Asia and giz data lab we gained first-hand experience and would like to share our learnings and approaches with you. The question was what do communities do differently to sustain tree cover?

Stage 1: Assess Problem-Method Fit

The tree cover in northern Ghana is under threat. Forests and trees are disappearing. The reasons are manifold: bushfires, changed land use, such as for agriculture, dwelling and animal rearing and herding as well as the increased need for firewood among others.

For rural and vulnerable communities, sufficient tree cover is however necessary for economic activities in the North (such as the production of shea butter, and sustainable wood use) and it protects from floods and droughts.

In northern Ghana, 97.9% of the households are engaged in crop farming, with few in other forms of farming including poultry and livestock. Agricultural production is the main activity, practiced mainly on seasonal and subsistence levels. The sector remains the largest source of employment with many being smallholder farmers.

In the dry season, charcoal burning becomes an alternative livelihood strategy and support system. There is demand for the sale since charcoal provides about 64% of domestic energy requirements in Ghana. In addition to other factors such as security and bad farming practices, tree cover is at risk. Deforestation is rising.

This notwithstanding, the assessment of the suitability of the application of the DPPD (Data Powered Positive Deviance) methodology, it was realized that amidst these trends, positive deviants (communities preserving their tree cover) exist and the identification and scaling up of the practices could significantly impact discussions on resilience among communities.

On feasibility, the capturing of the contextual reality of the target group is based on traditional and non-traditional data. Data from statistical services and municipal and district assemblies aided in the identification of homogeneous communities used at target groups. The use of satellite images normalized differential vegetation index, and change detections together with surveys aids in the derivation of outcome data. The methodology is desirable since positive deviant communities may have some advantage in being resilient as compared to the others.

Stage 2: Determine Positive Deviant

Earth observation through satellite data was used to measure the performance of the communities in the project area with normalized difference vegetation index as the observed outcome measure.

From the workflow LANDSAT data attained from USGS (United States Geological Survey) was used for this work. Band combination was carried out to achieve a composite band image for further analyses. The Normalized Difference Vegetation Index was used to ascertain tree cover for 2017 and 2022. The change detection algorithm in Erdas Imagine 2015 was applied to assess changes in the tree cover in the two images. The output was 5 classes based on 10% changes in pixel values, thus, decreased, some decreased, unchanged, some increase, and some increased. The areas where there have been some decreases and decreases were taken as the normal expected outcome in the study.

Positive deviants were determined were areas remained unchanged or with an increase in tree cover. Thus, communities that had their tree cover maintained and or had some increase up to 10% over the 5 years. The community resource extents for 48 rural communities in the project area were overlaid on the output of the satellite image analyses. Based on the spatial location selection algorithm and ranking per aggregation, 10 communities were identified as possible positive deviants.

Stage 3: Discover Underlying Factors

The current stage of the Data Powered Positive Deviance methodology

The team is presently at this stage of the Data-Powered Positive Deviance methodology. Thus, unearthing the underlying factors to the maintaining and increase in tree cover amidst diverse factors contributing to deforestation in the project area.

Initial hypotheses are strong bylaws, a strong perception of climate risks and how tree cover is connected, and available water sources, among others.

By understanding these factors, communities can adopt effective adaptation strategies.

Possible deviant communities which are currently being reviewed