The NUANCES-DEED Cycle

Describe, Explain, Explore, Design

Different system analytical methods are employed in the NUANCES framework, which combines participatory research, farm typologies, data-mining, experiments and modelling tools to identify intervention opportunities and pathways towards the sustainable intensification of smallholder systems in sub-Saharan Africa. 

The DEED Approach

Different steps in the methodology are articulated using the ‘DEED’ approach to: 
  1. Describe current production systems and their constraints; 
  2. Explain the consequences of current farmers’ decisions on resource allocation; 
  3. Explore options for agro-technological improvement for a range of possible future scenarios; 
  4. Design, together with the farmers, new management systems that contribute to the sustainable intensification of smallholder agriculture.

 

A first step in farming systems analysis and scenario evaluation is to define representative prototypes of fields, cropping sequences, farms or localities that capture the key management, socio-economic and agro-ecological aspects of the systems under study. Their heterogeneity and diversity at different scales needs to be categorised, relying on solid understanding of the key drivers of such variability and using methodologies that allow comparisons across systems. Such cross-scale categorization may also serve to define recommendation domains or socio-ecological niches (e.g. Ojiem et al., 2006) to which resources/technologies can be targeted. The exploration of future scenarios is done together with farmer groups using participatory methods in a form of action research to test the applicability of the alternative ‘best-fit’ technologies. Co-learning with farmers and key stakeholders takes place at all stages in the cycle.

 

Meta-database for Storing Legacy Data and Current Research Results

A meta-database was developed as part of the AfricaNUANCES Project in 2007 (see http://metadatabase.africanuances.nl/). The purpose of the database is to facilitate data exchange between researchers and to increase the utilization of past research on African farming systems. This database will be continued and extended, and has the potential to become the key data source for agricultural research data from Africa.

Recent experience in Africa indicates that current generation of researchers are very poorly informed of what research has been done in their own countries – and in many cases even in their own institutes – in the past. Emphasis is placed on extracting grey literature in unpublished reports – which can often be a treasure trove of data – and scanning these to make pdf files.