Experiences with implementing the Sustainable Intensification Assessment
Framework [SIAF] in East and Southern Africa
Lieven Claessens and Mateete Bekunda
International Institute of Tropical Agriculture
This poster is licensed for use under the Creative Commons Attribution 4.0 International Licence.
January 2019
We thank farmers and local partners in Africa RISING sites for their contributions to this work. We also acknowledge the
support of all donors which globally support the work of the CGIAR centers and their partners through their
contributions to the CGIAR system
Introduction
• SI evolved to include non-environmental dimensions
• 5 domains (productivity, economic, environment, human, social)
• SIAF systematic means to identify tradeoffs and opportunities
Discussion
• Clearly, the majority of scientists do not have data to meet the
needs of the SIAF yet
• The demand that generation of SIAF data in subsequent
research work plans is the way to go…
• Experience with inclusion of domains data in workplan
requirements shows gaps - either because of limited knowledge
or interest in going beyond comfort zones in data generation
(appreciating the needs to generate data in non-familiar
domains)
• Discipline approaches dominate, reflecting failure to implement
together even after planning multiple-interventions guided by
influence diagrams
• How can we synthesize/convert single discipline SIAF data into
systems SIAF data?
• Most data available are at plot-level. How do we plan for
household and community levels, including elevation of
available plot data?
• Recognise multiple ways of presenting SIAF data (Malawi vs
Babati) – for different audiences?
• 15 SI indicators for four technologies (Snapp et al., 2018)
• Based on trials, surveys and crop models
The Kongwa Kiteto [Tanzania] example: An attempt at
multidiscipline, multi-indicator presentation (farm system
performance).
• Influence and system diagrams developed
• Data available for productivity and economic domains
• Data largely lacking for other domains
• Communication and sharing data among scientists should be
improved
• Need for ‘stocktaking’ and ‘legacy’ workshops and training on
data collection and indicator assessment
The Babati [Tanzania] example: Comparing technologies
within one discipline – single indicator per domain.
• Maize-pigeon pea, 5 ISFM treatments
• Environmental domain missing
• Indicator selection (feasible vs useful) and weighting
• ‘Win-wins’, no tradeoffs
The Malawi example: Comparing technologies within one
discipline - multiple indicators per domain