Farming systems analysis: what have we learnt?.pptx
FBaudron
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21 slides
Jun 15, 2024
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About This Presentation
Presentation given at the official farewell of Prof Ken Gillet at Wageningen on 13 June 2024
Size: 32.76 MB
Language: en
Added: Jun 15, 2024
Slides: 21 pages
Slide Content
Successes, remaining challenges & new demands placed on farming systems analysis in Africa. A personal view. Frédéric Baudron Ken Giller’s official farewell , Wageningen, 13 June 2024
Minimum soil disturbance Soil cover Crop diversification Intense promotion in Southern Africa Silver bullets: antheses of farming systems research. The (in)famous example of conservation agriculture in Africa
Taking into account: Multiple domains – multi-disciplinarity (from soil conservation to labour productivity, livestock feeding, intra-household gender dynamics, etc ) The diversity of farmers (affordability of herbicides, level of mechanization, demand for crop residues as feed and/or fuel, etc ) Different scales (from plot intra-household dynamics, trade-offs at farm scale, equity issues at landscape-level, etc ) Silver bullets: antheses of farming systems research. The (in)famous example of conservation agriculture in Africa
Taking into account: Multiple domains – multi-disciplinarity (from soil conservation to labour productivity, livestock feeding, intra-household gender dynamics, etc ) The diversity of farmers (affordability of herbicides, level of mechanization, demand for crop residues as feed and/or fuel, etc ) Different scales (from plot intra-household dynamics, trade-offs at farm scale, equity issues at landscape-level, etc ) Silver bullets: antheses of farming systems research. The (in)famous example of conservation agriculture in Africa
My PhD research in the Mid Zambezi Valley (2007-2011)
My PhD research in the Mid Zambezi Valley (2007-2011) Cotton farming as main driver of land use change Farming limited by labour more than by land (preventing farmers to adopt labour-intensive technologies such as CA) Shift from cotton to less labour-intensive commodities likely to accelerate land use change
10% 18% 20% (Baudron et al., 2022) Fast forward ~15 years later…
“Societal negotiation processes are often of limited quality, and science can assist in making them more equitable, explicit, concrete, creative, and integrative” Insights into biodiversity conservation provided by farming systems research
Land sparing Land sharing Insights into biodiversity conservation provided by farming systems research
Simple landscape Intermediate landscape Complex landscape (Baudron et al. 2017, 2019; Daum et al. 2023; Duriaux Chavarría et al. 2018; Wood et al. 2018) Research at landscape-scale: the missing middle?
(Daum et al., 2023) Labour : major constraint to the adoption of “more sustainable” practices
Contributing to (at least measuring) equity (Duriaux Chavarría et al. 2018 ) Simple landscape Intermediate landscape Complex landscape
R4D work on appropriate-scale mechanization from a business model development & farming system research angle Market intelligence informing crop breeding & future market segments Data science transforming the way agronomy R4D is conducted « Like Marxism , interdisciplinarity has remained an ideologogy » (Proposition belonging to F. Baudron thesis , Wageningen, 8th September 2011) Benefits of looking at a “research object” from a different angle Disciplinary ultidisciplinary Interdisciplinary (Wright Morton et al., 2015)
R4D work on appropriate-scale mechanization from a business model development & farming system research angle Market intelligence informing crop breeding & future market segments Data science transforming the way agronomy R4D is conducted Breadth of experience , Knowledge & skills Depth of high- level expertise in one discipline T- shaped profile « Like Marxism , interdisciplinarity has remained an ideologogy » (Proposition belonging to F. Baudron thesis , Wageningen, 8th September 2011) Benefits of looking at a “research object” from a different angle
From ecology e.g., multi-dimensional scaling From medical research e.g., data mining From social sciences e.g., structural equation modeling From economics e.g., stochastic frontier analysis Etc Using new approaches, borrowed from other disciplines
Big data… but grounded in field work New insights from data revolution in farming systems research Growing availability of data, increasingly performant computational tools Danger of global/regional studies with little grounding Field work Crucial to connect (& inform) theory with local realities But time, resources, scientific isolation, safety Reward system? (e.g., publication rate vs. empirical datasets, community outreach etc ) (Silva et al., under review )
Co-designing & scaling… without compromising on creativity? The risk of “participatory lock-in” Innovation platforms, living labs, etc Transformation? Widening the range of possibilities? Pressure to demonstrate impact at scale and value for money “Scaling-ready” technologies, “quick wins”, “low hanging fruits”; “accelerators”, etc Technological fixes vs. systemic changes
Between naivety & disillusion: understanding the politics Knowledge politics Who sets the agenda? Who funds it? How are particular framings & narratives supported (or undermined)? Make the politics more explicit: who benefits, how and why? Be opened to debates, break-down “epistemic community” siloes
“Communicating complexity” to diverse audiences The 3 states of development-oriented research ( Voituriez , 2024) Solid (scientific production) Liquid (networking, contributing to agenda setting) Gaseous (story telling, building a narrative) A balance between “ It depends ” & “success stories”
Agriculture at the center of some of the most pressing issues we are facing today (e.g., climate change, biodiversity crisis, emerging diseases, nutrition, conflicts, etc ) Calling for a systems approach Several (academic) successes But remaining challenges (e.g., landscape research, labour dimension, equity) And new demands that need to be managed (e.g., communicating successes, serving particular interests) Conclusions