Systems science at the scale of impact
Fergus SinclairWorld Agroforestry Centre
Bangor University, Wales, UKCATIE, Costa Rica
Fertiliser
Cropvarieties
Agro-chemicals
Higher yield
The cropping ‘system’ concept
Agronomy
Assumes that the crop is fairly independent of the rest of the livelihood system – which (with due regard to sustainability) is appropriate for a subset of wealthier farmers in Africa
Package of measures to increase crop productivity by using inputs to make the environment as suitable as possible for the crop – need to watch sustainability esp. with respect to soil carbon and micronutrients
Crops within a livelihood system reality
Fertiliser
Cropvarieties
Agro-chemicals
Higher yield
Agronomy
Thinnings and residues
Manure
Fodder at key times
Livestock
Non agricultural activity e.g. trade
Trees
CropsHousehold
Agro-chemicals
Higher yield
Agronomy
Thinningsand residues
Fodder at key times
Livestock
Non
Trees
Livelihood systems within a landscape context
Strategic placement of trees within the landscape can increase water infiltration, reduce soil erosion and improve water productivity
Livestock move nutrients within and between landscapes often from common forest and grazing areas to crop land; livestock may be owned by farm households within the landscape or by separate pastoralist communities
Binding social capital
Bridging social capital
Food
Water Energy
Systems research
• Farmers don’t follow agronomicrecommendations for maize– plant at high population density, use thinings for
fodder end up with low densities– intercrop – including with tree cover (globally
almost half agricultural land has >10% tree cover)– apply fertiliser purposively (precision farming?)– 30% increase in maize yield through participatory
varietal selection in Nepal (Tiwari et al., 2009)
Fertiliser
Agro-chemicals
Higher yield
Agronomy
Thinningsand residues
Fodder at key times
Livestock
Non
Trees
used by individual households collectively used
BARImaize/millet intercroppingwith fodder trees on cropterrace risers
GRASSLAND F
O
R
E
S
TKHETpaddy rice
LIVESTOCK
grazing
tree fodder
tree fodder/crop residues
manure
crop residues
Systems interaction references
• Tiwari, T.P., Brook, R.M. and Sinclair, F.L. (2004) Implications of hill farmers' agronomic practices in Nepal for crop improvement in maize. Experimental Agriculture 40: 1-21
• Tiwari, T.P, Virk, D.S. and Sinclair, F.L. (2009). Rapid gains in yield and adoption of new maize varieties for complex hillside environments through farmer participation. I. Improving options through participatory varietal selection (PVS). Field Crops Research 111: 137–143
• Tiwari, T.P., Brook, R.M., Wagstaff, P. and Sinclair, F.L. (2012) Effects of light environment on maize in hillside agroforestry systems of Nepal. Food Security 4: 103-114.
The challenge
• Fine grained variation in:
– soil (biota)
– climate (altitude)
– farming practices
– household characteristics
– market opportunities
– social capital
– policy and its implementation
Pruned trees
Free growing trees
Earthworm cast weight
Sample with no
earthworm casts
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 5 10 15
Separation distance (m)
Sem
ivariance
Cross-semivariogram
Greater soil biological activity (earthworms) near trees but effect greater for some tree species than others
Pauli et al 2010 Pedobiologia
The challenge
• Fine grained variation in:
– soil (biota)
– climate (altitude)
– farming practices
– household characteristics
– market opportunities
– social capital
– policy and its implementation
The challenge
• Fine grained variation in:
– soil (biota)
– climate (altitude)
– farming practices
– household characteristics
– market opportunities
– social capital
– policy and its implementation
The challenge
• Fine grained variation in:
– soil (biota)
– climate (altitude)
– farming practices
– household characteristics
– market opportunities
– social capital
– policy and its implementation
The challenge
• Fine grained variation in:
– soil (biota)
– climate (altitude)
– farming practices
– household characteristics
– market opportunities
– social capital
– policy and its implementation
The challenge
• Fine grained variation in:
– soil (biota)
– climate (altitude)
– farming practices
– household characteristics
– market opportunities
– social capital
– policy and its implementation
The challenge
• Fine grained variation in:
– soil (biota)
– climate (altitude)
– farming practices
– household characteristics
– market opportunities
– social capital
– policy and its implementation
What to scale up?
• Participation (PAR) largely replaced systems methods (farmer or community integrates)
• Options refined through PAR at a few sites don’t scale because context varies, BUT
• scaling only innovation processes (rather than options to improve livelihood systems) is not cost effective. Options are:
Technology (components and their management)
Effective delivery mechanisms / markets
Appropriate enabling policy and institutional environment+ +
Ingredients that can be combined in different ways across scales
Characterize variation in context across scaling domain
Influence development projects so that sufficient intensification options are offered to farmers across
sufficient range of variation in drivers of adoption
Initial matrix of intensification and resilience options and the contexts in which they work (soils, climate, farming system, planting niche, resource availability, institutions)
Participatory monitoring and evaluation system for the performance of options
Scaling upSimple to use tools to match options to sites and circumstances across the scaling domain
Generate understanding of suitability of options in
relation to context – and the cost effectiveness of
different combinations
refined characterization
refined options
Scaling outApplication of understanding about cost effective options for different contexts beyond the current scaling domain
Global comparative understanding of how to improve livelihood systems, emergent from the place-based research complex.
Coe, R., Sinclair, F. and Barrios, E.(2014). Scaling up agroforestry requires research ‘in’ rather than ‘for’ development. Current Opinion in Environmental Sustainability, 6: 73–77.
Genotype x Environment (GxE) interaction
Drought stress
Crop yield
A
B
Used by breeders
Genotype x Environment (GxE) interaction
Drought stress
Crop yield
AB
C
Used by breeders
GxE → OxC
Tree species
Management package
Training approach
Organisationalmodel
Genotype
Climate
Soil
Farm resource endowment
Market integration
Gender, HH type
Ethnic group
Environment Production
Risk
Profitability
Acceptability
Env impact
X =
X
Option Context PerformanceX =
=
An
y style of p
articipatio
n
Qu
antitative -
Qu
alitative -M
ixed
Local effects - trees increase crop yields from meta analysis of >90 trials across sub Saharan Africa
• Mean yield of maize after coppiced and non-coppiced tree fallows (various species) is > 1 t ha-1 doubling default practice of many farmers in many years (no nutrient inputs).
• Very large standard error around the mean – indicates performance varies with circumstances – we need to know where particular trees will increase yields by a large enough amount to merit farmer input in the technology
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
Nat
ural f
allo
w
HG
MLs
Non-c
oppicin
g
Coppic
ing
Full fe
rtili
zer
Yie
ld d
iffe
ren
ce (
t h
a-1
)
Yield difference = Treatment-control yield
Control = maize without nutrient input
HGMLs = herbaceous green manure legumes
Sileshi G, Akinnifesi FK, Ajayi OC and Place F (2008) Meta-analysis of maize yield response to planted fallow and green manure legumes in sub-Saharan Africa. Plant and Soil 307: 1-19.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
-1.0 0.0 1.0 2.0 3.0 4.0 5.0
Increase in maize yield over control after sesbania fallow (t ha-1)
Cumulativeprobability
From: Sileshi et al., 2010. Field Crops Research. Based on meta analyses of over 90 trials across sub-saharan Africa
50% probability of no increase in yield or worse on Nitosols (saturated fertility?)
Risk – what is the probability that a farmer will get a threshold increase in yield on different soils?
60% probability of > 1 t ha-1 increase in yield on Luvisols
Simple examples from southern Africa
Landscape position Effect (t/ha)
foot 1.38ridge 0.21slope 0.68upland 0.81
Elevation Effect (m) (t/ha)
500 1.631000 0.461500 -0.74
Crop Effect (t/ha)cotton -0.25groundnut 0maize +3.40soya +0.70
Gliricidia effects
Faidherbia effects
Sesbania effects
+ Social, economic + other performance measures
Images and analyses courtesy of Thomas Gumbricht
Matching technologies to fine scale variation in food resilience
RATA – Resilience indicator
Roots of recoveryA tale of two villages – Africa Rising
Ministries go for cocoa options in Peru Rediscovering our trees - DRC
I am trying things out here first and if they work well, I will expand to other areas of my farm over there
Learning from experience - teasing out ingredients of success and the contexts they are appropriate for: scaling domains for ingredients and their combination
Key steps
• Optimise the system not one component– total factor productivity
• Vertical and horizontal integration– across scales– food, water, energy
• Options by context not silver bullets– systematic large N trials of range of options over range of context– nested scale options (T+M/E+P/I) and planned comparisons– measure performance with appropriate indicators including of
resilience– refine recommendations through action of feedback loops to create
easy to use tools (co-learning) at right scales and resolution– partner with development organisations and the private sector– bridge knowledge systems (local, global science, policy makers)
It’s more than…
• … classic ‘participatory research’– aiming for scale– integrating testing of options, delivery, institutional
arrangements
• …project M and E– planned to generate needed information efficiently– contribute to global K-base, not just project
requirements.
• … action research– learning principles, contributing to global K-base– not just optimising locally
from farmer empowerment
to
empowering millions of farmers
Top Related