Systems science at the scale of impact

Fergus Sinclair World Agroforestry Centre

Bangor University, Wales, UK CATIE, Costa Rica

Fertiliser

Crop varieties

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

Crop varieties

Agro-chemicals

Higher yield

Agronomy

Thinnings and residues

Manure

Fodder at key times

Livestock

Non agricultural activity e.g. trade

Trees

Crops Household

Agro-chemicals

Higher yield

Agronomy

Thinnings and 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 agronomic recommendations 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

Thinnings and 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

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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 up Simple 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 out Application 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

A B

C

Used by breeders

GxE → OxC

Tree species

Management package

Training approach

Organisational model

Genotype

Climate

Soil

Farm resource endowment

Market integration

Gender, HH type

Ethnic group

Environment Production

Risk

Profitability

Acceptability

Env impact

X =

X

Option Context Performance X =

=

An

y style of p

articipatio

n

Qu

antitative - Q

ualitative - 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

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ural f

allo

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MLs

Non-c

oppicin

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ing

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rtili

zer

Yie

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iffe

ren

ce (

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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.

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Increase in maize yield over control after sesbania fallow (t ha-1)

Cumulative probability

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 1.38 ridge 0.21 slope 0.68 upland 0.81

Elevation Effect (m) (t/ha) 500 1.63 1000 0.46 1500 -0.74

Crop Effect (t/ha) cotton -0.25 groundnut 0 maize +3.40 soya +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 recovery A 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