Fergus Sinclair, co-convenor of the Transformative Partnership Platform on Agroecology and chief scientist at CIFOR-ICRAF, focuses on developing holistic metrics for agricultural and food system performance to enhance sustainability and food security. The document discusses various agroecological approaches and emphasizes the need for context-specific metrics that consider both quantitative and qualitative data. It highlights the importance of integrating these metrics with Sustainable Development Goals (SDGs) and stresses a comprehensive assessment framework for effective evaluation.

1. Fergus Sinclair
Co-convenor of the Transformative Partnership Platform on Agroecology
Chief Scientist, CIFOR-ICRAF (Centre for International Forest Research – World Agroforesrtry)
Leader, Livelihood Systems Flagship, CGIAR Research Program on Forests, Trees and Agroforestry
School of Natural Sciences, Bangor University, Wales, UK
Development of holistic metrics of
agricultural and food system performance
https://glfx.globallandscapesforum.org/topics/21467/page/TPP-home

2. Develop and apply
holistic performance
metrics
Sinclair,F., Wezel,A., Mbow, C., Chomba, C., Robiglio,V.,andHarrison, R. (2019). The contributionof agroecological approaches to realizingclimate-resilientagriculture.BackgroundPaper.
Global Commissionon Adaptation.Rotterdam. https://gca.org/reports/the-contributions-of-agroecological-approaches-to-realizing-climate-resilient-agriculture/

3. HLPE 2019. Agroecological and other innovative approachesforsustainable agriculture and foodsystemsthat enhance foodsecurityand nutrition.A report by the High Level Panel of
Experts on Food Securityand Nutritionof the Committee on World Food Security,Rome http://www.fao.org/3/ca5602en/ca5602en.pdf

4. Indifference = equal value/utility
y
x
Biodiversity
1
1
0
0
The food planner?
Minimum
acceptable
Farm production
Minimum
acceptable
Space for
options
The conservationist?
Caution – principles for overcoming dangers with metrics
1. Contextspecificity
local relevance versus global comparison
2. Quantitativeand qualitativeinformation
triangulation,
explanation.
3. Multidimensionality
tryany of the one number index / monetisation,
differential weighting,
non-linearality,
thresholds,
limits to trade-offs(compensation).
Protocolsfor using and interpretingmetrics as importantas
the metrics themselves
Need comprehensive assessmentframeworksANDcommon
databases from which users can operate syntheses relevent
to their purpose, rather than justa few iconic indices

5. Immediate traction from combining SDG indicators
SDG 2.1prevelance of
undernourishment(PoU)
- percentageof total
population
SDG 15.3proportionof
land thatis degraded
(PD) - percentageof total
land area
𝛥 PoU / 𝛥 PD
O’Neill,D.W.,Fanning,A.L.,Lamb, W.F. et al. A good life forall withinplanetary
boundaries. NatSustain 1, 88–95 (2018). https://doi.org/10.1038/s41893-018-0021-4

6. Field scale: from 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 =
=
Any
style
of
participation
Quantitative
-
Qualitative
-
Mixed

7. Options x Context

8. Farm / Livelihood scale
Fertiliser
Crop
varieties
Agro-
chemic
als
Higher yield
Agrono
my
Thinnings and
residues
Manure
Fodder
at key
times
Livestock
Non agriculturalactivity
e.g. processingand trading
Trees
Crops
Household

9. Farmer decisions takewhole
livelihood into account: maize
in the mid hillsof Nepal
• Farmers don’t follow agronomic recommendations
• plant at high population density,use thinnings for fodder end up with lower
than recommended 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
A
g
r
o
-
c
h
e
m
i
c
a
l
s
Higher yield
A
g
r
o
n
o
m
y
Thinni
ngs
and
residu
es
Fo
dd
er
at
ke
y
ti
m
es
Livestock
Non
Trees
Total factor productivity and resilience of whole livelihood
rather than yeild of one component

10. used by individual households collectively used
BARI
maize/millet intercropping
with fodder trees on crop
terrace risers
GRASSLAND F
O
R
E
S
T
KHET
paddy rice
LIVESTOCK
grazing
tree fodder
tree fodder/
crop residues
manure
crop residues
Tiwari,T.P., Brook, R.M. and Sinclair,
F.L. (2004) Implicationsof hill
farmers' agronomic practices in
Nepal for crop improvementin
maize. Experimental Agriculture 40:
1-21
Tiwari,T.P, Virk,D.S. and Sinclair,F.L.
(2009). Rapid gainsin yieldand
adoptionof new maize varietiesfor
complex hillside environments
through farmerparticipation.I.
Improvingoptionsthrough
participatory varietal selection(PVS).
Field CropsResearch 111: 137–14-21
Tiwari,T.P., Brook, R.M., Wagstaff,P.
and Sinclair,F.L. (2012) Effectsof
lightenvironmentonmaize in
hillside agroforestrysystemsof
Nepal. Food Security 4: 103-114

11. DOCUMENTING AND EVALUATING
THE SOCIO-ECONOMIC VIABILITY
OF AGROECOLOGICAL PRACTICES
ACROSS AFRICA