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Measure, Analyze, Distill, Act
The science and pragmatism of
threads and tiers in evaluating
change in agricultural landscapes
Sandy Andelman CI, Cheryl Palm Columbia, Bob Scholes CSIR,
Monika Zurek & Kate Schneider BMGF -> Vital Signs
Stanley Wood, Policies Team, Ag. Development, BMGF
• Vital Signs: A “gold standard” approach
for assessing, and informing responses
to, change in agricultural landscapes
• Priorities for and harmonization of
investments in improved measurement,
analysis & DSSs for sustainable
livelihoods in agricultural landscapes?
• Points to ponder
Where did the Millennium Ecosystem Assessment end?
Source: Ernesto Viglizzio, INTA 2003 (in MA Cultivated Systems chapter)
An Integrated Monitoring System
for Agricultural Landscapes
• Ecosystem Services
• Agricultural Production
• Human Wellbeing
Integrated Monitoring of Agricultural Landscapes
To inform multi-tiered decision making
Co – location of ecosystem, ag. production, and human
wellbeing data systems in space and time – to support
assessment of tradeoffs and synergies
Use of existing systems and data as far as possible –
often adding the environmental components
Ownership by governments to link with national data
collection efforts (e.g. LSMS-ISA)
Build national capacity on data collection, storage, analysis
and use across implementation agencies
SAGCOT DEVELOPMENT CLUSTERS
AND PROTECTED AREAS
VITAL SIGNS APPROACH: Threads
Dashboards,
Scorecards,
Infographics
DECISIONSUPPORT
INDICATORS
a small set of monitoring &
decision indicators generated
Tradeoff Analysis
“Nextgen” integrated/land use
decision maker focused models
ANALYSIS
MEASUREMENT
mathematical models and
algorithms are applied
“Digital Design”
“Statistics from space”
consistent metrics gathered on the
VITAL SIGNS DECISION INDICATORS
CATEGORIES
Ecosystems
Services
Indicator
Climate Forcing
Net AFOLU Climate Forcing
X
Biodiversity
Biodiversity Security
X
Wood Fuel
Wood fuel Energy Security
Livestock
Agriculture
Human
wellbeing
Thread
X
Rangeland degradation
X
X
Forage Adequacy
X
X
Water
Water Security
X
X
X
Resilience
Resilience or buffering index
X
X
X
Inclusive Wealth
Sustainability index
X
X
X
Food Security
Food Security Index
X
X
Soil Health
Soil Health Index
X
Ag. Intensification
Yield Growth (%)
X
Poverty
Poverty
X
Health
Prevalence of malaria, diarrhea,
anemia
X
Nutrition
% overweight, under weight,
stunting, and wasting
X
X
Woodfuel Energy
Security
Indicator Threads
Loss of forest
area
Thread for
Wood Fuel
Supplydemand
v1 Sep 12
Wood
production
Tree production
Model
(Shackleton &
Scholes)
Wood
consumption
Annual
rainfall
Woody
biomass
Colgan et al
algorithm
Tree cover
MODIS
Tree height
ICESAT
Allometry
Nickless &
Scholes
2011
Tree basal
area
Tree
height
Tree species
Household
Woodfuel
consumption
Populatio
n
VITAL SIGNS DECISION INDICATORS
CATEGORIES
Agriculture
Human
wellbeing
Ecosystems
Services
Thread
Indicator
Climate Forcing
Net AFOLU Climate Forcing
X
Biodiversity
Biodiversity Security
X
Wood Fuel
Wood fuel Energy Security
X
Better tradeoff/integrated
Rangeland degradation models can
Livestock (a) capture best empirical/scientific knowledge
Forage Adequacy
X
X
about
the
structural relationshipsX amongstX
Water
Water
Security
indicators
Resilience
Resilience or buffering index
X
X
(b) inform
interpretation of multipleXchanging X
Inclusive Wealth
Sustainability index
indicators
over time (past and projected)
Food Security
Food Security Index
X
X
through
ability to generate “with”X and “w/o”
Soil Health
Soil Health Index
(counterfactual) indicator estimates
Ag. Intensification Yield Growth (%)
X
Poverty
Poverty
X
Health
Prevalence of malaria, diarrhea,
anemia
X
Nutrition
% overweight, under weight,
stunting, and wasting
X
X
X
X
X
X
X
Sampling Framework: Measurement Scales/Tiers
GLOBAL
REGION
Facilitating
Providing insights
comparisons among and information at
different regions
the scale on which
agricultural
investment
decisions are made
Tiers 1 and 2
LANDSCAPE FIELD/PLOT HOUSEHOLD
Measuring relationships
between agricultural
intensifications,
ecosystem services
and human wellbeing
Tiers 3 and 4
Tracking agricultural
production,
including inputs
and outputs
Using surveys on
health, nutritional
status, income
and assets
Sampling Framework
• Tier 1:
– simple measures, complete regional coverage at moderate
resolution, based on models and remote sensing
– Land cover, vegetation type, biomass, modeled NPP – yields
• Tier 2A: 1 ha plots, in situ detail, statistically valid sample - to
validate Tier 1 and measure things not ‘seen’ by RS (250-500 plots
sampled;
• Tier 2B: 500+ HHs depending on national surveys
- Population, disaggregated national statistics
• Tier 3: Flow based, continuous sampling – weather station,
hydrological flows
• Tier 4: Process-oriented studies at high resolution– Five to ten 10X10 km landscapes per region
– 30-40 households per landscape with associated fields
Pondering Points
- Fundamental importance of (joint) data collection
systems in appropriate sampling framework (e.g.,
questions, scales, time frames, data points )
- What is the most “efficient” set of indicators to
address the most pressing questions?
- Need for a taxonomy (even a “MIP”) of different
“agricultural landscape” approaches and elements?
- How is knowledge/learning from different initiatives
codified and shared? (e.g., are there consensus ubercommunities?)
Pondering Points
- Is there clear evidence that the additional complexity
of “gold standard” landscape approach leads to better
human well-being and landscapes? (are the approaches
cost-effective and actionable at scale?)
- What is are the most binding constraints to progress?
Landscape ecologists? Data points? Scientific
knowledge? Understanding decision maker needs?
Communicating complex outputs/findings?
- How can technology (existing or yet to be developed)
best contribute to making sustainable livelihoods/
landscapes ? And how not?