Presented by Roseline Remans, Columbia University at the Africa RISING–CSISA Joint Monitoring and Evaluation Meeting, Addis Ababa, Ethiopia, 11-13 November 2013

1. Vital Signs
An Integrated Monitoring System for Agricultural Landscapes
Roseline Remans, Columbia University
Africa RISING–CSISA Joint Monitoring and Evaluation Meeting,
Addis Ababa, Ethiopia, 11-13 November 2013

2. An Integrated Monitoring System
for Agricultural Landscapes
• Ecosystem Services
• Agricultural Production
• Human Wellbeing

3. ETHIOPIA
GHANA
UGANDA
RWANDA
Vital Signs is
starting in
SubSaharan Africa
TANZANIA
MOZAMBIQUE

4. Regions of impending agricultural change

5. Integrated Monitoring of Agricultural Landscapes
For decision making
Co – location of data in space and time – to assess tradeoffs and
synergies
Use of existing systems and data as much as possible –
often adding the environmental components
Ownership by governments to link with national data collection
efforts
Build national capacity on data collection, storage, analysis and
use

6. Vital Signs Approach - 1. Analysis threads
development agencies,
private sector, donors,
NGOs, farmer associations,
national governments
data +
metadata
archive and
management
decision
support
dashboard
analytics engine
(models and trade off
analysis + algorithms)
analytical
outputs
decision
layer
analytical layer
other
networks
and data
sources
LSMS,
AfSIS,
FAO,
GEO.....
remotely
sensed
+ in situ
measurement layer
6

7. 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 Target (%)
X
Poverty
Poverty
X
Health
Prevalence of malaria, diarrhea,
anemia
X
Nutrition
% overweight, under weight,
stunting, and wasting
X
X

8. Thread for
Soil Health
October 2013
Par al
nutrient
budget
indicator
crit vals
-20,
-5,-20
kg/ha/
crop
Net
nutrient
budget
for N, P,
K
Soil fer lity
indicator
Soil
critical
values for
Ca, Mg,
K, P, S
(AfSIS;
Shepherd
Vagen)
Soil
Exchangeable,
Available
Ca, Mg, K, P, S
Nutrients (N, P, K)
added to farm
plots
Nutrients (N, P,
K) removed from
farm plots
Soil Health
Index
Soil Health
critical
value
composites
Soil acidity
indicator
Soil C deficit
indicator
Soil erosion
indicator
crit
deficit
25%
Soil pH
critical
value of
5.5
Soil
pH
AfSIS
map
data
crit val
20 t/ha
Revised
Universal Soil
Loss Equation
(Rahman et
al, 2009)
Soil C
capacity
(Hassink et
al., 1997)
Soil C –
topsoil
Soil
texture
Slope
steepness
(S) and
slope
length (L)
Soil cover
and
management (C)
land
cover
Rainfall
erosivity (R)
Digital elevation
model
Rain rates

9. Thread for
Biodiversity
October 2013
Biodiversity
Security
Biodiversity
intactness
model
(modified
from
Scholes &
Biggs 2005)
Red list
indicator
IUCN
rules
for
threat
% Ecosystem
protec on
%
Remaining
habitat
Habitat
suitability
Protected
area
network
Land use
Thread for
Wood Fuel
October 2013
Habitat
Woodfuel
Energy
Security
Loss of forest
area
Species
richness
Niche
Models
MaxEnt
conec vity
Leaf forage on
Degrada
index
to livestock
thread
Supplydemand
spp
presence
Wood
produc on natl surveys
Potential
vegetation
From
Livestock
Tree
thread production
Species
abundance
Model
(Shackleton & from nat/
sub-nat
Scholes)
surveys
Land cover
class
Woody
biomass
Thread for
Species
Food Security
Presence
Octoberfrom Plots
2013
Many previous
studies in
literature
Species
Abundance
Annual
from Plots
rainfall
Tree cover
MODIS
Tree height
ICESAT
Food purchased/
Security
sold
index
VS modified
algorithm based on
EIU Food secrurity
index (2012)
Household
size
To climate thread
Food
availability
Colgan et
al
algorithm
Wood
Wood
consump on
% Under
Nutrition
Thread for
Gap
Nutrition
assesment
October 2013
Dietary
diversity scor e
Dietary
intake
Composite
index of
anthropometr
FAO.FAN ic failure **
Nutrition as integrative indicator
Calories and
essential
nutrients
Tree
available per
capita
height
FAO.FAN
TA
Tree
algorithm
species
(2007)
Risk of
food waste
Household
Food
Production
(from Ag
Intensificatio
n Thread )
Spatial
disaggregation
Subjective
food
availability
index
Allometry
Nickless &
Scholes
2011
Tree basal
area
Food utilization
Food access
Food sold and
purchased per
capita per day
Household % of
Minimum cost
Woodfuel
of nutritious
household
Pop%
consumption
diet
income spent ulation
on food Overweight
DHS
Subnat
statistics
Self
reportedNumber of
months of
food
insecurity
Per capita
consumption*
Save the
Children
(2009)
25
cutoff
Food
consumption*
DHS
Subnt
stats
TA (2007)
%
Underweight
18.5
cutoff Price of food
items on
markets
-2
SD
cutoff
7 day recall data on cuthousehold food
off
consumption of
different food groups
Weight for
age z-score
BMI
% Was ng
-2
SD
-2
SD
cutoff
% Stun ng
Height for age
z-score
Weight for
height z-score
*Overlap from the poverty thread
WHO
(2006)
Quetelet’s
Index
TIER 4
Gender
Age
WHO
(2006)
Weight
Height
WHO(2006)
MUAC
CIAF – 2
TIERModel developed by Svedberg 2000 used extensively in current literature
125
mm

10. Graphical tradeoff analysis
Thread for
Sustainable Agricultural Intensification
October 2013
Degree of
intensifica on
Climate
index
Per yield
All crop, all
year yield
Yield gap
From climate thread
Biodiversity
loss
Per yield
From biodiv thread
Water use
Per yield
Target yieldRealised yield
multiplied
Frac on of
area under ag
land use
Input
intensity
Target yield
per crop
Realized yield
per crop
Nutrient use
Per yield
From nutrient inputs
In realized crop yield
subthread
multiplied
Input/
target
input
Inputs for
target yield
Farmer inputs
Tillage, fertiliser,
irrigation, seed,
pesticides
VS Land cover
map
MGMT PRACTICES
Irrigation, Fertilizer
use, Residue,
Planting date,
Harvest date
From water thread
SPATIAL
WEATHER DATA
SET (eg CRU)
Temperature,
Precipitation,
Solar radiation,
Humidity
DSSAT -CSM
Crop model
(Koo et al.,
2012; Jones et
al., 2003) for
specific crops
Area harvested
per season by
crop
Area harvested
per season by
crop
Yield per hectar e
per season by
crop
Yield per hectare
per season by cr op
Spatial Disaggregation
AFSIS SOIL
MAP DATA: Soil
type, Soil
carbon, Soil
water content,
Soil Texture
Crop Yields
(Harvest
Choice; FAO;
District)

11. Vital Signs Approach - 2. Sampling framework and
Measurement scales
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

12. 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

13. Tanzania SAGCOT development clusters and
protected areas

14. Ghana Tier 2a plots and Tier 4 landscapes

15. Ecosystem stocks, functions, services
Natural
Systems
Slash & Burn
Agriculture;
shortened
fallows
Degraded
Systems
Rehabilitation
through
intensification
Bonsaaso, Ghana`
Mbola, Tanzania
Ruhiira, Uganda
Sauri, Kenya
Koraro, Ethiopia
Time and Population Density
Intensive
Management

16. Regions of impending agricultural change

17. SAGCOT DEVELOPMENT CLUSTERS
AND PROTECTED AREAS