Testing the CLEANED framework in
Lushoto, Tanzania
Mats Lannerstad (ILRI), An Notenbaert (CIAT), Ylva Ran (SEI), Simon Fravel (ILRI), Birthe Paul
(CIAT), Simon Mugatha (ILRI), Edmund Githoro (ILRI)
CLEANED validation, synthesis and planning workshop
Machakos, Kenya, 30-31 October 2014

The Lushoto pilot
• Aim = to provide a proof of concept
• From generic framework to practical implementation
• Livestock and Fish CRP connection: transforming the
dairy value chain in Tanzania
– Tanga and Morogoro
• Presented here = the Lushoto case
2

The step-wise procedure
A. Setting the baseline
• Stratification
• Description
– Land use and management practices
– Stocks and flows
– Value chains
– Vulnerable and limiting resources
B. Ex-ante assessment
• Intervention description
• Local impact assessment
• Out-scaling
• Flagging of risks

Describe systems, practices and VCs
4

• Primary data
Data sources
– Participatory GIS
– Expert consultations
– Household surveys
• Secondary data
– HH-level
– Spatial data
• Literature
• Expert knowledge
5

• Aim:
Participatory GIS
– Collect and calibrate
spatially-explicit data
– Explore scenarios
of change
– Assessments produced
aligned to and rooted in local understanding
• Resulting maps (with qualitative descriptions):
– Different dairy production systems
– Areas of dedicated feed production
– Environmental resources (status and risk)
6

Stratification of Lushoto (TZ)
Forest
• 3 main production systems:
– Intensive (cut&carry)
– Semi-intensive (some grazing)
– Extensive (pastoralism)
• Feed baskets:
Fodder
% grass/residues/other
Area
%
Milk yield
l/yr/LU
Extensive 75/20/5 11 400
Semi-intensive 65/22/12 5 1250
Intensive 50/35/15 29 1250

Vulnerable and limiting resources

Losses along the VC
9
Waste/loss as a “multiplying factor”
0% 2% 10% 2% 2%
15.3%

Full description
10
Intensive Semi Extensive Waste %
Area (ha) 119000 20500 43600 Feed 0 100.0
Grassland fraction 0.15 0.3 0.85 LS 2 98.0
Cropland fraction 0.75 0.6 0.05 Transport/processing 10 88.2
Crop residue removal (fraction) 0.9 0.6 0.85 Distribution 2 86.4
Grass removal (fraction) 0.5 0.5 0.4 Consumption 2 84.7
Cattle (number) 10000 1500 3000 Overall loss 15.3
Livestock density (head/ha) 0.08 0.07 0.07
Manure production (kg/head/day) 4 3 3
Total manure available (kg/ha/year) 122.69 80.12 75.34
Milk yield (l/head/year) 1250 1250 450
Manure to cropland (fraction) 1 0.75 0.33
Nmanure (kg/kg) 0.03 0.03 0.03
Manure N loss from volitilization
0.7 0.7 0.7
(ratio)
Nfertiliser (kg/kg) 0.18 0.18 0.18
Annual precipitation (mm/yr) 1100 1000 900
Soil type (FAO) Acrisol Acrisol Acrisol
SoilN (g/kg) 2.5 1.5 1.1
SoilC (g/kg) 35 30 21
Soilclay (%) 40 40 40
Soil depth (m) 0.2 0.2 0.2
Bulk density (g/cm3) 1230 1230 1230
LS factor 3.20 3.00 3.00
K factor 0.2 0.2 0.2
P factor 1 1 1.00
Soil loss (kg/ha) 21,403 18,920 13,273
Waste (% milk) 15 15 15

Intervention description
11

Scenario of change: intensification –
no land use change
Scenario1
Fodder
%
grass/residues/other
Livestock
population
LU
Milk yield
l/yr
/livestock unit
Extensive 75/10/15 12,500 1250
Semi-intensive 70/20/10 1,875 2750
Intensive 64/23/13 3,750 2750
• 25% increase in animal numbers
• Increase in fodder, concentrates and rice straw
• 100% increase in fertilizer input, with an associated yield
increase of 50%
• 50% reduction of waste at the transport/processing stage

Intervention description
• The level of detail ~ the assessment methods
– Changes in relevant input parameters
• Suitability to the different livestock production
systems and VCs
13

Quantification of impacts
14

Pathways and key indicators
15
1. Water availability and quality:
• Appropriation of available resources
• Change in soil water holding capacity
• Change in water quality
2. Soil and land health:
• Soil erosion
• Change in soil organic matter
• Nutrient
3. GHG emissions:
• Total emissions of methane, nitrous oxide, carbon dioxide
4. Biodiversity loss:
• Species diversity
• Landscape multi-functionality

Soil and Land
16

Soil and Land – Soil erosion
• Removal of valuable topsoil:
– Disturbance of seeds and plants
– Loss of nutrients
Impacts on crop emergence, growth and yield
• Deposited downstream:
– Disturbance of plant growth downstream
– Filling up and/or contaminating reservoirs and rivers
17

Soil and Land – Soil erosion
• Revised Universal Soil Loss Equation (RUSLE):
Annual soil loss (kg/ha/yr)
=
R * K * LS * C * P
R = Erosivity
K = Erodibility
LS = Slope length and steepness factors
C = Cover management factor
P = Support practice factor
18

Preliminary results: soil loss
1. Absolute:
Small increase in soil loss
2. Efficiency (compared to milk
gain): Gains across the board
Kg /ha/yr
25000
20000
15000
10000
5000
0
Kg/1000l
3000
2500
2000
1500
1000
500
0

Soil and Land – Nutrient balance
• Soil fertility decline
 Impacts on crop yields
• Losses to air and water
Water quality and GHG
 need to find a good balance!
20

Soil and Land – N NUTMON calculations
(IN1 + IN2 + IN3 + IN4) – (OUT1 + OUT2 + OUT3 + OUT4 + OUT5)
21
INPUT/OUTPUT ID NAME FORMULA
IN1 Mineral fertilizer Amount of fertilizer (kg/ha)*fertilizer N
(kg/kg)*area (ha)
IN2 Animal manure Amount of manure × manure N*area (ha)
IN3 Atmospheric deposition 0.14*p½*area (ha)
IN4 Biological N fixation Non-symbiotic N fixation {2+(p-1350)*0.005} +
Symbiotic N-fixation (% uptake attributed to N
fixation*total N uptake) *area (ha)
OUT1 Harvested crop products N content in harvested product(kg/kg)*yield of
crop (kg/ha)
OUT2 Crop residue N content in crop residues(kg/kg)*yield of crop
(kg/ha) *area (ha)
OUT 3 N leaching (if clay<35%) …(Soil N + Fertilizer N†)*(2.1×10ˉ²†×p+3.9) *area
(ha)
N leaching (if clay >35% and
<55%)
…(Soil N +Fertilizer N†)*(2.1*10ˉ²†*p+0.71) *area
(ha)
N leaching (if clay>55%) (Soil N +Fertilizer N†)*(2.1*10ˉ³*p+5.4) *area
(ha)
OUT 4 Gaseous losses (Soil N +Fertilizer N† )*(-9.4+0.13*clay+0.01p)
*area (ha)
OUT 5 Soil erosion
Soil loss (kg/ha/year)*Soil N*1.5

Preliminary results: N balance
1. Absolute:
Increase in nutrient mining,
leaching, gaseous losses
2. Efficiency (compared to milk
gain): Gains across the board
Kg /ha/yr Kg/1000l
0.00
-10.00
-20.00
-30.00
-40.00
-50.00
-60.00
-70.00
-80.00
0.00
-1.00
-2.00
-3.00
-4.00
-5.00
-6.00

Soil and Land – Next steps
• Improve calculations and feedback loops, e.g.:
– Add “organic” fertiliser
– Link manure production to DM feed intake
– Add Soil organic matter calculations
• Triangulate assumptions with HH-level data, values
from literature and expert opinion
• Convert quantitative calculations into qualitative
assessment
• Link to GIS and produce maps
• User-friendly “tool”
23

Water
24

Water – Why?
Water scarcity is a rising global problem
Vital for humans and functioning ecosystems
In livestock production:
• Provides drinking and servicing water
• Supports growth of animal feed and grazing
But - water resource use is highly complex to analyze
• Considers a moving resource in a landscape
• Variability of time and space
• Hidden in animal feed consumption
25

Water quantity
• Calculation of actual evapotranspiration (ET) per system
using the Aquacrop model
Soil water holding capacity (SWHC)
• Long term perspective of water availability for crops
• Comparing different land use management practices for
interventions
Water quality
• Change in water quality due to management practices
• Combined risk index based on fertilizers , chemicals and soil
erosion
Water – How?
26

Water – How?
Underlying assumptions:
– Area proportion per crop in the system reflects feed
composition
– Modelled ET is indicative of actual ET
– Two cropping/growth seasons corresponding to rain
periods
– Same growing conditions are assumed across the
study area
– An average harvest index of 35 % leaves 65 % of
crop biomass as residues – entirely used for fodder
27

Water - Results
28
Water
quantity
70
60
50
40
30
20
10
0
I S-I E
ET/MAR (m3/m3, %)
Baseline Scenario
1800
1600
1400
1200
1000
800
600
400
200
0
I S-I E
ET/feed (m3 /ton)
Baseline Scenario
180
160
140
120
100
80
60
40
20
0
I S-I E
ET/milk (m3/tton)
700
600
500
400
300
200
100
0
I S-I E
ET mm/year & system)
ET/
milk
ET/
feed
ET/
system
ET/
MAR
I = Intensive
S-I = Semi-intensive
E = Extensive

Water - Results
29
Scenario
System
(Lushoto)
SWHC
rating
Water
quality
rating
Intensive Low Low
Semi-intensive Low Low
Extensive Low Low
Water quality:
- Little impact
- Low levels of fertilizers &
chemicals applied – most
taken up by plants
SWHC calculation:
- Organic mulch
- Fertilizer
- Cropping patterns and tillage
- Impact in Lushoto is very low – especially
compared to increase WP

Water – Next steps
 Weight the 3 result components into a single score
• Enables to capture small impacts and “flag” them
even though they are not significantly changing the
final score
 Create water score maps for each component, and
the single score
• Indicating the difference between components and
the overall water score in red-green light over the
landscape
30

Water – Lessons learned
• Water for livestock is complex! Everything is
interlinked!
• The results for water use changes depending on
the lens you are looking through
Decreased SWHC indicates a negative result. But
management radically increases yield and WP -
thus leaving water quantity positive
 How do we properly capture that in weighting the
components in a final score?
• Results will be equally complex and need to be
visualized, component for component, but also
together to provide a water impact measure
31

Biodiversity
32

Biodiversity: Rationale
• Agriculture depends on biodiversity
• Gene pool of crops and animals: risks and missed
opportunities
• Future generations
33

Biodiversity: Loss drivers
• Long history of Agriculture:
Species selection, cultivation practices and
converting natural vegetation
• Drivers in Tanzania:
Agriculture, unsustainable harvesting, mining,
built environments, contamination of soil and
waterways.
34

Biodiversity: Scope and method
Scope: Vulnerable, threatened and endangered species
Method:
• Intersect of IUCN Redlist and study area
• Investigate
– Source of threat,
– Geographical extent
• Group species
• Mitigating strategies for all relevant species
35

Biodiversity: Preliminary results
• In Lushoto: 18 species
threatened by
agriculture
• Average extent globally:
4,300 km2
• Causal link with dairy
development?
– If only minor driver, still
an opportunity to raise
awareness
36

Biodiversity: Next steps
• Develop management strategies.
Potential groups:
Group 1) Birds – 6 species
Group 2) Other insectivores, similar location - 8 species
Group 3) Other reptiles – 6 species
• Incorporate botanical and aquatic species
• Links with water pathway on water quality
• Indicators for landscape multi-functionality
37

Biodiversity: Lessons
• Limited data for insect and agricultural biodiversity
• Individual species easier to analyse than ecosystem
interactions
• Causal links challenging – LUC can indirectly increase
habitat pressure.
• Identifying risks and mitigation options can be more
practical than quantified ‘impact’.
38

Greenhouse Gas Emissions
39

GHGs: rationale
• A long term global issue of global warming –
climate variability and sea level rise.
• Relevance to farmers
– lost energy and nutrients
– Linking with environmental
• Donors have a long term view of development and
potential risks
40

GHGs: scope and method
Scope: On farm emissions
• Livestock emissions
– Ruminant / IPCC CH4 enteric fermentation guidelines (eq 15)
– IPCC manure management emissions (eq 10.23, 10.25,10.27)
• Land management changes
– IPCC rice cultivation guidelines (Cool farm tool)
– IPCC guidelines on cropland (Cool farm tool)
• Land use change
– IPCC land use change guidelines / PAS:2050
41

GHGs: Lushoto results
42
Emissions in CO2 – equivalent
(annual) Baseline
Extensive Intensive/Semi Intensive
Enteric fermentation (head) 1152 1838
Manure management (head) 878 1092
Fertiliser emissions (head) 0.57 52
FPCM yield (l) 421 1315
Emission intensity FPCM 4.8 2.2
Net emissions* CO2-e (head) 2031 2982
Scenario
Extensive Intensive/Semi Intensive
Enteric fermentation (head) 1816 2882
Manure management (head) 1092 1520
Fertiliser emissions 1 93
FPCM yield (l) 1315 2892
Emission intensity FPCM 2.3 1.6
Net emissions* CO2-e (head) 2909 4495
• Net emissions
increase in Lushoto by
circa +35%
• Emission intensity
decreases from 3 to
1.7 kg CO2-e / 1l
*Background N2O emissions were excluded, but would be consistent FPCM
between baseline and scenario

GHGs: next steps
• Test more complex scenarios incorporating age at
first calving and manure management
• Test accuracy of results with more detailed data and
complex modeling
• Review allocation of emissions over the lifecycle
43

GHGs: lessons
• Post farm gate scenarios and emission estimates
• Scenarios have to be fleshed out – liveweights,
milk yields by season, feed baskets.
44

First Reflections I
• Pathways / impact categories:
– capture the most important issues – true?
• Pathway indicators:
– Subjectively selected – can we do better?
– Should other indicators be possible to use in other
contexts, e.g. aquaculture? Pastoral vs. mixed?
– Absolute vs. efficiency???
• Pathway calculations:
– How to better capture seasonality?
– Not all VCs are the same: for now only captures in the
”waste assessment” – how to improve
– How to indicate some kind of confidence level?
45

First Reflections II
• Intervention descriptions:
– Based on lots of assumptions and expert knowledge - Is
it possible to make this user-friendly?
– Single interventions as well as more systemic changes
• Is it a rapid tool?
– Calculations difficult to set up
– But as soon as set – should be quick
• Visualisation
– Aggregate how far?
• Indicator/pathway and trade-offs vs. Overall impact
• Per system, VC, study area
– Traffic light on map – feels good
• Same spatial unit all pathways
46

First Reflections III
• Implementation of the framework:
– Participatory approach (e.g. Through PGIS) might
increase trust in/use of results
• Use of the framework
– Useful for comparing interventions WITHIN a study area
– Only environment
• But it captures several dimensions, not only GHGs
– Other assessments will answer other questions
• Income, productivity, equality
– Follow up assessment might be required (e.g. In red
flagged areas/pathways)
– How to ensure use by different of stakeholders?
47

First Reflections IV
• Appears to work for dairy
– needs strenghtening and refining
- Needs further testing in other VCs and other systems
48