Opportunities for LED in agriculture in East Africa:
a regional perspective
Lutz Merbold, Klaus Butterbach-Bahl, Polly Ericksen, John P. Goopy, Daniel
Korir, Sonja Leitner, Paul Mutuo, Phyllis Ndung’u, Alice Onyango, Jesse
Owino, David E. Pelster, George Wanyama and the Mazingira Team
Low emissions livestock: Supporting policy making and implementation through science in East Africa
Workshop, Addis, 2-4 July 2018.

Climate Change -> Livestock -> Climate Change
2
Rojas-Downing et al. 2018, Climate Risk Management

AFOLU and GHG emissions
Africa
Americas
Asia
Europe
Oceania
Approx. 60-70% of
emissions related to
livestock production
%-contribution of continents
to total AFOLU GHG
emissions
Manure applied to soils
Enteric fermentation
Manure left on pasture
Manure management
Burning - savanna
Synthetic fertilizer
Rice cultivation
Crop residues
Cultivation org. soils
Burning – crop res.
GHG-emissions by sector
FAO, Tubiello et al. 2014 3

Verifiably mitigate GHGEs - reliable baseline!
Currently available data is based on modelling
• Cheaper, faster and easier than measurements, but has
(a lot) of limitations
• It is a capital mistake to theorize before one has data - Sherlock
Holmes
• You can have data without information, but you cannot have
information without data - Daniel Keys Moran
• GIGO Principal: Garbage IN = Garbage OUT
• We need to model emissions (scaling), but we need
LOCALLY SOURCED data to run the model
4

What should we measure?
It’s all mostly about the feed:
• Intake and digestibility
• 70-80% of variability in enteric GHGEs
• Very difficult to measure intake in
smallholder situations
• Key assumptions about estimating intake violated (esp. ad
lib intake)
• The “next best” is to estimate intake from energy
expenditure
• Thus, we measure LW, ∆LW, milk production and loco-
motion in ruminants on smallholder farms, then apply
appropriate algorithms to estimate intake -> estimate
enteric CH4 emissions. 5

What have we found so far? – 1st site
• Emissions factors (up to) 40% less than Tier 1 (default) estimates
Mean live weight (kg) and Emission Factors (CH4 kg/animal/year) for
three classes of cattle in the three topographic regions of the Nyando
basin, Kenya.
Goopy et al. 2018, Agricultural Systems
6
Mean liveweight and CH4 emission factors

What have we found so far? – 2nd site
Ndung’u et al. accepted, Animal Production Science, 7
• Emissions factors are up to 20% higher than Tier 1 (default)
estimates (approx. 41 kg CH4/yr for female cattle)
Emission Factors (CH4 kg/animal/year) for five classes of cattle in the
three topographic regions of the Nandi county, Kenya

What about manure?
• Tier 1 approach should be closest to the system relevant in the
country –> largest number of animal in pastoral systems –> all
manure from cattle is deposited during grazing (1 kg
CH4/animal/year) – no manure management
• Farm household survey (336 farms, 3 months, 1MSc student, one
county) including questions about farm size, animal numbers,
confinement system etc. followed by lab experiment
• Current confinement systems can be classified in 4 categories:
i) grazing/pastoral, (ii) fence, (iii) fence and roof and (iv)
fence, roof and floor
Owino et al. in prep.
8

What have we found so far?
Smallholder Dairy Farmers (Nandi county, n = 336)
Manure Management (> 99%)
No MM
(< 1%)
Fence only (89.8%) Fence and Roof (2.8%) Fence, Roof, Floor (7.4%)
Fence/
Floor
(0%))
Heap fresh (41.3%)
Heap dry (37.7%)
Slurry (0%)
Biogas (1.8%)
Compost (0.4%)
Store urine (0%)
Heap fresh (2.4%)
Heap dry (2.1%)
Slurry (0%)
Biogas (0.3%)
Compost (0.4%)
Store urine (0%)
Heap fresh (5.7%)
Heap dry (4.5%)
Slurry (2.7%)
Biogas (0.6%)
Compost (0.3%)
Store urine (0.3%)
9

What have we found so far?
• Manure methane emission factors per cattle category in Nandi
county
10
Owino et al. in prep.
Tier 1 (kg CH4 animal-1
yr-1)
Improved Tier 1 (kg
CH4 animal-1 yr-1)
Tier 2 (kg CH4 animal-1
yr-1)
Category
Oxen (adult) 1 8.01 0.02
Females (adult,
pregnant)
1 9.72 0.02
Females (adult) 1 8.89 0.02
Heifers 1 6.02 0.01
Young dairy (male) 1 4.55 0.01
Calves (male) 1 1.92 0.004
Calves (female) 1 2.20 0.004

• Two breeds (Boran – local; Friesian –
imported), three diets, 12 animals
• Animals crossed over all 3 diets
• Urine and manure applied separately
• 1 kg manure (FW) and 250 mL urine
• Used static chambers, measured daily for 3 d
prior to application, twice the day of
application, daily (1st week) and 3-times per
week for 3 additional weeks
What about the manure?
11
Pelster et al. 2016, Journal of Environmental Quality

CH4emissionfactors
(gCH4-Cyr-1250kganimal-1)
IPCC
Tier 1
valueD1 = Basal diet
D2 = Daily
supplementation (1X)
D3 = Bi-daily
supplementation (2X)
Why? likely related to poor quality diets, resulting in low quality
manure (high C:N ratio, low CP) limiting the production of GHG
when compared to other regions (ie. developed world)
IPCC
Tier 1
value
N2Oemissionfactors
(%appliedNlostasN2O-N)
What about the manure?
Pelster et al. 2016, Journal of Environmental Quality
12

13
What about the manure?
Zhu et al. in review, Global Biogeochemical Cycles

What about the soil?
• Selected 60 plots on smallholder farms in western Kenya and
measured greenhouse gas (GHG) fluxes
• Used static GHG chambers, measured weekly for one year
• Plots included annual crops, perennial crops, woodlots and grazing land
• Farmer-managed
14
Pelster et al. 2017, Biogeosciences

Baseline soil GHG emissions from smallholder farms
• Soils were minor sinks for CH4 (approx. -2.4 kg CH4-C ha-1 yr-1)
• Mean N2O emissions were approximately 0.2 kg N2O-N ha-1 yr-1
• No differences between crop types or landscape position
annuals grass trees/shrubs
468101214
CO2Flux(kgCO2−Cha-1
yr-1
)
annuals grass trees/shrubs
−6−4−202
Crop Type
CH4Flux(kgCH4−Cha-1
yr-1
)
annuals grass trees/shrubs
0.00.51.01.5
Crop Type
N2OFlux(kgN2O−Nha-1
yr-1
)
Pelster et al. 2017, Biogeosciences
15

What does this mean and steps forward?
• current EFs (Tier 1) are incorrect compared to in-situ
measurements (improved Tier 1, Tier 2)
• potentially invalidate mitigation practices because baselines
are incorrect, also reporting under UNFCCC is biased
Steps forward:
• climatic zones differ, breeds differ, intervention testing
• pastoral systems only partially covered yet (ongoing TZ work
- more difficult to monitor)
• spatial variability in agricultural production system (intensity
of farm varies) – ETHIOPIA, UGANDA and KENYA under joint
Program for Climate-Smart Livestock System (GIZ, WB, ILRI)
16

• With agricultural intensification – total GHG emissions are
likely to go up,
• but at the same time productivity will go up, faster than
emissions -> GHG emissions vs. amount of product decreases
-> GHG emissions intensities go down
• continuous standardized observations are key
Environmental infrastructures are useful
-> SEACRIFOG project
• closing nutrient cycles, avoiding nutrient losses, will be
beneficial for productivity, CC adaptation and mitigation
17
What does this mean and steps forward?

Mazingira Centre Team
Lutz Merbold
Head of Mazingira
Alice Onyango
Consultant
Daniel Sifael
MSc student
Stanley Mwangi
Office assistant
Samuel Mugo
MSc student
Phyllis Ndung‘u
PhD candidate
Klaus Butterbach-Bahl
Advisor (25%)
Stephen Okoth
PhD candidate
Victoria Carbonell
PhD candidate
Jesse Gakige
PhD candidate
Daniel Korir
PhD candidate
Ibrahim Wanyama
PhD candidate
Francis Njenga
Research Technician
Peter Mosongo
PhD candidate
Sonja Leitner
PostDoc
Sheila Wachiye
PhD candidate
Yuhao Zhu
PhD candidate
Jesse Owino
PhD candidate
Peter Kirui
MSc student
Rodgers Rogito
MSc student
Nelson Saya
Research Assistant
Joseph Macharia
PhD candidate
Paul Mutuo
Research Technician
Erik Kiprotich
Research Assistant
George Wanyama
Chemical Analyst
Jane Gitonga
MSc student
John Goopy
Consultant (50%)
Edinah Ombogo
MSc student
Thank you for your attention. Questions?