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Better lives through livestock
Environmental footprint of African livestock systems-
case studies in Kenya
Phyllis Ndung’u
Graduate Fellow
International Livestock Research Institute (ILRI)
Better lives through livestock
Tropentag Conference
15 September, 2021
2
Background of the study
 African livestock systems are of low productivity, heterogeneous and multi-
purpose.
 How does that affect the environmental footprint of these livestock systems?
 Objective: of our study was determining the greenhouse gas (GHG) performance
of smallholder livestock systems.
Descriptive Factors Nandi Bomet Nyando
% Adult females in the herd 42 45 37
% Adult males in the herd 4 5 23
% Growing in the herd 19 15 6
% Calves in the herd 35 35 34
% of lactating Females 53 60 22
Average daily milk yield (kg/day) 4.1 3.6 2.2
Average Herd size (numbers) 9.1 8.7 8.5
3
Materials and Methods
 Sample size: 313 smallholder farms
• Conducted a life-cycle assessment (LCA), accounted
for emissions from;
 Enteric fermentation (Tier 2 estimates)
 Manure management: manure on pasture,
enclosure and piles (Tier 2 emission estimates)
 Feed production – Interviews and emissions
from ecoinvent
• System boundary was set at “Cradle-to-farmgate.”
• Functional unit was set as “Crude Protein” which
encompasses both milk and meat.
NOTE: Emissions were calculated on animal-by-
animal basis to account for weak-links e.g.,
unproductive animals in the herd
4
Results: Enteric methane emission factors - Cattle
• Nyando lower emission factors compared to Nandi and Bomet.
• IPCC Tier 1 estimates (2019) are 12-70% higher.
5
Results: Farm-Level Emission Intensity Distribution - Cattle
Region EI (kg CO2-
eq/kg Milk
Range
Nandi* 2.1 0.78 – 18.42
Bomet* 2.2 1.01 – 35.42
Nyando* 4.9 0.49 – 178.4
Western Kenya* 2.3 0.49 – 178.4
Kaptumo, Kenya 2.6 0.9 – 4.3
Europe 1.3 -
* Represents figures reported in Median
• Differences in emissions intensities (EI = emissions per unit of product) are attributed to differences
in farm CP outputs – the higher the CP output the lower the EI
6
Take Home message
• Improved absolute emissions estimates are important especially for inventory
reporting.
• Simultaneous presence of farms with very high and very low EIs even between
neighboring enterprises.
• To move to low EI operations, we need to increased farm outputs achieved at a
minimal increase in emissions.
• Emissions intensities demonstrate a farm’s overall GHG performance especially
where there is presence of unproductive livestock owned for a variety of non-
economic reasons has shown to be a major cause of large on-farm emissions.
Environmental footprint of African livestock systems- case studies in Kenya

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Environmental footprint of African livestock systems- case studies in Kenya

  • 1. Better lives through livestock Environmental footprint of African livestock systems- case studies in Kenya Phyllis Ndung’u Graduate Fellow International Livestock Research Institute (ILRI) Better lives through livestock Tropentag Conference 15 September, 2021
  • 2. 2 Background of the study  African livestock systems are of low productivity, heterogeneous and multi- purpose.  How does that affect the environmental footprint of these livestock systems?  Objective: of our study was determining the greenhouse gas (GHG) performance of smallholder livestock systems. Descriptive Factors Nandi Bomet Nyando % Adult females in the herd 42 45 37 % Adult males in the herd 4 5 23 % Growing in the herd 19 15 6 % Calves in the herd 35 35 34 % of lactating Females 53 60 22 Average daily milk yield (kg/day) 4.1 3.6 2.2 Average Herd size (numbers) 9.1 8.7 8.5
  • 3. 3 Materials and Methods  Sample size: 313 smallholder farms • Conducted a life-cycle assessment (LCA), accounted for emissions from;  Enteric fermentation (Tier 2 estimates)  Manure management: manure on pasture, enclosure and piles (Tier 2 emission estimates)  Feed production – Interviews and emissions from ecoinvent • System boundary was set at “Cradle-to-farmgate.” • Functional unit was set as “Crude Protein” which encompasses both milk and meat. NOTE: Emissions were calculated on animal-by- animal basis to account for weak-links e.g., unproductive animals in the herd
  • 4. 4 Results: Enteric methane emission factors - Cattle • Nyando lower emission factors compared to Nandi and Bomet. • IPCC Tier 1 estimates (2019) are 12-70% higher.
  • 5. 5 Results: Farm-Level Emission Intensity Distribution - Cattle Region EI (kg CO2- eq/kg Milk Range Nandi* 2.1 0.78 – 18.42 Bomet* 2.2 1.01 – 35.42 Nyando* 4.9 0.49 – 178.4 Western Kenya* 2.3 0.49 – 178.4 Kaptumo, Kenya 2.6 0.9 – 4.3 Europe 1.3 - * Represents figures reported in Median • Differences in emissions intensities (EI = emissions per unit of product) are attributed to differences in farm CP outputs – the higher the CP output the lower the EI
  • 6. 6 Take Home message • Improved absolute emissions estimates are important especially for inventory reporting. • Simultaneous presence of farms with very high and very low EIs even between neighboring enterprises. • To move to low EI operations, we need to increased farm outputs achieved at a minimal increase in emissions. • Emissions intensities demonstrate a farm’s overall GHG performance especially where there is presence of unproductive livestock owned for a variety of non- economic reasons has shown to be a major cause of large on-farm emissions.