Why Kenyan rangeland cattle need new methane emission factors

In Kenya, livestock are not only an essential source of income but also a significant contributor to greenhouse gas (GHG) emissions, particularly methane (CH₄), resulting from enteric fermentation. Livestock contribute about 40% of Kenya’s agricultural GDP and 12% of national GDP, making it both an economic pillar and a climate-relevant sector. Recent studies by the International Livestock Research Institute (ILRI) provide important information on how emissions from cattle in Kenya's varied pastoral and ranching systems compare against the default estimates for Africa and highlight the importance of generating data for national representative estimates for impactful climate action.

Why global defaults fall short

The Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodology is widely used to estimate enteric CH₄ emissions in African livestock systems due to the limited availability of data to move up to Tier 2 estimates. Tier 1 applies default emission factors (EFs) uniformly across all cattle, while Tier 2 estimates emissions based on production parameters.

A recent study by ILRI’s Mazingira Centre in southern Kenya highlights that Tier 1 estimates, which ignore key differences in feed quality, cattle breed, and herd management, can substantially over- or underestimate emissions depending on the production system. These discrepancies compromise the accuracy of national GHG inventories and misguide climate mitigation efforts.

Why it matters

Accurate emissions data is essential for designing effective national GHG inventories and mitigation strategies. The researchers showed that:

• IPCC Tier 1 default values can over- or underpredict GHG emissions depending on the production system.
• Breed, body weight, feed quality, and animal mobility significantly affect emissions.
• Emission factors for rangeland and pastoral systems in Kenya must be system-specific to reflect actual conditions.

If these differences are overlooked, climate policies risk being ineffective or even counterproductive.

What they did

Researchers from ILRI and partners studied enteric CH₄ emissions in three ranching sites in southern Kenya:

• Kapiti Research Station – a semi-arid ranching system with improved livestock management.
• Olkirimatian Community Ranch – a traditional Maasai pastoral system.
• Shompole Community Ranch – another traditional Maasai pastoral system.

The team applied the IPCC Tier 2 methodology, incorporating site-specific data on animal activity, feed composition, and productivity. The study tracked 1,486 cattle (815 at Kapiti, 347 at Olkirimatian, and 324 at Shompole) over wet and dry seasons. GPS collars were used to record daily travel distances, allowing for more accurate estimation of energy requirements for movement during grazing.

What they found

System-specific emissions

The Tier 1 approach assumes uniform emission factors regardless of breed or production system. However, the researchers collected site-specific data which showed notable variation:

• Cattle at Kapiti were heavier and had higher annual emissions of 64.5 kg CH₄/head/year.
• Olkirimatian and Shompole cattle had lower body weights, resulting in emissions of 51.7 and 41.8 kg CH₄/head/year, respectively.
• Kapiti emissions were 18% higher than Tier 1 estimates.
• Shompole and Olkirimatian were 28% and 7% lower, respectively. 

These differences revealed breed and management system substantially influence emissions and that a one-size-fits-all factor can misrepresent actual outputs.

This further emphasizes the value of Tier 2, system-specific data to improve accuracy and guide mitigation strategies.

Comparison of digestible energy estimates with IPCC Tier 1 defaults

The IPCC Tier 1 method assumes a fixed digestible energy (DE) value of 58% for estimating methane emissions from enteric fermentation. However, site-specific data from Kenya show significant variation:

• Shompole pastures have a higher DE of 66%, suggesting better feed quality than assumed in Tier 1. Using the Tier 1 default would underestimate actual intake and possibly emissions in this context.
• Kapiti pastures have a lower DE of 55%, indicating poorer feed quality than the Tier 1 default. In this case, the Tier 1 assumption would likely overestimate intake and emissions.
• Olkirimatian pastures have a DE of 59%, comparable to the 58 % Tier 1 default value for Africa.

This matters because default Tier 1 data may overstate emissions from low-input pastoral systems like Shompole, while underestimating emissions in more intensive systems like Kapiti, potentially skewing mitigation priorities.

What should change

Collection of data must move from Tier 1 to Tier 2 emission to account for variations in animal breeds and livestock management systems. This new study demonstrates the value of generating local data to move to more accurate emission factors for Kenyan and other African livestock systems. By tracking nearly 1,500 animals across seasons and systems, the research provides robust, policy-relevant evidence. Shifting to Tier 2 estimates, especially in pastoral and ranching systems, will support climate-smart livestock policies and guide effective national climate strategies.

Read the full paper here.

This research was funded by:

• European Union, through the EU-DeSIRA programme, under the ESSA project (Earth observation and environmental sensing for climate-smart sustainable agropastoralism ecosystem transformation in East Africa).
• International Fund for Agricultural Development (IFAD), through:
  • Greening Livestock: Incentive-Based Interventions for Reducing the Climate Impact of Livestock in East Africa (Grant No. 2000000994)
  • Programme of Climate Smart Livestock (PCSL) (Programme No. 2017.0119.2)
• New Zealand Government, in support of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases.
• CGIAR Research Initiatives:
  • Livestock and Climate
  • Mitigate+: Low-Emission Food Systems
    both supported by CGIAR Trust Fund contributors.
• CGIAR Science Programs on:
  • Sustainable Animal and Aquatic Foods
  • Climate Action
  • Multifunctional Landscapes
    with support from CGIAR Trust Fund contributors.