CONTEXT: Accurate reporting of livestock greenhouse-gas (GHG) emissions is important
in developing effective mitigation strategies, but the cost and labour requirements associated
with on-farm data collection often prevent this effort in low- and middle-income countries.
AIM: The aim of this study was to investigate the precision and accuracy of simplified activity data
collection protocols in African smallholder livestock farms for country-specific enteric-methane
emission factors.
METHOD: Activity data such as live weight (LW), feed quality, milk yield, and
milk composition were collected from 257 smallholder farms, with a total herd of 1035 heads of
cattle in Nandi and Bomet counties in western Kenya. The data collection protocol was then
altered by substituting the actual LW measurements with algorithm LW (ALG), feed quality
(FQ) data being sourced from the Feedipedia database, reducing the need for daily milk yield
records to a single seasonal milk measurement (MiY), and by using a default energy content of
milk (MiE). Daily methane production (DMP) was calculated using these simplified protocols and
the estimates under individual and combined protocols were compared with values derived
from the published (PUBL) estimation protocol.
KEY RESULTS: Employing the algorithm LW
showed good agreement in DMP, with only a small negative bias (7%) and almost no change in
variance. Calculating DMP on the basis of Feedipedia FQ, by contrast, resulted in a 27% increase
in variation and a 27% positive bias for DMP compared with PUBL. The substitutions of milk
(MiY and MiE) showed a modest change in variance and almost no bias in DMP
CONCLUSION: . It is
feasible to use a simplified data collection protocol by using algorithm LW, default energy content of
milk value, seasonal single milk yield data, but full sampling and analysis of feed resources is required
to produce reliable Tier 2 enteric-methane emission factors.
IMPLICATIONS: Reducing enteric
methane emissions from the livestock is a promising pathway to reduce the effects of climate
change, and, hence, the need to produce accurate emission estimates as a benchmark to
measure the effectiveness of mitigation options. However, it is expensive to produce accurate
emission estimates, especially in developing countries; hence, it is important and feasible to
simplify on-farm data collection.
