Limitations of using simple indicators for evaluating agricultural emission reductions at farm level — evidence from Kenyan smallholder dairy production

National-scale carbon footprints of livestock production are commonly computed from a set of production system
characteristics that serve as inputs for greenhouse gas (GHG) emission models. We evaluated the feasibility of
using such equations at a finer scale to derive a simple farm-scale indicator of emission intensity (milk yield per
head). Using probabilistic simulations, we quantified the impact of input variable uncertainty on emission estimates
for smallholder dairy farms in Kenya. We simulated emissions for farm-scale scenarios generated from a survey of
414 households and published or expert-estimated uncertainty bounds. We simulated the impacts of five
interventions: changing breeds, retiring unproductive males, keeping fewer replacement males, feeding forage
supplements, and balancing animal diets. Impacts were assessed against a true counterfactual and against a more
realistic scenario affected by random effects. We estimated errors incurred in classifying farms into adopters and
non-adopters of the innovations based on changes in milk yield per animal. Given the current uncertainty, such
classification would either miss a large percentage of adopters or misclassify many non-adopters as adopters. As a
critical uncertainty, we identified the milk yield of dairy cows. Added precision on this metric reduced but did not
eliminate classification errors. We remain cautiously optimistic about using milk yield per head to proxy emission
intensity, but its effective use will require further reduction of critical uncertainties. Replacing generic
recommendations of parameter uncertainties with context-specific error estimates might lead to a more efficient quantification of the carbon footprint of milk production on smallholder farms.