Most sub-Saharan Africa smallholder farming systems are highly heterogeneous. Direct quantification of greenhouse gas emissions from these farming systems is hampered by diversity at farm-level. Each farm contributes differently to greenhouse gas (GHG) emissions and consequently GHG inventories. Typologies can be used as a mechanism of addressing farming systems’ heterogeneity by grouping them into specific farm types. With the GHG quantification simplification initiatives in mind, we developed smallholder farm typologies based on soil fertility inputs. We assessed nitrogen application rate, soil fertility management technologies and the socio-economic factors diversity among the farm typologies in the central highlands of Kenya. We used data from a cross-sectional household survey with a sample size of 300 smallholder farmers. We characterized the farm types using principal component analysis (PCA). To develop farm typologies, we subjected the PCA-derived typologies related factors to cluster analysis (CA). The results showed six farm types: Type 1, comprising cash crop and hybrid cattle farmers; Type 2, comprising food crop farmers; Type 3, composed of coffee-maize farmers; Type 4, comprising millet-livestock farmers; Type 5, comprising highly diversified farmers, and Type 6, comprising tobacco farmers. Land size owned, total tropical livestock unit, the proportion of land and nitrogen applied to different cropping systems were significant in the construction of farm typologies. Univariate analysis showed the household head's level of education, hired labour, group membership, access to extension services, and proportion of income from cropping activities as critical factors influencing farm typologies in the study area. This study demonstrates the importance of smallholder farm typologies in identifying greenhouse gas emissions hotspots, designing quantification experiment and policy framing. We concluded that policies and intervention measures targeting climate-smart agriculture at smallholder farms should consider not only farm-level soil fertility management technologies but also socio-economic characteristics that influence their adoption.