Homogeneous land-use sequences in heterogeneous small-scale systems of Central Kenya: Land-use categorization for enhanced greenhouse gas emission estimation

The current understanding of the link between land management practices and GHG (greenhouse gas) emissions is limited in the small-scale farm sector of sub-Sahara regions due to insufficient or fragmented land-use data. Land-use categories recognized in the national GHG inventories in Kenya are coarse. Therefore, they do not adequately account for the diversity in small-scale land uses. Characterization of land-use and knowledge of key drivers of land-use change is necessary to improve national GHG inventories in the SSA (Sub-Sahara Africa) region. We implemented a cross-sectional survey to characterize land-use and determine factors which influenced changes in land use within small-scale farms of Tharaka-Nithi County, Kenya. We sampled 300 farmers using multistage sampling and collected crop sequence recall data at plot level for three years (seven seasons). We grouped crop sequences into clusters using the 'TraMineR' R package. We derived four clusters including banana, tea, and declining fallows (cluster 1, 19.2% of plots), cereal-legume systems (cluster 2, 55.1%), fodder (cluster 3, 11.7%) and coffee (cluster 4, 14.0%). We observed higher N application rates in perennial cropping systems, than in annual crops, including cereal-legume systems. We observed that farmers in higher potential agro-ecological zones, male-managed farms, with higher per capita land area, higher remittances and higher total house-hold incomes, were associated with a higher propensity to adjust crop enterprises, leading to more unstable land-use sequences. Contrariwise, farmers with higher education, credit access, secure land tenure, increasing slope, good soil fertility, and longer farming experiences recorded a lower propensity to adjust their land uses, resulting in more stable crop sequences. Farmer socio-demographic characteristics influenced land-use change, which is directly linked with soil GHG emissions. Our findings propose the adoption of Tier II GHG quantification approaches which disaggregate between annual and perennial crop enterprises. GHG emissions are likely to be more generalizable in stable perennial crop systems than annual systems. Thus, better disaggregation of GHG sampling in annual crop systems is needed due to high diversity in crop and soil fertility management, and the dynamic nature of C and N cycling in these systems.