Characterization of Smallholder Farming Systems And Greenhouse Gas Emissions Simulation From Maize Cropping System in Tharaka-Nithi County, Kenya
Abstract
The influence of soil fertility management technologies on crop production has widely
been researched in Tharaka-Nithi County. However, data on their contribution towards
national greenhouse gas budget is scanty. This study aimed at characterising smallholder
farming systems and simulating greenhouse gas emissions, maize yields, yield scaled
nitrous oxide (N2O) emissions and N2O emission factors from different soil fertility
management technologies in Tharaka-Nithi County. Three hundred households were
interviewed to obtain data for farming systems characterisation and evaluation of socioeconomic
factors
influencing
the
diversity
of
farm
typologies.
Interview
schedules
were
administered
using
open
data
kit
collect
mobile
App.
Multivariate
analysis
was
done
to
characterise
smallholder farming systems. To evaluate socio-economic factors
influencing farm diversity, Chi-square, t-test, and multinomial regression analysis were
carried out using the Statistical Package for Social Sciences (SPSS version 23). For
calibration and validation of the DeNitrification DeComposition (DNDC) model, a oneyear
soil greenhouse gas quantification experiment data were used. The data were
obtained from a field experiment conducted in Kigogo primary school. It was laid out in
randomised complete block design under four soil fertility treatments as control (no
external inputs), inorganic fertiliser (NP, 23.23, 120 kg N ha
), animal manure (goat
manure, 120 kg N ha
-1
-1
yr
-1
)
replicated thrice. Climate, soil properties, N2O fluxes, maize yields and farm
management data were used. The model was evaluated using modelling efficiency, mean
error, coefficient of determination, mean absolute error, and root mean square error
(RMSE). The experimental data were subjected to Analysis of Variance in SAS 9.4
software and mean separation done using least significance difference at p = 0.05. The
results showed six farm types: Type 1, comprising cash crop and hybrid cattle farmers;
Type 2, involving food crop farmers; Type 3, composed of coffee-maize farmers; Type 4,
consisting of millet-livestock farmers; Type 5, comprising highly diversified farmers, and
Type 6, had tobacco farmers. Land size, total tropical livestock unit, the proportion of
land and amount of nitrogen applied to different cropping systems were significant in the
construction of farm typologies. The DNDC model was fair in simulating daily N2O
fluxes (54% ≤ normalized RMSE (nRMSE) ≤ 68% and 0.26 ≤ modelling efficiency
(MEi) ≤ 0.49) and good to excellent performance in simulating cumulative annual soil
N2O fluxes (6.16 ≤ nRMSE ≤ 12.86 and 0.63 ≤ MEi ≤ 0.86) across soil fertility
treatments. The cumulative observed and simulated annual soil N2O fluxes ranged
between 0.21±0.01 and 0.38±0.02 kg N2O-N ha
-1
yr
) and animal manure + inorganic fertiliser (120 kg N ha
-1
-1
yr
(control) to 0.38 (fertiliser) kg N2O-N ha
-1
-1
yr
and 0.20 kg N2O-N ha
-1
-1
yr
. The simulated N2O yield scaled
emissions, and emission factors ranged from 0.022 to 0.029 g N Kg
-1
-1
yr
grain yield and
0.03 % to 0.14% under manure and fertiliser treatments, respectively. Based on the low
observed and simulated emission factors, using the Intergovernmental Panel on Climate
Change (IPCC) Tier 1 default factor of 1% overestimates agricultural soils GHG
emissions in the Central Highlands of Kenya. Manure and fertiliser combination should
be promoted to enhance the three pillars of climate-smart-agriculture (CSA) as food
security, climate change mitigation and adaptation.