Parameterization, calibration and validation of the DNDC model for carbon dioxide, nitrous oxide and maize crop performance estimation in East Africa

Dynamic biogeochemical models are crucial tools for simulating the complex interaction between soils, climate and plants; thus the need for improving understanding of nutrient cycling and reduction of greenhouse gases (GHG) from the environment. This study aimed to calibrate and validate the DeNitrification-DeComposition (DNDC) model for soil moisture, temperature, respiration, nitrous oxide and maize crop growth simulation in drier sub-humid parts of the central highlands of Kenya. We measured soil GHG fluxes from a maize field under four different soil fertility management practices for one year using static chambers and gas chromatography. Using experimental data collected from four management practices during GHG sampling period, we parameterized the DNDC model. The results indicate that the DNDC model simulates daily and annual soil moisture, soil temperature, soil respiration (CO2), nitrous oxide (N2O), N2O yield-scaled emissions (YSE), N2O emission factors (EFs) and maize crop growth with a high degree of fitness. However, the DNDC simulations slightly underestimated soil temperature (2–6%), crop growth (2–45%) and N2O emissions (5–23%). The simulation overestimated soil moisture (9–17%) and CO2 emissions (3–10%). It however, perfectly simulated YSE and EFs. Compared to the observed/measured annual GHG trends, the simulation results were relatively good, with an almost perfect fitting of emission peaks during soil rewetting at the onset of rains, coinciding with soil fertilisation. These findings provide reliable information in selecting best farm management practice, which simultaneously improves agricultural productivity and reduces GHG emissions, thus permitting climate-smart agriculture. The good DNDC simulated YSE and EFs values (Tier III) provide cheaper and reliable ways of filling the huge GHG data gap, reducing uncertainties in national GHG inventories and result to efficient targeting of mitigation measures in sub-Saharan Africa. Animal manure, Climate-Smart Agriculture; Emission factors; Inorganic fertiliser; Maize; Yield-scaled greenhouse gas emissions.