Soil Greenhouse Gas Fluxes From Maize Production Under Different Soil Fertility Management Practices in East Africa

Abstract

In sub‐Saharan Africa (SSA), few studies have quantified greenhouse gas (GHG) emissions following application of soil amendments, for development of accurate national GHG inventories. Therefore, this study quantified soil GHG emissions using static chambers for two maize cropping seasons (one full year) of four different soil amendments in the central highlands of Kenya. The four treatments were (i) animal manure, (ii) inorganic fertilizer, (iii) combined animal manure and inorganic fertilizer, and (iv) a no‐N control (no amendment) laid out in a randomized complete block design. Cumulative annual soil fluxes (February 2017 to February 2018) ranged from −1.03 ± 0.19 kg CH4‐C ha−1 yr−1 from the manure inorganic fertilizer treatment to −0.09 ± 0.03 kg CH4‐C ha−1 yr−1 from the manure treatment, 1,391 ± 74 kg CO2‐C ha−1 yr−1 from the control treatment to 3,574 ± 113 kg CO2‐C ha−1 yr−1 from the manure treatment, and 0.13 ± 0.08 to 1.22 ± 0.12 kg N2O‐N ha−1 yr−1 in the control and manure treatments, respectively. Animal manure amendment produced the highest cumulative CO2 emissions (P < 0.001), N2O emissions (P < 0.001), and maize yields (P = 0.002) but the lowest N2O yield‐scaled emission (YSE) (0.5 g N2O–N kg−1 grain yield). Manure combined with inorganic fertilizer had the highest cumulative CH4 uptake (P < 0.001) and N2O YSE (2.2 g N2O–N kg−1 grain yield). Our results indicate that while the use of animal manure may increase total GHG emissions, the concurrent increase in maize yields results in reduced yield‐scaled GHG emissions.

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

“…The study area is characterised by relatively short seasons which are predominant in the drier low agricultural potential areas (Ngetich et al, 2014). However, the study has been found suitable for the low-land drought tolerant crop varieties of green grams, sorghum, cowpea, chicken pea, maize, common beans amongst other crops (Macharia et al, 2020).…”

“…The experiment is composed of twelve different treatments containing organics, inorganics and their combinations alongside treatments with no external input of nutrients (control) under maize production. However, only four treatments were selected for this study on soil greenhouse gas quantification and simulation: i) inorganic fertiliser; ii) animal manure; iii) animal manure combined with inorganic fertiliser, the three applied at 120 kg N ha À1 yr À1 ; and iv) a control (no external inputs) (Macharia et al, 2020). Selection of the four treatments were based on their high adoption level in the area (Macharia et al, 2014).…”

“…The weather files for the above climate parameters for the period of study were prepared following DNDC guide (version 9.5). Soils were sampled from each of the plots prior to setting up of the experiment (February 2017) and taken to the laboratory for analysis following standard procedures as described by Macharia et al (2020). They were tested for mineral nitrogen, bulk density, texture, soil organic carbon (SOC) and soil pH.…”

“…Field capacity, slope, porosity, conductivity and wilting point had earlier on been determined in the same study site by Ngetich et al (2014). Cropping data (the type of crop, planting and harvesting), tillage data (method of ploughing, day and month of land preparation, biomass fraction and biomass C/N ratio), fertiliser data (type, amount and method of fertiliser applied and the day and month of application) and manure amendments (type, amount, method of application and day and month applied) were all collected from the experiment as described in Macharia et al (2020).…”

“…Thus, most developing countries cannot afford to establish these empirical studies and therefore rely heavily on default Tier I emission factors (EF) from the Intergovernmental Panel on Climate Change (IPCC) to report their nationally determined contributions (NDCs) as an obligation to the Paris Climate Agreement of 2015. According to Macharia et al (2020) and Musafiri et al (2020), agriculture-related Tier I emission factors tend to over-estimate GHG status in sub-Saharan Africa (SSA) leading to inflated national GHG inventories that may result in poor targeting of efficient adaptation and mitigation measures (Pelster et al, 2017). However, to overcome uncertainties in national GHG inventories, process-based models have been developed and which over time has been validated and found reliable to provide supplementary information on large spatial-temporal scales from areas with which it would have been too costly and impractical to acquire relevant information (Giltrap et al, 2010).…”