Greenhouse Gas Emissions from Fertilization Practices in Maize Cropping in Sub-Saharan Africa: Toward Climate-Smart Agriculture

Abstract

The intensive use of nitrogen (N) fertilizers in maize (Zea mays L.) cropping in sub-Saharan Africa (SSA) contributes significantly to nitrous oxide (N₂O) emissions. Due to limited data on emissions and emission factors (EFs) in SSA, this study investigates GHG emissions and proposes EFs under different fertilization regimes in maize cropping in Burkina Faso (West Africa). A randomized complete block design was used with five treatments: (i) control: no fertilizer (CK), (ii) cattle manure (M), (iii) chemical fertilizer (NPK), (iv) a combination of chemical fertilizer and cattle manure (NPKM) at the national recommended rate, and (v) farmers’ practices, which involve chemical fertilizer combined with manure at the farmers’ rate (NPKM+). Cumulative N₂O emissions varied significantly among treatments (p < 0.05), with the highest under NPKM (2.86 kg N₂O-N ha⁻¹) and the lowest under CK (1.93 ± 0.11 kg N₂O-N ha⁻¹). NPKM also showed the highest methane (CH₄) uptake (−0.62 kg CH₄-C ha⁻¹; p < 0.001), while CK exhibited an increasing trend (0.74 kg CH₄-C ha⁻¹). The highest N₂O EF was recorded for NPK (0.37 ± 0.05%), 63% lower than the Intergovernmental Panel on Climate Change default value. Although NPKM treatment resulted in the highest global warming potential and maize yield, it also achieved the lowest greenhouse gas intensity per unit of yield, highlighting a more efficient trade-off between productivity and climate impact with nitrogen fertilizer use. NPKM+ was the most effective in maintaining high maize productivity with lower yield-scaled N₂O emissions and GHG intensity. These findings suggest that an integrated approach combining organic and inorganic fertilizers can mitigate soil GHG emissions. Further research is needed to refine climate-smart fertilizer combinations for sustainable maize production in SSA.

1. Introduction

In recent decades, due to rapid population growth, agriculture has become a major source of greenhouse gas (GHG) emissions and a significant contributor to anthropogenic global warming [1]. Nitrogen fertilizer (NF) application has been one of the most common practices to increase crop production worldwide over the past several decades [2,3]. Globally, fertilizer use supports food production for approximately 50% of the world’s population [4]. In regions with low-fertility soils, farmers commonly apply both organic and inorganic fertilizers to sustain and improve crop yields [5,6]. Indeed, during fertilizer application, most of the NF applied to crops is lost in gaseous forms, particularly in ammonia and nitrous oxide [2,7] as well as other gaseous compounds (NO_x and N₂), through the processes of nitrification and denitrification [8]. Nitrogen fertilizer application from the agriculture sector is a major source of N₂O emissions, accounting for at least 30% of total global N₂O emissions to the atmosphere [9,10]. These substantial losses, which significantly contribute to greenhouse gas emissions, highlight the need for a more precise and efficient fertilization strategy.

According to the Intergovernmental Panel on Climate Change (IPCC), annual global N₂O emissions in 2019 were estimated at 6600 Kt N₂O yr⁻¹, equivalent to 1.8 Gt CO₂-eq yr⁻¹, representing 69% of the global net anthropogenic GHG emissions from agriculture, forestry, and other land use [1]. Agricultural activities in Sub-Saharan Africa (SSA) have accounted for 64% of the region’s total N₂O emissions, contributing to global GHG emissions [11]. Nitrogen and carbon released from organic and inorganic fertilizers significantly influence carbon dioxide (CO₂) and methane (CH₄) emissions, further contributing to global warming [12,13,14]. Therefore, when assessing the impact of agriculture on GHG emissions, it is essential to consider the combined effects of all GHGs [15].

Currently, N₂O emissions from agricultural soils are primarily estimated using the emission factors (EFs) method [16,17]. The IPCC recommends a global default EF of 1% for upland crops refers to 0.01 kg of N₂O-N emitted per kg of nitrogen applied (kg N₂O-N per kg⁻¹ N) [16]. This value is associated with considerable uncertainty due to the omission of climate, soil characteristics, and management practices, however [2,18]. Moreover, most developing countries rely on the IPCC Tier 1 approach for national greenhouse gas inventories [19,20]. Tier 1 method is a default approach using generalized emission factors to estimate GHG emissions at a local level when specific emission factors are not available [17]. Globally, emission and EF data remain scarce, with limited precise quantifications across diverse cropping systems [21]. Indeed, EFs are poorly documented in regions such as Northern Europe, Central America, Southeast Asia, and Northern South America [18]. In East Africa, some data on emissions exist [19,22,23]; however, field measurements specifically focused on EFs remain scarce across this region, the rest of SSA, and other developing countries [24,25]. Only [26,27] have provided EFs for carbon associated with biomass burning. This results in significant data gaps and uncertainties in national and regional GHG inventories [1,6]. Field research on GHG emissions and EFs in West Africa remains particularly limited. To our knowledge, [28] is the only study providing nitrous oxide emission factor (N₂O EF) data for the region. However, conditions in western SSA may differ significantly from those in other regions due to variations in soil types, climate, altitude, and management practices [22]. Expanding research on emissions and EFs could help reduce current uncertainties.

Maize (Zea mays L.) is a key food crop in SSA, supporting over 70% of the population [29]. Financial constraints force most smallholder farmers to apply low fertilizer doses (<25 kg N ha⁻¹) alone or in combination with animal manure, thus limiting yields in traditional maize cultivation [22,30]. To enhance agricultural production, an increasing number of farmers are adopting higher fertilizer doses. Numerous studies have demonstrated significant yield improvements with NF application [31,32]. However, this practice also influences GHG emissions [11,33]. To develop effective emission reduction strategies, the Paris Agreement (COP 21) recommends that each country provide activity data and EFs for GHG inventories [17,34]. Additionally, the IPCC encourages countries to adopt Tier 2 (using country-specific EFs) or Tier 3 (model-based) methods to improve estimate accuracy and reduce uncertainty when sufficient national data are available [16,35]. However, the lack of accurate data poses a significant challenge; many developing nations, particularly in SSA, struggle to provide regular updates. This hinders their ability to set complementary targets and achieve net-zero GHG emissions while ensuring food security [4,36,37]. Here, we assess GHG emissions from different fertilizer management practices currently used in SSA to establish a synoptic baseline for the continent, updating GHG inventories and supporting future mitigation strategies. Specifically, this study examines average daily GHG fluxes, cumulative emissions, and key emissions parameters, including EF and global warming potential (GWP), under different fertilization regimes in maize cropping in Burkina Faso. We address three main objectives by (1) assessing the effects of different fertilizer treatments on cumulative GHG emissions, (2) determining N₂O EF for different NFs, and (3) evaluating the relationship between GHG emissions and maize yields across fertilization regimes. We hypothesize that mixture fertilization at recommended rates for tropical regions like Burkina Faso increases N₂O in maize cropping systems. The findings of this study contribute to improving the accuracy of GHG emission estimates, establishing emissions baselines, and informing mitigation strategies. Ultimately, they provide insights into optimizing fertilization practices in SSA in the context of climate-smart agriculture.

2. Materials and Methods

2.1. Study Area

This study was conducted in Banaorodougou (11°20′91″ N; 4°22′41″ W) (Figure 1) in a smallholder maize farm located in the northern part of Bobo-Dioulasso, the second-largest city in western Burkina Faso. A dry tropical climate in the Sudanian climate zone characterizes the study site [38], with five months of humid season (June to September) and seven dry months (October to May). The mean annual rainfall and mean temperature at the site from 2013 to 2022 were 1153.48 ± 203 mm a⁻¹ and 28.15 ± 0.25 °C, respectively. The daily mean temperature and precipitation during the maize growing seasons (Figure 2) were obtained from the nearest (5 km away) agrometeorological station of the Institut de l’Environnement et de Recherches Agricoles (INERA). Soils are characterized as Lixisols (soils with subsurface accumulation of low activity clays and high base saturation) according to the Harmonized World Soil Database map [39]. The soil physicochemical properties in the 0–20 cm layer before the start of the experiments include pH (H₂O, 1:2.5): 5.64 ± 0.05, electrical conductivity (1:5): 87.6 ± 3.5 µS cm⁻¹, soil organic carbon (C) content: 0.74 ± 0.06%, total nitrogen (N) content: 0.06 ± 0.005%, C/N: 11.80 ± 0.3, total potassium (K⁺) content: 1015 ± 43.3 mg kg⁻¹, total phosphorus content: 141.43 ± 11.4 mg kg⁻¹, bulk density: 1.35 ± 0.01 g cm⁻³, and granulometry with clay: 23.53 ± 1.13%, sand: 45.42 ± 0.65%, silt: 31.05 ± 0.65%.

2.2. Experimental Design and Crop Management

To assess the effects of existing fertilization regimes in maize cropping on N₂O, CH_4, and CO₂ emissions from soil, an experimental site was established in July 2023. Farmers generally adopt different fertilization regimes in maize cropping in Burkina Faso. The treatments, along with the corresponding fertilizers and nitrogen application rates, are presented in

Table 1. Description of treatments with corresponding fertilizers and nitrogen rates.

Treatment	CK	NPKM	M	NPK	NPKM+
Characteristic	Control (no fertilizer)	Conventional mixture fertilization (manure + chemical fertilizers)	Manure fertilization	Chemical fertilization (NPK)	Local mixture fertilization (informal combination)
Fertlizers rate	0	5 t ha−1 M + 200 kg ha−1 NPK	5 t ha−1 M	200 kg ha−1 NPK	3.6 t ha−1 M + 100 kg ha−1 NPK
Nitrogen rate (kg N ha−1)	0	26.5	30	34.08	56.5

Note: The conventional mixed fertilization corresponds to the fertilizer application rates recommended by the Burkina Faso Ministry of Agriculture.

. The fertilization regimes, including control plots, were arranged in a randomized complete block design with three replicates for each treatment. Treatments were implemented in individual plots of 30 m² (6 m × 5 m). Buffer strips of 2 m separated individual plots and 4 m between blocks. The cattle manure used in this study had the following chemical composition: 16% organic matter, 0.53% total nitrogen (N), and 9.4% carbon (C), resulting in a C/N ratio of 17.7. The NPK fertilizer applied was a compound formulation with a nutrient content of 15-15-15 (N–P₂O₅–K₂O). The cultivar “SR21” of Zea mays L. was sown directly after tillage, with around three seedlings per hole into the soil. Maize plants were maintained at the density of 62.500 plants ha⁻¹, compressing 2 plants per pocket, with a row spacing of 80 × 40 cm. Before maize planting, cattle manure was incorporated as basal fertilizer with the hoe into the corresponding plots approximately 1 week before seeding. Two weeks after sowing the maize, NPK fertilizer was spread as basal fertilizers by side placement about 5–7 cm away from seedlings. Weeding was carried out manually twice, followed by hoeing, while other management practices implemented in the experimental fields were completely consistent with those applied by local farmers.

2.3. Gas Sampling and Measurements

N₂O, CH₄, and CO₂ fluxes from the soil were estimated using the manual closed static chamber method, as employed in several studies [24,40]. Static chambers were deployed according to the Global Research Alliance N₂O chamber methodology guidelines [41,42]. The chambers were made from opaque polyvinyl chloride plastic (40 cm height, 25 cm diameter). In each plot, three base rings (12 cm height) were inserted into a 5 cm soil surface to avoid lateral gas diffusion. Three chambers were installed along the diagonal of each plot to capture soil spatial variability, which may affect GHG emissions. The rings were removed only during management events (fertilization, tillage) and returned immediately after these activities. One chamber per plot was connected to a datalogger (GP2-advanced datalogger and controller/Delta-T Devices, Cambridge, UK) equipped with a sensor to measure the inside temperature, relative air humidity, and air pressure. All chambers were equipped with fans for air mixture and pressure equalization during sampling. Gas sampling was performed per block (all replications of each treatment) as recommended by [41]. To minimize diurnal variation in the flux pattern, measurements were conducted on each sampling day from 10:00 a.m. to 3:00 p.m., following the recommendation of [43] which demonstrated that gas fluxes measured during this time frame closely approximate daily averages. Gas was collected one day before fertilization for reference emissions, daily for 7 days following topdressing application, and weekly during the maize growth period. Measurements were also conducted before the harvest when gas was assumed to have dropped to its baseline rates. In total, gas sampling was conducted on 20 days during maize cropping season. To calculate gas fluxes, samples were taken at 0, 10, 20, and 30 min after the closure using a 20 mL polypropylene syringe and stored in 12 mL vacuum glass vials (Labco Exetainer, Labco Ltd., Lampeter, UK). Samples were subsequently sent to the Laboratory of Plant Biology and Ecology at the Université Joseph Ki-Zerbo (Burkina Faso) for analysis by gas chromatography (8610C, SRI Instruments, Torrance, CA, USA) with a flame ionization detector (FID) for CH₄ and CO₂ analysis and an electron capture detector (ECD) for N₂O analysis. GHG fluxes were calculated based on the concentration change within the headspace of the flux chambers, determined as the slope of a linear regression between concentration and time, according to Equation (1). The calculation was accepted if the coefficient of determination R² estimated from the linear regression was above 0.90 for each measurement [23,44].

$$\mathrm{F}\left(\mathrm{gas}\right)={\displaystyle \frac{\mathrm{b}\times {\mathrm{V}}_{\mathrm{Ch}}\times {\mathrm{MW}}_{\left(\mathrm{gaz}\right)}\times {10}^6}{{\mathrm{A}}_{\mathrm{Ch}}\times {\mathrm{MV}}_{\mathrm{corr}}\times {10}^9}}$$

(1)

where F (_gas) = flux rate [(N₂O $=$ μg N m⁻² h⁻¹); (CO₂ $=$ mg C m⁻² h⁻¹) and (CH₄ $=$ mg C m⁻² h⁻¹)]; b = mixing ratio increase (ppb h⁻¹ for N₂O and ppm h⁻¹ for CO₂ and CH₄, respectively); V_Ch = chamber volume (m³); MW N₂O-N $=$ molecular weight of N₂O-N (28 g mol⁻¹); MW_CO2-C $=$ molecular weight of CO₂-C (12 g mol⁻¹); MW_CH4-C $=$ molecular weight of CH₄-C (12 g mol⁻¹); A_Ch = base area of chamber (m²); and MV_corr = pressure- and temperature-corrected molar volume of air (m ³ mol⁻¹), calculated used Equation (2):

$${\mathrm{MV}}_{\mathrm{corr}}=0.02241 \times \left({\displaystyle \frac{273.15+\mathrm{t}}{273.15}}\right)\times \left({\displaystyle \frac{{\mathrm{p}}_0}{{\mathrm{p}}_1}}\right)$$

(2)

where t $=$ air temperature during measurements (°C), p₀ = standard atmospheric air pressure (Pa), and p₁ $=$ air pressure during measurements (Pa).

2.4. Auxiliary Measurements

Parallel to gas flux measurements, soil temperature (°C) and volumetric water content (%) were monitored at a depth of 5 cm next to each chamber by a handheld digital device (WET150-Kit, Delta-T Devices Ltd., Cambridge, UK), calibrated for generic mineral soil. Soil temperature measurements from the replicated plots under the same treatments were averaged to produce a mean value for the treatment on each sampling date. Water-filled pore space (WFPS) was calculated based on volumetric water content, soil bulk density, and particle density, which was assumed to be 2.65 Mg m⁻³ [29,45]. Bulk density to a depth of 20 cm was calculated by dividing the weight of the oven-dried soil at 105 °C for 24 h by the volume of the core.

2.5. Estimation of Grain Yields

For each treatment, 3 subplots of 6 m² (3 m $\times$ 2 m) were installed at the maturity stage for grain yield assessment. Within subplots, the number of maize plants was counted, and their ears were harvested. Grains were oven-dried at 75 °C until constant weight. The average grain yield per subplot was determined and then multiplied by the plant density at the main plot level to obtain the final grain yield, which was adjusted to a 14% moisture level.

2.6. Soil Sampling and Analysis

Soil samples were collected before crop cultivation to analyze their physical and chemical characteristics. Three soil sampling points were chosen in each plot. Soil samples were collected at 0–20 cm soil depths from each point using a soil auger and mixed into one composite sample. Soil granulometry (clay, silt, and sand contents) was determined by the hydrometer method after the destruction of organic matter with hydrogen peroxide (H₂O₂). Soil carbon (C) and total nitrogen (N) were determined by dry combustion. Available soil nutrients, K and P, were determined by inductively coupled plasma–optical emission spectrometry (ICP-OES) after soil extraction using the ethylene-di-amine-tetra-acetic acid (EDTA) method. Soil pH (1:2.5 soil water suspension) and soil conductivity (1:5 soil water suspension) were analyzed using a digital pH meter and a conductivity meter. Bulk density was analyzed using Keen’s box method [46].

2.7. Data Calculation

2.7.1. Cumulative CO₂, CH₄, and N₂O Emissions

The cumulative CO₂, CH_4, and N₂O emissions during the maize growing season were calculated directly from the fluxes and by linear interpolation for days when no measurements were made using Equation (3) [47,48]:

$$\mathrm{Cumulative} {\mathrm{CO}}_2, {\mathrm{CH}}_4 {\mathrm{and} \mathrm{N}}_2\mathrm{O} \mathrm{emissions}={\sum }_1^{\mathrm{n}}({\mathrm{R}}_{\mathrm{i}}\times {\mathrm{D}}_{\mathrm{i}})$$

(3)

where R_i is the daily emission rate of CO₂, CH₄, and N₂O (kg ha⁻¹ d⁻¹) in the ith sampling interval; D_i is the number of days in the ith sampling interval and n is the total number of sampling days. The number of days in the growing season was 103 days.

2.7.2. Grain Yield Calculation

The grain yield was determined using the formula provided by [49], as outlined in Equation (4):

$$\mathrm{GY}(\mathrm{t}/\mathrm{ha})={\displaystyle \frac{[\mathrm{Grain} \mathrm{weight} (\mathrm{kg})\times 10\times (100- \mathrm{MC}\left)\right]}{\left[\right(100-\mathrm{adjusted} \mathrm{MC})/\mathrm{Plot} \mathrm{area} ({\mathrm{m}}^2\left)\right]}}$$

(4)

where grain weight is in kg, the crop moisture content (MC) is in percentage (%), and plot area is in m².

2.7.3. Global Warming Potential (GWP) and Greenhouse Gas Intensity (GHGI)

The global warming potential (GWP) of CH₄ and N₂O was calculated in a mass of CO₂ equivalent (kg CO₂ eq. ha⁻¹) over the 100-yr time horizon. Radiative forcing potential of 1 for CO₂, 27 for CH₄, and 273 for N₂O, as recently published by the IPCC, were used [50]. Greenhouse gas intensity (GHGI) was calculated by taking the ratio of GWP and corresponding grain yield for each treatment. GWP and GHGI were calculated using Equations (5) and (6), respectively [47,51].

$$\mathrm{GWP} \left(\mathrm{kg} {\mathrm{CO}}_2\mathrm{eq} {\mathrm{ha}}^{-1} \right)=1 {\mathrm{CO}}_2 \left(\mathrm{kg} {\mathrm{CO}}_2 {\mathrm{ha}}^{-1}\right)+27 {\mathrm{CH}}_4 (\mathrm{kg} {\mathrm{CH}}_4 {\mathrm{ha}}^{-1})+273 {\mathrm{N}}_2\mathrm{O} (\mathrm{kg} {\mathrm{N}}_2\mathrm{O} {\mathrm{ha}}^{-1})$$

(5)

$$\mathrm{GHGI} \left({\mathrm{kg} \mathrm{CO}}_2 \mathrm{eq}. {\mathrm{kg}}^{-1} \mathrm{grain} \mathrm{yield}\right)={\displaystyle \frac{\mathrm{GWP} ({\mathrm{kg} \mathrm{CO}}_2 \mathrm{eq}. {\mathrm{ha}}^{-1})}{\mathrm{Maize} \mathrm{grain} \mathrm{yield} (\mathrm{kg} {\mathrm{ha}}^{-1})}}$$

(6)

2.7.4. Calculation of Yield-Scaled N₂O Emissions

The yield-scaled N₂O emission (YSNE) was calculated using Equation (7) [24] to estimate the N₂O emissions per kilogram of grain yield produced during the cultivation period, a crucial metric for regulating food production and lowering the environmental impact.

$$\mathrm{YSNE}={\displaystyle \frac{{\mathrm{N}}_2\mathrm{O} \mathrm{emissions} \mathrm{over} \mathrm{crop} \mathrm{growing} \mathrm{period} }{\mathrm{Grain} \mathrm{yield}}}$$

(7)

where yield-scaled N₂O emissions is the amount of N₂O emitted per grain yield produced (g N₂O-N kg⁻¹ grain⁻¹), N₂O emissions is the cumulative emissions of this gas (kg N₂O ha⁻¹) during the growing season, and grain yield is the annual grain yield produced (kg ha⁻¹).

2.7.5. Nitrous Oxide Emissions Factors Calculation

The direct N₂O EF specific to the treatment during the growing season was calculated using Equation (8) [42]. EF values were calculated by subtracting the cumulative N₂O emissions occurring in a control treatment where no N was added (N₂O-control) from the cumulative N₂O emissions in each experimental treatment where N was added (N₂O-treatment), then dividing this by the amount of N applied (N_applied):

$$\mathrm{EF} (\%)=\left({\displaystyle \frac{{{\mathrm{N}}_2\mathrm{O}}_{\mathrm{treatment}}-{{\mathrm{N}}_2\mathrm{O} }_{\mathrm{control}}}{{\mathrm{N}}_{\mathrm{applied}}}}\right)\times 100$$

(8)

where EF N₂O is the N₂O emission factor (%) of N input lost as N₂O-N, N₂O_treatment is the cumulative N₂O-N emission from each treatment applied (from sowing to harvest) in the fertilized and intercropped treatments, N₂O_control is the cumulative emission from the control treatment (background emission), and N_applied the estimated total input of N.

2.8. Statistical Tests

One-way ANOVA analyses were performed to test the effects of fertilization treatments on GHG emissions, GWP, GHGI, YSNE, and maize yields. Differences between treatments were highlighted using Fisher’s least significant difference (LSD) test at a 5% significance level. When needed, data were log-transformed to meet the assumptions of the ANOVA application, especially the normal distribution of the data and the homogeneity of the variance of their residuals, which were checked using the Shapiro–Wilk test and Levene’s test. In addition, linear regressions were performed to assess the relationship between the N rate and cumulative N₂O fluxes and between the N rate and maize yields. The goodness of the relationships was evaluated based on the magnitude of the r-squared values and the significance of corresponding p-values. All statistical calculations, including some data visualizations, were performed using R programming (R 4.4.0) [52].

3. Results

3.1. Environmental Variables

The average daily air temperature was 31.4 °C, ranging from 27.8 to 33.7 °C. During the measurement period, total precipitation amounted to 210.2 mm, with heavy rainfall concentrated between 22 and 24 July 2023 (Figure 2). Variations in soil temperature and WFPS were consistent with those of air temperature and rainfall, respectively (Figure 2 and Figure 3a,b). Higher rainfall concentrations (>23 mm in three consecutive days) and lower air temperature (<31 °C) led to an increase in soil WFPS (58.16%) and a decrease in soil temperature (32.1 °C) under dry climatic conditions. Soil temperature and water-filled pore space were not significantly affected by fertilization treatment or date of sampling. The soil temperature during gas measurements ranged between 28.9 and 36.3 °C at a 0–5 cm depth across all treatments (Figure 3a). Because of higher rainfall in the rainy season (between 22 and 24 July), the overall WFPS during the maize season (average of 58.1%) exceeded that during the other days (Figure 3b).

3.2. Dynamics of N₂O, CH_4, and CO₂ Fluxes

3.2.1. Daily Dynamics

The dynamics of GHG emissions across different treatments exhibited varying fluctuation ranges during manure incorporation and NPK application (Figure 4a,c,e). The N₂O fluxes were initially lower but increased during each fertilization. The highest N₂O flux peaks (0.06 kg N₂O-N ha⁻¹ d⁻¹) typically occurred within 4 days following chemical fertilization in the NPKM treatment, while the lowest average N₂O flux (0.003 kg N₂O-N ha⁻¹ d⁻¹) occurred under manure treatment (M). The peaks exhibited a decreasing trend of average values across the NPK (0.047), NPKM+ (0.043), M (0.040), and CK (0.023 kg N₂O-N ha⁻¹ d⁻¹) treatments (Figure 4a). Soil CH₄ fluxes were mostly positive but occasionally negative in all treatments with values ranging from −0.038 to 0.033 kg CH₄-C ha⁻¹ d⁻¹ throughout the study period (Figure 4c). Maximum fluxes were observed earliest in the control treatment (0.033 kg CH₄-C ha⁻¹ d⁻¹), while the negative flux (−0.0034 kg CH₄-C ha⁻¹ d⁻¹) was observed with conventional mixed treatment three days after manure incorporation. The fluxes remained less variable during the rest of the growing season, except for the fertilization times with NPK, where mostly positive CH₄ fluxes were observed from all treatments. The CO₂ flux was not significantly affected by manure and inorganic N fertilization (Figure 4e). Although manure has been incorporated, CO₂ flux did not peak considerably during these periods. Fertilization events seem to have led to minor variations in CO₂ dynamics. Overall, soil CO₂ emissions ranged between 4.27 and 17.38 kg CO₂-C ha⁻¹ d⁻¹ for local fertilization practices (NPKM+) and chemical fertilization (NPK). Carbon dioxide emissions in the control treatment remained low, with the main rates commonly below 10.9 kg CO₂-C ha⁻¹ d⁻¹, while the mixed treatment at the recommended rate recorded the highest rates, approximately 7% higher.

3.2.2. Cumulative Emissions of N₂O, CH₄ and CO₂

Cumulative emissions of N₂O and CH₄ during the maize growing season were significantly (p < 0.001) affected by fertilization (Table S1). Using both chemical and organic fertilizers at a recommended rate significantly increased N₂O emissions. The cumulative N₂O emissions from NPKM treatment were higher (2.86 ± 0.08 kg ha⁻¹) than other treatments (Figure 4b). Compared to NPKM, the treatments NPK, M, NPKM+, and CK decreased the cumulative N₂O emissions by 12.5%, 17%, 18.9%, and 32.5%, respectively. The cumulative emissions of CH₄ from NPKM treatment (−0.62 kg CH₄-C ha⁻¹) were substantially lower than those under M (0.14 ± 0.17 kg CH₄-C ha⁻¹), NPK (0.10 ± 0.27 kg CH₄-C ha⁻¹), and NPKM+ (0.53 ± 0.26 kg CH₄-C ha⁻¹). However, the control treatment recorded the highest value of methane (0.74 ± 0.22 kg CH₄-C ha⁻¹). The cumulative CO₂ emissions were not significantly different between treatments (p > 0.05). The NPKM treatment had the highest CO₂ emission, and the NPKM+ treatment had the lowest (Figure 4f).

3.3. N₂O Emission Factor, Yield-Scaled N₂O Emission, Global Warming Potential, and GHG Intensity

Fertilizer treatments did not significantly affect EF, and YSNE (p > 0.05) (

Table 2. Effect of different fertilizers treatment on N₂O emission factor, yield-scaled N₂O emission, global warming potential, and greenhouse gas intensity.

Treatment	Emission Factors (%)	Total GWP (kg CO2 eq. ha−1)	GHGI (kg CO2 eq. kg−1 Grain Yield)	YSNE (g N2O-N kg−1 Grain)	Gas Contribution in GWP (%)
					N2O	CH4	CO2
CK	-	1670.93 ± 136.18 a	1.15 ± 0.16 a	1.34 ± 0.17 a	31.54 ± 1.23 a	1.20 ± 0.13 a	67.26 ± 1.25 a
NPKM	0.31 ± 0.05 a	1968.32 ± 114.82 b	0.69 ± 0.006 b	1.01 ± 0.04 a	39.85 ± 1.88 b	−0.89 ± 0.74 b	61.04 ± 1.15 b
M	0.32 ± 0.08 a	1829.65 ± 51.12 ab	0.89 ± 0.10 ab	1.16 ± 0.07 a	35.53 ± 0.27 ab	0.14 ± 0.32 ab	64.33 ± 0.43 ab
NPK	0.37 ± 0.05 a	1850.21 ± 71.93 ab	0.94 ± 0.10 ab	1.27 ± 0.06 a	37.07 ± 0.61 b	0.20 ± 0.19 ab	62.73 ± 0.55 b
NPKM+	0.22 ± 0.10 a	1767.74 ± 107.47 ab	0.74 ± 0.03 b	0.98 ± 0.03 a	35.95 ± 0.29 ab	0.85 ± 0.49 ab	63.20 ± 0.74 ab
p-Value	0.580	0.0545	0.0025	0.074	0.003	0.052	0.006

Different lowercase letters in a column indicate significant differences at the 5% level by the least significant difference test (LSD). Values are means ± standard errors (n = 3).

). However, GWP showed a marginal significance (p = 0.054), indicating notable differences among the treatments, while GHGI varied considerably between fertilizer treatments (p ˂ 0.01). The EF values of N₂O ranged from 0.22% to 0.37% in the NPKM+ and NPK treatments, respectively, with an average of 0.30% (Table 2). In general, mixed fertilization demonstrated a relative potential to reduce EFs by at least 19.4% compared to single NPK fertilizers. Regarding GWP, the results indicated that existing fertilization regimes significantly increase their values compared to the control. The highest GWP value was recorded in NPKM treatment (769.23 ± 14.32 kg CO₂ eq. ha⁻¹) and the lowest in NPKM+ (649.65 ± 10.02 kg CO₂ eq. ha⁻¹), representing an 18.4% reduction. In general, in terms of GWP contribution, CO₂ is the main contributor, followed by N₂O. Among the treatments, NPKM shows the highest N₂O contribution to the GWP, accounting for approximately 40%. Conversely, the same treatment contributes the least in terms of CO₂ emissions (Table 2). The results showed that the no-fertilization treatment (CK) exhibited a higher GHGI of 1.15 ± 0.16 kg CO₂ eq kg⁻¹ grain yield, while the lowest rates were observed under NPKM treatment (0.69 ± 0.006 kg CO₂ eq kg⁻¹ grain yield). Additionally, the control treatment produced the highest YSNE, estimated at 1.27 ± 0.06 g N₂O-N kg⁻¹ grain, which was statistically similar to the other treatments and followed the order NPK > M > NPKM > NPKM+ (Table 2).

3.4. Relationship Between N Rate and N₂O Emissions During Maize Growth Season

Linear regression highlighted a correlation (R² = 0.77) between N rate and cumulative N₂O emissions. Emissions were positively and significantly correlated with N inputs (Figure 5). N₂O emissions were 48% higher at the highest nitrogen levels compared to the control, which received no nitrogen input.

3.5. Effect of Fertilizer Application on Maize Grain

Fertilization treatments had significant effects on grain yields (p ˂ 0.001). The average grain yield ranged from 2850.58 ± 81.72 to 11,453.21 ± 73.14 kg ha⁻¹ (Table S1). NPKM treatment exhibited the best-performing yields. Considering NPKM fertilization as a reference, grain yield was reduced by 27.6% under manure fertilization. Maize responses to existing fertilization regimes in terms of grain yields are illustrated in Figure 6a. Additionally, positive correlations were found between nitrogen rate and grain yield (Figure 6b).

4. Discussion

4.1. Effect of Fertilizer Treatment on N₂O Emissions

The increase in soil N₂O emissions immediately after fertilizer application coincided with precipitation events, which led to increased soil moisture. This could have triggered substrate availability, microbial activity, and mineralization of C and N, resulting in a pulse of N₂O fluxes [5]. Rainfall increases soil moisture, which can reduce oxygen availability and create anaerobic conditions that favor denitrification. Fertilizer application adds nitrogen, typically in the form of ammonium or nitrate, and may also enhance the availability of organic carbon. Both nitrate (NO₃⁻) and organic carbon serve as essential substrates for denitrifying bacteria. Under moist and low-oxygen conditions, these microbes convert nitrate into gaseous forms of nitrogen. However, when the process is incomplete or environmental conditions are suboptimal, nitrogen is emitted primarily as nitrous oxide (N₂O) instead of being fully reduced to dinitrogen gas (N₂) [53,54]. This aligns with the findings of [23], who observed peak N₂O fluxes following N fertilizer applications. In terms of cumulative N₂O emissions, conventional mixed fertilization (NPKM) resulted in the highest emissions, likely due to the increased nitrogen rate, which directly affects soil nitrogen availability and bioactivity [35,55]. The increase in N₂O from NPKM could be also related to the high NH₄⁺ and NO₃⁻ contents from fertilizers, which facilitate the activity of nitrite reducers (nirS and nirK genes), thereby leading to N₂O production through the denitrification process [56]. In addition, the observed increase in N₂O under NPKM treatments may result from conditions that promote incomplete denitrification such as high nitrate availability, insufficient carbon, or transient anaerobic conditions, which hinder the full reduction of N₂O to N₂ [53]. The use of animal manure as an organic amendment in mixture fertilization could result in increased labile C and N substrates for microbial N₂O consumption and production [7]. While this may enhance N₂O production through increased substrate availability and partial anaerobic conditions, it may also enable N₂O consumption via complete denitrification depending on whether conditions support the full reduction of N₂O to N₂ [53]. The combination of chemical fertilizer and cattle manure could increase the abundance of nirS-harboring bacteria responsible for denitrification and emissions [37,57]. The minimal difference in soil N₂O emissions between treatments (M, NPK, NPKM+) may be due to the lower total nitrogen content in both organic and inorganic fertilizers. In the manure-only treatment (M), N₂O emissions were reduced to 17% of those in the NPKM treatment, aligning with the findings of [58,59], who reported reductions of 16% and 55% in emissions from manure compared to inorganic nitrogen, respectively.

Manure-derived nitrogen reduces organic nitrogen mineralization and autotrophic nitrification rates [55], thereby promoting nitrogen uptake by plants and enhancing microbial assimilation of mineral nitrogen [59]. Organic fertilizer is potentially more stable to microbial decomposition than chemical fertilizer, as it can significantly mitigate GHG emissions. The seasonal cumulative N₂O emissions found in this study were higher than those found in SSA studies [23,24,44] that reported N₂O fluxes ranging between 0.02 and 1.5 kg N₂O-N ha⁻¹. Conversely, in other regions (China and Spain), some studies [36,60] recorded the highest emissions at up to 29.4 ± 1.1 kg N₂O-N ha⁻¹. These differences may result from soil types, climate conditions, and land use management, including fertilizer type, rate, and quality [7,29,61].

4.2. Effect of Fertilizer Treatment on CH₄ Emissions

CH₄ fluxes varied significantly over time throughout the experiment. The slight decline in CH₄ emissions in the NPKM treatment at the beginning of the sampling period was likely due to the combined effects of carbon from manure and reduced soil oxygen levels caused by precipitation. During nitrogen fertilization, the soil water content may have influenced methanogen activity, impacting CH₄ emissions by increasing uptake [62]. Cumulative soil CH₄ emissions were significantly affected by the fertilization treatment throughout the experiment (Figure 4d). The highest CH₄ emissions in the control treatment could be largely attributed to the high WFPS (Figure 3b) controlling CH₄ production and consumption in soils [63], but also to enhanced methane oxidation because of the relatively high pH [23]. This study found that CH₄ uptake was highest under mixed fertilizer (NPKM) due to degradable organic matter from manure, which improved soil aeration, water retention, and root respiration [29,31,64]. The negative CH₄ flux in NPKM was attributed to enhanced diffusion of atmospheric O₂ and CH₄, promoting uptake by methanotrophs. Methane oxidation by bacteria and methanogens further increased CH₄ uptake [19,23]. These results can also be explained by methane oxidation by methanotrophic bacteria, but also by methanogens [65] to CO₂, thus increasing CH₄ uptake. The study observed higher soil CH₄ fluxes under farmers’ fertilizer practices (Figure 4d). Co-fertilizing manure with chemical fertilizers increased methanogenic diversity and abundance, with labile carbon from manure supporting methanotrophic populations. Additionally, cattle manure may release CH₄ upon application [24]. The study found that microbial activity and CH₄ uptake were low in mineral fertilizer treatments due to reduced oxidation activity. Cumulative CH₄ emissions were slightly higher than those reported in SSA [19,24], where agricultural soils mainly acted as CH₄ sinks. However, our results differed from those of [44] in Cameroon, who reported higher emissions. These differences were attributed to variations in soil properties, which affect GHG flux dynamics.

4.3. Effect of Fertilizer Treatment on CO₂ Emissions

Across different fertilizers, CO₂ fluxes showed a similar pattern of variation, consistent with the findings of a previous study [54]. The fluxes of CO₂ peaked after fertilization, which mainly stemmed from the increase in soil respiration and microbial activity associated with the application of N fertilizer and increased soil moisture following a rainfall event [54,55]. The N-dependent C-use efficiency associated with soil CO₂ emissions observed here is consistent with previous findings [5,66]. CO₂ emissions are directly proportional to the rate of organic amendments input. A high amount of animal manure had a positive correlation with CO₂ emissions. Even though no significant differences were observed between treatments, the use of manure and its combination with chemical fertilizers at the recommended rate led to increased cumulative CO₂ emissions. The increasing organic matter combined with the loading of nutrients from manure can stimulate the microbiome activity, thereby enhancing soil quality [12,31]. Additionally, rainfall and frequent soil tillage were observed during the experiment [29,38], which may have promoted CO₂ emissions. However, the low CO₂ emissions observed in plots receiving only chemical fertilizers or under control conditions were attributed to low soil organic carbon (SOC) resulting from the absence of organic fertilization. The cumulative CO₂ fluxes in the present study ranged from 1118 to 1203 kg CO₂-C ha⁻¹; higher than those observed under tropical conditions by others [19,29].

4.4. Effect of Fertilizer Treatments on N₂O Emission Factors

Areas with both high and low EFs exist across all continents, with EFs for maize worldwide ranging from 0.08% to 3.77% [18]. The direct N₂O EFs determined in this study were lower than the default 1% EF currently proposed by the IPCC for global and dry climates [16]. This demonstrated that using the IPCC’s EF may overestimate more than 78% of N₂O emissions for dry climate zones. Several experimental studies on maize crops carried out in tropical climates have reported emission factors (EF) ranging from 0.1 % to 0.9% across maize cropping systems in SSA, including findings by [28] in Ghana, refs. [24,61] in Kenya, ref. [20] in Tanzania, and [25] in Ethiopia. The mean EF observed in this study was lower than both the global EF (0.97%) proposed by [2] for various environmental and ecological zones and the global nitrous oxide emission factor (0.57%) reported in a meta-analysis by [64]. It was also lower than Australia’s EF (0.57%) reported by [67], the UK’s EF (0.6%) by [68], and China’s EF (0.56%) proposed by [69].

The lower N₂O EFs observed in our study compared to the IPCC default value and most previous studies may be due to lower nitrogen input levels as well as differences in environmental conditions, soil types, and management practices [2,18,33]. This may also be due to the duration of the experiment. When comparing emission factors based on fertilizer types, our results showed that maize cultivation under mineral fertilization had higher N₂O EFs than under organic fertilization, which is consistent with previous studies finding higher EFs from inorganic NF [35,70]. Globally, the nitrogen application rate has a positive effect on EF [2]. However, some observations have also confirmed a nonlinear response to the N application rate [70].

4.5. Yield-Scaled N₂O Emissions, Global Warming Potential, and GHG Intensity Following Fertilization Practices

The YSNE did not differ across N fertilization treatments, although emissions showed significant variation. This could be attributed to the negative correlation between N inputs and YSNE, where an increase in nitrogen inputs does not necessarily lead to higher YSNE. The combination of low emissions from local practices and higher yields contributed to a lower YSNE value, weakening the relationship between N₂O emissions and YSNE and resulting in this non-significant effect between all treatments. There was an optimal N input rate that reduced YSNE. This finding aligns with global YNSE from the study of [4], with a lower YSNE average of 0.211 g N₂O-N kg⁻¹ grain. Conversely, low crop yields, probably due to low fertilization, resulted in high YSNE, as mentioned by [6,22]. These results suggest that local farming practices that reduce YSNE could be key to developing effective strategies for balancing food security and N₂O emissions reduction. Yield-scaled N₂O emissions could be lowered if maize yield is increased with optimal N input [71].

In our study, the combined fertilizer (NPKM) and the single inorganic fertilizer application (NPK) exhibited the highest GWP value. This is largely explained by the impact of N₂O rather than CO₂, as CO₂ emissions are statistically similar across the different treatments. This is due primarily to higher cumulative N₂O emissions compared to other fertilizer treatments, as N₂O contributed the largest share to the overall GWP, especially in dryland agriculture [29,31]. The contribution of N₂O to the GWP in the NPKM treatment can be explained by elevated N₂O emissions, which are themselves associated with high nitrogen inputs. The observed differences in GWP between treatments in our context are mainly due to variations in nitrogen application rates [33], whereas discrepancies in other studies are largely attributed to differences in environmental conditions, crop types, and fertilization methods [51]. Adding manure to NF increased N₂O emissions, likely due to the higher mineral nitrogen content (particularly NH₄⁺) in the organic matter [36]. Regarding CO₂, the NPKM treatment shows the lowest contribution, partly due to negative CH₄ values. This negative CH₄ balance indicates net CH₄ uptake by methanotrophic bacteria, which oxidize CH₄ into CO₂. Although this microbial process increases CO₂ emissions, it ultimately reduces the overall GWP contribution by mitigating CH₄, a gas with a higher global warming potential than CO₂. This underscores the importance of implementing additional strategies, such as using nitrification inhibitors, to reduce N₂O emissions and enhance nitrogen conservation in manure [33].

The greenhouse gas intensity (GHGI) decreased with increasing levels of nitrogen fertilizer and GWP. This decrease was primarily driven by an increase in yield at high nitrogen application levels (NPKM treatment), as higher nitrogen inputs led to higher maize grain production. This result occurred because the increased grain yield had a positive effect on GHGI, while lower yields corresponded to higher GHGI values, particularly for the NPK and CK (Figure 6a and Table 1) treatments. Similar findings were observed by demonstrating the balancing effect of crop yields on GHGI [19,72]. Moreover, small changes in maize yield can result in substantial differences in GHGI across treatments [29].

4.6. Effect of Maize Grain Yield to Nitrogen Fertilizers

Our study found that the recommended mixed fertilizer (NPKM) produced the highest yield, while the NPKM+, NPK, and M treatments showed no significant differences in grain yield. The higher grain yields observed under the NPKM treatment can be largely attributed to the combined benefits of both fertilizer types, which supply a diverse range of essential nutrients. When organic fertilizer and inorganic fertilizer are applied together, crops preferentially absorb the nutrients released by inorganic fertilizers, readily available for plant uptake [33]. The study highlights the long-term benefits of organic fertilizers, particularly cattle manure, which enhance soil structure, increase organic carbon, and improve nitrogen availability, leading to higher maize yields [7,37,73]. Combining organic and chemical fertilizers is an effective strategy for maintaining yields [74,75]. Although local practices combine both manure and NPK, yields are still slightly lower than recommended practices. This is due to the low doses, which are almost two times lower than those recommended. The N fertilization rate is significantly lower than what plants require. Chemical fertilizers revealed low yields. Similar findings were noted by [76], who found that chemical fertilizer did not improve yield but slightly acidified the soil. In our study, the average grain yield under conventional fertilization was lower than maize yields in Sub-Saharan Africa and tropical or subtropical regions worldwide, despite conventional NF producing relatively higher yields [22]. This study also found that nitrogen application rates influence grain yield, with higher inputs leading to greater yield potential. However, low nitrogen inputs and small farm sizes contribute to significant yield gaps in Sub-Saharan Africa [3,30]. Addressing these gaps while reducing greenhouse gas emissions through climate-smart agriculture remains a key challenge for smallholder farmers and stakeholders in the region.

5. Conclusions

This study highlights the impact of current fertilization practices on greenhouse gas (GHG) emissions, emission factors (EFs), global warming potential (GWP), greenhouse gas intensity (GHGI), and yield-scaled nitrogen efficiency (YSNE) in maize cropping in Burkina Faso. As endorsed by the Burkina Faso Ministry of Agriculture, the recommended mixed fertilization approach increased nitrous oxide (N₂O) emissions by 20.1% and 48.18% compared to single manure and the control treatment, respectively. The methane (CH₄) uptake was relatively high in the NPKM treatment, suggesting that methanotrophic populations had increased due to microbial activity, enhancing CH₄ diffusion. Surprisingly, carbon dioxide showed no significant response to these treatments. Among the various fertilizers, the single application of NPK fertilizer resulted in the highest direct EFs.

In line with findings from other studies in Sub-Saharan Africa (SSA), the N₂O EFs observed in this study were lower than the default EFs provided by the IPCC, suggesting that these default values may overestimate N₂O emissions. Overall, the use of manure fertilizer reduces the GWP in maize cropping. Mixture fertilization at the recommended dose has the greatest positive effect on maize yield compared to a single application of manure or chemical fertilizer. However, since this fertilization regime also increases GHG emissions, further research is needed to develop a smart mixed fertilization strategy that minimizes emissions while maintaining optimal maize yields. Based on our findings, a potential approach for designing such a strategy could involve reducing the rate of synthetic nitrogen application. Organic fertilizers, such as manure, may offer a more sustainable alternative to chemical fertilizers, significantly mitigating greenhouse gas emissions. The one-year experiment conducted in this study may not provide sufficient insight into the long-term effects of different fertilization regimes on GHG fluxes. We recommend establishing long-term experiments across various agroecological zones with varying types of soil to provide a robust GHG emission profile in maize cropping systems.

This research provides emissions data and emission factors that can be used to develop tailored guidance addressing gaps in greenhouse gas emissions accounting and reporting challenges. Nevertheless, our measurements in SSA show results comparable to those from other tropical and subtropical regions, reinforcing confidence that our projections offer valuable insights into future patterns of soil N₂O emissions. This approach supports consistent, comprehensive, and transparent reporting, helping to strengthen capacities for the upcoming global stocktake. Our findings will aid decision-making processes related to the implementation of climate-smart agriculture in Sub-Saharan Africa.