Impacts of intensifying or expanding cereal cropping in sub-Saharan Africa on greenhouse gas emissions and food security

2.1 Four scenarios which achieve cereal self-sufficiency in 2050

In this study, we used four scenarios to assess GHG emissions as affected by agricultural intensification or area extension for 10 countries in SSA (i.e. Burkina Faso, Ethiopia, Ghana, Kenya, Mali, Niger, Nigeria, Tanzania, Uganda and Zambia) and five cereals (i.e. maize, millet, rice, sorghum and wheat). The underlying principles of the scenarios are consistent with those used in van Ittersum et al. (2016), but were updated to the reference year 2015—Scenario 1: In 2050, cereal yields are the same as today (the year 2015); Scenario 2: Actual cereal yield trends over the period 1991–2014 are extrapolated to 2050; Scenario 3: In 2050, cereal yields are 50% of water-limited potential yield (Y_w); Scenario 4: In 2050, cereal yields are 80% of Y_w. Actual and water-limited potential cereal yields for each of the scenarios were obtained from the Global Yield Gap Atlas (www.yieldgap.org; Grassini et al., 2015; Van Bussel et al., 2015) as also used in van Ittersum et al. (2016).

2.2 Cereal yields and cereal areas required for self-sufficiency

For each of the four scenarios, we take the view that individual countries aim for self-sufficiency in cereal production in 2050. While we acknowledge that full self-sufficiency of cereals is generally not an explicit aim, it is generally agreed that substantial dependence on food imports is only possible if economic development is sufficient to afford them, while economic development of low-income countries to support such imports requires a strong agricultural development (Chang, 2012; Johnston & Mellor, 1961). Here, we briefly describe how calculations regarding self-sufficiency were performed; a complete overview can be found in van Ittersum et al. (2016).

Self-sufficiency is calculated as the ratio between domestic cereal production and cereal demand and is equal to 1 to obtain full self-sufficiency. Cereal demands for 2050 were derived from recent population projections (medium fertility variant of the UN population projections; United-Nations, 2015), and per capita consumption (Robinson et al., 2015). The predicted per capita cereal consumption includes direct human consumption of cereals, but also cereals as used for animal feed and other purposes like bioenergy and brewing. Predicted increased consumption per capita in Robinson et al. (2015) is similar to other forecast studies such as that of Alexandratos and Bruinsma (2012). In all forecast studies, predicted increase in cereal demand for 2050 compared to 2015 is mainly determined by the population growth in SSA, and to less extent to the increased per capita consumption of livestock products (Alexandratos & Bruinsma, 2012; van Ittersum et al., 2016). We expressed production and demand data at standard moisture content (maize: 15.5%; rice, sorghum, millet: 14%; wheat: 13.5%).

Each scenario has a different intensification level, with corresponding cereal yield and necessity to expand the production area to achieve self-sufficiency. We assumed that higher levels of intensification come with higher cereal yields per hectare, and therefore, less area needs to be converted to agricultural land to obtain full self-sufficiency. In our scenarios, cereal area expansion occurs through conversion of present grassland and/or forest (in proportion of current availability per country), and it is assumed that the productivity of newly converted land is equal to that of existing cereal land. Data on current areas of cereals, grassland, and forest were obtained from FAOSTAT (FAO, 2019). For estimations of GHG emissions in 2015, we included recent land conversion, considering any land conversion which occurred in the last 20 years (1995–2015) for cereal production (the default time period used in IPCC calculations for the transition between SOC equilibrium values; IPCC, 2006b).

The potential area available for expansion of crop production was taken from Chamberlin, Jayne, and Headey (2014), who considered per country the land area suitable for cropland expansion as land which is currently not cultivated, not part of a national park or other gazetted area, not of a low-yield potential and with a low population density. The potential cereal area for expansion was the land area suitable for cropland expansion multiplied by the current share of cereal land in the total cropland.

2.3 Nitrogen input requirements

Data on current N inputs were obtained from FAOSTAT (FAO, 2019; which is the average N input for all arable and permanent crops, as it is not specified per crop type). N input requirements for the future scenarios were estimated with two methods: (a) minimum nitrogen input requirements (i.e. high-efficiency variant); and (b) nitrogen input requirement using a current mean value of agronomic nitrogen use efficiency (N-AE, i.e. extra grain yield per kg of N applied) in SSA (i.e. low-efficiency variant). Both variants use the underlying principles of ten Berge et al. (2019) and are briefly explained below.

2.4 Minimum N input requirement (high-efficiency variant)

In the high-efficiency variant, N input for each of the scenarios (S1–S4) is estimated by the minimum nutrient input approach. It is postulated that the annual application rates of macronutrients (N, P, K) should at least be equal to total nutrient uptake in the aboveground crop biomass (grain and stover) of a given target yield, Y_T.

The nutrient input requirements for a given Y_T are calculated from: (a) coefficients to express physiological efficiency (kg grain per kg uptake), uptake efficiency (kg uptake per kg applied) and agronomic efficiency (kg grain per kg applied) of each nutrient (Table S2); (b) balanced nutrition implying that overall efficiency is best served if the different nutrients (N, P, K) are taken up in optimum stoichiometric ratios to one another; (c) the above efficiency coefficients and the crop nutrient ratios are constant up to a given relative target yield (Y_T/Y_w; here 0.62); and (d) beyond this point, the efficiency coefficients decrease non-linearly to a minimum value reached when the target yield approaches the yield ceiling Y_w. The constant efficiency values in the yield domain below  Y_T/Y_w = 0.62 are referred to as 'initial' values. This variant is suitable for long-term calculations as it assumes soil N content being in equilibrium with a given input regime. This approach implies N-AE values of 52 kg/kg for Scenarios 1–3, and 46 kg/kg for Scenario 4.

2.5 N application rate under current mean N-AE in SSA (low-efficiency variant)

In the low-efficiency variant, N input is estimated by the short-term nutrient input requirement, and assuming an initial N-AE of 14.3 kg/kg. This value corresponds with the mean value currently found for maize in on-farm field trials or on-station experiments in SSA (ten Berge et al., 2019). Thus, our N input requirement first increases linearly with target yield according to the SSA-mean N-AE, and then increases more steeply for relative target yields exceeding 0.62. This variant does not assume a steady-state equilibrium of soil N but accounts, instead, for current soil N supply. This method is therefore more compatible for short-term assessments. Soil N uptake was estimated in two steps: (a) extrapolation of current actual N inputs and yields with N-AE (14.3 kg/kg) to obtain yield at zero N input; (b) from this yield level at zero N input, the crop N uptake from soil was calculated using the physiological efficiency (Section 2.4).

2.6 GHG emissions

Total GHG emissions from cereal production consist of emissions from land (forest or grassland) conversion to cereal area, the use of fertilizer, the production of mineral fertilizer and from flooding of rice fields. GHG emissions from removal of crop residues are not included. All emissions were converted to CO₂ equivalents. For GHG emission calculations and parameter values, we used the IPCC tier 1 approach, unless it is specifically indicated that another data source was used (Tables S1 and S2).

2.7 CO₂ emission from land use change

Emission from land use change from either forest area or grassland to cereal cropland in the year 2050 is the total emission due to change in soil organic carbon (SOC) content, removal of forest biomass, and/or removal of grassland biomass. Data on the fraction of forest and grassland area in the specific country are obtained from FAOSTAT (FAO, 2019). We assume that the land use change from 2015 until 2050 is linear, and that the default time period for transition between equilibrium SOC values is 20 years (IPCC, 2006b). This means that the total forest or grassland area which needs to be converted to cropland to obtain full self-sufficiency in 2050 is equally distributed over the years.

The CO₂ emission from removal of forest is the aboveground carbon content of the forest times the land use change area (discounted over 20 years) and the proportion of forest of the total forest and grassland area in the specific country. We obtained aboveground forest biomass (AGB) per country by combining a forest cover map (forest defined as more than 10% tree cover; Hansen et al., 2013) with a biomass map (Zarin et al., 2016). Both maps have a resolution of 30 m, and are dated circa 2000. Average AGB was converted to aboveground forest carbon content by using a conversion factor of 0.5.

The CO₂ emission from the removal of grassland is similarly defined, namely the aboveground biomass of the grassland times the land use change area (discounted over 20 years) and the proportion of grassland of the total forest and grassland area in the specific country.

The CO₂ emission from SOC loss due to land use change depends on the SOC before conversion minus SOC after conversion. SOC before conversion is the total carbon content from the forest minus the aboveground carbon content. The total carbon content of the forest was obtained from conversion of the total biomass content of the forest using a conversion factor of 0.5. The total biomass map was derived from the AGB density map by applying the equation: Total biomass = AGB + 0.489AGB^0.89 (Saatchi et al., 2011). SOC stock after land use conversion was obtained by multiplying the SOC stock before conversion with the relative stock change factors for cropland for land use, tillage and inputs used (Tables S1 and S2).

2.8 N₂O emission from fertilizer use

Total emission from nitrogen input is composed of direct N₂O-N emission from applied fertilizer, indirect N₂O emission through NH₃ and NO_x volatilization and indirect N₂O-N emission from leaching and run-off. For brevity, we assumed that all nitrogen inputs come from mineral fertilizer only (see Section 2.3).

The direct N₂O-N emission from fertilizer application was estimated as the mineral fertilizer N applied multiplied by the emission factor for direct N₂O emission. The indirect emission of N₂O-N by volatilization of N as NH₃ and NO_x, was estimated as the mineral fertilizer N applied multiplied by the fraction of NH₃ and NO_x volatilized and the emission factor for N volatilization. The indirect emission of N₂O-N by leaching and run-off from land of N was estimated as the mineral fertilizer N applied multiplied by the fraction of N leached and run-off and the emission factor for leaching and run-off (Table S1).

2.9 CO₂ emission from production of mineral fertilizer

The CO₂ emission from the production of mineral fertilizer was calculated as the amount of mineral fertilizer of a specific type multiplied by the emission factor for that specific type of fertilizer. We took world average emission factors for all fertilizer types (Table S2). The types of fertilizer used are based on FAOSTAT (FAO, 2019), and were assumed to remain the same in 2050 compared to 2015.

2.10 CH₄ emission from rice fields

The CH₄ emission from rice cultivation was calculated as the default world CH₄ emission constant multiplied by a scaling factor for either irrigated or rainfed cultivation (Table S2).

2.11 Negative emissions

We assumed that cereal area is converted to either forest and/or grassland area when the land area required to obtain full self-sufficiency of cereals in 2050 is less than the current cereal area (country by scenario). This means uptake of CO₂ (indicated with negative emissions) due to change in SOC content, sequestration of carbon in forest biomass, and/or sequestration of carbon grassland. Recovery of SOC of forest until equilibrium takes also 20 years similar to what was assumed for SOC breakdown (Guo & Gifford, 2002), but for SOC of grassland, it was assumed to take 53 years (Guo & Gifford, 2002). Recovery of grass biomass was assumed to take 20 years, and natural regeneration of forest 100 years, but carbon increment is fastest in first 20 years and differs per climate type (Albanito et al., 2016).

2.12 Uncertainty and sensitivity analysis

3.1 GHG emissions from intensification and land use conversion—high-efficiency variant

Analysis revealed that cereal intensification to 80% yield gap closure (Scenario 4) will require an enormous increase in nitrogen (N) application per hectare of at least 16 times the current use in SSA (Table 1). Current N input is very low (< 10 kg N/ha) (Table 1), and therefore does not even compensate for N offtake in actual yields. We estimate that just to sustain current yields, N input per hectare has to increase by almost a factor 4 (Scenario 1).

The increase in N application as a result of cereal intensification through yield gap closure will come with substantial GHG emissions (Figure 1). The fertilizer-induced emission (grey bars in Figure 1) consists for the largest part of direct N₂O emission from soils and CO₂ emission due to the production of fertilizer, respectively, on average across the four scenarios 45% and 41%. Indirect N₂O emission through leaching and run-off, and from NH₃ and NO_x volatilization accounted, respectively, for 10% and 4%. Area expansion results in large GHG emissions especially due to the removal of C from standing biomass of forest, which accounted on average across the scenarios for 63% of the total emissions from land use change (green bars in Figure 1). CH₄ emission from rice is the largest emitter of GHGs per unit area compared to the other cereal crops, followed by maize, wheat, sorghum and millet; therefore, for countries with rice cultivation, this is a substantial part of the total emissions (yellow bars in Figure 1).

For all countries, Scenario 1 in which yields do not increase has the largest GHG emissions (Figure 1). For the 10 countries jointly, emissions from Scenario 1 are more than threefold those of Scenario 4, while Scenarios 2 and 3 take intermediate positions. In Tanzania and Nigeria, the largest GHG emissions are projected; the share of both countries together in the total GHG emissions of the 10 SSA countries is on average 56% across the scenarios. This is due to their high population and large cereal demand in 2050 and current limited cereal area, and thus relatively more cereal area expansion is needed (Table S3).

Irrespective of the scenario chosen, GHG emissions of the 10 countries jointly for 2050 are always higher compared to 2015; for Scenario 1, they are almost sixfold more, while for Scenario 4, they are 1.5 times more (Figure 1). The increase between 2015 and 2050 differs, however, hugely between countries. In countries which faced relatively rapid crop area expansion over the past decades (Ethiopia, Tanzania, Burkina Faso and Mali), our estimated emissions in 2050 for intensification scenarios are lower than estimated emissions in 2015, as still CO₂ is omitted originating from this expansion.

For Ethiopia, Burkina Faso and Mali, intensification on current cropland (Scenario 4) is assessed to result in more production than required for full self-sufficiency, and potentially land savings and reforestation could take place (apparent through the negative GHG emissions). Nevertheless, for most countries, Scenario 4 still requires land use conversion to meet self-sufficiency in 2050 (Table S3). The results of the analysis at the national level also reveal that matching food demand through land use conversion, instead of intensification, is not always an option as land area required for such expansion is not available in most SSA countries (Table 1; Table S3). For example, Niger and Uganda do not have the required cropland area for any of the scenarios, while only Zambia potentially has the land area required for land use conversion for all four scenarios. For the 10 SSA countries jointly, there is only sufficient land suitable for land use conversion for Scenario 4.

3.2 Robustness of scenario results for maize

In the previous section, we used the high-efficiency variant to estimate N input requirements for all cereals and scenarios. This implies that for maize, an agronomic nitrogen use efficiency (N-AE, extra grain yield per kg of fertilizer applied) was assumed of 52 kg/kg for Scenarios 1–3, and 46 kg/kg for Scenario 4 (ten Berge et al., 2019). In reality, such efficiencies are currently not achieved in SSA, and hence, larger N applications may be necessary than we assumed. Therefore, we also estimated N input requirements with a low-efficiency variant for maize only, using an initial N-AE of 14.3 kg/kg which is the mean current N-AE found for maize in farmer field trials in SSA (ten Berge et al., 2019). The high-efficiency variant (Section 3.1) results in minimum N application rates; N application and thereby also GHG emission are much lower than in the low-efficiency variant for Scenarios 2–4 (Figure 2a,b). However, for Scenario 1 the GHG emissions are higher  in the high-efficiency variant because the latter sustains—unlike the low-efficiency variant and today's practice—soil N supply by extra N input (Table S4; Figure 2a,b).

Scenarios which reach self-sufficiency mainly with land use conversion (both forest and grassland) had much larger GHG emissions than scenarios which mainly rely on intensification of current agricultural land, according to the high-efficiency variant. Using the low-efficiency variant, intensification still leads to lower GHG emissions until Scenario 3 (50% of Y_w), but not anymore with Scenario 4 (80% of Y_w; Figure 2b; note that Scenario 4 is still superior to Scenarios 1 and 2). However, the standard deviation of the total GHG emission arising from the uncertainty in model parameters (represented by the error bars in Figure 2a,b) is large. While with the high-efficiency variant, the trend—that intensification results in less GHG emissions compared to extension—is robust when accounting for uncertainty, the trend of the low-efficiency variant is highly uncertain. We decomposed the uncertainty to identify model parameters that attribute most to the total uncertainty (Figure 2c,d). For both variants, the main source of uncertainty in Scenario 1 is emissions from forest biomass, and for Scenarios 3 and 4, emissions from fertilizer use (Figure 2c,d). Within fertilizer use, the main contributors to uncertainty are the estimation of the direct N₂O emission factor for the high-efficiency variant (Figure 2c), and of N-AE for the low-efficiency variant (Figure 2d).

3.3 Maize intensification and agronomic N use efficiencies

The effects of initial N-AE on the outcomes were further explored for maize, by using a range of initial N-AE values instead of the fixed value of 14.3 kg grain yield kg/N as assumed in the low-efficiency variant. Figure 3 shows for maize at which initial N-AE value intensification is still superior to land use conversion in terms of GHG emissions. As already noted, with current nutrient management (i.e. an initial N-AE of 14.3 kg grain yield/kg N), Scenario 3 results in less GHG emission than Scenario 4, and we estimate that it would be most optimal in terms of minimum GHG emissions to intensify maize production until ca. 60% of Y_w, leading to a total GHG emissions of 92 Mton CO₂ eqv. (Figure 3b). When initial N-AE exceeds 20 kg grain yield/kg N, Scenario 4 results in less GHG emission than Scenario 3 (Figure 3a). With an initial N-AE of 30 kg grain yield kg/N, intensifying maize production until ca. 70% of Y_w would be most optimal and emissions would be reduced to 50 Mton CO₂ eqv. Note that at an initial N-AE of 30, Scenarios 3 and 4 are clearly superior to Scenarios 1 and 2 (Figure 3a).

4.1 Nutrient inputs

There is currently extensive soil nutrient depletion, which is general practice in SSA (Giller, Witter, Corbeels, & Tittonell, 2009), due to low nutrient inputs. We showed that a large increase in nitrogen (N) application is required to sustain current yields. Intensifying cereal production will require even more N application, thereby reaching application levels which are similar to European Union average values (Van Grinsven et al., 2012).

Irrespective of the scenario chosen, increased N input should always come with efficient nutrient management, as inefficient nutrient management (i.e. low values of agronomic N use efficiency, N-AE) results in high emissions of GHGs (plus other types of nutrient losses) from fertilizer use independent of the intensification level. The N-AE is of key importance in determining whether intensification is more favourable than area expansion for climate change mitigation and how much. If current N-AE of 14 kg grain yield kg/N would be increased to 30 kg grain yield kg/N, this would on average across the scenarios already result in a reduction of 26% in GHG emissions. We suggest that an N-AE of 30 kg grain yield kg/N can be well achieved in SSA, as in some locations, it is already obtained (ten Berge et al., 2019), and it is also a common efficiency achieved in for instance the United States (Ciampitti & Vyn, 2012). Yet, such enhanced use efficiency requires substantial improvements in current management practices, including good seed quality of the right crop cultivars, good planting densities, balanced crop nutrition, integrated soil fertility management (Vanlauwe et al., 2010) and improvements in controls of weeds, pests and diseases. There is thus a need for farmers to adopt new strategies, but adoption of these measures is currently already difficult for smallholder farmers and might become more difficult in the future as more climate variability is expected (Burke & Lobell, 2010). This points at the need to invest in research and development on nutrient management to go hand in hand with good agronomy to enhance the nutrient use efficiency of fertilizers.

4.2 Avoiding crop area expansion

Our study reveals that compared to scenarios in which area expansion is the main pathway to increase production (which has been the case in recent decades), intensification of cereal production with efficient use of fertilizers will lead to much lower GHG emissions and might conserve forest and/or permit reforestation. Forest conversion to agriculture might conflict with SSA countries' global commitments set out in the Cancun Agreements on REDD+ (Reducing Emissions from Deforestation and Forest Degradation and forest conservation, sustainable management of forests and enhancement of forest carbon stocks) in order to address the challenge of climate change (UNFCCC, 2010). It contradicts also with the SSA countries' strategic response to climate change effects (e.g. Ethiopia's climate resilient green economy strategy; Federal-Democratic-Republic-of-Ethiopia, 2011), and to the national targets set in their National Determined Contribution. While we show that intensification results in lower emissions because of reducing the need for area expansion, we also note that higher yields may not necessarily spare land for nature, and might end up driving deforestation rather than reducing it (Ewers, Scharlemann, Balmford, & Green, 2009). Whether yield increases spare land or stimulate expansion depends on various factors such as markets for agricultural products and forest governance and conservation policies (Ewers et al., 2009).

4.3 Methodological considerations and uncertainties

We showed how our results depend on various assumptions and uncertainty in parameters. The IPCC tier 1 estimate for direct N₂O emissions, which is directly linked to mineral fertilizer N applied (IPCC, 2006a), contributed most to the uncertainty in the resulting GHG emissions of each scenario. Despite a recent meta-analysis of the N₂O emission factor (Albanito et al., 2017), it is widely recognized that especially for Africa, a better estimate for the N₂O emission factor is required, due to the limited availability of data in the region (Albanito et al., 2017; Reay et al., 2012). More attention should therefore be given in future research to obtain more precise estimates of this emission factor.

In this analysis, we assumed that mineral fertilizer is used to fulfil the nutrient input requirements, but other sources of nutrients can also be used, such as leguminous crops and animal manure. Per kg of N applied, animal manure results in similar direct N₂O emissions (IPCC, 2006a), but have additional GHG emissions from amongst other storage and methane emissions from animals (Monteny, Bannink, & Chadwick, 2006), while no CO₂ emissions from fertilizer production. The specific size of the effect of including animal manure on GHG emissions is therefore unknown, but generally manure is only sparsely available in most of SSA. In each of the four presented scenarios, the available amount of manure which can be used will be similar, thereby probably not changing the observed trends and our main conclusions.

Inclusion of leguminous crops in cropping systems can reduce the mineral fertilizer N requirement for the subsequent crop (Jensen et al., 2012). A meta-analysis for SSA revealed that this residual effect of legumes can result in 450–700 kg/ha extra maize yield (Franke, Van Den Brand, Vanlauwe, & Giller, 2017). This potentially lowers the input of mineral fertilizer by 0–51 kg N/ha resulting in 0.03–12.18 Mton CO₂ eqv. less total GHG emissions depending on the scenario. However, Palm et al. (2010) showed for two contrasting sites in SSA that GHG emissions per unit maize produced is lower if only mineral fertilizer is used in comparison to using only green manure. Apparently, the increased N₂O emissions from legume residue incorporation outweigh the benefits of reduced needs for mineral fertilizer inputs.

In this study, we included current climate variability, but did not consider the implications of long-term climate change. In addition, we also did not take into account the adoption of technological and genetic improvements which may partly offset negative effects of climate change. Furthermore, until 2050, the projected effect of climate change is not only highly uncertain but also relatively small compared to the large yield gaps (see van Ittersum et al., 2016 for more details). It seems likely that due to climate change, potential yields will be affected (varying between a slightly positive impact, up to 10%, in high elevation regions of east SSA to negative impact up to approximately 20% elsewhere in SSA; Niang et al., 2014; Porter et al., 2014), but how climate change will affect N-AE, and thus, N requirements remains unclear. If we assume climate change has no effect on N-AE, it will result in the need for more area expansion (assuming average yields will somewhat decrease), and thus, climate change will favour intensification scenarios rather than expansion scenarios in terms of GHG emissions. If N-AE is negatively affected by climate change, the sensitivity analysis of N-AE revealed that this will favour the expansion scenarios, but at the same time, climate change results in the need for more expansion because of lower yields. So, overall, we argue that short-term climate change is likely to have neutral to aggravating effects on relative advantages of intensification scenarios over expansion scenarios.