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

Cropping is responsible for substantial emissions of greenhouse gasses (GHGs) worldwide through the use of fertilizers and through expansion of agricultural land and associated carbon losses. Especially in sub-Saharan Africa (SSA), GHG emissions from these processes might increase steeply in coming decades, due to tripling demand for food until 2050 to match the steep population growth. This study assesses the impact of achieving cereal self-sufficiency by the year 2050 for 10 SSA countries on GHG emissions related to different scenarios of increasing cereal production, ranging from intensifying production to agricultural area expansion. We also assessed different nutrient management variants in the intensification. Our analysis revealed that irrespective of intensification or extensification, GHG emissions of the 10 countries jointly are at least 50% higher in 2050 than in 2015. Intensification will come, depending on the nutrient use efficiency achieved, with large increases in nutrient inputs and associated GHG emissions. However, matching food demand through conversion of forest and grasslands to cereal area likely results in much higher GHG emissions. Moreover, many countries lack enough suitable land for cereal expansion to match food demand. In addition, we analysed the uncertainty in our GHG estimates and found that it is caused primarily by uncertainty in the IPCC Tier 1 coefficient for direct N2 O emissions, and by the agronomic nitrogen use efficiency (N-AE). In conclusion, intensification scenarios are clearly superior to expansion scenarios in terms of climate change mitigation, but only if current N-AE is increased to levels commonly achieved in, for example, the United States, and which have been demonstrated to be feasible in some locations in SSA. As such, intensifying cereal production with good agronomy and nutrient management is essential to moderate inevitable increases in GHG emissions. Sustainably increasing crop production in SSA is therefore a daunting challenge in the coming decades.

Can sub-Saharan Africa feed itself?

Although global food demand is expected to increase 60% by 2050 compared with 2005/2007, the rise will be much greater in sub-Saharan Africa (SSA). Indeed, SSA is the region at greatest food security risk because by 2050 its population will increase 2.5-fold and demand for cereals approximately triple, whereas current levels of cereal consumption already depend on substantial imports. At issue is whether SSA can meet this vast increase in cereal demand without greater reliance on cereal imports or major expansion of agricultural area and associated biodiversity loss and greenhouse gas emissions. Recent studies indicate that the global increase in food demand by 2050 can be met through closing the gap between current farm yield and yield potential on existing cropland. Here, however, we estimate it will not be feasible to meet future SSA cereal demand on existing production area by yield gap closure alone. Our agronomically robust yield gap analysis for 10 countries in SSA using location-specific data and a spatial upscaling approach reveals that, in addition to yield gap closure, other more complex and uncertain components of intensification are also needed, i.e., increasing cropping intensity (the number of crops grown per 12 mo on the same field) and sustainable expansion of irrigated production area. If intensification is not successful and massive cropland land expansion is to be avoided, SSA will depend much more on imports of cereals than it does today.

Plant-plant interactions mediate the plastic and genotypic response of Plantago asiatica to CO2: an experiment with plant populations from naturally high CO2 areas.

BACKGROUND AND AIMS: The rising atmospheric CO2 concentration ([CO2]) is a ubiquitous selective force that may strongly impact species distribution and vegetation functioning. Plant-plant interactions could mediate the trajectory of vegetation responses to elevated [CO2], because some plants may benefit more from [CO2] elevation than others. The relative contribution of plastic (within the plant's lifetime) and genotypic (over several generations) responses to elevated [CO2] on plant performance was investigated and how these patterns are modified by plant-plant interactions was analysed. METHODS: Plantago asiatica seeds originating from natural CO2 springs and from ambient [CO2] sites were grown in mono stands of each one of the two origins as well as mixtures of both origins. In total, 1944 plants were grown in [CO2]-controlled walk-in climate rooms, under a [CO2] of 270, 450 and 750 ppm. A model was used for upscaling from leaf to whole-plant photosynthesis and for quantifying the influence of plastic and genotypic responses. KEY RESULTS: It was shown that changes in canopy photosynthesis, specific leaf area (SLA) and stomatal conductance in response to changes in growth [CO2] were mainly determined by plastic and not by genotypic responses. We further found that plants originating from high [CO2] habitats performed better in terms of whole-plant photosynthesis, biomass and leaf area, than those from ambient [CO2] habitats at elevated [CO2] only when both genotypes competed. Similarly, plants from ambient [CO2] habitats performed better at low [CO2], also only when both genotypes competed. No difference in performance was found in mono stands. CONCLUSION: The results indicate that natural selection under increasing [CO2] will be mainly driven by competitive interactions. This supports the notion that plant-plant interactions have an important influence on future vegetation functioning and species distribution. Furthermore, plant performance was mainly driven by plastic and not by genotypic responses to changes in atmospheric [CO2].

How light competition between plants affects their response to climate change.

How plants respond to climate change is of major concern, as plants will strongly impact future ecosystem functioning, food production and climate. Here, we investigated how vegetation structure and functioning may be influenced by predicted increases in annual temperatures and atmospheric CO2 concentration, and modeled the extent to which local plant-plant interactions may modify these effects. A canopy model was developed, which calculates photosynthesis as a function of light, nitrogen, temperature, CO2 and water availability, and considers different degrees of light competition between neighboring plants through canopy mixing; soybean (Glycine max) was used as a reference system. The model predicts increased net photosynthesis and reduced stomatal conductance and transpiration under atmospheric CO2 increase. When CO2 elevation is combined with warming, photosynthesis is increased more, but transpiration is reduced less. Intriguingly, when competition is considered, the optimal response shifts to producing larger leaf areas, but with lower stomatal conductance and associated vegetation transpiration than when competition is not considered. Furthermore, only when competition is considered are the predicted effects of elevated CO2 on leaf area index (LAI) well within the range of observed effects obtained by Free air CO2 enrichment (FACE) experiments. Together, our results illustrate how competition between plants may modify vegetation responses to climate change.

High-resolution global maps of yield potential with local relevance for targeted crop production improvement.

Identifying untapped opportunities for crop production improvement in current cropland is crucial to guide food availability interventions. Here we integrated an agronomically robust bottom-up approach with machine learning to generate global maps of yield potential of high resolution (ca. 1 km2 at the Equator) and accuracy for maize, wheat and rice. These maps serve as a robust reference to benchmark farmers' yields in the context of current cropping systems and water regimes and can help to identify areas with large room to increase crop yields.

Daily bias-corrected weather data and daily simulated growth data of maize, millet, sorghum, and wheat in the changing climate of sub-Saharan Africa.

Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies -WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water-limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes.

Costs and benefits of synthetic nitrogen for global cereal production in 2015 and in 2050 under contrasting scenarios.

Cereals are the most important global staple crop and use more than half of global cropland and synthetic nitrogen (N) fertilizer. While this synthetic N may feed half of the current global population, it has led to a massive increase in reactive N loss to the environment, causing a suite of impacts, offsetting the benefits of N fertilizers for food security and agricultural economy. To address these complex issues, the NBCalCer model was developed to quantify the global effects of N input on crop yields, N budgets and environmental impacts and to assess the associated social benefits and costs. Three Shared Socioeconomic Pathway scenarios (SSPs) were considered with decreasing N agri-environmental ambitions, through contrasting climate and N policy ambitions: sustainability (SSP1H), middle-of-the-road (SSP2M) and fossil-fueled development (SSP5L). In the base year the contribution of synthetic N fertilizer to global cereal production was 44 %. Global modelled grain yield was projected to increase under all scenarios while the use of synthetic N fertilizer decreases under all scenarios except SSP5L. The total N surplus was projected to be reduced up to 20 % under SSP1H but to increase under SSP5L. The Benefit-Cost-Ratio (BCR) was calculated as the ratio between the market benefit of increased grain production by synthetic N and the summed cost of fertilizer purchase and the external cost of the N losses. In base year the BCR was well above one in all regions, but in 2050 under SSP1H and SSP5L decreased to below one in most regions. Given the concerns about food security, environmental quality and its interaction with biodiversity loss, human health and climate change, the new paradigm for global cereal production is producing sufficient food with minimum N pollution. Our results indicate that achieving this goal would require a massive change in global volume and distribution of synthetic N.

Spatial frameworks for robust estimation of yield gaps.

Food security interventions and policies need reliable estimates of crop production and the scope to enhance production on existing cropland. Here we assess the performance of two widely used 'top-down' gridded frameworks (Global Agro-ecological Zones and Agricultural Model Intercomparison and Improvement Project) versus an alternative 'bottom-up' approach (Global Yield Gap Atlas). The Global Yield Gap Atlas estimates extra production potential locally for a number of sites representing major breadbaskets and then upscales the results to larger spatial scales. We find that estimates from top-down frameworks are alarmingly unlikely, with estimated potential production being lower than current farm production at some locations. The consequences of using these coarse estimates to predict food security are illustrated by an example for sub-Saharan Africa, where using different approaches would lead to different prognoses about future cereal self-sufficiency. Our study shows that foresight about food security and associated agriculture research priority setting based on yield potential and yield gaps derived from top-down approaches are subject to a high degree of uncertainty and would benefit from incorporating estimates from bottom-up approaches.