The potential consequences of grain-trade disruption on food security in the Middle East and North Africa region.

The Middle East and North Africa (MENA) region has seen remarkable population growth over the last century, outpacing other global regions and resulting in an over-reliance on food imports. In consequence, it has become heavily dependent on grain imports, making it vulnerable to trade disruptions (e.g., due to the Russia-Ukraine War). Here, we quantify the importance of imported grains for dietary protein and energy, and determine the level of import reductions at which countries are threatened with severe hunger. Utilizing statistics provided by the Food and Agriculture Organization (FAO), we employed a stepwise calculation process to quantify the allocation of both locally produced and imported grains between the food and feed sectors. These calculations also enabled us to establish a connection between feed demand and production levels. Our analysis reveals that, across the MENA region, 40% of total dietary energy (1,261 kcal/capita/day) and 63% of protein (55 g/capita/day) is derived from imported grains, and could thus be jeopardized by trade disruptions. This includes 164 kcal/capita/day of energy and 11 g/capita/day of protein imported from Russia and Ukraine. If imports from these countries ceased completely, the region would thus face a severe challenge to adequately feed its population. This study emphasizes the need for proactive measures to mitigate risks and ensure a stable food and feed supply in the MENA region.

A global dataset for the production and usage of cereal residues in the period 1997-2021.

Crop residue management plays an important role in determining agricultural greenhouse gas emissions and related changes in soil carbon stocks. However, no publicly-available global dataset currently exists for how crop residues are managed. Here we present such a dataset, covering the period 1997-2021, on a 0.5° resolution grid. For each grid cell we estimate the total production of residues from cereal crops, and determine the fraction of residues (i) used for livestock feed/bedding, (ii) burnt on the field, (iii) used for other off-field purposes (e.g. domestic fuel, construction or industry), and (iv) left on the field. This dataset is the first of its kind, and can be used for multiple purposes, such as global crop modelling, including the calculation of greenhouse gas inventories, estimating crop-residue availability for biofuel production or modelling livestock feed availability.

Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.

A shift from cattle to camel and goat farming can sustain milk production with lower inputs and emissions in north sub-Saharan Africa's drylands.

Climate change is increasingly putting milk production from cattle-based dairy systems in north sub-Saharan Africa (NSSA) under stress, threatening livelihoods and food security. Here we combine livestock heat stress frequency, dry matter feed production and water accessibility data to understand where environmental changes in NSSA's drylands are jeopardizing cattle milk production. We show that environmental conditions worsened for ∼17% of the study area. Increasing goat and camel populations by ∼14% (∼7.7 million) and ∼10% (∼1.2 million), respectively, while reducing the dairy cattle population by ∼24% (∼5.9 million), could result in ∼0.14 Mt (+5.7%) higher milk production, lower water (-1,683.6 million m3, -15.3%) and feed resource (-404.3 Mt, -11.2%) demand-and lower dairy emissions by ∼1,224.6 MtCO2e (-7.9%). Shifting herd composition from cattle towards the inclusion of, or replacement with, goats and camels can secure milk production and support NSSA's dairy production resilience against climate change.

Beyond livestock carrying capacity in the Sahelian and Sudanian zones of West Africa.

We applied the process-based model, LandscapeDNDC, to estimate feed availability in the Sahelian and Sudanian agro-ecological zones of West Africa as a basis for calculating the regional Livestock Carrying Capacity (LCC). Comparison of the energy supply (S) from feed resources, including natural pasture, browse, and crop residues, with energy demand (D) of the livestock population for the period 1981-2020 allowed us to assess regional surpluses (S > D) or deficits (S < D) in feed availability. We show that in the last 40 years a large-scale shift from surplus to deficit has occurred. While during 1981-1990 only 27% of the area exceeded the LCC, it was 72% for the period 2011-2020. This was caused by a reduction in the total feed supply of ~ 8% and an increase in feed demand of ~ 37% per-decade, driven by climate change and increased livestock population, respectively. Overall, the S/D decreased from ~ 2.6 (surplus) in 1981 to ~ 0.5 (deficit) in 2019, with a north-south gradient of increasing S/D. As climate change continues and feed availability may likely further shrink, pastoralists either need to source external feed or significantly reduce livestock numbers to avoid overgrazing, land degradation, and any further conflicts for resources.

Heat stress will detrimentally impact future livestock production in East Africa.

Climate change-induced increases in temperature and humidity are predicted to impact East African food systems, but the extent to which heat stress negatively affects livestock production in this region is poorly understood. Here we use ERA-Interim reanalysis data to show that the frequency of 'Severe/Danger' heat events for dairy cattle, beef cattle, sheep, goats, swine and poultry significantly increased from 1981 to 2010. Using a multi-model ensemble of climate change projections for 2021-2050 and 2071-2100 (under representative concentration pathway (RCP) 4.5 and 8.5 by the coordinated regional-climate downscaling experiment for Africa (CORDEX-AFRICA)), we show that the frequency of dangerous heat-stress conditions and the average number of consecutive days with heat stress events will significantly increase, particularly for swine and poultry. Our assessment suggests that 4-19% of livestock production occurs in areas where dangerous heat stress events are likely to increase in frequency from 2071 to 2100. With demand for animal products predicted to grow in East Africa, production-specific heat-stress mitigation measures and breeding programmes for increasing heat tolerance are urgently needed for future livestock sector productivity-and future food security-in East Africa.

Livestock enclosures in drylands of Sub-Saharan Africa are overlooked hotspots of N2O emissions.

Sub-Saharan Africa (SSA) is home to approximately » of the global livestock population, which in the last 60 years has increased by factors of 2.5-4 times for cattle, goats and sheep. An important resource for pastoralists, most livestock live in semi-arid and arid environments, where they roam during the day and are kept in enclosures (or bomas) during the night. Manure, although rich in nitrogen, is rarely used, and therefore accumulates in bomas over time. Here we present in-situ measurements of N2O fluxes from 46 bomas in Kenya and show that even after 40 years following abandonment, fluxes are still ~one magnitude higher than those from adjacent savanna sites. Using maps of livestock distribution, we scaled our finding to SSA and found that abandoned bomas are significant hotspots for atmospheric N2O at the continental scale, contributing ~5% of the current estimate of total anthropogenic N2O emissions for all of Africa.