Will dairy cattle production in West Africa be challenged by heat stress in the future?

This study focuses on heat stress conditions for dairy cattle production in West Africa under current and future climatic conditions. After testing the accuracy of the dynamically downscaled climate datasets for simulating the historical daily maximum temperature (Tmax) and relative humidity (RH) in West Africa for 50 meteorological stations, we used the dataset for calculating the temperature-humidity index (THI), i.e., an index indicating heat stress for dairy cattle on a daily scale. Calculations were made for the historical period (1981–2010) using the ERA-Interim reanalysis dataset, and for two future periods (2021–2050 and 2071–2100) using climate predictions of the GFDLESM2M, HadGEM2-ES, and MPI-ESM-MR Global Circulation Models (GCMs) under the RCP4.5 emission scenario. Here, we show that during the period from 1981 to 2010 for > 1/5 of the region of West Africa, the frequency of severe/danger heat events per year, i.e., events that result in significant decreases in productive and reproductive performances, increased from 11 to 29–38 days (significant at 95% confidence level). Most obvious changes were observed for the eastern and southeastern parts. Under future climate conditions periods with severe/danger heat stress events will increase further as compared with the historical period by 5–22% depending on the GCM used. Moreover, the average length of periods with severe/danger heat stress is expected to increase from ~ 3 days in the historical period to ~ 4–7 days by 2021–2050 and even to up to 10 days by 2071–2100. Based on the average results of three GCMs, by 2071–2100, around 22% of dairy cattle population currently living in this area is expected to experience around 70 days more of severe/danger heat stress (compare with the historical period), especially in the southern half of West Africa. The result is alarming, as it shows that dairy production systems in West Africa are jeopardized at large scale by climate change and that depending on the GCMused, milk production might decrease by 200–400 kg/year by 2071–2100 in around 1, 7, or 11%. Our study calls for the development of improved dairy cattle production systems with higher adaptive capacity in order to deal with expected future heat stress conditions

A redistribution of nitrogen fertiliser across global croplands can help achieve food security within environmental boundaries

A major societal challenge is to produce sufficient food for a growing global population while simultaneously reducing agricultural nitrogen pollution to within safe environmental boundaries. Here we use spatially-resolved, process-based simulations of cereal cropping systems (at 0.5° resolution) to show how redistribution of nitrogen fertiliser usage could meet this challenge on a global scale. Focusing on major cereals (maize, wheat and rice), we find that current production could be (i) maintained with a 32% reduction in total global fertiliser use, or (ii) increased by 15% with current nitrogen fertiliser levels. This would come with substantial reductions in environmental nitrogen losses, allowing cereal production to stay within environmental boundaries for nitrogen pollution. The more equal distribution of nitrogen fertiliser across global croplands would reduce reliance on current breadbasket areas, allow regions such as Sub-Saharan Africa to move towards self-sufficiency and alleviate nitrogen pollution in East Asia and other highly fertilised regions

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

Livestock enclosures in drylands of Sub-Saharan Africa are overlooked hotspots of N2_{2}O emissions

Sub-Saharan Africa (SSA) is home to approximately ¼ 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 N$_{2}$O 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 N$_{2}$O at the continental scale, contributing ~5% of the current estimate of total anthropogenic N$_{2}$O emissions for all of Africa

Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

A multivariate bias correction based on N-dimensional probability density function transform (MBCn) technique is applied to four different high-resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias-correction on the historical (1980–2005) period, the inter-variable relationships, and the measures of spatio-temporal consistency are investigated. The focus is on the discrepancies between the original and the bias-corrected results over five agro-ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara

Multivariate bias‐correction of high‐resolution regional climate change simulations for West Africa: performance and climate change implications

A multivariate bias correction based on N‐dimensional probability density function transform (MBCn) technique is applied to four different high‐resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near‐surface air temperature, near‐surface maximum air temperature, near‐surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias‐correction on the historical (1980–2005) period, the inter‐variable relationships, and the measures of spatio‐temporal consistency are investigated. The focus is on the discrepancies between the original and the bias‐corrected results over five agro‐ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara

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

Cassava yield gap—A model-based assessment in Nigeria

Introduction: Cassava production is essential for food security in sub-Saharan Africa and serves as a major calorie-intake source in Nigeria. Estimating the yield gap in Nigeria is essential to indicate the most important limiting factors for production, and identify the yield gap hotspot areas. Secondly, these assessments may help set agendas in policy development and research prioritization where current information is scarce. Materials and methods: Here, Wwe used a crop model, LINTUL5, calibrated for five different cassava varieties based on field experiments embedded into a modeling framework SIMPLACE to estimate potential, water- and nutrient (current) limited cassava yield gaps (YG) as affected by climate factors and contributing a better understanding of yield gaps and its potentials in 30 states of Nigeria. Results: Our study shows that cumulative radiation and precipitation were the most significant factors associated with cassava yield variability (p < 0.01). The YG averaged across states was estimated as 18.2 Ton7ha-1, with a maximum of 31.2 Ton7ha-1 35 in Kano state. Across the states, nutrient limitation accounts for 55.3% of the total cassava yield gap, while the remaining 44.7% is attributed to water limitation. The highest untapped water-limited yields were estimated in the northern states, such as Bauchi, Gombe, and Sokoto, characterized by the short rainy season. Conclusion: Our results showed that most northern states are better equipped to become leading cassava producers in Nigeria under adequate crop management practices involving irrigation and soil fertility enhancement. We reached this conclusion because the northern states usually receive the highest radiation from their characteristic reduced cloud cover, even Therefore, policy and management interventions can be prioritized in these areas. Conclusively, the current cassava yield levels can be increased by a factor of five by emphasizing nutrient and soil health management and irrigation, particularly in areas characterized by a shorter rainy season (Sudan Savanna) in Nigeria