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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 GFDL-ESM2M, 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 GCM used, 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.

Recent estimates show that one third of the world's land and water resources are highly or moderately degraded. Global economic losses from land degradation (LD) are as high as USD $10.6 trillion annually. These trends catalyzed a call for avoiding future LD, reducing ongoing LD, and reversing past LD, which has culminated in the adoption of Sustainable Development Goal (SDG) Target 15.3 which aims to achieve global land degradation neutrality (LDN) by 2030. The political momentum and increased body of scientific literature have led to calls for a 'new science of LDN' and highlighted the practical challenges of implementing LDN. The aim of the present study was to derive LDN soil organic carbon (SOC) stock baseline maps by comparing different digital soil mapping (DSM) methods and sampling densities in a case study (Otjozondjupa, Namibia) and evaluate each approach with respect to complexity, cost, and map accuracy. The mean absolute error (MAE) leveled off after 100 samples were included in the DSM models resulting in a cost tradeoff for additional soil sample collection. If capacity is sufficient, the random forest DSM method out-performed other methods, but the improvement from using this more complex method compared to interpolating the soil sample data by ordinary kriging was minimal. The lessons learned while developing the Otjozondjupa LDN SOC baseline provide valuable insights for others who are responsible for developing LDN baselines elsewhere.The staggering evidence of the magnitude and rate of LD, and its continued negative impacts on food and fiber production and human well-being, catalyzed a call for avoiding future LD, reducing ongoing LD, and reversing past LD. The concept of 'zero net land degradation' (ZNLD) was first mentioned at the UN Convention to Combat Desertification (UNCCD) in 2011 [7].The following year, ZNLD was proposed at the UN Conference on Sustainable Development (Rio+20). Two important events in 2015 further solidified the importance of Land Degradation Neutrality (LDN). The Sustainable Development Goals (SDGs) were adopted by the UN General Assembly and SDG Target 15.3 specifically aims to "combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world" by 2030. Later that year, during the twelfth UNCCD Conference of the Parties (COP), parties decided to integrate LDN into the implementation of the UNCCD.The political momentum (i) translated into new debates, and (ii) increased the body of scientific literature including calls for a 'new science of LDN' [8]. The LDN debates, thus far, have focused on its feasibility [9,10], its scientific conceptual framework [11,12], practical issues concerning operationalization [8,13], and its potential to unify the three Rio Conventions [14]. Meanwhile, the scientific community agreed to connect LDN to the SDG Target 15.3 indicator, which is the 'proportion of land that is degraded over total land area,' and this is measured with t...

Heat stress is a global issue constraining pig productivity, and it is likely to intensify under future climate change. Technological advances in earth observation have made tools available that enable identification and mapping livestock species that are at risk of exposure to heat stress due to climate change. Here, we present a methodology to map the current and likely future heat stress risk in pigs using R software by combining the effects of temperature and relative humidity. We applied the method to growing-finishing pigs in Uganda. We mapped monthly heat stress risk and quantified the number of pigs exposed to heat stress using 18 global circulation models and projected impacts in the 2050s. Results show that more than 800 000 pigs in Uganda will be affected by heat stress in the future. The results can feed into evidence-based policy, planning and targeted resource allocation in the livestock sector.

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.