In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.

This review aims to elucidate the contemporary methods of measuring and estimating methane (CH4) emissions from ruminants. Six categories of methods for measuring and estimating CH4 emissions from ruminants are discussed. The widely used methods in most CH4 abatement experiments comprise the gold standard respiration chamber, in vitro incubation, and the sulfur hexafluoride (SF6) techniques. In the spot sampling methods, the paper discusses the sniffer method, the GreenFeed system, the face mask method, and the portable accumulation chamber. The spot sampling relies on the measurement of short-term breath data adequately on spot. The mathematical modeling methods focus on predicting CH4 emissions from ruminants without undertaking extensive and costly experiments. For instance, the Intergovernmental Panel on Climate Change (IPCC) provides default values for regional emission factors and other parameters using three levels of estimation (Tier 1, 2 and 3 levels), with Tier 1 and Tier 3 being the simplest and most complex methods, respectively. The laser technologies include the open-path laser technique and the laser CH4 detector. They use the laser CH4 detector and wireless sensor networks to measure CH4 flux. The micrometeorological methods rely on measurements of meteorological data in line with CH4 concentration. The last category of methods for measuring and estimating CH4 emissions in this paper is the emerging technologies. They include the blood CH4 concentration tracer, infrared thermography, intraruminal telemetry, the eddy covariance (EC) technique, carbon dioxide as a tracer gas, and polytunnel. The emerging technologies are essential for the future development of effective quantification of CH4 emissions from ruminants. In general, adequate knowledge of CH4 emission measurement methods is important for planning, implementing, interpreting, and comparing experimental results.

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