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Livestock are an important source of livelihoods in agricultural systems in sub-Saharan Africa (SSA), while also being the largest source of national greenhouse gas (GHG) emissions in most African countries. As a consequence, there is a critical need for data on livestock GHG sources and sinks to develop national inventories, as well as conduct baseline measurements and intervention testing to mitigate GHG emissions and meet ambitious national climate goals. Our objective was to review studies on GHG emissions from livestock systems in SSA, as well as soil carbon storage in livestock-dominated systems (i.e., grasslands and rangelands), to evaluate best current data and suggest future research priorities. To this end, we compiled studies from SSA that determined emission factors (EFs) for enteric methane and manure emissions, along with studies on soil organic carbon (SOC) stocks in SSA. We found that there has been limited research on livestock GHG emissions and SOC relative to national ambitions for climate change mitigation in SSA. Enteric methane emission factors (EFs) in low productivity cattle systems may be lower than IPCC Tier 1 default EFs, whereas small ruminants (i.e. sheep and goats) had higher EFs compared to IPCC Tier 1 EFs. Manure EFs were equal to or lower than IPCC Tier 1 EFs for deposited manure (while grazing), manure applied as fertilizer, and manure management. SOC stocks for grasslands and rangelands in SSA show broad agreement with IPCC estimates, but there was a strong geographic bias and many studies did not report soil type, bulk density, or SOC stocks at >30 cm depth. In general, the largest data gaps included information for manure (quantity, quality, management), small ruminants, agropastoral/pastoralist systems, and in general from West Africa. Future research should focus on filling major data gaps on locally appropriate mitigation interventions and improving livestock activity data for developing Tier 2 GHG inventories in SSA. At the science-policy interface, all parties would benefit from enhanced coordination within the research community and between researchers and African governments to improve Tier 2 inventories and harmonize measurement for mitigation in livestock systems in SSA.

Additional file 16: Review history.

Additional file 1: Fig. S1. Heatmaps displaying counts of enzymes belonging to each CAZyme family, in each MAG. Clusters of CAZyme families involved in the breakdown of selected polysaccharides (highlighted by coloured boxes). Heatmaps generated from CAZymes present in African MAGs. Figure 7a in high resolution.

Additional file 3: Fig. S3. Distribution of estimated statistical power. Sampling with replacement generated 1000 Euclidean distance matrices based on our MAG abundance data for a range of sample size groupings (3:3:3, 4:4:4, 4:5:5 and 5:5:5) representing 40%:60%:80% MER diet treatments respectively. The bootstrap PERMANOVA function estimated statistical power for each matrix based on 10,000 bootstrap permutations. Distribution of the power estimates is shown for each experimental design and at P = 0.05 (panel A), P = 0.01 (panel B) and P = 0.001 (panel C).

Additional file 2: Fig. S2. Heatmaps displaying counts of enzymes belonging to each CAZyme family, in each MAG. Clusters of CAZyme families involved in the breakdown of selected polysaccharides (highlighted by coloured boxes). Heatmaps generated from CAZymes present in Scottish RUGs. Figure 7b in high resolution.

Test scores are intended to provide a measure of examinee's estimate of ability. High ability examinees are expected to get few easy items wrong and low ability examinees are exepcted to get few difficult items right. But there are occasions when the test-taking behavior of some atypical examinees may be so unsual that their test scores cannot be regarded as an appropriate measure of ability. An atypical examinee can have a spuriously low or a spuriously high score. However, appropriateness indices can be used to identify examinees with potentially inaccurate total scores. Appropriateness indices provide quantitative, measures of response pattern atypicality. These indices fall into two major categories: (a) IRT-based and (b) non-IRT based indices. The dependency of non-IRT based indices on the item difficulty order of a particular group has rendered them inadequate for detecting aberrant reponse patterns. IRT-based indices are group invariant. Researchers have investigated the effectiveness and the distributions of these indices under varying conditions of testing. However, some test situations might require efficient and accurate indices of appropriateness measurement for restricted samples. It might be helpful, for example, to accurately identify examinees with potential spuriously low scores falling just the below the criterion of a minimum competency test, on a certification test, it might be helpful to concentrate on identifying examinees with spuriously high scores. Therefore, the effects of item difficulty 7 and examinee ability distributions on the effectiveness and the distributional characeristics of LZ and ECIZ4 (IRT-based) appropriateness indices were investigated in this study. To examine the effects of item difficulty and ability distributions on the distributional characteristics of LZ and ECIZ4, data were generated in nine combinations of item difficulty and ability distributions to simulate the responses of 2000 examinees to 60 test items according to the three-parameter model. Three uniform distributions of item difficulty were used. Items typical of diagnostic tests were generated in the interval -3.0 to +1.2; items typical of power tests were generated in the interval of -3.0 to +3.0; and items typical of certification and licencing tests were generated in the interval of -1.2 to +3.0. Three distributions of ability were used. Thetas typical of low, medium, and high ability examinees were generated to have normal distributions with the means of -1.2, 0.0, and +1.2 respectively and each with a standard deviation of 0.6. The mean, standard deviation, skewness, kurtosis, and the percentile estimates of LZ and ECIZ4 were significantly affected by the variations of item difficulty and ability distributions. The distributions of the two indices approximated a normal distribution when the ability estimates matched the item difficulty. Overall, the distributions of LZ approximated a normal distribution better than the distribution of ECIZ4. To examine the effectiveness of LZ and ECIZ4 in detecting aberrant response patterns, two samples, each consisting of 500 response patterns (for spuriously low and spuriously high) were generated for each of the nine combinations of item difficulty and ability distribution and subjected to spurious treatments. Twenty percent and 10% spuriously high scores were created by randomly selecting 20% or 10% of the original responses and changing incorrect answers to correct. Twenty percent and 10% spuriously low scores were created by randomly selecting 20% or 10% of the original responses and changing correct answers to incorrect. The percentile estimates obtained were used as cutoff points to classify response patterns as aberrant or non-aberrant. Spuriously low aberrant response patterns were easier to detect by the two indices under the low item difficulty and spuriously high aberrant response patterns were easier to detect under high item difficulty. At low (0.01 and 0.05) false positive rates, LZ had higher detection rates of spuriously high and spuriously low aberrant response patterns than ECIZ4 under the high item difficulty; and ECIZ4 had higher detection rates than LZ under the medium and under the low item difficulty. Twenty percent treatment samples were easier to detect by the two indices than the 10% treatment samples.