research paper on ethiopian culture

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 11 June 2021

Evidence of the interplay of genetics and culture in Ethiopia

• Saioa López   ORCID: orcid.org/0000-0003-2936-4070 1 , 2   na1 ,
• Ayele Tarekegn 3   na1 ,
• Gavin Band   ORCID: orcid.org/0000-0002-1710-9024 4 ,
• Lucy van Dorp   ORCID: orcid.org/0000-0002-6211-2310 1 , 2 ,
• Nancy Bird   ORCID: orcid.org/0000-0003-2596-874X 1 , 2 ,
• Sam Morris 1 , 2 ,
• Tamiru Oljira   ORCID: orcid.org/0000-0002-8186-1667 5 ,
• Ephrem Mekonnen   ORCID: orcid.org/0000-0003-0416-649X 6 ,
• Endashaw Bekele 7 ,
• Roger Blench 8 , 9 ,
• Mark G. Thomas   ORCID: orcid.org/0000-0002-2452-981X 1 , 2 ,
• Neil Bradman 10 &
• Garrett Hellenthal   ORCID: orcid.org/0000-0002-5760-8020 1 , 2  

Nature Communications volume  12 , Article number:  3581 ( 2021 ) Cite this article

21k Accesses

25 Citations

52 Altmetric

Metrics details

• Genetic variation
• Population genetics

The rich linguistic, ethnic and cultural diversity of Ethiopia provides an unprecedented opportunity to understand the level to which cultural factors correlate with–and shape–genetic structure in human populations. Using primarily new genetic variation data covering 1,214 Ethiopians representing 68 different ethnic groups, together with information on individuals’ birthplaces, linguistic/religious practices and 31 cultural practices, we disentangle the effects of geographic distance, elevation, and social factors on the genetic structure of Ethiopians today. We provide evidence of associations between social behaviours and genetic differences among present-day peoples. We show that genetic similarity is broadly associated with linguistic affiliation, but also identify pronounced genetic similarity among groups from disparate language classifications that may in part be attributable to recent intermixing. We also illustrate how groups reporting the same culture traits are more genetically similar on average and show evidence of recent intermixing, suggesting that shared cultural traits may promote admixture. In addition to providing insights into the genetic structure and history of Ethiopia, we identify the most important cultural and geographic predictors of genetic differentiation and provide a resource for designing sampling protocols for future genetic studies involving Ethiopians.

Similar content being viewed by others

Genetic and linguistic non-correspondence suggests evidence for collective social climbing in the Kol tribe of South Asia

Genetic history of Calabrian Greeks reveals ancient events and long term isolation in the Aspromonte area of Southern Italy

Anatolian genetic ancestry in North Lebanese populations

Introduction.

Ethiopia is one of the world’s most ethnically and culturally diverse countries, with over 70 different languages spoken across more than 80 distinct ethnicities ( www.ethnologue.com ). Its geographic position and history (briefly summarised in Supplementary Note  1 ) motivated geneticists to use blood groups and other classical markers to study human genetic variation 1 , 2 . More recently, the analysis of genomic variation in the peoples of Ethiopia has been used, together with information from other sources, to test hypotheses on possible migration routes at both ‘Out of Africa’ and more recent “Migration into Africa” timescales 3 , 4 . The high genetic diversity in Ethiopians facilitates the identification of novel variants, and this has led to the inclusion of Ethiopian data in studies on the genetics of elite athletes 5 , 6 , 7 , adaptation to living at high elevation 8 , 9 , 10 , 11 , milk drinking 12 , 13 , 14 , tuberculosis 15 , 16 , and drug metabolising enzymes 17 , 18 , 19 .

While the relationships of Beta Israel with other Jewish communities have been the subject of focused research following their migration to Israel 20 , 21 , 22 , studies involving genomic analyses of the history of wider sets of Ethiopian groups have been more limited 23 , 24 . Although as early as 1988 Cavalli-Sforza et al. 25 , drew attention to the importance of bringing together genetic, archaeological and linguistic data, there have been few attempts to systematically do so in studies of Ethiopia 4 , 26 , 27 , 28 , 29 , 30 , 31 . Generally, studies have been limited to analysing data from single autosomal loci, non-recombining portion of the Y chromosome and mitochondrial DNA 24 , 26 , 32 , 33 , 34 and/or relatively few ethnic groups 3 , 27 , 30 , 31 , 35 , 36 , which has limited the inferences that can be drawn. Furthermore, hitherto there has been little exploration of how genetic similarity is associated with shared cultural practices (see however van Dorp et al. 37 ) despite the considerable variation known to exist in cultural practices, particularly in the southern part of the country (The Council of Nationalities, Southern Nations and Peoples Region, 2017 38 ). For example, Ethiopian ethnic groups have a diverse range of religions, social structures and marriage customs, which may impact which groups intermix, and hence provide an on-going case study of socio-cultural selection 39 , 40 , The Council of Nationalities, Southern Nations and Peoples Region, (2017) 38 that can be explored using DNA.

Here we analyse autosomal genetic variation data at 534,915 single nucleotide polymorphisms (SNPs) in 1214 Ethiopian individuals that include 1082 previously unpublished samples and 132 samples from Lazaridis et al. 41 , Gurdasani et al. 42 , and Mallick et al. 28 , 41 , 42 . Our study includes people from 68 distinct self-reported ethnicities (8–73 individuals per ethnic group) that comprise representatives of most of the major language groups spoken in Ethiopia, including Nilo-Saharan (NS) speakers and three branches (Cushitic, Omotic, Semitic) of Afroasiatic (AA) speakers, as well as languages of currently uncertain classification (Chabu, and the speculated, possibly extinct language of the Negede-Woyto) ( www.ethnologue.com ) (Fig.  1a , Supplementary Fig.  1 , Supplementary Data  1 , 2 , Supplementary Note  2 ). Newly genotyped individuals were selected from a larger collection on the basis that their self-reported ethnicity, and typically birthplace, matched that of their parents, maternal grandmother, paternal grandfather, and any other grandparents recorded, analogous to recent studies of population structure in Europe 43 , 44 . For these individuals we also recorded their self-reported religious affiliation (four categories), first language (66 total classifications) and/or second language (40 total classifications) (Supplementary Data  1 ). Furthermore, some of the authors of this study (A.T., N.B.) translated into English and edited a compendium (originally published in Amharic) that documented the oral traditions and cultural practices of 56 ethnic groups of the Southern Nations, Nationalities and Peoples’ Region (SNNPR) of Ethiopia through interviews with members of different ethnic groups (The Council of Nationalities, Southern Nations and Peoples Region, 2017) 38 . From this new resource, we compiled a list of 31 practices that were reported as cultural descriptors by members of 47 different ethnic groups out of the 68 in this study (see “Methods”). These practices include self-declared cultural practices such as male and female circumcision, and 29 different pre-marital and marriage customs, including arranged marriages, polygamy, gifts of beads or belts, and covering the bride in butter.

a Locations of sampled Ethiopians based on birthplace (in some cases slightly moved due to overlap), with landscape colours showing elevation and coloured symbols depicting the language category (plus unclassified languages Negede Woyto, Chabu) of each individual’s ethnic identity. The legend for the symbols is provided in Supplementary Fig.  1 . b Fitted model for genetic similarity (1-TVD; under the “Ethiopia-internal” analysis) between pairs of individuals versus geographic distance, with points depicting the average genetic similarity within 25 km bins, for all individuals (black; dots) or restricting to individuals who report the same group label (green; diamonds), same first language (orange; open squares), same second language (blue; triangles), same religious affiliation (purple; asterisks), or whose reported ethnicities are from the same language group (red; closed squares). Labels at right give permutation-based p values when testing the null hypothesis of no increase in genetic similarity among individuals sharing the given trait (see “Methods”).

We compared SNP patterns in each present-day Ethiopian to those in all other present-day Ethiopians and to the 4500 year-old Ethiopian sample “Mota”, a forager from southern Ethiopia that represents the only presently available ancient genome from the country 4 . We also compared them to a further 16 labelled groups comprised of 39 ancient individuals 36 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 (Supplementary Table  1 ) and 264 present-day non-Ethiopian groups 28 , 41 , 42 , 51 , 54 , 55 comprised of 2678 individuals (average group sample size = 10, range: 1–100), including 106 unpublished samples from nine groups (Supplementary Data  3 ). We focus on inferring patterns of haplotype sharing among individuals, which has increased resolution over commonly-used allele-frequency based techniques 56 , 57 when identifying latent population structure and inferring the ancestral history of peoples sampled from relatively small geographic regions, such as within a country 43 , 58 , 59 .

Our results provide a comprehensive understanding of the relative strength to which different socio-cultural factors are associated with genetic distance in present-day Ethiopians. We provide evidence that recent intermixing is increased among groups, sometimes from distantly-related linguistic affiliations, that live nearby and/or share cultural practices. We also provide an inferred recent admixture history for members of 68 ethnic groups.

Genetic distance is broadly associated with geography, ethnicity, linguistics and shared culture in Ethiopia

Principal components analysis (PCA) 57 , 60 applied to sampled African individuals revealed Ethiopians to be more genetically similar to each other and sampled groups from other east African countries (Kenya, Somalia, Sudan, Tanzania) than to other African populations (Supplementary Fig.  2b ). Runs-of-homozygosity 61 and inferred proportions of genome that are identical-by-descent (IBD) 62 among individuals of the same ethnicity vary substantially across Ethiopian groups (Supplementary Fig.  3a, b ). Ethiopia’s two largest ethnic groups, Amhara and Oromo, have the lowest levels of within-group IBD-sharing (Supplementary Fig.  3a ), and we observe a significant ( p val < 0.001) decrease of homozygosity with increasing population census size across ethnic groups in the SNNPR (Supplementary Fig.  3c ; census from 2007: The Council of Nationalities, Southern Nations and Peoples Region, 2017 38 ).

To measure genetic similarity between pairs of individuals, we calculated the total variation distance (TVD) 43 between their haplotype-sharing patterns inferred by CHROMOPAINTER 63 (see “Materials and Methods”). Mimicking van Dorp et al. 37 , we performed two CHROMOPAINTER analyses in order to infer the broad time periods over which lines of ancestry between individuals diverged (see schematic of approach in Supplementary Fig.  4 ). The first, which we call “Ethiopia-internal,” compares haplotype patterns in each Ethiopian to those in all other sampled individuals. TVD based on this analysis can be thought of as a haplotype-based analogue of the commonly-used F ST 64  genetic distance measure, and the two are correlated in our analyses (Pearson’s r  = 0.63; Mantel-test p value < 0.00001). However, TVD estimates have been shown to be more powerful at distinguishing subtle genetic differences among e.g., African groups 65 . The second, which we call “Ethiopia-external,” instead compares patterns in each Ethiopian only to those among individuals in non-Ethiopian groups. As the “Ethiopia-internal” analysis compares haplotype patterns in each Ethiopian to those in other Ethiopians, including other members of the same ethnic group, it is more sensitive for detecting endogamy effects and admixture among Ethiopian groups 37 , 59 . In contrast, the “Ethiopia-external” analysis mitigates signals related to both of these factors, while remaining sensitive for inferring whether Ethiopians having varying proportions of ancestry related to non-Ethiopian sources due to e.g., different admixture histories 43 . We illustrate this in simulations mimicking our real data (Supplementary Fig.  5 , Supplementary Note  3 ).

We first considered how pairwise genetic similarity among Ethiopians is related to several factors. Under both the “Ethiopia-internal” and “Ethiopia-external” analyses, we found significant associations ( p val < 0.05) between genetic distance and each of geographic distance, elevation difference, ethnicity and first language, after controlling each factor for the others where possible (Fig.  1b , Supplementary Figs.  6, 7 , Supplementary Tables  2 – 6 ). In contrast, we found no significant association ( p val > 0.2) between genetic distance and each of religion and second language (Fig.  1b , Supplementary Fig.  6 , Supplementary Tables  2 – 6 ). However, within six of 16 groups for which we sampled at least five individuals from different religions, we found some nominal evidence (permutation-based p val < 0.05) of genetic isolation between people reporting as Christians versus those reporting as Muslims or those reporting as practicing traditional religions (Supplementary Table  7 ).

We next averaged pairwise genetic similarity values among individuals from the same versus different group labels (Supplementary Fig.  8 ). Consistent with the relationships depicted by PCA (Supplementary Fig.  2 ), on average Ethiopian groups are more genetically similar to other Ethiopian groups than they are to the non-Ethiopian groups included in this study (Supplementary Fig.  8 , Supplementary Data  5, 6 ). We found a significant association between genetic similarity and reporting shared cultural traits among SNNPR groups under the “Ethiopia-internal” analysis (Mantel-test p value < 0.03), which remained after accounting for geographic or elevation distance (partial Mantel-test p value < 0.05) or language group (partial Mantel-test p value < 0.03) (Supplementary Table  8 ).

To facilitate comparisons of genetic patterns among groups, we generated an interactive map that graphically displays the genetic similarity among groups under each of the “Ethiopia-internal” and “Ethiopia-external” analyses ( https://www.well.ox.ac.uk/~gav/projects/ethiopia/ ), with averages summarised in Supplementary Fig.  8 and Supplementary Data  5, 6 . As examples, we provide three observations based on these findings. The first observation is that, under the “Ethiopia-internal” analysis, Ari and Wolayta people who work as cultivators or weavers are more genetically similar to members of other ethnicities on average than they are to people from their own ethnicities who work as potters, blacksmiths and tanners (top left squares in Fig.  2a ). This is consistent with the social marginalisation reported to be associated with occupational classes in these ethnic groups 66 , 67 . Despite this, under the “Ethiopia-external” analysis, Ari and Wolayta are more genetically similar to members of their own ethnicities on average, regardless of occupation (bottom right of squares in Fig.  2a ). Therefore, in contrast to indications given from the “Ethiopia-internal” analysis (Supplementary Data  5 ) and F ST (Supplementary Data  11 ), the “Ethiopia-external” results suggest that individuals from different occupations within the same ethnic group are more recently related to each other than they are to any other ethnic group.

Average pairwise genetic similarity (1-TVD) between individuals from different Ethiopian labelled groups (coloured on axis by language category–see Fig. 1 a), under the “Ethiopia-internal” analysis (top left, red colour scale) versus the “Ethiopia-external” analysis (bottom right, blue colour scale). a Genetic similarity between Ari and Wolayta (Wol) occupational groupings (C = cultivator, P = potter, S = blacksmith, T = tanner, W = weaver), with green asterisks denoting relatively high similarity above the green lines in legend at right. This illustrates how the “Ethiopia-external” analysis shows increased similarity between groups of the same ethnicity relative to that seen under the “Ethiopia-internal” analysis. b Average pairwise genetic similarity among individuals from four language classifications (italics), and the average genetic similarity between individuals from these four language groups and those from eight ethnic groups (non-italics). For each of the eight ethnic groups, the cyan squares denote the language group with the highest average genetic similarity to that ethnic group under each analysis. This illustrates how the linguistically-unclassified Chabu and Negede-Woyto are most genetically similar on average to Nilo-Saharan and Afroasiatic Semitic speakers, respectively, and highlights six other groups that are more genetically similar to members from a different language group than they are to members of their own language group.

The second example concerns the two sampled groups in our study for which Ethnologue ascribes no linguistic classification, the Chabu and Negede-Woyto. Each are significantly differentiable ( p val < 0.001) from all other ethnic groups under the “Ethiopia-internal” analysis (Fig.  2b , Supplementary Fig  8a , Supplementary Data  5 ). The Chabu, a hunter-gatherer group and linguistic isolate, exhibit the strongest overall degree of genetic differentiation from all other ethnic groups, consistent with previous analyses highlighting their genetic distinctiveness 30 , 31 . However, under the “Ethiopia-external” analysis, the Chabu show similar genetic patterns to NS speaking groups, while the Negede-Woyto are not significantly distinguishable from multiple ethnic groups representing all three branches of AA (Fig.  2b , Supplementary Fig  8b , Supplementary Fig.  9 ). The Chabu’s similarity to NS speakers reflects previous findings based on genetics 30 , where the Chabu were referred to as Sabue 31 ), and linguistics 68 , 69 . However, we clarify this further by showing the Chabu to be significantly more genetically similar to the Mezhenger sample than other samples examined here (Supplementary Fig.  8a ), with whom they have been suggested to share recent origins 70 .

Third, we find unexpectedly high genetic similarities among groups classified into distantly related linguistic categories (Fig.  2b , Supplementary Fig.  8 ). For example, the AA-speaking Karo and Dasanech are on average more genetically similar to NS speakers than to other AA speakers. In contrast, the NS speaking Meinit and Berta are more similar to AA speakers. At a finer linguistic level, the AA Cushistic-speaking Agaw and Qimant are most genetically similar to sampled AA Semitic-speakers, with the Qimant and AA Semitic-speaking Beta Israel having been reported previously to be related linguistically to the Agaw 71 . These observations demonstrate that shared linguistic affiliation, even using broad categories, is not always a reliable predictor of relatively higher genetic similarity. However, on average individuals from the AA Cushitic, AA Omitic, AA Semitic, and NS classifications, as well as individuals from separate sub-branches within each of these categories, are genetically distinguishable from each other under both the “Ethiopia-internal” and “Ethiopia-external” analyses ( p val < 0.001; Supplementary Note  4 ; Supplementary Fig.  9 ; Supplementary Data  9-10 ), consistent with Pagani et al. 27 . This suggests that speakers of the first three tiers of Ethiopian language classifications at www.ethnologue.com are genetically –distinguishable on average, and that these genetic differences are not solely attributable to endogamy effects but also to differential ancestry related to non-Ethiopians. We also find that several groups spanning the three AA classifications of Cushitic, Omotic, and Semitic show high genetic similarity to each other on average and less genetic similarity to NS speakers (Fig.  2b , Supplementary Figs. 8 , 9 ). We find no clear genetic evidence Omotic is an outgroup to other AA language groups, as previously claimed 29 , at least among Ethiopians.

The recent admixture history of Ethiopia

We explore the ancestry of different Ethiopian groupings by comparing their haplotype sharing patterns under the “Ethiopia-external” analysis to those in a set of reference populations intended to reflect ancestral source populations. To do so, we first used fineSTRUCTURE 63 to assign Ethiopians into 78 clusters of relative genetic homogeneity (Supplementary Fig.  10 , Supplementary Data  4 ). Unsurprisingly, given our previous genetic similarity results (Fig.  1b , Supplementary Fig.  6 , Supplementary Fig.  8 ), these clusters were associated with ethnic label (Supplementary Fig.  10 ), with clusters inferred using the alternative approach ADMIXTURE (Alexander et al. 56 ) also often categorising genomes according to ethnic group (Supplementary Fig.  11 ). However, using clusters rather than self-reported label can increase power to infer ancestral histories by merging ethnic groups with similar genetic variation patterns. This also can clarify ancestry inference, as it does not assume that all individuals reporting the same ethnicity share recent ancestry. We applied SOURCEFIND 72 and GLOBETROTTER 58 to infer and describe admixture events in each of these 78 clusters (see “Methods”, Supplementary Note  5 ). Simulations mimicking patterns we observe showed that our approach accurately infers sources and dates of admixture (Supplementary Fig.  5d , Supplementary Note  3 ).

GLOBETROTTER infers clear admixture events in 68 of the 77 Ethiopian clusters containing more than one individual, with dates ranging from ~100 to 4200 years ago (Supplementary Note  5 , Fig.  3 , Supplementary Fig.  12 , Supplementary Data  7 ). Out of 275 reference populations, SOURCEFIND infers only 13 contributed >5% towards describing ancestry patterns within any of these 68 clusters: the 4500-year-old Ethiopian Mota and 12 present-day groups from Chad, Egypt, Kenya, Saudi Arabia, Somalia, Sudan, Tanzania, Uganda and Yemen (Fig.  3 , Supplementary Fig.  12 , Supplementary Data  7 ). Strikingly, the percentage of matching to Mota decreases with increasing spatial (geographic and elevation) distance between where Mota was discovered and the average location of individuals in each cluster (linear regression p value < 0.0005, Supplementary Fig.  13 , Supplementary Table  9 ).

(top-left) FINESTRUCTURE-inferred genetically homogeneous clusters of Ethiopians, with location placed on the map by averaging the latitude/longitude of each cluster’s individuals. Colours denote which of six types of admixture event (1-6 below) each cluster falls into and symbols provide the most-represented language group among individuals’ ethnicities in each cluster. (top-right) A subset of the 264 non-Ethiopian present-day reference populations, plus the 4.5 kya Ethiopian Mota (Gallego-Llorente et al. 4 ; cyan triangle), that DNA patterns in each Ethiopian cluster were compared to under the “Ethiopian-external” analysis. Filled circles (legend at bottom) indicate reference populations that contributed >5% of ancestry to at least one Ethiopian using SOURCEFIND. (Middle) Inferred admixture dates in generations from present (symbols give means and correspond to legend in the top-left panel, line = 95% CI, sample sizes given in Supplementary Fig.  12 ), coloured by the six types of admixture event. (Bottom) SOURCEFIND-inferred ancestry proportions for each Ethiopian cluster (key for numbers in Supplementary Data  7 ). Blue and green borders in the ancestry composition highlight different admixing sources. In particular we enclose the reference populations representing one of the inferred admixing sources with a thick blue line. In Ethiopian groups with >2 inferred sources, we also enclose the reference populations representing the second source with a thick green line. Using this information, we highlight six types of inferred admixture events among: (1) three sources related to the Baganda, Dinka and Sengwer, (2) two sources related to Mota and Dinka/Sengwer, (3) two sources related to Rendille and Mota or Sengwer, (4) three sources related to Rendille, Mota and Sengwer, (5) three sources related to Egypt/W.Eurasia, Rendille and Mota/Iraqw, and (6) two sources related to Egypt/W.Eurasia and Rendille/Mota.

We infer six broad categories of admixture, correlated with both geography and linguistics (Fig.  3 , Supplementary Fig.  12 ). For example, 12 clusters primarily containing individuals from NS-speaking groups (clusters 1–5, 7, 9, 10, 15, 48–50 on Fig.  3 , Supplementary Fig.  12 ) show evidence of admixture involving a source related to Bantu (Baganda) and/or NS Nilotic (Sengwer, Dinka) speakers, with date estimates <30 generations ago in all but two of these clusters. Similar admixture is inferred in the AA Omotic speaking Karo (cluster 6), AA Cushitic speaking Dasanech (cluster 11) and linguistically-unclassified Chabu (cluster 8), which each show relatively high genetic similarity to NS-speakers (Fig.  2a ). In contrast, clusters primarily containing AA speakers, including all Ari and Woylata clusters (clusters 22, 24, 25, 39, 41, 43, 45, 54, 56) and a cluster containing the linguistically-unclassified Negede-Woyto (cluster 58), typically show evidence of admixture between two or more sources related to the 4.5 kya Ethiopian Mota, Cushitic-speaking Rendille from Kenya and Egypt/W.Eurasian groups, over a broader range of dates (5-147 generations ago). Among these, five northern clusters containing AA Semitic-speakers and the AA Cushitic-speaking Agaw (clusters 62, 64, 66–68), plus two geographically nearby clusters containing the AA Cushitic-speaking Qimant (cluster 63) and AA Omotic-speaking Shinasha (cluster 65), show the highest amounts of Egypt-like ancestry in our dataset and similar admixture dates (point estimates 71–85 generations ago).

Pervasive recent intermixing among groups is associated with geographic proximity and shared cultural practices

The surprising genetic similarity among people speaking dissimilar languages may be attributable in part to relatively recent language adoption and/or high levels of recent intermixing among distinct Ethiopian groups. To test for the latter, we also applied GLOBETROTTER to each of the 77 Ethiopian clusters under the “Ethiopia-internal” analysis, which includes Ethiopians as surrogates for admixing sources and hence can characterize intermixing that has occurred among Ethiopian groups. GLOBETROTTER found evidence of admixture in 61 clusters in this analysis, 46 (75.4%) of which had estimated dates <30 generations ago (<900 years ago) (Supplementary Data  8 ). Across clusters, inferred dates under the “Ethiopia-internal” analysis typically are more recent than those inferred under the “Ethiopia-external” analysis (Fig.  4a ). This indicates that the “Ethiopia-internal” analysis captures recent intermixing among Ethiopian groups that is missed under the “Ethiopia-external” analysis; otherwise dates under the two analyses would be similar. Furthermore, we inferred that recent intermixing occurred more frequently than expected among clusters whose individuals reside geographically near to each other ( p value < 0.00002, Fig.  4b ).

a GLOBETROTTER-inferred dates (generations from present) for each Ethiopian cluster inferred to have a single date of admixture under each of the “Ethiopia-external” and “Ethiopia-internal” analyses. Inferred dates typically are more recent under the latter, indicating this analysis is picking up relatively more recent intermixing among sources represented by present-day Ethiopian clusters. Colours match those in Fig. 3 for these clusters. b GLOBETROTTER-inferred ancestry sources under the “Ethiopia-internal” analysis. Each Ethiopian cluster X , also including Mota, has a corresponding colour (outer circle). Lines of this colour emerging from X indicate that X was inferred as the best surrogate for the admixing source contributing the minority of ancestry to each other cluster it connects with. The thickness of lines is proportional to the contributing proportion. Ethiopian clusters, with labels coloured by language category according to Fig. 1 a, are ordered by the first component of a principal components analysis applied to the geographic distance matrix between groups, i.e., so that geographically close groups are next to each other. The “geographic proximity score” gives the average ordinal distance between an admixture target and the surrogate that best represents the source contributing a minority of the admixture, with the one-sided p value testing the null hypothesis that admixture occurs randomly between groups (i.e., independent of the geographic distance between them) based on permuting cluster labels around the circle.

We next explored whether groups that share cultural practices also show evidence of recent intermixing. Supporting this, we found a significant association ( p value < 0.05) between genetic similarity and shared cultural practices only under the “Ethiopia-internal” analysis that is sensitive to intermixing among Ethiopian groups (Supplementary Table  8 ). Six traits out of the 20 reported by more than one ethnic group exhibited nominally higher ( p value < 0.05) genetic similarity among ethnic groups participating in the practice relative to those who did not participate or whose participation in the practice was unknown (Fig.  5 ). These practices include male and female circumcision and four different marriage practices (see Supplementary Note  6 for details). The average genetic similarity among groups sharing one of these six cultural traits in common was higher than that expected based on linguistic affiliation and spatial distance (Fig.  5 ), and we see increased evidence of recent intermixing among groups reporting male/female circumcision and sororate/cousin marriages relative to other SNNPR groups (Fig.  5 , Supplementary Table  10 , see “Methods”, Supplementary Note  5 ). As an example, GLOBETROTTER infers admixture occurring 16 generations ago (95% CI: 11-21) in a cluster of the AA Cushitic-speaking Dasanech (cluster 11 in Fig.  4b ), from a source most genetically related to a cluster containing the NS-speaking Murle and Nyangatom that share practices of arranged and abduction marriages (Fig.  4b , Supplementary Data  8 ).

Boxplots depict the pairwise genetic similarity (under the “Ethiopia-internal” analysis) among ethnic groups that reported practicing (“Y”), not practicing (“N”) or gave no information about practicing (“U”) each of six different cultural traits (labelled above heatmaps, with text colours matching the corresponding boxplots’ colours). Numbers of groups in each category are in parentheses in red. Each boxplot depicts the median (horizontal black bar), interquartile range (box), minimum and maximum (endpoints) values across pairwise comparisons. Stars above the boxplots denote whether there is a significant increase (one-sided empirical p val < 0.05, based on re-sampling groups, and without adjusting for multiple comparisons) in genetic similarity among groups in (black) “Y” versus “U” or (green) “Y” versus “N”. The bottom right of each heatmap shows the increase (red) or decrease (blue) in average genetic similarity relative to that expected based on the ethnicities’ language classifications (key in bottom right heatmap), after accounting for the effects of spatial distance, between every pairing of ethnicities who reported practicing (“Y”) the given trait (axis labels coloured by language group as in Fig.  1a ; group labels given in Supplementary Data  1 ). Green squares in the top left portion of the heatmaps indicate whether > =1 pairings of individuals from different ethnic groups share atypically long DNA segments relative to all other comparisons of people from the two groups, which is indicative of recent intermixing between the two groups (see Supplementary Note  5 ); p values provided in Supplementary Table  10 .

Here we analyse a large-scale Ethiopian cohort densely sampled across ethnicities and geography, and annotated for cultural practices (Supplementary Note  2 ). This resource enabled us to disentangle several factors shaping genetic structure in Ethiopians. Wherever possible we only included individuals whose ethnicity matched that reported for parents and grandparents, which—if accurate—should exclude instances of ethnic re-identification and between-group intermixing occurring within the last two generations. This inclusion criterion implies that the patterns we have inferred reflect genetic patterns in Ethiopia approximately two generations prior to the present-day. This plausibly underrepresents genetic similarity and intermixing among ethnic groups that would be observable in a random sample, though our results support widespread recent intermixing among ethnic groups nonetheless (Fig.  4 ).

Our simulations demonstrate how two different types of analyses, which we term “Ethiopia-internal” and “Ethiopia-external”, can disentangle relatively recent from ancient shared ancestry to better understand the origins of different ethnic groups (Supplementary Figs. 4 , 5 ). In the real data, groups referred to as socially marginalised occupational minorities in the social anthropology literature, such as the Manjo from Kefa Sheka 66 , the Manja from Dawro 73 , the Ari/Wolayta Blacksmiths/Potters/Tanners 39 , 74 the Chabu and the Negede-Woyto 70 , 75 , 76 , each show relatively high genetic distance from other Ethiopians using F ST (Supplementary Data  11 ) and under the “Ethiopia-internal” analysis (Fig.  2a , Supplementary Fig.  8a , Supplementary Data  5 ). However, under the “Ethiopia-external” analysis, these genetic distances become relatively small (Fig.  2a , Supplementary Fig.  8b , Supplementary Data  6 ), suggesting that the high levels of genetic differentiation between marginalised and other Ethiopians groups (e.g. measured by F ST ) have arisen through their relatively recent isolation. Consistent with this isolation, these groups also exhibit signatures of recent endogamy as reflected by higher degrees of genetic homogeneity (Supplementary Fig.  3a, b ), with each forming a distinct cluster in ADMIXTURE analysis (Supplementary Fig.  11 , Lawson et al. 77 ).

In the Ari, we infer very similar sources and dates of admixture in independent analyses of distinct clusters that correspond to occupational groups (clusters 22, 24 and 25 in Fig.  3 , Supplementary Fig.  12 ) under the “Ethiopia-external” analysis, with overlapping 95% confidence intervals spanning 42–146 generations (Supplementary Data  7 ). A parsimonious explanation of these findings, consistent with our simulations (Supplementary Fig.  5 ), is that the ancestors of the Ari were a single population when these admixture events occurred. This in turn suggests the ancestors of different Ari occupational groups became isolated from one another only within the past ~146 generations (<4200 years, assuming 28 years per generation 78 ). This corresponds to the time period during which iron working is thought to have first appeared in Ethiopia 79 and supports the marginalisation theory of their origins 80 consistent with previous genetic studies 31 , 37 .

Analogous to this, in the Chabu, who are not linguistically classified by Ethnologue, we infer admixture events (dated to 300–900 years ago) and ancestry proportions that are similar to those inferred in the Mezhenger (Fig.  3 , Supplementary Fig.  12 , Supplementary Data  7 ). These inferences are consistent with a high degree of intermarrying among the Chabu and Mezhenger, as has been proposed 31 , 81 , and/or that these two groups split within the last ~900 years and had subsequently distinct linguistic trajectories. Nonetheless, among the Ethiopian groups, the Chabu are the strongest outliers under F ST and “Ethiopia-internal” analyses, consistent with previous claims of a decline in genetic diversity over the past 1000 years in the Chabu 31 . For the Negede-Woyto, the other group in this study for which there is no established linguistic classification in Ethnologue, we infer a relatively high amount of Egyptian-related ancestry (Fig.  3 , Supplementary Fig.  12 ), which is consistent with the group’s own origin narrative of a migration from Egypt by way of the Abay river 75 . The ancestry proportions and admixture dates inferred in the Negede-Woyto are similar to those for the Beta Israel and Agaw, whom some scholars have proposed possible genealogical relationships with 76 , and show the highest average similarity to AA Semitic speakers ( p val > 0.05; Supplementary Fig.  9 ).

A caveat to the interpretation that groups with similar inferred admixture sources and proportions under the “Ethiopian-external” analysis share similar recent ancestry is that this analysis will have reduced (or no) power to discriminate between Ethiopian groups that indeed have separate ancestral sources if we have not included relevant non-Ethiopian groups to represent these sources. The large number of non-Ethiopian groups included in this sample, particularly those geographically proximal to Ethiopia, diminishes this possibility, but more samples from other sources, in particular from ancient individuals in Ethiopia, may increase our ability to identify older ancestral differences between Ethiopians using these techniques.

Both the “Ethiopia-internal” and “Ethiopia-external” analyses show a strong concordance between genetic differences and geographic distance among individuals (Fig.  1b , Supplementary Fig.  6 ), analogous to that shown previously among peoples sampled from European 43 , 82 , African 30 , 83 and worldwide countries 84 . We also identify a correlation between genetic similarity and elevation difference, even after correcting for genetic similarity over geographic distances. Strikingly, we also see a correlation between spatial distance and the degree of genetic ancestry related to Mota, an ancient individual 4 whose remains were found in the Gamo Highlands of present-day Ethiopia 4500 years ago (Supplementary Fig.  13 , Supplementary Table 9). This suggests a notable preservation of some population structure in parts of Ethiopia over the intervening period 4 , 31 .

The “Ethiopia-external” SOURCEFIND and GLOBETROTTER results indicate that Ethiopians in the southwest, typically NS speakers plus a few non-NS speaking groups (Chabu, Dasanech, Karo), share more recent ancestry with non-Ethiopian Bantu and NS Nilotic speakers. In contrast, Ethiopian AA speakers in the northeast share more recent ancestry with Egyptians and West Eurasians (Fig.  3 , Supplementary Fig.  12 ). The inferred timing and sources of admixture related to Egypt/W.Eurasian-like sources, starting around 100–125 generations ago (~2800–3500 years ago; Fig.  3 , Supplementary Fig.  12 ), as in previous findings 27 , 85 , is consistent with significant contact and gene flow between the peoples of present day Ethiopia and northern Africa even before the rise of the kingdom of D’mt and interactions with the Saba kingdom of southern Yemen which traded extensively along the Red Sea 79 . This timing is also consistent with trading ties between the greater Horn and Egypt. dating back only to 1500 BCE, when a well-preserved wall relief from Queen Hateshepsut’s Deir el-Bahari temple shows ancient Egyptian seafarers heading back home from an expedition to what was known as the Land of Punt (Supplementary Note  1A ). On the other hand, inferred admixture dates in groups with varying amounts of ancestry related to Bantu and NS Nilotic speakers are dated to <1100 years ago, with the exceptions of the NS-speaking Kwegu (~1500 years ago) and a second inferred older date (>1400 years ago) in the NS-speaking Meinit, which may reflect recent intermixing of NS-speakers with other Ethiopians. Such recent intermixing is consistent with mixed ancestry signals we see in some NS groups (e.g., see clusters containing Berta, Meinit and Nyangatom in clusters 15, 48–50 in Fig.  3 , Supplementary Fig.  12 ).

To facilitate comparison, our SOURCEFIND analysis included reference groups related to the four proxies used for ancestry sources in ancient and present-day East African groups reported in Prendergast et al. 36 (see Supplementary Note  5 ). We excluded their aDNA samples as reference groups, because they reported them to have admixture from these four sources. While using different reference groups and techniques complicates direct comparisons, our inferred sources of ancestry broadly agree with that study. For example, the Agaw (clusters 66, 67) have relatively more Levant-like ancestry (which we match most closely to Egypt), the Ari (clusters 22, 24, 25; called Aari in Prendergast et al. 36 ), have relatively more Mota-like ancestry, and the Ethiopian Mursi (cluster 2) have relatively more Dinka-like ancestry (Fig.  3 , Supplementary Fig.  12 ). Simulations mimicking the admixture inferred here show high accuracy in inferred dates and sources, though illustrate a limitation whereby older dates of admixture (e.g., those reported in Prendergast 36 ) may be masked by more recent admixture (Supplementary Fig.  5 ). Thus complex intermixing events, such as those exhibited here, can be difficult to dissect fully with these approaches and sample sizes, e.g., distinguishing between multiple pulses or continuous admixture. A potential example are the NS-speaking Berta (clusters 48, 50), in which we infer only a single recent date of admixture but whom have complicated sources of ancestry that suggest multiple events (Fig.  3 , Supplementary Fig.  12 ).

Interestingly, the association between cultural and genetic similarity is only apparent under the “Ethiopia-internal” analysis, which is more sensitive to recent shared ancestry (Supplementary Table  8 ). Another example consistent with this trend is that the NS-speaking Suri, Mursi, and Zilmamo, the only three Ethiopian ethnic groups that share the practice of wearing decorative lip plates, show atypically high genetic similarity under the “Ethiopia-internal” analysis but similarity levels comparable to other NS speakers under the “Ethiopia-external” analysis (Supplementary Fig.  14 , Supplementary Data  13 ). This suggests a recent separation of these groups, i.e., more recently than they separated from all other sampled Ethiopian groups, and/or recent intermixing among them.

Overall the above examples illustrate how genetic data provide a rich additional source of information that can either corroborate or conflict with claims from other disciplines (linguistics, geography, archaeology, anthropology, sociology and history) while adding further details and/or novel insights and directions for future investigation. Our interactive map is designed to facilitate evaluation of genetic evidence for such claims, providing results from both the “Ethiopia-internal” and “Ethiopia-external” analysis to enable comparisons analogous to the examples above. Future work can compare these and other published genetic results (e.g., 30 , 31 , 36 ) to oral histories recorded for various ethnic groups. For example, some Mezhenger report that their ancestors originally migrated from Sudan to the present-day Gambella Regional State where Anuak lived, after which they migrated with the AA Omotic-speaking Sheko for a period before settling in their present-day homeland (The Council of Nationalities, Southern Nations and Peoples Region 37 ). Consistent with this, in the “Ethiopia-external” analysis the Mezhenger have high inferred ancestry matching to the Sudanese Dinka (Fig.  3 , Supplementary Fig.  12 , Supplementary Data  7 ), and in the “Ethiopia-internal” analysis they have an inferred admixture event ~300–600 years ago among three sources that are best represented by clusters containing the Anuak, Sheko and other NS-speaking groups near the Mezhenger (Supplementary Data  8 ).

Our study also highlights the importance of considering topographical and cultural factors, in particular language, ethnicity and in some cases occupation, when designing sampling strategies for future Ethiopian genetic studies, e.g. genome-wide association studies (GWAS), which our interactive map can also assist with. Similar sampling strategies may be necessary to capture the genetic structure of peoples in some other African countries that also exhibit relatively high levels of genetic diversity and structure 65 , 83 . Finally, our analyses illustrate how cultural practices, e.g., participation in certain cultural and marriage customs, can operate as both a barrier and a facilitator of gene flow among groups, and consequently act as an important factor shaping human diversity and evolution.

DNA samples from the 1082 Ethiopians whose autosomal genetic variation data are newly reported in this study (following quality control, see below) were collected in several field trips from 2000 to 2010, through a long-standing collaboration including researchers at University College London and Addis Ababa University. All study participants, including non-Ethiopians whose genetic variation data are newly reported in this study, gave their informed consent. Local permissions were obtained in all cases where applicable local ethical approval and regulations existed, e.g., Cameroon, Ministry of Higher Education and Scientific Research, Permits 0188/MINREST/B00/D00/D10/ D12 and 317/MINREST/B00/D00/D10 and University of Yaounde I; Ethiopia, Ethiopian Science and Technology Commission and National Ethics Review Committee. Sample collection/usage for all unpublished data included in this study were approved by the UK ethics committee London Bentham REC (formally the Joint UCL/UCLH Committees on the Ethics of Human Research: Committee A and Alpha, REC reference number 99/0196, Chief Investigator MGT). The analyses reported here were approved by UCL REC (Project ID: 5188/001).

Buccal swab samples were collected from anonymous donors over 18 years of age, unrelated at the paternal level. For all individuals we recorded their, their parents’, paternal grandfather’s and maternal grandmother’s village of birth, language, cultural ethnicity and religion. In order to mitigate the effects of admixture from recent migrations that may be causing any genetic distinctions between ethnic groups to blur, analogous to Leslie et al. 43 , where possible we genotyped those individuals whose grandparents’ birthplaces and ethnicity were coincident 43 . However, for a few ethnic groups (Bana, Meinit, Negede Woyto, Qimant, Shinasha, Suri), we did not find any individuals fulfilling this birthplace condition; in such cases we randomly selected individuals whose grandparents had the same ethnicity. In these cases, the geographical location was calculated as the average of the grandparents’ birthplaces (see Supplementary Note  2 ). We did not have geographic or birthplace information for Beta Israel individuals whose genetic variation data is newly released in this study. Information about elevation was obtained using the geographic coordinates of each individual in the dataset with the “Googleway” package. All the Ethiopian individuals included in the dataset are classified into 75 groups based on self-reported ethnicity (68 ethnic groups) plus occupations (Blacksmith, Cultivator, Potter, Tanner, Weaver) within the Ari and Wolayta ethnicities. Supplementary Data  1 shows the number of samples from each Ethiopian population and ethnic group that passed genotyping QC and were used in subsequent analyses. Figure  1a shows the geographic locations (i.e., birthplaces) of the Ethiopian individuals, though jittered to avoid overlap.

For comparison, we also incorporated 2678 non-Ethiopians (after quality control below) from 264 labelled present-day populations, and 40 high coverage aDNA genomes (including Mota), as described in this paragraph. Among these, non-Ethiopian samples newly released in this study include 23 Arabs from Israel, 13 Arabs from Palestine, 8 Bedouins from Saudi Arabia, 18 Berbers from Morocco, 7 Kotoko from Cameroon, 6 Muganda/Baganda from Uganda, 6 Mussese from Uganda, 13 Senegalese and 12 Syrians. All newly reported DNA samples in this study were genotyped using the Affymetrix Human Origins SNP array, which targets 627,421 SNPs (prior to our quality control), and merged with the Human Origin datasets published by Lazaridis et al. 41 and Lazaridis et al. 86 , excluding their haploid samples (some ancient humans and primates) 41 , 86 . To these data we added present-day Indians and Iranians published by Broushaki et al. 52 , and Lopez et al. 55 , and genomes from present-day Africans published by Skoglund et al. 54 , Gurdasani et al. 42 and Mallick et al. 28 (Supplementary Data  3 ) 28 , 42 , 52 , 54 , 55 . We also included 21 high coverage published ancient samples (>1X average coverage) from Africa 36 , 51 , 87 , including GB20 ‘Mota’ from Ethiopia 4 , and 19 high coverage (>5X) published ancient non-African samples 45 , 46 , 48 , 52 , 53 (Supplementary Table  1 ).

BAM files for ancient samples were downloaded from the ENA website ( https://www.ebi.ac.uk/ena ), with each file checked for correct format and metadata using PicardTools. We estimated post-mortem damage using ATLAS 88 with “pmd”, recalibrating each BAM file using ultra-conserved positions from UCNE ( https://ccg.epfl.ch/UCNEbase/ ) and running ATLAS with “recal”, and then generated maximum likelihood genotype calls and phred-scaled genotype likelihood (PL) scores for each position using ATLAS with “call”. We used Conform-GT ( https://faculty.washington.edu/browning/conform-gt.html ) to ensure that strand was consistent with 1000 Genomes 89 across present-day and ancient datasets, merging the data and running Beagle 4.1 90 with “modelscale = 2” and the genetic maps at http://bochet.gcc.biostat.washington.edu/beagle/genetic_maps/plink.GRCh37.map.zip to re-estimate genotypes and impute missingness. We used vcf2gprobs, gprobsmetrics and filterlines ( https://faculty.washington.edu/browning/beagle_utilities/utilities.html ) to filter SNPs with an imputation accuracy of less than 0.98, and then we phased all samples using shapeit4 91 with “–pbwt-depth 16” and using their provided genetic maps.

To identify putatively related individuals, we used PLINK v1.9 61 with “–genome” to infer pairwise PI_HAT values, after first pruning for linkage disequilibrium using “–indep-pairwise 50 10 0.1”. Instead of using the same fixed PI_HAT threshold value for all populations, we identified individuals with outlying PI_HAT values relative to other members of the same group label, in order to avoid removing too many individuals from populations with relatively low genetic diversity. Specifically, we found all pairings of individuals from populations (i,k) that had PI_HAT > 0.15 and PI_HAT > min(X_i + 3*max{0.02,S_i}, Y_i + 3*max{0.02,D_i}, X_k + 3*max{0.02,S_k}, Y_k + 3*max{0.02,D_k}), where {X_i, Y_i, S_i, D_i} are the {mean, median, standard deviation, median-absolute-deviation}, respectively, of pairwise PI_HAT values among individuals from population i. For populations with <=2 sampled individuals, the standard deviation and median-absolute-deviations are undefined or 0; therefore in such cases we added to the list any pairings with PI_HAT > 0.15 that contained >=1 person from that population. Using a stepwise greedy approach, we then selected individuals from this list that were in the most pairs to be excluded from further analysis, continuing until at least one individual had been removed from every pair. This resulted in a total of 234 individuals removed, including 62 Ethiopians. All remaining Ethiopian pairs after this procedure had PI_HAT < 0.2.

Following the quality control described above, the total number of samples in the merge was 3892, analyzed at 534,915 autosomal SNPs. We performed a principal-components-analysis (PCA) on the SNP data using smartpca 57 , 60 from EIGENSOFT version 7.2.0, with standard parameters and the lsqproject option. For the PCA of all individuals (Supplementary Fig.  2a ), we performed PCA on all individuals and used five outlier removal iterations (default). For the PCA of only African individuals (Supplementary Fig.  2b ), we performed PCA on 2,110 present-day Africans and 8 Saudi-Bedouins without performing any outlier removal iterations to prevent excluding more isolated populations, subsequently removing the Saudi Bedouins from the plot and projecting the 21 ancient African samples including Mota.

Genetic diversity and homogeneity

We used three different approaches to assess within-group genetic homogeneity in the Ethiopian ethnic groups. First, we computed the observed autosomal homozygous genotype counts for each sample using the–het command in PLINK v1.9 61 , taking the median value within each group. Second, we pruned SNP data based on linkage disequilibrium (–indep-pairwise 50 5 0.5), which left us with 359,281 SNPs, and used PLINK v1.9 to detect runs of homozygosity (ROH). This ROH procedure find runs of consecutive homozygous SNPs within groups that are identical-by-descent; here we report the total length of these runs per individual (Supplementary Fig.  3b ). Third, we used FastIBD 62 , implemented in the software BEAGLE v3.3.2, to find tracts (in basepairs) of identity by descent (IBD) between pairs of individuals. For each population and chromosome, fastIBD was run for ten independent runs using an IBD threshold of 10 −10 , as recommended by Browning and Browning 62 , for every pairwise comparison of individuals 62 . For each population, we report the fraction of the genome that each pair of individuals shares IBD (Supplementary Fig.  3a ).

We assessed whether the degree of genetic diversity in Ethiopian ethnic groups was associated with census population size, by comparing different measures of genetic diversity described above (homozygosity, IBD and ROH) with the census population size using standard linear regression (Supplementary Fig.  3c ). As population census are not always available and can be inaccurate, we limited this analysis to ethnic groups in the SNNPR, for whom census information was recently reported (The Council of Nationalities, Southern Nations and Peoples Region, 2017).

Using chromosome painting to evaluate whether genetic differences among ethnic groups are attributable to recent or ancient isolation

To quantify relatedness among individuals, we employed a “chromosome painting” technique, implemented in CHROMOPAINTER 63 , that identifies strings of matching SNP patterns (i.e., shared haplotypes) between a phased target haploid and a set of phased reference haploids. By modelling correlations among neighboring SNPs (i.e., “haplotype information”), CHROMOPAINTER has been shown to increase power to identify genetic relatedness over other commonly-used techniques such as ADMIXTURE and PCA 43 , 58 , 63 . In brief, at each position of a target individual’s genome, CHROMOPAINTER infers the probability that a particular reference haploid is the one which the target shares a most recent common ancestor (MRCA) relative to all other reference haploids. These probabilities are then tabulated across all positions to infer the total proportion of DNA for which each target haploid shares an MRCA with each reference haploid. We can then sum these total proportions across the reference haploids assigned to each of K pre-defined groups.

Following van Dorp et al. 37 , we used two separate CHROMOPAINTER analyses that differed in the K pre-defined groups used:

1. “Ethiopian-external”, which matches (i.e., paints) DNA patterns of each sampled individual to that of non-Ethiopians from K  = 264 groups only (Supplementary Data  3 ).

2. “Ethiopia-internal”, which matches DNA patterns of each sampled individual to that of all sampled groups, comprising 264 non-Ethiopian groups plus the 78 Ethiopian clusters defined in Supplementary Fig.  10 and the 4 Ethiopian groups from Mallick et al. 28 , leading to K  = 346 groups total 28 .

Relative to our genetic similarity score (1-TVD, described in the next section) under the “Ethiopia-internal” analysis, our score under the “Ethiopia-external” analysis mitigates the effects of any recent genetic isolation (e.g., endogamy) that may differentiate a pair of Ethiopians. This is because individuals from groups subjected to such isolation typically will match relatively long segments of DNA to only a subset of Ethiopians (i.e., ones from their same group) under analysis (1). However, this isolation will not affect how the same individuals match to each non-Ethiopian under analysis (2), for which they typically share more temporally distant ancestors. Consistent with this, in our sample the average size of DNA segments that an Ethiopian individual matches to another Ethiopian is 0.68 cM in the “Ethiopia-internal” analysis, while the average size that an Ethiopian matches to a non-Ethiopian is only 0.23 cM in the “Ethiopia-external” analysis, despite the latter analysis matching to substantially fewer individuals overall and hence having a higher a priori expected average matching length per individual.

Following López et al. 55 , van Dorp et al. 59 , and Broushaki et al. 52 , for each analysis (1) and (2) we estimated the CHROMOPAINTER algorithm’s mutation/emission (Mut, “-M”) and switch rate (Ne, “-n”) parameters using ten steps of the Expectation-Maximisation (E-M) algorithm in CHROMOPAINTER applied to chromosomes 1, 8, 15 and 22 separately, analysing only every ten of 4081 individuals as targets for computational efficiency 52 , 55 , 59 . This gave values of {321.844, 0.0008304} and {178.8922, 0.0006667} for {Ne, Mut} in CHROMOPAINTER analyses (1) and (2), respectively, after which these values were fixed in a subsequent CHROMOPAINTER run applied to all chromosomes and target individuals. The final output of CHROMOPAINTER includes two matrices giving the inferred genome-wide total expected counts (the CHROMOPAINTER “.chunkcounts.out” output file) and expected lengths (the “.chunklengths.out” output file) of haplotype segments for which each target individual shares an MRCA with every other individual.

Inferring genetic similarity among Ethiopians under two different CHROMOPAINTER analyses

Separately for each of the “Ethiopia-internal” and “Ethiopia-external” CHROMOPAINTER analyses, for every pairing of Ethiopians i,j we used total variation distance (TVD) 43 to measure the genetic differentiation (on a 0-1 scale) between their K -element vectors of CHROMOPAINTER-inferred proportions (with K defined above for both analyses), i.e:

where \({f}_{k}^{i}\) is the total proportion of genome-wide DNA that ind i vidual i is inferred to match to individuals from group k (see schematic in Supplementary Fig.  4 ). Throughout we report \(1-TV{D}_{ij}\) , which is a measure of genetic similarity. When calculating the genetic similarity between two groups, we average \((1-TV{D}_{ij})\) across all pairings of individuals (i,j) where the two individuals are from different groups (e.g., for Figs.  2 , 5 , Supplementary Figs.  8, 9 , Supplementary Data  5, 6 ). We note an alternative approach to measure between-group genetic similarity is to first average each \({f}_{k}^{i}\) across individuals from the same group, and then use (1) to calculate TVD between the groups by replacing each \({f}_{k}^{i}\) with its respective average value. Potentially this could give more power by reducing noise in the inferred copy vector for each group through averaging. However, here we instead use our approach of averaging \((1-TV{D}_{ij})\) across individuals because of the considerable reduction in computation time when performing large numbers of permutations when assessing significance.

To test whether individuals from group A are more genetically similar on average to each other than an individual from group A is to an individual from group B , we repeated the following procedure 100 K times. Let \({n}_{A}\) and \({n}_{B}\) be the number of sampled individuals from A and B , respectively, with \({n}_{X}=min({n}_{A},{n}_{B})\) . First we randomly sampled \(floor({n}_{X}/2)\) individuals without replacement from each of A and B and put them into a new group C . If \({n}_{X}/2\) is a non-integer, we added an additional unsampled individual to C that was randomly chosen from A with probability 0.5 or otherwise randomly chosen from B , so that C had \({n}_{X}\) total individuals. We then tested whether the average genetic similarity, \({\sum }_{i,j}\frac{1-TV{D}_{ij}}{({n}_{X}choose2)}\) , among all \(({n}_{X}choose2)\) pairings of individuals ( i,j ) from C is greater than or equal to that among all \(({n}_{X}choose2)\) pairings of \({n}_{X}\) randomly selected (without replacement) individuals from group Y , where Y ∈ {A,B} (tested separately). We report the proportion of 100 K such permutations where this is true as our one-sided p value testing the null hypothesis that an individual from group Y has the same average genetic similarity with someone from their own group versus someone from the other group (Supplementary Fig.  8 , Supplementary Data  5, 6 ). Overall this permutation procedure tests whether the ancestry profiles of individuals from A and B are exchangeable, while accounting for sample size and avoiding how some permutations may by chance put an unusually large proportion of individuals from the same group into the same permuted group.

For each Ethiopian group A , in Supplementary Fig.  8 and Supplementary Data  5, 6 we also report the other sampled group \({A}_{max}\) with highest average pairwise genetic similarity to A . To test whether \({A}_{max}\) is significantly more similar to group A than sampled group B is, we permuted the group labels of individuals in \({A}_{max}\) and B to make new groups \({A}_{max}^{{p}}\) and \({B}^{p}\) that preserve the respective sample sizes. We then found the average genetic similarity between all pairings of individuals where one in the pair is from \({B}^{p}\) and the other from A , and subtracted this from the average genetic similarity among all pairings of individuals where one is from \({A}_{max}^{p}\) and the other is from A . Finally, we found the proportion of 100 K such permutations where this difference is greater than that observed in the real data (i.e., when replacing \({B}^{p}\) with B and \({A}_{max}^{p}\) with \({A}_{max}\) ), reporting this proportion as a p value testing the null hypothesis that individuals from group \({A}_{max}\) and group B have the same average genetic similarity to individuals from group A . For each A , any group B where we cannot reject the null hypothesis at the 0.001 type I error level (not adjusting for multiple testing) is enclosed with a white rectangle in Supplementary Fig.  8 and reported in Supplementary Data 5, 6.

As individuals are not allowed to match to themselves under the CHROMOPAINTER model, one potential issue with our paintings of Ethiopians under the “Ethiopian-internal” analysis is that each Ethiopian is allowed to match to one less individual in the cluster to which it is assigned relative to Ethiopians outside that cluster. For example, if cluster A contains ten Ethiopians, each of those Ethiopians are allowed to match to nine people from cluster A under the “Ethiopia-internal” analysis, while Ethiopians outside of cluster A are matched to all ten. This may create a slight discrepancy in the \({f}_{k}^{i}\) values among Ethiopians for the 78 elements of k representing the Ethiopian clusters, which in turn may affect differences in TVD among Ethiopian group labels. To test this, we repeated the above using an alternative “Ethiopia-internal” painting where each Ethiopian is matched to all other Ethiopians from their cluster and \({n}_{k}-1\) Ethiopians from each other Ethiopian cluster k after randomly removing one individual, while matching to all individuals from every non-Ethiopian group as before. This gives a K = 346 length vector of \({f}_{k}^{i}\) values for each Ethiopian i as before, but where each Ethiopian now has been painted against the same numbers of individuals from the K groups. We found that results change very little, e.g., with the TVD values among all pairwise combinations of Ethiopian groups (Supplementary Fig.  8A , Supplementary Data  5 ) having correlation r  > 0.999. This likely reflects how, for the given sample sizes in the k clusters, removing one individual from a cluster k results in people matching slightly more to the remaining \({n}_{k}-1\) individuals in that cluster, so that the total matching to k remains relatively unchanged. For comparison, in Supplementary Data  5 we provide columns at the far right end showing which groups were the closest match under this alternative “Ethiopia-internal” analysis; we note there are few changes relative to the original “Ethiopia-internal” analysis.

Testing for associations between genetic similarity and spatial distance, shared group label, language and religious affiliation

To test for a significant association between genetic similarity and spatial distance, we used statistical tests that are analogous to the commonly-used Mantel test 92 but that account for the non-linear relationships between some variables and/or adjust for correlations among more than three variables. We calculated genetic similarity ( G ij ) between individuals i and j as G ij = 1 − TVD ij , geographic distance ( d ij ) using the haversine formula applied to the individuals’ location information, and elevation distance ( h ij ) as the absolute difference in elevation between the individuals’ locations. We assessed the significance of associations between G ij and d ij and between G ij and h ij using 1000 permutations of individuals’ locations.

When using distance bins of 25 km, we noted that the mean genetic similarity across pairs of individuals showed an exponential decay versus geographic distance in the “Ethiopia-internal” analysis (Fig.  1b ). Therefore, we assumed

To infer maximum likelihood estimates (MLEs) for ( α,β,λ ), we first used the “Nelder-Mead” algorithm in optim() in R to infer the value of λ that minimizes the sum of e ij 2 across all pairings of individuals i , j when α = 0 and β = 1, and then found the MLE for α and β under simple linear regression using this fixed value of λ . As the main observed signal of association between genetic and spatial distance is the increased G ij at small values of d ij , (e.g. d ij = 0, which is not always accurately fit via the Nelder-Mead algorithm), our reported p values are the proportion of permutations for which the mean G ij among all ( i,j ) with permuted d ij  < 25 km is greater than or equal to that of the (unpermuted) real data (Supplementary Table  5a ).

In contrast, we noted a linear relationship between mean G ij and d ij in the “Ethiopia-external” analysis (Supplementary Fig.  6b ) and between mean G ij and h ij when using 100 km elevation bins under both analyses (Supplementary Fig.  6a, c ). Therefore, for these analysis we assumed:

where x ij = d ij or h ij . Separately for each analysis, we found the MLEs for ( γ,δ ) using lm() in R. When testing for an association with elevation, we only included individual pairs ( i,j ) whose elevation distance was less than 2500 km, which occurred in 730,880 (99.6%) of 733,866 total comparisons, to avoid undue influence from outliers. As we expect (and observe) the change in genetic similarity δ to be negative as spatial distance increases, our reported p values provide the proportion of permutations for which the MLE of δ in the 1000 permutations is less than or equal to that of the real data (Supplementary Table  5b–d ).

As d ij and h ij are correlated (r = 0.22, Supplementary Fig.  7c, d ), we also assessed whether each was still significantly associated with G ij after accounting for the other under the “Ethiopia-internal” analysis. To test whether geographic distance was still associated with genetic similarity after accounting for elevation difference, we assumed:

and used lm() in R to infer maximum likelihood estimates for ( η,θ ). Then to test for an association between genetic similarity and geographic distance after accounting for elevation, we used Eq. ( 2 ) but replacing G ij with the fitted residuals ε ij = G ij − γ − δh ij from Eq. ( 2 ) and replacing d ij with the fitted residuals к ij = d ij − η − θ h ij from Eq. ( 4 ). We then repeated the procedure described above to calculate permutation-based p values, first shifting к ij to have a minimum of 0 (Supplementary Table  5a, c ). Similarly, to test for an association between genetic similarity and elevation difference after accounting for geographic distance, we replaced x ij in Eq. ( 3 ) with the fitted residuals from an analogous model to (4) that instead regresses elevation on geographic distance, and replaced G ij in Eq. ( 3 ) with the fitted residuals e ij = G ij − α − β exp(− λ d ij ) from Eq. ( 2 ). We used the same permutation procedure described above to generate p values (Supplementary Table  5b, d ).

We then tested whether sharing the same (A) self-reported group label, (B) language category of reported ethnicity, (C) self-reported first language, (D) self-reported second language, or (E) self-reported religious affiliation were significantly associated with increased genetic similarity after accounting for geographic distance or elevation difference. We used 75 group labels for (A) (Supplementary Data  1 ), 66 first languages for (C), and 40 s languages for (D). For (B), we used the four labels in the second tier of linguistic classifications at www.ethnologue.com for which we have data (i.e., Afroasiatic Omotic, Afroasiatic Semitic, Afroasiatic Cushitic, Nilo-Saharan Core-Satellite), excluding the Negede-Woyto and Chabu as they have not been classified into any language family. For (E), we compared genetic similarity across three religious affiliations (Christian, Jewish, Muslim), excluding religious affiliations recorded as “Traditional” as practices within these affiliations may vary substantially across groups.

To test whether each of these factors are associated with genetic similarity, we repeated the above analyses that use Eqs. ( 2 )–( 4 ) while restricting to individuals (including permuted individuals) that share the same variable Y, separately for Y={A,B,C,D,E}. Our reported p values give the proportion of permutations for which genetic similarity among permuted individuals sharing the same Y is more extreme than or equal to that of the real (un-permuted) data. For the “Ethiopia-internal” analysis when testing genetic similarity against geographic distance, this is the same p value procedure as above, i.e., the proportion of permutations for which the mean G ij among all ( i,j ) with permuted d ij  < 25 km is greater than or equal to that of the (unpermuted) real data (Supplementary Table  5a ). When testing genetic similarity against geographic distance under the “Ethiopia-external” analysis, or testing genetic similarity against elevation difference under either analysis, this was instead defined as having any fitted value of G ij , at 48 equally-spaced bins of d ij ∈ {12.5,1187.5 km} or 25 equally-spaced bins of h ij ∈ {50,2450 m}, greater than or equal to that of the observed data.

As group label, language and religion can also be correlated with spatial distance and with each other (e.g. see Supplementary Fig.  7a, b ), we performed additional permutation tests where we fixed each of (A)-(E) when carrying out the permutations described above. For example, when fixing (A), we only permuted birthplaces and each of (B)-(E) across individuals within each group label, hence preserving the effect of group label on G ij . Applying this permutation procedure for each of (A)-(E), we repeated all tests described above, reporting p values in Supplementary Table  5 .

For each of geographic distance, elevation difference, and (A)-(E), our final p values reported in the main text and Fig.  1b and Supplementary Fig.  6 that test for an association with genetic similarity are the maximum p values across the six permutation tests that permute all individuals freely or fix each of (A)-(E) while permuting (i.e., the maximum values across rows of Supplementary Table  5 ), with the following two exceptions. First, relative to the distances between birthplaces among all individuals, Ethiopians who share the same group label or who share the same first language live near each other (Supplementary Table  6 ), so that permuting birthplaces while fixing group label or first language do not permute across large spatial distances. Therefore, we ignore those permutations when reporting our final p values for geographic distance and elevation difference (i.e., in the main text and Fig.  1b , Supplementary Fig.  6 ). Second, the high correlation between group label and first language (Supplementary Fig.  7a, b ) makes accounting for one challenging (in terms of loss of power) when testing the other. Furthermore, few permutations are possible when testing language group while accounting for group label (0 permutations available) or first language. Therefore, we excluded permutations fixing group and fixing first language when testing each of group, first language and language group when reporting our final p values in the main text and Supplementary Fig.  6 . Note we do observe a significant association with genetic similarity and ethnicity after accounting for spatial distance (geographic or elevation) and major language group, suggesting ethnicity explains genetic similarity beyond that of classifications according to the second language tier of at Ethnologue. We caution that these analyses assume that the relationships among genetic, geographic and elevation distance can be modelled with simple linear or exponential functions, which is sometimes debatable (Supplementary Fig.  7c, d ), indicating larger sample sizes may reveal deviations from these assumptions.

Classifying Ethiopians into genetically homogeneous clusters

We used fineSTRUCTURE 63 to classify 1268 Ethiopians (which includes all sampled Ethiopians except the eight Ethiopians from Mallick et al. 28 that were added later) into clusters of relative genetic homogeneity 28 . To do so, we first used SHAPEIT 93 to jointly phase individuals using default parameters and the linkage disequilibrium-based genetic map build 37 (available at https://github.com/johnbowes/CRAFT-GP/find/master ). We then employed CHROMOPAINTER to paint each individual against all others, i.e., in a manner analogous to the “Ethiopian-internal” analysis, though using a slightly different set of reference populations (e.g., samples from Mallick et al. 28 were not included due to unavailability at the time) and hence slightly different {Ne, Mut} values of {192.966, 0.000801}. We used default parameters, with the fineSTRUCTURE normalisation parameter “c” estimated as 0.20245. To focus on the fine-scale clustering of Ethiopians, we fixed all non-Ethiopian samples in the dataset as seven super-individual populations (Africa, America, Central Asia Siberia, East Asia, Oceania, South Asia and West Eurasia) that were not merged with the rest of the tree. We performed 2,000,000 sample iterations of Markov-Chain-Monte-Carlo (MCMC), sampling an inferred clustering every 10,000 iterations. Following Lawson et al. 63 , we next used fineSTRUCTURE to find the single MCMC sampled clustering with highest overall posterior probability 63 . Starting from this clustering, we then performed 100,000 additional hill-climbing steps to find a nearby state with even higher posterior probability. This gave a final inferred number of 180 clusters containing Ethiopians. Results were then merged into a tree using fineSTRUCTURE’s greedy algorithm. We used a visual inspection of this tree to merge clusters, starting at the bottom level of 180 clusters, that had small numbers of individuals of the same ethnicity, as shown in Supplementary Fig.  10 . After merging, we ended up with a total of 78 Ethiopian clusters.

We followed Leslie et al. 43 to generate a measure of cluster certainty using the last 100 fineSTRUCTURE MCMC samples 43 . In particular for each of these 100 MCMC samples, we assigned a certainty score for each individual i being assigned to each final cluster j (out of 78) as the percentage of individuals assigned to the same cluster as individual i in that MCMC sample that are found in final cluster j. (For each individual i, note these percentages sum to 100% across the 78 final clusters.) For each combination of individual and final cluster, we averaged these certainty scores across all 100 MCMC samples. For each of our 78 final clusters, in Supplementary Data  4 we report the average certainty score of being assigned to that cluster across all individuals assigned to that cluster. This average certainty score had a mean of 44.7% across all clusters (range: 5.6–88.8%). For comparison, the average certainty score of being assigned to a cluster other than the final classification we used had a mean of 0.7% across all clusters (range: 0.1–1.2%). We note that clusters do not necessarily correspond to distinct groups that split from one another in the past, but instead provide a convenient means to increase power and clarity of ancestry inference by (i) merging people with similar genetic variation patterns, and (ii) separating individuals of the same self-identified label that have different genetic variation patterns.

Clustering Ethiopians using ADMIXTURE

We also used ADMIXTURE v.1.3.0 56 to cluster Ethiopians. To do so, we first pruned the dataset for SNPs in linkage disequilibrium using PLINK v.2 61 , removing SNPS with an r 2  > 0.1 within a 50-SNP window, which left 139,032 SNPs. We then applied ADMIXTURE to the Ethiopians using these SNPs and a varying number of clusters K = 2 − 15 and default parameters.

Describing the genetic make-up of Ethiopians as a mixture of recent ancestry sharing with other groups

We applied SOURCEFIND 72 to each of the 78 clusters to infer the proportion of ancestry that each clusters’ individuals share most recently with 275 ancestry surrogate populations, consisting of 264 present-day non-Ethiopian populations and aDNA samples from 11 populations including Mota (Supplementary Note  5 ). Briefly, SOURCEFIND identifies the reference groups for which each Ethiopian cluster shares most recent ancestry, and at what relative proportions, while accounting for potential biases in the CHROMOPAINTER analysis e.g. attributable to sample size differences among the surrogate groups. To do so, first each surrogate group and Ethiopian cluster k is described as a vector of length 264, where each element i in the vector for group k contains the total amount of genome-wide DNA that individuals from k are, on average, inferred to match to all individuals in group i under the “Ethiopia-external” CHROMOPAINTER analysis. These elements are proportional to the \({f}_{k}^{i}\) described in the section “Inferring genetic similarity among Ethiopians under two different CHROMOPAINTER analyses” above. SOURCEFIND then uses a Bayesian approach to fit the vector for each Ethiopian cluster as a mixture of those from the 275 surrogate populations, inferring the mixture coefficients via MCMC 72 . In particular SOURCEFIND puts a truncated Poisson prior on the number of non-Ethiopian groups contributing ancestry to that Ethiopian cluster. We fixed the mean of this truncated Poisson to 4 while allowing 8 total groups to contribute at each MCMC iteration, otherwise using default parameters. For each Ethiopian cluster, we discarded the first 50 K MCMC iterations as “burn-in”, then sampled mixture coefficients every 5000 iterations, averaging these mixture coefficients values across 31 posterior samples. In Supplementary Data  7 and Fig.  3 , Supplementary Fig.  12 , we report the average mixture coefficients as our inferred proportions of ancestry by which each Ethiopian cluster relates to the 275 reference groups, though noting only 13 of these 275 contribute >5% to any cluster in these results.

Identifying and dating admixture events in Ethiopia

Under each of the “Ethiopia-internal” and “Ethiopia-external” analyses, we applied GLOBETROTTER 58 to each Ethiopian cluster to assess whether its ancestry could be described as a mixture of genetically differentiated sources who intermixed (i.e., admixed) over one or more narrow time periods (Supplementary Note  5 ). GLOBETROTTER assumes a “pulse” model whereby admixture occurs instantaneously for each admixture event, followed by the random mating of individuals within the admixed population from the time of admixture until present-day. When testing for admixture in each Ethiopian cluster under the “Ethiopia-external” analysis, we used 130 groups (119 present-day groups and 11 ancient groups) as potential surrogates to describe the genetic make-up of the admixing sources, excluding non-African groups that contributed little in the SOURCEFIND analysis for computational efficiency. When testing for admixture under the “Ethiopia-internal” analysis, we added as surrogates 64 of the 78 inferred Ethiopian clusters, removing 14 clusters (marked by asterisks in the first column of Supplementary Data  4 ) that contained small numbers of individuals from several ethnic groups and hence would confuse interpretation of results.

GLOBETROTTER requires two paintings of individuals in the target population being tested for admixture: (1) one that is primarily used to identify the genetic make-up of the admixing source groups (used as “input.file.copyvectors” in GLOBETROTTER), and (2) one that is primarily used to date the admixture event (used as the “painting_samples_filelist_infile” in GLOBETROTTER). For both the “Ethiopia-external” and “Ethiopia-internal” analyses, we used the respective paintings described in “Using chromosome painting to evaluate whether genetic differences among ethnic groups are attributable to recent or ancient isolation” above to define the genetic make-up of each group for painting (1). For (2), following Hellenthal et al. 58 , we painted each individual in the target cluster against all other individuals except those from the target cluster, using ten painting samples inferred by CHROMOPAINTER per haploid of each target individual 58 . For the “Ethiopia-external” analysis, by design the painting in (2) is the same as the one used in (1). For the “Ethiopia-internal” analysis, we had to repaint each individual in the target cluster for step (2); to do so we used the previously estimated CHROMOPAINTER {Ne, Mut} parameters of {180.5629, 0.000610556}.

In all cases, we ran GLOBETROTTER for five mixing iterations (with each iteration alternating between inferring mixture proportions versus inferring dates) and performed 100 bootstrap re-samples of individuals to generate confidence intervals around inferred dates. We report results for null.ind = 1, which attempts to disregard any signals of linkage disequilibrium decay in the target population that is not attributable to genuine admixture when making inference 58 . All GLOBETROTTER results, including the inferred sources, proportions and dates of admixture, are provided in Supplementary Data  7-8 and summarized in Fig.  3 and Supplementary Fig.  12 ; see Supplementary Note  5 for more details. To convert inferred dates in generations to years in the main text, we used years ~= 1975 − 28 x (generations + 1), which assumes a generation time of 28 years 78 and uses an average birthdate of 1975 for sampled individuals that matches our recorded information.

Permutation test to assess significance of genetic similarity among individuals from different linguistic groups

To test whether individuals from language classification A are more genetically similar to each other than an individual from classification A is to an individual from classification B , we followed an analogous procedure to that detailed above to test for genetic differences between group labels A and B . Again let \({n}_{A}\) and \({n}_{B}\) be the number of sampled individuals from A and B , respectively, with \({n}_{X}=min({n}_{A},{n}_{B})\) . For each of 100 K permutations, we first randomly sampled \(floor({n}_{X}/2)\) individuals without replacement from each of A and B and put them into a new group C . If \({n}_{X}/2\) is a fraction, we added an additional unsampled individual to C that was randomly chosen from A with probability 0.5 or otherwise randomly chosen from B , so that C had \({n}_{X}\) total individuals. We then tested whether the average genetic similarity, \(\mathop{\sum}\nolimits_{i,j}\frac{1-TV{D}_{ij}}{({n}_{X}choose2)}\) , among all \(({n}_{X}choose2)\) pairings of individuals ( i,j ) from C is greater than or equal to that among all \(({n}_{X}choose2)\) pairings of \({n}_{X}\) randomly selected (without replacement) individuals from group Y , where Y ∈ {A,B} (tested separately).

Individuals from the same ethnic/occupation label (i.e., those listed in Supplementary Data  1 ) are often substantially genetically similar to one another (Supplementary Fig.  8 , Supplementary Data  5, 6 ), which may in turn drive similarity among individuals within the same language classification. Therefore, whenever a language classification contained more than two different ethnic/occupation labels, we restricted our averages to only include pairings ( i,j ) that were from different ethnic/occupation labels (including in permuted group C individuals). We report the proportion of 100 K such permutations where this is true as our one-sided p value testing the null hypothesis that an individual from language classification Y has the same average genetic similarity with someone from their own language group versus someone from the other language group (Supplementary Fig.  9 , Supplementary Data  9, 10 ). To test whether classifications A and B are genetically distinguishable, we take the minimum such p value between the tests of Y = A and Y = B (Supplementary Fig.  9 ), which accounts for how some linguistic classifications include more sampled individuals and/or more sampled ethnic groups that therefore may decrease their observed average genetic similarity.

Genetic similarity versus cultural distance

Between each pairing of 46 sampled SNNPR ethnic groups, we calculated a cultural similarity score as the number of practices, out of 31 reported in the SSNPR book (The Council of Nationalities, Southern Nations and Peoples Region, 2017) and described in Supplementary Note  6 , that the pair reported either both practicing or both not practicing (see Supplementary Data  12 for all groups’ recorded practices). Despite the SSNPR book also containing information about the Ari, we did not include them among these 46 because of the major genetic differences among occupational groups (Fig.  2a ). For the Wolayta, we included individuals that did not report belonging to any of the occupational groups analysed here.

We also calculated a second cultural similarity score whereby practices shared by many groups contributed less to a pair’s score than practices shared by few groups. To do so, if H ethnic groups in total reported participating in a practice, any pair of ethnicities that both reported participating in this practice added a contribution of 1.0/H to that pair’s cultural similarity score, rather than a contribution of 1 as in the original cultural similarity score. Similarly, if Z ethnic groups in total reported not participating in a practice, any pair of ethnicities that both reported not participating in this practice added a contribution of 1.0/Z to that pair’s cultural similarity score.

Genetic similarity, geographic distance and elevation difference between two ethnic groups A , B were each calculated as the average such measure between all pairings of individuals where i is from A and j from B . We then applied a mantel test using the mantel package in the vegan library in R with 100,000 permutations to assess the significance of association between genetic and cultural similarity across all pairings of ethnic groups (Supplementary Table  8 ). We also used separate partial mantel tests, using the mantel.partial function in R with 100,000 permutations, to test for an association between genetic and cultural similarity while accounting for one of (i) geographic distance, (ii) elevation difference, or (iii) shared language classification (Supplementary Table  8 ). To account for shared language classification, we used a binary indicator of whether A,B were from the same language branch: AA Cushitic, AA Omotic, AA Semitic, NS Satellite-Core.

For each of the 31 cultural practices, all 46 ethnic groups were classified as either (i) reporting participation in the practice, (ii) reporting not participating in the practice or (iii) not reporting whether they participated in the practice. For cultural practices where at least two of (i)-(iii) contained > =2 groups, we tested the null hypothesis that the average genetic similarity among groups assigned to category X was equal to that of groups assigned to Y, versus the alternative that groups in X had a higher average genetic similarity to each other. To do so, we calculated the difference in mean genetic similarity among all pairs of groups assigned to X versus that among all pairs assigned to Y. We then randomly permuted ethnic groups across the two categories 10,000 times, calculating p values as the proportion of times where the corresponding difference between permuted groups assigned to X versus Y was higher than that observed in the real data. For 16 of 31 cultural practices, we tested X=(i) versus Y=(iii). For one cultural practice, we tested X=(ii) versus Y=(iii). For three cultural practices, we tested {X=(i) versus Y=(ii)}, {X=(i) versus Y=(iii)}, and {X=(ii) versus Y=(iii)}.

Six practices gave a p value < 0.05 for one of the above permutation tests (Fig.  5 ). These p values remained after first adjusting for spatial distance as described in this paragraph. We calculated the average genetic similarity between all ethnic groups sharing these six practices after accounting for the effects of spatial distance and language classification. To account for spatial distance, we used Eqs. ( 2 )–( 4 ) above, first adjusting geographic distance out of each of genetic similarity and elevation difference, and then regressing the residuals from the genetic similarity versus geographic distance regression against the residuals from the elevation difference versus geographic distance regression. We take the residuals for individuals i,j from this latter regression as the adjusted genetic similarity between individuals i and j (denoted G* ij ). In each of the above regressions, we fit our models using all pairs of Ethiopians that were not from the same language classification at the branch level (i.e., AA Cushitic, AA Omotic, AA Semitic, NS Satellite-Core), in order to account for only spatial distance effects that are not confounded with any shared linguistic classification. We calculate the average spatial-distance-adjusted genetic similarity between each ethnic group A,B as the average G* ij between all pairings of individuals where i is from A and j from B . Then to adjust for language classification, we calculated the expected spatial-distance-adjusted genetic similarity for each pairing of language branches C,D as the average adjusted genetic similarity across all pairings of ethnic groups A, B where A is from C and B is from D . For each pair of ethnic groups that share a reported cultural trait shown in Fig.  5 , we show the adjusted genetic similarity between that pair minus the expected spatial-distance-adjusted genetic similarity based on their language classification. This therefore illustrates the genetic similarity between the two groups after adjusting for that expected by their spatial distance from each other and their respective language classifications (lower right triangles of heatmaps in Fig.  5 ).

For each of these six cultural practices shown in Fig.  5 , we also assessed whether there was evidence of recent intermixing among people from pairs of groups that both reported the given practice (see Supplementary Note  5 ). To do so, we indicate in the upper left triangles of the heatmaps in Fig.  5 whether >=1 pairings of individuals, one from each group, have average MRCA segments >= 2.5 cM longer than the median length of average inferred MRCA segments across all such pairings of individuals from the separate groups. We calculated the average MRCA segment length between two individuals as the total inferred cM length of matching between the two divided by the total inferred number of segments matching between the two, as inferred by CHROMOPAINTER under the “Ethiopia-internal” analysis. We calculated the proportion out of 10,000 random samples of n groups (sampled from the 46 SNNPR groups analysed here) where a greater or equal number of group pairings showed this trend, also considering various different values of excess average MRCA segment size (Supplementary Table  10 ).

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Genotype data, birthplace information and self-reported group label, first language, second language and religious affiliation for newly genotyped individuals are available for non-commercial use at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001005171 . Previously published data were obtained from: www.ebi.ac.uk/ena/browser/view/PRJEB32086 , www.ebi.ac.uk/ena/browser/view/PRJEB21878 , https://doi.org/10.6084/m9.figshare.5223583.v1 , www.ebi.ac.uk/ena/browser/view/PRJEB11848 , www.ebi.ac.uk/ena/browser/view/PRJEB11450 , www.ebi.ac.uk/ena/browser/view/PRJEB22660 , www.ebi.ac.uk/ena/browser/view/PRJEB2830 , www.ebi.ac.uk/ena/data/view/PRJEB9021 , www.ncbi.nlm.nih.gov/sra?term=PRJNA230689 , genetics.med.harvard.edu/reichlab/Reich_Lab/Datasets.html, africangenome.org/Main_Page, www.ebi.ac.uk/ena/browser/view/PRJEB32086 , www.ebi.ac.uk/ena/browser/view/PRJEB31373 , www.ebi.ac.uk/ena/browser/view/PRJEB22660 , www.ebi.ac.uk/ena/browser/view/PRJEB14180 , www.ebi.ac.uk/ena/browser/view/PRJEB8448 .

Mourant, A. E. et al. The blood groups and haemoglobins of the Kunama and Baria of Eritrea, Ethiopia. Ann. Hum. Biol. 1 , 383–392 (1974).

Harrison, G. A. Genetic and anthropological studies in the human adaptability section of the International Biological Programme. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 274 , 437–445 (1976).

Pagani, L. et al. Tracing the route of modern humans out of Africa by using 225 human genome sequences from Ethiopians and Egyptians. Am. J. Hum. Genet. 96 , 986–991 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Gallego Llorente, M. et al. Ancient Ethiopian genome reveals extensive Eurasian admixture throughout the African continent. Sci. (80-.) 350 , 820–822 (2015).

Article   ADS   CAS   Google Scholar  

Rankinen, T. et al. No evidence of a common DNA variant profile specific to world class endurance athletes. PLoS ONE 11 , e0147330 (2016).

Ash, G. I. et al. No association between ACE gene variation and endurance athlete status in Ethiopians. Med. Sci. Sports Exerc 43 , 590–597 (2011).

Article   PubMed   Google Scholar  

Scott, R. A. et al. Mitochondrial DNA lineages of elite Ethiopian athletes. Comp. Biochem. Physiol. - B Biochem. Mol. Biol. 140 , 497–503 (2005).

Article   PubMed   CAS   Google Scholar  

Huerta-Sánchez, E. et al. Genetic signatures reveal high-altitude adaptation in a set of Ethiopian populations. Mol. Biol. Evol. 30 , 1877–1888 (2013).

Stobdan, T. et al. Endothelin receptor B, a candidate gene from human studies at high altitude, improves cardiac tolerance to hypoxia in genetically engineered heterozygote mice. Proc. Natl. Acad. Sci. USA. 112 , 10425–10430 (2015).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Simonson, T. S. Altitude adaptation: a glimpse through various lenses. High. Alt. Med. Biol. 16 , 125–137 (2015).

Ronen, R., Zhou, D., Bafna, V. & Haddad, G. G. The genetic basis of chronic mountain sickness. Physiology 29 , 403–412 (2014).

Liebert, A. et al. World-wide distributions of lactase persistence alleles and the complex effects of recombination and selection. Hum. Genet. 136 , 1445–1453 (2017).

Jones, B. L. et al. Diversity of lactase persistence alleles in Ethiopia: Signature of a soft selective sweep. Am. J. Hum. Genet. 93 , 538–544 (2013).

Ingram, C. J. E. et al. Multiple rare variants as a cause of a common phenotype: Several different lactase persistence associated alleles in a single ethnic group. J. Mol. Evol. 69 , 579–588 (2009).

Mekonnen, E. & Bekele, E. An ancestral human genetic variant linked to an ancient disease: A novel association of FMO2 polymorphisms with tuberculosis (TB) in Ethiopian populations provides new insight into the differential ethno-geographic distribution of FMO2*1. PLoS ONE 12 , e0184931 (2017).

Mekonnen, E., Bekele, E. & Stein, C. M. Novel polymorphisms in TICAM2 and NOD1 associated with tuberculosis progression phenotypes in Ethiopian populations. Glob. Heal. Epidemiol. Genomics 3 , e1 (2018).

Creemer, O. J. et al. Contrasting exome constancy and regulatory region variation in the gene encoding CYP3A4: An examination of the extent and potential implications. Pharmacogenet. Genomics 26 , 255–270 (2016).

Browning, S. L., Tarekegn, A., Bekele, E., Bradman, N. & Thomas, M. G. CYP1A2 is more variable than previously thought: A genomic biography of the gene behind the human drug-metabolizing enzyme. Pharmacogenet. Genomics 20 , 647–664 (2010).

Sim, S. C. et al. A common novel CYP2C19 gene variant causes ultrarapid drug metabolism relevant for the drug response to proton pump inhibitors and antidepressants. Clin. Pharmacol. Ther. 79 , 103–113 (2006).

Article   CAS   PubMed   Google Scholar  

Behar, D. M. et al. The genome-wide structure of the Jewish people. Nature 466 , 238–242 (2010).

Article   ADS   CAS   PubMed   Google Scholar  

Thomas, M. G. et al. Founding mothers of Jewish communities: Geographically separated Jewish groups were independently founded by very few female ancestors. Am. J. Hum. Genet. 70 , 1411–1420 (2002).

Non, A. L., Al-Meeri, A., Raaum, R. L., Sanchez, L. F. & Mulligan, C. J. Mitochondrial DNA reveals distinct evolutionary histories for Jewish populations in Yemen and Ethiopia. Am. J. Phys. Anthropol. 144 , 1–10 (2011).

Tadesse, L., Tafesse, F. & Hamamy, H. Communities and community genetics in ethiopia. Pan Afr. Med. J. 18 , 115 (2014).

Poloni, E. S. et al. Genetic evidence for complexity in ethnic differentiation and history in East Africa. Ann. Hum. Genet 73 , 582–600 (2009).

Cavalli-Sforza, L. L., Piazza, A., Menozzi, P. & Mountain, J. Reconstruction of human evolution: Bringing together genetic, archaeological, and linguistic data. Proc. Natl. Acad. Sci. USA . 85 , 6002–6006 (1988).

Boattini, A. et al. MtDNA variation in East Africa unravels the history of afro-asiatic groups. Am. J. Phys. Anthropol. 150 , 375–385 (2013).

Pagani, L. et al. Ethiopian genetic diversity reveals linguistic stratification and complex influences on the Ethiopian gene pool. Am. J. Hum. Genet. 91 , 83–96 (2012).

Article   CAS   Google Scholar  

Mallick, S. et al. The simons genome diversity project: 300 genomes from 142 diverse populations. Nature 538 , 201–206 (2016).

Baker, J. L., Rotimi, C. N. & Shriner, D. Human ancestry correlates with language and reveals that race is not an objective genomic classifier. Sci. Rep. 7 , 1–10 (2017).

Scheinfeldt, L. B. et al. Genomic evidence for shared common ancestry of East African hunting-gathering populations and insights into local adaptation. Proc. Natl Acad. Sci. USA 116 , 4166–4175 (2019).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Gopalan, S. et al. Hunter-gatherer genomes reveal diverse demographic trajectories following the rise of farming in East Africa. bioRxiv https://doi.org/10.1101/517730 (2019).

Semino, O., Santachiara-Benerecetti, A. S., Falaschi, F., Cavalli-Sforza, L. L. & Underhill, P. A. Ethiopians and Khoisan share the deepest clades of the human Y-chromosome phylogeny. Am. J. Hum. Genet. 70 , 265–268 (2002).

Kivisild, T. et al. Ethiopian mitochondrial DNA heritage: tracking gene flow across and around the gate of tears. Am. J. Hum. Genet. 75 , 752–770 (2004).

Messina, F. et al. Linking between genetic structure and geographical distance: study of the maternal gene pool in the Ethiopian population. Ann. Hum. Biol. 44 , 53–69 (2017).

Scheinfeldt, L. B. et al. Genetic adaptation to high altitude in the Ethiopian highlands. Genome Biol. 13 , R1 (2012).

Prendergast, M. E. et al. Ancient DNA reveals a multistep spread of the first herders into sub-Saharan Africa. Science . 365 , eaaw6275 (2019).

van Dorp, L. et al. Evidence for a common origin of blacksmiths and cultivators in the Ethiopian Ari within the last 4500 years: lessons for clustering-based inference. PLoS Genet 11 , 1005397 (2015).

Tarekegn, A. A profile of the Nations, Nationalities and Peoples of the Southern Region, Second Edition. (Berhanena Selam Printing Enterprise, 2017).

Pankhurst, A. ‘Caste’ in Africa: the evidence from south-western Ethiopia reconsidered. Afr. (Lond.) 69 , 485–509 (1999).

Article   Google Scholar  

Levine, D. N. Greater Ethiopia: the evolution of a multi-ethnic society. (The University of Chicago Press, 2000).

Lazaridis, I. et al. Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513 , 409–413 (2014).

Gurdasani, D. et al. The African genome variation project shapes medical genetics in Africa. Nature 517 , 327–332 (2015).

Leslie, S. et al. The fine-scale genetic structure of the British population. Nature 519 , 309–314 (2015).

Byrne, R. P. et al. Insular Celtic population structure and genomic footprints of migration. PLoS Genet. 14 , (2018).

Hofmanová, Z. et al. Early farmers from across Europe directly descended from Neolithic Aegeans. Proc. Natl Acad. Sci. USA. 113 , 6886–6891 (2016).

Mathieson, I. et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature 528 , 499–503 (2015).

Schlebusch, C. M. et al. Southern African ancient genomes estimate modern human divergence to 350,000 to 260,000 years ago. Sci. (80-.) 358 , 652–655 (2017).

Keller, A. et al. New insights into the Tyrolean Iceman’s origin and phenotype as inferred by whole-genome sequencing. Nat. Commun. 3 , 698 (2012).

Allentoft, M. E. et al. Population genomics of Bronze Age Eurasia. Nature 522 , 167–172 (2015).

Olalde, I. et al. Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European. Nature 507 , 225–228 (2014).

Lipson, M. et al. Ancient West African foragers in the context of African population history. Nature 577 , 665–670 (2020).

Broushaki, F. et al. Early Neolithic genomes from the eastern Fertile Crescent. Science. (80-.) 353 , 499–503 (2016).

Haak, W. et al. Massive migration from the steppe was a source for Indo-European languages in Europe. Nature 522 , 207–211 (2015).

Skoglund, P. et al. Reconstructing prehistoric African population structure. Cell 171 , 59–71.e21 (2017).

López, S. et al. The genetic legacy of Zoroastrianism in Iran and India: insights into population structure, gene flow, and selection. Am. J. Hum. Genet 101 , 353–368 (2017).

Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. https://doi.org/10.1101/gr.094052.109 (2009).

Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38 , 904–909 (2006).

Hellenthal, G. et al. A genetic atlas of human admixture history. Sci. (80-.) 343 , 747–751 (2014).

van Dorp, L. et al. Genetic legacy of state centralization in the Kuba Kingdom of the Democratic Republic of the Congo. Proc. Natl Acad. Sci. USA 116 , 593–598 (2019).

Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet 2 , 2074–2093 (2006).

Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4 , 7 (2015).

Browning, S. R. & Browning, B. L. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12 , 703–714 (2011).

Lawson, D. J., Hellenthal, G., Myers, S. & Falush, D. Inference of population structure using dense haplotype data. PLoS Genet. 8 , e1002453 (2012).

Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution (N. Y) 38 , 1358 (1984).

CAS   Google Scholar  

Busby, G. B. et al. Admixture into and within sub-Saharan Africa. Elife 5 , e15266 (2016).

Freeman, D. & Pankhurst, A. A. Peripheral people: the excluded minorities of Ethiopia. (Hurst and Company, 2003).

Todd, D. M. The origins of outcastes in Ethiopia: reflections on an evolutionary theory. Abbay Doc. pour Serv. à l’histoire la Civilis. éthiopienne 9 , 148–158 (1978).

Google Scholar  

Blench, R. Archaeology, Language, and the African Past. (Rowman Altamira, 2006).

Ehret, C. Do Krongo and Chabu belong in Nilo-Saharan? in Nilo-Saharan Linguistic Analysis and Documentation (Rüdiger Köppe Verlag, 1992).

Dira, S. J. & Hewlett, B. S. The Chabu hunter-gatherers of the highland forests of Southwestern Ethiopia. Hunt. Gatherer Res . 3 , 232–353 (2018).

Appleyard, D. Preparing a Comparative Agaw Dictionary. in Cushitic & Omotic Languages (eds. Griefenow-Mewis, C. & Voigt, R.) (1996).

Chacón-Duque, J. C. et al. Latin Americans show wide-spread Converso ancestry and imprint of local Native ancestry on physical appearance. Nat. Commun. 9 , 5388 (2018).

Dea, D. Rural livelihoods and social stratification in Dawro, southern Ethiopia. in Social anthropology dissertation series no.14 (2007).

Biasutti, R. Pastori, agricoltori e cacciatori nell’Africa Orientale’. Bolletino del. R. Soc. Geogr. Ital. 6 , 155–179 (1905).

Teclehaimanot, G. S. The Wayto of Lake Tana: an Ethno-History. (1984).

Legesse, W. Religion and Everyday Life: Negede-Woyto Muslim Community of Bahir Dar Town Ethiopia. (Addis Ababa University, 2013).

Lawson, D. J., van Dorp, L. & Falush, D. A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nat. Commun. 9 , 3258 (2018).

Fenner, J. N. Cross-cultural estimation of the human generation interval for use in genetics-based population divergence studies. Am. J. Phys. Anthropol. 128 , 415–423 (2005).

Phillipson, D. Foundations of an African Civilisation: Aksum and the northern Horn, 1000 BC - AD 1300. (Boydell & Brewer, 2012).

Lewis, H. S. Historical problems in Ethiopia and the Horn of Africa. Ann. N. Y. Acad. Sci. 96 , 504–511 (1962).

Anbessa, T. & Unseth, P. The blood groups and haemoglobins of the Kunama and Baria of Eritrea, Ethiopia.. in Topics in Nilo-Saharan linguistics (Helmut Buske, 1989).

Novembre, J. et al. Genes mirror geography within Europe. Nature 456 , 98–101 (2008).

Tishkoff, S. A. et al. The genetic structure and history of Africans and African Americans. Sci. (80-.) 324 , 1035–1044 (2009).

Li, J. Z. et al. Worldwide human relationships inferred from genome-wide patterns of variation. Sci. (80-.) 319 , 1100–1104 (2008).

Pickrell, J. K. et al. Ancient west Eurasian ancestry in southern and eastern Africa. Proc. Natl Acad. Sci. USA. 111 , 2632–2637 (2014).

Lazaridis, I. et al. Genomic insights into the origin of farming in the ancient Near East. Nature 536 , 419–424 (2016).

Schlebusch, C. M. et al. Ancient genomes from southern Africa pushes modern human divergence beyond 260,000 years ago. Science. (80-.) 358 , 652–655 (2017).

Link, V. et al. ATLAS: analysis tools for low-depth and ancient samples. bioRxiv https://doi.org/10.1101/105346 (2017).

Auton, A. et al. A global reference for human genetic variation. Nature 526 , 68–74 (2015).

Article   ADS   PubMed   CAS   Google Scholar  

Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98 , 116–126 (2016).

Delaneau, O., Zagury, J. F., Robinson, M. R., Marchini, J. L. & Dermitzakis, E. T. Accurate, scalable and integrative haplotype estimation. Nat. Commun. 10 , 5436 (2019).

Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 27 , 209–220 (1967).

Delaneau, O., Marchini, J. & Zagury, J. F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9 , 179–181 (2012).

Download references

Acknowledgements

This work is funded by BBSRC (Grant Number BB/L009382/1). GH is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 098386/Z/12/Z) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. We also acknowledge the UCL Biosciences Big Data equipment grant from BBSRC (BB/R01356X/1). GB is a member of the MalariaGEN resource centre, supported by Wellcome [204911/Z/16/Z]. We thank David Reich and the Children’s Hospital of Philadelphia for genotyping the samples on the Human Origins array. We thank Karl Skorecki for assistance with sampling. Samples analysed in this study are drawn from a collection assembled and managed over many years. We thank the very many collectors, donors, students and technical staff who have contributed to this enterprise.

Author information

These authors contributed equally: Saioa López, Ayele Tarekegn.

Authors and Affiliations

Research Department of Genetics, Evolution & Environment, University College London, London, UK

Saioa López, Lucy van Dorp, Nancy Bird, Sam Morris, Mark G. Thomas & Garrett Hellenthal

UCL Genetics Institute, University College London, London, UK

Department of Archaeology and Heritage Management, College of Social Sciences, Addis Ababa University, New Classrooms (NCR) Building, Second Floor, Office No. 214, Addis Ababa University, Addis Ababa, Ethiopia

Ayele Tarekegn

Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

Genomics & Bioinformatics Research Directorate (GBRD), Ethiopian Biotechnology Institute (EBTi), Addis Ababa, Ethiopia

Tamiru Oljira

Institute of Biotechnology, Addis Ababa University, Addis Ababa, Ethiopia

Ephrem Mekonnen

College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, Ethiopia

Endashaw Bekele

McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, UK

Roger Blench

Department of History, University of Jos, Jos, Nigeria

Henry Stewart Group, London, UK

Neil Bradman

You can also search for this author in PubMed   Google Scholar

Contributions

A.T., T.O., E.M., E.B., and N. Bradman performed sample collection. N. Bradman oversaw and managed the sample collection programme. M.G.T. designed and managed post-collection sample processing procedures. S.L., L.v.D., N. Bird, S.M. and G.H. performed the analyses. S.L., A.T., N. Bradman, and G.H. wrote the paper with input from co-authors including R.B. G.B. designed the webpage resource.

Corresponding authors

Correspondence to Saioa López , Ayele Tarekegn or Garrett Hellenthal .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, description of additional supplementary files, supplementary data 1-13, reporting summary, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

López, S., Tarekegn, A., Band, G. et al. Evidence of the interplay of genetics and culture in Ethiopia. Nat Commun 12 , 3581 (2021). https://doi.org/10.1038/s41467-021-23712-w

Download citation

Received : 17 December 2019

Accepted : 13 May 2021

Published : 11 June 2021

DOI : https://doi.org/10.1038/s41467-021-23712-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Influence of socio-demographic factors on medicinal plant knowledge among three selected ethnic groups in south-central ethiopia.

• Sintayehu Tamene
• Mesele Negash
• Linley Chiwona-Karltun

Journal of Ethnobiology and Ethnomedicine (2024)

A systematic review exploring the diversity and food security potential of wild edible plants in Ethiopia

• Daniel Tadesse
• Getinet Masresha
• Melaku Wondafrash

Scientific Reports (2024)

The Allen Ancient DNA Resource (AADR) a curated compendium of ancient human genomes

• Swapan Mallick
• David Reich

Scientific Data (2024)

Medieval DNA from Soqotra points to Eurasian origins of an isolated population at the crossroads of Africa and Arabia

• Kendra Sirak
• Julian Jansen Van Rensburg

Nature Ecology & Evolution (2024)

Whole genomes from Angola and Mozambique inform about the origins and dispersals of major African migrations

• Sam Tallman
• Maria das Dores Sungo
• Sandra Beleza

Nature Communications (2023)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Research article
• Open access
• Published: 27 October 2022

Practices and challenges of cultural heritage conservation in historical and religious heritage sites: evidence from North Shoa Zone, Amhara Region, Ethiopia

• Habtamu Mekonnen 1 ,
• Zemenu Bires   ORCID: orcid.org/0000-0002-4156-3235 2 &
• Kassegn Berhanu   ORCID: orcid.org/0000-0001-9981-5901 3  

Heritage Science volume  10 , Article number:  172 ( 2022 ) Cite this article

62k Accesses

30 Citations

Metrics details

Cultural heritage treasures are precious communal assets that show the past human legacy. It depicts present and future way of life as well as cultural values of a society, and enhances solidarity and social integration of communities. This study is designed to investigate the practices and challenges of cultural heritage conservations in North Shoa Zone, Central Ethiopia. The research employed a mixed research approach and cross-sectional descriptive and explanatory research design. The researchers applied multiple data gathering instruments including questionnaire survey, interview, focus group discussion and observation. Concerning sampling techniques, systematic random sampling technique was applied to select samples from local communities, and purposive sampling was designed to choose interviewees from government authorities, and culture and tourism office experts of North Shoa Zone and respective districts. The actual and valid sample size of the study is 236. The findings of the study revealed that the cultural heritage properties in North Shoa are not safeguarded from being damaged and found in a poor status of conservation. The major conclusion sketched from the study is that the principal factors affecting heritage conservation are lack of proper management, monitoring and evaluation, lack of funds and stakeholder involvement, urbanization, settlement programs and agricultural practice, poor government concern and professional commitment, poor attitude towards cultural heritage and low level of community concern, vandalism and illicit trafficking, low promotions of cultural heritage, and natural catastrophes such as invasive intervention, climate change (humidity and frost, excessive rainfall and flood, heat from the sun). The study implied that the sustainability of cultural heritage in the study area are endanger unless conservation practice is supported by conservation guidelines, heritage site management plans and research outputs, stakeholders’ integration, and community involvement. Most importantly, the study recommends the integration of heritage conservation and sustainable development, and the promotion of conservation is a way of achieving economic and social sustainability.

Introduction

Heritage is our legacy from the past, what we live with today, and what we pass on to the future generations. Our cultural and natural heritage resources are both irreplaceable sources of life and inspiration. They are our touchstones, our points of reference, and our identity [ 1 ]. Cultural heritage is the legacy of physical artifacts, cultural property, and intangible attributes of a group or society that are inherited from past generations, maintained in the present, and bestowed for the benefit of future generations [ 2 , 3 ].

According to Bleibleh and Awad [ 4 ], and the United Nations Educational, Scientific and Cultural Organization (UNESCO, 1972: Article 1), cultural heritage includes monuments: architectural works, sculpture, painting, inscriptions, archaeological structure, cave dwellings; buildings: groups of separate or connected buildings and their architectures, homogeneity or place in the landscape; and sites: man made creativity or the combined work of nature and man. Cultural heritage should have outstanding universal value from the historical, architectural, commemorative, aesthetic, ethnological or anthropological point of view. Cultural heritage provides communities, groups, and individuals with a sense of identity and continuity, helping them to visualize their world and giving meaning to their way of living together [ 5 ].

In Ethiopian nations, nationalities, and people’s context, the definition of cultural heritage could be used to incorporate their varied social, economic, political, administrative, moral, religious, and psychological conditions [ 6 ]. Ethiopia is a great country with its fabulous 3000 years history [ 7 ], a population of about 114 million people endowed with astonishingly rich linguistic and cultural diversity with more than 80 living languages and 200 dialects, spoken by as many ethno-linguistic communities [ 8 ].

In this era of globalization, there is a growing fear that culture around the world will become more uniform, leading to a decrease in cultural diversity. To counter this potential homogeneity, strategies have been developed to preserve culture of various communities whose very existence could be threatened. Living culture is highly susceptible to becoming extinct [ 9 , 10 ]. Currently, the surge of interest in culture is creating new possibilities for safeguarding cultural heritage as a major component in building a sustainable cultural vision for the world [ 11 ]. In the context of UNESCO’s activities, the value and the importance of safeguarding cultural heritage is universally recognized [ 1 ].

Conservation of cultural heritage can be defined as all measures and actions aimed at safeguarding cultural heritage while ensuring its accessibility to present and future generations. Conservation embraces preventive preservation, adaptation, reconstruction, and restoration. All measures and actions should respect the significance and physical properties of the cultural heritage item [ 12 ].

In Ethiopia, the Authorities for Research and Conservation of Cultural Heritage (ARCCH) within the Ministry of Tourism and UNESCO Addis Ababa Office established a joint work plan (2006–2007) concerning inventorying and safeguarding both tangible and intangible cultural heritage in the country [ 7 ]. Besides, both the 1995 constitution and the 1997 cultural policy of Ethiopia refers to equal safeguard, recognition of and respect for all Ethiopian languages, heritage, history, handicraft, fine arts, oral literature, traditional lore, beliefs, and other cultural features. Following the ratification of the 2003 Convention for the Safeguarding of the Cultural Heritage, the ARCCH designed a strategy on the identification, safeguarding, and promotion of cultural heritage through a national inventory-making exercise.

In principle, in Ethiopia, there are policies, guidelines and regulations of cultural heritage conservation. In practice, however, the majority of the heritage attractions are in poor conservation status (for instance light shelter protection to the world heritage site of Lalibela Rock hewn Churches); demolished due to ignorance (e.g. Ankober Archaeological site); intentionally destructed due to misinterpretation and interethnic conflict (as evidenced on Ras Mekonnen Monument in Harar, Ethiopia), and the destruction of Al-Negash Mosque in Tigray, Ethiopia due to the war between the Federal Government of Ethiopia and Tigray Liberation Front.

North Shoa is a special focus from cultural and historical perspectives. Historically, the region had been administrative centres or seats of government for the Kings of Shoa and Ethiopia from the reign of Amde-Tsion (1314–1344) and Zera Yakob (1434–1468) up to Emperor Menelik II (1865–1913). In this regard, the historical sites such as Menz, Tegulet, Debre Berhan, Sela Dingay, Ankober, Liche, and Angolela had served as a headquarter of the government of Ethiopia in the medieval history and in the second half of nineteenth century.

When almost all African nations were under European colonization in the late 19 th and in the first half of twentieth century, Shoa in general and North Shoa in particular, was in position to establish formal diplomatic relations with the Europeans countries. Consequently, European embassies (for instance British, France and Italy) were opened at Ankober for the first time. Most importantly, the region is a birth place of prominent patriots (e.g. Ras Abebe Aregay, Hailemariam Mamo, Buayalew Abate, Fiwtarari Gebeyehu to mention few among many) who sacrificed a lot in defending the sovereignty of Ethiopia against foreign aggressors.

Culturally, North Shoa is also rich with Christian religious sites such as churches, monasteries, and holy water. Famous religious sites include but not limited to Tsadikanie St. Mary Church, Kukyelesh St. Mary, Abune Melike Tsedik monastery, Zebir Gabriel church, Seminesh Kidane   Mihiret church which are known for their annual religious ceremonies, holy water that cure diseases and cleanse sins. Important traditional games such as hockey and horse racing or horse galloping are practiced along with feast days. Besides, North Shoa is not only a special attention for the Christians, but also known for its rich history and incredible Islamic heritage relics. The sultanates of Shoa (9th–thirteenth century), Ifat (thirteenth–fifteenth century) as well as the 13th medieval great mosque of Goze (still existing Islamic architecture) are some of the evidences of the historical and religious Islamic civilizations [ 13 , 14 ].

However, despite the presence of plenty of cultural and historical heritage in Ethiopia in general and North Shoa Zone in particular, their sustainability is in question and the contribution of heritage tourism to the host community is very low due to various impacts such as developmental projects near or on heritage sites, absence of demarked buffer zones, lack of awareness or ignorance, theft and looting, embezzlement, inappropriate conservation practices, and natural damage/ deteriorations. The most widely known problems of cultural heritage include archaeological looting, destruction of cultural sites, and the theft of works of art from churches and museums all over the world are testimonies of cultural heritage destructions [ 15 ].

According to Eken, Taşcı, and Gustafsson [ 16 ] cultural heritage properties are vulnerable to various physical, chemical, natural and anthropogenic factors that worsening the sustainability of heritage attractions. Though North Shoa has a paramount significance from historical and cultural perspectives, it has never received due attention from the government, researchers and other conservationists stakeholders as bold as its potentials. Besides, scholarly works regarding cultural heritage conservation are not sufficient in East Africa in general and in Ethiopia in particular. Hence, to address this research gap, the need to research on challenges and practices of cultural heritage conservation is one of the top priorities.

Literature review

Issues of cultural heritage, cultural ownership, rights, politics and representation.

When the homogenization and standardization of heritage occur, the politics of cultural identity emerges as a critical issue. This is particularly true since heritage is not just a matter of the past, but very much a conduit for constructing the future [ 17 ]. In other words, how the local communities present their cultural heritage to the outside visitors affects the way the community members envisage their future. This has been observed in numerous cross-cultural ethnographic cases [ 18 , 19 ]. Needless to say, how to represent the cultural heritage reflects the present condition of political hierarchies that exist within the society.

Members of local communities have diverse opinions that are positioned in different contexts of their lives. A unified representation of cultural heritage may not be something that some members of the community can easily accept [ 20 ]. This may affect the community negatively in both socio-cultural and political domains. Sometimes, the cohesiveness within the community is weakened, and some members even decide to leave the community altogether which is a serious breach of the cultural rights of these members.

Identification and documentation of cultural heritage

Inventories should identify threats that certain elements of cultural heritage is facing. Based on such information, a plan for safeguarding or revitalization can be developed. When conservation of heritage property is impossible due to lack of funds and experts; digital preservation deemed to be an alternative means of safeguarding cultural heritage. According to Koiki- Owoyele, Alabi and Egbunu [ 21 ] heritage digitization is a process of taking photographs or scanning a material and transferring it to a computer. The dissemination of digital preserved heritage on websites, social media platforms and Google search optimization helps to reach more users which in turn reduce the cost and energy of users to undertake a journey to a library, archive or museum to visit the heritage. Digital preservation is a long lasting solution to threats such as decay, war, fire and flood and enables to secure the availability of useful resources for academicians of future generations [ 22 ].

Danger of extinction

According to Karin and Philippe [ 23 ] the new alternative approaches to cultural heritage conservation recognize the importance of preserving vital and living elements of culture. Because of natural and human factors, developments around cultural heritage, conflict of interest among stakeholders, theft and vandalism, and inappropriate conservational practices, and hence, the danger of losing them is sometimes underestimated [ 24 ].

Truscott [ 25 ] argued that local communities themselves often do not see the importance of preserving their cultural heritage properties. They may consider their cultural heritage as backward and as a hindrance to their ability to access "modern society" and economic wealth. It is essential, therefore, not only to create a system that values and respects minority culture but also to encourage communities to become aware of their cultural treasures and to help them find ways to preserve those treasures [ 26 ].

Roy and Kalidindi [ 27 ] stated that rapid growth of urbanization, mass tourism, lack of funds, improper project selection, lack of traditional know-how among conservation professionals, poor handling system or heritage management, corruption, and erroneous conservation policy are responsible for the poor performance of heritage conservation projects [ 28 ]. Besides, adverse factors that threaten heritage conservation include heritage trafficking, limited community participation in conservation, cultural degradation, and inadequate attention from government bodies, and poor coordination among stakeholders [ 29 ]. Other critical issues of heritage conservation encompass indigenous claims of ownership and access to material culture, authentic, original value embodied in material culture [ 30 ]; removal of monuments from their original site, damage through the flooding of agricultural land, resettlement programs and rebuilding of urban centres [ 31 ].

Cultural heritage properties have been attacked in wars of conquest and colonization, during interstate and civil conflicts, by governments, protestors or rebels across the world [ 32 ]. Monuments such as historical buildings and statues; religious sites like synagogues, mosques, temples, monasteries, churches; material culture exhibitions and collection sites (e.g. museums, art galleries, and libraries) which depict the collective narratives, stories and memories of people have become vulnerable to destructions [ 33 ].

It has been documented that over 13,000 cultural heritage sites were destroyed in the Middle East particularly in Iraq, Syria, Yemen and Libya [ 34 ]. The widespread devastation or attacks include world heritage sites. For instance, the six UNESCO World Heritage sites of Syria such as the Ancient City of Damascus, the Ancient City of Bosra, the Site of Palmyra (ancient temples, tombs and antiquities with the age of more than 2000 years), the Ancient City of Aleppo, Crac des Chevaliers and Qal’at Salah El-Din, and the Ancient Villages of Northern Syria or Dead Cities are either destroyed or partially damaged during the armed conflict between ISIS (also called IS, ISIL, Da’esh or the Islamic State) and state government [ 35 , 36 ].

The ISIS has systematically been destroying the cultural heritage (ancient monuments, mosques, shrines, cemeteries, works of art at museums and libraries) blowing up the Armenian, Syrian Orthodox and Roman Catholic churches, monasteries and the tombs of prophets. Thousands of archaeological and cultural sites (including those aged in the Bronze, Iron, Greek, Roman, Byzantine and Islamic periods) in Syria are victims of the on-going fighting or war [ 37 ].

The destruction of Yazidi shrines and the obliteration of ancient sculptures called “lamassu", a vital symbol to the modern Assyrian Christian population, and the devastation and vandalism of other Christian relics and churches in the Tadmor and Palmyra area were deliberate to deface the minority religious and cultural sites as well as to terrorize and subdue the minorities [ 38 ].

As noted in the work of Wollentz [ 39 ] during the Yugoslavian Civil War, cultural heritage such as the medieval Stari Most Bridge was destroyed, and the old town of Dubrovnik, one of the first sites inscribed by UNESCO as World Heritage Sites was bombarded.

In Africa, the most outstanding cultural site of Timbuktu (Mali) famous for its world heritage sites of mausoleums and mosques having exceptional cultural, historical and spiritual significance were targeted to destruction [ 40 ].

By the same token, the Eritrea–Ethiopia war in 1998–2000 was responsible for the devastation of an essential archaeological monument nearby the Ethio-Eritrea border [ 41 ].

The major causes for the destruction of cultural heritage and systematic cultural cleansing include civil war, ignorance and negligence, religious differences or fundamentalism and radical ideologies (for instance, ISIS perceived that most of the cultural and religious heritage in the middle east are false idols that are heretical to Islam) [ 42 , 43 ], a mission to accomplish military, political, and economic objectives [ 34 ] and developmental projects such as urban reconstruction [ 44 ].

Lack of funds and experts, and organizational structure problem

The custodian of cultural heritage is not always good at organizing or management of funds [ 31 ]. On the other hand, those who are experts in organizing and managing funds are not always experts or even interested in cultural heritage. So the solution has been creating collaboration between these two kinds of people: between the cultural heritage custodians and those who are experts in managing and organizing these kinds of projects [ 45 ]. Another mechanism of securing funds and initiating experts is devising means of discussions regarding the values of cultural heritage on different media such as social media, broadcast media, and printed media. The other issue mentioned by Mancacaritadipura [ 45 ] the younger generation is less interested in the local culture. To overcome this issue, Mancacaritadipura suggested that the school curriculum should include cultural heritage at local content [ 45 ]. Besides the main curriculum, Mike and David [ 46 ] forwarded that awareness creation about the significance and promotion of cultural heritage should be undertaken in schools, colleges, and universities.

As observed in many African countries, states have not yet created an official section and positions in the Department of Culture and Tourism to be specifically responsible for cultural heritage [ 26 ]. Truscoot (2000) forwarded that the government may create a sub-directorate of cultural heritage which will make it easier to do long-term programs [ 25 ]. Besides, UNESCO (2005) has been identified difficulties in finding qualified human resources to participate in efforts to preserve and develop cultural heritage [ 47 ].

Opportunities for safeguarding cultural heritage

Stakeholders involvement.

Cultural heritage must be thoughtfully managed if it is to survive in an increasingly globalized world [ 47 ]. True partnerships are required between all relevant stakeholders, particularly governments, private tourism sectors, NGOs, and local communities. Through mutual understanding, key stakeholders can build on their shared interest in cultural assets, in close consultation with local communities, the ultimate bearers of humankind’s cultural legacy [ 48 ]. The awareness and attitude of among stakeholders towards the conservation of cultural heritage is crucial to have a common stake among interest groups towards cultural heritage and development, to keep sustainable conservation management, and to promote cultural tourism [ 49 ]. Community-based tourism projects allow for direct communication between communities and heritage tourism while sustainably developing cultural assets as tourism products [ 50 ].

Community participation

Communities must be actively involved in safeguarding and managing their cultural heritage since it is only the one who can consolidate their presence and ensure its future [ 51 ]. Each community, using its collective memory and consciousness of its past, is responsible for the identification as well as the management of its heritage [ 52 ]. Communities, in particular indigenous communities, groups, and, in some cases, individuals, play an important role in the production, safeguarding, maintenance, and re-creation of the intangible cultural heritage. Within the framework of safeguarding the cultural heritage, each state party shall endeavour to ensure the widest possible participation of communities, groups, and, where appropriate, individuals that create, maintain, and transmit such heritage, and to involve them actively in its management [ 53 ]. Apart from stakeholders’ participation and community involvement, resource mobilization, ecotourism activities, and corporate fundraising mechanisms could be devised to achieve conservation programmes, and contribution should be based on willingness and abilities of stakeholders [ 54 ].

UNESCO committee and convention for safeguarding cultural heritage

Today, even in a world of mass communication and global cultural flows, many forms of cultural heritage properties are being preserved or conserved in every corner of the world [ 55 ]. Other forms and elements of cultural heritage resources which are more fragile, and some are even endangered and needs measures called for by the UNESCO Convention of safeguarding cultural heritage at the national and international levels can help communities to ensure that their heritage remains available to their descendants for decades and centuries to come [ 56 ]. The Convention recognizes that the communities, groups, and, in some cases, individuals who safeguard and maintain cultural heritage must be its primary stewards and guardians, but their efforts can be supported or undercut by state policies and institutions [ 5 ]. The challenges facing such communities, and those who work on their behalf, are to ensure that their children and grandchildren continue to have the opportunity to experience the heritage of the generations that preceded them and that measures intended to safeguard such heritage are carried out with the full involvement and the free, prior and informed consent of the communities, groups, and individuals concerned [ 56 ].

Theoretical framework of the study

Recently, heritage conservation domains received adequate attention from both the academia and practitioners [ 57 ]. According to Sinamai [ 58 ] the practices of heritage conservation and management must align with the principle of community-based cultural heritage conservation which recognizes the communities’ well-being and empowers the host community through the harnessing of endogenous knowledge and skills. And, heritage conservation practices shall respect local culture such as vernacular architecture. Certain principles shall be adhered when cultural heritage conservation is applied. The heritage shall continue to be used according to its earlier purpose, and when this is not feasible, a compatible use should be sought with minimal alteration to the heritage and its context. Conservation techniques shall also focus on repairing rather than replacing. Since, heritage relics are authentic evidence of our past, historic fabrics should be kept as much as possible. While repairing and maintaining the heritage, emphasis shall be paid to respect the heritage context, location and significant views shall be maintained [ 59 ]. Cultural heritage can be deteriorated, damaged or destructed due to anthropogenic and natural factors. The anthropogenic or human factors include conflict of interest and ownership issues, contestation and cultural politics [ 12 , 60 ], negligence, ignorance and poor handling system, theft and illicit trafficking, civil war, unprofessional conservation, urbanization, developmental projects, large scale agriculture and mining activities [ 58 ]. The natural factors may encompass climatic and geological factors such as solar radiation, rainfall, humidity, wind pressure, and natural catastrophes such as earth quake, flooding, lighting and thunder as well as biological factors like plants (e.g. invasive specious, weeds) and animals such as rat can harm the heritage [ 16 ]. Depending on the level of impact on the heritage, various conservation approaches can be applied or practiced. These are: Maintenance -continuous protective care of the fabric and setting of heritage [ 57 ]; Preservation - maintaining the fabric of heritage in its existing state and retarding deterioration [ 61 ]; Restoration -returning the existing fabric of a place to a known earlier state by removing accretions or by reassembling existing components without the introduction of new material [ 62 ]; Reconstruction - returning a place to a known earlier state and is distinguished from restoration by the introduction of new material into the fabric [ 61 ]; and Adaptation - modifying a place to suit the existing use or a proposed use [ 63 ].

Based on the literature review and theoretical framework, a conceptual framework is formulated as illustrated in Fig.  1 .

Conceptual framework of the study (Own compilation, 2021)

Methods and materials

Description of the study area.

North Shoa Zone of Amhara regional state is located in the central part of Ethiopia, north of the capital city of Ethiopia, Addis Ababa. North Shoa Zone is blessed with plenty of cultural, historical, and natural tourism resources [ 64 ]. The study area is chosen due to its rich medieval Christian and Islamic historical and cultural heritage relics of Ankober historical site, Koremash of bullet factory, Angolela Tera of King Sahilesillassie palace and Goze Mosque (See Fig.  2 ).

Map of the Study Area (Researchers own map, 2021)

Research approach and data analysis techniques

The research employed both qualitative and quantitative research approaches which is a mixed research approach. A descriptive and explanatory method of cross-sectional research design was used. The descriptive research design helps to describe the current heritage conservation practices and challenges. And, explanatory research design was used to examine the impacts of predictors or explanatory variables such as anthropogenic and natural factors on cultural heritage conservations.

The quantitative data was collected through a questionnaire survey whereas qualitative data was gathered using interviews, site observations, focus group discussions and document analysis. Due to the nature of the study, the researchers applied multiple data gathering instruments as stated above. For instance, survey questionnaire helps to collect information regarding community’s sense of belongingness, access to capacity building trainings, community’s concern or attitude of cultural heritage. And, information such as status of cultural heritage conservation, on-going conservation practices, and buffer zones demarcation can be obtained through field observations. Interview and focus group discussions help to get information with respect to roles of stakeholders towards cultural conservation, promotion of cultural heritage, fund and expert issues. Document analysis helps to gather information such as action plans of respective offices, conservation procedures and guidelines and management of heritage.

The subjects of this study include the local communities, North Shoa Zone and district’s Culture and Tourism office staff, Authority for Research and Conservation of Cultural Heritage (ARCCH) and religious institutions having direct and indirect involvement in tourism activities. Self-administered questionnaire were disseminated using random sampling techniques to 384 households.

Informants for interview were selected purposively based on their knowledge and closeness to the research problem under study. A total of 10 purposively selected individuals (from North Shoa Culture and Tourism, Debre Berhan Culture and Tourism Office, Angolola and Tera Culture and Tourism Office, Ankober Culture and Tourism Office, and ARCCH) were interviewed. Focus group discussants were selected from local representatives such as religious leaders, local elders, and 4 focus group discussions (total 28 discussants) was performed at prominent heritage sites, namely: Ankober, Koremash, Goze and Angolela district. The interview and focus group discussions were undertaken through taking notes and recording followed by transcribing.

The quantitative data was analysed through descriptive statistics (frequency and percentage, mean and standard deviations) and inferential statistics such as exploratory factor analysis, correlations and regressions whereas, content analysis was employed to thematically analyse the qualitative data.

Reliability and validity analysis

The reliability and validity test has been conducted to assure the appropriateness of the instrument and the consistency of the results using the pilot study. The validity of the research explains how well the collected data covers the actual area of investigation [ 36 ]. Hence, to assure the validity of the instruments, the research adapted the standardized questionnaires and interview checklists from literature [ 8 , 15 , 18 , 27 , 29 , 50 , 57 , 59 , 60 , 61 , 65 , 66 ] and the items were checked by consulting the research advisors and subject area experts. Hence 15 questionnaires were distributed to tourism and heritage management experts working at universities, culture and tourism offices, and ARCCH to check content validity. And, experts forwarded important inputs regarding the contents, layout and structure of the questionnaire.

Besides, the reliability concerns the extent to which a measurement of a phenomenon provides stable and consist result, or it is all about the consistency of the result to measure inter-item homogeneity of each construct using Cronbach’s alpha value greater than or equal to 0.70 and the inter-item correlations were greater than or equal to 0.30 were included to collect data and included in the analysis [ 67 , 68 , 69 , 70 ]. According to Sharma [ 71 ] reliability statistics is classified the depending on the Cronbach alpha value: α ≥ 0.90 = Excellent, 0.90 > α ≥ 0.80 = Good, 0.80 > α ≥ 0.70 = Acceptable, 0.70 > α ≥ 0.60 = Questionable, 0.60 > α ≥ 0.50 = Poor and α < 0.50 = Unacceptable.

In the present study, the reliability analysis was made by employing 58 observations which are nearly 15% of the total sample population [i.e., 15%*384 = 57.6) for a pilot survey. The items from each of the constructs having very low inter-item correlation below.30 were removed. The reliability analysis (see Table 1 ) revealed the Cronbach alpha coefficient that exhibited the consistency of the results that ranges from 0.741 to 0.802 that made the result acceptable [ 69 , 70 ].

Results and discussion

Respondents characteristics.

From a total of 384 disseminated questionnaires, 198 valid observations (52% response rate) were useful for analysis, and the majority of the respondents were males that account for 143 (72. 2%) whereas 55 (27.8%) were female respondents (see Table 2 ). And, the majority of them were youngsters under 18–35 years of age that accounting for 175 (79.3%). The survey indicates the youngsters are the majority of employees working and residing around cultural heritage which can be basic to apply cultural heritage conservation practices for better off.

Regarding place of residence and livelihood strategy of the respondents, 68 (34.3%), 52 (26.3%) and 44 (22.2%) reside in and around heritage sites namely, Ankober Medahnealem , Koremash and Goze whereas few respondents accounted for 34 (17.2%) lived in Angolela Kidanemihret area. Regarding the livelihood strategy people employed, the majority of the respondent led their household through employment in government offices followed by engaging in agriculture and working as a private employee accounts for 61.6%, 12.1% and 9.6% respectively (see Table 3 ).

Practices of cultural heritage conservation

The research finding indicates that 12.1% and 30.8% of respondents strongly disagreed, and disagreed respectively whereas 33.8% and 7.6% of respondents agreed and strongly agreed regarding an attempt of cultural heritage conservation in the study areas. The result revealed that there is insufficient attempt to conserve the heritage. Similar to this study, in Africa and many developing countries, cultural heritage have been facing hindrances of multiple platforms in unplanned manner that didn’t account for heritages sustainable use [ 72 ]. Unlike the finding of the present study, Ekwelem, Okafor and Ukwoma [ 72 ] pointed that the preservation of cultural heritage properties enhances historical and cultural continuity, fosters social cohesion, enables to visualization of the past and envisioning the future, and hence it is indispensable for sustainable development. Another study that supports this argument revealed that a need for conservation of heritage is subjected to a desire to transfer away from object oriented conservation and preservation practices, and the theoretical commitment to social constructivism that consider heritage a socio-cultural process [ 73 ]. The aforementioned two findings assured that heritage conservation practices should not only prepare for their objective value like source of economy but also as a social and cultural process that could maintain history which in turn escalate social cohesion, promote identity and proud. The finding revealed that the local community has a sense of belongingness and identity to the cultural heritage as it is portrayed by the respondents' response shown by 34.3% and 7.1% of agreement and strong agreement. This significant level of community belongingness and awareness about the cultural heritage will overpoweringly support the conservation efforts at heritage sites [ 74 ].

The practice of cultural heritage conservation in the study area is not based on research as 16.2% & 38.4% of the respondents strongly disagreed and disagreed in this regard. The present finding suggests that in-depth and strong research to develop conservation guidelines and undertake conservation activities in heritage sites. According to Garrod and Fyall [ 75 ], conservation management should consider timeliness and managerial prudence. The timeliness concept stated that conservation funds should be allotted in a timely fashion to save high conservation costs in the future. From the managerial prudence angle, parallel measures or techniques should be designed to prevent further deterioration [ 75 ]. Moreover, the study of Oevermann [ 76 ] scrutinized the “Good Practice Wheel” that is composed of management, conservation, reuse, community engagement, sustainable development and climate change, education, urban development, and research that expresses each of the good practice criteria spinning wheels which also needs the consideration of those criteria while practising heritage conservation. In this regard, the conservationist expert from ARCCH (personal communication, 21 June 2021) also underlined that,

Though there are efforts by the conservationists to undertake in-depth research, there are initiations mainly from the political leaders showing a commitment to conserve the heritage without adequate research and analysis.

Another participant from the Authority for Conservation of Cultural Heritage (Head, Conservators, personal communication, June 17, 2021) portrayed;

The basis and detrimental problem in the practices of conservation especially in cultural heritage is either lack of original material to conserve perfectly as it was or unavailability of raw materials that resemble originality which makes the conservation practice less effective. He added that the problem exacerbated by the lack of conservationists in the field makes the Ethiopian Heritage in danger.

Besides, regular follow-up of existing status for conservation hasn't been made with 20.7% and 37.9% of strong disagreements and disagreements that revealed poor status of conservation. Similarly, capacity building training on heritage conservation is not delivered at different times to the communities, conservationists and other key stakeholders that are exhibited by a total of 65.7% level of disagreement (where 26.3 replied with strong disagreement and 29.4% replied with a disagreement scale). Only 17.7% of respondents were found in the agreement response category whereas 16.7% were unable to fall in the two categories either (see Table 4 ). Hence, the finding of this study revealed that there is a low-level practice of cultural heritage which needs to be improved. Analogues to this, the conservation of heritage requires the three most important elements of heritage conservation underlined by professionals (curators, academics and consultants) are training and expertise of maintenance staff, budget and financial planning, and conservation plan [ 77 ]. Conservation efforts should be monitored that could follow up information for condition, risks and value assessment, strengths and support strategic heritage planning regularly which in turn should be developed based on an inventory system that requires continuous monitoring [ 78 ].

Challenges of cultural heritage conservation

This study was also concerned with the investigation of the various barriers that hinder cultural heritage conservation practices for better management and sustainability of cultural heritage. Thus, to identify these factors, factor analysis was employed to extract the list of factors and to group each of the linear components onto each factor if found significant. A total of 22 items or linear component factors (variables) were employed after checking the reliability of items in the pilot survey. Those variables were coded as: 01-The local community have no positive attitude towards cultural heritage; 02- The local community are not concerned to the cultural heritage; 03- Population growth and settlement programs have impacts on cultural heritage of the area; 04- Conflict of interest among stakeholders to safeguard the cultural heritage; 05- A practices of heritage conservation without the involvement of professional; 06- Practice of illicit trafficking of cultural objects; 07- The cultural heritage is not promoted for sustainable tourism development; 08- Practice of farming in and around the cultural heritage; 09- Adequate budget/ financial allocation for conservation of cultural heritage; 10- Little concern of government and local authorities about the heritage; 11- Professionals lack enough commitment to engage in conservation practices; 12- Media failed to expose the problems of heritage to the community in time; 13- Travel agents and tour operators are negligent to the sustainability of heritage; 14- Inappropriate conservation practices of cultural heritage; 15- Lack of buffer zone demarcations of the heritage sites; 16- Natural catastrophes and climate variations (flooding, frost, acidic rain, storm, heat from the sun) deteriorate cultural heritage; 17- Development projects such as buildings, roads affect the sustainability of heritage; 18- The heritage hosts more than its carrying capacity during different events; 19- Funding agencies lack willingness to provide aids and loans to cultural heritage; 20- There is no regular monitoring and evaluation of cultural heritage status by the concerned body; 21- The growth of vegetation over the heritage, and 22- The heritage are challenged by biological factor such as rat and other biological organisms.

The assumptions of relationship, randomness and sampling adequacy were checked in the analysis of exploratory factor analysis (EFA).

The descriptive statistics revealed that all the 22 linear component factors or variables have a mean value greater than 3 with a range varied from 3.41 to 3.95 for a total of 198 valid observations made for analysis. And, there was no missing data in the analysis.

The Kaiser–Meyer–Olkin Measure of Sampling Adequacy and Bartlett's Test of Sphericity (see Table 5 ) also indicated that the sample size employed was adequate and the assumption is met with the KMO and Bartlett’s Test of Sphericity value of 0.768 and Sig. = 0.000. A value varies between 0 and 1 where the value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors. Kaiser [ 79 ] recommends accepting values greater than 0.5 as acceptable. Hence, the current value of KMO Bartlett’s Test of Sphericity meets the assumption [ 79 ].

The communalities table also presented the relationship of one of the variables with the other variables before rotation with which a value greater or equal to 0.30 indicates the employed sample is acceptable and results will not be distorted. The current finding has confirmed this assumption of factor analysis with the value ranging from 0.315 to 0.784 which is significantly above 0.30.

Factor extraction and variance explained

The present finding indicated that 59.51% of the total variance is explained by the seven factors extracted out of 21 linear components variables included in the model with Eigenvalues greater than one. Hence, the Rotation Sums of Squared Loadings indicated that the first factor contributed about 14.726% and the 2nd contributes 9.412% whereas the 3rd and 4th factors accounted for 8.808% and 7.503% of the variance explained. The 5th, 6th, and 7th factors contributed to about 7.222%, 6.509% and 5.325% of variance explained in cultural heritage conservation (see Table 6 ).

Factor rotation

The rotated factor matrix indicates the rotated component matrix (also called the rotated factor matrix in factor analysis) which is a matrix of the factor loadings for each variable on to each factor. The component loadings for each factor are positive that shows the positive relationship between the variable and each principal component. The values below 0.45 were suppressed while extracting the factors, and are not displayed in the rotated component matrix and the factor loadings were sorted by size. The orthogonal rotation was used with the assumption that the variables are independent of each other [ 80 ]. Before rotation, most variables loaded highly onto the first factor (21.554% variance explained) and the remaining factors didn't get a look in. However, the rotation of the factor structure has clarified things considerably with the equivalence of variance explained. As can be depicted in the rotated matrix table, there are seven components or factors that have been extracted as a factor hindering the management of cultural heritage conservation. Hence, Principal Component factor analysis with Varimax rotation was conducted to assess the underlying structure for the 22 items of the challenges of cultural heritage conservation practices. The assumption of independent sampling, normality, linear relationships between pairs of variables, and the variables being correlated at a moderate level were checked.

Seven factors were extracted after rotation, the first factor accounted for 14.726% of the variance and was composed of seven items related to lack of proper management, monitoring and evaluation, whereas the second factor accounted for 9.412% that consisted of a cluster of three variables that were related to lack of stakeholder involvement and population settlement. The third factor accounted for 8.808% and it comprised of two items which are related to lack of government concern and professional commitment. The fourth factor consisted of three items and it is related to lack of community concern, illicit trafficking and promotion for sustainable development and accounted for 7.503%. The group of two items related to poor destination management and conservation practice make the fifth factor that accounted for 7.222% whereas the sixth factor accounted for 6.509% and comprises two items that are related to natural catastrophes and agricultural practices (see Table 7 ). The 7th factor encompasses only a single variable that is related to the lack of communities' positive attitudes towards cultural heritage.

Table 7 displays the items and factor loadings for the rotated factors, with factor loadings less than 0.40 omitted to improve clarity. Similar to the present study, heritage properties can be affected by the impacts of visitors such as overcrowding which may result in wear and tear including trampling, handling, humidity, temperature, pilfering and graffiti [ 75 ].

Mathematical representations of factor loadings

Like regression, a linear model of the mathematical equation can be applied to the scenario of describing a factor. The factor loadings are represented by b ‘s. According to Field [ 80 ], the equation can be written as.

Fi = b1X1i + b2X2i + … + bnXni.

Where Fi is the estimate of the ith Factor; b 1 is the weight or factor loading of variable X1, b2 is the factor loading of variable X2, bn is the factor loading of variable Xn, and n is the number of variables.

Accordingly, it was stated that seven factors were found underlying the construct Factors affecting cultural heritage conservation. Consequently, an equation can be constructed for each factor in terms of the items that have been measured.

Factor 1 = 0.671(X1) + 0.668 (X2) + 0.664 (X3) + 0.626(X4) + 0.571 (X5) + 0.571 (X6) + 0.485(X7).

By substituting the mean value of each item (question), the approximate percentage variance that factor 1 can explain can be calculated.

Factor1 = 0.671(3.58) + 0.668 (3.36) + 0.664 (4.33) + 0.626(3.60) + 0.571 (3.60) + 0.571 (2.88) + 0.485(2.86) = 14.2

Factor 2 = 0.742(X8) + 0.710(X9) + 0.576(X10) = 0.742 (4.53) + 0.710(4.42) + 0.576(4.72) = 9.23.

Applying similar formula for the remaining factors, and adding the calculated values together, or the summation of all factors will be a total of 58.51 which means using the mathematical equations, the seven factors together can explain 58.51% of the variance. As explained before in the total variance explained in Table 7 , in the rotated sums of squared loadings column, it has been said that the seven components explained 59.51% of the variance. Hence with a minor difference, values calculated from the equation and summations of a percentage of variance in the total variance explained Table 7 provide an approximately similar result. The difference may be resulted either from using the approximate values after the decimal point or the factor loadings less than 0.4 that were suppressed.

After conducting the exploratory factor analysis and extracting the seven factors, the multiple linear regressions was applied to confirm which factors affect the practice of cultural heritage conservation.

Assumptions of multiple linear regression

The relationship between the independent variable and dependent variables is linear. This assumption was confirmed as it is reflected by the scatter plot that showed the relationship is linear for all independent variables: lack of proper management, monitoring and evaluation, lack of stakeholder involvement and population settlement, lack of government concern and professional commitment, lack of community concern, illicit trafficking and promotion towards sustainable development, poor destination management and conservation practice, natural catastrophes and agricultural practices, and the local community have no positive attitude towards cultural heritage conservation.

There is no multicollinearity in the data set. Multicollinearity exists when the correlation coefficient r between independent variables is above 0.80. Hence, no independent variable was found to have multicollinearity problems with each other with all below 0.80 where the highest Pearson correlation value of 0.688. Besides, the multicollinearity issue can be checked by VIF and tolerance level where VIF is below 10 and tolerance level > 0.20 [ 81 ]. Hence, VIF and Tolerance are found within the acceptable region.

The values of the residual are independent. The residuals of the data set in the sample stratum were found independent or uncorrelated which can also be tested based on Durbin-Watson statistics (above one and below 3). The Durbin Watson statistics is 1.821.

The assumption of homoscedasticity: the assumption that shows the variation in the residual is a similar constant at each point of the model. As it can be shown, the closer the data points to a straight line when plotted, the points are about the same distance from the line meaning the data points have the same scatter. This can be shown by the normality probability curve of the scatter plot (see Fig.  3 ).

The values of the residual are normally distributed. This assumption can be tested by looking at the p-p plot for the model. The closer the dote lies to the diagonal line; the closer to normal the residuals are distributed. The normal p-p plot dotes (see Fig.  4 ) line indicates that the assumption of normality has not to be violated.

Scatter Plot; Conservation of Cultural Heritage (Field Survey, 2021)

Normal P-P Plot of Dependent Variable (Field Survey, 2021)

Regression results

The Pearson`s correlation table indicates (see Table 8 ) that there was a significant relationship between the independent variables and the dependent variable i.e. cultural heritage conservation at a p value of 0.05 level of significance. However, lack of stakeholder involvement and population settlement, poor destination management and conservation practice and lack of the local community positive attitude towards cultural heritage were not significantly correlated with the cultural heritage conservation practice (r = 0.057, sig = 0.212; r = − 0.008, sig. = 0.458 and r = 0.016, Sig = 0.410). Thus, the indicators were removed from the regression model.

The Analysis of Variance (ANOVA) table (see Table 9 ) exhibits the goodness of fit of the model and revealed the model is appropriate and the introduction of the independent variables has improved by at least one predictor (P = 0.001) significant at 1% level of significance. Thus, the model is the best-fitted model presenting the regression that presents the significant independent variables that significantly explain the dependent variable.

The model summary shows the predicted variable i.e. practices of cultural heritage conservation is explained by the introduced independent variables viz., natural catastrophes and agricultural practices, lack of community concern, illicit trafficking, promotion towards sustainable development, lack of government concern and professional commitment, and lack proper management, monitoring and evaluation accounted for 7.9% with an adjusted R square value of 0.079 (see Table 10 ). The variance explained in the model summary table is also supported by the coefficients table that exhibited some of the extracted factors that were significant.

The coefficient result shows that the largest β value is the greatest predictor of heritage conservation. Among the independent variables, lack of community concern, illicit trafficking and promotion towards sustainable development was found the most significant factor affecting practices of cultural heritage conservation (β = − 0.213, p < 0.05) followed by natural catastrophes and agricultural practices (β = − 0.132, p < 0.05). Besides, lack of stakeholder involvement and population settlement was the factor found to be significant β-value (β = 0.179 & Sig. = 0.007). Furthermore, there was a negative relationship between lack of community concern, illicit trafficking and promotion towards sustainable development, and natural catastrophes and agricultural practices with the predicted variable.

As far as this study was concerned, lack of community concern, illicit trafficking and lack of promotion towards sustainable tourism development with β = − 213; p. = 0.002 and natural catastrophes and agricultural practices in and around the cultural heritage with β = − 0.132; p = 0.026 were found to be significant challenges hindering the heritage conservation practices (see Table 11 ). This finding was confirmed by the previous studies that revealed air pollution; biological causes like invasive intervention, humidity and vandalism have negative consequences on the survival of heritage tourism. The present finding was also in line with the findings of Irandu and Shah [ 82 ] that portrayed the cultural heritage conservation of Kenya faced challenges such as funding, poor enactment of policies, land grabbing and lack of adequate trained personnel. Besides, another finding revealed that tackling the calamities of climate change mainly global warming and extreme weather events combined with the implementation of varied strategies to moderate the impact of a growing tourist demand towards heritage sites become the growing problem in the conservation efforts of cultural heritage conservation which supports the present finding [ 83 ]. This finding also revealed the land use issue is an emerging problem for conservation. Therefore, the present study underlines that effective planning, proper land use strategy and environmental conservation policies shall be enhanced by the local and national governments.

Unlike the present study, as noted in the work of Eken, Taşcı, and Gustafsson [ 16 ] public participation along with governmental strategies is vital to deciding preventive conservation. Their finding indicated that local communities have an awareness regarding the significance and preservation of the World Heritage Site of Visibility, but they were not adequately cognizant of the practical aspect of preservation. The other issues raised by the authors are difficulties concerning guidance and promotion of regular maintenance which is also similar to the present study. Besides, restoration works have been carried out without a detailed report of the current condition of the cultural heritage [ 16 ]. On the opposite, the interview was found in line with the aforementioned previous study revealing the disintegration of the heritage concerned authorities, the poor intervention of the government and inadequate collaboration of the local and regional governments with the local communities. Besides, the political implication of understanding the heritage also nailed our challenge in the conservation of cultural heritage. Similar to the present finding, the study scrutinized owing to conflicting claims, representations and discourse of urban heritages become contested [ 84 ]. Unlike the present finding, the study of Tweed and Sutherland [ 85 ] indicates that conserving heritage properties contributes to the sustainability of the built environment, and it is a crucial element of the cultural identity of the community which describes the character of a place.

Moreover, lack of stakeholder involvement and population settlement was identified as a significant challenge with β = 0.179; p = 0.007 in the present study. The previous findings revealed that the lack of collaborations to date in terms of managing the assets between the local authority and other stakeholders was found a significant challenge in cultural heritage conservation [ 86 ]. The findings of this study were supported by the findings of the previous study on the adaptation of land use for new purposes and functions, especially for the heritage buildings which demand new strategies for the indoor quality and efficiency of heritage for the new functional use was found the challenge that affects heritage conservation and management of heritage sites [ 83 ]. To overcome this problem, stakeholder collaboration and involvement, community empowerment and the adaptive reuse approach should be adopted that in turn increases the tourism demand and receipts which again escalate the multiplier effects within the industry combined with the job creation [ 87 ] and livelihood diversification through the enhancement of conservation enterprises around protected areas [ 88 ]. It is argued that cultural heritage sustainability relies on training and education that can produce competent human capital who are in charge of heritage protection and promotion [ 89 ]. The cultural heritage understudy is facing various natural and manmade problems which were verified by the interview made with officials of ARCCH who are working at the department of Heritage Restoration and Conservation (Personal communication, 21 June 2021) that revealed structural problems of the heritage authority from federal to the local level, lack of skilled manpower, and lack of clear proclamations and guidelines regarding private heritage conservation. This finding was supported by the technical aspects such as limited availability of experts (lack of skilled forces, absence of educational training for new skills, and lack of technical staff in the heritage maintenance team) and availability of original or authentic materials were the major constraints in conservation projects [ 90 ]. Besides, the interviewees added lack of sufficient funds for restoration and conservation and the difficulty of conservation of heritage in and nearby urban areas due to urbanization and urban renovation were significant challenges for conservation. In line with the interview, the findings of Dias Pereira et al. [ 83 ] pinpointed the conservation of cultural heritage and the maintenance of its original characteristics and identity which could have been exacerbated by the unavailability of raw materials for conservation. Moreover, the unavailability of raw materials for restoration and maintenance of heritage, and keeping authenticity was found a very serious problem in the applicability of cultural heritage conservation practices [ 91 ]. Besides, there is an increasing interest to replace old cultural heritage with modern buildings, and hiding movable heritage are problems in escalating conservation efforts. An ideal example is the church of Ankober Medahnealem Church where only remnants or ruins of buildings are visible and the historical ruins of old church was replaced with the new modern buildings. Generally, the finding of the present study indicates the various challenges that should be overcome to assure the sustainability of cultural heritage. This was also supported by the study of [ 85 ], whose heritage conservation theme encompasses technical, environmental, organizational, financial and human issues.

Practical implications

There should be a mechanism and plan to evaluate, follow up and supervise the conservation status of heritage side by side with the activities of heritage inventory made each year in each study area by the respective district. In this regard, it has been suggested that heritage sites shall receive an urgent response from the government in collaboration with the host community [ 92 ].

Appropriate guidelines for conservation should be developed based on research and scientific evidence to escalate the conservation practices. In line with this, to make the conservation effort effective, the right heritage management professionals and appropriate mapping guidelines should be hired to conduct the management of cultural heritage conservations and preservations [ 92 , 93 ].

Besides, conservation activities should be made through allocating sufficient budget, training, technical support and human resources equipped with the latest technology and required raw materials to keep the authenticity of the heritage.

Furthermore, heritage conservation funds should be organized institutionally and come into the practice to support conservation efforts. The local communities, the private travel and tourism organizations and government bodies should be engaged in the planning, execution and monitoring of the heritage conservation and renovation process. In addition, better platforms for stakeholder collaboration should be developed and management of conflict of interest threats should be seriously addressed. The study of Aas, Ladkin, and Fletcher [ 94 ] recommended that the function of involving the local communities and all other key stakeholders in decision making and the view of right participation which in turn can empower the stakeholders’ engagement in conservation activities [ 66 , 95 ].

Though there are a few attempts, especially at Angolela Kidanemihret Site at King Sahle Sellassie Palace and Goze Mosque, the practices of cultural heritage conservation were found to be very low which needs to be enhanced to assure the sustainability of cultural heritage. The finding of this study revealed that local communities feel as if the heritage belongs to them and consider it as part and parcel of their identity. However, conservation of activities was not based on research, conservation practices and the status of heritage follow-up are not made on regular basis and capacity buildings are not provided for the sustainable conservation of cultural heritage in the study areas.

Concerning the status and practice of cultural heritage conservation, lack of community concern, illicit trafficking and promotion of sustainable tourism development and natural catastrophes and agricultural practices in and around the cultural heritage were found to be significant factors affecting the heritage conservation practices in the study areas. Lack of stakeholder involvement and population settlement around the heritage sites were also identified as the challenges hindering the conservation of cultural heritage and their environs. On top of these, lack of government concern, community interest, lack of appropriate funding and skilled manpower were also found to be significant factors that hinder the practices of conservation of cultural heritage. Moreover, the structural weakness of the heritage-related government institutions and political implication of leaders and the urbanization and urban renovation programs added are exacerbating the existence and practices of cultural heritage conservation. From the findings of the present study, it can be understood that the conservation of cultural heritage is not an easy task which cannot be undertaken by a single actor such as the government or heritage destination managers. The multitude of the contribution of various relevant stakeholders is demanding to upscale the conservation efforts and grant sustainability of cultural heritage. The sustainable conservation of cultural heritage will also be important for the wise use of the heritage for many purposes such as a means for enhancing socio-cultural ties, building the image of a place or destination and fosters tourism development.

Generally, poor conservation practices of cultural heritage and insufficient commitment of concerned bodies to conserve cultural heritage exacerbated by various manmade and natural factors demand strong and vivid solutions to the problems to reverse the existing severe conditions of the cultural heritage. The present study revealed that the likelihood of cultural heritage conservation highly depends on not only man-made bottlenecks but also natural catastrophes such as flooding, climatic variations and invasive species. Thus, to improve the effective conservation and use of cultural heritage, especially in developing countries like Ethiopia, government and political leaders’ positive attitude and understanding of the relevance of cultural heritage to the society and the country at large should play a fundamental role in this regard. The improved view of the leaders toward cultural heritage has the potential to enhance funding possibilities and pave the way for a meaningful participation of stakeholders.

Moreover, the enhancement of conservation practices and sustainable use of cultural heritages should be supported through proper land use planning around heritage sites, preparation of heritage conservation plans and efficient heritage destination management. This tells us the practices of conservation efforts for cultural heritage and heritage sites demand the involvement of various actors from various sectors viz., tourism, agriculture, government administration bodies, religious and community institutions and heritage conservation organizations, environmentalists and development agencies to assure sustainability and community benefits from the heritage.

Furthermore, the conservation of cultural heritage shall be seen in a wider scope beyond the conservation of heritage property itself. It should include the vitality of cultural heritages for promotion of destination and country image, enhancement of socio-cultural bondage, and serving as a tool of economic integration through tourism. Therefore, a system of management of cultural heritage needs to be developed that takes significant issues and challenges into consideration through participatory decision-making process to optimize the values and sustainability of cultural heritage in Ethiopia.

Availability of data and materials

Not applicable.

Abbreviations

Analysis of Variance

Authorities for Research and Conservation of cultural heritage

Kaiser–Meyer–Olkin

United Nations Educational, Scientific and Cultural Organization

UNESCO. Globalization and intangible heritage. Belgium; 2005.

Kurin R. Safeguarding intangible cultural heritage in the 2003 UNESCO convention: a critical appraisal. Mus Int. 2004;56(1–2):66–77.

Article   Google Scholar  

Bolin A. Imagining genocide heritage: material modes of development and preservation in Rwanda. J Mater Cult. 2019. https://doi.org/10.1177/1359183519860881 .

Bleibleh S, Awad J. Preserving cultural heritage: shifting paradigms in the face of war, occupation, and identity. J Cult Herit. 2020. https://doi.org/10.1016/j.culher.2020.02.013 .

UNESCO. Safeguarding intangible heritage and sustainable cultural tourism: opportunities and challenges. UNESCO-EIIHCAP Regional Meeting.Hué; Viet Nam; 2007.

FDRE (Federal Democratic Republic of Ethiopia). The Federal Democratic Republic of Ethiopia Cultural Policy. Addis Ababa: FDRE. 1997.

Ministry of Culture and Tourism, ARCCH and UNESCO. Meeting on inventorying; Addis Ababa, Ethiopia; 2007.

Mengistu G. Heritage tourism in Ethiopia. Addis Ababa, Ethiopia; 2008.

Pietrobruno S. Cultural research and intangible heritage. J Curr Cult Res. 2009;1(2):227–47.

Nesbeth G.Art and globalization: the place of intangible heritage in a globalizing environment, Ph.D. dissertation. University of South Africa.2013.

Lévi-Strauss C. Anthropology confronts the problems of the modern world. Cambridge, London, England: Harvard University Press; 2013.

Google Scholar  

ICOM‐CC (International Council for Museums – Conservation Committee).Terminology to Characterize the Conservation of Tangible Cultural Heritage. 2008., http://www.icom‐cc.org/242/about‐icom‐cc/what‐is‐conservation. Accessed September 14 2020.

Begashew K. The Archaeology of Islam in North East Shoa. 16th International conference of Ethiopian Studies. 2009; 11–22.

Omer AH. Centres of traditional Muslim education in Northern Shäwa (Ethiopia): a historical survey with particular reference to the twentieth century. J Ethiopian Studies. 2006;39(1/2):13–33.

Stiffman E. Cultural Preservation in Disasters, War Zones. Presents Big Challenges in the Chronicle of Philanthropy. 2015.

Eken E, Taşcı B, Gustafsson C. An evaluation of decision-making process on maintenance of built cultural heritage: the case of Visby Sweden. Cities. 2019;94:24–32.

Herzfeld M. A place in history: social and monumental time in a Cretan town. Princeton New Jersey, UK: Princeton University Press; 1991.

Babb FE. Theorizing gender, race, and cultural tourism in Latin America: a view from Peru and Mexico. Lat Am Perspect. 2012;39(6):36–50.

Olwig KF. The burden of heritage: claiming a place for a West Indian culture. Am Ethnol. 1999;26(2):370–88.

During R. Cultural heritage and identity politics. Hong Kong, China: Silk Road Research Foundation; 2011.

Koiki- Owoyele AE, Alabi AO, Egbunu AJ. Safeguarding Africa’s cultural heritage through digital preservation. J Applied Inform Sci Technol. 2020;13(1):76–86.

Moseti I. Digital preservation and institutional repositories: case study of universities in Kenya. J South African Society Archivists. 2016;49:137–54.

Karin C, Philippe D. Preserving intangible cultural heritage in Indonesia: A pilot project on oral tradition and language preservation UNESCO. Jakarta: Indonesia; 2003.

Czermak K, Delanghe P,Weng W. Preserving intangible cultural heritage in Indonesia. In Conference on Language Development, Language Revitalization and Multilingual Education in Minority Communities in Asia. 2003; 1–8.

Truscott M. Intangible values as heritage in Australia. Hist Environ. 2000;14(5):22–30. https://doi.org/10.3316/informit.591886148622588 .

Munjeri D. Intangible heritage in Africa: could it be a case of much ado about nothing? ICOMOS Newsletter; 2000. Retrieved from https://www.international.icomos.org/munjeri-eng.htm . Accessed 25 Feb 2021.

Roy D, Kalidindi S. Critical challenges in the management of heritage conservation projects in India. J Cult Heritage Manag Sustaina Dev. 2017;7(3):290–307.

Berhanu E. Potentials and challenges of religious tourism development in Lalibela, Ethiopia. African J Hosp Tour Leis. 2018;7(4):1–17.

Wharton G. Indigenous claims and heritage conservation: an opportunity for critical dialogue. Public Archaeol. 2005;4(2–3):199–204.

Le Mentec K. The Three Gorges Dam Project—Religious Practices and Heritage Conservation. A study of cultural remains and local popular religion in the xian of Yunyang (municipality of Chongqing). China Perspectives. 2006;65:1–15. https://doi.org/10.4000/chinaperspectives.626 .

Navaneethakrishnan S. Preservation and documentation of intangible cultural heritage: the Strategic Role of the Library and Information Science Professionals in Sri Lanka. J Univ Librarians Association Sri Lanka. 2013;17(1):58–65.

Brosché J, Legnér M, Kreutz J, Ijla A. Heritage under attack: motives for targeting cultural property during armed conflict. Int J Herit Stud. 2017;23(3):248–60.

Azzouz A. A tale of a Syrian city at war. City. 2019;23(1):107–22. https://doi.org/10.1080/13604813.2019.1575605 .

Danti M, Branting S, Penacho S. The American schools of oriental research cultural heritage initiatives: monitoring cultural heritage in Syria and Northern Iraq by Geospatial imagery. Geosciences. 2017;7(4):1–21. https://doi.org/10.3390/geosciences7040095 .

Cunliffe E, Pedersen W, FiolM, Jellison T, Saslow C,Bjørgo E, Boccardi G. Satellite-based Damage Assessment to Cultural Heritage Sites in Syria . A report submitted to United Nations Institute for Research and Training. Geneva: Switzerland; 2014; Retrieved from https://unosat.web.cern.ch/unitar/downloads/chs/FINAL_Syria_WHS.pdf . Accessed 20 May 2021.

Muddie E. Palmyra and the radical other on the politics of monument destruction in Syria : deliberate destruction of heritage properties. Otherness: Essays and Studies. 2018;6(2):140–60.

Clapperton M, Jones M, Smith R. Iconoclasm and strategic thought: Islamic State and cultural heritage in Iraq and Syria. Int Aff. 2017;93(5):1205–31.

Fisher J. Violence against architecture: The lost cultural heritage of Syria and Iraq. 2017; City University of New York. Thesis. Retrieved from https://academicworks.cuny.edu/cgi/viewcontent.cgi. Accessed on 30 March 2022 http://academicworks.cuny.edu/gc_etds/1898 .

Wollentz G. Making a home in Mostar: heritage and the temporalities of belonging. Int J Herit Stud. 2017;23(10):928–45.

Wierczynska K, Jakubowski A. Individual responsibility for deliberate destruction of cultural heritage: contextualizing the ICC judgment in the Al-Mahdi case. Chin J Int Law. 2017;16(4):695–721.

Jakubowski A. Resolution 2347: Mainstreaming the protection of cultural heritage at the global level. Questions International Law. 2018;48:21–44.

Kalman H. Destruction, mitigation, and reconciliation of cultural heritage. Int J Herit Stud. 2017;23(6):538–55.

Zarandona J, Albarrán-Torres C, Isakhan B. Digitally mediated iconoclasm: the Islamic State and the war on cultural heritage. Int J Herit Stud. 2018;24(6):649–71.

Harrowell E. Looking for the future in the rubble of Palmyra: destruction, reconstruction, and identity. Geoforum. 2016;69:81–3.

Mancacaritadipura G. Safeguarding the Intangible Cultural Heritage of Indonesia: Systems, Schemes, Activities and Problem. In The 30th International Symposium on the Conservation and Restoration of Cultural Property, Japan; 2007.

Mike R, David P. Tourism, culture and sustainable development. UNESCO : Spain: Madrid. 2006.

UNESCO. Safeguarding Intangible Heritage and Sustainable Cultural Tourism: Opportunities and Challenges. Bangkok; 2008

Xulu, M. Indigenous Culture, Heritage and Tourism: An Analysis of the Official Tourism Policy and Its Implementation in the Province of Kwazulu-Natal: A Thesis submitted To the Faculty of Arts in Partial Fulfilment of the Requirements For the Degree of Doctor of Philosophy in The Centre for Recreation and Tourism at the University of Zululand. 2007.

MohdAriffin NF, Ahmad Y, Alias A. Stakeholders’ Attitude on the Willingness-To-Pay value for the conservation of the George Town, Penang World Heritage Site. J Survey Construct Property. 2015;6(1):1–10.

Eleonora L. Intangible Cultural Heritage Valorization: A New Field for Design Research and Practice. Italia: Milano; 2007.

European Construction Technology Platform. Cultural heritage guide, technology integration division: Travel world news section. 2008.

UNESCO-ICOMOS. Documentation Centre.Convention for the Safeguarding of Intangible Cultural Heritage.2011.

Richard K . Museums and Intangible Heritage Culture Dead or Alive? : ICOM news. 2004

Jamieson W. The Challenges of Sustainable Community Cultural Heritage Tourism. Asian Institute of Technology: Bangkok, Thailand; 2000.

Rukwaro R. Community participation in conservation of gazetted cultural heritage sites: a case study of the Agikuyu shrine at MukurwewaNyagathanga. In: Deisser AM, editor. Conservation of Natural and Cultural Heritage in Kenya. London: UCL Press; 2016. Retrieved from  https://ucldigitalpress.co.uk/Book/Article/19/44/1421/ . Accessed 19 Jan 2022.

Boonyakiet C. Introduction to the Intangible Cultural Heritage and Museums Field School by Alexandra Denes, Anthropology Centre, Thailand Intangible Cultural Heritage and Museums Learning Resources. 2011.

Gursoy D, Zhang C, Chi OH. Determinants of locals’ heritage resource protection and conservation responsibility behaviors. Int J Contemp Hosp Manag. 2019;31(6):2339–57. https://doi.org/10.1108/IJCHM-05-2018-0344 .

Sinamai A. African cultural heritage conservation and management: theory and practice from southern Africa. Conserv Manag Archaeol Sites. 2018;20(1):52–4. https://doi.org/10.1080/13505033.2018.1430444 .

Tadesse B. Visual representation in the Ethiopian orthodox church: church paintings and conservation problems. Asian Man Int J. 2012;6(1):1–7.

Haspel J. Contrast versus context: A conflict between the authenticity of the pastand the authenticity of the present? In Conservation and Preservation: Interactions between Theory and Practice: In Memoriam AloisRiegl (1858–1905): Proceedings of the International Conference of the ICOMOS International Scientific Committee for the Theory and the Philosophy of Conservation; Polistampa: Vienna, Austria; 2008; 1000–1017.

Umar SB A Conservation Guideline for Traditional Palaces in Nigeria for the Resilience of Cultural Heritage and Identity in a Cultural Milieu Universiti Teknologi Malaysia: Skudai: Malaysia; 2018

Vaccaro A. Restoration and anti-restoration. In: Historical and Philisophical Issues in the Conservation of Cultural Heritage. Los Angeles: The Getty Conservation Institute; 1996. p. 308–13.

YazdaniMehr S. Analysis of 19th and 20th century conservation key theories in relation to contemporary adaptive reuse of heritage buildings. Heritage. 2019;2(1):920–37.

Hailu WA. Prospects for Environmental Protection and Eco-Tourism in Semen Shewa. A Project Proposal. Retrieved from http://www.cyberethiopia.com/net/docs/hailu.pdf . Accessed 18 Mar 2021.

Adane Y. Practices and Challenges of Cultural Heritage Conservation in Ethiopia: The Case of Ankober, Ethiopia. 2019.

Abebayehu B. Key stakeholders’integration for religious heritage site conservation: The case of AngolelaSemineshKidanemihret Monastery North Shewa Zone, Ethiopia. Doctoral Dissertation: University of Gondar; 2021.

Taherdoost H. Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. SSRN Electronic J. 2016. https://doi.org/10.2139/ssrn.3205040 .

Carmines EG, Zeller RA. Reliability and validity assessment. Quantitative Applications in the Social Sciences. Thousand Oaks, California, USA: Sage Publications; 1979.

Book   Google Scholar  

Cronbach LJ, Warrington WG. Time-limit tests: estimating their reliability and degree of speeding. Psychometrika. 1951;16(2):167–88.

Article   CAS   Google Scholar  

Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;2:53.

Sharma B. A focus on reliability in developmental research through Cronbach’s Alpha among medical, dental and paramedical professionals. Asian Pac J Health Sci. 2016;3(4):271–8.

Ekwelem VO, Okafor VN, Ukwoma SC. Preservation of cultural heritage: The strategic role of the library and information science professionals in South East Nigeria. Library Philosophy and Practice, 2011. p. 14

Sterling C. Critical heritage and the posthumanities: problems and prospects. Int J Herit Stud. 2020;26(11):1029–46. https://doi.org/10.1080/13527258.2020.1715464 .

Tan SK, Tan SH, Kok YS, Choon SW. Sense of place and sustainability of intangible cultural heritage—The case of George Town and Melaka. Tourism Manag. 2018;67:376–87. https://doi.org/10.1016/j.tourman.2018.02.012 .

Garrod B, Fyall A. Managing heritage tourism. Ann Tour Res. 2000;27(3):682–708. https://doi.org/10.1016/S0160-7383(99)00094-8 .

Oevermann H. Good practice for industrial heritage sites: systematization, indicators, and case. J Cultural Heritage Manag Sustain Dev. 2019;10(2):157–71. https://doi.org/10.1108/JCHMSD-02-2018-0007 .

Idrus A, Khamidi F, Sodangi M. Maintenance management framework for conservation of heritage buildings in Malaysia. Modern Appl Sci. 2010;4(11):66–77.

Heras VC, Wijffels A, Cardoso F, Vandesande A, Santana M, Van Orshoven J, Steenberghen T, Van Balen K. A value-based monitoring system to support heritage conservation planning. J Cult Heritage Manage Sustain Dev. 2013;3(2):130–47.

Kaiser HF. An index of factorial simplicity. Psychometrika. 1974;39(1):31–6.

Field A. Discovering statistics using SPSS (and sex and drugs and rock ’n’ roll). 3rd edn. London: Sage Publications Ltd.; 2009.

Miles E, Crisp RJ. A meta-analytic test of the imagined contact hypothesis. Group Processes Intergroup Relations. 2014;17(1):3–26.

Irandu E, Shah P. Development of cultural heritage tourism in Kenya: a strategy for diversification of tourism products. Conservation of Natural and Cultural Heritage in Kenya. A Cross-Disciplinary Approach. 2016; 154–171.

Dias Pereira L, Tavares V, Soares N. Up-To-Date challenges for the conservation, rehabilitation and energy retrofitting of higher education cultural heritage buildings. Sustainability. 2021;13(4):2061. https://doi.org/10.3390/su13042061 .

Liu Y, Jin X, Dupre K. Engaging stakeholders in contested urban heritage planning and management. Cities. 2022;1(122): 103521.

Tweed C, Sutherland M. Built cultural heritage and sustainable urban development. Landscape Urban Plan. 2007;83(1):62–9.

Ismail N, Masron T, Ahmad A. Cultural heritage tourism in Malaysia: Issues and challenges. InSHS Web of Conferences 2014 (Vol. 12, p. 01059). EDP Sciences.

Chong KY, Balasingam AS. Tourism sustainability: Economic benefits and strategies for preservation and conservation of heritage sitesin Southeast Asia. Tourism Review. 2019;74(2):268–79. https://doi.org/10.1108/TR-11-2017-0182 .

Bires Z, Raj S. Social media as a pathway to environmental conservation in protected areas: a case study on Lake Tana Biosphere Reserve. J Cultural Heritage Manag Sustain Dev. 2020;11(4):457–70.

Berhanu K, Raj S. The trustworthiness of travel and tourism information sources of social media: perspectives of international tourists visiting Ethiopia. Heliyon. 2020;6(1):e03439. https://doi.org/10.1016/j.heliyon.2020.e03439 .

Ngulube P. Implications of technological advances for access to the cultural heritage of selected countries in sub-Saharan Africa. Gov Inf Q. 2004;21(2):143–55.

Azizi NM, Razak AA, Din MM, Nasir NM. Recurring Issues in Historic Building Conservation. Procedia Soc Behav Sci. 2016;222:587–95. https://doi.org/10.1016/j.sbspro.2016.05.217 .

Tilahun M. Survey of Cultural Heritage in Soddo Woreda south Guragea Zone, Ethiopia. 2019.

Nel JL, Roux DJ, Abell R, Ashton PJ, Cowling RM, Higgins JV, Thieme M, Viers JH. Progress and challenges in freshwater conservation planning. Aquatic Conservation Marine Freshwater Ecosystems. 2009;19(4):474–85.

Aas C, Ladkin A, Fletcher J. Stakeholder collaboration and heritage management. Ann Tour Res. 2005;32(1):28–48.

Jamal TB, Getz D. Collaboration theory and community tourism planning. Ann Tourism Res. 1995;22(1):186–204.

Download references

Acknowledgements

First of all, we would like to express our deepest gratitude to the Omnipotent God for the strength and determination to accomplish this academic research paper. Praise God! God Grace! The authors are really delighted to express sincere gratitude to Debre Berhan University, College of Social science and Research and Community Service Directorate for funding, duplicating questionnaire, writing letters to concerned offices and stakeholders during data collection. The authors are also indebted to all the respondents (local communities, culture and tourism offices, Authority for Research and Conservation of Cultural Heritage (ARCCH), Angolola Seminesh Kedanemihiret Chruch, Goze and Koremash Mosque) who participated in the interview sessions and filling the questionnaire or survey. The authors are also very grateful to Dr. Lemma Demissie for his diligent editing and proofreading of the manuscript. The Authors would love to express their genuine thanks to Rashmi Jenna JEO Assistant of Heritage Science, Professor Richard Brereton, Editorial teams, and anonymous reviewers for their constructive comments. Thank you all for your invaluable information, precious time, enthusiasm and cooperation.

This research received internal/ local funding from Debre Berhan University.

Author information

Authors and affiliations.

Department of History and Heritage Management, DebreBerhan University, P.O.Box: 445, Debre Berhan, Shoa, Ethiopia

Habtamu Mekonnen

Senior Lecturer & Researcher, Department of Tourism and Hotel Management, Debre Berhan University, Debre Berhan, Shoa, Ethiopia

Zemenu Bires

Senior Lecturer and Researcher, Department of Tourism and Hotel Management, Debre Berhan University, P.O.Box: 445, Debre Berhan, Shoa, Ethiopia

Kassegn Berhanu

You can also search for this author in PubMed   Google Scholar

Contributions

KB develops proposal, review the literature and conceptualise the paper; HM prepared items and edited the manuscript; ZB writes the analysis and discussion part of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kassegn Berhanu .

Ethics declarations

Ethics approval and consent to participate.

The study was investigated as per the research guidelines of Debre Berhan University, Ethiopia, and the final findings was presented to the university research committee and got acceptance on 20 October 2021.

Competing interests

The authors declare that the study has no any conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Mekonnen, H., Bires, Z. & Berhanu, K. Practices and challenges of cultural heritage conservation in historical and religious heritage sites: evidence from North Shoa Zone, Amhara Region, Ethiopia. Herit Sci 10 , 172 (2022). https://doi.org/10.1186/s40494-022-00802-6

Download citation

Received : 24 December 2021

Accepted : 04 October 2022

Published : 27 October 2022

DOI : https://doi.org/10.1186/s40494-022-00802-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cultural Heritage
• Conservation

The Nexus between Culture, Indigenous Knowledge and Development in Ethiopia: Review of Existing Literature

Table of contents, 1. background, 2. b) indigenous knowledge, 3. c) the role of culture and indigenous knowledge to development, 4. d) the ethiopian context, 5. objectives, 7. review method a) screening process, 8. b) inclusion criteria, 9. c) review technique, 10. table 2, 11. d) limitations, 13. findings e) profiles of the reviewed materials, 14. f) focuses of the publications, 15. g) methods and objectives of the publications used, 16. h) research questions, 17. table 3 i) main findings, 18. references références referencias, 19. identified gaps, 20. a) emphasis given to study the links between culture, indigenous knowledge and development, 21. b) limited sense of ownership of studying culture, indigenous knowledge and development, 22. c) the qualities of the reports, 24. conclusion.

a) Defining Culture he term "culture" is derived from a French term, which in turn is originated from the Latin word "colere," which means to tend to the earth and grow, or cultivation and nurture. Yitbarek (2009, p. 203 ) describes culture as "?the experiences, knowledge, beliefs, values, norms, and attitudes that a certain social group constitutes and reproduces in daily life." The same author describes culture as dynamic and the concept is broader than a given language group or even a nation. People who share same culture can have more than one language families and can reside in more than a nation. Culture measures the quality of life, the vitality and the health of the society. Through culture, people develop personal and cognitive growth and the ability to emphasize and relate to each other. Culture is also reflected in the history of a society, in the heritage and in how society members express ideas and creativity. Banks, and McGee (1989) provide a comprehensive definition of culture.

The essence of culture is not its artifacts, tools, or other tangible cultural elements but how the members of the group interpret, use, and perceive them. It is the values, symbols, interpretations, and perspectives that distinguish one people from another in modernized societies; it is not material objects and other tangible aspects of human societies. People within a culture usually interpret the meaning of symbols, artifacts, and behaviours in the same or in similar ways (p.8).

Indigenous knowledge is defined as ways of knowing, seeing and thinking that are passed down orally from generation to generation. The ways of knowing reflects experimentation and innovation in topics like agriculture, animal husbandry, child rearing practices, education systems, medicine and natural resource management, among many others (International Centre for Indigenous Knowledge, 2015). Warren (1995; cited in Sithole, 2006 , p.2) defines indigenous knowledge as "local knowledge that is unique to a given culture or society. [It] is the systematic body of knowledge acquired by local people through accumulation of informal experiences and intimate understanding of the environment in a given culture."

Fenta (2000; cited in United Nations, 2004) describes traditional knowledge is "?embedded in the community's practices, institutions, relationships and rituals. It is the total of the knowledge and skills that people in particular geographic areas possess and that enable them to get the most out of their [social and] natural environment" (p.25).The use of indigenous knowledge for local development is recognized by the international communities such as the World Bank and a lot has been invested to revitalize the contribution of such knowledge to community strengthening. The use of traditional knowledge is expressed in a form of customary conflict resolution, agricultural practices, community mobilization and networking, spiritual services, health protection, soil conservation, neighbourhood security, economic support via traditional lending, labour cooperation and philanthropic services.

Culture and indigenous knowledge have paramount contribution to community wellbeing and development. It is, therefore, necessary to devise protection mechanisms to culture and indigenous knowledge. Protection conveys the measures of preserving, promoting, controlling the use and ensuring to the owners' proper share of the benefits from the use of such knowledge (United Nations, 2004 ). Culture as one of the determinant factors for development has given less emphasis for long times. For example, the cause for underdevelopment of nations in the South was ascribed mainly to external factors such as colonialism, neo-colonialism, and dependency; and internal factors such as poor macroeconomic policy, inefficient economic system and bad political institutions, whereas, in actual facts culture influences the speedup or slowdown of development. It is very recently that culture becomes the agenda in the development discourses.

Contemporary writers began to amplify that culture matters for economic and social development (Yitbarek, 2009; Grenier, 1998; Yitbarek, 2009) .

Coming to the Ethiopian context, the Country's earlier civilization serves as evidence for extent and rationality of traditional knowledge. The domestication of certain crops like coffee, teff, enset, etc; and the development of bench terrace systems are important cases of achievements in agriculture using the indigenous knowledge of communities in Ethiopia. The Country with written language over 2000 years, owns manuscripts for over 500 years old, is the indications for the long period preservation of traditional knowledge (Fenta, 2000) . Ethiopia is endowed with hundreds and thousands of tangible and intangible cultural heritages. Each tribe and nationality is full of dozens of cultural traits and indigenous knowledge. For example, a recently published inventory of five ethnic groups' intangible cultural heritages indicates existence of various social, economic, and cultural practices patented to these ethnic groups as summarized in the table below.

The objective of this paper is to make a synthesis of existing literature on culture, indigenous knowledge and development and; identify existing gaps (if any) in terms of thematic coverage, methodology, findings, attentions paid to policy matters and overall qualities of the publications. The review covers (if any) in terms of thematic coverage, methodology , findings, attentions paid to policy matters and overall published materials in a form of journals, books, conference proceedings and theses/dissertations. The review delimited itself to materials published from the year 2000-2014.

We applied various techniques to identify published materials on the subject area of culture, indigenous knowledge and development. We used subject index as a main technique to identify the publications from printed and electronic sources. We also consulted annotated bibliographies to identify related topics to the study. The Store and AcademicJournals.com were the two most electronic sources visited from which over 500 journal publications related to the key terms (culture, indigenous knowledge and development) were retrieved irrespective of our focus on Ethiopia. We further filtered the sources to select those materials published on Ethiopia related to the key terms mentioned above. Only 199 materials were found written on Ethiopia. These materials mentioned some cultural, indigenous knowledge or development issues in relation to Ethiopia. We went to a third stage scrutinizing of the articles/books to identify only those which fulfill our inclusion criteria.

The inclusion criteria to select materials relevant for our review include the following: (1) the material should be published on or after 2000, (2) materials have to appear in journal publications, books or workshop/ conference proceedings/theses/PhD dissertations, (3) the title of the material should contain either culture, indigenous knowledge or development or a combination of two or all; and (4) there should be clear author, date and source of publication. Consequently, only 29 of the materials fulfilled the criteria and were subject for detail review and analysis.

Upon completion of listing the sources, we continued reviewing each material in accordance to the predetermined set of focuses. We paid attentiontoextract information on issues summarized in the template below.

Our data analysis passed through the following sequences. The first task was extensive reading of each material, followed by extraction of basic information in accordance to the focuses presented in the template above, and developing a source data that comprises the necessary information. Once the necessary data were generated from the desk review, such data were regrouped under the major themes/topical statements we identified in advance. The topics include: (1) profiles of reviewed materials, (2) topical focuses, (3) methods used and objectives set in the studies, (4) research question, (5) findings and (6) conclusions/ recommendations made. Following review and analysis of the publications we made a critical reflection on the gaps identified within the publications (thematic focus, content, objectives and question formulation, methodology, and comprehensiveness).In the final section, we make our own conclusion.

The first limitation was lack of well organized database from which we could retrieve published materials. We applied a free search engine which might not be strong enough to explore all possible publications in the subject area we conducted the review. Therefore, the sources we relied for review may not be as inclusive as we expected to explore all available materials. Some retrieved materials lacked clear focus for which we dropped them from considering in the review process. Many resourceful materials were omitted since they were not published in journals/books or were not presented in conferences. The materials selected for review were heterogeneous in terms of method, content, focus/objective and scope of their geographic and population targets. This made the review to become cumbersome. In order to make the review process more rigorous and trustable, we were careful enough to rely only on those materials which fulfilled our inclusion criteria.

Among the total of 199 published materials on Ethiopia in relation to culture, indigenous knowledge and development, only 29 of them were found relevant for review and analysis. These materials were published during 2000-2014. Among the selected materials, the highest number of publications retrieved were published in 2005 and 2013 where six and four materials, respectively. The following table illustrates year of publications of materials which were subject for review and analysis. Publication status of the 29 materials indicates that 24 were journal articles, three conference/workshop proceedings and two Master theses. Another focus of presenting profiles of the publications was to indicate who the authors of the materials were in terms of national origin and solo-co-authorship. Out of the 29 publications,14 were published either by Ethiopian authors or published in co-authorship with other nationals. The remaining 15 were exclusively published by foreign authors. Eighteen publications were found to be solo-authored out of which only eight were published by Ethiopian authors. From the remaining 11 publications which were co-authored, only two were coauthored with Ethiopian authors.

The topics of the 29 reviewed materials indicate that 16 of them were dedicated to pronounce indigenous knowledge in connection to other focus areas to study. Only six of the materials have culture as their topic of study. The remaining seven had combined topics where culture and indigenous knowledge or culture and development are merged with other issues such as religion, politics, policy or other macro-concepts such as society. Whereas, development was less pronounced in topical sentences; indigenous knowledge was presented as major topical phrases in majority of the publications, within which culture is being quoted in some sections of the publications.

A critical observation of mentioning the term "method" or "methodology" was used as a parameter to specify the presence or absence of this important section of scholastic writing. In addition, the extent of describing the method used was applied to measure the attention given by the authors to present clear research method/methodology. Our overall impression in relation to the study methods is that, there is a problem of clarity to describe the specific method/approach used in many of the reviewed materials.

A tally of each of the reviewed articles indicates that authors of the 14 articles never mentioned at all the term "method" or "methodology" in their publications. Other 15 at least mentioned method/methodology in the texts they wrote. A further investigation of the level of clarity of methods/methodologies indicate that out of the 15 publications which mentioned the method/ methodology, nine of them simply mentioned the type of method applied, such as qualitative or quantitative and never described what it means or why they have chosen such method. This is considered in our review as lack of clarity of a method in the publications. Some authors also used study designs as alternative to describe method. For example, terms and phrases such as ethnographic study, participatory rural appraisal, or ethno-botanical approach are used as terms to describe research methods. In our view, the above listed terminologies are study designs falling either under qualitative, quantitative or a mixed method being used ina given study.

Many authors have paid attention to describe objectives compared to explaining their study methods. Although the authors did not give specific topic to state their study objectives, as long as they describe in a form of statements anywhere in the background of their articles, we considered the publications as having stated their objectives. In this regard, 22 publications stated their study objectives, out of which 19 have described such objectives clearly. The level of clarity could be subjective depending on how we analyze the views of authors in presenting and explaining their objectives. The level of clarity both for the objectives and methods was measured against whether or not the authors explained these issues in detail or were presented in scanty manner.

As far as the publications we reviewed are published in a form of journals, books or presented in conferences/workshops, we believed the studies have clearly stated research questions and followed scientific inquiries. In actual fact, only12of the 29publications have stated research questions in one way or another. Authors of some materials explicitly listed their research questions while others simply described in a form of statements. In this regard, nineof them have listed the research questions clearly and the remaining three articles have presented ambiguous questions. Out of the 12 publications that presented their research questions, eight of them have attempted to respond to a single research question and four paid attentions to answer multiple questions.

It is not easy and simple to summarize and present findings of already published articles in a concrete and consolidated manner, where the quality of the materials is found to be anomalous. Major constraints to summarize the findings emanated from the huge number of reading materials to cover diverse style of presentation of findings and ambiguities in presenting such findings so that it could be easy to communicate to the reader. Despite these limitations, we have exerted at most effortto develop common themes where findings of the 29 articles can be grouped and synthesized. Our findings are summarized as follows.

i. Findings on Culture One major finding of a study conducted by Keeley and Scoons (2000) indicates the strong influence of cultural networks on political decision making. This same paper continues to explain the influence of cultural traits on protection of environment as well as agricultural and natural resources management. This paper tried to explain how culture contributes to facilitate or deter development in general and political decision making and natural environment protection in particular. Culture is defined in a broader term by some authors and used as lenses to study political ideologies. Tronvoll (2001) describes three forms of political culture that were practiced by some groups in Ethiopia. According to the author, accommodationist, assimilationist and secessionist are three political cultures used to exist in Ethiopia of which the accommodationist political culture got the upper hand.

Culture and religion is another major focus that draws the attention of authors considered in this review of existing literature. Kaplan (2004 Kaplan ( , 2009) ) describes the influence of culture in the process of conversion to a specific religion. According to the author, for someone to be considered converted to a specific religion (Islam, Christian) the person has to pass through culture rites that approve the immersion to that particular religion. For example, in old days, receiving a Christian name was the essential rites for those wishing to join a Christian community.

Despite its long existed heritages within a society's way of life, culture is found to be reshaped by a state policy. As described by Abbink (2000) , a significant factor that is reshaping local cultures and group relations in Ethiopia and elsewhere is state policy. On the other hand, culture can also be influenced by some gift of nature. Cultural practices and rituals can be shaped by nature including plants, mountains and other sort of topographic features. For instance, plants, especially those medicinal plants can shape cultural elements of a given society (Bahiru, Asfaw & Demissew, 2012).

ii. Indigenous/Traditional Knowledge Indigenous/traditional knowledge is pronounced by scholars as source of strength for natural resource conservation practices. Some Ethiopian writers such as Amsalu Aklilu (2001) notes that "making good use of and building upon indigenous knowledge and practice of the land users in the development and implementation of conservation technologies could bring about effective technological transfer and sustainable land use." Some traditional practices and indigenous knowledge are considered as part and parcel of cultural values in many communities in Ethiopia. The soil conservation practice in the Konso community has, for example, contributed for the change of survival mechanisms in the face of climatic changes. Mulat (2013) underlines that in the Konso community; the deep indigenous knowledge on soil conservation mechanism is deeply embedded in their culture.

There exists controversial agreement between modernity and preservation of traditional/indigenous knowledge. Some argue that while people continue to attend modern education and start to live modern way of life, they start to forget the existed indigenous knowledge that passed through generation to generation. This seems a valid argument as documented by Legesse, Teferi and Baudouin (2013) . In their study of the Gedeo community on the use of indigenous knowledge on agro-forestry, the authors found that those young Gedeo's who attended formal education and who engaged in off-farm activities were found to be less knowledgeable on the existing indigenous knowledge.

iii. Culture, Indigenous Knowledge and Development Among the authors whose works are reviewed in this article, it is fair to say that majority the of them paid little attention to link culture and i ndigenous knowledge with development. The concept development is rarely mentioned although elements of development such as soil conservation, natural resources management, and preservation of important plant species are mentioned in their reports.

The on ly famous article out of the 29 reviewed works that directly stated the connection between culture, indigenous knowledge and development is the one written by Unasho (2013) . Unasho (2013) states "development that does not pay attention to culture and envi ronment cannot produce fruits. The author, in the study of the Zaysitelanguage quoted the comments given by the respondents of the study and described that there was a direct link between linguistically encoded indigenous environmental knowledge and biodiv ersity conservation.

iv. What did the Studies Recommended? Although many of the authors are lacking to provide recommendations in their works, very few Volume XVII Issue IV Version I

recommended that in the study of culture and indigenous knowledge, deeper studies with comprehensive nature that apply both quantitative and qualitative methodology and encompass wider social groups within a cultural setting are needed to better understand the contemporary nature of culture and indigenous knowledge. Some specific recommenddations given by few of the study inclined towards giving the assignment to the Ethiopian government to conserve existing cultural practices that contribute towards conservation of natural resources (Bahiru, Asfaw & Demissew, 2012; Unasho, 2013).

It may not be fair to use a review of only 29 articles written on such huge areas of culture, indigenous knowledge and development and try to speak boldly about gaps. However, admitting our own limitations of the small sample size of publications, it is still possible to mention some gaps we identified. The gaps we want to describe are related to the following areas.

Our review clearly tells existence of only few published materials that paid attention to study culture and indigenous knowledge. Studying the link between culture and indigenous knowledge to development is a totally missing agenda by many of the articles we reviewed. While culture and indigenous knowledge are two sides of a coin, which can directly affect development, lack of attention given by researchers to show their importance for policy consideration is a serious flashback.

The review process makes it clear that study of culture and indigenous knowledge is predominantly conducted by foreign researchers. If Ethiopians are participating, in most cases, they are co-authors. Studies conducted by Ethiopians are mainly post graduate theses which remain unpublished; otherwise, our argument would have been reversed.

Regarding the qualities of the materials we made review, we tried our best to see the quality of each article in terms of content, methodology, research questions, objectives, and conclusions/ recommendations the authors made. As we presented in the findings of this report, a significant number of articles have suffered from lack of clear objectives, ambiguity/ absence of methodology, and unable to describe their research questions. In the absence of clear research questions, objectives, and methods, it is very difficult to witness whether findings of the studies are reliable or not. With such doubts reliability is compromised. We can't be confident that findings from such study with less quality can be useful for policy and programme design.

Another element of the articles subject for comment is the contents/findings. Findings of many of the articles suffer from insufficient presentations of data. Even those with clear data do not witness whether such data respond to the research questions or meet their objectives due to absence of clearly stated research questions and objectives as stated in the previous sections. In sum, many of the studies are not rigor enough to contribute towards development by providing sufficient knowledge on culture and tradition.

From our observation of the literature review, it is safe to conclude that knowledge production in the areas of culture and indigenous knowledge in Ethiopia is at the infancy stage. Similarly, researchers in the areas of culture and indigenous knowledge have paid no or very little attention to magnify the contribution of cultural heritage and indigenous knowledge to development. Another critical observation is many of the existing studies are not pioneered by Ethiopian scholars. On the other hand, studies conducted by Ethiopians are not published that widens the gap on knowledge transmission.

The lack of appropriate storage and retrieving systems for existing publications informs that available resources are not in a proper use. As a result, it is very difficult to make a concrete statement which areas of culture and indigenous knowledge should get priority attention for research and knowledge production.

• Indigenous agro-forestry knowledge transmission and young people' participation in agro-forestry practices: The case of Wonago Woreda, Gedeo Zone, and Southern Ethiopia . A Legesse , B Teferi , Baudouin . Acta Geographica -Trondheim: Serie A, Nr 2013. 26 (26) .
• Natural resource management in Ethiopia: Making good use of indigenous knowledge . Amsalu Aklilu . Ethiopian Development Forum 2001. 2 (3) p. .
• Language as genes of culture and biodiversity conservation: The case of "Zaysite" language in southern region of Ethiopia . A Unasho . International Journal of Modern Anthropology 2013. 2004. 6 (1) p. . (United Nations)
• bêG<M? ½ xêE ½ x!éB ½ k#¥Âyx!T×ùà î¥l@ B/@rîC §Y ytmzgb# x!N¬NjBL AE? §êEQRîC. Q{ 7 sn@ . AElSLÈN 2007. 2007.
• Knowledge, power and politics: The environmental policy-making process in Ethiopia . J & Keeley , I Scoones . Source: The Journal of Modern African Studies 2000. 38 (1) p. .
• Multicultural education , J A Banks , C A Mcgee . 1989. Needham Heights, MA : Allyn & Bacon .
• Comparing culture in southern Ethiopia: From ethnography to generative explanation . J G Abbink . Northeast African Studies 2000. 7 (3) p. .
• The challenges faced by African libraries and information centres in documenting and preserving indigenous knowledge. World Library and Information Congress , J Sithole . 2006. 20-24 August. Seoul, Korea .
• Voting, violence and violations: Peasant voices on the flawed elections in Hadiya, southern Ethiopia . K Tronvoll . The Journal of Modern African Studies 2001. 39 (4) p. .
• Working with indigenous knowledge: A guide for researchers. Dakar: International Develop-ment Research Centre , L Grenier . 1998.
• Themes and methods in the study of conversion in Ethiopia: A review essay . S Kaplan . DOI:10.1163/ 15700660417225475. Journal of Religion in Africa 2004. 34 (3) p. .
• Dominance and diversity: Kinship, ethnicity, and Christianity in Orthodox Ethiopia . S Kaplan . Church History and Religious Culture 2009. 89 (1/3) p. .
• Vignettes on culture and development in Ethiopia . S Yitbarek . International Journal of Ethiopian Studies 2009. 4 (1/7) p. .
• Indigenous knowledge on plant species of material culture used by the Afar and Oromo nations in and around the Awash National Park . T Bahru , Z Asfaw , S &demissew . Ethiopia. Global Journal of Human Science Geography 2012. 12 (11) p. .
• Biodiversity conservation and utilization on the basis of farmers traditional knowledge: Ethiopian experience. Ethiopian Science and Technology Commission , T Fenta . 2000. Addis Ababa .
• What is indigenous knowledge? Accessed on , http://icik.psu.edu/psul/icik/aboutik.html 2015. June 30. 2015. (International Centre for Indigenous Knowledge)
• Indigenous knowledge practices in soil conservation at Konso people, South Western Ethiopia . Y Mulat . American Journal of Rural Development 2013. 2 (2) p. .

Introduction: Background and History of Ethiopia and Cultural Context

• First Online: 12 October 2021

Cite this chapter

• Adrienne Wynn 11 ,
• Greg Wiggan 11 ,
• Marcia J. Watson-Vandiver 12 &
• Annette Teasdell 13  

Part of the book series: Palgrave Studies in Race, Inequality and Social Justice in Education ((PSRISJE))

280 Accesses

In this chapter, we provide background on Ethiopian history as context for our investigation regarding Ethiopian immigrants in the U.S. This case study provides analysis for educators, public policy makers, and social change agents with information to support and assist Ethiopian women with their transition into U.S. schools and society. Using critical race feminism and Afrocentricity theory as guiding frameworks for the entire book, we analyze race and the effects of acculturation and assimilation on school achievement, identity development, and concepts of beauty among Ethiopian immigrant women.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Adejumobi, S., & Olukoshi, A. (2008). The African union and new strategies for development in Africa . Cambria Press.

Google Scholar  

Adogamhe, P. G. (2008). Pan-africanism revisited: Vision and reality if African unity and development. African Review of Integration, 2 (2), 1–34.

African Immigration Council. (2012, June 28). African immigrants in America: A demographic overview. African Immigration Council . https://www.americanimmigrationcouncil.org/research/african-immigrants-america-demographic-overview

Akokpari, J., Ndinga-Muvumba, A., & Murithi, T. (2008). The African union and its institutions . Fanele.

Altbach, P. G. (2004). Higher education crosses borders: Can the United States remain the top destination for foreign students. Change, 36 (2), 18–25. https://doi.org/10.1080/00091380409604964

Article   Google Scholar  

Anderson, M. (2017, February 14). African immigrant population in U.S. steadily climbs. Pew Research Center. https://www.pewresearch.org/fact-tank/2017/02/14/african-immigrant-population-in-u-s-steadily-climbs/

Anderson, M., & López, G. (2018, January 24). Key facts about Black immigration in the U.S. Pew Research Center. https://www.pewresearch.org/fact-tank/2018/01/24/key-facts-about-black-immigrants-in-the-u-s/

Ani, M. (1994). Yurugu: An Afrikan-centered critique of European cultural thought and behavior . Nkonimfo Publications.

Aristotle. (350 B.C.E./1966). Metaphysics, Vol 1. (W. D. Ross, Trans.). Oxford Clarendon Press (Original work published in 350 B.C.E.).

Asante, M. K. (1990). Kemet , Afrocentricity, and knowledge . African World Press.

Asante, M. K. (2010). Afrocentricity and africology: Theory and practice in the discipline. In J. R. Davidson (Ed.), African American studies (pp. 35–52). Edinburgh University Press.

Asante, M. K. (2011).  Afrocentric idea revised . Temple University Press.

Barak, G., Leighton, P., & Flavin, J. (2007). Class, race, gender, crime: The social realities of justice in America . Rowman & Littlefield.

BBC News. (2012, February 1). Profile: African union. BBC News . http://news.bbc.co.uk/2/hi/africa/country_profiles/3870303.stm

BBC News. (2019, April 8). How did US and Ethiopia become so close? BBC News. https://www.bbc.com/news/world-us-canada-47203691

Bell, D. (1980). ‘Brown v. Board of Education’ and the interest-convergence dilemma.  Harvard Law Review ,  93 (3), 518–533. https://doi.org/10.2307/1340546

Bell, D. (1987). And we are not saved: The elusive quest for racial justice . Basic Books.

Bell, D. (1995). Who’s afraid of critical race theory. University of Illinois Law Review , 893.

Beoku-Betts, J. (2004). African women pursuing graduate studies in the sciences: Racism, gender bias, and third world marginality. Feminist Formations, 16 (1), 116–135. https://doi.org/10.2979/NWS.2004.16.1.116

Beoku-Betts, J., & Njambi, W. N. (2005). African feminist scholars in women’s studies: Negotiating spaces of dislocation and transformation in the study of women. Meridians, 6 (1), 113–132.

Bodovsky, D., & Avni, B. (1989). Immigrants’ families from Ethiopia, issues in immigration and readaptation.  Jerusalem: Beiteinu Betazin Pub [In Hebrew Translated to English].

Budge, E. A. W. (Ed.). (2001).  Kebra Nagast. The Queen of Sheba and her only son Menyelek (I): Being the “Book of the Glory of Kings”. Routledge.

Camara, S. (2020, June 8). Why I march for “Black Lives Matter” as an African immigrant in America. Tantv Studios . https://www.tantvstudios.com/posts/why-i-march-for-black-lives-matter-as-an-african-immigrant-in-america

Carroll, J., & Ryan, J. (Eds.). (2007).  Teaching international students: Improving learning for all. Routledge.

Chacko, E. (2003). Identity and assimilation among young Ethiopian immigrants in metropolitan Washington. Geographical Review, 93 (4), 491–506. https://doi.org/10.1111/j.1931-0846.2003.tb00044.x

Chaillot, C. (2012). The Ethiopian Orthodox Church . In The Wiley-Blackwell companions to religion (pp. 234–240). Wiley. https://doi.org/10.1002/9781118258813.ch15

Childers-McKee, C. D., & Hytten, K. (2015). Critical race feminism and the complex challenges of educational reform. The Urban Review, 47 (3), 393–412. https://doi.org/10.1007/s11256-015-0323-z

Collins, P. H. (2000). Black feminist thought: Knowledge, consciousness, and the politics of empowerment . Routledge.

Collins, P. H., & Bilge, S. (2016). Intersectionality (Key concepts) . Polity Press.

Constantine, M. G., Anderson, G. M., Berkel, L. A., Caldwell, L. D., & Utsey, S. O. (2005). Examining the cultural adjustment experiences of African international college students: A qualitative analysis. Journal of Counseling Psychology, 52 (1), 57.

Conyers, J. L. (Ed.). (2015).  Afrocentricity and the academy: Essays on theory and practice . McFarland.

Cooper, A. J. (1892).  A voice from the South . Reprinted (1988). Oxford University Press.

Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43 (6), 1241–1299. https://doi.org/10.2307/1229039

Crenshaw, K. (2016). The urgency of intersectionality. TEDWomen 2016. https://www.ted.com/talks/kimberle_crenshaw_the_urgency_of_intersectionality?language=en

Crenshaw, K., Ritchie, A., Anspach, R., Gilmer, R., & Harris, L. (2015). Say her name: Resisting police brutality against black women . African American Policy Forum.

Dei, G. J. S. (1994). The women of a Ghanaian village: A study of social change.  African Studies Review ,  37 (02), 121–-145. https://doi.org/10.2307/524769

Dei, G. J. S. (1996). The role of Afrocentricity in the inclusive curriculum in Canadian schools. Canadian Journal of Education, 21 (2), 170–186. https://doi.org/10.2307/1495088

Dei, G. J. S. (2003). Why write “Black”? Reclaiming African culture resource knowledges in Diasporic contexts. In J. L. Conyers (Ed.), Afrocentricity and the academy: Essays on theory and practice (p. 211). McFarland.

Dei, G. J. S. (2012). Teaching Africa: Towards a transgressive pedagogy . Springer.

Delgado, R., & Stefancic, J. (2017).  Critical race theory . New York University Press.

deMarrais, K., & LeCompte, M. (1999). The way schools work . Longman.

Edwards, S., & Harris, D. (2017). Black lives matter . Essential Library, an imprint of Abdo Publishing.

Eloundou, N. G. (2019, December). Private sector strengthening as pillar of South Korea’s development assistance toward Cameroon. World Economic Brief, 9 (26), 1–7.

Evans-Winters, V. E. & Esposito, J. (2010). Other people’s daughters: Critical race feminism and Black girls’ education. Educational Foundation, 11–24.

Ferree, M. M., & Tripp, A. M. (2006).  Global feminism: Transnational women's activism, organizing, and human rights . New York University Press.

Gambino, C. P., Trevelyan, E. N., & Fitzwater, J. T. (2012). The foreign-born population from Africa: 2008-2012 . U.S. Census Bureau .

Garza, A. (2014). A herstory of the# BlackLivesMatter movement . https://collectiveliberation.org/wp-content/uploads/2015/01/Garza_Herstory_of_the_BlackLivesMatter_Movement.pdf

Getahun, S. A. (2007). The history of Ethiopian immigrants and refugees in America, 1900-2000 . LFB Scholarly Publishing.

Gomez, M. (2005). Reversing sail: A history of the African Diaspora . Cambridge University Press.

Guy-Sheftall, B. (1995).  Words of fire: An anthology of African-American feminist thought . The New Press.

Hanassab, S. (2006). Diversity, international students, and perceived discrimination: Implications for educators and counselors. Journal of Studies in International Education, 10 (2), 157–172. https://doi.org/10.1177/1028315305283051

Harris, J. E. (1993). Global dimensions of the African diaspora . Howard University Press.

Henderson, J., Lynch, J., & Parks, B. (2020, July 9). An officer told George Floyd it took ‘a lot of oxygen to talk,’ body camera transcripts show. CNN . https://www.cnn.com/2020/07/08/us/george-floyd-police-body-camera-transcripts/index.html

Herodotus. (440 B.C.E./2014). The history of Herodotus, volume 1 . (G. C. Macaulay, Trans.). CreateSpace Independent Publishing Platform (Original work published in 440 B.C.E.).

Hill, E., Tiefenthäler, A., Triebert, C., Jordan, D., Willis, H., & Stein, R. (2020, May 31). How George Floyd was killed in police custody. The New York Times. https://www.nytimes.com/2020/07/08/us/george-floyd-body-camera-transcripts.html

Hilliard, C. B. (1998). Intellectual traditions of pre-colonial Africa. McGraw-Hill Press.

Hooghe, M., & Dassonneville, R. (2018). Explaining the Trump vote: The effect of racist resentment and anti-immigrant sentiments. American Political Science Association, 51 (3), 528-534.

hooks, b. (1981). Ain’t I a woman: Black women and feminism . South End Press.

Huerta, A. (2020, May 15). The right to ethnic studies in higher education. Inside Higher Ed. https://www.insidehighered.com/advice/2020/05/15/why-students-should-be-required-take-ethnic-studies-opinion

Human Rights Watch. (2020, May 12). Covid-19 fueling anti-Asian racism and xenophobia worldwide. Human Rights Watch . https://www.hrw.org/news/2020/05/12/covid-19-fueling-anti-asian-racism-and-xenophobia-worldwide

Immigrant Legal Resource Center. (2020, June 18). Understanding the 2020 supreme court decision on DACA. Immigrant Legal Resource Center. https://www.ilrc.org/understanding-2020-supreme-court-decision-daca?gclid=EAIaIQobChMI5aK2jdKx6gIV6giICR1fuQWqEAAYASAAEgLVGfD_BwE

Jackson, J. G. (1970). Introduction to African civilizations . Kensington Publishing Group.

Jonas, R. (2011). The Battle of Adwa . Harvard University Press.

Book   Google Scholar  

Karenga, M. (2002). Introduction to Black studies . University of Sankore Press.

Kendi, I. X. (2017).  Stamped from the beginning: The definitive history of racist ideas in America . Random House.

Kendi, I. X. (2019). How to be an antiracist . One World.

Kibour, Y. (2001). Ethiopian immigrants’ racial identity attitudes and depression symptomatology: An exploratory study. Cultural Diversity and Ethnic Minority Psychology, 7 (1), 47. https://doi.org/10.1037/1099-9809.7.1.47

King, J. E., & Swartz, E. E. (2015). The Afrocentric praxis of teaching for freedom: Connecting culture to learning . Routledge.

Kishimoto, K. (2018). Anti-racist pedagogy: From faculty’s self-reflection to organizing within and beyond the classroom. Race Ethnicity and Education, 21 (4), 540–554. https://doi.org/10.1080/13613324.2016.1248824

Ladson-Billings, G., & Tate, W. (1995). Toward a critical race theory of education. Teachers College Record, 97 (1), 47–68.

Ladson-Billings, G. (1998). Just what is critical race theory and what’s it doing in a nice field like education? Qualitative Studies in Education, 11 (1), 7–24. https://doi.org/10.1080/095183998236863

Lee, E. (2019). America for Americans: A history of xenophobia in the United States . Basic Books.

Lee, J. J. (2010). International students’ experiences and attitudes at a US host institution: Self-reports and future recommendations. Journal of Research in International Education, 9 (1), 66–84. https://doi.org/10.1177/1475240909356382

Lee, J. J., & Rice, C. (2007). Welcome to America? International student perceptions of discrimination. Higher Education, 53 , 381–409. https://doi.org/10.1007/s10734-005-4508-3

Lemert, C. (2004/2016). Social theory: The multicultural and classic readings (4th ed.). Westview Press.

Loo, B. (2019, March 26). International students and experiences with race in the United States. World Education News and Reviews . https://wenr.wes.org/2019/03/international-students-and-experiences-with-race-in-the-united-states

Mana, A., Orr, E., & Mana, Y. (2009). An integrated acculturation model of immigrants’ social identity. The Journal of Social Psychology, 149 (4), 450–473. https://doi.org/10.3200/SOCP.149.4.450-473

Maxwell, L. A. (2014, August, 19). U.S. school enrollment hits majority-minority milestone. Education Week . https://www.edweek.org/ew/articles/2014/08/20/01demographics.h34.html

May, S. (Ed.). (1999).  Critical multiculturalism: Rethinking multicultural and antiracist education . Psychology Press.

Milkias, P., & Metaferia, G. (Eds.). (2005). The Battle of Adwa: Reflections on Ethiopia's historic victory against European colonialism . Algora Publishing.

Morrison, K. (2006).  Marx, Durkheim, Weber: Formations of modern social thought. Sage.

National Center for Education Statistics. (2017). Number and percentage distribution of teachers in public and private elementary and secondary schools, by selected teacher characteristics: Selected years, 1987-88 through 2015-16. U.S. Department of Education. https://nces.ed.gov/programs/digest/d17/tables/dt17_209.10.asp?current=yes

National Science Foundation. (2001, July 12). Earliest human ancestors discovered in Ethiopia; Discovery of bones and teeth date fossils back more than 5.2 million years. Science Daily . www.sciencedaily.com/releases/2001/07/010712080134.htm

National Center for Education Statistics. (2019a). Status and trend in the education of racial and ethnic groups 2018. U.S. Department of Education . https://nces.ed.gov/pubs2019/2019038.pdf

National Center for Education Statistics. (2019b). Enrollment and percentage distribution of enrollment in public elementary and secondary schools, by race/ethnicity and level of education: Fall 1995 through fall 2029. U.S. Department of Education . https://nces.ed.gov/programs/digest/d19/tables/dt19_203.50.asp?current=yes

Nawyn, S. J., & Park, P. (2017). Gendered segmented assimilation: Earning trajectories of African immigrant women. Ethnic and Racial Studies, 42 (2), 216–234. https://doi.org/10.1080/01419870.2017.1400085

Obot, N. E., (2020). Highly-skilled Black African immigrant women’s narratives on healthcare workplace experiences and socioeconomic integration (Doctoral Dissertation). ProQuest Dissertations and Theses Database.

Ohito, E. O. (2016). Making the emperor’s new clothes visible in anti-racist teacher education: Enacting a pedagogy of discomfort with white preservice teachers. Equity & Excellence in Education, 49 (4), 454–467. https://doi.org/10.1080/10665684.2016.1226104

Open Doors Report. (2019). All places of origin: International student data from the 2019 Open Doors Report. Institute of International Education . https://www.iie.org/en/Research-and-Insights/Open-Doors/Data/International-Students/Places-of-Origin

Oppel, R. A. Jr., & Barker, K. (2020, July 8). New transcripts detail last moments for George Floyd. The New York Times . https://www.nytimes.com/2020/07/08/us/george-floyd-bodycamera-transcripts.html

Parrillo, V. N. (Ed.). (2008).  Encyclopedia of social problems . Sage.

Plato. (348 B.C.E./1968). Laws. (R.G. Bury, Trans.). Harvard University Press (Original work published in 348 B.C.E.).

Plutarch. (75 C.E./2012). Parallel lives vol. 1 . SMK Books (Original work published in 75 C.E.).

Pratt-Clarke, M. (2010).  Critical race, feminism, and education: A social justice model . Springer.

Redden, E. (2018, February 1). A year of travel bans. Inside Higher Education . https://www.insidehighered.com/news/2018/02/01/year-later-trump-administrations-travel-restrictions-opposed-many-higher-ed-are

Reese, D. (2017, December 14). African immigrants doing well in United States. Courthouse News Service . https://www.courthousenews.com/african-immigrants-doing-well-in-united-states/

Relethford, J. H. (2008). Genetic evidence and the modern human origins debate. Heredity, 100 , 555–563. https://doi.org/10.1038/hdy.2008.14

Romanucci-Ross, L., De Vos, G. A., & Tsuda, T. (2006). Ethnic identity: Problems and prospects for the twenty-first century . Rowman Altamira.

Showers, F. (2014). Being Black, foreign and woman: African immigrant identities in the United States. Ethnic and Racial Studies, 38 (10), 1815–1830. https://doi.org/10.1080/01419870.2015.1036763

Sleeter, C. E. (2005). Un-standardizing curriculum: Multicultural teaching in the standards-based classroom. Teachers College Press.

Sleeter, C. E., & Grant, C. A. (1987). An analysis of multicultural education in the United States. Harvard Educational Review, 57 (4), 421–445. https://doi.org/10.17763/haer.57.4.v810xr0v3224x316

Sleeter, C., & Grant, C. (2005). Illusions of progress: Business as usual. Critical social issues in American education: Democracy and meaning in a globalizing world , 89–107.

Taylor, K. Y. (2016). From# BlackLivesMatter to black liberation . Haymarket Books.

U.S. Census Bureau. (2015). Projections of the size and composition of the U.S. population: 2014 to 2060: Estimates and projections .

Waters, M. C. (1990). Ethnic options: Choosing identities in America . University of California Press.

Watson-Vandiver, M. J. (2018). The history of 20 th and 21 st century Black migration: Implications for educators and schools. Black History Bulletin, 81 (2), 6–9. https://doi.org/10.5323/blachistbull.81.2.0006

Wilder, C. S. (2014). Ebony and ivy: Race, slavery and troubled history of America’s universities . Bloomsbury Publishing USA.

Williams, C. (1987). The destruction of Black civilization: Great issues of a race from 4500 B.C. to 2000 A.D . Third World Press.

Wilton, L., & Constantine, M. G. (2003). Length of residence, cultural adjustment difficulties, and psychological distress symptoms in Asian and Latin American international college students. Journal of College Counseling, 6 (2), 177–186. https://doi.org/10.1002/j.2161-1882.2003.tb00238.x

Wood, P. H. (1996). Black majority: Negroes in colonial South Carolina from 1670 through the Stono rebellion . W. W. Norton.

Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race, Ethnicity and Education, 8 (1), 69–91. https://doi.org/10.1080/1361332052000341006

Younis, M. (2018). Race, the world and time: Haiti, Liberia and Ethiopia (1914–1945). Millennium: Journal of International Studies, 46 (3), 352–70. https://doi.org/10.1177/0305829818773088

Download references

Author information

Authors and affiliations.

College of Education, University of North Carolina at Charlotte, Charlotte, NC, USA

Adrienne Wynn & Greg Wiggan

Elementary Education Department, Towson University, Towson, MD, USA

Marcia J. Watson-Vandiver

Department of Curriculum and Instruction, Clark Atlanta University, Atlanta, GA, USA

Annette Teasdell

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Wynn, A., Wiggan, G., Watson-Vandiver, M.J., Teasdell, A. (2021). Introduction: Background and History of Ethiopia and Cultural Context. In: Race, Class, Gender, and Immigrant Identities in Education. Palgrave Studies in Race, Inequality and Social Justice in Education. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-75552-2_1

Download citation

DOI : https://doi.org/10.1007/978-3-030-75552-2_1

Published : 12 October 2021

Publisher Name : Palgrave Macmillan, Cham

Print ISBN : 978-3-030-75551-5

Online ISBN : 978-3-030-75552-2

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Ethiopia: Historical Background and Modern Issues Research Paper

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Introduction

Ethiopia is one of the oldest countries on global scene with rich history that remains untold. This research paper examines the historical background of Ethiopia in terms of its political setting, economic, and cultural backgrounds. In addition, the paper seeks to examine and review the contemporary issues defining its current position relative to other African states.

Ethiopia is one of the African countries with oldest history and culture. The country is located in East-Central Africa occupying an area of about 1,127,127 square kilometers. The country borders Kenya on south, Eritrea on north, Sudan on the west, and Somalia and Djibouti on the east (Marcus, 1994).

Historical background

The country has many detailed backgrounds from political, economic, culture and the social contexts. Indeed, Ethiopia has a fertile history regarding its political life. Its political history stretches into the ancient times with and held high by among African countries in respect of the fight for liberation. According to Zewde (2001), the historical background Ethiopia goes back as far as 6 millions ago.

Some scientists perceive the country as the home of the earliest man on earth. The ample evidence retrieved from the foundation of Ardipithecus ramidus shows that country serves to demonstrate this element. However, the present nation is a consolidation of smaller Kingdoms (Adejumobi, 2007).

The political history

The country has made a big stride in the political lines. The country got its independence in 1896 after a fierce war that defeated the Italian, who had invaded it. The country resisted and defeated the Italians through Emperor Menelik II. After the sad death of Menelik II, many and challenging experiences lead to political disorders, which caused the crowing of his daughter and her cousin in 1917.

However, after a period of about 15 years the empress died, thus accommodating the cousin to the might emperor. The country under the rule of Haile Selassie outlawed slavery where in efforts endured to show commitments towards redeeming the seemingly scattered population. Not surprisingly, Italy again attacked Ethiopia in 1935 raising hot foot to the emperor forcing him to exile in May 1936.

This provided a quality and opportune moment for the Italians, who later began to establish their Kingdom in Ethiopian. Its name became the Italian East Africa. Unfortunately, they were flashed by the strong British troops in 1941. This provided room for the return and rule of Haile Selassie later after the establishment of the British rule (Adejumobi, 2007).

After the World War II, the Emperor demonstrated many efforts in pursuit to modernize the country. The tremendous growth commenced immediately with first high school and college born in 1950. The country continued with rational thoughts and democratization of the state. In 1955, the country promulgated its new constitution five years after its material independence.

The document gave the members of parliament more undeserved powers with a belief that they will dedicate them rationally and equitably to relevant developments of the country. In the view of constantly changing political environment, the emperor formulated strategies to acquire Eritrea through declaring Eritrea as the fourteenth province of Ethiopia.

According to Paul B. Henze (qtd. in Pankhurst, 1998), most of the Ethiopians were personality thinkers as contrasted to ideologist, thus they held notions that the emperor was the only icon of change that constituted their think-tank. The country was in cold war as the imperialists were among the primary elites of the growing Ethiopia Marxist movement.

The country came into fierce battle against poverty and other challenges such as disease and mortality. The government failed to implement significant political and economic reforms, which led to political instability. However, this evidenced a positive step toward a refined country within the confines of politics (Pankhurst, 1998).

Economic background

Most countries are demonstrating much effort towards attaining sustainable growth in relation to economic growth and development. Ethiopia is among the poorest countries in the world exhibiting a low-level economy. The economic history of the country is unpleasant; in fact, it has inflicted a lot of injustice to her subjects.

Although the country is one the oldest in Africa in respect of gaining independence, its economic base does not speak the same language. However, the modern government is fully acquiring innovative budgetary allocations in its wide range of ministries for many years. This aims at dedicating its commitment in providing the best services to its populations.

The country enjoys rich resource in minerals such as gold, copper, potash, and natural gas. The country has an estimated GDP of US$6 billion annually and per capita income of about US$100 per annum. However, the country ails from chronic trade deficits since the start of 2005. Agriculture remains Ethiopia’s economic backbone due to its good rainfall and terrain.

The constantly growing population surpasses the provisions of the available resources, hence creating food insecurity. Retail trade and transportation constitute the second largest earners of income for the economy. The country depends heavily on international donors and grants to finance its budgetary allocations.

People and culture

According to the census report of 2004, the Ethiopian population was about 70 million with an annual growth rate of about 2.3 percent. The country boasts of 70 different languages spoken as the first languages. A majority of the language groups belongs to the Semitic, Cushitic and omotic families.

A small fraction belongs to the Nilo-Saharan family. It is worth noting that the largest Semitic speaking groups are Amhara. However, no religion has a largest share of the population of the country (Marcus, 1994).

Ethiopia has a great interesting and much uncovered history and culture both in time and space. The country has a unique and exclusive mix of culture compared to other African countries. Although the country has a low GDP, it continues to make improvements in its economy to match the changing times and market dynamics.

Adejumobi, S. (2007). The history of Ethiopia . New York: Greenwood Publishing Group.

Marcus, H. (1994). A history of Ethiopia . University of California: University of California Press.

Pankhurst, R. (1998). The Ethiopians . Malden: Blackwell Publishers.

Zewde, B. A. (2001). History of modern Ethiopia, 1855-1991. New York: James Currey

• Argentina’s Geographical Information
• Energy Consumption: The State of Maryland as the Residential Region
• The Japanese Tea and Ethiopian Coffee Ceremonies
• Education, Health, and Employment in Ethiopia
• Ethiopia's Economic and Financial Trends
• Central Asia: Uzbekistan Profile
• Balkans Physical Geography
• Kosovo Geography and Demographics
• Transformation of the Canadian North (Arctic)’S Natural and Social Environment by the Extraction Industry
• GIS and groundwater management
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2019, May 16). Ethiopia: Historical Background and Modern Issues. https://ivypanda.com/essays/the-ethiopian-history-research-paper/

"Ethiopia: Historical Background and Modern Issues." IvyPanda , 16 May 2019, ivypanda.com/essays/the-ethiopian-history-research-paper/.

IvyPanda . (2019) 'Ethiopia: Historical Background and Modern Issues'. 16 May.

IvyPanda . 2019. "Ethiopia: Historical Background and Modern Issues." May 16, 2019. https://ivypanda.com/essays/the-ethiopian-history-research-paper/.

1. IvyPanda . "Ethiopia: Historical Background and Modern Issues." May 16, 2019. https://ivypanda.com/essays/the-ethiopian-history-research-paper/.

Bibliography

IvyPanda . "Ethiopia: Historical Background and Modern Issues." May 16, 2019. https://ivypanda.com/essays/the-ethiopian-history-research-paper/.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center
• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Last »
• Ethiopian Studies Follow Following
• Djibouti Follow Following
• Ethiopian History Follow Following
• Horn of Africa Follow Following
• Eritrea Follow Following
• African Studies Follow Following
• Anthropology of Ethiopia Follow Following
• Ethiopian archaeology Follow Following
• Somalia Follow Following
• Ethiopian Christianity Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Journals
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024