The coffee species Coffea arabica is facing numerous challenges regarding climate change, pests and disease pressure. Improved varieties will be part of the solution. Making optimal use of the scarce genetic diversity of the species is hence essential. In this paper, we present the first study of C. arabica genetic diversity covering its complete native habitat in Ethiopia together with its main domestication centers: Yemen and Hararghe region in Ethiopia. All in all, 555 samples were analyzed with a set of Single Sequence Repeat markers. Through admixture genetic analysis, six clusters were identified. A total of two “Core Ethiopian” clusters did not participate in the domestication of the species. There were four clusters that were part of the “Domestication Pathway” of C. arabica. The first one was named “Ethiopian Legacy” as it represents the genetic link between “Core Ethiopia” and the “Domestication Pathway” in Yemen and Hararghe. The geographic origin of this cluster in Ethiopia was the south of Ethiopia, namely Gedio, Guji and Sidama, which hence appears as the source of coffee seeds that led to the domestication of C. arabica. In Yemen, in addition to the “Ethiopian Legacy” cluster, we confirmed the “Typica/Bourbon” and “New-Yemen” clusters. In Hararghe, the “Harrar” cluster, never described before, likely originates from a re-introduction of domesticated coffee from Yemen into this region of Ethiopia. Cultivated varieties around the world today originate from the “Ethiopian Legacy” and “Typica/Bourbon” clusters and but none are related to the “new-Yemen” and “Harrar” clusters. Implications for breeding strategies are discussed.

While Ethiopia and South Sudan are the native habitats for Coffea arabica, Yemen is considered an important domestication center for this coffee species as most Arabica coffee grown around the world can be traced back to Yemen. Furthermore, climatic conditions in Yemen are hot and extremely dry. As such, Yemeni coffee trees likely have genetic merits with respect to climate resilience. However, until recently, very little was known about the genetic landscape of Yemeni coffee. The Yemeni coffee sector identifies coffee trees according to numerous vernacular names such as Udaini, Tufahi or Dawairi. However, the geographical landscape of these names and their correlation with the genetic background of the coffee trees have never been explored. In this study, we investigated the geographic occurrence of vernacular names in 148 coffee farms across the main coffee areas of Yemen. Then, we used microsatellite markers to genotype 88 coffee trees whose vernacular name was ascertained by farmers. We find a clear geographical pattern for the use of vernacular coffee names. However, the vernacular names showed no significant association with genetics. Our results support the need for a robust description of different coffee types in Yemen based on their genetic background for the benefit of Yemeni farmers.

Post-harvest processing (PHP) is a key determinant of coffee quality, flavor profile, and market classification, yet verifying PHP claims remains a significant challenge in the specialty coffee industry. This study introduces near-infrared spectroscopy (NIRS) coupled with chemometrics as a rapid, non-destructive approach to classify green coffee beans based on PHP. For the first time, seven distinct PHP categories—Alchemy, Anaerobic Processing (Deep Fermentation), Dry-Hulled, Honey, Natural, Washed, and Wet-Hulled—were discriminated using NIRS, encompassing 20 different processing protocols under varying environmental and fermentation conditions. The NIR spectra (350–2500 nm) of 524 green Arabica coffee samples were analyzed using PCA-LDA models (750–2450 nm), achieving classification accuracies up to 100% for underrepresented categories and strong performance (91–95%) for dominant PHP groups in an independent test set. These results demonstrate that NIRS can detect subtle chemical signatures associated with diverse PHP techniques, offering a scalable tool for quality assurance, fraud prevention, and traceability in global coffee supply chains. While limited sample sizes for some PHP categories may influence model generalization, this study lays the foundation for future work involving broader datasets and integration with digital traceability systems. The approach has direct implications for producers, traders, and certifying bodies seeking reliable, real-time PHP verification.