José Antonio Ramírez-Peralta

In recent years, Peruvian coffee production has increasingly focused on specialty coffees, with cup quality (CQ) as a key criterion. While previous studies have characterized the Coffee Germplasm Collection of the National Institute of Agrarian Innovation (CGC‐INIA), few studies have analyzed coffee cup quality. The present study assessed the association between agro‐morphological traits and sensory quality of CGC‐INIA. A total of 123 accessions from six departments were evaluated during two crop seasons (2022–2023 and 2023–2024). Fourteen agro‐morphological traits (morphological, reproductive, and phytosanitary) and cup quality were recorded. Mean cup quality of the accessions was 81.56 points, which is classified as “very good” according to the Specialty Coffee Association. Most traits remained stable across seasons, except for fruit production and two phytosanitary traits (leaf miner infestation and rust incidence). Correlation analysis showed weak associations between individual agro‐morphological traits and CQ ( r < 0.3). Principal component analysis explained 28.6% of the total variance in the first two components, indicating limited overall association between agro‐morphological variation and sensory quality. Generalized linear model results showed that Plant height (PH) and young shoot color (YSC) were the only traits positively associated with CQ. However, the weak associations and small effect sizes indicate that PH and YSC should be considered complementary indicators in CQ evaluation, and only within integrated selection frameworks that combine phenotypic, genetic, environmental, and management factors to improve specialty coffee quality.

The present study evaluated the potential use of leaf morphometry for the taxonomic identification of six Podocarpaceae species. We collected botanical samples from 17 forests across five departments in northern, central, and southern Peru (Cajamarca, Pasco, Junín, Apurimac, and Cusco), and we also performed a multivariate comparison of leaf morphometric variables (length, width, thickness, and area). Hierarchical clustering (dendrogram analysis) successfully discriminated the species, grouping localities with the presence of the same species and identifying six clusters. A Principal Component Analysis (PCA) yielded two components (PCs) that explain 96.1% of the variability of the data; PC1 (71%) is associated with leaf length, width, and area, while PC2 is explained by leaf thickness. Podocarpus celatus and Podocarpus magnifolius exhibit wide and long leaves, Prumnopitys montana and Retrophyllum rospigliosii have small and thin leaves, and Podocarpus glomeratus and Podocarpus oleifolius have medium-sized leaves. Finally, we present a brief synopsis of the evaluated species identification keys for the evaluated species, based on leaf morphometry, distribution, conservation status, botanical illustrations, and LCDP plates.