Phenotypic variability of tarwi (Lupinus mutabilis S.) in Peruvian germplasm collections - Ortega Quispe, Kevin Abner (2026)
The growing global loss of genetic diversity, phenotypic characterization becomes essential for identifying resilient varieties capable of diversifying and strengthening the agricultural production of underutilized crops such as tarwi (Lupinus mutabilis S.). This study aimed to characterize the phenotypic variability of 41 tarwi accessions conserved in the germplasm bank of the National Institute of Agricultural Innovation (INIA) of Peru. The accessions were evaluated over two consecutive agricultural seasons at the Santa Ana Agrarian Experimental Station under local conditions. Thirty morphological descriptors (17 qualitative and 13 quantitative) were used following IBPGR guidelines. Data were analyzed using descriptive statistics, principal component analysis, hierarchical clustering and correlation analysis for quantitative descriptors, as well as frequency tables and the Shannon-Weaver diversity index for qualitative descriptors. The results revealed high phenotypic variability, particularly in traits related to yield, plant architecture and floral attributes. The accessions were grouped into three morpho-agronomic types: (1) highly productive accessions, (2) accessions with vigorous vegetative development, and (3) short-cycle plants with moderate yields. Yield per plant was significantly associated with the total pod number, total seed mass in hundred seeds and seed thickness. The study revealed considerable phenotypic diversity, characterized by significant correlations among key agronomic traits, the delineation of three distinct phenotypic clusters, and the identification of valuable qualitative attributes, which reinforces their potential for conservation and breeding programmes. However, expanded germplasm evaluation and multi-environment trials are required to validate genotype stability and refine selection criteria. However, additional accessions and further analyses are needed to validate the observed patterns.