Grouping rice varieties in Morocco based on key agronomic traits: A PCA and HCA approach

Abstract

The identification and classification of rice varieties based on key agronomic traits are essential for enhancing productivity and adaptability in diverse growing environments. This study focused on 18 rice varieties cultivated in Morocco, comprising 14 Korea-Africa Food and Agriculture Cooperation Initiative (KAFACI) lines, three National Institute of Agricultural Research (Fr.: Institut national de la recherche agronomique – INRA) Morocco varieties, and a control cultivar, ‘Lagostino’, widely used by Moroccan farmers. The experiment was conducted at the Sidi Allah Tazi Experimental Domain (Fr.: Domaine Expérimental de Sidi Allal Tazi) using a randomised complete block design (RCBD) with three replications. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were utilised to group varieties based on significant agronomic traits. The analysis revealed four distinct clusters. Cluster 1, including ‘KF190066’, showed high values for plant height, 1000-seed weight, and panicle length. Cluster 2, represented by the control cultivar ‘Lagostino’, was characterised by an earlier heading and fewer panicles per plant. Cluster 3, including ‘KF190064’ and ‘KF190065’, displayed the highest straw and seed yields despite shorter panicles. Cluster 4, comprising ‘Ka WS 9294292’, ‘Nachat’ (INRA Morocco), ‘CB MS11’, and ‘Hayat’ (INRA Morocco), exhibited extended heading and maturity durations, alongside higher tiller and panicle counts per plant. These findings highlight the agronomic diversity potential of rice varieties in Morocco, providing critical insights for breeding programs. The identification of superior varieties, such as ‘KF190064’, ‘Hayat’, and ‘KF190066’, reinforces their potential for boosting rice production and sustainability under Morocco’s agro-climatic conditions.