Growth Curve Models and Clustering Techniques for Studying New Sugarcane Hybrids

Sugarcane (Saccharum spp.) is a crop of significant industrial and nutritional value, essential for producing various products. Due to its importance, genetic improvement programs involve a rigorous selection process. In this study, growth curve models were used to analyze the maturity curves of 33 hybrids (currently in the adaptability testing phase) and 6 control varieties (MEX 69-290, ITV 92-1424, CP 72-2086, COLMEX 94-8, COLMEX 95-27, RB 85-5113) during both plant and ratoon periods at the Melchor Ocampo Sugar Mill fields in Jalisco, México. With the use of clustering techniques, the materials were classified into four maturity groups: early, early–intermediate, intermediate–late, and late. Hybrids with a larger intercept and smaller slope were classified as having early and early–intermediate maturity. Conversely, hybrids with a smaller intercept and larger slope were classified as having intermediate–late and late maturity. According to the Connectivity and Dunn indexes, the DBSCAN algorithm provides the best clustering structure for materials in the plant cycle, while for the ratoon cycle, the k-means algorithm offers the best clustering structure. This highlights the versatility of each algorithm in the context of hybrid and varietal maturity analysis. These results are crucial for optimizing the productivity and sustainability of the crop, with significant implications for the sugar industry.