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Revista de Ciencias Agrícolas
versión impresa ISSN 0120-0135
Resumen
PANTOJA G, Darío; MUNOZ Z, Keily y CHECA C, Oscar. Evaluation and correlation of yield componentsin advanced lines of pea Pisum sativum with afila gene. Rev. Cienc. Agr. [online]. 2014, vol.31, n.2, pp.24-39. ISSN 0120-0135. https://doi.org/10.22267/rcia.143101.40.
In the department of Nariño, the cultivated varieties of pea Pisum sativum L. are predominantly those with tall plants and abundant branching, which require expensive tutoring systems to prevent tipping and the spread of diseases. Producing changes in the plant’s architecture, using pea varieties with the afil gene, which replaces leaves for tendrils, may help the development of simpler tutoring systems and decrease the use of polypropylene fibers used to tie the plant, reducingcrop production costs. This research was conducted at the Lope farm belonging to the Servicio Nacional de Aprendizaje (SENA) in Pasto-Colombia,with the goal of evaluatinggenotypes of pea P. sativum L. and contributing to the development of new varieties with the afila gene to improve the competitiveness of the legume in the department of Nariño. A randomized complete block design with3 replicas and 22 treatments was conducted during semester A of 2013. The treatments consisted of 20 F5 lines of climbing semileafless peas obtained at the Universidad de Nariño, and two control nonafila varieties (Andina and Sindamanoy). We observed differences between genotypes in terms of the variables: number of grains per pod, green pods weight, weight of green grains per pod, and grain yield. Yield correlated genotypically with the number of grains per pod, green pod weight, and weight of green grains per pod. Path analysis showed that the highest indirect positive contribution wasmade by the weight of the grain per green pod throughthe green pod weight; therefore,these were used as selection criteria that allowed the identification of UDENAR11, UDENAR12, UDENAR14, UDENAR19, and UDENAR20 as promising lines.
Palabras clave : Pea; path analysis; genotypic correlation.