Phenotypic diversity of native potatoes in indigenous communities of the Pastos ethnic group (Nariño, Colombia): Ecological agriculture for food security and rural development

En este trabajo se describe el modo de conservación in situ del sistema de producción y la diversidad fenotípica de papas nativas en los resguardos indígenas de la etnia de los Pastos. En el estudio se utilizaron dos metodologías: 1) una caracterización de los sistemas tradicionales de producción mediante investigación acción participativa en los resguardos indígenas de Males Córdoba y El Gran Cumbal y 2) una caracterización morfológica de las papas nativas mediante 26 descriptores cualitativos. Se encontró que el 16% de las familias cultivan al menos una variedad de papa nativa, distribuidas en zonas de subpáramo y páramos entre 2900 y 3500 m de altitud, en un agroecosistema de producción autóctono llamado “Shagra”, con manejo tradicional de labranza mínima del suelo “Guachado” y áreas cultivadas inferiores a 600 m2. Se identificaron 38 variedades clasificadas en dos tipos, según los indígenas: chauchas y guatas, que representaron el 65 y el 35% respectivamente. Con el análisis de conglomerados se identificaron siete grupos discriminados por chauchas, guatas, lugar de procedencia y características morfológicas. Estas comunidades indígenas preservan el conocimiento ancestral y los recursos genéticos, cultivando una alta diversidad de papas nativas en asociación con cultivos andinos, lo que contribuye a la seguridad y soberanía alimentaria.This paper describes the in situ conservation mode of the production system and phenotypic diversity of native potatoes in the indigenous reserves of the Los Pastos ethnic group. Two methodologies were used in the study: 1) a characterization of traditional production systems through participatory action research in the indigenous reservations of Males Córdoba and El Gran Cumbal, and 2) a morphological characterization of native potatoes through 26 qualitative descriptors. We found that 16% families cultivate at least one variety of native potato, distributed in sub-páramo and páramo areas between 2,900 and 3,500 m above sea level, in an autochthonous production agroecosystem named “Shagra”, with traditional minimum soil tillage management (“Guachado”) and cultivated areas less than 600 m2. Thirty eight varieties were identified and classified in two types, according to the indigenous people: chauchas and guatas, representing 65 and 35%, respectively. The cluster analysis identified seven groups, discriminated by chauchas, guatas, place of origin, and morphological characteristics. These indigenous communities preserve their traditional knowledge and genetic resources by growing a diversity of native potatoes in association with other Andean crops, which contributes to food security and sovereignty

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization

Memorias del primer Simposio Nacional de Ciencias Agronómicas

Primer simposio nacional de Ciencias Agronómicas: El renacer del espacio de discusión científica para el Agro colombiano

Memorias del primer Simposio Nacional de Ciencias Agronómicas

Primer simposio nacional de Ciencias Agronómicas: El renacer del espacio de discusión científica para el Agro colombiano