Servicios Personalizados
Revista
Articulo
Indicadores
- Citado por SciELO
- Accesos
Links relacionados
- Similares en SciELO
Compartir
Revista mexicana de ciencias agrícolas
versión impresa ISSN 2007-0934
Resumen
AGUILAR TLATELPA, Martin et al. Concentration and extraction of macronutrients in four strawberry varieties. Rev. Mex. Cienc. Agríc [online]. 2019, vol.10, n.6, pp.1287-1299. Epub 02-Oct-2020. ISSN 2007-0934. https://doi.org/10.29312/remexca.v10i6.1552.
Strawberry (Fragaria x ananassa) nutrition is a fundamental factor in achieving high yields and fruit quality. For this, fertilizer sources, timing and concentrations must be considered. The plant is adapted to subtropical and temperate conditions but is sensitive to ionic fluctuations in the nutrient solution and to the nutrient content in soil and substrate. The analysis of nutrient absorption dynamics is one of the most used strategies to infer the needs at each phenological stage. For this the need arises to formulate an algorithm that allows to know the amount of nutrient that the plant requires in each phenological stage. The objective was to determine, through regression models, the nutritional demand during the crop cycle of the strawberry varieties Albion, Festival, Jacona and Zamorana. The hypothesis was that the concentration and absorption of nutrients is differential in each variety and phenological stage of the plant, in addition the absorption of nutrients can be described by multiple linear regression models. The study was conducted using a completely random sampling for the collection of plant material under field cultivation conditions. Nutrient concentration was determined by chemical analysis. The nutritional extraction was obtained and related to each phenological stage. The reference values for the concentration and nutritional extraction were obtained for N, P, K, Ca, Mg and S, using mathematical models that determine the nutritional needs of the plants at each stage of their development.
Palabras llave : Fragaria x ananassa; nutritional concentration; nutrient extractions; regression models.