Estimating the area and weight of cactus forage cladodes using linear dimensions



Título del documento: Estimating the area and weight of cactus forage cladodes using linear dimensions
Revista: Acta scientiarum. Agronomy
Base de datos: PERIÓDICA
Número de sistema: 000459856
ISSN: 1679-9275
Autores: 1
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Instituciones: 1Universidade Federal Rural de Pernambuco, Serra Talhada, Pernambuco. Brasil
Año:
Volumen: 43
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico, descriptivo
Resumen en inglés The forage palm is one of the main forages of ruminants in semiarid regions. Measurements of leaf area are required in agronomic studies because they are one of the main parameters used to evaluate plant growth. The objective of this study was to validate and define the best models for estimating the area and weight of Giant Sweet clone (Nopalea cochenillifera) forage cladodes in a non-destructive way based on the linear dimensions of length, width and thickness. There were 432 randomly measured cladodes at 550 days after planting. The length, width and thickness of each cladode were measured using a digital calliper. The cladodes were weighed individually. The cladode area was calculated by the gravimetric method. The power regression model was the most efficient method to explain the cladode area as a function of the product of length by width, while the gamma model was the most efficient method to explain the weight of cladodes as a function of the product of length by width and thickness. The power model, R C A ̂ = L W 0.982, and gamma model, W C ̂ = 0.536 T + 0.028 L W, were used to determine the area and weight of Nopalea cochenillifera Giant Sweet clone cladodes, respectively, based on the values of linear dimensions measured independently of the order of the cladode
Disciplinas: Agrociencias
Palabras clave: Fitotecnia,
Cactaceae,
Nopalea cochenillifera,
Forraje,
Metodos no destructivos,
Regresión lineal
Keyword: Crop husbandry,
Cactaceae,
Nopalea cochenillifera,
Feedstuff,
Non destructive methods,
Linear regression
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