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Univariate and multivariate analysis on processing tomato quality under different mulches Scientia Agricola
Moreno,Carmen; Mancebo,Ignacio; Tarquis,Ana María; Moreno,Marta María.
The use of eco-friendly mulch materials as alternatives to the standard polyethylene (PE) has become increasingly prevalent worldwide. Consequently, a comparison of mulch materials from different origins is necessary to evaluate their feasibility. Several researchers have compared the effects of mulch materials on each crop variable through univariate analysis (ANOVA). However, it is important to focus on the effect of these materials on fruit quality, because this factor decisively influences the acceptance of the final product by consumers and the industrial sector. This study aimed to analyze the information supplied by a randomized complete block experiment combined over two seasons, a principal component analysis (PCA) and a cluster analysis (CA) when...
Tipo: Info:eu-repo/semantics/article
Ano: 2014 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000200004
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Mulch materials in processing tomato: a multivariate approach Scientia Agricola
Moreno,Marta María; Moreno,Carmen; Tarquis,Ana María.
Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L.) crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2), one oxo-biodegradable material (OB), two types of paper (PP1, PP2), and one barley straw cover (BS) were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]). A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied,...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Mulching; Principal component analysis; Cluster analysis; Discriminant analysis.
Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162013000400005
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