Model of pre-harvest quality of pineapple guava fruits (Acca sellowiana (O. berg) burret) as a function of weather conditions of the crops

• Autor(a) principal: Parra-Coronado,Alfonso
• Data de Publicação: 2017
• Outros Autores: Fischer,Gerhard, Camacho-Tamayo,Jesús Hernán
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Bragantia
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052017000100177
• topic: total soluble solids; total titratable acidity; firmness; growing degree days

ABSTRACT Weather conditions influence the quality parameters of pineapple guava fruit during growth and development. The aim of this study was to propose a model of pre-harvest fruit quality as a function of weather conditions in the cultivation area. Twenty trees were flagged per farm in 2 localities of the Department of Cundinamarca, Colombia: Tenjo (2,580 m.a.s.l.; 12.5 °C; relative humidity between 74 and 86%; mean annual precipitation 765 mm) and San Francisco de Sales (1,800 m.a.s.l.; 20.6 °C; relative humidity between 63 and 97%; mean annual precipitation 1,493 mm). Measurements were performed every 7 days during 2 harvest periods starting on days 96 (Tenjo) and 99 (San Francisco de Sales) after anthesis and until harvest. The models were obtained using Excel® Solver, and a set of data was obtained for the 2 different cultivar periods and each study site. The results showed that altitude, growing degree days, and accumulated precipitation are the weather variables with the highest influence on the physicochemical characteristics of the fruit during growth. The models of fresh weight, total titratable acidity, and skin firmness better predict the development of fruit quality during growth and development. Equations were obtained for increases of length and diameter as a function of fruit weight and for days from anthesis as a function of growing degree days and altitude. The regression analysis parameters showed that the models adequately predicted the fruit characteristics during growth for both localities, and a cross-validation analysis showed a good statistical fit between the estimated and observed values.