Soluble solids content is an important element of strawberry fruit quality. Approximately 80% to 90% of the SSC consists of sugars (Perkins-Veazie, 1995), and high levels have frequently been associated with favorable sensory ratings in taste panel evaluations (Jouquand et al., 2008; Wozniak et al., 1997). Either SSC alone or its value relative to the percentage of titratable acidity in the fruit correlates positively with the level of sweetness perceived by panelists (Sims et al., 1997).
Studies examining the inheritance of SSC in strawberries (Shaw 1988, 1990; Shaw et al., 1987) have estimated varying levels of additive and dominance control and demonstrated some selection response for this trait. Shaw (1988) detected substantial differences in SSC across harvests and also found significant genotype × harvest interactions that accounted for 30% of the phenotypic variance. Jouquand et al. (2008) and Whitaker et al. (2011) reported similar interactions during Florida’s growing season, and they noted that the fruit SSC for some genotypes was more variable than for others.
Differences in strawberry SSC across harvest dates have been previously associated with changes in a number of plant physiological states and environmental conditions. For example, sugar uptake rates have been shown to be higher in primary fruit (Forney and Breen, 1985), which predominate at the beginning of harvest cycles (MacKenzie and Chandler, 2009). In addition, Olsen et al. (1985) detected changes in the source-sink ratio throughout the fruiting period that may also affect SSC. Recently, MacKenzie et al. (2011) implicated increasing temperature as the major cause of declining SSC at the end of the season in Florida. The SSC decline appeared to be independent of changes in yield in this study. Regardless of the underlying causes for SSC variation in strawberry fruit, cultivars that maintain stable SSC throughout the season would be highly desirable as a result of their more uniform eating quality. The typical fruiting season in west–central Florida extends from late November to late March and is characterized by widely varying environmental conditions, making Florida an ideal location to examine SSC stability over time.
A number of procedures have been proposed to analyze the stability of genotypes across different environments. Extensive reviews of these methods have been prepared by Becker and Leon (1988) and Freeman (1973). Most were developed to evaluate agronomic crops that are harvested only once in a season and are assessed for stability (usually yield stability) across multiple environments (unique combinations of location and year). However, by modeling correlations for observations arising from the same plant, the regression approach introduced by Finlay and Wilkinson (1963) can be adapted to within-season stability analyses by considering each harvest as a separate “environment.”
In the regression approach, the performance of individual genotypes is linearly regressed against the population mean for each environment or against the mean of an independent set of genotypes (Freeman and Perkins, 1971) in each environment. These environmental means reflect the combined effects of physiological and environmental conditions that were common to all genotypes in the environments and estimate how favorable conditions were for the trait of interest. Unstable genotypes are sensitive to small changes in field conditions and are associated with steeper slopes. Genotypes with slope estimates close to zero are considered stable according to the static concept in that they do not react as much to changes in field conditions. Genotypes with slopes close to 1.0 (or close to the population’s slope) have average stability across the season, but they may also be considered stable according to the dynamic concept of stability (Becker and Leon, 1988). The regression approach to stability has been successfully applied in a number of crops and traits, including such diverse examples as yield in triticale [× Triticosecale (Goyal et al., 2011)], fruit size in bell pepper [Capsicum annuum (Stoffella et al., 1995)], ripening date of blueberry [Vaccinium sp. (Gupton et al., 1996)], and isoflavone content (Murphy et al., 2009), phosphorus concentration (Maupin et al., 2011), and oleic acid content (Lee et al., 2012) in soybean (Glycine max). A retrospective study in processing tomato (Solanum lycopersicum) used regression to examine soluble solids stability across environments (Ashburner et al., 2003). They determined that soluble solids stability was a better predictor of cultivar adoption than mean soluble solids across the environments.
The objective of this study was to examine the within-season stability of SSC for a representative subset of genotypes in the strawberry breeding program of the University of Florida. By 1) examining the variation for SSC stability in this set of germplasm; 2) exploring the relationship between SSC stability and other traits such as mean SSC and yield; and 3) obtaining a preliminary estimate of the heritability of SSC stability, the potential for measuring and making improvements in this trait and selecting stable individuals can be more accurately assessed. To our knowledge, this study is the first application of the regression approach to examine stability across harvest dates within environments.
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