In Queensland, Australia, strawberries (Fragaria ×ananassa Duchesne) are grown in open fields and rainfall events can damage fruit. Cultivars that are resistant to rain damage may reduce losses and lower risk for the growers. However, little is known about the genetic control of resistance and in a subtropical climate, unpredictable rainfall events hamper evaluation. Rain damage was evaluated on seedling and clonal trials of one breeding population comprising 645 seedling genotypes and 94 clones and on a second clonal population comprising 46 clones from an earlier crossing to make preliminary estimates of heritability. The incidence of field damage from rainfall and damage after laboratory soaking was evaluated to determine if this soaking method could be used to evaluate resistance to rain damage. Narrow-sense heritability of resistance to rain damage calculated for seedlings was low (0.21 ± 0.15) and not significantly different from zero; however, broad-sense heritability estimates were moderate in both seedlings (0.49 ± 0.16) and clones (0.45 ± 0.08) from the first population and similar in clones (0.56 ± 0.21) from the second population. Immersion of fruit in deionized water produced symptoms consistent with rain damage in the field. Lengthening the duration of soaking of ‘Festival’ fruit in deionized water exponentially increased the proportion of damage to fruit ranging in ripeness from immature to ripe during the first 6-h period of soaking. When eight genotypes were evaluated, the proportion of sound fruit after soaking in deionized water in the laboratory for up to 5 h was linearly related (r 2 = 0.90) to the proportion of sound fruit in the field after 89 mm of rain. The proportion of sound fruit of the breeding genotype ‘2008-208’ and ‘Festival’ under soaking (0.67, 0.60) and field (0.52, 0.43) evaluations, respectively, is about the same and these genotypes may be useful sources of resistance to rain damage.
Mark E. Herrington, Craig Hardner, Malcolm Wegener, Louella L. Woolcock and Mark J. Dieters
Mark E. Herrington, Craig Hardner, Malcolm Wegener, Louella Woolcock and Mark J. Dieters
The Queensland strawberry (Fragaria ×ananassa) breeding program in subtropical Australia aims to improve sustainable profitability for the producer. Selection must account for the relative economic importance of each trait and the genetic architecture underlying these traits in the breeding population. Our study used estimates of the influence of a trait on production costs and profitability to develop a profitability index (PI) and an economic weight (i.e., change in PI for a unit change in level of trait) for each trait. The economic weights were then combined with the breeding values for 12 plant and fruit traits on over 3000 genotypes that were represented in either the current breeding population or as progenitors in the pedigree of these individuals. The resulting linear combination (i.e., sum of economic weight × breeding value for all 12 traits) estimated the overall economic worth of each genotype as H, the aggregate economic genotype. H values were validated by comparisons among commercial cultivars and were also compared with the estimated gross margins. When the H value of ‘Festival’ was set as zero, the H values of genotypes in the pedigree ranged from –0.36 to +0.28. H was highly correlated (R 2 = 0.77) with the year of selection (1945–98). The gross margins were highly linearly related (R 2 > 0.98) to H values when the genotype was planted on less than 50% of available area, but the relationship was non-linear [quadratic with a maximum (R 2 > 0.96)] when the planted area exceeded 50%. Additionally, with H values above zero, the variation in gross margin increased with increasing H values as the percentage of area planted to a genotype increased. High correlations among some traits allowed the omission of any one of three of the 12 traits with little or no effect on ranking (Spearman’s rank correlation 0.98 or greater). Thus, these traits may be dropped from the aggregate economic genotype, leading to either cost reductions in the breeding program or increased selection intensities for the same resources. H was efficient in identifying economically superior genotypes for breeding and deployment, but because of the non-linear relationship with gross margin, calculation of a gross margin for genotypes with high H is also necessary when cultivars are deployed across more than 50% of the available area.