Influence of plant traits on production costs and profitability of strawberry in southeast Queensland

Herrington, M. E.; Wegener, Malcolm K.; Hardner, Craig; Woolcock, Louella L.; Dieters, Mark J.

doi:10.1016/j.agsy.2011.11.003

Cited by 10 publications

(12 citation statements)

References 9 publications

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“…It is generally assumed by Florida strawberry growers that greater early yields would result in increased profitability. An economic analysis conducted in Queensland, Australia, a region with similar climate to Florida and using some of the same cultivars, showed that a 10% redistribution of yield from the end to the beginning of the season should result in a gross margin increase of 23% (Herrington et al, 2012). However, it is possible that increased yields in Florida above a certain point may saturate the market and lead to decreased prices.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Genetic Parameters for 12 Fruit and Vegetative Traits in the University of Florida Strawberry Breeding Population

Whitaker¹,

Osorio²,

Hasing³

et al. 2012

J. Amer. Soc. Hort. Sci.

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The University of Florida strawberry (Fragaria ×ananassa) breeding population has been continuously improved by recurrent selection since 1968. However, there is a lack of information on genetic parameters that may inform breeding decisions. Parameters were estimated in this population using 19 full-sib families from a 5 × 4 factorial mating design plus six additional biparental crosses and 14 control genotypes including some of the parents. During the 2010–11 season, clonal replicates of the seedling and parental genotypes were distributed within and among two field locations in west–central Florida. Twelve commercially important traits were measured including fruit chemical traits (soluble solids content and titratable acidity), other fruit and yield traits (early and total marketable yields, proportion of total cull fruit, proportion of misshapen fruit, proportion water-damaged fruit, and shape score), and vegetative traits (plant height and total runners). Heritabilities, genotype by environment interaction, and multiple correlations (phenotypic, genotypic, and genetic) were estimated using general mixed model analyses. Narrow-sense heritabilities varied from low to moderate (h2 = 0.13 ± 0.07 to 0.32 ± 0.09) except for shape score (h2 = 0.06 ± 0.04) and total average weight (h2 = 0.52 ± 0.07). Broad-sense heritabilities were larger (H2 = 0.18 ± 0.03 to 0.53 ± 0.04), and for more than half of the traits, over 50% of the total genetic variation was non-additive. Large genetic and genotypic correlations were found for some traits, most notably between soluble solids content and early marketable yield (–0.68 ± 0.22). Genetic gains for this pair of traits based on a Monte Carlo simulation illustrated the tradeoff between these two traits, showing that a 27% increase in early yield could be obtained through selection but at the expense of an 8% decrease in soluble solids. However, moderate gains can be made in both traits using the appropriate index coefficients.

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Section: Discussionmentioning

confidence: 99%

Estimation of Genetic Parameters for 12 Fruit and Vegetative Traits in the University of Florida Strawberry Breeding Population

Whitaker¹,

Osorio²,

Hasing³

et al. 2012

J. Amer. Soc. Hort. Sci.

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“…Linear mixed models were used to analyze the unbalanced clonal trial data to estimate genetic parameters and predict clonal value as a best linear unbiased prediction using ASReml-W (version 4.1.1051) (Gilmour et al, 2006) combined across the eight yield experiments over the 7 years. Consistent with traits that were identified as highly influencing profitability (Herrington et al, 2012(Herrington et al, , 2014)-namely, early production and larger fruit size-'Red Rhapsody' had yields in May, June, July, and August that were greater than 'Strawberry Festival', and in May and June they were greater than 'Florida Radiance', but in all months they were similar to 'Suncoast Delight'. Total yield of 'Red Rhapsody' was similar to 'Florida Radiance' and 'Suncoast Delight', and greater than total yield of 'Strawberry Festival' (Table 1).…”

Section: Descriptionmentioning

confidence: 65%

‘Red Rhapsody’ Strawberry

Herrington¹,

Neal²,

Woolcock³

et al. 2019

horts

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“…IDENTIFICATION OF SOURCES OF INCOME AND EXPENSE. All sources of income and expense in commercial strawberry production in southeast Queensland were previously identified and a production function and profit equation developed (Herrington et al, 2012). The gross margin (Australian dollars per hectare) of this function was based on a whole of industry production approach (i.e., assuming farms are using similar production systems).…”

Section: Methodsmentioning

confidence: 99%

“…The analyses included the incorporation of full pedigree information, adjustment for trial spatial effects, amalgamation of sampling dates within years and combination of data across years (breeding cycles). The traits chosen included those expected to most strongly influence economic outcomes with an emphasis on cost of production (Herrington et al, 2012). As a result of unavailability of economic data, consumer-oriented traits (e.g., sweetness, internal color, external color, glossiness, and acidity) were not included in these analyses.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, although selection indices based on economic criteria have been developed (Wenzel, 1980), there is no report of an aggregate economic genotype (breeding objective) involving multiple traits in strawberry. Herrington et al (2012) described the contribution of 12 individual plant and fruit traits to farm cost and income under subtropical Queensland conditions with the overall goal of improving the profitability of the industry through the release of new strawberry cultivars. The study involved specifying the production and marketing system in southeast Queensland, developing a spreadsheet to assess the economic impact of changes to the system, and identifying plant traits that influenced outcomes from the system.…”

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confidence: 99%

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Estimating an Aggregate Economic Genotype to Facilitate Breeding and Selection of Strawberry Genotypes in Southeast Queensland

Herrington¹,

Hardner

Wegener³

et al. 2014

J. Amer. Soc. Hort. Sci.

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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 (R2 = 0.77) with the year of selection (1945–98). The gross margins were highly linearly related (R2 > 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 (R2 > 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.

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Influence of plant traits on production costs and profitability of strawberry in southeast Queensland

Cited by 10 publications

References 9 publications

Estimation of Genetic Parameters for 12 Fruit and Vegetative Traits in the University of Florida Strawberry Breeding Population

Estimation of Genetic Parameters for 12 Fruit and Vegetative Traits in the University of Florida Strawberry Breeding Population

‘Red Rhapsody’ Strawberry

Estimating an Aggregate Economic Genotype to Facilitate Breeding and Selection of Strawberry Genotypes in Southeast Queensland

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