Strawberry (Fragaria ×ananassa) cultivation is a global enterprise with nearly every country with temperate or subtropical climates having some level of production (Chandler et al., 2012). The expansive market and cultivation range results in numerous public and private breeding efforts for specifically targeted regional adaptabilities. Breeders have begun focusing their efforts over the previous decades on pest resistance and a wide range of fruit quality attributes (Chandler et al., 2012; Faedi et al., 2002; Yue et al., 2012, 2014). Fortunately, breeders have a wide array of germplasm from ≈20 species around the globe, in addition to various ploidy levels (diploid, tetraploid, hexaploid, and octoploid) in these species to achieve specified regional resistance (Hancock et al., 1996, 2008).
Strawberry has been bred since the 1800s using controlled, biparental crosses to produce progeny that undergo evaluation and selection based on fruit and plant characteristics. In 2010, the relatively small diploid genome of woodland strawberry [Fragaria vesca (2n = 2x = 14)] was sequenced, which is homologous to subgenomes of the allooctoploid genome of cultivated strawberry (2n = 8x = 56) (Shulaev et al., 2011; Whitaker, 2011). This homology enables the development of DNA markers or specific pieces of DNA located near genes controlling traits of interest. Associations between DNA markers and variation of traits in a population for a trait of interest are analyzed using statistical methods (Miles and Wayne, 2008), and an identified genomic location associated with variation for the trait is called a quantitative trait locus (QTL) (Frey et al., 2004). These advancements in genetic understanding have led to the development of a new method of plant breeding. Breeders can use DNA marker tests to identify individual plants (hereafter referred to as individuals) possessing the desired QTL, enabling faster breeding decisions earlier in a program.
One method of DNA-informed breeding involves examination of DNA of seedlings, and it is referred to as marker-assisted seedling selection (MASS). Another method involves examination of parent material before crosses occur and is commonly referred to as marker-assisted parental selection (MAPS). These are two types of marker-assisted selection (MAS) useful for their potential to improve rate of selection and quality in a breeding program (Collard and Mackill, 2008). Studies have explored the use of MAS in field crops, such as maize [Zea mays (Johnson, 2003; Knapp, 1998; Stromberg et al., 1994)], potato [Solanum tuberosum (Slater et al., 2013)], soybean [Glycine max (Hoeck et al., 2003)], and wheat [Triticum aestivum (Kuchel et al., 2005)], and a subset have examined economic impacts associated with this technology. Alpuerto et al. (2009) showed that MAS incorporation in rice (Oryza satvia) breeding resulted in additional costs compared with conventional breeding but reduced the time to cultivar release. Quicker cultivar release resulted in increased economic gains by balancing higher MAS-related costs with reduced length of costs occurring, ultimately resulting in cost-effective MAS. Consistent with these findings, Dreher et al. (2003) and Morris et al. (2003) compared MAS and conventional breeding methods in agronomic crops and showed that incorporation of MAS in breeding schemes reduces time or cost requirements.
Without knowledge of how MAS could impact a breeding program economically, breeders may be less likely to adopt this technology; and to our knowledge, limited studies have investigated economic impacts of MAS in horticultural breeding programs (Ru et al., 2015). One study concluded that the probability of more cost-effective MAS is impacted by conditions such as the inheritance of the trait, the timing of trait expression, MAS application timing, conventional screening costs, robust marker-trait associations, and the probability of selecting superior individuals using MAS (Luby and Shaw, 2001). A second study by Edge-Garza et al. (2015) used these conditions to generate the MASS Efficiency Calculator v. 1.0 using apple (Malus ×domestica), grape (Vitis vinifera), and strawberry as model crops. They concluded that the stage in the breeding program in which MASS was employed was less important in predicting cost-effectiveness than both removal rates’ reduction due to MASS and conventional seedling removals. They further determined that application of MASS as early as possible was not necessary if MASS occurred before additional labor costs are incurred from seedling handling [e.g., planting in fields with annual maintenance (Edge-Garza et al., 2015)].
In this study, we incorporate the dynamic nature of perennial plant breeding into a decision support tool to provide an overview of a day-neutral strawberry breeding program. Breeding programs differ in procedures, costs, or both, which results in every program having unique cost structures. This makes the creation of adaptable spreadsheet-based calculators challenging and often requires new development of specialized budgets, which may prevent its use by breeders (Ru et al., 2015). Overcoming this challenge requires detailed inputs from breeders, breeding program records, and a flexible decision support tool (Wannemuehler et al., 2019). The decision support tool created by Wannemuehler et al. (2019) should be capable of accommodating a breeding program’s unique objectives, thus enabling breeders to make breeding program cost estimations for different scenarios.
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