Peach (Prunus persica) cultivation began in China as early as 1100 bce (Bassi and Monet, 2008). Yet modern breeders continue striving for peach cultivars with improved disease resistance, environmental adaptability, and fruit quality (Gallardo et al., 2015; Janick and Moore, 1996; Yue et al., 2012, 2014). Peach production is plagued by susceptibility to devastating diseases such as sharka (Plum pox virus), leaf curl (Taphrina deformans), and Xanthomonas species (Hancock et al., 2008). Additionally, changing climates create adverse growing environments with unstable temperatures for some peach breeders, forcing them to modify chilling requirements for better fruit production. Complex traits such as disease resistance or environmental adaptability are difficult to measure in the field using conventional breeding methods. However, breeding methods combining conventional practices with DNA marker–assisted technologies provide greater opportunities for improvement of these complex traits (Martínez-García et al., 2013). Breeders, including peach breeders, are seeking ways to increase the cost-effectiveness of their programs. Each breeding program is different, and thus the cost-effectiveness of DNA-informed breeding in apple (Malus ×domestica) and other crops does not mean it is cost-effective in peach. It is therefore worthwhile to explore whether it is cost-effective to apply in a peach breeding program. RosBREED included both large-scale breeding programs that already apply DNA-informed breeding and relatively small-scale breeding programs that are exploring whether it is worthwhile to apply DNA-informed breeding. In this study, we use a decision support tool to examine the cost-effectiveness of implementing DNA-informed breeding in a small-scale peach breeding program exploring use of MAS.
DNA marker–assisted breeding consists of identifying a DNA marker, which is a fragment of DNA closely associated with the presence or absence of a target trait (Frey et al., 2004; Johnson, 2003). The association between the marker and the trait allows breeders to screen a population by testing for the presence or absence of the marker and select among offspring for individuals with the desired trait. This technique has been largely used in agronomic crops such as rice (Oryza satvia) and maize (Zea mays) (Alpuerto et al., 2009; Hoeck et al., 2003). In rice breeding, MAS has been used to increase crop yield (Hoeck et al., 2003), salinity tolerance (Alpuerto et al., 2009), and pyramid disease resistance [i.e., developing seedlings with multiple genetic regions associated with resistance to a disease (Suh et al., 2013)]. In peach, DNA markers have been associated with traits such as fruit quality, disease resistance, maturity date, and postharvest quality (Abbott et al., 2002; Hancock et al., 2008).
However, many traits are not controlled by a single large-effect gene (qualitative) but rather by multiple genes (quantitative) influencing the trait to varying degrees (Tartarini and Sansavini, 2002). Qualitative traits are more easily selected in conventional breeding with controlled crosses, but conventional breeding lacks screening methods to effectively select for quantitative traits (Lande and Thompson, 1990). For example, in disease-resistant plants, more than one resistance gene can elicit similar symptoms or responses and may be indistinguishable in the field by observation. Variations between resistance genes can be observed on the genetic level, allowing markers to select for multiple sources of resistance in a single plant, increasing the effectiveness of a breeding program (Chandler et al., 2012; Dreher et al., 2003; Whitaker, 2011).
Studies comparing MAS and conventional breeding methods in agronomic crops, such as maize and rice, show that incorporation of MAS into breeding schemes may reduce time requirements, costs, or both depending on implementation strategy (Dreher et al., 2003; Morris et al., 2003). A study by Slater et al. (2013) suggests a relationship between MAS cost-effectiveness and MAS application timing. They identified a cost-effective MAS breeding scheme in which MAS was used in the field before conventional disease resistance trials (Slater et al., 2013). These studies show that cost-effective incorporation of MAS in a breeding program depends on the conventional methods’ costs relative to MAS costs, timing of marker application, and the efficiency of markers to identify elite seedlings. Most studies describing methods of cost-effective MAS focus on agronomic crops such as maize (Dreher et al., 2003, Morris et al., 2003; Stromberg et al., 1994), rice (Suh et al., 2013), and wheat [Triticum aestivum (Kuchel et al., 2005)]. Interest and incorporation of MAS in perennial crop breeding programs is growing due to prolonged maintenance costs as plants mature and the ability of MAS to remove inferior seedlings earlier in the program potentially reducing these costs (Collard and Mackill, 2008).
In perennial crops, the value of MAS is promising because DNA from young plants can be used to screen for the presence of traits expressed in mature plants. With conventional breeding methods, evaluation of traits related to fruit quality are delayed until after completion of the juvenile phase, which increases costs due to maintenance of inferior seedlings in addition to superior ones. Using marker-assisted seedling selection (MASS), a form of MAS, allows breeders to remove inferior seedlings earlier in their program before costs for seedling maintenance become significant (Collard and Mackill, 2008). Few studies have described MAS as a breeding tool and examined the costs associated with MAS incorporation for rosaceous crops [Rosaceae (Edge-Garza et al., 2009; Ru et al., 2015, 2016; Tartarini and Sansavini, 2002; Testolin, 2002)]. Luby and Shaw (2001) concluded that if conditions related to trait inheritance, trait expression timing, and testing costs were met, then MAS had a higher probability of improving the efficiency of selection. Meeting each of these requirements ensures that the use of MAS is more cost-effective than conventional plant breeding, but not every condition is necessary to achieve cost-effectiveness.
A study by Edge-Garza et al. (2015) used trait inheritance, trait expression timing, and testing costs conditions to generate a decision support tool for MASS using apple, grape (Vitis vinifera), and strawberry (Fragaria ×ananassa) as case study crops. This study determined that application of MASS as early as possible was not necessary as long as MASS occurred before significant labor costs are incurred from seedling handling (e.g., planting in orchards and maintenance).
Using the decision support tool they developed, Wannemuehler et al. (2019) modeled a large-scale apple breeding program (here defined as >4000 seedlings per year) and showed that it is cost-effective to apply MAS in the program compared with conventional breeding. The tool can be adjusted to accommodate the unique characteristics of different breeding programs with varying breeding capacities. It provides an overview of a program’s entire breeding process by taking into consideration the dynamic nature of breeding over multiple years and each program’s unique procedures and costs. These characteristics of breeding programs are challenges to the development of a decision support tool capable of modeling multiple breeding programs. However, these challenges are overcome by incorporating detailed information from breeders and breeding program records, allowing the tool to capture the variability among programs. This decision support tool provides breeders with a tailored decision-aid tool to model and estimate comparative costs quickly for their breeding methods at any program stage.
However, whether the application of MAS is cost-effective for relatively smaller-scale rosaceous breeding programs remains unknown. Conducting a cost-effectiveness analysis of DNA markers use requires a program to maintain detailed costs for breeding practices, which is lacking for many breeding programs. Collecting and analyzing the cost data requires professionals who have interdisciplinary knowledge: researchers who understand the detailed peach breeding process and the economic tools to analyze the data. The U.S. Department of Agriculture–funded RosBREED project brought researchers from different disciplines to work as a team, which overcomes the boundaries between different disciplines and makes this work achievable. We use the decision support tool developed by Wannemuehler et al. (2019) to assess the cost-effectiveness of a small-scale (defined here as generating <2000 seedlings per year) peach breeding program’s adoption of MAS and explore the versatility of the decision support tool.
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