Commercial red raspberry cultivars suited to machine-harvest and process markets need to have a high yield of good-quality fruit that is easily removed during the harvest operations. In the PNW, this has been achieved using the cultivar Meeker, which was developed in the 1960s (Moore and Daubeny, 1993). This cultivar is only two and three generations removed from wild species representing the native North American red raspberry, Rubus idaeus ssp. strigosus, and European red raspberry, R. idaeus ssp. vulgatus. Since ‘Meeker’ was released, a number of breeding programs have developed new red raspberry types for machine-harvest (Kempler et al., 2006, 2007; Moore and Finn, 2007). Nevertheless, ‘Meeker’ still remains the dominant process red raspberry cultivar grown in the PNW, although ‘Wakefield’, released in 2009, accounted for 19% of new plantings in Washington State in 2011.
One of the major difficulties with breeding new red raspberry cultivars is the time-consuming nature of collecting fruit yield and fruit quality data on individual genotypes from seedling populations. Typically breeders might rely on visual scores for a number of key traits, including yield, which may not be accurate. The lack of objective measurements on seedling populations may be a contributing factor to the low number of successful new commercial machine-harvest cultivars.
Ways to reduce the cost associated with yield measurements in red raspberry have been investigated in New Zealand by Stephens et al. (2009) and in the PNW by Stephens et al. (2012). In Washington State, Stephens et al. (2012) identified two key components of yield (berry weight and lateral length) that, when measured in the first two fruiting years from planting, were able to predict total yield in later years (r = 0.55), thus enabling a breeder to concentrate on the most promising genotypes when harvesting.
In their study, Stephens et al. (2012) used fruit harvested by hand as the measure of total yield. This is time-consuming and not necessarily applicable to breeding programs focused on developing cultivars suited to machine-harvesting. For machine-harvesting, fruit must be firm and coherent, separate from the receptacle easily with little pedicel breakage, and plant laterals must be strong. However, machine-harvesting individual seedlings for yield measurement is not currently practical because the machine has to stop in a clear space after each seedling plant to allow fruit to be cleared from catcher plates and belts before weighing. For large numbers of seedlings that must be harvested many times in one season, this is not feasible. A previous study by Hall et al. (2002) tried to attribute red raspberry machine-harvestability to single traits such as lateral length, receptacle morphology, and fruit firmness. However, no single trait was found to be responsible, indicating that many traits contribute to successful machine-harvest.
Various strategies have been proposed and/or adopted to breed high-yielding cultivars for machine-harvested crops. The modified ear-to-row procedure developed for maize breeding involves selection of best families from bulk harvest of all individuals within a family followed by a recombination event between the best families (growing open-pollinated seed from best families) and selection for best individuals within families (Hallauer et al., 2010). Similarly, among-family selection before within-family selection is commonly used in sugarcane, which is another machine-harvested crop (Bischoff and Gravois, 2004; Kimbeng and Cox, 2003; Stringer et al., 2011). Falconer and Mackay (1996) state that among-family selection is useful for traits with low heritability and previous studies have shown that red raspberry yield has low heritability (Stephens et al., 2009, 2012) and thus maybe suited to this strategy. Furthermore, Kimbeng and Cox (2003) suggest among-family selection is especially suited to mechanical harvesting after the advent of mobile weighing machines.
The benefits of using mixed models and best linear unbiased predictors (BLUP) through the animal model to estimate the additive genetic variance and breeding value of individuals have been outlined by Lynch and Walsh (1998) and Piepho et al. (2008). For BLUP estimations of breeding value, one of the key advantages over traditional linear models (that derive the general combining ability of an individual) is the use of the relationship matrix, which adjusts the BLUP value based on performance of an individual’s relatives. The BLUP breeding value estimate derived in this fashion effectively incorporates among- and within-family genetic variance, which allows among- and within-family selection for an individual simultaneously.
This study examined an alternative strategy for developing machine-harvested red raspberries. A machine was used to bulk harvest all seedlings within a full-sib family plot to get bulk (combined) yield and then the yield data were apportioned to individual seedlings within the full-sib plot by using key yield component data developed by Stephens et al. (2012). The strategy was used to eliminate the need for costly and time-consuming hand-harvest operations and would allow measurements to be carried out on large seedling populations. A combined breeding strategy could be applied whereby the machine-harvest yield was used to select better families and yield components used to select the best individuals within the better families. This strategy allows for selection of parents with improved combining ability for machine-harvest and also for selection for machine-harvest yield early in the development of new cultivars.
In the red raspberry pairwise genetic study described by Stephens et al. (2012), two plants within each plot of six full-sib seedlings were hand-harvested, whereas the remaining four were machine-harvested to give a bulk machine-harvest, whereas yield components were measured on all plants. Using these data, we determined if yield components could be used to apportion our bulk machine-harvest yield to individuals within a family, thus allowing selection among and within families based on an estimate of machine-harvest yield. We further investigated the implications such an approach would have on genetic gain per generation for a fixed cost.
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