A method for breeding new cultivars of machine-harvested raspberries with high yield

The Pacific northwestern (PNW) region of the United States is well known for production of machine-harvested red raspberries (Rubus idaeus) for process markets. The cultivar Meeker, developed in the 1960s, is well suited to this area and for machine-harvesting, but it is susceptible to raspberry bushy dwarf virus and root rot caused by Phytophthora rubi. Despite the efforts of several breeding programs, ‘Meeker’ is still the predominant cultivar for commercial production in the PNW. One of the major difficulties with breeding new berry fruit cultivars is the time consuming nature of collecting fruit yield and quality data on large seedling populations. For fruit yield, visual scoring assessment methods are commonly used for seedling populations, but these may be poor predictors of yield. Consequently, visual scores for yield can result in less genetic improvement and thus can adversely affect successful cultivar development. Total yield measured by hand-harvesting is labor-intensive and does not assess machine-harvestability, but machine-harvesting is not practical to measure on individual plants. In this study we set out to see if we could bulk machine-harvest full-sib family plots for among-family selection and use yield component data on individuals within the plots for within-family selection. Using best linear unbiased predictors, we estimated machine-harvest yield breeding values for our individual seedlings and found higher genetic gain per generation using estimated individual machine-harvest breeding values (7.6%) than using hand-harvested breeding values (6.5%). Implications for breeding machine-harvest red raspberries are discussed

Genetic parameters and breeding for yield in red raspberry

For most small fruit-breeding programs, high yield is a key objective and breeders face a number of challenges breeding for high yield, including interaction of environmental influences and the high cost of yield measurements. Red raspberry (Rubus idaeus) yield is determined by a number of yield components (YC), including cane number, cane length, number of fruiting laterals, fruit numbers, and fruit size. The ultimate goal for breeders would be to be able to select for high-yield genotypes using key YC as early in the life of the plant as possible. In this study we set out to determine how individual components of yield are inherited, determine which components contribute the most to total yield, and investigate whether it is possible using key components to make selections for high-yielding genotypes on 1- and 2-year-old plants. We estimated variance components, heritabilities, phenotypic and genotypic correlations, and breeding values for yield and YC from 1008 genotypes based on 85 families derived from 45 parents harvested over three seasons in Washington state. Narrow-sense heritability estimates varied from moderately low [0.2 for number of canes (NCAN)] to moderately high [0.69 for berry weight (BWT)]. In general, all YCs were positively correlated with total yield (TYLD). The highest genetic correlation with TYLD was found for BWT (0.8), followed by cane length (CLEN) (0.54) and number of fruit per lateral (NFRT) (0.5). NCAN had the lowest genetic correlation with TYLD (–0.03). Genotype x year (GxY) interaction was higher for some YCs than others. Berry weight, lateral length (LLEN), and NFRT were found to be the most stable overall seasons and the interaction was higher between the first and second years than between the second and third years of the study. To determine the most important YC, we calculated the correlations between the product of all combinations of subsets of the YC breeding values and TYLD. Berry weight, CLEN, and cane diameter (CDIA) were found to be the most important for 2009, 2010, and 2011, respectively. The two most important YCs were LLEN and BWT and this was consistent overall seasons. We demonstrate that it is possible to select high-yielding genotypes by measuring key components such as LLEN, CLEN, and BWT in the first and second fruiting seasons

Genetic parameters and development of a selection index for breeding red raspberries for processing

New commercial red raspberry (Rubus idaeus) cultivars suited to machine-harvesting and processing markets need to be high-yielding and have good fruit quality attributes, including fruit firmness, sugar content, acidity, flavor, and health properties. Combining many traits in one genotype is a challenge for breeders, especially for traits negatively correlated with yield. Despite its potential, the use of multiple-trait selection through selection indices has had limited application in fruit breeding. In this study, we estimated variance components, heritabilities, phenotypic and genetic correlations and breeding values for total yield (TYLD), harvest span, mid-harvest day and fruit quality traits, firmness (FIRM), soluble solids (SS), acidity (ACID), total anthocyanins (TACY), and total ellagitannins (TELG) from 1008 seedling genotypes based on 85 families derived from 45 parents harvested over three seasons in Washington state. Narrow-sense heritability estimates ranged from moderately low (0.22 for TYLD) to moderately high (0.73 for SS). All traits measured had positive genetic correlations with TYLD except for ACID (–0.35) and TACY (–0.28). Genotype x year (GxY) interaction was high for TYLD and low for fruit quality attributes FIRM, SS, ACID, TACY, and TELG, and interactions were higher between the first (2009) and second (2010) seasons than between the second (2010) and third (2011) seasons. Using economic weights and breeding values derived from multivariate analysis for TYLD, FIRM, SS, and TACY, we constructed a selection index designed to assist with multiple-trait selection for population improvement and the development of commercial raspberry cultivars

Equivalence of Electron-Vibration Interaction and Charge-Induced Force Variations: A New O(1) Approach to an Old Problem

Calculating electron-vibration (vibronic) interaction constants is computationally expensive. For molecules containing N nuclei it involves solving the Schrödinger equation for Ο(3N) nuclear configurations in addition to the cost of determining the vibrational modes. We show that quantum vibronic interactions are proportional to the classical atomic forces induced when the total charge of the system is varied. This enables the calculation of vibronic interaction constants from O(1) solutions of the Schrödinger equation. We demonstrate that the O(1) approach produces numerically accurate results by calculating the vibronic interaction constants for several molecules. We investigate the role of molecular vibrations in the Mott transition in κ-(BEDT-TTF)2Cu[N(CN)2]Br

Network Decontamination

The Network Decontamination problem consists in coordinating a team of mobile agents in order to clean a contaminated network. The problem is actually equivalent to tracking and capturing an invisible and arbitrarily fast fugitive. This problem has natural applications in network security in computer science or in robotics for search or pursuit-evasion missions. In this Chapter, we focus on networks modeled by graphs. Many different objectives have been studied in this context, the main one being the minimization of the number of mobile agents necessary to clean a contaminated network. Another important aspect is that this optimization problem has a deep graph-theoretical interpretation. Network decontamination and, more precisely, graph searching models, provide nice algorithmic interpretations of fundamental concepts in the Graph Minors theory by Robertson and Seymour. For all these reasons, graph searching variants have been widely studied since their introduction by Breish (1967) and mathematical formaliza-tions by Parsons (1978) and Petrov (1982). This chapter consists of an overview of algorithmic results on graph de-contamination and graph searching