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The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive-and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree-or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.

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High rates of N loss have been observed from N fertilizers applied directly on the surface in no‐till corn (Zea mays L.) production systems. Field experiments were conducted at four locations over a three‐year period to determine what effects N source and N placement had on N losses in both no‐till and conventional till corn production systems. Soils used were: Stoy loam, an Aquic Hapludalf; Clermont silt loam, a Typic Ochraqualf; Avonberg silt loam, an Aeric Fragiaqualf; Chalmers silty clay loam, a Typic Argiaquoll; and Lyles fine sandy loam, a Typic Haplaquoll. Nitrogen sources used were anhydrous ammonia (NH3), urea‐ammonium nitrate solutions (UAN), solid urea and solid ammonium nitrate (NH4NO3). Placement variables used were injection of NH3 and UAN 20 cm below the soil surface and broadcasting UAN, urea and NH4NO3 on the soil surface with no incorporation. Nitrogen rates used were 0 and 165 kg N/ha. Injecting NH3, or UAN below the surface resulted in consistently higher corn grain yields than applying UAN, NH4NO3 or urea directly on the soil‐residue surface. Percent N in leaf and grain also reflected an increase in N use efficiency with subsurface N placement. Percent N in leaf was significantly higher where NH3 or UAN were injected as compared to UAN or urea surface applied.

Genetic resistance to disease incited by necrotrophic pathogens is not well understood in plants.Whereas resistance is often quantitative, there is limited information on the genes that underpin quantitative variation in disease resistance. We used a population genomic approach to identify genes in loblolly pine (Pinus taeda) that are associated with resistance to pitch canker, a disease incited by the necrotrophic pathogen Fusarium circinatum. A set of 498 largely unrelated, clonally propagated genotypes were inoculated with F. circinatum microconidia and lesion length, a measure of disease resistance, data were collected 4, 8, and 12 weeks after inoculation. Best linear unbiased prediction was used to adjust for imbalance in number of observations and to identify highly susceptible and highly resistant genotypes (''tails''). The tails were reinoculated to validate the results of the full population screen. Significant associations were detected in 10 single nucleotide polymorphisms (SNPs) (out of 3938 tested). As hypothesized for genes involved in quantitative resistance, the 10 SNPs had small effects and proposed roles in basal resistance, direct defense, and signal transduction. We also discovered associated genes with unknown function, which would have remained undetected in a candidate gene approach constrained by annotation for disease resistance or stress response.