M arker-assisted selection for quantitative traits has traditionally relied on fi rst identifying markers linked to quantitative trait loci (QTL). A specifi c form of marker-assisted selection in maize (Zea mays L.) is marker-assisted recurrent selection (MARS) in which (i) one generation of phenotypic selection in the target environment is conducted, (ii) markers with signifi cant eff ects are used to predict the performance of individual plants, and (iii) several generations of marker-only selection are performed in a year-round nursery or greenhouse. Empirical results from private breeding programs have shown MARS to be eff ective at improving quantitative traits in maize, soybean [Glycine max (L.) Merr.], and sunfl ower (Helianthus annuus L.) ( Johnson, 2004;Eathington et al., 2007). Specifi cally, gains from selection in 248 maize breeding populations were more than twice as large with MARS than with standard phenotypic selection methods (Eathington et al., 2007).In contrast to previous MARS or other QTL-based selection strategies, genomewide selection (GWS, also called genomic selection; Meuwissen et al., 2001) does not involve tests of signifi cance and uses all available markers to predict performance. Simulation ABSTRACT Genomewide selection (GWS) is markerassisted selection without identifying markers with signifi cant effects. Our previous work with the intermated B73 × Mo17 maize (Zea mays L.) population revealed signifi cant variation for grain yield and stover-quality traits important for cellulosic ethanol production. Our objectives were to determine (i) if realized gains from selection are larger with GWS than with markerassisted recurrent selection (MARS), which involves selection for markers with signifi cant effects; and (ii) how multiple traits respond to multiple cycles of GWS and MARS. In 2007, testcrosses of 223 recombinant inbreds developed from B73 × Mo17 (Cycle 0) were evaluated at four Minnesota locations and genotyped with 287 single nucleotide polymorphism markers. Individuals with the best performance for a Stover Index and a Yield + Stover Index were intermated to form Cycle 1. Both GWS and MARS were then conducted until Cycle 3. Multilocation trials in 2010 indicated that gains for the Stover Index and Yield + Stover Index were 14 to 50% larger (signifi cant at P = 0.05) with GWS than with MARS. Gains in individual traits were mostly nonsignifi cant. Inbreeding coeffi cients ranged from 0.28 to 0.38 by Cycle 3 of GWS and MARS. For stover-quality traits, correlations between wet chemistry and near-infrared refl ectance spectroscopy predictions decreased after selection. We believe this is the fi rst published report of a GWS experiment in crops, and our results indicate that using all available markers for predicting genotypic value leads to greater gain than using a subset of markers with significant effects.

Maize (Zea mays L.) breeders evaluate many single-cross hybrids each year in multiple environments. Our objective was to determine the usefulness of genomewide predictions, based on marker effects from maize single-cross data, for identifying the best untested single crosses and the best inbreds within a biparental cross. We considered 479 experimental maize single crosses between 59 Iowa Stiff Stalk Synthetic (BSSS) inbreds and 44 non-BSSS inbreds. The single crosses were evaluated in multilocation experiments from 2001 to 2009 and the BSSS and non-BSSS inbreds had genotypic data for 669 single nucleotide polymorphism (SNP) markers. Single-cross performance was predicted by a previous best linear unbiased prediction (BLUP) approach that utilized marker-based relatedness and information on relatives, and from genomewide marker effects calculated by ridge-regression BLUP (RR-BLUP). With BLUP, the mean prediction accuracy (r(MG)) of single-cross performance was 0.87 for grain yield, 0.90 for grain moisture, 0.69 for stalk lodging, and 0.84 for root lodging. The BLUP and RR-BLUP models did not lead to r(MG) values that differed significantly. We then used the RR-BLUP model, developed from single-cross data, to predict the performance of testcrosses within 14 biparental populations. The r(MG) values within each testcross population were generally low and were often negative. These results were obtained despite the above-average level of linkage disequilibrium, i.e., r(2) between adjacent markers of 0.35 in the BSSS inbreds and 0.26 in the non-BSSS inbreds. Overall, our results suggested that genomewide marker effects estimated from maize single crosses are not advantageous (cofmpared with BLUP) for predicting single-cross performance and have erratic usefulness for predicting testcross performance within a biparental cross.

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Spun polyester row covers, alone and in combination with insecticides, were evaluated for management of bacterial wilt of muskmelon at three locations in Iowa during 2003 and 2004. Following removal of row covers at bloom, more spotted cucumber beetles were counted in the plots with row covers than in the non-covered plots. Row covers delayed the onset of bacterial wilt symptoms and reduced bacterial wilt incidence. In both years, row covers increased both number and yield of marketable melons. There were no significant differences in beetle counts or melon yield among insecticide treatments in 2003. In 2004, however, each of the insecticide treatments had more marketable melons than the non-treated control. Row covers may enable cucurbit growers to reduce reliance on insecticides for managing bacterial wilt. Accepted for publication 31 July 2006. Published 20 October 2006.