, P level, and cultivar, we used a stratified sampling approach wherein 15 to 20 storage roots were randomly sampled from each treatment combination (planting date × P level × cultivar). 3D image acquisition. The major components of the 3D image
Arthur Villordon, Jeffrey C. Gregorie, and Don LaBonte
Khalid Ibrahim and John Juvik
Vegetables are a rich source of dietary carotenoids and tocopherols, powerful antioxidants that have the capacity to protect cells against oxidative damage caused by free radical reactions. There is evidence for a negative correlation between the incidence of certain types of cancer, age-related macular degeneration, cataract development, and cardiovascular disease with increased carotenoid and tocopherol intake. Development of elite vegetable germplasm with enhanced levels of these phytochemicals will potentially promote health among the consuming public. To assess the feasibility for genetic improvement in phytochemical content, it is necessary to partition the phenotypic variability into its component sources (genotype, environment, and genotype by environment interaction). To provide data for comparison and partition of phenotypic variation, 41 sweet corn and 13 broccoli genotypes were grown and harvested in one location for 3 years and analyzed for phytochemical content by HPLC. The most abundant form of carotenoids and tocopherols were lutein and gamma-tocopherol in sweet corn and beta-carotene and alpha-tocopherol in broccoli. Analysis of variance showed that, in sweet corn, the differences among genotypes described most of the phenotypic variation (76% for lutein, and 78% for gamma-tocopherol). Genotype by year interaction was a second significant factor, while variation affiliated with the year was found to be a minor component. In contrast, in broccoli, the three sources of variability contributed equally to describe the total phenotypic variation for beta-carotene and alpha-tocopherol. These results suggest that elite sweet corn and broccoli germplasm with improved carotenoid and tocopherol levels can be developed using conventional breeding protocols.
Julie Villand, James Nienhuis, Paul Skroch, and Jan Tivang
Precise cultivar descriptions are necessary to support Plant Variety Protection and utility applications for patent protection. However, accurate discrimination among cultivars is contingent upon the dependability of the method used to delineate lines. The efficiency and reliability of Amplified Fragment Length Polymorphisms (AFLPs), Random Amplified Polymorphic DNAs (RAPDs), microsatellite polymorphisms, and phenotypic traits were studied in order to determine a method's ability to accurately predict pedigree relationships among a set of 20 California processing tomato cultivars. All molecular marker and phenotypic trait data sets were independently produced using identical cultivar seed sources. Data was reduced to a genetic distance measure and presented as a multidimensional scaling (MDS) plot. Principal component analysis using the scored quantitative phenotypic traits was computed and is compared to molecular marker data results. Experimental error, sampling variance, and independence of scored bands for each molecular marker technique are presented. These estimates should assist breeders to determine a sufficient level of characterization, determine a minimum distance considered to be unique, and defend pedigree relationships.
Ji Yeon Kang, Khalid E. Ibrahim, John A. Juvik, Doo Hwan Kim, and Wha Jeung Kang
Strong evidence exists to suggest that increased consumption of glucosinolates from Brassica vegetables is associated with reduced risk of cancer induction and development. Development of elite germplasm of these vegetables with enhanced levels of glucosinolates will putatively enhance health promotion among the consuming public. To evaluate levels of glucosinolate phenotypic variation in Chinese cabbage tissue and partition the total phenotypic variation into component sources (genotype, environment, and genotype-by-environment interaction), a set of 23 Brassica rapa L. var. pekinensis genotypes were grown in two different environments (field plots and greenhouse ground beds). Gluconasturtiin and glucobrassicin were found to account for ≈80% of total head glucosinolate content. Significant differences were found in glucosinolate concentrations between the lowest and highest genotypes for glucobrassicin (6-fold) and for gluconasturtiin (2.5-fold). Analysis of variance showed that for the three major glucosinolates (gluconasturtiin, glucobrassicin, and progoitrin), the genotypic effects described most of the phenotypic variation (62% averaged over the three compounds). The next most important factor was genotype × environment interaction (29%), whereas variation affiliated with the environment was found to be relatively minor (8%). These results suggest that genetic manipulation and selection can be conducted to increase glucosinolate content and the putative health promotion associated with consumption of Chinese cabbage.
William Terry Kelley
Statistical analysis of agricultural research has traditionally been via the use of fixed model methods. However, recent advances in statistical software have made analysts through random or mixed model methods more practical. Errant or inappropriate use of statistical programs to analyze data has been a recurring problem in the reporting of agricultural research findings. Often variables are all considered to be fixed in order to facilitate analysis, when in reality some variables in field research are nearly always random. Proper selection of error terms and calculation of standard errors are also frequently done incorrectly when statistical analysts packages are not used correctly. Unbalanced data is also quite normal in field research due to unforseen circumstances that result in lost information. Most of these situations can be more early handled with a mixed model approach. In this work, a broccoli field trial involving tillage and planting dates was analyzed using the General Smear Models procedure in SAS and the General Elmer Mixed Models Procedure in GLMM. Comparison of the analyses revealed that conclusions would differ somewhat with balanced data and even more with unbalanced data. Since variance components from all random effects are used to calculate standard errors in GLMM, standard errors in the mixed model were larger, but likely more accurate Inference space was also broader and allowed prediction space to include the entire population of experimental units which were sampled in the experiment. The mixed model procedure was more efficient and thus more sensitive to differences in treatments.
Erik J. Sacks and David M. Francis
The genetic and environmental variation for flesh color of tomato (Lycopersicon esculentum Mill.) fruit was quantified using 41 red-fruited breeding lines, open-pollinated cultivars, and hybrids that are representative of the diversity of tomatoes grown for whole-peel processing in the midwestern and eastern United States and Ontario, Canada. Objective color measurements were made for 2 years from replicated experiments with 2 to 4 blocks per year. Genotypes differed significantly in lightness value (L*), saturation (chroma), and hue angle. Variation within fruit and among fruit in plots accounted for more than 75% of the environmental variation for the color traits. The crimson locus (ogc) accounted for less than one-third of the variation in fruit color among genotypic means, and explained 18% to 27% of the genotypic variation for L*, chroma, and hue. Estimates of variance components were used to develop sampling strategies for improving selection efficiency. Genotypes were identified that may be useful for studying genetic differences that lead to quantitative variation for fruit color in red-fruited populations of tomato.
Valdomiro A.B. de Souza, David H. Byrne, and Jeremy F. Taylor
Seedlings of 108 families from crosses among 42 peach [Prunus persica (L.) Batsch] cultivars and selections were evaluated for six plant characteristics in 1993, 1994, and 1995. The data were analyzed by using a mixed linear model, with years treated as fixed and additive genotypes as random factors. Best linear unbiased prediction (BLUP) was used to estimate fixed effects. Restricted maximum likelihood (REML) was used to estimate variance components, and a multiple trait model was used to estimate genetic and phenotypic covariances among traits. The narrow-sense heritability estimates were 0.41, 0.29, 0.48, 0.47, 0.43, and 0.23 for flower density, flowers per node, node density, fruit density, fruit set, and blind node propensity, respectively. Most genetic correlations among pairs of traits were ≥0.30 and were, in general, much higher than the corresponding phenotypic correlations. Flower density and flowers per node (ra = 0.95), fruit density and fruit set (ra = 0.84) and flower density and fruit density (ra = 0.71) were the combinations of traits that had the highest genetic correlation estimates. Direct selection practiced solely for flower density (either direction) is expected to have a greater effect on fruit density than direct selection for fruit density.
Glenn M. Ito and James L. Brewbaker
Pericarp thickness in maize (Zea mays L.) was analyzed by generation mean analysis for backcross and F2 populations from eight hybrids, derived from two thin-pericarped sweet corn inbreds—AA8 and 677a (55 and 51 μm)—crossed with four field corn inbreds—B37, B68, H55, and Hi26 (range 82-132 μm). Average heterosis was −12.5% and segregating progeny distributions were skewed toward those of thin-pericarped parents. Narrow-sense heritability was high, averaging 55.2%, and the number of effective factors was low, ranging from 1.4 to 5.9 and averaging 3.3. Epistatic effects were as large as additive or dominance effects in many crosses, urging caution in applying models that exclude gene interactions to determine variance components and heritabilities. The mode of action in reducing pericarp thickness appeared to differ among the two thin parents, with AA8 affecting the differential thickening of germinal vs. abgerminal walls, and 677a reducing the number of pericarp cell layers. All genetic parameters suggested that genetic progress in backcross conversions to thin pericarp in sweet corn breeding would be rapid irrespective of the pericarp thickness of exotic parents.
Knowledge of the level of cold hardiness and how hardiness is inherited in sour cherry is essential to germplasm collection and cultivar development. Twig samples of two sweet cherries (Prunus avium L.), 12 sour cherries (P. cerasus L.), and one ground cherry (P. fruticosa Pall.) of diverse geographic origins were collected in Jan. 1990 and monthly from Aug. 1990 to Mar. 1991, preconditioned to induce maximum cold resistance, and subjected to freeze tests and differential thermal analysis. Low temperature exotherms (LTEs) were detected in all stems of P. cerasus investigated and correlated to xylem incipient injury temperatures (ITs) from December to February (r = 0.84, P ≤ 0.01). March had the best correlation of LTEs to xylem ITs with r = 0.84, P ≤ 0.01. LTEs were strongly correlated to phloem-cambium ITs in November, representing the acclimation period. The correlation coefficient (r) for the phloem-cambium ITs and the twig LTEs during November was 0.68, significant at P ≤ 0.01. Cortical tissue and vegetative bud injuries were not correlated to the stem LTEs. Xylem ITs were selected for evaluating the cold resistance of sour cherry in December to March and phloem-cambium ITs were selected for November. The degree of supercooling and hardiness of the phloem-cambium in late fall and early spring appears significant in determining the stem hardiness and commercial range of P. cerasus. Phloem-cambium tissue, expressed the most rapid deacclimation response. The average decrease in hardiness for the phloem-cambium, xylem, and cortical tissues between February and March was 4 °C, 0.32 °C, and 2.14 °C, respectively. Principal component (PC) analyses of the phloem-cambium and cortical tissues depicted gradations between minimum survival temperatures of the two presumed progenitor species of sour cherry, i.e., sweet cherry and ground cherry. The first principal component (PC1), which accounted for 61% of the total variance, was used to separate among cultivars and seedlings. Cultivars and seedlings at the negative end of PC1 exhibited hardier phloem-cambium tissue at critical injury times, October, December, January, and March than cultivars and seedlings at the positive end of the PC1 axis. Cultivars and progeny of crosses of northern origin parents showed hardiness values more comparable to ground cherry than did selections of less-cold-hardy parents suggesting that cold is a major selective force, contributing to sour cherry population variation.
Shengrui Yao*, Ian A. Merwin, Janice E. Thies, and George S. Abawi
An apple (Malus domestica cv. Empire on M9/MM111 rootstock) orchard groundcover management systems (GMSs) study has been underway since 1992 in Ithaca, N.Y. Four GMS treatments are applied each year in 2-m wide tree-row strips: Pre-emergence herbicides (Pre-H: diuron + norflurazon + glyphosate); Post-emergence herbicide (Post-H: glyphosate); mowed-sod (Grass); and composted hardwood bark mulch (Mulch) treatment. The soil (silty clay loam) physical and chemical conditions have been monitored continuously. In May and Sept. 2003, we sampled topsoil beneath trees in each GMS and used PCR-DGGE combined with sequencing to characterize soil microbial community composition. Mulch had more culturable soil bacteria than the Pre-H treatment. Soil in Grass plots had the most culturable soil fungi. Soil microbial respiration rates were higher in Mulch than Grass and herbicide GMSs. Surface vegetation in the Grass and Post-H plots strongly influenced soil bacterial community composition. In Principal Component Analyses, Post-H and Grass treatments comprised one variance cluster, and Pre-H and Mulch treatments another. The soil fungal community was less diverse (fewer DGGE bands) than the bacterial community, and was less affected by GMS. Treatments with more surface vegetation (Post-H and Grass) also had more free-living and phytonematodes than Pre-H and Mulch. A total of 47 clones from 12 DGGE bands yielded 31 unique DNA sequences. Of these, 15 were novel sequences with no matches in the GenBank (NCBI) database. Another 10 (27 clones) could be matched with known fungal species at 96-100% identity. The primer pair used, ITS1F/ITS2, amplified a considerable number of Basidiomycetes and Ascomycetes, but there was no amplification for Zygomycetes and Oomycetes.