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Dawn M. VanLeeuwen, Rolston St. Hilaire, and Emad Y. Bsoul

Statistical analysis of data from repeated measures experiments with missing factor combinations encounters multiple complications. Data from asynchronous cyclic drought experiments incorporate unequal numbers of drought cycles for different sources and provide an example of data both with repeated measures and missing factor combinations. Repeated measures data are problematic because typical analyses with PROC GLM do not allow the researcher to compare candidate covariance structures. In contrast, PROC MIXED allows comparison of covariance structures and several options for modeling serial correlation and variance heterogeneity. When there are missing factor combinations, the cross-classified model traditionally used for synchronized trials is inappropriate. For asynchronous data, some least squares means estimates for treatment and source main effects, and treatment by source interaction effects are inestimable. The objectives of this paper were to use an asynchronous drought cycle data set to 1) model an appropriate covariance structure using mixed models, and 2) compare the cross-classified fixed effects model to drought cycle nested within source models. We used a data set of midday water potential measurements taken during a cyclic drought study of 15 half-siblings of bigtooth maples (Acer grandidentatum Nutt.) indigenous to Arizona, New Mexico, Texas, and Utah. Data were analyzed using SAS PROC MIXED software. Information criteria lead to the selection of a model incorporating separate compound symmetric covariance structures for the two irrigation treatment groups. When using nested models in the fixed portion of the model, there are no missing factors because drought cycle is not treated as a crossed experimental factor. Nested models provided meaningful F tests and estimated all the least squares means, but the cross-classified model did not. Furthermore, the nested models adequately compared the treatment effect of sources subjected to asynchronous drought events. We conclude that researchers wishing to analyze data from asynchronous drought trials must consider using mixed models with nested fixed effects.

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J. Logan and M.A. Mueller

Tennessee is located in an area of diverse topography, ranging in elevation from <100 m to ≈2000 m, with numerous hills and valleys. The physiography makes it very difficult to spatially interpolate weather data related to vegetable production, such as spring and fall freeze dates and growing degree days (GDD). In addition, there is a poor distribution of cooperative weather stations, especially those with 30 years or more of data. There are climate maps available for Tennessee, but they are of such a general format as to be useless for operational applications. This project is designed to use a geographic information system (GIS) and geospatial techniques to spatially interpolate freeze (0 °C) dates and GDD for different base temperatures and make the data available as Internet-based maps. The goal is to develop reasonable climate values for vegetable growing areas <1000 m in elevation at a 100 square km resolution. The geostatistics that we are evaluating include Thiessen polygons, triangulated irregular network (TIN), inverse distance weighting (IDW), spline, kriging, and cokriging. Data from 140 locations in and around Tennessee are used in the analysis. Incomplete data from 100 other locations are used to validate the models. GDD, which have much less year-to-year variability than freeze dates, can be successfully interpolated using inverse distance weighting (IDW) or spline techniques. Even a simple method like Thiessen produces fairly accurate maps. Freeze dates, however, are better off analyzed on an annual basis because the patterns can vary significantly from year to year. The annual maps can then be superimposed to give a better estimate of average spring and fall freeze dates.

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Amy K. Freidig and Irwin L. Goldman

calcium levels and oxalate, especially in regard to length of the growing period. Roots. We evaluated the interactions between year and cultivar for the standard planting date as a result of incomplete data for the late planting date. There were no

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Arthur Villordon, Ron Sheffield, Jose Rojas, and Yin-Lin Chiu

probabilistically describe the relationship between causal variables and the outcome of interest. An added benefit is that BBNs can learn from small and incomplete data sets that contain constant, discrete, and continuous variables ( McCann et al., 2006 ; Uusitalo

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Jian-Feng Geng, Cheng-Song Zhu, Xiao-Wei Zhang, Yan Cheng, Yuan-Ming Zhang, and Xi-Lin Hou

, N.M. Rubin, D.B. 1977 Maximum likelihood from incomplete data via EM algorithm J. R. Stat. Soc. B. 39 1 38 Ferriol, M. Picó, B. Nuez, F. 2003 Genetic diversity of a germplasm collection

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Lifei Chen, Youping Sun, Genhua Niu, Qiang Liu, and James Altland

multivariate parameters at both harvests. The relative leaf area and SPAD data in EC 10 were excluded in the cluster analysis because of incomplete data set resulting from dead plants. Performance index. Performance index was affected by elevated salinity with

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Eder J. Oliveira, Maria Lucia C. Vieira, Antonio Augusto F. Garcia, Carla F. Munhoz, Gabriel R.A. Margarido, Luciano Consoli, Frederico P. Matta, Michel C. Moraes, Maria I. Zucchi, and Maria Helena P. Fungaro

cytogenetic characterization of somatic hybrids Caryologia 58 220 228 Dempster, A.P. Laird, N.M. Rubin, D.B. 1977 Maximum likelihood from incomplete data via the EM algorithm J. Royal Stat. Soc. Ser. B 39 1

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Derek W. Barchenger, Robert A. Clark III, Paul A. Gniffke, Dolores R. Ledesma, Shih-wen Lin, Peter Hanson, and Sanjeet Kumar

, data collection, and experimental design ( Lin et al., 2014 ). A total of 103 collaborators in 36 different countries received ICPN15, but only 21 provided feedback. Due to inconsistencies in experimental design, incomplete data, or other problems such

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Justine E. Vanden Heuvel and Martin C. Goffinet

function. Statistical analysis. Least square means and se s of the means were calculated using Proc GLM in SAS (SAS Institute, Cary, NC). As a result of the incomplete data set (detailed in Table 1 , as a result of the loss of temperature control

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Paul R. Fantz

identification of names and species of Ophiopogon currently found in the green industry and in botanic gardens in the southeastern United States. Multiple traits were used when possible for each couplet with contrasting characters, but incomplete data for some