Selection of Stable Cultivars Using a Safety-First Rule

The presence of genotype by environment interaction is of major concern to plant breeders, since large interactions can reduce gains from selection and complicate identification of superior cultivars. Numerous statistics have been proposed to characterize stability of cultivars, yet none of these methods explicitly indicate how stability may be combined with mean yield in choosing superior cultivars. It is assumed that the plant breeder prefers a cultivar with a small probability of low yield. Using a decision-theory concept known as safety-first to model such behavior, an index incorporating mean yield and stability is developed for each of four different definitions of stability. Data from an international experimental maize (Zea mays L.) yield trial are used to illustrate the application of these indices when genotype by environment interaction is present. It is concluded that safety-first selection indices can be useful to plant breeders when genotype by environment interaction is large and poor yield has severely adverse consequences

BIAS IN PRINCIPAL COMPONENTS ANALYSIS DUE TO CORRELATED OBSERVATIONS

A common practice in many scientific disciplines is to take measurements on several different variables on each unit from a designed experiment. This practice is cost efficient and results in data that may be analyzed using multivariate statistical methods. Usually, principal components analysis (PCA) is conducted by decomposing the covariance matrix of the several dependent variables using eigenanalysis without accounting for possible correlations among the observations. To evaluate how correlated observations bias PCA results, we used algebraic derivation and simulation for several different types of correlation structures. Our results indicated that sampling error generally had a much larger impact on the bias of PCA results than correlation between the observations. If we ignore the sampling error and there are no time trends or treatment effects, the PC\u27s and the percent variance explained by a PC is not affected by correlated observations, however the eigenvalues are biased. If the sampling error is considered, for moderate sized correlations between observations and reasonably sized designs, bias was generally small enough to ignore for the first PC, otherwise SAS PROC MIXED may be used to easily correct for correlated observations, resulting in less bias in the PCA results

CLUSTERING ENVIRONMENTS BASED ON CROSSOVER INTERACTIONS AND USING GRAPHICAL APPROACHES TO VISUALIZE CLUSTERS

Crossover interactions occur in evaluation trails when ranks of cultivars change across environments. Determining groups of environments within which crossover interactions are minimized may facilitate making cultivar recommendations. Model-based approaches to finding such clusters have been previously described. Our goal was to describe a new, non-model based approach of defining these clusters and then apply this method to a 59 environment x eight maize (Zea mays L.) cultivar data set. Hierarchical clustering of a 59 x 59 distance matrix defined two environmental clusters within which the total crossover interaction was reduced by approximately one-third and four clusters within which the crossover interaction was reduced by one-half. Four graphical approaches to visualizing the environmental clusters in this data set also were considered. Multi-dimensional scaling (MDS) allowed visualization of clusters when the dimensionality of the crossover space was reduced by considering only some of the crossover interactions between pairs of cultivars. Another benefit of MDS may be identification of specific environmental variables associated with crossover interactions

Distinguishing between yield advances and yield plateaus in historical crop production trends

Food security and land required for food production largely depend on rate of yield gain of major cereal crops. Previous projections of food security are often more optimistic than what historical yield trends would support. Many econometric projections of future food production assume compound rates of yield gain, which are not consistent with historical yield trends. Here we provide a framework to characterize past yield trends and show that linear trajectories adequately describe past yield trends, which means the relative rate of gain decreases over time. Furthermore, there is evidence of yield plateaus or abrupt decreases in rate of yield gain, including rice in eastern Asia and wheat in northwest Europe, which account for 31% of total global rice, wheat and maize production. Estimating future food production capacity would benefit from an analysis of past crop yield trends based on a robust statistical analysis framework that evaluates historical yield trajectories and plateaus

Distinguishing between yield advances and yield plateaus in historical crop production trends

Food security and land required for food production largely depend on rate of yield gain of major cereal crops. Previous projections of food security are often more optimistic than what historical yield trends would support. Many econometric projections of future food production assume compound rates of yield gain, which are not consistent with historical yield trends. Here we provide a framework to characterize past yield trends and show that linear trajectories adequately describe past yield trends, which means the relative rate of gain decreases over time. Furthermore, there is evidence of yield plateaus or abrupt decreases in rate of yield gain, including rice in eastern Asia and wheat in northwest Europe, which account for 31% of total global rice, wheat and maize production. Estimating future food production capacity would benefit from an analysis of past crop yield trends based on a robust statistical analysis framework that evaluates historical yield trajectories and plateaus

MULTI-TRAIT QTL MAPPING USING A STRUCTURAL EQUATION MODEL

Research on mapping quantitative trait loci (QTL) often results in data on a number of traits that have well established causal relationships. Many multi-trait QTL mapping methods, taking into account the correlation among the multiple traits, have been developed to improve the statistical power of the test for QTL and the precision of parameter estimation. However none of these methods are capable of incorporating the causal structure among the traits with the consequence that genetic functions of the QTL may not be fully understood. Structural equation modeling (SEM) allows researchers to explicitly characterize the causal structure among the variables and to decompose the effects into direct, indirect, and total effects. In this paper, we developed a multi-trait SEM method of QTL mapping that takes into account the causal relationships among traits. The performance of the proposed method is evaluated by simulation study. Compared with single trait analysis and the multi-trait least-squares analysis, our proposed model (Multitrait SEM) provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects, which is generally not possible with other methods. The approach also helps with building models that more realistically reflect complex relationships among QTL and traits, and is more precise and efficient in QTL mapping than single trait analysis

Examining the Reliability, Validity and Factor Structure of the DRS-15 with College Athletes

Hardiness, comprising feelings of commitment, control and challenge, is most frequently measured with the Dispositional Resiliency Scale (DRS), but little work has been done with the brief 15-item version. To examine the factor structure, reliability, validity, and item functioning for gender invariance of the 15-item DRS with 525 collegiate athletes from a wide range of sports. Convergent and divergent validity were examined through relationships with mental toughness, grit and competitive anxiety. Participants completed measures of mental toughness, optimism, grit, competitive anxiety, and the DRS-15. Confirmatory Factor Analysis revealed a poor fit for the three-factor hardiness model, and subsequent exploratory factor analysis yielded a four-factor model with better fit than the three-factor structure. Additionally, several items appear to be biased towards males or females. The fourth factor may be unique to the collegiate athlete population, and related to perceived lack of control in future life directions. Convergent and divergent validity were supported through correlations of DRS scores with related measures. The four-factor model should be tested with different samples to determine if these changes should be adapted when using the DRS-15 in collegiate athletics or other settings

A Comparison of Term Clusters for Tokenized Words Collected from Controlled Vocabularies, User Keyword Searches, and Online Documents

Tokenized word terms were collected from three sources: controlled vocabulary headings, user keyword searches, and html documents all dealing with issues in water quality. Distances were calculated between word pairs using the Jacquard formula. Distances from the three sources were compared using Spearman rank correlations and clusters were calculated on distances transformed for non-normality using the SAS pseudo-centroid method. Word pair distances from controlled vocabularies were more closely correlated to keyword searches than document distances were to users’ keywords. The mean distance of controlled vocabularies was also closer to that of users. Clusters produced from the three sources were most similar for word pairs with small distances

A Comparison of Term Clusters for Tokenized Words Collected from Controlled Vocabularies, User Keyword Searches, and Online Documents

Tokenized word terms were collected from three sources: controlled vocabulary headings, user keyword searches, and html documents all dealing with issues in water quality. Distances were calculated between word pairs using the Jacquard formula. Distances from the three sources were compared using Spearman rank correlations and clusters were calculated on distances transformed for non-normality using the SAS pseudo-centroid method. Word pair distances from controlled vocabularies were more closely correlated to keyword searches than document distances were to users’ keywords. The mean distance of controlled vocabularies was also closer to that of users. Clusters produced from the three sources were most similar for word pairs with small distances

Effect of N-nitrosoatrazine on Embryogenesis in Avian Embryos

Preliminary studies suggest that the nitrosamine N-nitrosoatrazine (NNAT) is teratogenic and mutagenic. We hypothesized that the embryos exposed to NNAT would have higher mortality and lower growth rate compared to unexposed embryos. In this study, we evaluated growth and mortality in chick embryos after exposure to NNAT dissolved in dimethyl sulfoxide (DMSO). The study was divided into three experiments depending treatment design. First, the effect of DMSO on embryo growth and survival was tested. Second, we compared growth and survival between embryos treated with DMSO, 50:50 DMSO:water and NNAT at 0.245 µmol/l. Finally, we compared growth and survival between embryos treated with DMSO and varying doses of NNAT (1.11 µmol/l, 2.22 µmol/l, 3.33 µmol/l) dissolved in DMSO. Based on this, we determine the LD50 (lethal dose for 50% of a test population) for NNAT. In terms of mortality, the first experiment shows that there is no effect of DMSO compared to water and blank, but the third experiment shows that there is a linear relationship between NNAT doses where high NNAT dose level will reduce the survival rate of the embryos. From this relationship, we determine that LD50 to be 2. 85µmol/l. We continue the analysis on the survive embryos and reveals that DMSO and NNAT had no effect on the growth of embryos in all three experiments.https://digitalcommons.unmc.edu/coph_pres/1010/thumbnail.jp