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

SIMULATION STUDY OF SPATIAL-POISSON DATA ASSESSING INCLUSION OF SPATIAL CORRELATION AND NON-NORMALITY IN THE ANALYSIS

Spatial correlation and non-normality in agricultural, geological, or environmental settings can have a significant effect on the accuracy of the results obtained in the statistical analyses. Generalized linear mixed models, spatial models, and generalized linear models were compared in order to assess how critical the inclusion of non-normality and spatial correlation is to the analysis. Spatially correlated data with a Poisson distribution were generated in a completely randomized design (CRD) with 2 treatments and 18 repetitions. Four analyses: spatial Poisson, non-spatial Poisson, spatial normal, and non-spatial normal, were conducted on the simulated data to compare their power functions. The degree of spatial correlation, size of the mean, the dimension of the plots and difference between the two treatment means were altered to investigate how the ability to detect differences between the treatments is affected. In addition, the range covariance parameter was estimated and compared among the spatial models. Some covariance parameter estimates were under-estimated. The size of the field plot and the treatment means were increased to assess their effects on estimation of the range. The Reduced Maximum Likelihood (REML) covariance parameter estimates were compared to those obtained using Maximum Likelihood (ML) estimates. The analysis that incorporated the spatial correlation of the observations and used ML to estimate the covariance parameters had the highest power and most accurate range parameter estimates

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

PREDICTING SOIL TEMPERATURES IN HIGH TUNNELS USING A DYNAMIC MODEL BASED ON NEWTONIAN LAW OF COOLING

High tunnels are low cost temporary greenhouses that are often used to extend the growing season for high value crops such as tulips, muscari, sweet pea cultivars, and hyacinth beans. Profitability depends on selection and timing of crops to optimize use of these structures. Predicting soil temperatures in high tunnels as a function of outside temperature is a critical factor in crop selection and timing. However, predicting soil temperatures is difficult because air temperatures constantly change from hour to hour and day to day. We develop a model to account for temperature dynamics in high tunnels by modifying the fundamental differential equation in Newtonian law of cooling. We fit the model to data from high tunnels located in two states - Nebraska, Kansas and predict soil temperature as a function of external air temperatures. The model fits reasonably well at all high tunnel stations with most predictions being within 2° C of the observed value. We also found that the model could be used to adequately predict soil temperatures at one site based on parameter estimates of another nearby site. Thus we conclude that the model is an adequate tool in making high tunnel placement decisions and is useful for selection and timing of crops within established high tunnels

UNREPLICATED VARIETY TRIALS: EFFECTS OF CHECK PLOT DENSITY AND FIXED VERSUS RANDOM TREATMENTS

Crop researchers performing germplasm screenings are often unable to replicate their plots due to scarcity of seed and the large numbers of genotypes being evaluated. The use of known check varieties is a common method of overcoming the difficulties associated with unreplicated trials. In this simulation, we explored the effect of check plot density on the effectiveness of the resulting analysis. We also explored the effect of analyzing treatments as random versus fixed. Our study considers ten different designs with check densities ranging from 5% of the plots to 50%. The designs and analyses were then compared on the basis of the correlation of the actual treatment effects with the following: observed yield, LSMEANs for treatments fixed, and BLUPs for treatments random. Finally, we observed the frequency with which the analysis ranked the top 10% of the treatments within the top 15% of the LSMEANs or BLUPs. It was found that the LSMEANs and BLUPs from the spatial analysis provide more accurate results than the observed Y-values. Also, if the treatments are analyzed as fixed and the LSMEANs are used as estimates, then there seems to be a certain point beyond which not much additional information is gained by adding more check plots. This plateau is reached near a check plot density of approximately 30%. Finally, the BLUPs seem to be a more accurate estimate of the true treatment effects than are the LSMEANs at the lower densities; in fact, the BLUPs perform relatively well even at check densities of only 5% or 10%

ASSESSING REFINEMENTS IN MODELING SINUSOIDAL CONDITIONS USED TO DRIVE CATTLE BODY TEMPERATURES

A model, termed the PET model, is used to estimate body temperature in cattle challenged by hot cyclic chamber temperatures. The model is based on Newton\u27s law of cooling, driven by an estimated sinusoidal function. In practice, it is often difficult to maintain hot sinusoidal fluctuations in chamber temperatures. However, it is possible to model cyclic chamber temperatures using a discrete Fourier series. By increasing the precision in estimating the cyclic temperature driving function, we can more precisely estimate the parameters in the PET model. Simulation studies were performed to investigate the effect of under- and over-parameterization on accuracy of estimates, performance of a number of model selection criteria, and on nonlinear behavior such as intrinsic and parameter-effects curvature, bias, excess variance, and skewness. Our results will help researchers decide how to model ambient temperatures producing heat stress in cattle and improve estimates for evaluating management strategies

Dietary Fiber in Sow Gestation Diets - An Updated Review

Twenty-four published reports dating from 1975 to 2007 were examined to determine the overall effects of feeding gestation sows additional fiber. Sow and litter traits among trials were weighted by the number of litters for each treatment within each trial. Overall, sows can successfully consume high-fiber diets during gestation with few deleterious effects. Positive effects from feeding high-fiber diets were evident in litter size (0.2 to 0.6 pigs/litter) and sow lactation feed intake (0.5 to 0.8 lb/day), but they are not largely evident until the second reproductive cycle following exposure to the diet. It\u27s possible that to ensure sow and litter performance improvements from feeding fiber, fiber must be included in the diet before mating

Correlation of Omega-3 Fatty Acids Intakes with Acculturation and Socioeconomic Status in Midwestern Latinas

Background: Low socioeconomic status (SES) and acculturation of Latino immigrants in the U.S. are linked to a decrease in diet quality. Methods: Interviews were conducted with 162 first-generation Latinas to examine the association of SES and acculturation with intake of omega-3 (n − 3) fatty acids. Each participant provided dietary intake by use of a validated n − 3 food frequency questionnaire administered twice, 4 weeks apart, three 24-h recalls, sociodemographic information and completed the 5-item Short Acculturation Scale. Results: Mean intakes of Total n − 3, α-linolenic acid (ALA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (g/d) were 1.2 ± 0.7, 1.1 ± 0.6, and 0.1 ± 0.1, respectively. After adjusting for energy intake, education was significantly correlated with EPA + DHA intakes, and acculturation was significantly correlated with Total n − 3, ALA and EPA + DHA intakes. Foods sources of EPA + DHA eaten by at least 50% of participants were chicken, shrimp, tuna and eggs. Discussion: Given the beneficial cardiovascular effects of n − 3 fatty acids, it is important to understand sociocultural factors affecting adequate intake towards an improvement in diet quality in minorities