Life Sciences Commons^™

The Effect Of Design And Dose Level Choice On Estimatlng The Optimal Dose In A Quantitative Dose-Response Experiment, Henry R. Rolka, George A. Milliken, James R. Schwenke, Marta Remmenga

Conference on Applied Statistics in Agriculture

D-optimality is a commonly used criterion to evaluate a design with respect to parameter estimation. The variance of the optimal dose estimate is another criterion for evaluating a design. The quantitative dose-response experiment involves fitting a model to data and estimating an optimal dose. Two techniques for estimating an optimal dose and three models are used to compare the variances of optimal dose estimates over nine equally spaced balanced designs and five fixed unequally spaced six-point designs. The results show that a design which is more D-optimal than another design does not necessarily produce optimal dose estimates with less variance.

Analysis Of Genotype X Environment Interaction By Graphical Techniques, George C.J. Fernandez

Conference on Applied Statistics in Agriculture

Genotype x Environment interactions results from the changes in the magnitude of differences among genotypes (non-crossover or quantitative interactions) or changes in the relative ranking of the genotypes (crossover or qualitative interactions) in different environments. Non-crossover interactions are usually associated with variance heterogeneity and non-additivity. The analysis of variance combined with joint regression analysis failed to differentiate between the crossover and non-crossover interactions. Tedious computations are necessary in comparisons of all possible pairs of genotypes in all possible pairs of environments in the crossover detection tests. Therefore, differentiating the non-crossover interaction caused by variance heterogeneity and non-additivity from crossover interaction …

Analysis Of The Spatial Distribution Of Sugarbeet Plants, Stephen D. Kachman, John A. Smith

Conference on Applied Statistics in Agriculture

The spatial distribution of emerged sugarbeet plants is an important aspect of the performance of sugarbeet planters. Three major components influencing the spatial distribution are the ability to drop a single seed at a time, the ability to drop the seeds a fixed distance apart, and the ability of the seed to emerge. A model has been developed to describe the distribution of the spacing between emerged sugarbeet plants. The model consists of a mixture of normal and gamma distributions. The spatial data consists of the distance between neighboring emerged plants. Spatial data was collected on 7 planters operated at …

Evaluation Of Four Covariate Types Used For Adjustment Of Spatial Variability, Paul N. Hinz, John P. Lagus

Conference on Applied Statistics in Agriculture

Four types of covariates are used to account for spatial variability in data from a field experiment for evaluating 620 soybean varieties for iron chlorosis. The covariates are calculated as the average of 4 and of 14 neighboring residuals and of 4 and of 14 neighboring observations. The residual mean square from the analysis of covariance was smaller' when residuals were used in calculation of the covariates than when observations were used. Moreover, use of 14 neighbors resulted in smaller residual mean squares than did use of 4 neighbors. Differences among 4 covariate types were small and not practically important. …

Multi-Product Dry Milling Yields Prediction When Products Are Not Independent, Aziz Bouzaher, Alicia L. Carriquiry

Conference on Applied Statistics in Agriculture

The yield of products in the dry milling industry is largely determined by the physical properties of the corn kernel. The main objective of this paper is to investigate several statistical models of dry milling yield prediction based on physical characteristics of corn. Data consisting of one hundred corn samples representing a range of genetic traits and quality differences are used. For each corn sample, sixteen physical and chemical properties together with six dry milling product yields were measured, in a controlled laboratory environment .

For each corn sample, we consider a vector of dry milling product yields, and a …

Messy Experimental Designs, Dallas E. Johnson

Conference on Applied Statistics in Agriculture

This paper describes the statistical analysis of an agricultural experiment that was conducted in a very complex, but somewhat reasonable, experimental design. A correct analysis of data collected from the experimental design used requires the estimation of 8 error terms.

A Statistical Analysis Of The Performance Of Milking System Vacuum Regulators, Linda J. Young, Gerald R. Bodman, Eugene C. Boilesen, Walter W. Stroup

Conference on Applied Statistics in Agriculture

Milking machine vacuum regulators were tested at dairies across the United States over a period of twelve years. The drop in vacuum level with increasing air flow for each regulator tested was modeled using segmented regression. Three measures of regulator performance were considered: the slope of the line in the first phase, the variability about the first line, and the join point (after which vacuum pressure began to drop rapidly). The distribution of the join point was estimated based on an accelerated failure time model with censoring, Weibull errors, a model effect, and a linear effect of set point vacuum. …

Evaluation Of Methane Gas Production In A Simultaneous Regression System, Mark W. Jenner, Jon Maatta, Dennis M. Sievers

Conference on Applied Statistics in Agriculture

Methane gas production is a function of volatile solids activity in anaerobic digesters. Increasing the solids retention time of the swine manure digester system without increasing the hydraulic retention time would theoretically increase the methane gas production efficiency. Coagulation treatments were performed on the effluent of the second digester in a system of two digesters in series .

The objective of this paper is to describe mathematically the relationship of the Coagulation treatments in the second digester to biogas production and volatile solids retention. An initial, single equation, ordinary least squares regression produced statistically significant parameter estimates, but failed to …

A Reevaluation Of The Growth Decline In Pine In Georgia, And In Georgia-Alabama Combined, Z. Ouyang, H. T. Schreuder, J. Li

Conference on Applied Statistics in Agriculture

Using an improved testing procedure based on bootstrap and weighted jack-knife confidence intervals with the same model as used in Bechtold et al. (1991) and Ruark et al. (1991), analysis in this paper generally confirm the results of a significant decrease in growth rate in pine in Georgia and Alabama for 1972 - 1982 (5th cycle) relative to 1961 - 1972 (4th cycle) discussed in these papers.

Shrinkage In Ternary Mixes Of Container Media, Silvia Bures, Franklin A. Pokorny, Glenn O. Ware

Conference on Applied Statistics in Agriculture

Based on functional relationships established for binary mixes of container media, a mathematical model is proposed for ternary component mixtures. Shrinkage values are generated for three-component mixtures based on mathematical equations. Empirically observed shrinkage values for corresponding three-component mixtures are determined and used as the basis for assessing the reliability of the proposed mathematical model for characterizing shrinkage in mixtures of container media. . Regression equations were developed and compared for both theoretical and empirical results.

Nonlinear Estimation Of Growth Curve Models For Germination Data Analysis, Bahman Shafii, William J. Price, Jerry B. Swensen, Glen A. Murray

Conference on Applied Statistics in Agriculture

Logistic, Gompertz, Richards and Weibull growth curves were evaluated for their suitability as mathematical and empirical models to represent cumulative germination. By avoiding the limitations associated with the method of moments and single-value germination indices, the fitted models provided superior description of the time course of germination. The four-parameter Weibull model gave the best fit across a relatively wide range of seed species and germination conditions, and the resulting parameter estimates reflected identifiable aspects of the germination process. The nonlinear estimation of the germination response included a parameter summary, together with their asymptotic standard errors and correlation matrix, along with …

Straight Line Regression When Both Variables Are Subject To Error, Norman R. Draper

Conference on Applied Statistics in Agriculture

This expository note discusses the problem of fitting a straight line when both variables are subject to error. A brief review of the literature is undertaken, and one fitting method, the geometric mean functional relationship, is spotlighted and illustrated with two sets of example data. The emphasis is on providing practical advice. All methods have drawbacks, but the geometric mean functional relationship method appears to provide a sensible course of action in many practical problems, and could benefit from further investigation.

Editor's Preface, Table Of Contents, And List Of Attendees, George A. Milliken

Conference on Applied Statistics in Agriculture

These proceedings contain papers presented at the third annual Kansas State University Conference on Applied Statistics in Agriculture, held in Manhattan, Kansas, April 28 through 30, 1991.