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1995

Conference on Applied Statistics in Agriculture

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Full-Text Articles in Physical Sciences and Mathematics

Sample Design In The Finnish Agricultural Income Statistics, Paavo Väisänen Apr 1995

Sample Design In The Finnish Agricultural Income Statistics, Paavo Väisänen

Conference on Applied Statistics in Agriculture

The Finnish Agricultural Income Statistics. published yearly by Statistics Finland, are based on a survey in which the data are collected from the farms in connection with taxation. The sampling design is stratified simple random sampling, in which Neyman allocation is used to calculate the sample sizes for the strata. The Farm Register is used as the sampling frame where variables such as region, production sector and arable land are available for stratification. The total incomes of farms from the previous survey serve as the allocation variable. Stratification and Neyman allocation rendered the estimates of most income variables more effective …

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Mixed Models Approach To On-Farm Trials: An Alternative To Meta-Analysis For Comparing One Treatment To Possibly Different Controls, Peter M. Njuho, George A. Milliken Apr 1995

Mixed Models Approach To On-Farm Trials: An Alternative To Meta-Analysis For Comparing One Treatment To Possibly Different Controls, Peter M. Njuho, George A. Milliken

Conference on Applied Statistics in Agriculture

The estimator of effect size, the sample mean difference divided by the sample standard error of the difference is studied in the context of mixed models and is related to the analysis of on-farm trials. A single treatment is compared against possibly different controls using a completely randomized design on each farm. A lower (1-α)100% confidence limit on mean difference of the treatment and the average control is obtained. The best linear unbiased predictors (BLUPs) of the mean difference of the treatment and the individual controls as well as the lower (1-α)100% prediction limits are provided. The effect of omitting …

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Using Tree Regression To Identify Nutritional And Environmental Factors Affecting Sugarcane Production, Kenneth M. Portier, David L. Anderson Apr 1995

Using Tree Regression To Identify Nutritional And Environmental Factors Affecting Sugarcane Production, Kenneth M. Portier, David L. Anderson

Conference on Applied Statistics in Agriculture

A prediction function is developed for sugarcane yield using preplant soil nutrition levels, cultivar, and soil type. A tree regression approach is used because the resulting function encompasses the complexity of response between yield, multiple nutrients and other factors, while handling large amounts of data and providing information useful in the development of fertilizer and other production recommendations. Data collected from 148 control plots of experiments performed on commercial fields in the Everglades Agricultural Area of Florida are used to illustrate the method.

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Step-Stress Testing In Agricultijre, Imad H. Khamis, James J. Higgins Apr 1995

Step-Stress Testing In Agricultijre, Imad H. Khamis, James J. Higgins

Conference on Applied Statistics in Agriculture

Step-stress testing has been used for a munber years in engineering. An item is placed on test for a specified period of time. If it does not fail in that time, the stress is increased. This process is repeated for a specified number of stress levels until the item fails. In agriculture, animals or plants may be the test items and dosage of a chemical, amount of fertilizer, temperature, etc, the stress variable. In this paper we suggest several potential applications of step-stress testing in agriculture and present inferential procedures for observations that are distributed exponentially.

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Two-Factor Agricultural Experiment With Repeated Measures On One Factor In A Complete Randomized Design, Armando Garsd, María Del C. Fabrizio, María V. López Apr 1995

Two-Factor Agricultural Experiment With Repeated Measures On One Factor In A Complete Randomized Design, Armando Garsd, María Del C. Fabrizio, María V. López

Conference on Applied Statistics in Agriculture

A typical agricultural experiment involves comparisons of several treatments at different points in time. The ensuing lack of independence between observations of the same experimental unit may then impair the attainment of statistical significance by the standard analysis of variance, and calls for the application of more powerful methods. This paper addresses one such method, the so-called two-factor experiment with repeated measures on one factor. We discuss the adequacy of this model in the context of three concrete examples drawn from agricultural experimentation.

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Sequential Analysis Of Agricultural Experiments, Armando Garsd, María V. López, María Del C. Fabrizio Apr 1995

Sequential Analysis Of Agricultural Experiments, Armando Garsd, María V. López, María Del C. Fabrizio

Conference on Applied Statistics in Agriculture

Interim monitoring of accumulating data has been widely used in clinical trials, but it has not received the same attention in agricultural experimentation. The methodology, however, can be a useful tool in agronomic trials designed to find better production techniques or optimal animal treatments at low cost, plus the possible economic advantages resulting from correct early decisions. These sequential procedures for testing hypothesis with available data in successive periods of time dictate termination of the experiment when a significant difference is detected, or otherwise continuation of the experiment to the end of the stipulated time or until all the planned …

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Spatial Analysis Of Grasshopper Density As Influenced By Anthropogenic Habitat Changes, Bahman Shafii, William J. Price, Dennis J. Fielding, Merlyn A. Brusven Apr 1995

Spatial Analysis Of Grasshopper Density As Influenced By Anthropogenic Habitat Changes, Bahman Shafii, William J. Price, Dennis J. Fielding, Merlyn A. Brusven

Conference on Applied Statistics in Agriculture

The rangeland environment in southern Idaho has been heavily impacted by human activities. Invasion by exotic plant species, frequent fires, grazing pressure, and other ecological disturbances have greatly affected the structure and dynamics of grasshopper populations. Quantification of spatial patterns of grasshopper density and species composition is important in order to determine their influence on grassland ecosystems, as well as evaluating managerial decisions concerning vegetation manipulations, grazing practices, and spraying programs. A spatial statistical approach to modeling the heterogeneity of grasshopper populations is presented, and the impact of vegetation and grazing treatments on grasshopper density is investigated. Empirical applications are …

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A Classification Of Unreplicated Factorial Experiments For Use With The Analysis Of Deterministic Simulation Models, J L. Willers, B. T. Vinyard Apr 1995

A Classification Of Unreplicated Factorial Experiments For Use With The Analysis Of Deterministic Simulation Models, J L. Willers, B. T. Vinyard

Conference on Applied Statistics in Agriculture

Deterministic simulation models are important in agricultural applications and their use is becoming increasingly common. Therefore, statistical procedures that interpret the output and evaluate the performance of deterministic models are necessary. The fact that deterministic computer simulation experiments cannot be replicated provides opportunities for using several procedures applicable to unreplicated factorial experiments. We discuss a classification scheme that selects the correct technique for most deterministic simulation experiments. The value of these techniques is their capability to estimate the experimental error variance for unreplicated computer experiments. Using these estimates of error, model developers and practitioners can more thoroughly analyze their deterministic …

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Estimation Of And Adjustment For Residual Effects In Dairy Feeding Experiments Utilizing Changeover Designs, Ramon C. Littell, Charles J. Wilcox, H. H. Van Horn, A. P. Tomlinson Apr 1995

Estimation Of And Adjustment For Residual Effects In Dairy Feeding Experiments Utilizing Changeover Designs, Ramon C. Littell, Charles J. Wilcox, H. H. Van Horn, A. P. Tomlinson

Conference on Applied Statistics in Agriculture

A procedure is presented which demonstrates estimation of and adjustment for residual effects in changeover designs. The method utilizes all data collected in an experiment by including treatments imposed on animals prior to initiation of data collection. Estimation is achieved via general linear models. An example is given of a nutrition experiment conducted with dairy cattle. Such analyses should increase efficacy of changeover designs and reduce concern by researchers about biased estimates of direct effects which could result from residual effects. Methods from popular computer programs for estimating direct effect treatment means are compared. Practical problems encountered in computing standard …

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Issues In Analysis Of A Long-Term Integrated Pest Management Field Study, J. R. Alldredge, F. L. Young Apr 1995

Issues In Analysis Of A Long-Term Integrated Pest Management Field Study, J. R. Alldredge, F. L. Young

Conference on Applied Statistics in Agriculture

A team of 14 scientists conducted a 6-year, 16-ha, integrated pest management field study in the dryland wheat production area of the Pacific Northwest. Objectives were to develop a profitable crop production system that controls weeds effectively and reduces soil erosion. Farm-size machinery was used to till, plant, and harvest crops grown in either a continuous wheat (Triticum aestivum L.) sequence or a 3-year crop rotation of winter wheat-spring barley (Hordeum vulgare L.) -spring pea (Pisum sativum L.) in conservation and conventional tillage systems. Main plot factor levels were two tillage systems and three …

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Bayesian Inference On Variance Components In Generalized Linear Mixed Models: An Evaluation Of Different Methods, Robert J. Tempelman Apr 1995

Bayesian Inference On Variance Components In Generalized Linear Mixed Models: An Evaluation Of Different Methods, Robert J. Tempelman

Conference on Applied Statistics in Agriculture

Generalized linear mixed models are now popular in the animal breeding and biostatistics literature as these models allow inference on fixed and random effects for the exponential family of data distributions. In animal breeding, particular attention is directed towards variances of the random effects. We investigate three methods for marginal inference on variance components in binary data, including (1) the conventional expectation-maximization (EM) type algorithm, (2) Laplace's method, and (3) "exact" Gibbs sampling methods. A simulation study involving probit animal models was used to compare the modal estimates computed under the three methods. It was found that EM estimates were …

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Variance As A Factor Effect In Interdisciplinary Studies Of Agricultural Systems, Cathryn S. Miller, Dawn M. Vanleeuwen, Jill Schroeder, Mike Kenney Apr 1995

Variance As A Factor Effect In Interdisciplinary Studies Of Agricultural Systems, Cathryn S. Miller, Dawn M. Vanleeuwen, Jill Schroeder, Mike Kenney

Conference on Applied Statistics in Agriculture

Studies of interrelationships among factors typically focus on factor effects related to the mean response. In some instances, response variances, as well as, or even rather than, response means, may be affected by the factors under consideration. In this paper, generalizations of Levene's test and the Jackknife test to two-factor experimental designs are studied via simulation studies to assess their ability to identify differences in the variance as an interaction effect or as a factor main effect. These tests are then applied to a particular example where relationships between chile plants and two prominent pests of chile plants -nematodes and …

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Predicting The Date Of First Catch Of The Corn Earworm, Helicoverpa Zea, In Central U.S., J. H. Matis, S. Yang, N. Castiaux, J. K. Westbrook, K. R. Beerwinkle, J. D. Lopez Jr. Apr 1995

Predicting The Date Of First Catch Of The Corn Earworm, Helicoverpa Zea, In Central U.S., J. H. Matis, S. Yang, N. Castiaux, J. K. Westbrook, K. R. Beerwinkle, J. D. Lopez Jr.

Conference on Applied Statistics in Agriculture

This paper develops predictive (or correlative) models for the date of first catch of the com earworm, Helicoverpa zea, as a basis for identifying biotic and abiotic factors that influence dispersal and migration. Data described in Goodenough et al. (1988, J. Econ. Entomol.) on the catch of H. zea gathered at over 150 sites predominantly in the central U.S. from 1983 to 1986 are analyzed. The dependent variables, Y1 and Y2, are date of first meaningful catch and date when cumulative catch exceeds 5, respectively; the independent variables are latitude, longitude and elevation of the site. …

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Sampling Schemes To Detect Very Low Concentrations, Terry C. Nelsen Apr 1995

Sampling Schemes To Detect Very Low Concentrations, Terry C. Nelsen

Conference on Applied Statistics in Agriculture

Many mycotoxins and certain drug residues can be important at very low concentrations in feeds and foods. Government regulatory agencies establish maximum acceptance concentrations (Action Levels) to avoid proven effects of known toxins and provide Advisory Levels where effects are not yet well established. The Action Levels for the aflatoxins range from 5 to 300 ppb (ng/g) depending on the particular toxin and the intended use of the feed or food. Other naturally occurring mycotoxins, such as DON, zearalenones, and fumonisons, have advisory levels in the range of 1 to 5 ppm (j.A-g/g) . Chemotherapeutic agents in feeds must be …

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Sampling Segments In An Area Frame With A Distance Threshold, M. Fuentes, F. J. Gallego Apr 1995

Sampling Segments In An Area Frame With A Distance Threshold, M. Fuentes, F. J. Gallego

Conference on Applied Statistics in Agriculture

A simple random sample in an area frame usually gives a number of pairs of elements that are close to each other. These elements give redundant information since there is usually a high spatial autocorrelation at short distances. The efficiency of sampling is generally improved if we impose that the distance between two elements of the sample cannot be less than a certain threshold. However applying this restriction can introduce a significant perturbation of the sampling probability. Elements near the borders of the region are more likely to be selected. In the case of aligned sampling by repetition of a …

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Covariance Analysis With A Covariate Interaction: An Example Of A Simple Linear Regression Comparison Technique, D. E. Palmquist, C. A. Stockwell Apr 1995

Covariance Analysis With A Covariate Interaction: An Example Of A Simple Linear Regression Comparison Technique, D. E. Palmquist, C. A. Stockwell

Conference on Applied Statistics in Agriculture

Many real data sets that would normally lend themselves to being analyzed by an analysis of covariance, have a covariate interaction present with one or more of the factors in the experiment. Because this violates the assumption of same-slope covariate effect across all treatments, an analysis of covariance should not be performed. The course normally taken when there is such an interaction is to derive regression equations for the dependent variable as a function of the covariate, at each level of the factor(s) being tested. A general linear model F-test can then be used to test whether there are any …

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A Review Of Analysis Of Experimental Data, Roger Mead Apr 1995

A Review Of Analysis Of Experimental Data, Roger Mead

Conference on Applied Statistics in Agriculture

Initial Assumptions, We assume that it is standard practice to base an initial analysis of experimental data on a linear model including terms for blocks, treatments and covariates. This produces a summary analysis of variance, indicating the major components of variation relative to the fitted model, and tables of means (one- or two-way) as indicated from the analysis of variances, with appropriate standard errors.

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Editor's Preface, Table Of Contents, And List Of Attendees, James R. Schwenke Apr 1995

Editor's Preface, Table Of Contents, And List Of Attendees, James R. Schwenke

Conference on Applied Statistics in Agriculture

These proceedings contain papers presented in the seventh annual Kansas State University Conference on Applied Statistics in Agriculture, held in Manhattan, Kansas, April 23 through 25, 1995.

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