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Articles 1 - 9 of 9
Full-Text Articles in Agriculture
Modeling Ratios With Potential Zero-Inflation To Assess Soil Nematode Community Structure, Joanna Zbylut, Leigh Murray, S. H. Thomas, J. Beacham, J. Schroeder, C. Fiore
Modeling Ratios With Potential Zero-Inflation To Assess Soil Nematode Community Structure, Joanna Zbylut, Leigh Murray, S. H. Thomas, J. Beacham, J. Schroeder, C. Fiore
Conference on Applied Statistics in Agriculture
The southern root-knot nematode (SRKN) and the weedy perennials, yellow nutsedge (YNS) and purple nutsedge (PNS) are simultaneously-occurring pests in the irrigated agricultural soils of southern New Mexico. Previous research has characterized SRKN, YNS and PNS as a mutually beneficial pest complex and has shown their enhanced population growth and survival when they occur together. In addition, it was shown that the density of nutsedge in a field could be used as a predictor of SRKN juveniles in the soil. In addition to SRKN, which is the most harmful of the plant parasitic nematodes, in southern New Mexico other species …
Developing Prediction Equations For Carcass Lean Mass In The Prescence Of Proportional Measurement Error, Zachary J. Hass, Ziqi Zhou, Bruce A. Craig
Developing Prediction Equations For Carcass Lean Mass In The Prescence Of Proportional Measurement Error, Zachary J. Hass, Ziqi Zhou, Bruce A. Craig
Conference on Applied Statistics in Agriculture
Published prediction equations for carcass lean mass are widely used by commercial pork producers for carcass valuation. These regression equations have been derived under the assumption that the predictors, such as back fat depth, are measured without error. In practice, however, it is known that these measurements are imperfect, with a variance that is proportional to the mean. In this paper, we consider both a linear and quadratic true relationship and compare regression fits among two methods that account for this error versus simply ignoring the additional error. We show that biased estimates of the relationship result if measurement error …
Check Based Stability Analysis Method And Its Application To Winter Wheat Variety Trials, Jixiang Wu, Karl Glover, Nathan Mueller
Check Based Stability Analysis Method And Its Application To Winter Wheat Variety Trials, Jixiang Wu, Karl Glover, Nathan Mueller
Conference on Applied Statistics in Agriculture
Finley-Wilson (FW) regression based stability analysis is highly dependent on the testing varieties and environments being used. In this study, we proposed a check based regression method to determine yield stability. One advantage of this method is its capability to determine yield stability through widely acceptable varieties and thus to provide more meaningful information to evaluate the potential use of new varieties. In addition, with integration a resampling technique, bootstrapping method, yield stability can be compared among different varieties/genotypes from either the same or different testing environments. As a demonstration, we applied this method to analyze the 2009- 2011 winter …
Use Of The Posterior Predictive Distribution As A Diagnostic Tool For Mixed Models, Matthew Kramer
Use Of The Posterior Predictive Distribution As A Diagnostic Tool For Mixed Models, Matthew Kramer
Conference on Applied Statistics in Agriculture
The posterior predictive distribution (the distribution of data simulated from a model) has been used to flag model-data discrepancies in the Bayesian literature, and several approaches have been developed. The approach taken here differs from the others both conceptually and as realized. It works by comparing the "distance" between the data and model (as represented by pseudo-data simulated from a model) with "distance" within the model. The distance within the model is calculated by generating pseudo-data from it, using each set of these pseudo-data to reestimate the model, and then generating pseudo-data from them, matching the way the original data …
Bayesian Inference For A Covariance Matrix, Ignacio Alvarez, Jarad Niemi, Matt Simpson
Bayesian Inference For A Covariance Matrix, Ignacio Alvarez, Jarad Niemi, Matt Simpson
Conference on Applied Statistics in Agriculture
Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly modeled, e.g. random-intercept, random-slope models. A Bayesian analysis of these problems requires a prior on the covariance matrix. Here we compare an inverse Wishart, scaled inverse Wishart, hierarchical inverse Wishart, and a separation strategy as possible priors for the covariance matrix. We evaluate these priors through a simulation study and application to a real data set. Generally all priors work well with the exception of the inverse Wishart when the true variance is small relative to prior mean. In this …
Modeling Sleep And Wake Bouts In Drosophila Melanogaster, Gayla R. Olbricht, V. A. Samaranayake, Sahitya Injamuri, Luyang Wang, Courtney Fiebelman, Matthew S. Thimgan
Modeling Sleep And Wake Bouts In Drosophila Melanogaster, Gayla R. Olbricht, V. A. Samaranayake, Sahitya Injamuri, Luyang Wang, Courtney Fiebelman, Matthew S. Thimgan
Conference on Applied Statistics in Agriculture
Adequate sleep restores vital processes required for health and well-being; but the function and regulation of sleep is not well understood. Unfortunately, a definition of adequate sleep is unclear. On an hours-long timescale, consolidated and cycling sleep results in better health and performance outcomes. At shorter timescales, older studies report conflicting results regarding the relationship between sleep and wake bout durations. One approach to this problem has been to simply analyze the distribution of bout durations. While informative, this method eliminates the time relationship between bouts, which may be important. Here, we develop a model that describes the relationship between …
Multivariate Statistical Analysis Of Coleoptera Spectral Reflectance, Sarah E.M. Herberger, Bahaman Shafii, Stephen P. Cook, Christopher J. Williams, William J. Price
Multivariate Statistical Analysis Of Coleoptera Spectral Reflectance, Sarah E.M. Herberger, Bahaman Shafii, Stephen P. Cook, Christopher J. Williams, William J. Price
Conference on Applied Statistics in Agriculture
The insect order Coleoptera, commonly known as beetles, comprises 40% of all insects which in turn account for half of all identified animal species alive today. Coleopterans frequently have large elytra (the hardened front wings) that can have a wide range of colors. Spectral reflectance readings from these elytra may be used to uniquely identify coleopteran taxonomic groups. Multiple samples of eleven species of wood boring beetles were selected from the University of Idaho William Barr Entomology Museum. Spectrometer readings for each specimen were then fit to normal distribution mixture models to identify multiple peak reflectance wavelengths. Eighteen prominent peaks …
Response Of Soybean Yield And Yield Components To Phosphorus Fertilization In South Dakota, Adams Kusi Appiah, Rebecca Helget, Yi Xu, Jixiang Wu
Response Of Soybean Yield And Yield Components To Phosphorus Fertilization In South Dakota, Adams Kusi Appiah, Rebecca Helget, Yi Xu, Jixiang Wu
Conference on Applied Statistics in Agriculture
Increased demand for soybean [Glycine max (L.) Merrill] production for industrial, human, and animal consumption has provided many incentives for farmers and producers to increase their production. In many soils used for soybean production, phosphorus (P) becomes a major limiting factor to soybean growth and grain production. A field experiment was conducted in five locations across Eastern South Dakota in 2013 to study the response of soybean yield and yield components to phosphorus fertilizer applications. The experiment was laid out in a randomized complete block (RCB) design with four replications. The treatments consisted of five P levels 0, 20, 40, …
Editor's Preface And Table Of Contents, Weixing Song
Editor's Preface And Table Of Contents, Weixing Song
Conference on Applied Statistics in Agriculture
These proceedings contain papers presented in the twenty-sixth annual Kansas State University Conference on Applied Statistics in Agriculture, held in Manhattan, Kansas, April 27 - April 29, 2014.