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Discussion on Competition for Spatial Statistics for Large Datasets J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210724
Roman Flury, Reinhard FurrerWe discuss the experiences and results of the AppStatUZH team’s participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different subcompetitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging

Discussion on “Competition on Spatial Statistics for Large Datasets” J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210723
Denis Allard, Lucia Clarotto, Thomas Opitz, Thomas RomaryWe discuss the methods and results of the RESSTE team in the competition on spatial statistics for large datasets. In the first subcompetition, we implemented block approaches both for the estimation of the covariance parameters and for prediction using ordinary kriging. In the second subcompetition, a twostage procedure was adopted. In the first stage, the marginal distribution is estimated neglecting

Spatial Generalized Linear Models with NonGaussian Translation Processes J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210720
Robert RichardsonIt is not generally feasible to pick any given marginal distribution and assume there will be a way to apply a link function to add fixed and random effects in a spatial generalized linear model. We introduce an adjustment to spatial copula processes called a nonGaussian translation process that will allow for the specification of any marginal distribution with a closedform density function in a

Discussion on Competition for Spatial Statistics for Large Datasets J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210713
Yasumasa MatsudaThe team of Tohoku University attended subcompetition 2b in the competition on spatial statistics for large datasets, where prediction on 100,000 testing points were to be constructed conditional on 900,000 training points. We chose a covariance tapering approach in a simplified way to manage one million spatial data points. Dividing \([0,1]^2\) into \(30\times 30\) subregions with equal area, we

Competition on Spatial Statistics for Large Datasets J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210708
Huang Huang, Sameh Abdulah, Ying Sun, Hatem Ltaief, David E. Keyes, Marc G. GentonAs spatial datasets are becoming increasingly large and unwieldy, exact inference on spatial models becomes computationally prohibitive. Various approximation methods have been proposed to reduce the computational burden. Although comprehensive reviews on these approximation methods exist, comparisons of their performances are limited to small and medium sizes of datasets for a few selected methods

A HigherOrder Singular Value Decomposition Tensor Emulator for Spatiotemporal Simulators J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210630
Giri Gopalan, Christopher K. WikleWe introduce methodology to construct an emulator for environmental and ecological Spatiotemporal processes that uses the higherorder singular value decomposition (HOSVD) as an extension of singular value decomposition (SVD) approaches to emulation. Some important advantages of the method are that it allows for the use of a combination of supervised learning methods (e.g., random forests and Gaussian

Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210512
Jonatan A. González, Francisco J. RodríguezCortés, Elvira Romano, Jorge MateuSpatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the secondorder properties of the point pattern, and it may be done locally by using local secondorder characteristics (LISA). Some properties of this local structure solve the problem of classification into feature and clutter

Correction to: Correcting Bias in Survival Probabilities for Partially Monitored Populations via Integrated Models J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210510
Blanca Sarzo, Ruth King, David Conesa, Jonas HentatiSundbergA Correction to this paper has been published: https://doi.org/10.1007/s13253020004231

Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210509
ShinFu Tsai, ChihChien Shen, ChenTuo LiaoBayesian optimization is incorporated into genomic prediction to identify the best genotype from a candidate population. Several expected improvement (EI) criteria are proposed for the Bayesian optimization. The iterative search process of the optimization consists of two main steps. First, a genomic BLUP (GBLUP) prediction model is constructed using the phenotype and genotype data of a training set

Generalized Single Index Models and Jensen Effects on Reproduction and Survival J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210430
Zi Ye, Giles Hooker, Stephen P. EllnerEnvironmental variability often has substantial impacts on natural populations and communities through its effects on the performance of individuals. Because organisms’ responses to environmental conditions are often nonlinear (e.g., decreasing performance on both sides of an optimal temperature), the mean response is often different from the response in the mean environment. Ye et al. (Ann Appl Stat

MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210413
Douglas R. M. Azevedo, Marcos O. Prates, Dipankar BandyopadhyayExploring spatial patterns in the context of disease mapping is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. In most cases, multiple count responses (corresponding to disease incidences of multiple types, such as cancer in men and women) are recorded at each spatial location, which may exhibit similar spatial patterns in addition to diseasespecific

Semiparametric MixedEffects Ordinary Differential Equation Models with HeavyTailed Distributions J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210405
Baisen Liu, Liangliang Wang, Yunlong Nie, Jiguo CaoOrdinary differential equation (ODE) models are popularly used to describe complex dynamical systems. When estimating ODE parameters from noisy data, a common distribution assumption is using the Gaussian distribution. It is known that the Gaussian distribution is not robust when abnormal data exist. In this article, we develop a hierarchical semiparametric mixedeffects ODE model for longitudinal

Correction to: Variance Propagation for Density Surface Models J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210403
Mark V. Bravington, David L. Miller, Sharon L. HedleyA Correction to this paper has been published: https://doi.org/10.1007/s13253021004382

VaryingCoefficient Stochastic Differential Equations with Applications in Ecology J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210326
Théo Michelot, Richard Glennie, Catriona Harris, Len ThomasStochastic differential equations (SDEs) are popular tools to analyse time series data in many areas, such as mathematical finance, physics, and biology. They provide a mechanistic description of the phenomenon of interest, and their parameters often have a clear interpretation. These advantages come at the cost of requiring a relatively simple model specification. We propose a flexible model for SDEs

A Statistical Perspective on the Challenges in Molecular Microbial Biology J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210324
Pratheepa Jeganathan, Susan P. HolmesHigh throughput sequencing (HTS)based technology enables identifying and quantifying nonculturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal

Augmented Block Designs for Unreplicated Trials J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210307
Linda M. HainesThis paper is concerned with augmented block designs for unreplicated trials for which the underlying model comprises fixed block and fixed treatment effects. Explicit expressions for the average scaled variances and the maximum variances of estimates of the pairwise differences between controls, between unreplicated test lines and between controls and unreplicated test lines are developed and demonstrate

Joint Modeling of Distances and Times in PointCount Surveys J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210306
Adam MartinSchwarze, Jarad Niemi, Philip DixonRemoval and distance modeling are two common methods to adjust counts for imperfect detection in pointcount surveys. Several recent articles have formulated models to combine them into a distanceremoval framework. We observe that these models fall into two groups building from different assumptions about the joint distribution of observed distances and first times to detection. One approach assumes

Spatially Varying Coefficient Models with Sign Preservation of the Coefficient Functions J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210228
Myungjin Kim, Li Wang, Yuyu ZhouThis paper considers the estimation and inference of spatially varying coefficient models, while preserving the sign of the coefficient functions. In practice, there are various situations where coefficient functions are assumed to be in a certain subspace. For example, they should be either nonnegative or nonpositive on a domain by their nature. However, optimization on a global space of coefficient

Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210226
Yuan Yan, HsinCheng Huang, Marc G. GentonMotivated by the need to analyze readily available data collected in space and time, especially in environmental sciences, we propose a parsimonious spatiotemporal model for time series data on a spatial grid. In essence, our model is a vector autoregressive model that utilizes the spatial structure to achieve parsimony of autoregressive matrices at two levels. The first level ensures the sparsity

Variance Propagation for Density Surface Models J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210223
Mark V. Bravington, David L. Miller, Sharon L. HedleySpatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a twostage approach for estimating spatially varying density from distance sampling data. First, detection probabilities—perhaps depending on covariates—are estimated based on details of individual encounters; next, local

Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210222
Kuangyu Wen, Ximing Wu, David J. LeathamThis study is motivated by the estimation of many crop yield densities, each with a small number of observations. These densities tend to resemble one another if they are spatially proximate. To gain flexibility and improve efficiency, we propose kernelbased estimators refined by empirical likelihood probability weights derived under spatially smoothed moment conditions. We construct spatially smoothed

Judgment Poststratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210218
Omer Ozturk, Olena KravchukThis paper presents novel estimators for a judgment poststratified (JPS) sample, which combine the ranking information from different methods or rankers. A JPS sample divides the units in the original simple random sample (SRS) into several ranking groups based on the relative positions (ranks) of the units in their individual small comparison sets. Ranks in the comparison sets may be assigned with

A Sample CovarianceBased Approach For Spatial Binary Data J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210128
Sahar Zarmehri, Ephraim M. Hanks, Lin LinThe field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an infectious disease. We investigate two genetic patterns in spatially referenced singlenucleotide polymorphisms (SNPs): isolation by distance and isolation

ContinuousTime DiscreteState Modeling for Deep Whale Dives J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210116
Joshua Hewitt, Robert S. Schick, Alan E. GelfandUnderstanding unexposed/baseline behavior of marine mammals is required to assess the effects of increasing levels of anthropogenic noise exposure in the marine environment. However, quantifying variation in the baseline behavior of whales is challenging due to the fact that they spend much of their time at depth, and therefore, their diving behavior is not directly observable. Data collection employs

Correcting Bias in Survival Probabilities for Partially Monitored Populations via Integrated Models J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210115
Blanca Sarzo, Ruth King, David Conesa, Jonas HentatiSundbergWe provide an integrated capture–recapture–recovery framework for partially monitored populations. In these studies, live resightings are only observable at a set of monitored locations, so that if an individual leaves these specific locations, they become unavailable for capture. Additional ringrecovery data reduce the corresponding bias obtained in the survival probability estimates from capture–recapture

Combining Environmental Area Frame Surveys of a Finite Population J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210107
Wilmer Prentius, Xin Zhao, Anton GrafströmNew ways to combine data from multiple environmental area frame surveys of a finite population are being introduced. Environmental surveys often sample finite populations through area frames. However, to combine multiple surveys without risking bias, design components (inclusion probabilities, etc.) are needed at unit level of the finite population. We show how to derive the design components and exemplify

Optimizing the Allocation of Trials to Subregions in Multienvironment Crop Variety Testing J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20210107
Maryna Prus, HansPeter PiephoNew crop varieties are extensively tested in multienvironment trials in order to obtain a solid empirical basis for recommendations to farmers. When the target population of environments is large and heterogeneous, a division into subregions is often advantageous. When designing such trials, the question arises how to allocate trials to the different subregions. We consider a solution to this problem

Hierarchical Modeling of Structural Coefficients for Heterogeneous Networks with an Application to Animal Production Systems J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20201028
K. Chitakasempornkul, G. J. M. Rosa, A. Jager, N. M. BelloUnderstanding the interconnections between performance outcomes in a system is increasingly important for integrated management. Structural equation models (SEMs) are a type of multiplevariable modeling strategy that allows investigation of directionality in the association between outcome variables, thereby providing insight into their interconnections as putative causal links defining a functional

Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire $$\hbox {PM}_{2.5}$$ Concentration Forecasting J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20201015
Suman Majumder, Yawen Guan, Brian J. Reich, Susan O’Neill, Ana G. RappoldFine particulate matter, PM$_{2.5}$, has been documented to have adverse health effects and wildland fires are a major contributor to PM$_{2.5}$ air pollution in the US. Forecasters use numerical models to predict PM$_{2.5}$ concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify

Modeling Crop Phenology in the US Corn Belt Using Spatially Referenced SMOS Satellite Data J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20201014
Colin LewisBeck, Zhengyuan Zhu, Victoria Walker, Brian HornbuckleSatellite measurements follow the growth and senescence of vegetation aid in monitoring crop development within and across growing seasons. For example, identifying when crops reach their peak growth stage or modeling the seasonal growing cycle is useful for agronomists and climatologists. In this paper, we analyze remote sensing data from an intensively cultivated agricultural region in the Midwest

Testing Independence Between Two Spatial Random Fields J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20201009
ShihHao Huang, HsinCheng Huang, Ruey S. Tsay, Guangming PanIn this article, we consider testing independence between two spatial Gaussian random fields evaluated, respectively, at p and q locations with sample size n, where both p and q are allowed to be larger than n. We impose no spatial stationarity and no parametric structure for the two random fields. Our approach is based on canonical correlation analysis (CCA). But instead of applying CCA directly to

Multilevel Block Designs for Comparative Experiments J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20201008
Rodney N. EdmondsonComplete replicate block designs are fully efficient for treatment effects and are the designs of choice for many agricultural field experiments. For experiments with a large number of treatments, however, they may not provide good control of variability over the whole experimental area. Nested incomplete block designs with a single level of nesting can then improve ‘withinblock’ homogeneity for moderate

Guest Editors’ Introduction to the Special Issue on “Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture” J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20201001
HansPeter Piepho, Robert J. Tempelman, Emlyn R. WilliamsThe Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture covers a select set of topics currently of primary importance in the field. Efficient use of resources in agricultural research, as well as valid statistical inference, requires good designs, and this special issue

Optimization of Selective Phenotyping and Population Design for Genomic Prediction J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200926
Nicolas Heslot, Vitaliy FeoktistovGenomic prediction, the joint analysis of highdensity molecular marker data and phenotype to predict the performance of individuals for breeding purpose, is now a method used in routine in many plant and animal breeding programs. This opens several new design questions such as how to select a subset of preexisting individuals for phenotyping based on the molecular marker data to estimate marker effects

Linear Variance, Psplines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related? J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200918
Martin P. Boer, HansPeter Piepho, Emlyn R. WilliamsNearestneighbour methods based on first differences are an approach to spatial analysis of field trials with a long history, going back to the early work by Papadakis first published in 1937. These methods are closely related to a geostatistical model that assumes spatial covariance to be a linear function of distance. Recently, Psplines have been proposed as a flexible alternative to spatial analysis

Spatial Sampling Design Using Generalized Neyman–Scott Process J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200915
Sze Him Leung, Ji Meng Loh, Chun Yip Yau, Zhengyuan ZhuIn this paper we introduce a new procedure for spatial sampling design. It is found in previous studies (Zhu and Stein in J Agric Biol Environ Stat 11:24–44, 2006) that the optimal sampling design for spatial prediction with estimated parameters is nearly regular with a few clustered points. The pattern is similar to a generalization of the Neyman–Scott (GNS) process (Yau and Loh in Statistica Sinica

Flexible Modeling of Variable Asymmetries in CrossCovariance Functions for Multivariate Random Fields J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200910
Ghulam A. Qadir, Carolina Euán, Ying SunThe geostatistical analysis of multivariate spatial data for inference as well as joint predictions (cokriging) ordinarily relies on modeling of the marginal and crosscovariance functions. While the former quantifies the spatial dependence within variables, the latter quantifies the spatial dependence across distinct variables. The marginal covariance functions are always symmetric; however, the

Do Spatial Designs Outperform Classic Experimental Designs? J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200829
Raegan Hoefler, Pablo GonzálezBarrios, Madhav Bhatta, Jose A. R. Nunes, Ines Berro, Rafael S. Nalin, Alejandra Borges, Eduardo Covarrubias, Luis DiazGarcia, Martin Quincke, Lucia GutierrezControlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency

Bias Correction in Estimating Proportions by Imperfect Pooled Testing J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200819
Graham Hepworth, Brad J. BiggerstaffIn the estimation of proportions by pooled testing, the MLE is biased. Hepworth and Biggerstaff (JABES, 22:602–614, 2017) proposed an estimator based on the bias correction method of Firth (Biometrika 80:27–38, 1993) and showed that it is almost unbiased across a range of pooled testing problems involving no misclassification. We now extend their work to allow for imperfect testing. We derive the estimator

A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200817
Stephen N. Freeman, Nicholas J. B. Isaac, Panagiotis Besbeas, Emily B. Dennis, Byron J. T. MorganBiodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of timevarying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a statespace formulation for new multispecies biodiversity indicators, based on rates

Nonparametric Bayesian Functional MetaRegression: Applications in Environmental Epidemiology J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200809
Jaeeun Yu, Jinsu Park, Taeryon Choi, Masahiro Hashizume, Yoonhee Kim, Yasushi Honda, Yeonseung ChungTwostage metaanalysis has been popularly used in epidemiological studies to investigate an association between environmental exposure and health response by analyzing timeseries data collected from multiple locations. The first stage estimates the locationspecific association, while the second stage pools the associations across locations. The second stage often incorporates locationspecific predictors

Spatial Spread Sampling Using Weakly Associated Vectors J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200725
Raphaël Jauslin, Yves TilléGeographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting wellspread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion

The Design of EarlyStage Plant Breeding Trials Using Genetic Relatedness J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200716
Brian R. Cullis, Alison B. Smith, Nicole A. Cocks, David G. ButlerThe use of appropriate statistical methods has a key role in improving the accuracy of selection decisions in a plant breeding program. This is particularly important in the early stages of testing in which selections are based on data from a limited number of field trials that include large numbers of breeding lines with minimal replication. The method of analysis currently recommended for earlystage

Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200715
Yuanzhi Huang, Steven G. Gilmour, Kalliopi Mylona, Peter GoosBiochemical mechanism studies often assume statistical models derived from Michaelis–Menten kinetics, which are used to approximate initial reaction rate data given the concentration level of a single substrate. In experiments dealing with industrial applications, however, there are typically a wide range of kinetic profiles where more than one factor is controlled. We focus on optimal design of such

A Nonstationary Spatial Covariance Model for Processes Driven by Point Sources J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200703
Joshua L. WarrenWe introduce a new nonstationary spatial covariance model for analyzing geostatistical pointreferenced data that contain point sources (i.e., known locations that impact the outcome). Our model is based on viewing the spatial domain on the polar coordinate scale, with the point source representing the reference location. As a result, we incorporate distances from the point source and angles of the

Vecchia Approximations of GaussianProcess Predictions J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200623
Matthias Katzfuss, Joseph Guinness, Wenlong Gong, Daniel ZilberGaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussianprocess approximations that use basis functions at multiple resolutions to achieve fast inference and that can (approximately) represent any spatial covariance structure. We consider two special cases of this multiresolutionapproximation

PseudoLikelihood or Quadrature? What We Thought We Knew, What We Think We Know, and What We Are Still Trying to Figure Out J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200621
Walt Stroup, Elizabeth ClaassenTwo predominant computing methods for generalized linear mixed models (GLMMs) are linearization, e.g., pseudolikelihood (PL), and integral approximation, e.g., Gauss–Hermite quadrature. The primary GLMM package in R, LME4, only uses integral approximation. The primary GLMM procedure in SAS®, PROC GLIMMIX, was originally developed using linearization, but integral approximation methods were added in

A Bayesian Markov Model with PólyaGamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200615
Toryn L. J. Schafer, Christopher K. Wikle, Jay A. VonBank, Bart M. Ballard, Mitch D. WeegmanThe use of accelerometers in wildlife tracking provides a finescale data source for understanding animal behavior and decision making. Current methods in movement ecology focus on behavior as a driver of movement mechanisms. Our Markov model is a flexible and efficient method for inference related to effects on behavior that considers dependence between current and past behaviors. We applied this

Adjusting for Spatial Effects in Genomic Prediction J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200605
Xiaojun Mao, Somak Dutta, Raymond K. W. Wong, Dan NettletonThis paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect phenotypic measurements of plants. We consider a Gaussian random field model with an additive covariance structure that incorporates genotype effects, spatial effects and subpopulation effects. An empirical study shows the

A Distancebased Method for Spatial Prediction in the Presence of Trend J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200601
Carlos E. Melo, Jorge Mateu, Oscar O. MeloA new method based on distances for modeling continuous random data in Gaussian random fields is presented. In nonstationary cases in which a trend or drift is present, dealing with information in regionalized mixed variables (including categorical, discrete and continuous variables) is common in geosciences and environmental sciences. The proposed distancebased method is used in a geostatistical

Systematic Statistical Analysis of Microbial Data from Dilution Series J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200528
J. Andrés Christen, Albert E. ParkerIn microbial studies, samples are often treated under different experimental conditions and then tested for microbial survival. A technique, dating back to the 1880s, consists of diluting the samples several times and incubating each dilution to verify the existence of microbial colonyforming units or CFU’s, seen by the naked eye. The main problem in the dilution series data analysis is the uncertainty

Estimating Changes in the Observed Relationship Between Humidity and Temperature Using Noncrossing Quantile Smoothing Splines J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200515
Karen A. McKinnon, Andrew PoppickThe impacts of warm season heat extremes are dependent on both temperature and humidity, so it is critical to properly model their relationship, including how it may be changing. This presents statistical challenges because the bivariate temperature–humidity (measured here by dew point) distribution is complex and spatially variable. Here, we develop a flexible, semiparametric model based on quantile

History of the Statistical Design of Agricultural Experiments J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200505
L. Rob VerdoorenIn Section 1 the approach of improving crop yields by the development of agriculture and addition of various mineral or organic substances in the last 200–300 years is investigated. In Section 2 the principle of randomized experiments is treated. Section 3 describes the variety trials of field crops. The elimination of effects in two dimensions is shown in Section 4 on Row–Column designs. Fertilizer

Obtaining a Balanced Area Sample for the Bureau of Land Management Rangeland Survey J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200330
Cindy L. Yu, Jie Li, Michael G. Karl, Todd J. KruegerIn agricultural and environmental surveys, obtaining spatially balanced area samples that are also representative probability samples in the presence of auxiliary variables is a challenge, especially when the study regions have fragmentary boundaries and possess holes of various shapes. This paper describes a sampling procedure that achieves this goal and is implemented in the U.S. Department of the

Projecting FloodInducing Precipitation with a Bayesian Analogue Model J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200327
Gregory P. Bopp, Benjamin A. Shaby, Chris E. Forest, Alfonso MejíaThe hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment factors such as topography that cause runoff accumulation. Temporal dependence can also increase flood risk as storm water drainage systems operating at capacity

Estimation in Complex Sampling Designs Based on Resampling Methods J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200312
Bardia PanahbehaghGenerally, to select a representative sample of the population, we use a combination of several probabilistic sampling methods which is called a complex sampling design. A complex sampling design usually needs very sophisticated mathematical calculations to provide unbiased estimators of the population parameters. Therefore, only a limited number of sampling designs are commonly used in practice. In

Modeling Partially Surveyed Point Process Data: Inferring Spatial Point Intensity of Geomagnetic Anomalies J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200312
Kenneth A. Flagg, Andrew Hoegh, John J. BorkowskiMany former military training sites contain unexploded ordnance (UXO) and require environmental remediation. For the first phase of UXO remediation, locations of geomagnetic anomalies are recorded over a subregion of the study area to infer the spatial intensity of anomalies and identify high concentration areas. The data resulting from this sampling process contain locations of anomalies across narrow

Substitutes for the Nonexistent Square Lattice Designs for 36 Varieties J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200304
R. A. Bailey, Peter J. Cameron, L. H. Soicher, E. R. WilliamsSquare lattice designs are often used in trials of new varieties of various agricultural crops. However, there are no square lattice designs for 36 varieties in blocks of size six for four or more replicates. Here, we use three different approaches to construct designs for up to eight replicates. All the designs perform well in terms of giving a low average variance of variety contrasts. Supplementary

Sampling Strategies to Estimate Deer Density by Drive Counts J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200226
Lorenzo Fattorini, Alberto Meriggi, Enrico Merli, Paolo VaruzzaThe best evaluation of deer density can be achieved by accurate drive counts of deer performed in all the suitable wooded patches of the area of interest. This would provide the true density within drive areas which, in turn, should be akin to the true density within the study area. Because the drive of all these areas is prohibitive, only a subset is usually driven. Results are highly dependent on

Estimating Reproduction and Survival of Unmarked Juveniles Using Aerial Images and Marked Adults J. Agric. Biol. Environ. Stat. (IF 1.524) Pub Date : 20200222
Perry J. Williams, Cody Schroeder, Pat JacksonMethods for estimating juvenile survival of wildlife populations often rely on intensive data collection efforts to capture and uniquely mark individual juveniles and observe them through time. Capturing juveniles in a time frame sufficient to estimate survival can be challenging due to narrow and stochastic windows of opportunity. For many animals, juvenile survival depends on postnatal parental care