2000 - 12th Annual Conference ProceedingsCopyright (c) 2020 Kansas State University Libraries All rights reserved.
https://newprairiepress.org/agstatconference/2000/proceedings
Recent Events in 2000 - 12th Annual Conference Proceedingsen-usThu, 23 Jul 2020 17:17:43 PDT3600SEPARATION OF SINGLE GENE EFFECTS FROM ADDITIVE-DOMINANCE GENETIC MODELS
https://newprairiepress.org/agstatconference/2000/proceedings/20
https://newprairiepress.org/agstatconference/2000/proceedings/20Sun, 30 Apr 2000 15:45:00 PDT
Separation of single gene and polygenic effects would be useful in crop improvement. In this study, additive-dominance model with a single qualitative gene based on diallel crosses of parents and progeny F_{1}s (or F_{2}s) was examined. The mixed linear model approach, minimum norm quadratic unbiased estimation (MINQUE), was used to estimate the variance and covariance components and single gene effects. Monte Carlo simulation was used to evaluate the efficiency of each parameter estimated from the MINQUE approach for this genetic model. The results of 200 simulations indicated that estimates of variance components and single gene effects were unbiased when setting different single gene effects for parents and F_{1}s (or F_{2}s). Results also indicated that estimates of variances and single gene effects were very similar for both genetic populations. Therefore, single gene effects could be effectively separated and estimated by this approach. This research should aid the extension of this model to cases that involve multiple linked or unlinked genes (or genetic markers) and other complex ploygenic models. For illustration, a real data set comprised of eight parents of upland cotton (Gossypium hirsutum L.) with normal leaf and one parent with okra leaf, and their 44 F_{2}s were used to estimate the variance components and the genetic effects of the okra leaf gene on fiber traits.
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Jixiang Wu et al.PERFORMANCE OF THE EXACT AND CHI-SQUARE TESTS ON SPARSE CONTINGENCY TABLES
https://newprairiepress.org/agstatconference/2000/proceedings/19
https://newprairiepress.org/agstatconference/2000/proceedings/19Sun, 30 Apr 2000 14:30:00 PDT
A cross-sectional observational study design was used to determine the prevalence of Escherichia coli 0157:H7 in wild deer feces. Samples were voluntarily submitted at a number of different locations. In order to determine if the proportions of E. coli 0157: H7 positive samples submitted were equal for each of the 26 locations, a 26 by 2 contingency table was analyzed. There were only four E. coli 0157:H7 positive samples, which resulted in a sparse table. It is possible to obtain statistically significant results in sparse tables using Fisher's exact test, whereas the chi-square test is generally unreliable in such situations. Thus, Fisher's exact test should be considered when small expected cell counts bring into question the validity of the chi-square test. However, the statistical conclusions based on either the exact test or an asymptotic chi-square test are shown to vary drastically by slight alterations in the distribution of non-empty cells. Therefore, a different statistical conclusion very easily could have been reached if a volunteer had submitted a sample at a different location. In addition, we show that the computational times for exact tests in SASĀ® can be an applicational limitation.
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D. G. Renter et al.AN AUTOREGRESSION MODEL FOR A PAIRED WATERSHED COMPARISON
https://newprairiepress.org/agstatconference/2000/proceedings/18
https://newprairiepress.org/agstatconference/2000/proceedings/18Sun, 30 Apr 2000 14:00:00 PDT
Analysis of water quality data from a paired watershed design is needed to determine if a best fertilizer management practice reduces a specific water quality variable compared to a conventional fertilizer management practice. This study examines an existing recommended method of analysis for paired watershed designs, simple analysis of covariance (ANCOVA) on time aggregated data, then offers two autoregression analyses (AR) as alternatives. The first approach models the sequence of paired differences and estimates its 95% confidence band. The second approach develops individual watershed AR models then examines the joint 95% confidence interval about the predicted difference. A reliability analysis on the water quality data reveals that the data for the controlled watershed, i.e., the covariate, has a sizable measurement error, a factor that is not considered in the usual ANCOVA model. The AR methods avoid the measurement error and other inherent problems with the published recommended method. Graphically both AR analyses are similar and reveal three distinct trend phases: a period of continued similarity; a period of transition; and a period of sustained change. The model for the sequence of paired differences is the easier one of the two AR methods to use and interpret because its trend model of splined linear segments readily defines each response phase. Hence, we recommend it over the given alternatives. It offers water resources researchers an effective and readily adoptable analysis option.
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D. Meek et al.OPTIMUM DESIGN FOR EXPONENTIAL MODEL USING AN EXPONENTIAL LOSS FUNCTION AND ITS APPLICATIONS IN AGRICULTURE
https://newprairiepress.org/agstatconference/2000/proceedings/17
https://newprairiepress.org/agstatconference/2000/proceedings/17Sun, 30 Apr 2000 13:30:00 PDT
Accelerated life testing has been used for years in engineering. Test units are run at high stress and fail sooner than at design stress. The lifetime at design stress is estimated by extrapolation using a regression model. This paper considers the optimum design of accelerated life tests in which two levels of stresses, high and low are constantly applied. For the exponential model the expected value of an exponential loss function of the arameter is to be used. The initial sample proportion allocated to the high stress which minimizes the expected loss function is determined. In the agriculture context, plants or animal may be the items placed on test and dosage of a chemicals, amount of fertilizer, may be the stress variable. In this paper I suggest several potential applications of constant testing in agriculture and present inferential procedure in the case in which observations have the exponential distribution.
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Imad H. KhamisUNCERTAINTY ANALYSIS OF A PIPE MODEL BASED ON CORRELATED DISTRIBUTIONS
https://newprairiepress.org/agstatconference/2000/proceedings/16
https://newprairiepress.org/agstatconference/2000/proceedings/16Sun, 30 Apr 2000 13:00:00 PDT
Traditionally, uncertainty analysis of complex simulation models has been conducted based on the assumption of that the components of the model are independent. In practice, correlation is universal in ecosystems. This study applied Bayesian estimation and rejection sampling to generate correlated random samples for an uncertainty analysis of a process based forest growth model, a pipe model. Comparison of error budgets built using independent and correlated distributions shows that correlated distributions are very important to provide reasonable and realistic simulation and uncertainty analysis.
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Shoufan Fang et al.A SIMULATION STUDY TO EVALUATE PROC MIXED ANALYSIS OF REPEATED MEASURES DATA
https://newprairiepress.org/agstatconference/2000/proceedings/15
https://newprairiepress.org/agstatconference/2000/proceedings/15Sun, 30 Apr 2000 12:00:00 PDT
Experiments with repeated measurements are common in pharmaceutical trials, agricultural research, and other biological disciplines. Many aspects of the analysis of such experiments remain controversial. With increasingly sophisticated software becoming available, e.g. PROC MIXED, data analysts have more options from which to choose, and hence more questions about the value and impact of these options. These dilemmas include the following. MIXED offers a number of different correlated error models and several criteria for choosing among competing models. How do the model selection criteria behave? How is inference affected if the correlated error model is misspecified? Some texts use random between subject error effects in the model in addition to correlated errors. Others use only the correlated error structure. How does this affect inference? MIXED has several ways to determine degrees of freedom, including a new option to use Kenward and Roger's procedure. The Kenward-Roger procedure also corrects test statistics and standard errors for bias. How do the various degree-of-freedom options compare? When is the bias serious enough to worry about and how well does the Kenward-Roger option work? Some models are prone to convergence problems. When are these most likely to occur and how should they be addressed? We present the results of several simulation studies conducted to help understand the impact of various decisions on the small sample behavior of typical situations that arise in animal health and agricultural settings.
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LeAnna Guerin et al.SOME STRATEGIES FOR SELECTING AND FITTING COVARIANCE STRUCTURES FOR REPEATED MEASURES
https://newprairiepress.org/agstatconference/2000/proceedings/14
https://newprairiepress.org/agstatconference/2000/proceedings/14Sun, 30 Apr 2000 11:45:00 PDT
Since in longitudinal studies the covariance structure is often regarded as a nuisance parameter, the strategy has been to use a parsimonious covariance model that describes adequately the observed data and permits better inference on the parameters of interest. In this paper we present some diagnostic tools to choose an appropriate covariance structure and discuss some strategies for fitting it. The main diagnostic tool is the "residual", computed as the standardized difference between the elements of the fitted covariance (concentration or correlation) matrix and the corresponding unstructured matrix. SAS Proc Calis is a very efficient procedure that fits many covariance structures in models with no fixed effects. Based on this procedure, we discuss some strategies to choose initial values and improve convergence problems in certain commonly used structures.
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Raul E. MacchiavelliBIAS IN PRINCIPAL COMPONENTS ANALYSIS DUE TO CORRELATED OBSERVATIONS
https://newprairiepress.org/agstatconference/2000/proceedings/13
https://newprairiepress.org/agstatconference/2000/proceedings/13Sun, 30 Apr 2000 11:30:00 PDT
A common practice in many scientific disciplines is to take measurements on several different variables on each unit from a designed experiment. This practice is cost efficient and results in data that may be analyzed using multivariate statistical methods. Usually, principal components analysis (PCA) is conducted by decomposing the covariance matrix of the several dependent variables using eigenanalysis without accounting for possible correlations among the observations. To evaluate how correlated observations bias PCA results, we used algebraic derivation and simulation for several different types of correlation structures. Our results indicated that sampling error generally had a much larger impact on the bias of PCA results than correlation between the observations. If we ignore the sampling error and there are no time trends or treatment effects, the PC's and the percent variance explained by a PC is not affected by correlated observations, however the eigenvalues are biased. If the sampling error is considered, for moderate sized correlations between observations and reasonably sized designs, bias was generally small enough to ignore for the first PC, otherwise SAS PROC MIXED may be used to easily correct for correlated observations, resulting in less bias in the PCA results.
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Hong Jiang et al.DEVELOPMENT OF WILD OAT SEED DISPERSAL DISTRIBUTIONS USING AN INDIVIDUAL-PLANT GROWTH SIMULATION MODEL
https://newprairiepress.org/agstatconference/2000/proceedings/12
https://newprairiepress.org/agstatconference/2000/proceedings/12Sun, 30 Apr 2000 11:10:00 PDT
An individual-plant growth simulation model for quantifying competition between spring barley and wild oat has been previously described (price, Shafii, and Thill, 1994). Individual plants within a population were modeled independently and competition between plants was determined by resource demand within plant specific areas-of-influence. Calibration of the model to spring barley and wild oat biomass data was performed and shown to have a high degree of accuracy under mono culture conditions. The work presented here applies the specified model to a larger scale simulation for the purpose of demonstrating seed dispersal in wild oat. This is accomplished by breaking the annual cycle of wild oat seeds into the three integrated phases: Growth and development, dissemination, and dormancy. The growth and development phase is handled using the individual-plant growth model. The subsequent dispersal of seeds is described using two-dimensional stochastic processes. Finally, a life table analysis, based on predetermined transition probabilities, is used to establish the makeup of populations in the following season. A sensitivity analysis which examines various biological, ecological, and mechanical components over a 10 year period is carried out and the potential use in weed science education is demonstrated.
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William J. Price et al.APPLICATION OF COMPUTER INTENSIVE METHODS TO EVALUATE THE PERFORMANCE OF A SAMPLING DESIGN FOR USE IN COTTON INSECT PEST MANAGEMENT
https://newprairiepress.org/agstatconference/2000/proceedings/11
https://newprairiepress.org/agstatconference/2000/proceedings/11Sun, 30 Apr 2000 11:00:00 PDT
A scouting protocol for cotton insect pests was developed which combines high resolution, multispectral remotely sensed imagery with a belt transect that crosses rows of cotton. Imagery was used to determine sample site selection while estimating plant bug abundance in a more than 200 ac. cotton field in 1997. Tarnished plant bug (Lygus lineolaris) counts were acquired using a standard drop cloth for each of eight rows along a transect. The sample data indicated that plant bug population densities spatially vary as a function of different spectral (color) classes present on the imagery. We postulate that such classified images correlate to differences in crop phenology, and plant bug populations (especially from early to mid-season) aggregate themselves by these habitat differences. Therefore, the population dynamics of Lygus, and possibly other species, can be better understood by combining the transect-based sampling plan with remotely sensed imagery. To verify and validate this claim, a computer intensive approach was utilized to simulate the performance of different sampling plans. The comparison is accomplished with a combinatorial algorithm that exhaustively enumerates the original data into unique subsets. These subsets correspond to results that could be expected from the use of traditional or alternative sampling plans and compared to results from the candidate plan actually used. The results of the enumerative analysis show the benefit of multi-band, remotely sensed imagery combined with the use of large sized sample units to improve sampling efficiency (and without the need to have large sample sizes). It is of great benefit that the enumerative algorithm provided answers to questions of interest without having to complete additional fieldwork.
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J. L. Willers et al.