Research Methods in Weed Science: Statistics

Ritz, Christian; Kniss, Andrew R.; Streibig, J. C.

doi:10.1614/ws-d-13-00159.1

Cited by 59 publications

(49 citation statements)

References 22 publications

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“…For simplicity, we have restricted attention here to polynomial models, but the general principles, including lack-of-fit testing, can also be applied to intrinsically non-linear models with or without asymptote such as the sigmoidal growth curves (logistic, Gompertz, etc.) (Schabenberger & Pierce, 2002;Chapter 5;Ritz, Kniss, & Streibig, 2015). Also, one may apply a generalized linear model framework (Lee et al, 2006;McCullagh & Nelder, 1989), where inverse polynomials provide a particularly convenient way to model non-linear relationships, also when the treatment design entails qualitative factors.…”

Section: Discussionmentioning

confidence: 99%

A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels

Piepho

Edmondson²

2018

J Agronomy Crop Science

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Agronomic experiments are often complex and difficult to interpret, and the proper use of appropriate statistical methodology is essential for an efficient and reliable analysis. In this paper, the basics of the statistical analysis of designed experiments are discussed using real examples from agricultural field trials. Factorial designs allow for the study of two or more treatment factors in the same experiment, and here we discuss the analysis of factorial designs for both qualitative and quantitative level treatment factors. Where treatment factors have quantitative levels, models of treatment effects are essential for efficient analysis and in this paper we discuss the use experimental data is now readily available, and we demonstrate the use of two alternative software packages, the SAS package and the R language. The main purpose of the paper is to exemplify standard statistical methodology for routine analysis of designed experiments in agricultural research, but in our discussion we also provide some references for the study of more advanced methodology.factorial analysis, linear mixed models, polynomial regression, R, repeated-measures analysis, response surface models, SAS, split-plot analysis | INTRODUCTIONAgronomic experiments are often difficult and complex both to plan and to execute and may depend on many factors that can affect both efficiency and reliability. They are usually expensive and may sometimes take many years to accomplish. In addition, agronomic experiments are typically subject to high background variability due to effects such as fertility trends in fields or initial weight differences between animals or year-to-year variability in weather as well as to the high natural variability of biological and agronomic systems. wileyonlinelibrary.com/journal/jac | 1 the aims of the experiment and produces accurate and efficient information that provides a simple and parsimonious description of the results while still taking proper account of the error structure of the design. A properly chosen analysis will provide not only a full summary of the observed data but will also give additional insight into the effects of the treatments on the system under study.The focus of this paper is on designed experiments with treatment structures that can be modelled by polynomial regression methods. All the experiments considered are comparative, meaning that the focus is exclusively on comparisons between treatments in the same experiment (Bailey, 2008;Giesbrecht & Gumpertz, 2004;Hinkelmann & Kempthorne, 1994). Polynomial regression is a standard linear model methodology that fits naturally within the standard analysis of variance. We will show how modern mixed model computer software can be used for the routine analysis of blocked experiments based on a range of block and treatment structures, including designs with repeated observations on the same experimental units.The paper does not explicitly discuss the design of experiments, but it will be assumed that the example data sets have been col...

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Section: Discussionmentioning

confidence: 99%

A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels

Piepho

Edmondson²

2018

J Agronomy Crop Science

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“…B. subalternans percentage of control was visually evaluated at 21 days after treatment (DAT), using a scale of 0 to 100%, in which 0% represents no control, and 100% represents plant death. Data were subjected to analysis of variance (ANOVA) and the four‐parameter nonlinear logistic regression model was fitted using the drc package in R software version 3.5.1 (R Core Team, Vienna, Austria) . The dose providing 50% response (LD 50 ) was determined and the RF was calculated as the ratio of LD 50 between the GR and GS biotypes.…”

Section: Methodsmentioning

confidence: 99%

“…Data were subjected to analysis of variance (ANOVA) and the four-parameter nonlinear logistic regression model was fitted using the drc package in R software version 3.5.1 (R Core Team, Vienna, Austria). 16 The dose providing 50% response (LD 50 ) was determined and the RF was calculated as the ratio of LD 50 between the GR and GS biotypes.…”

Section: Glyphosate Dose-response and Effect Of Low Temperaturesmentioning

confidence: 99%

A novel TIPT double mutation in EPSPS conferring glyphosate resistance in tetraploid Bidens subalternans

Takano

Fernandes

Adegas

et al. 2019

Pest Management Science

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BACKGROUND Bidens subalternans (greater beggarticks) is a tetraploid and troublesome weed infesting annual crops in most tropical regions of the world. A glyphosate‐resistant (GR) B. subalternans biotype was detected in a soybean field from Paraguay. A series of physiological and molecular analyses were conducted to elucidate its resistance mechanisms. RESULTS The GR biotype had a high level of resistance (> 15‐fold LD50), relative to a glyphosate‐susceptible (GS) biotype. Shikimate accumulation was up to ten‐fold greater for GS compared with GR. We found no differences in sensitivity when plants were treated and kept under lower (10/4 °C) or higher temperatures (25/20 °C). GS and GR had the same relative EPSPS gene copy number, and similar glyphosate absorption and translocation rates. Neither biotype metabolized glyphosate. A double amino acid substitution (TIPT – Thr102Ile and Pro106Thr) was found in only one EPSPS allele from one of the two EPSPS homoeologs present in tetraploid GR B. subalternans. CONCLUSION This is the first report of a TIPT double mutation conferring high levels of glyphosate resistance in a weed species. The presence of both wild‐type and TIPT mutant EPSPS on the polyploid genome of GR B. subalternans may offset a potential fitness cost, requiring additional research to confirm the absence of deleterious effects. © 2019 Society of Chemical Industry

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“…The relationship between spring wheat grain yield loss and wild oat density was simulated using rectangular hyperbolic function described by Cousens (1985) and Ritz et al (2012). To fit the rectangular hyperbolic curve drm function from the package drc was used, specifying the two-parameter Michaelis-Menten as the model, then function yieldLoss was used to estimate the parameters I and A.…”

Section: Methodsmentioning

confidence: 99%

Influence of wild oat plant density on spring wheat yield

Ņečajeva¹,

Erdmane²,

Isoda-Krasovska³

et al. 2017

Zemdirbyste-Agriculture

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Wild oat (Avena fatua L.) is the most serious weed infesting cereal fields in Latvia. To predict possible yield loss at different wild oat densities field trials were established in four consecutive years, each year in a different spring wheat field in the southern part of Latvia. Growth dynamics and biomass of both crop and wild oat and grain yield were measured. Yield loss model developed by Cousens (1985) and exponential decay model were fitted to the data from each year. Grain yield and biomass of both spring wheat and wild oat significantly differed between the years. The average grain yield of spring wheat in all treatments was 4443.38-7127.02 kg ha -1 . The targeted wild oat density was 0, 1, 2, 4, 8, 16, 32, 100, 200 and 500 plants m -2 . The achieved density in the field trial reached from 0 to 332-466 plants m -2 . The emergence of wild oat seedlings lasted for 35 to 56 days after sowing, depending on the year. The initial hypothesis that low wild oat plant density may cause grain yield loss of spring wheat was supported by the results of the field trials. There was substantial variation in model parameters among the years of the trials, 5% yield loss was estimated to result from wild oat density ranging from 3 to 54 plants m -2 in different years. The variation of spring wheat yield loss and wild oat competitiveness might be explained by different meteorological conditions in the years 2013-2016 and different nutrient supply in the trial fields. Further research is necessary to find the most influential factors that determine yield loss caused by wild oat. The results are important to raise awareness of the harm caused by wild oat in cereals in the Baltic region.

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Research Methods in Weed Science: Statistics

Cited by 59 publications

References 22 publications

A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels

A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels

A novel TIPT double mutation in EPSPS conferring glyphosate resistance in tetraploid Bidens subalternans

Influence of wild oat plant density on spring wheat yield

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