Using height to adjust for interplot interference in spring wheat yield trials

1999 ◽  
Vol 79 (2) ◽  
pp. 169-174 ◽  
Author(s):  
F. R. Clarke ◽  
R. J. Baker ◽  
R. M. Depauw
Keyword(s):  
Historical Data ◽  
Final Height ◽  
Wheat Yield ◽  
Analysis Of Covariance ◽  
Data Sets ◽  
Average Height ◽  
Test Plot ◽  
Height Difference ◽  
Interplot Interference ◽  
Yield Trials

Interplot interference can occur in yield trials when genotypes vary for height. We investigated the possibility of adjusting yield for interplot interference by using height measurements in an analysis of covariance. Computer simulation showed that genetic correlation between height and yield would have no impact on analysis of covariance, as well as showing that analysis of covariance would reduce precision in the absence of interference due to height difference. Sixty-five data sets from yield trials conducted in Saskatchewan in 1992, 1993 and 1994 were analyzed to see if analysis of covariance would improve precision of yield estimates. Forty percent of the historical data sets showed evidence of significant interplot interference. In those trials in which interference occurred, yield decreased an average of 0.39% for every centimetre by which the average height of the flanking plots exceeded that of the test plot. Using final height to adjust for interplot interference is effective when genotypes vary for height and when the regression coefficient is significant. Key words: Interplot interference, yield adjustment, height difference, covariate

USE OF MOVING MEANS IN WHEAT YIELD TRIALS

1973 ◽  
Vol 53 (3) ◽  
pp. 447-450 ◽  
Author(s):  
T. F. TOWNLEY-SMITH ◽  
E. A. HURD
Keyword(s):  
Experimental Error ◽  
Wheat Yield ◽  
Error Variance ◽  
Analysis Of Covariance ◽  
Test Plant ◽  
Lower Error ◽  
Yield Trials

The efficiency of adjustments employing repeated controls was compared with the efficiency of moving mean adjustments in yield of wheat. Results reported show the moving mean of adjacent hybrid plots to give superior control of the experimental error. The best number of adjacent controls was tested and found to vary widely from test to test. Plant breeders may have to run several analyses to obtain the most accurate adjustment. An analysis of covariance was made on some trials to see if this technique would avoid overadjustment and give lower error variance. With few exceptions neither the use of covariance nor control plots gave as great a reduction in error variance as the optimum adjustment obtained by using the moving mean of adjacent plots.

Language change across a lifetime: A historical micro-perspective

2021 ◽  
Vol 7 (s2) ◽  
Author(s):  
Alexander Bergs
Keyword(s):  
Communities Of Practice ◽  
Social Factors ◽  
Language Use ◽  
Language Change ◽  
Historical Data ◽  
Data Sets ◽  
The Social ◽  
Challenges And Opportunities ◽  
Linguistic Behavior ◽  
Micro Analysis

Abstract This paper focuses on the micro-analysis of historical data, which allows us to investigate language use across the lifetime of individual speakers. Certain concepts, such as social network analysis or communities of practice, put individual speakers and their social embeddedness and dynamicity at the center of attention. This means that intra-speaker variation can be described and analyzed in quite some detail in certain historical data sets. The paper presents some exemplary empirical analyses of the diachronic linguistic behavior of individual speakers/writers in fifteenth to seventeenth century England. It discusses the social factors that influence this behavior, with an emphasis on the methodological and theoretical challenges and opportunities when investigating intra-speaker variation and change.

Reduced space optimal analysis for historical data sets: 136 years of Atlantic sea surface temperatures

1997 ◽  
Vol 102 (C13) ◽  
pp. 27835-27860 ◽  
Author(s):  
Alexey Kaplan ◽  
Yochanan Kushnir ◽  
Mark A. Cane ◽  
M. Benno Blumenthal
Keyword(s):  
Historical Data ◽  
Sea Surface ◽  
Data Sets ◽  
Sea Surface Temperatures ◽  
Optimal Analysis ◽  
Surface Temperatures ◽  
Reduced Space

Developing an Empirical Yield-Prediction Model Based on Wheat and Wild Oat (Avena fatua) Density, Nitrogen and Herbicide Rate, and Growing-Season Precipitation

Weed Science ◽  
2007 ◽  
Vol 55 (6) ◽  
pp. 652-664 ◽  
Author(s):  
N. C. Wagner ◽  
B. D. Maxwell ◽  
M. L. Taper ◽  
L. J. Rew
Keyword(s):  
Field Data ◽  
Wheat Yield ◽  
Growing Season ◽  
Predictor Variables ◽  
Data Sets ◽  
Wild Oat ◽  
Best Fitting ◽  
Weed Infestation ◽  
Growing Season Precipitation ◽  
Better Than

To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R2= 0.71), a double-hyperbolic equation including three input predictor variables (R2= 0.63), and a nonlinear model including all five predictor variables (R2= 0.56). The double-hyperbolic, three-predictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the five-variable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our five-variable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with anR2of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation.

Experimenting on the past: a case study on changing analysability in English ly-adverbs

2017 ◽  
Vol 21 (2) ◽  
pp. 317-340 ◽  
Author(s):  
HENDRIK DE SMET ◽  
FREEK VAN DE VELDE
Keyword(s):  
Negative Correlation ◽  
Historical Data ◽  
Frequency Change ◽  
Data Sets ◽  
The Past ◽  
Analogous Composition ◽  
Using Data ◽  
Complex Words ◽  
Experimental Findings

While it is undoubtedly true that historical data do not lend themselves well to the reproduction of experimental findings, the availability of increasingly extensive data sets has brought some experimenting within practical reach. This means that certain predictions based on a combination of synchronic observations and uniformitarian thinking are now testable. Synchronic evidence shows a negative correlation between analysability in morphologically complex words and various measures of frequency. It is therefore expected that when the frequency of morphologically complex items changes, their analysability will change along with this. If analysability decreases, this should in turn be reflected in decreasing sensitivity to priming by items with analogous composition. The latter prediction is in principle testable on diachronic data, offering a way of verifying the diachronic effect of frequency change on analysability. In this spirit, the present article examines the relation between changing frequency and priming sensitivity, as a proxy to analysability. This is done for a sample of 250 English ly-adverbs, such as roughly, blindly, publicly, etc. over the period 1950–2005, using data from the Hansard Corpus. Some of the expected relations between frequency and analysability can be shown to hold, albeit with great variation across lexical items. At the same time, much of the variation in our measure of analysability cannot be accounted for by frequency or frequency change alone.

Nonstationarity of Production Relationships in Exploited Populations

1987 ◽  
Vol 44 (S2) ◽  
pp. s156-s165 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Environmental Changes ◽  
Historical Data ◽  
Stock Assessment ◽  
Policy Implications ◽  
Data Sets ◽  
Stock Structure ◽  
Stock Size ◽  
Rate Processes ◽  
And Shifts ◽  
Time Invariant

Stock assessment usually proceeds from the assumption that there are time-invariant relationships between stock size and rate processes such as recruitment, although such relationships are difficult to discern due to noise caused by factors other than stock size. There are good biological reasons not to trust this assumption in exploited populations, where persistent environmental changes and shifts in stock structure may cause various parameters to change. Graphical and statistical procedures can be used to detect this nonstationarity in historical data sets for which stock size has varied so as to repeatedly sample a range of sizes. The policy implications of nonstationarity depend on whether the changes are clearly observable as deviations from known, Song-term baseline responses. If the changes are observable, it is usually best to pretend that the current deviation will persist unless strong constraints on policy change make it necessary to plan for changes that may occur far into the future. If the changes are not observable (the usual case), then it is necessary to make a difficult policy choice between passively waiting for informative stock responses versus actively experimenting with harvest rates so as to quickly get information about responses over a range of stock sizes.

Using SNP arrays to leverage historical data sets for improved prediction accuracy and study of G×E×M in peach

2021 ◽  
pp. 133-140
Author(s):  
C. Hardner ◽  
K. Gasic ◽  
C. da Silva Linge ◽  
M. Worthington ◽  
D. Byrne ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Historical Data ◽  
Data Sets ◽  
Snp Arrays

scholarly journals Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials

Crop Science ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 491-507 ◽  
Author(s):  
Brian P. Ward ◽  
Gina Brown-Guedira ◽  
Priyanka Tyagi ◽  
Frederic L. Kolb ◽  
David A. Van Sanford ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Wheat Yield ◽  
Early Generation ◽  
Selection Models ◽  
Yield Trials

Defusing Technology:Technology Diffusion in British Columbia

1993 ◽  
Vol 9 (1) ◽  
pp. 46-61 ◽  
Author(s):  
Arminée Kazanjian ◽  
Kathryn Friesen
Keyword(s):  
British Columbia ◽  
Historical Data ◽  
Current Data ◽  
Fiscal Year ◽  
Data Sets ◽  
Canadian Province ◽  
Data Set ◽  
Time Periods ◽  
Institutional Profile ◽  
Areas Of Interest

AbstractIn order to explore the diffusion of the selected technologies in one Canadian province (British Columbia), two administrative data sets were analyzed. The data included over 40 million payment records for each fiscal year on medical services provided to British Columbia residents (2,968,769 in 1988) and information on physical facilities, services, and personnel from 138 hospitals in the province. Three specific time periods were examined in each data set, starting with 1979–80 and ending with the most current data available at the time. The detailed retrospective analysis of laboratory and imaging technologies provides historical data in three areas of interest: (a) patterns of diffusion and volume of utilization, (b) institutional profile, and (c) provider profile. The framework for the analysis focused, where possible, on the examination of determinants of diffusion that may be amenable to policy influence.

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