An evaluation of the efficiency of plant protection products via nonlinear statistical methods – a simulation study

Ewa Skotarczak; Ewa Bakinowska; Kamila Tomaszyk

Biometrical Letters (2014)

  • Volume: 51, Issue: 2, page 171-179
  • ISSN: 1896-3811

Abstract

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A nonlinear statistical approach was used to evaluate the efficiency of plant protection products. The methodology presented can be implemented when the observations in an experiment are recorded as success or failure. This occurs, for example, when following the application of a herbicide or pesticide, a single weed or insect is classified as alive (failure) or dead (success). Then a higher probability of success means a higher efficiency of the tested product. Using simulated data sets, a comparison was made of three methods based on the logit, probit and threshold models, with special attention to the effect of sample size and number of replications on the accuracy of the estimation of probabilities.

How to cite

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Ewa Skotarczak, Ewa Bakinowska, and Kamila Tomaszyk. "An evaluation of the efficiency of plant protection products via nonlinear statistical methods – a simulation study." Biometrical Letters 51.2 (2014): 171-179. <http://eudml.org/doc/268766>.

@article{EwaSkotarczak2014,
abstract = {A nonlinear statistical approach was used to evaluate the efficiency of plant protection products. The methodology presented can be implemented when the observations in an experiment are recorded as success or failure. This occurs, for example, when following the application of a herbicide or pesticide, a single weed or insect is classified as alive (failure) or dead (success). Then a higher probability of success means a higher efficiency of the tested product. Using simulated data sets, a comparison was made of three methods based on the logit, probit and threshold models, with special attention to the effect of sample size and number of replications on the accuracy of the estimation of probabilities.},
author = {Ewa Skotarczak, Ewa Bakinowska, Kamila Tomaszyk},
journal = {Biometrical Letters},
keywords = {threshold model; logistic model; plant protection; simulated data},
language = {eng},
number = {2},
pages = {171-179},
title = {An evaluation of the efficiency of plant protection products via nonlinear statistical methods – a simulation study},
url = {http://eudml.org/doc/268766},
volume = {51},
year = {2014},
}

TY - JOUR
AU - Ewa Skotarczak
AU - Ewa Bakinowska
AU - Kamila Tomaszyk
TI - An evaluation of the efficiency of plant protection products via nonlinear statistical methods – a simulation study
JO - Biometrical Letters
PY - 2014
VL - 51
IS - 2
SP - 171
EP - 179
AB - A nonlinear statistical approach was used to evaluate the efficiency of plant protection products. The methodology presented can be implemented when the observations in an experiment are recorded as success or failure. This occurs, for example, when following the application of a herbicide or pesticide, a single weed or insect is classified as alive (failure) or dead (success). Then a higher probability of success means a higher efficiency of the tested product. Using simulated data sets, a comparison was made of three methods based on the logit, probit and threshold models, with special attention to the effect of sample size and number of replications on the accuracy of the estimation of probabilities.
LA - eng
KW - threshold model; logistic model; plant protection; simulated data
UR - http://eudml.org/doc/268766
ER -

References

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  2. Bakinowska E., Pilarczyk W., Osiecka A., Wiatr K. (2012): Analysis of downy mildew infection of field pea varieties using the logistic model. Journal of Plant Protection Research 52(2): 264-270. 
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  9. Ritz C., Pipper C.B., Strebig J.C. (2013): Analysis of germination data from agricultural experiments, European Journal of Agronomy 45: 1-6.[WoS][Crossref] 
  10. SAS Institute (1997): SAS/STAT software: Changes and enhancements through release 6.12. SAS Inst., Cary, NC, USA. 
  11. Sørensen D.A., Andersen S, Gianola D., Kørsgaard I. (1995): Bayesian inference in threshold models using Gibbs sampling. Genetics Selection Evolution 27: 229-249.[WoS][Crossref] 
  12. Sørensen D.A, Gianola D. (2002): Likelihood, Bayesian and MCMC methods in quantitative genetics. Springer-Verlag, New York. Zbl1013.62105
  13. Skotarczak E., Molińska A., Moliński K. (2002): Zastosowanie modelu progowego do oceny skuteczności działania wybranych herbicydów. Colloquium Biometryczne 32: 125-132. 

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