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

• Autorzy: Ewa Skotarczak , Ewa Bakinowska , Kamila Tomaszyk
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

threshold model; logistic model; plant protection; simulated data

• Wydawca: De Gruyter Open
• Czasopismo: Biometrical Letters
• Rocznik: 2014
• Tom: 51
• Numer: 2
• Strony: 171-179

Daty

wydano

2014-12-01

online

2014-12-20

Twórcy

autor

Ewa Skotarczak

• Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, Poznań 60-637, Poland,

autor

Ewa Bakinowska

• Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, Poznań 60-637, Poland

autor

Kamila Tomaszyk

• Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, Poznań 60-637, Poland

Bibliografia

• Bakinowska E., Kala R. (2007): An application of logistic models for comparison of varieties of seed pea with respect to lodging. Biometrical Letters 44(2): 143-154.
• 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.
• Burgos N.R., Tranel P.J., Streibig J.C., Davis V.M., Shaner D., Norsworthy J.K., Ritz C. (2013): Review: confirmation of resistance to herbicides and evaluation of resistance levels. Weed Science 61(1): 4-20.[Crossref]
• Falconer D.S. (1960): Introduction to quantitative genetics. Ronald Press Co, New York.
• Finney D.J. (1979): Bioassay and the practice of statistical inference. International Statistical Review 47(1): 1-12.[Crossref]
• Moliński K., Szydłowski M., Szwaczkowski T., Dobek A., Skotarczak E. (2003): An algorithm for genetic variance estimation of reproductive traits under a threshold model. Archives of Animal Breeding 46(1): 85-91.
• McCullagh P., Nelder J.A. (1989): Generalized linear models. 2nd. ed., Chapman and Hall, London.
• Rao C.R., Toutenburg H. (1999): Linear Models. 2nd ed. Springer-Verlag, New York.
• 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]
• SAS Institute (1997): SAS/STAT software: Changes and enhancements through release 6.12. SAS Inst., Cary, NC, USA.
• 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]
• Sørensen D.A, Gianola D. (2002): Likelihood, Bayesian and MCMC methods in quantitative genetics. Springer-Verlag, New York.
• Skotarczak E., Molińska A., Moliński K. (2002): Zastosowanie modelu progowego do oceny skuteczności działania wybranych herbicydów. Colloquium Biometryczne 32: 125-132.

Identyfikatory

DOI

10.2478/bile-2014-0012