ORIGINAL ARTICLE
A mildew infection resistance study of winter barley varieties and their mixtures by the logistic model
More details
Hide details
1
Institute of Mathematics, Poznan University of Technology, poznan, Poland
2
Department of Pests Methods Forecasting and Plant Protection Economy, Institute of Plant Protection, National Research Institute, Poznan, Poland
3
Department of Genetics, Plant Breeding and Seed Production, Wroclaw University of Environmental and Life Sciences, Wroclaw, Poland
4
Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Poznan, Poland
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2020-01-13
Acceptance date: 2020-03-16
Online publication date: 2020-06-01
Corresponding author
Ewa Bakinowska
Institute of Mathematics, Poznan University of Technology, ul. Piotrowo 3A, 60-965, Poznan, Poland
Journal of Plant Protection Research 2020;60(2):207-214
KEYWORDS
TOPICS
ABSTRACT
Biological diversity within a mixture field allows for better use of habitat and agro-technical
conditions by the mixtures, which can be seen by higher and more stable yields
than varieties sown separately. Our studies were conducted in the growing seasons
2011/2012–2014/2015 as field experiments with four winter barley varieties (Bombaj, Gil,
Gregor, Bażant) and three, two- and three-component mixtures (Bombaj/Gil, Bombaj/Gregor,
Gil/Gregor/Bażant). Seven different chemical treatments with fungicides were applied.
The aim of this study was to compare the different varieties of winter barley with their mixtures
for resistance to powdery mildew infection. To achieve this aim the logistic model for
the analysis of data was used. Of the varieties under consideration, the best and the most resistant
variety was Gregor, while the weakest and the most susceptible to diseases (powdery
mildew) was Gil. This variety was also significantly weaker than any of the other mixtures
taken into account. Moreover, it was so weak that when it was included in mixtures with
other varieties, it weakened these mixtures as well.
FUNDING
This study was partially funded by the Ministry of Science and Higher Education (grant number 04/43/DSPB/0088)
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (20)
1.
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. DOI:
http://www.up.poznan.pl/biomet....
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. DOI:
https://doi.org/10.2478/v10045....
3.
Bakinowska E., Pilarczyk W., Zawieja B. 2016. Analysis of downy mildew data on field pea: An empirical comparison of two logistic models. Acta Agriculturae Scandinavica, Section B – Soil & Plant Science 66: 107–116. DOI:
https://doi.org/10.1080/090647....
4.
Chen Z., Kuo L. 2001. A note on the estimation of the multinomial logit model with random effects. The American Statistician 55: 89–95. DOI:
http://www.jstor.org/stable/30....
5.
Finckh M.R., Gacek E.S., Czembor H.J., Wolfe M.S. 1999. Host frequency and density effects on powdery mildew and yield in mixtures of barley cultivars. Plant Pathology 48: 807–816. DOI:
https://bsppjournals.onlinelib....
6.
Finckh M.R., Gacek E.S., Goyeau H., Lannou C., Merz U., Mundt C.C., Munk L., Nadziak J., Newton A.C., de Vallavieille-Poppe C., Wolfe M.S. 2000. Cereal variety and species mixtures in practice, with emphasis on disease resistance. Agronomie 20: 813–837. DOI:
https://doi.org/10.1051/agro:2....
7.
Hampel D., Hartmann J. 2011. Testing frost resistance for cereals in the Czech Republic. Cultivar Testing Bulletin 33: 83–90.
10.
Mccullagh P., Nelder J.A. 1989. Generalized Linear Models. 2nd ed., Chapman and Hall, London, UK.
11.
Mcculloch C.E., Searle S.R. 2001. Generalized, Linear, and Mixed Models. Wiley, New York, USA, 156 pp.
12.
Mila A.L., Carriquiry A.L., Yang X.B. 2004. Logistic regression modeling of prevalence of soybean sclerotinia stem rot in the north central Region of the United States. Phytopathology 94: 102–110.
13.
Miller M.E., Davis C.H.S., Landis J.R. 1993. The analysis of longitudinal polytomous data: Generalized estimating equations and connections with weighted least squares. Biometrics 49: 1033–1044. DOI: 10.2307/2532245.
14.
Newton A.C., Guy D.C., Nadziak J., Gacek E.S. 2002. The effect of inoculum pressure, germplasm selection and environment on spring barley cultivar mixtures efficacy. Euphytica 125: 325–335. DOI:
https://link.springer.com/arti....
15.
Newton A.C., Begg G.S., Swanston J.S. 2009. Deployment of diversity for enhanced crop function. Annals of Applied Biology 154 (3): 309–322. DOI:
https://doi.org/10.1111/j.1744....
16.
Philips S.L., Wolfe M.S. 2005. Evolutionary plant breeding for low input systems. Journal of Agricultural Science 143 (4): 245–254. DOI:
https://doi.org/10.1017/S00218....
17.
SAS Institute. 1997. SAS/STAT software: Changes and enhancements through release 6.12. SAS Inst., Cary, NC.
18.
Tratwal A., Law J., Philpott H., Horwell A., Garner J. 2007. The possibilities of reduction of winter barley chemical protection by growing variety mixtures. Part II. Effect on yield. Journal of Plant Protection Research 47 (1): 79–86.
19.
Tratwal A., Walczak F. 2010. Powdery mildew (Blumeria graminis) and pest occurrence reduction in spring cereals mixtures. Journal of Plant Protection Research 50 (3): 372–377. DOI: 10.2478/v10045-010-0068-3.