Filters
Environmental impact on downy mildew infection for different pea varieties
Summary In general, varieties of any cultivated species can differ in many aspects. One of the desirable traits in breeding new varieties is high yield, which can be significantly reduced by plant diseases. This study is an extension of previous research about the resistance of seed pea lines to downy mildew, and provides an extended analysis of this type of experiment. The probability of a degree of infection is determined based on a logistic model with a multinomial distribution. In the analysis the impact of a fixed effect of variety and random environmental effects is considered. It is shown that the environmental effects (combinations of years and location – macro-environments) significantly influence the resistance to diseases, and the differences among environments are larger than the variability of differences among genotypes over all environments. For two types of soils (light and rich) the most resistant varieties, which are significantly different from the control variety, are indicated.
Biochemical Properties of Bioactive Compounds in the Oil from Polish Varieties of Camelina sativa Cultivated in 2019–2022
AbstractCold‐pressed Camelina oil is a traditional oil registered as a traditional food in Poland. Camelina oil has health‐promoting properties and high oxidative stability. This may be due to the presence of various bioactive antioxidant compounds such as carotenoids, sterols and polyphenols. Bioactive compounds content in Camelina oil depends mainly on the varieties and on the conditions under which the crop was grown therefore the aim of the research was to analyse antioxidant bioactive compounds in oil from different cultivars of Camelina sativa seeds and to determine their relationship with oil parameters.
Notes on parametric functions in the linear model
Summary Starting from a simple Gauss–Markov model, this paper presents notes about criteria for the estimability of parametric functions of the vector of interest in linear models. The results obtained for the transformed models are compared with known results from the literature.
Testing Correlation in a Three-Level Model
AbstractIn this paper, we present a statistical approach to evaluate the relationship between variables observed in a two-factors experiment. We consider a three-level model with covariance structure $${\varvec{\Sigma }} \otimes {\varvec{\Psi }}_1 \otimes {\varvec{\Psi }}_2$$ Σ ⊗ Ψ 1 ⊗ Ψ 2 , where $${\varvec{\Sigma }}$$ Σ is an arbitrary positive definite covariance matrix, and $${\varvec{\Psi }}_1$$ Ψ 1 and $${\varvec{\Psi }}_2$$ Ψ 2 are both correlation matrices with a compound symmetric structure corresponding to two different factors. The Rao’s score test is used to test the hypotheses that observations grouped by one or two factors are uncorrelated. We analyze a fermentation process to illustrate the results. Supplementary materials accompanying this paper appear online.