The Section was established in 1973 on the basis of a didactic team of experimental theory and biometrics established in 1970. The Section was organised and headed for many years until 2000 by Prof. Tadeusz Przybysz, in the years 2000-2011 the head was Prof. Mirosława Wesołowska-Janczarek, and in the years 2011-2020 by Dr Izabela Kuna-Broniowska.

From 1 October 2020. The Department was renamed into the Laboratory of Experimental Theory and Biometry, with Dr Urszula Bronowicka-Mielniczuk appointed as its head.


Scientific research conducted at the Laboratory focuses primarily on the theory of experiment and applications of mathematical statistics in agricultural sciences, including agricultural engineering. Research topics include linear models of one and many variables, analysis of variance and covariance of various experimental systems, analysis of crop rotation experiments, estimation of parameters and study of their properties, study of effectiveness of systems, analysis of factorial experiments taking into account a control object, regression analysis, as well as analysis of growth curves taking into account accompanying variables. It also covered methods for estimating variance and covariance components in random and mixed models, and problems associated with determining sample size:

  • development of single and multivariable linear models for systems in crop rotation experiments
  • determine conditions for estimability of parameters and hypothesis testing
  • determination of the efficiency of these systems
  • methods of analysis of variance and covariance
  • provide an evaluation of the relative power of formulations in the most commonly used experimental systems used in agricultural research
  • to generalise Henderson's methods for estimating variance components to multiple variables
  • to develop different models for growth curves with time-varying background variables
  • Clarification of the conditions for estimability of parameters in the different variants of the profile sum model
  • generalisation of the Behoffer's theorem for determining sample sizes
  • to provide a new indicator for testing the goodness of fit of estimated functions
  • demonstrating the applicability of the multivariate growth curve method for the determination of regression equations and their comparison for different groups of objects under the assumption of heteroscedasticity of variance
  • comparing different tests for verifying hypotheses on the identity of regression equations for the studied groups