Browsing by Subject "Recovery of information"

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Connecting variety trialling systems across two countries
(2025) Piepho, Hans‐Peter; Malik, Waqas Ahmed; Piepho, Hans‐Peter; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Malik, Waqas Ahmed; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
In Europe, before acceptance to a country's national list, crop variety candidates must undergo testing for value for cultivation and use (VCU) in multi‐environment trials. Once a variety is accepted to the national list of a country, it can be marketed in that county. Moreover, it may be marketed in other European countries as well, even if it has not been tested for VCU, and hence, there is no performance assessment in those other countries. This paper investigates how VCU trialling systems of two countries can be connected and integrated so that performance can be assessed for both countries without altering the capacity of the trialling systems. Our statistical calculations based on VCU trial data for maize from Germany and Poland highlight the benefit of a joint analysis of data from two countries. Moreover, we show how the efficiency of each country's VCU testing system can be improved—without altering the overall capacity of the systems in terms of the number of trials and the number of plots per trial—by ensuring that each variety is tested in both countries.
How many checks are needed per cycle in a plant breeding or variety testing programme?
(2025) Piepho, Hans‐Peter; Laidig, Friedrich; Piepho, Hans‐Peter; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Laidig, Friedrich; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
Check varieties are used in plant breeding and variety testing for a number of reasons. One important use of checks is to provide connectivity between years, which facilitates comparison among genotypes of interest that are tested in different years. When long‐term data are available, such comparisons allow an assessment of realized genetic gain (RGG). Here, we consider the question of how many check varieties are needed per cycle for a reliable assessment of RGG. We propose an approach that makes use of variance component estimates for relevant random effects in a linear mixed model and plugs them into an analysis of dummy datasets set up to represent the design options being considered. Our results show that it is useful to employ a larger number of checks and to keep the replacement rate low. Furthermore, there is intercycle information to be recovered, especially when there are few checks and replacement rates are high, so modelling the cycle main effect as random pays off.