For centuries people have been breeding plants, trying to fine-tune individual plants to obtain or change a particular trait. Today plant breeders look at addressing concerns such as drought or environment tolerance, pesticide, herbicide, fungi or bacteria tolerance, improving nutritional value or increasing yields. This, by definition, ranges from “classical” propagation techniques to the modern methods of molecular breeding and genetical modification. However, one thing is very apparent: the scientists engaged in any form of plant breeding need to be sure their data analysis is based on sound, solid and secure statistics.
KWS is one of the world’s leading suppliers of seeds to the farming industry and it is therefore no surprise that research into plant breeding and seeds is a crucial part of their work. The research and development teams at KWS focus on yield, seed quality, resistance to disease and pests, enhancing the nutrient quality and improved processing capability of the plants.
Recently the researchers at KWS decided to change their data analysis software portfolio by introducing GenStat. During the last 2 years main emphasis was put on establishing GenStat for statistical analysis of phenomic data from their field.
GenStat was chosen, because it was possible to analyse data from trials with large Alpha Designs with the REML techniques. Plus, GenStat’s REML algorithm dealt with missing values in a more superior way than the previous software. REML itself can be used to analyse models with several types of error variation (multi-level models) and to fit models to correlated data like repeated measurements. GenStat’s powerful command language streamlined the data analyses, reducing the calculation time by at least 70%.
Additionally GenStat’s efficient data handling meant that far less data pre-processing was needed. GenStat allows the full integration into automated analysis pipelines, thus decreasing calculation times by 30%. This allows the flexible addition of new statistical methods into the pipelines.
KWS researchers use a wide variety of different factors in their trials, using Block Designs, Alpha Designs, Lattice, Split Plot, Multi factorial Experiments. GenStat allows the researchers to easily design and analyse these and other types of experiments. GenStat also effortlessly handles large and complex data sets, which will be essential for the analysis of genomic data and their association with phenomic informations. Combine these factors with GenStat’s ability to easily export and import data from other databases and the plant breeder has very powerful analysis tools.
As well as the extensive technical capabilities of GenStat, KWS chose GenStat because of its history within agricultural science and research; it was originally designed by statisticians working on agricultural research at Rothamsted Research; where Fisher, Yates and Nelder (to mention but 3) developed statistical techniques that are central to modern statistics. GenStat’s connection with Rothamsted continues today. Developments in each new version of GenStat reflect the needs of the agricultural scientist, including ANOVA, design of experiments a host of multivariate analysis techniques and linear mixed model analysis. A huge variety and complexity of data analysis is possible in GenStat, and an understanding of the requirements of experiments in this area is reflected in the terminology and thinking behind GenStat. But GenStat’s history not only shows its suitability to any form of agricultural research but also shows the stability of its performance; it has been tried and tested for over 30 years.
Coupled with the power and background of GenStat, VSNi were able to provide a bespoke consultancy service to KWS to assist with their specific needs; this is possible because our developers have been working with agricultural researchers and statistics for years. The VSNi statisticians were able to talk to the researchers at KWS, in their language to find more efficient ways to run their analyses.
In a subject area that demands precision and security GenStat provides the data analysis solution that removes the unknowns surrounding the research. You can trust the statistics within GenStat, because it is developed by people who know and understand the issues in agricultural research today.