How Can Data Science Provide Protection and Improve Growth in Forestry?
Genstat includes a set of comprehensive tools for modelling forestry tree breeding data, including linear mixed model facilities for spatial analyses, repeated measures, multi-environment and meta-trial data, QTL linkage and GWAS analysis for identifying the genetic factors underlying phenotypic variation in trees. You can also use GGE biplots, AMMI models, Finlay & Wilkinson joint regression analysis and stability coefficients for exploring the phenotypic performance of tree cultivars in different environments.
Additionally, Genstat includes experimental design tools to generate robust and efficient experimental designs, including randomised block, split-plot, row-column and cyclic designs for field trials and laboratory experiments.
Data from diverse sources is easily imported and made ready for analysis using efficient data preparation tools, and Genstat’s data visualization options help to identify insights from your statistical analyses to truly get the most from your data.