Forestry
Data Analysis for Forestry
The forestry sector is of major ecological, economic, and social importance. Foresters must plan and implement sustainable management of forest ecosystems while dealing with external factors such as climate change, population growth, loss of genetic diversity and naturally occurring fires. Technology such as (GIS) maps and satellite imagery makes it possible to precisely monitor forests, obtaining real-time data for measuring forest depth and growth.
The ability to gather accurate genomic information in forest breeding programs enables you to increase the genetic gain of tree species and varieties. VSNi provides the analytical solutions you need to bring viable research and results to the forestry sector.
How Can Data Science Provide Protection and Improve Growth in Forestry?
For over 20 years VSNi has developed a range of data and analytics software to support scientists, researchers and breeders within the biosciences. Designed by statisticians with non-statisticians in mind, our products are ideal for users looking for exceptional decision-making tools that can accommodate increasingly large and complex datasets
Today, those who work within forestry must be knowledgeable in general biology, botany, genetics, soil science, climatology, hydrology, economics and forest management. We understand this and our software is tailored to suit all such disciplines. Our role is to support you in achieving your goals and as such promise an unparalleled level of support to assist in your research and forest management.
Our data analysis software provides all the tools you need to bring viable results from your forestry data analyses. You can:
- Analyse large, complex datasets easily and efficiently
- Assist in defining factors that affect your research
- Define key traits to help you discover and create new methods of regeneration
- Discover patterns in vast sets of data
- Determine risk factors
- Examine traits of agro-economic importance
- Assist in increasing yield
- Integrate forestry and climatic data
Our Forestry Software Solutions
VSNi creates easy-to-use statistical software to help your forestry operation thrive. We also have fully documented user guides, knowledge bases and video tutorials, making VSNi’s products easy to learn – but should you need more assistance you can even get help directly from the team who developed the software (us!).
Genstat
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.
AsReml
ASReml is powerful statistical software specially designed for mixed models using Residual Maximum Likelihood (REML). Trials measured over several years can be evaluated with ASReml allowing you to model flexible temporal correlations together with random effects.
The traditional approach used to analyse tree breeding progeny trials in multiple sites was to either assume a unique error variance or to correct the data by the site-specific error variance. Using ASReml, it is possible to use alternative methods, either explicitly fitting a site-specific error variance (but keeping a unique additive genetic variance) or fitting site-specific additive and residual variances. Using a multivariate analysis approach in ASReml, where the expression of a trait in each site is considered a different variable, is recommended and commonly used by those in tree breeding programmes.
ASReml is commonly used in tree commercial breeding programs, so we have extensive model terms to allow for multi-trait and multi-environment (MET) analyses, and also for reading complex matrices, often involved with genomic selection. Linear mixed-effects models in ASReml provide a rich and flexible tool for the analysis of many data sets commonly arising in foresty sciences and breeding programs.
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.
ASReml is powerful statistical software specially designed for mixed models using Residual Maximum Likelihood (REML). Trials measured over several years can be evaluated with ASReml allowing you to model flexible temporal correlations together with random effects.
The traditional approach used to analyse tree breeding progeny trials in multiple sites was to either assume a unique error variance or to correct the data by the site-specific error variance. Using ASReml, it is possible to use alternative methods, either explicitly fitting a site-specific error variance (but keeping a unique additive genetic variance) or fitting site-specific additive and residual variances. Using a multivariate analysis approach in ASReml, where the expression of a trait in each site is considered a different variable, is recommended and commonly used by those in tree breeding programmes.
ASReml is commonly used in tree commercial breeding programs, so we have extensive model terms to allow for multi-trait and multi-environment (MET) analyses, and also for reading complex matrices, often involved with genomic selection. Linear mixed-effects models in ASReml provide a rich and flexible tool for the analysis of many data sets commonly arising in foresty sciences and breeding programs.
Case Studies
Our analytics software and consulting services are chosen by seed, plant, aqua and animal breeding companies worldwide to support and inform the development of new varieties, strains, stocks and breeds.
Competitive Genetics: Exploring the impact of direct and indirect genetic effects in tree breeding
Competitive Genetics: Exploring the impact of direct and indirect genetic…
Accelerating sugar beet breeding insights for SESVanderHave
Explore SESVanderHave’s H3 Pipeline: Advancing sugar beet breeding through phenotypic…
KWS: supplying seeds to the farming industry
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…
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