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Soil Science
Data Analysis for Soil Science
Humanity faces a dilemma: how do we combine the sustainable use of soil with its crucial role in food production? The connection between soil, energy, water and food production directly impacts factors like climate change and population growth. The soil degradation process, which includes erosion, acidification, and soil organic carbon depletion all compromise soil fertility and consequentially food security. Â
New technologies such as remote sensing, GIS, advanced modelling and data analysis techniques enable soil scientists to better understand soil processes and interactions.Â
At VSNi, we are committed to helping soil scientists to ensure accurate research and data analysis, allowing you to get the best results from your soil.
How Can Data Analysis Advance Soil Science?
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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 are able to accommodate increasingly large and complex datasets
Soil science is said to be one of the oldest agricultural sciences in the world. Not only is it one of the oldest but one of the most important; without soil, the earth could not support life — shame there isn’t an infinite supply. That’s one of the reasons that VSNi are committed to helping soil scientists to ensure accurate research and data analysis, giving you real-time insights that can help you make the best decisions for your soil.
- Spatial analysis and mapping using kriging and co-kriging
- Multivariate analysis techniques such as principal components and cluster analysis
- Integration between agri and climatic communities
Our data analysis software provides all the tools you need to bring viable results from your soil science data analyses. You can:
- Analyse large, complex datasets easily and efficiently
- Assist in defining factors that affect your soil
- Define key traits to help you discover and create new dynamics
- Discover patterns in vast sets of data
- Determine risk factors
- Examine traits of agro-economic importance
- Integrate agricultural and climatic data
- Produce graphs to explore and present your data
With thousands of analyses, procedures and directives a single webpage isn’t nearly enough to show off what our software can do. The following is a small sample of some of the possibilities:
- Design experiments and plan soil monitoring systems
- Analysis of variance to compare the effects of soil treatments and determine trends
- Mixed modelling (REML) to correlate soil properties
- Compare treatment effects on diversity, biomass, population, etc.
- Model soil parameters using generalized linear modelling
Our Soil Science Software Solutions
VSNI create easy-to-use statistical software to help your soil science research 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
Analysis of variance is a crucial step in extracting information from efficiently designed experiments and surveys in soil science. Genstat offers a statistically accurate ANOVA menu for analysis of variance when comparing treatments. Multivariate analysis in Genstat can uncover patterns in multivariate soil data by means of principal components analysis, cluster analysis, canonical correlation analysis and much more.
Genstat provides an easy-to-use environment where only a few simple menu clicks are needed to undertake simple or more complex analyses, bringing reliable and accurate analytics to your soil analysis. Make predictions from large soil science data sets using data mining techniques, such as support vector machines, regression trees, random regression forests, and neural networks.
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 and truly get the most from your data.
AsReml
ASReml  is specially designed for data analysis utilising mixed models using Residual Maximum Likelihood (REML). Using REML improves the estimation of the fixed (or random) treatment effects by modelling more accurately the spatial correlations between observations in two-dimensions using a linear mixed model. It supports big and complex data analysis and advanced statistics.  Â
ASReml strengths include:
- The analysis of (un)balanced longitudinal data
- Repeated measures data (multivariate analysis of variance and spline type models)
- The analysis of (un)balanced designed experiments,
- The analysis of multi-environment trials and meta-analysis
- The analysis of univariate and multivariate animal breeding and genetics data (involving a relationship matrix for correlated effects),
- The analysis of regular or irregular spatial data
Analysis of variance is a crucial step in extracting information from efficiently designed experiments and surveys in soil science. Genstat offers a statistically accurate ANOVA menu for analysis of variance when comparing treatments. Multivariate analysis in Genstat can uncover patterns in multivariate soil data by means of principal components analysis, cluster analysis, canonical correlation analysis and much more.
Genstat provides an easy-to-use environment where only a few simple menu clicks are needed to undertake simple or more complex analyses, bringing reliable and accurate analytics to your soil analysis. Make predictions from large soil science data sets using data mining techniques, such as support vector machines, regression trees, random regression forests, and neural networks.
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 and truly get the most from your data.
ASReml  is specially designed for data analysis utilising mixed models using Residual Maximum Likelihood (REML). Using REML improves the estimation of the fixed (or random) treatment effects by modelling more accurately the spatial correlations between observations in two-dimensions using a linear mixed model. It supports big and complex data analysis and advanced statistics.  Â
ASReml strengths include:
- The analysis of (un)balanced longitudinal data
- Repeated measures data (multivariate analysis of variance and spline type models)
- The analysis of (un)balanced designed experiments,
- The analysis of multi-environment trials and meta-analysis
- The analysis of univariate and multivariate animal breeding and genetics data (involving a relationship matrix for correlated effects),
- The analysis of regular or irregular spatial data
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.
Enhancing agronomic research through precise statistical analysis with Genstat
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