Agribusiness analytics with Genstat
Rewritten by C. Supakorn
“Crops and livestock animals compete for the same land. The prodigious appetite of affluent nations for food means agribusiness is an important part of modernizing our economy not only helping farmers but also preserving consumers”.
Agribusiness is used to describe the economic sector engaged in farming and farming-related business activities, such as crop production, farm machinery businesses, fertilizer companies, and agri-product processing, marketing, distribution, and sales. This business sector is comprised of interconnected subsectors working to provide goods and services to consumers around the world. With the need to accommodate economic, social, and environmental concerns, organizations, and managers in the sector share many of the challenges that exist in other business value chains.
Today, many individual stakeholders in the agribusiness sector regularly use data to help them make better and faster decisions. On an industry macro level, digital agriculture is pursuing a more integrated vision of the agribusiness value chain for all stakeholders.
Genstat software is a comprehensive statistical system to summarize, display, and analyse data. The software provides an easy-to-use environment, where only a few instructions or selections from a menu are needed to do standard or complex analyses. Genstat, a VSNi product, provides the tools you need to bring reliable and accurate analytics to your agribusiness, and help you reach your goals quickly and efficiently.
Here are some examples of how Genstat can help your agribusiness:
- Integrate agricultural and climatic data
- Identify factors that can benefit you and your clients
- Design and plan agricultural experiments
- Analyse large, complex datasets easily and efficiently
‣ Analysis of variance to compare varieties and treatments
‣ REML facilities to model spatial and temporal data, perform meta analyses and estimate breeding values
‣ GLM, GLMM and HGLM facilities to model non-normal data
‣ Identify patterns in data by means of PCA, cluster analysis and much more
‣ Predictive analysis to assist with optimization
‣ QTL analysis and GGE biplots to identify genetic factors and understand phenotypic variation
- Illustrate data with graphics such as histograms, boxplots, scatter plots, line graphs, trellis plots, contour and 3-dimensional surface plots
- Summarize and compare data with tabular reports, fitted distributions, and standard tests, such as t-tests and various non-parametric tests
- Generate reports, including information in non-English languages such as Chinese and Thai
With thousands of analyses, procedures, and directives a single page isn’t nearly enough to show off Genstat. The following is but a small example of some of the possibilities! To assess the full potential, why not try Genstat for yourself?