The VSNi Team25 October 2023
VSNi's vision: A world without hunger, powered by data-driven breeding
The world's population is growing rapidly, and the challenge of feeding everyone is becoming more and more pressing. According to the Food and Agriculture Organization, our global population is projected to reach 9.7 billion by 2050, therefore food production must increase by 70% to meet the needs of this growing population. Climate change, pests, and diseases are all threatening our ability to produce enough food to meet the needs of everyone on the planet. Two-thirds of human caloric intake are dependent on just four crops: wheat, rice, maize, and soybeans, whose yields are all negatively impacted by changes in global temperature.
But there is hope. Data-driven innovative breeding is helping us to overcome these challenges and create a more sustainable and food-secure future.
Data analysis is essential for addressing the challenges of food production and we at VSNi envision a future where these challenges are eradicated through the power of extracting meaningful insights from data. After all, most companies have a wealth of breeding data, but the question is: are they getting the best from it? In this blog, we explore the vital role of data analysis in plant, animal, and aquaculture breeding programs.
Advancing plant breeding for food security
Robust data analysis is essential to enhance global food security. Plant breeders use a variety of data analysis tools and techniques, such as genomic selection, phenomics, and multi-environment trials (MET).
Genomic selection identifies and predicts plant performance based on genetic markers. This information can then be used to select superior genotypes more efficiently to develop more adaptable, nutritious, and sustainable crops. Phenomics measures plant traits helping breeders identify traits associated with yield, disease resistance, and other important characteristics. MET analysis looks to ensure crop varieties are adaptable to diverse growing conditions in multiple environments. These data-driven approaches enable breeders to identify yield-influencing factors and select resilient crop varieties more efficiently.
Improving livestock performance through data strategies
In livestock breeding programs, data analysis is used to identify genetic markers associated with desirable traits, such as growth rate, feed conversion efficiency, and resistance to diseases such as mastitis, to select animals with the best genetic potential. Data analysis also involves the use of pedigree and performance records to estimate heritabilities and genetic correlations, helping breeders make informed decisions about which animals to mate to improve specific traits. These data-driven strategies enable animal breeders to develop livestock that are more efficient in converting feed into protein, disease-resistant, and adapted to various environmental conditions.
Enhancing aquaculture productivity
Data analysis is essential for enhancing aquaculture productivity and sustainability. By analysing data on water quality parameters, feeding regimes, growth rates, and disease prevalence, aquaculture operators can optimise production systems to maximise fish health and growth. Genetic analysis and performance data can also be used to develop aquaculture stocks that are better suited to specific production environments, resulting in higher yields and reduced environmental impact.
VSNi's ambition and commitment
VSNi's vision is to eradicate the challenges of global food production through the effective application of data analytics and technology. We believe that by empowering researchers, breeders, and scientists with world-leading analytical software and services, we can transform the global food landscape and overcome obstacles that hinder food security and sustainability.
Our flagship software products, ASReml and Genstat, provide highly valued and intuitively usable tools for data analysis and statistical modelling. Our consultancy services support customers in harnessing the full potential of their data to drive continuous, sustainable improvements in food production.
In conclusion, data analysis is indispensable for addressing the challenges of global food production. We believe that by working together, we can create a more sustainable and food-secure future for everyone.
Dr. Ruth Butler15 November 2023
Experimental Design: What are Balance, Efficiency and Independence?
Dr. Salvador A. Gezan08 November 2023
Statistical inference is the process of drawing some conclusions about a population based on the sample data at hand.
Salvador A. Gezan01 November 2023
An important aim when fitting linear mixed models (LMM) is the use of random effect estimates. In some analyses, such as genetic evaluations, the main objective of the analysis is to obtain these estimates.