Computational Breeding Market

Market Overview

The Global Computational Breeding Market size will significantly grow at a CAGR of 12.3% by forecast period.

The term "computational breeding" alludes to the use of cutting-edge computing techniques, data analytics, and machine learning algorithms to speed up and improve plant and animal breeding processes. In order to pinpoint desirable traits and forecast the results of various breeding situations, this method entails the analysis of sizable amounts of genetic and phenotypic data.

Breeders can expedite breeding, cut costs, and increase the precision of breeding forecasts by using computational methods. Additionally, they can breed for several traits at once, which can lead to breeding plans that are more effective and long-lasting.

Computational breeding methods come in a broad variety, including machine learning algorithms, quantitative trait loci mapping, genomic selection, and genome-wide association studies. By analyzing genetic and phenotypic information from both plants and animals, these methods can be used to predict how various breeding situations will impact the particular traits of interest.

The market for computational breeding is fueled by a number of factors, such as the need to address issues with global food security, the rising demand for breeding techniques that are more effective and sustainable, and the availability of cutting-edge computing and data analytics tools.

The market for computational breeding is the application of computer-based techniques and equipment to enhance plant and animal reproduction. This involves accelerating the breeding process, improving the precision of breeding predictions, and enhancing the effectiveness and sustainability of breeding programs through the use of data analytics, machine learning, and other cutting-edge technologies. Agriculture, forests, and animal breeding are just a few of the sectors where computational breeding has uses. It is driven by a variety of issues, such as the need to handle problems with global food security, the rising demand for more effective and sustainable breeding techniques, and the accessibility of cutting-edge computing and data analytics tools.

During the forecast period, which runs from 2021 to 2029, it is anticipated that growth of the computational breeding market a significant rate. The market was expanding steadily in 2021, and due to key players' increasing adoption of strategies, the market is anticipated to develop over the anticipated time frame.

Market Dynamics

Market Drivers

• Substantial rise in the demand for food

Market is driving as per the research by 2050, it's anticipated that there will be over 9 billion people on the planet, which will result in a substantial rise in the demand for food. By increasing the effectiveness of breeding programs and creating new agricultural and livestock varieties that are more productive and resilient, computational breeding can help in meeting this demand.

• More environmentally friendly and sustainable farming methods

An increasing need exists for more environmentally friendly and sustainable farming methods because agriculture plays a significant role in climate change. Crops and animals that are more resistant to pests and diseases, use less water and fertilizer, and emit fewer greenhouse gases can be developed with the aid of computational breeding.

• Growing adoption of precision agriculture and technology advancement

Large amounts of genetic and phenotypic data can now be analysed more rapidly and accurately than ever before due to the development of high-performance computing and big data analytics. For breeders and researchers, this has increased computational breeding's affordability as well as availability. Precision agriculture, which improves crop and livestock output using data and technology, is growing in popularity among farmers and agribusinesses. To create plants and animals that are better adapted to a range of growing environments, computational breeding can be combined with smart farming.

Market Restraints

• Inaccurate forecasts and unreliable breeding results can result from data of poor quality

When using genetic and genomic data in breeding systems, there are ethical and legal issues to take into account. The adoption and application of computational breeding methods may be further complicated by this. Also, the availability of high-quality data is one of the major issues facing the computational breeding industry. Inaccurate forecasts and unreliable breeding results can result from data of poor quality.

• High start-up expenses

Investments in hardware, software, and people may be necessary to implement computational breeding techniques. Smaller organizations or businesses with fewer means may find it difficult to enter the market because of this.  

Market Opportunities

• Significant investments in computational breeding research and development

Governments, academic institutions, and private businesses are all making significant investments in computational breeding research and development. As a result, new methods and tools have been created, increasing the effectiveness and precision of breeding initiatives. Additionally, improvements in technology and computing power are making it possible to create fresh and inventive breeding methods that could boost breeding programs' effectiveness and efficiency.

• Demand for customized breeding systems is rising, and collaborative research and development

Customized breeding programs that can cater to the unique requirements of farms and agribusinesses are becoming more and more in demand. By enabling breeders to recognize and select for particular traits that are most essential to their customers, computational breeding can help satisfy this demand. Collaboration between researchers, breeders, and industry stakeholders can help the computational breeding market trends surmount some of its challenges, such as data availability and quality, and speed the development and adoption of new techniques and tools.

Market Scope

Segmental Analysis

Segment by Type

• Molecular Breeding

Crossing two genetically different parents to create offspring with desired traits is known as hybrid breeding. Utilizing this method will increase crop output, disease resistance, and other crucial qualities.

• Hybrid Breeding

To create precise changes to the DNA of plants or animals, CRISPR/Cas9 or other molecular tools are used. Using this method, novel crop varieties with desirable properties, such as drought resistance or higher yield, can be produced.

• Genome Editing

This technique can be used to create new crop varieties with desirable traits, such as drought resistance or increased yield.

• Genetic Engineering

To produce novel crop or animal varieties with desired traits, genetic engineering includes the transfer of genes from one organism to another.

Segment by Application

• Oilseeds & Pulses

Pulses and oilseeds are significant food and feed products. New varieties with higher yields, enhanced disease resistance, and better nutritional profiles can be created using computational breeding.

• Cereals & Grains

Some of the most popular crops produced worldwide include grains and cereals. New varieties that are more productive, better adapted to particular growing circumstances, and more resistant to pests and diseases can be created using computational breeding.

• Fruits & Vegetables

Fruits and vegetables are important crops for human consumption. Computational breeding can be used to develop new varieties that have better flavour, texture, and nutritional value.

Regional Analysis

The Global Computational Breeding Market Analysis is segmented by region as North America, Europe, Asia Pacific, Latin America, and Middle East and Africa.

Asia Pacific

• Growing demand for food to feed this population

More than half of the world's population resides in the Asia-Pacific area, where food is in greater demand. In order to satisfy this demand, computational breeding can create new, more productive, and resilient crop and livestock varieties. Agribusinesses and farmers are adopting new technologies to maximize crop and livestock output, which is why precision agriculture is growing in popularity in Asia. To create plants and animals that are better suited to particular growing conditions, computational breeding can be combined with precision agriculture.

• Asian countries are supporting agricultural research and development

A large number of Asian countries are supporting agricultural research and development, including computational breeding. For instance, China has started a national program to create novel breeding tools and technologies, such as genome editing and genomic selection.

Asia is one of the regions most concerned about climate change, and there is an increasing need to create more resilient and sustainable farming methods. Crops and animals that are more resistant to diseases and pests, use less water and fertilizer, and emit fewer greenhouse gases can be developed with the help of computational breeding.

North America

Between 2021 and 2026, the market for computational breeding in North America is anticipated to expand at a CAGR of around 11%. The need for sustainable farming methods is rising along with knowledge of the effect of agriculture on the environment in North America. Using computational breeding, it is possible to create crops and animals that are less susceptible to pests and diseases, use less water and fertilizer, and emit fewer greenhouse gases.

• Aim to develop innovative breeding tools and technologies

In North America, a lot of states are funding agricultural research and development, including computational breeding. For instance, the United States Department of Agriculture (USDA) has a number of programs that aim to develop innovative breeding tools and technologies, such as genome editing and genomic selection.

Several significant technology firms, including IBM, Microsoft, and Google, are based in North America and are making significant investments in R&D in areas like artificial intelligence, big data analytics, and high-performance computing. Each of these tools is an essential aspect of computational breeding.

Europe

• Between 2021 and 2026, the market for computational breeding in Europe is anticipated to expand at a Rate of roughly 10.5%

Several significant technology firms with headquarters in Europe, including SAP, Siemens, and Atos, are making significant investments in R&D in areas like artificial intelligence, big data analytics, and high-performance computing. Each of these tools is a crucial part of computational breeding. Over the coming years, Europe is predicted to continue expanding rapidly thanks to a mix of rising demand for sustainable farming methods, technological advancements, rising adoption of precision agriculture, and government support.

Key Players

1. NRgene
2. NSIP
3. Computomics
4. GeneTwister
5. Keygene
6. GeneXPro
7. Hi Fidelity Genetics
8. Benson Hill

Recent Developments

In 2020- Indigo Agriculture is a business that uses computational tools to create crop varieties and microbial products that enhance plant health and yield. Indigo recently announced a partnership with the farm cooperative Grow mark to make its computational breeding platform available to member farmers in an effort to increase member farmers' productivity and sustainability.