Plant breeding works ahead of the future. It often takes many years to get from the first cross to a finished variety. Decisions made in the breeding nursery today therefore determine which varieties will be available to agriculture in ten or 20 years' time.

At the same time, scientific progress and new technological possibilities are changing breeding itself. Genome research, new breeding methods and the intelligent linking of growing volumes of data are opening up new perspectives. But much of this is only just beginning. The next stage of innovation in plant breeding must be shaped now.

What this innovation needs to deliver becomes tangible in the breeding nursery. A new variety should secure yield and quality while being as robust as possible against diseases, pests and changing environmental conditions. On top of this come requirements for the efficient use of water and nutrients and for adaptation to heat and drought.

This makes breeding more demanding, because traits do not act independently of one another. An improvement in one area can have negative effects on other traits. What matters, therefore, is not the individual trait but its combination with many other characteristics in a variety that actually performs in agricultural production.

Diversity as the foundation

The starting point for all breeding is plant genetic diversity. Breeders draw on varieties, breeding lines, genebank material and related wild species to unlock new traits. Resistance to plant diseases and pests, improved quality characteristics and adaptation to specific environmental conditions have time and again been derived from existing diversity.

What has fundamentally changed, however, are the ways of tapping into this diversity. For a long time, breeders assessed plants mainly on what they could see: growth habit, maturity, yield, disease incidence or quality. Since the 1980s, molecular markers have provided an ever more precise view of the genome. This allows traits to be identified earlier and plants to be selected in a more targeted way. Technical analysis methods and digitalisation have made breeding faster and more precise. Now the next step in this development is emerging.

Breeding becomes more forward-looking

Plant breeding is facing a data-driven transformation. Large amounts of information are already being generated about a plant's genome, its measurable characteristics and the environment in which it grows. But much of this data still exists in isolation. Its real potential only emerges when it can be systematically linked and made permanently usable for breeding.

This is precisely where one of the major research and innovation tasks of the coming years lies. Genomic data must be combined with information from phenotyping as well as with environmental and management data. This requires powerful analytical methods and artificial intelligence, but equally suitable data infrastructures, common standards and datasets that remain available over the long term. So far, these prerequisites are only partly in place.

If this linking succeeds, it can fundamentally change the selection process in breeding. Data-based models can help to assess the potential of crosses earlier, select promising genotypes in a more targeted way and better predict how plants will respond under different environmental conditions. Artificial intelligence can detect relationships in large and complex datasets that are difficult to capture with conventional methods.

The decisive step, therefore, is not to generate ever more data. Data must be turned into knowledge that breeders can use. To achieve this, the interactions between genotype, environment and management must be better understood and modelled.

The prospect is a more forward-looking approach to breeding: rather than merely reacting to changing conditions, being able to estimate early on which genetic combinations promise stable performance under tomorrow's conditions. However, considerable research and development work is still needed before this potential can be fully exploited.

The field remains indispensable

Great as the potential of data-driven methods is, plant breeding will not become a purely digital process. Models are only as good as the data on which they are based. And much of this data is generated where a new variety ultimately has to prove itself – in the field.

Only under real conditions does it become clear how a plant's genome interacts with location, weather and crop management. Field trials across different locations and years therefore remain indispensable.

The future lies in combining the two: precisely collected field data improve models and predictions. These, in turn, can help to design trials in a more targeted way and to identify promising material earlier. Breeding experience, biological research and data science complement one another – they do not replace one another.

While the data-driven transformation primarily changes how breeders select plants and predict their performance, New Genomic Techniques open up additional ways of creating genetic variation in a targeted manner. Methods such as CRISPR/Cas allow changes to be made at specific sites in the genome, thereby expanding the plant breeder's toolbox.

Such methods are of interest, for example, for disease resistance, quality traits or characteristics that help plants cope better with environmental stress. They can make development processes more targeted and open up new ways of accessing desired traits for breeding.

Time as a factor in innovation

New tools are also gaining importance because the requirements placed on varieties are changing faster than the long development cycles of plant breeding would suggest. Climatic conditions are shifting, pathogens are evolving and existing resistances can lose their effectiveness.

New methods can help to identify promising material earlier, plan crosses in a more targeted way and make development pathways more efficient. Nevertheless, biological processes cannot be shortened at will: plants have to grow, and new varieties have to be tested under different conditions. What matters, therefore, is not just breeding faster, but making the right decisions earlier.