Plant Variety Testing
To be recognised as a registered variety, and eligible for marketing in the EU, a plant variety must first pass both DUS and VCU testing. These tests are conducted at approved centres, following closely monitored protocols (VCU test: national protocols; DUS test: CPVO protocols).
DUS stands for Distinct, Uniform and Stable.
To be recognised as a registered variety, and eligible for marketing in the EU, a plant variety must pass DUS testing. These tests are conducted at approved centres, following closely monitored protocols under the regulation of CPVO.
Distinct: a variety can be described as distinct if one or more of the important features, known as characters, observed over the course of the evaluation is different from any other variety whose existence is a matter of common knowledge.
Uniform: this test requires that the measured features of several individual plants are similar (subject to the variation that may be expected from the particular features of its propagation) or genetically identical in its relevant characteristics.
Stable: this test is passed if important characteristics are true to their original description after repeated propagation or, in the case of a particular cycle of propagation, at the end of each such cycle.
DUS testing takes place over a minimum of two growing cycles.
VCU stands for Value for Cultivation and Use.
This test is used to determine if a new plant variety shows a significant advantage over existing registered varieties and evaluates its agronomic performance. Evaluation of varieties occurs over a minimum of two sowings and often in multiple locations to ensure robust evaluation in a range of soil and weather conditions. The agronomic performance of new plant varieties is evaluated against that of known “control” varieties. Protocols for this testing are set by national authorities in each country.
Phenomics is the study of observable physical characteristics throughout the life of a plant. InnoVar will use phenomics technology to overcome some of the bottlenecks in crop science. Specifically, the fact that field conditions are heterogeneous, and the inability to control environmental factors make results difficult to interpret. To address these limitations, InnoVar will develop and adopt cutting-edge phenomics technologies for plant variety testing. Adoption of digital technologies such as drone-based field phenomics fitted with the latest in infra-red camera technology will allow information on plant variety response to disease pressure to be captured that isn’t visible to the human eye. For VCU testing programs, InnoVar will focus on the characteristics of a plant variety that have agronomic benefits, such as yield and disease resistance, and for DUS testing programs we will assess characters that make plant varieties distinct from other varieties. We aim to use more accurately high throughput phenomic data can be more allowing the expansion and expedition of the phenotyping of DUS and VCU characters.
SNP genotyping: measures the occurrence of variants in DNA, called Single Nucleotide Polymorphisms (SNPs). These commonly occurring genetic variations can lead to changes in the way plants express genes, leading to changes in the plants’ characteristics. This is an important tool for plant variety breeding to help target desirable traits, and speed up their introduction via breeding.
GWAS: A Genome-Wide Association Study is a genomic study, which observes the whole genome of a plant for previously defined SNPs, and identifies which SNPs may be responsible, or linked to, certain plant traits. This type of study can help pin-point which genes might be important for agronomic traits and can help our understanding of their inheritance.
InnoVar will make the most of the advances in genomics technologies in recent years to develop next-generation variety testing. Project partners will combine existing and novel SNP data with phenotype data (data on plant characteristics) in genome-wide association studies, to identify SNPs and genes linked to characteristics of interest, such as grain yield and disease resistance. This will enable a better understanding of the genetic basis of the plant phenotype leading to greater efficiency of plant variety testing. This method has the potential to enhance the breeding of optimised varieties and thereby reduce the need for the on-farm application of chemistry to improve plant health, yield, and quality.‘
Machine learning is a rapidly developing area of computer science used in many different industries and is revolutionising how we communicate with each other, purchase goods online, detecting fraudulent activity to even writing film scripts. Variety testing schemes collect large amounts of diverse data on weather, soil and plant characteristics to provide bespoke variety recommendations to align with their individual environmental and business requirements.
The relationships between weather, soil and plant characteristics are complex and not fully understand. InnoVar will apply machine learning approaches to existing and de novo datasets to provide novel insight into the complex relationships of testing plant varieties across multiple countries, climates and weather conditions. This will leverage the power of big data to enhance current variety recommendations provide farmers with more personalised recommendations align with individual weather, soil and farm enterprise circumstances.