The harmonisation of Value for Cultivation and Use (VCU) protocols across EU member states offers a transformative opportunity to streamline plant variety testing and enhance agricultural innovation. Historically, variations in VCU methodologies across countries have led to inconsistencies, inefficiencies, and barriers to market access for breeders. The InnoVar project proposes a harmonised system that integrates advanced technologies like genomics, phenomics, and machine learning to improve the precision, efficiency, and adaptability of VCU trials. Genomic markers enable targeted breeding by identifying genetic traits linked to desirable characteristics, while phenomic imaging, particularly through UAVs, provides detailed plant data for consistent and high-throughput assessments. Machine learning further optimises trial designs and accelerates variety recommendations by analysing complex datasets. Harmonisation promotes sustainability and resilience by evaluating varieties under various input conditions and across diverse agro-climatic zones, supporting the development of crops suited to low-input and organic systems. Despite these benefits, challenges such as resistance to change, technological integration, and regulatory alignment must be overcome. With coordinated efforts, the harmonisation of VCU protocols can enhance efficiency, innovation, and sustainability in European agriculture, positioning it to meet the demands of a rapidly changing environment.

Over the past two decades, advances in genomics have revolutionized crop breeding, providing affordable tools to enhance the development and validation of distinctness, uniformity, and stability (DUS) in new crop varieties. The InnoVar project set out to validate the potential of these genomic tools to complement existing DUS testing methods, starting with wheat and extending the findings to other crops. Specifically, the project utilized the 90K Illumina SNP data for both bread wheat and durum wheat. In addition to exploring how this genomic data could support DUS testing, we also investigated its potential as a cross-over tool between DUS and VCU (Value for Cultivation and Use) testing. Our study further evaluated the use of transcriptomics to complement these cross-over traits, particularly focusing on the potential of both genomic and transcriptomic data to predict resistance to key wheat diseases, such as Septoria tritici blotch and Fusarium head blight. This study delved into the genetic dissection of DUS-related traits in wheat through genome-wide association studies (GWAS). We identified multiple significant SNP markers and haplotype blocks associated with these traits, highlighting their polygenic nature.  These findings offer valuable insights for wheat breeding programs, as they demonstrate the power of integrating phenotypic and genotypic data to accelerate the development of superior cultivars. The identified markers and candidate genes represent promising targets for future research and breeding efforts aimed at enhancing wheat yield and quality. 

There is currently a disparate array of systems across Europe for farmers to access information about variety performance and suitability. InnoVar reviewed information from VCU (Value for Cultivation and Use) testing authorities across 15 European countries and found reporting ranged from simply publishing the results of VCU trials to recommendations based on additional trials or expert panels. Most shared an emphasis on yield and disease resistance, with quality traits and lodging resistance also common. To address this diversity, a key outcome of the InnoVar project is a recommendation tool that can allow farmers across Europe to receive the most appropriate variety options for them based on their farm location, disease risk etc..  

To facilitate this, a system of categorising varieties, HPLR (High Performance, Low Risk) was developed; classes included diseases, lodging, resilience and sustainability. The web-based and mobile-friendly tools use data from InnoVar VCU trials but could incorporate data from any VCU trial. Users choose their country, what characteristics (eg disease) and level of incidence they are interested in, and a comparator base variety. For the chosen criteria, the tool displays the relative yields of varieties where they have been grown with full fungicide and no fungicide. The tool also calculates HPLR Resilience by taking account of absolute and stability of yield. HPLR Sustainability considers relative yield where no fungicide has been applied.