The AGMARDT Technology Transfer Award of the New Zealand Institute of Agricultural & Horticultural Science has been won by Brent Barrett and the AgResearch Pastoral Genomics Research Team. A celebration was held at Grasslands last week when Tony Conner presented the award on behalf of NZIAHS.

This accolade was for embedding the latest tools of plant genomics and genetics in the leading New Zealand seed companies associated with commercial ryegrass and white clover breeding. This has included the development and implementation of new plant breeding decision support systems, genomic selection in ryegrass and clover, phenomics to estimate dry matter accumulation, and pre-breeding for phosphate efficiency and drought tolerance in clovers.

The team included Craig Anderson, Sai Arojju, Mingshu Cao, Tracy Dale, Jim Crush, Marty Faville, Kioumars Ghamkhar, Andrew Griffiths, Angus Heslop, Tony Hilditch, Won Hong, Wajid Hussain, Jeanne Jacobs, Zulfi Jahufer, Anna Larking, Dongwen Luo, Peter Moran, Shirley Nichols, Jessica O’Connor, Lily Ouyang, Jana Schmidt, Prue Taylor, and Mike Trolove; and PhD students Lucy Egan and Grace Ehoche.

Nomination statement:

Pastoral Genomics is a joint venture co-funded by DairyNZ, Beef+Lamb NZ, Dairy Australia, AgResearch, Barenbrug Agriseeds, Grasslands Innovation and MBIE. The nominated Team have been contracted (2015-2020) by Pastoral Genomics for the science delivery associated with the co-development and transfer of technology associated with adoption of the latest tools of plant genomics and genetics by commercial ryegrass and white clover breeding programmes in the major New Zealand seed companies. This research programme has contributed globally leading research in genomics, phenomics and genetics of ryegrass and clover, coupled with industry engagement and uptake in four main areas:

  1.  New plant breeding decision support systems. In a global first, open-source decision support systems offering foresight and insight for pasture plant breeding have been co-created and supported for end-user adoption. This has included developing tactical and strategic design, simulation and analysis platforms adapted to pasture plant breeding. The tactical plant breeding decision support tool software package ‘DeltaGen’ that was released as freeware ( To date it has been used extensively by the New Zealand seed industry programme partners, and by plant breeders in over 60 countries. During the period December 2018 to February 2020, a total of 800+ new users and 3,000+ sessions have been recorded. In New Zealand, DeltaGen is actively used in cultivar development programs by breeders in commercial seed companies such as PGW Seeds (includes Grassland Innovation) and Barenbrug Agriseeds. The user-friendly software design, basic theory and associated references, provided in this easily accessed R/Shiny-based package, has resulted in it being used as a teaching resource in plant breeding courses in New Zealand and overseas. An updated version of QuLine, known as QuLinePlus, has also been developed that modernises and extends its repretoir plant breeding strategy, incorporating a comprehensive quantitative genetic model simulation for cross-pollinated populations, as required for most forage species (Hoyos-Villegas et al 2018). More recently this has been extended to encompass a genomic selection step. This allows simulation of genomic selection in a breeding programme, using functions developed in R programming language. Using an R/Shiny version of QuLinePlus reduces the complexity of running simulations and simplifies operations for a user-friendly experience by plant breeders.
  2. Genomic selection in ryegrass and white clover. Genomic selection is a breeding approach wherein the effects of high-density single nucleotide polymorphism (SNP) markers are used to predict breeding values as a basis for selection in plant breeding programmes. The team has further developed the emerging technology of Genotyping-By-Sequencing (GBS) to produce efficient pipelines from DNA extraction through to the analysis of genetic and genomic variation within the DNA of individuals. GBS provides a method for rapid and cost-effective generation of high-density SNP marker data. Populations from seed company breeding programmes were developed with the aim of training and assessing genomic prediction models in forages. The AgResearch developed statistical method KGD, designed specifically for GBS data was used to develop an unbiased genomic relationship matrix and genomic selection predictive models for traits measured in the ryegrass training set. The program has allowed the adoption of genomic selection in industry breeding programs via the determination of genomic estimated breeding values. The Team has recently completed a successful proof-of-concept for genomic selection in perennial ryegrass. This demonstrated successful and highly divergent selection of valuable traits in forage breeding based solely on genomic information. Using genomic prediction models trained for herbage accumulation (a proxy for dry matter yield potential) and days-to-heading in perennial ryegrass, they established substantially improved genetic gain using molecular marker information from genotyping-by-sequencing to predict breeding values in selection candidates. This peer-reviewed validation of genomic selection via progeny test has provided the confidence to the seed companies of the merits of genomic selection. To help facilitate the uptake of this technology in ryegrass and clover breeding programmes, the Team have recently provided genomic estimated breeding values (GEBVs) for over 1000 ryegrass seedlings to both Barenbrug Agriseeds and PGW Seeds (includes Grassland Innovation) in the commercial breeding pipeline of each respective company. Selections based on these GEBVs allows breeders to choose elite parents for the next generation while plants are still in the seedling phase. This results in rapid breeding cycles with savings in space, time and resources, and culminates in a substantial increase in the rate of genetic gain in breeding programmes
  3. Phenomics for measuring forage traits. In world-leading research, the team has developed systems utilising LiDAR and hyperspectral imaging to precisely estimate dry matter accumulation and forage composition in a single-pass non-invasive scanning procedure. This has involved the design, construction and implementation of remote-controlled machinery for rapid assessment of forage traits with high correlation to physical measurements. Critical to this has been the development of algorithms from images that allow the accurate and rapid estimation of forage parameters in a non-destructive manner. Using highly accurate GBS co-ordinates, this offers new opportunities for repeated measurements of the same plants/plots over time to calculate forage growth rates in real time. This will aid plant breeders to make selections on more relevant parameters involving re-growth after grazing. Using a combination of LiDAR and hyperspectral imaging, the team have been able to use phenomics to distinguish ryegrass and clovers in mixed swards. This provides a huge opportunity for non-destructive rapid assessment of the growth of the individual species within a mixed sward in a very cost-efficient manner. The rapid advancement of this phenomic technology over the programme has seen the initial development of an M5 machine and evolved into the M6 machine and more recently emergence of the state-of-the-art M7 machine featuring remote control, GPS, and capable of scanning speed >2000 plots per hour. The research team have built multiple versions of these machines and provided them for use in the seed companies’ commercial breeding pipeline. The availability of data from the seed companies has further allowed the refinement of the algorithms and fine-tuned the applications for phenomic tools for seed company adoption.
  4. Pre-breeding for drought tolerance and phosphate-use efficiency in clovers. The technical expertise of the team has been utilised for the development of novel interspecific clover hybrids and the transfer of novel traits to white clover. This has involved the use of Trifolium occidentale and Trifolium uniflorum as sources of drought tolerance and phosphate-use efficiency. From thorough evaluations of phenotypic components of these traits following several generations of backcrossing, promising clover lines have been identified for incorporation into industry breeding programmes. Applications of genomic tools has allowed the introgressions from the wild species to be tracked during backcrossing to white clover. The unique value to this novel clover germplasm is evident from the seed companies accessing more than 700 unique lines to date for incorporation into industry breeding programmes.

The co-development of the research programme with the partner seed companies and the active involvement of seed company plant breeders in the science delivery have been a hallmark to the successful adoption of the first wave of genomic selection and accompanying technologies by industry. This two-way communication has been critical for dissemination and uptake of the technology by allowing the industry needs to be fulfilled with marked influence from the science thought-leadership of the nominated team. This industry/science team interaction is well evidenced by the co-authorships of the scientific papers published from the programme. Of the 35 peer-reviewed scientific papers published to date, 14 papers have co-authors from the industry funding-partners, predominantly the seed companies.

Science excellence.

The success of the ‘AgResearch Pastoral Genomics Research Team’ is built upon the delivery of science excellence coupled with their commitment to making a difference in New Zealand’s primary industries. The Team quickly gained the confidence and trust of the seed companies via co-development of the research plan resulting in the rapid appreciation of the potential for genomic selection to increase the rate of genetic gain in ryegrass and white clover breeding programmes. The science excellence is evidenced by the publication of 35 key scientific papers. Of these publications, 26 are in the last three years which is indicative of a gain in publishing momentum with many more expected over the coming years as more research components reach maturity. Evidence of the science quality in the programme is validated by the high-ranking international plant science journals in which many of these science papers are published (ten in journals with Impact Factor > 4). Other papers have been deliberately published in local New Zealand journals with high engagement and interaction with the industry-good entities and the seed companies associated with the pastoral sector (e.g. Journal of New Zealand Grasslands) in order to target the transfer of knowledge and uptake of the latest genetic and genomic technologies by the wider pastoral industry sector.

Outreach and communication for effective uptake of science and technology.

Over the past six years the Team have delivered a strong outreach programme involving regular workshops and seminars to upskill the plant breeders in the seed companies. This has involved hands-on training in:

  1. The use of the new plant breeding decision support systems (DeltaGen and QuLinePlus).
  2. The general concepts of genomic selection and how to interpret genomic estimated breeding values (GEBVs) in decision making within a breeding programme.
  3. The use of the M5 to M7 machines for phenomic estimates of plant biomass via the embedded algorithms for determination and interpretation of trait values.
  4. The value of novel clover germplasm with introgressed traits for components of drought tolerance and phosphate-use efficiency derived from Trifolium occidentale and Trifolium uniflorum.

The nominated team have also made a substantial contribution to capability development with the embedding of PhD students and MSc students within the programme. This is resulting in ‘future primed’ graduates for the uptake of the latest tools of plant genomics and genetics in breeding programmes of seed companies.

Economic impact.

The adoption in industry breeding programmes of genotyping-by-sequencing and accompanying new data assemblage workflows and statistical tools for genomic selection will have immense impact and value for the New Zealand economy. For example, traditional phenotypic breeding in perennial ryegrass has produced a historical genetic gain of 0.7% per year. Adoption of genomic selection is expected to result in a genetic gain of 2.0% per year, a target recently validated by the nominated team (Faville et al 2020; Agronomy, 10: 340). Pastoral Genomics, the funding entity, contracted AbacusBio to undertake an independent assessment of the economic impact of this technology being applied to perennial ryegrass. Based on the assumptions that genomic selected cultivars would be available from 2026 with a rate of genetic gain of 2% per annum, it was estimated that improved yield and quality traits in perennial ryegrass would contribute $1B per annum to the New Zealand economy by 2040. When broken down by sector, values were $839M and $166M for Dairy and Meat & Fibre respectively. These benefits are mid values, with sensitivity analysis indicating value at 2040 ranges from $400M to $1.4B per annum. Applying the same analysis to white clover will also generate substantial economic impact of this technology to the New Zealand economy.

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