About the project

• Grassland-based forage production plays a critical role in Norwegian agriculture for producing milk and meat and is crucial for the farm economy. With increasing temperatures due to climate change, perennial ryegrass, which is a high yielding and nutritious grass species, can be grown further north and in more continental regions. Expansion of the cultivation area of perennial ryegrass, which is mostly grown in monoculture for leys and requires high rates of nitrogen fertilization, leads to increased GHG emissions, and nitrogen leaching.
• To address this challenge, it is crucial to breed future perennial ryegrass cultivars with a higher nitrogen use efficiency (NUE). To speed up the development of improved cultivars, Norwegian perennial ryegrass breeding must be more efficient by incorporating advanced breeding methods and techniques.
• This project will utilize ecotypes and modern cultivars characterized in a previous Nordic/Baltic “Public-Private-Partnership (PPP) project for pre-breeding in perennial ryegrass”. NUE of these populations will be evaluated at two locations in Norway by integrating phenomics and genomics, to select the best NUE populations to incorporate in the breeding program. Phenomics creates huge amounts of data from various field sensors by drones and robots.
• Currently, no platform can process such big data in real-time. This project aims to establish an AI/ML-based data analytics platform for autonomous processing of big data, creating a real-time reporting application for dry matter yield and forage quality in grasses assisting breeders with rapid selection of superior genotypes and farmers with optimal harvest time decisions. The project will develop knowledge for breeding perennial ryegrass cultivars with improved forage quality and lower environmental footprints. Precision farming aided by utilization of the AI-based big-data platform will increase the economic output for farmers.

• Background

  Climate shifts in Northern Europe, including rising temperatures, increased precipitation, and longer growing seasons, offer opportunities and challenges for perennial ryegrass (Lolium perenne L.) cultivation in Norway. While warmer conditions may expand cultivation areas and boost yields, they also increase nitrogen use and leaching, raising greenhouse gas emissions. Breeding resilient, sustainable ryegrass varieties with high nitrogen use efficiency (NUE) is crucial to mitigate environmental impacts while maintaining yields.

  Breeding for Climate Resilience

  Changing winter conditions and rainfall patterns will stress current ryegrass cultivars, reducing yield and quality. Advanced genomics tools, such as genomic selection and next-generation sequencing, can accelerate the breeding of varieties with improved nutrient efficiency, cold tolerance, and drought resistance. NUE-focused breeding reduces nitrogen fertilizer dependency, limiting nitrous oxide (N₂O) emissions and nutrient runoff while improving biomass production.

  High-Throughput Phenomics (HTP)

  Modern breeding relies on HTP to collect precise, large-scale plant trait data, overcoming the inefficiencies of manual field phenotyping. Robotics, sensors, and autonomous platforms enhance data accuracy and efficiency: - Aerial Systems: Drones equipped with multispectral and hyperspectral sensors rapidly capture large-scale field data. - Ground Systems: Robots with LiDAR and other sensors collect high-resolution, detailed trait measurements. Combining aerial and ground-based systems enhances data quality and coverage, although challenges in mission coordination remain.

  AI-Powered Data Platforms

  The agriculture sector generates significant data from sensors, robotics, and drones, but much remains siloed, limiting its utility. A standardized, scalable data platform integrating AI/ML analytics can transform this data into actionable insights. For example, farmers could receive real-time recommendations on fertilizer use, harvest timing, or optimal cultivar selection. Breeders could accelerate trait selection by combining genomics with phenomics data.

  While industries like oil and gas have leveraged big data, agriculture lags behind. Collaborations with technology providers and plant breeding companies can address this gap, enabling tools to estimate key nutritive traits (e.g., protein, carbohydrates, fiber) and advance precision agriculture.

  By integrating genomics, HTP, and AI-driven analytics, Norway can adapt ryegrass cultivation to future climate conditions, optimize nutrient use, reduce emissions, and maintain high yields and superior feed quality.

• Objectives

  The main objective of this project is to create a sustainable and efficient "data-led transformation solution" for forage grass farming by utilizing technology such as robotics, energy-efficient sensor networks, genomics, and AI/ML models.

  The secondary objectives includes:

  1. Designing a sustainable IoT sensor network that combines low-power communication and data storage with AI/ML techniques to assess soil quality and nitrogen use efficiency in perennial ryegrass.
  2. Enhancing the accuracy and robustness of phenomics protocols for collecting dry matter yield and nutritive traits using advanced robotics and sensing technology along with AI/ML models.
  3. Identifying and characterizing the genes related to nitrogen use efficiency through genome wide association studies.
  4. Developing an AI/ML data analytics platform for autonomous processing of big data collected from field sensors, creating a real-time reporting application for yield and quality data of perennial ryegrass.
• Participants

  

  Odd Arne Rognli

  Professor emeritus

  Tel. 67232790

  Send e-mail

  WP5 leader

  External project participants and partners

  Abhishesh Pal, Accenture Norge - WP4 leader
  Muath Alsheikh - GRAMINOR
  Nikolai Ødegaard - GRAMINOR
  Kristin Håland Gylstrøm - GRAMINOR