About the project

This project develops a digital twin-enhanced phenotyping framework to quantify and predict Nitrogen Use Efficiency (NUE) in forage grasses and oats. By integrating TraitFinder's high-resolution 3D phenotyping with drone-based field sensing and genomics, we will simulate trait dynamics under variable nitrogen regimes and identify the genetic basis of NUE.

Approach: Real-time NUE phenotyping protocols established in TraitFinder will feed digital twin models that simulate plant responses to nitrogen variation. Greenhouse predictions will be validated against drone and sensor data from perennial ryegrass and oat field trials. GWAS will pinpoint genomic regions underlying NUE traits, and genome-edited NUE mutants from NitroGenEdit and DLT-Farming projects will be phenotyped in controlled environments to confirm gene function.

Outcome: A validated phenotype-to-genotype pipeline linking controlled-environment simulation, field-scale sensing, and causal gene discovery to accelerate breeding for nitrogen-efficient forage and cereal crops.

• Background

  Nitrogen fertilisation underpins modern crop production, yet only 30–50% of applied N is recovered in harvested biomass — the remainder is lost to leaching, N₂O emissions, and economic waste (Mueller et al., 2012; Zhang et al., 2015). Improving nitrogen-use efficiency (NUE) is therefore both an environmental and economic imperative, especially for the forage grasses and cereals that form the backbone of Norwegian livestock feed, where the sector must deliver higher yields with reduced inputs.

  To address this, we will deploy the Norwegian Plant Phenotyping Infrastructure (PheNo) and its newly installed TraitFinder system, a multispectral 3D laser scanner capable of daily, non-invasive measurement of canopy architecture, chlorophyll status, and biomass accumulation. Controlled greenhouse trials with diverse genotypes of perennial ryegrass (Lolium perenne) and oat (Avena sativa) under low, standard, and high N regimes will generate an unprecedented, high-resolution NUE trait dataset (Garnett et al., 2009) and establish TraitFinder as a core research and training resource at NMBU.

• Objectives

  This project will develop a digital twin-enhanced phenotyping framework to quantify, simulate, and predict Nitrogen Use Efficiency (NUE) in forage grasses and oats.

  By integrating TraitFinder-based 3D phenotyping, drone field sensors, and genomic data, the project will (i) establish real-time NUE phenotyping protocols, (ii) build digital twin models from 3D point clouds to simulate trait dynamics, (iii) validate greenhouse predictions against field measurements in perennial ryegrass and oats, (iv) identify genomic regions controlling NUE through GWAS, and (v) confirm gene function by phenotyping genome-edited NUE mutants from NitroGenEdit and DLT-Farming projects.

  

• Participants

  NMBU participants

  External participants

  Dr. Bruno Rodrigrues Alves, Embrapa, Brazil