Deep rooting winter wheat genotypes can reduce nitrate leaching losses and increase N uptake. We aimed to investigate which deep root traits are correlated to deep N uptake and to estimate genetic variation in root traits and deep 15 N tracer uptake. In 2 years, winter wheat genotypes were grown in RadiMax, a semifield root-screening facility.Minirhizotron root imaging was performed three times during the main growing season.At anthesis, 15 N was injected via subsurface drip irrigation at 1.8 m depth. Mature ears from above the injection area were analysed for 15 N content. From minirhizotron imagebased root length data, 82 traits were constructed, describing root depth, density, distribution and growth aspects. Their ability to predict 15 N uptake was analysed with the least absolute shrinkage and selection operator (LASSO) regression. Root traits predicted 24% and 14% of tracer uptake variation in 2 years. Both root traits and genotype showed significant effects on tracer uptake. In 2018, genotype and the three LASSO-selected root traits predicted 41% of the variation in tracer uptake, in 2019 genotype and one root trait predicted 48%. In both years, one root trait significantly mediated the genotype effect on tracer uptake. Deep root traits from minirhizotron images can predict deep N uptake, indicating the potential to breed deep-N-uptake-genotypes.nitrogen isotope, plant breeding, plant roots, semifield | INTRODUCTIONDeeper rooting crops expand the soil depth from which nitrogen (N) can be taken up. This increases the N use efficiency of cropping systems and decreases leaching losses (Dresbøll & Thorup-Kristensen, 2014). The primary form of mineral N in temperate soils is nitrate, which is highly mobile in the soil water solution, as it is a negatively charged molecule (Allred et al., 2007). Therefore, nitrate percolates with excess precipitation and reaches deep soil layers easily. If subsequent crop roots do not penetrate to these soil layers, nitrate leached so deep that it is close to the bottom of the root zone will be lost from the cropping system. If we expand the rooting depth of crops, we can increase the uptake of leached nitrate (e.g., Thorup-Kristensen, 2006) and increase total crop N uptake (Thorup-Kristensen et al., 2009). The importance of deep roots for N acquisition in a leaching situation was suggested in the 'steep-cheap-deep' ideotype concept (Lynch, 2013).A 2-year Danish field experiment found significant variance in root depth between cultivars and that deeper roots extracted more N from deep soil (Rasmussen et al., 2015). These findings are supported by other studies that measured the deep root N uptake of crops through 15 N injection into deep soil layers (Chen et al., 2019;Saengwilai et al., 2014;Kristensen & Thorup-Kristensen, 2004a, 2004b. These studies found significant variation between species (Kristensen & Thorup-Kristensen, 2004a, 2004b) and within species (Chen

Root phenotyping describes methods for measuring root properties, or traits. While root phenotyping can be challenging, it is advancing quickly. In order for the field to move forward, it is essential to understand the current state and challenges of root phenotyping, as well as the pressing needs of the root biology community. In this letter, we present and discuss the results of a survey that was created and disseminated by members of the Graduate Student and Postdoc Ambassador Program at the 11th symposium of the International Society of Root Research. This survey aimed to (1) provide an overview of the objectives, biological models and methodological approaches used in root phenotyping studies, and (2) identify the main limitations currently faced by plant scientists with regard to root phenotyping. Our survey highlighted that (1) monocotyledonous crops dominate the root phenotyping landscape, (2) root phenotyping is mainly used to quantify morphological and architectural root traits, (3) 2D root scanning/imaging is the most widely used root phenotyping technique, (4) time-consuming tasks are an important barrier to root phenotyping, (5) there is a need for standardised, high-throughput methods to sample and phenotype roots, particularly under field conditions, and to improve our understanding of trait-function relationships.

No abstract

No abstract

No abstract

No abstract

Background and aims Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured planar root length density (pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods In the RadiMax semi-field root-screening facility 95/120 different winter wheat genotypes were phenotyped for root growth in 2018/2019, respectively. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and pRLD was extracted using a convolutional neural network. We developed ML models to explore whether the pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as drought resilience potential using natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both an analytical root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results Both analytical and ML models demonstrated clear correlations between pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018 than in 2019. Conclusions The results demonstrated that in the semi-field non-invasive root phenotyping setup, analytical and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake.