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Background and aims: Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_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 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results: Both parametrized and ML models demonstrated clear correlations between Sqrt_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. Conclusions: The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake. [ABSTRACT FROM AUTHOR]

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 15N 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, 15N was injected via subsurface drip irrigation at 1.8 m depth. Mature ears from above the injection area were analysed for 15N content. From minirhizotron image‐based root length data, 82 traits were constructed, describing root depth, density, distribution and growth aspects. Their ability to predict 15N 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. Summary Statement: Root traits of winter wheat were constructed from minirhizotron image data. Root traits predicted 24% of 15N tracer uptake, which was injected at anthesis at 1.8 m depth, and explained genotypic variation in tracer uptake. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is broadly recognised as an important parameter affecting soil quality, and can therefore contribute to improving a number of soil properties that influence crop yield. Previous research generally indicates that soil organic carbon has positive effects on crop yields, but in many studies it is difficult to separate the effect of nutrients from the effect of SOC in itself. The aim of this study was to analyze whether the SOC content, in itself, has a significant effect on potential yields of commonly grown cereals across a wider range of soil types in Denmark. The study draws on historical data sets from the Danish national field trials consisting of 560 winter wheat ( Triticum aestivum L.) trials and 309 spring barley ( Hordeum vulgare L.) trials conducted over the past 20 and 17 years, respectively. We hypothesised that for these two crops, the potential grain yield, the yield with no fertiliser N application and the N use efficiency would be positively affected by SOC level. A statistical model was developed to explore relationships between SOC and potential yield, yields at zero N application and N use efficiency (NUE). The model included a variety of variables and aimed to elucidate the sole effect of SOC by controlling for potential confounding variables. No significant effect of SOC on potential winter wheat was found, whilst for spring barley, only for the course sandy loam soil type was a borderline significantly positive effect of SOC on potential yields found. The relationship between unfertilized plot yields and SOC was positive for winter wheat, although not significant, whilst for spring barley a significant positive effect of SOC was found only for the coarse sandy soil type, and a borderline significant positive effect of SOC was found for the coarse sandy loam soil type. A significant negative relationship was found between SOC and NUE for both winter wheat and spring barley. Based on the large dataset analyzed, we cautiously challenge the importance of SOC in contributing to crop productivity in contexts with similar soils and climate, and we speculate that in situations where nutrient limitation does not occur, SOC levels above 1% may be sufficient to sustain yields. In light of the findings presented in this study, further work should be conducted which can further elucidate the effect of SOC on yields. [ABSTRACT FROM AUTHOR]