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Background: In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors. Results: The estimated heritabilities (h^ 2) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation (GCV) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest GCV observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2. Conclusions: The significant GCV and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines

Background and aims Deep roots are needed to allow uptake of nitrogen (N) and water available in the deeper soil layers, to help tolerate increasingly extreme climates. Yet few studies in the field have been able to identify genetic differences in deep roots and how this relates to N and water uptake. This study aimed to identify the relationship between deep roots and tolerance to drought, and how this varies by genotype and with differing N fertilization. Methods We grew 14 diverse genotypes of winter wheat in a semi-field facility in Denmark, in 2019 and 2020, with a soil depth gradient and a rain-out shelter to create a water stress. We used minirhizotron tubes reaching to 2.5 m depth to quantify differences in deep roots. We applied isotope tracers (15N and 2H labelled water) at 1.6-1.8 m at anthesis to assess differences in root function. Grain and straw 13C were used to assess drought stress. Results We found differences in deep roots between genotypes, and slightly less deep root growth when more N was applied. Deep roots were correlated with grain yield, uptake of deep-placed tracers of water and N, and tolerance to drought. Genotypes with deeper roots had the biggest decrease in water stress and increase in grain yield, when their roots had access to deeper soil. Conclusion Deeper roots were related to drought tolerance and increased yields. This suggests that deep rooting should be considered in future breeding efforts for more climate resilient crops., Background and aims: Deep roots are needed to allow uptake of nitrogen (N) and water available in the deeper soil layers, to help tolerate increasingly extreme climates. Yet few studies in the field have been able to identify genetic differences in deep roots and how this relates to N and water uptake. This study aimed to identify the relationship between deep roots and tolerance to drought, and how this varies by genotype and with differing N fertilization. Methods: We grew 14 diverse genotypes of winter wheat in a semi-field facility in Denmark, in 2019 and 2020, with a soil depth gradient and a rain-out shelter to create a water stress. We used minirhizotron tubes reaching to 2.5 m depth to quantify differences in deep roots. We applied isotope tracers (15N and 2H labelled water) at 1.6-1.8 m at anthesis to assess differences in root function. Grain and straw 13C were used to assess drought stress. Results: We found differences in deep roots between genotypes, and slightly less deep root growth when more N was applied. Deep roots were correlated with grain yield, uptake of deep-placed tracers of water and N, and tolerance to drought. Genotypes with deeper roots had the biggest decrease in water stress and increase in grain yield, when their roots had access to deeper soil. Conclusion: Deeper roots were related to drought tolerance and increased yields. This suggests that deep rooting should be considered in future breeding efforts for more climate resilient crops.

Cover crops can contribute to climate change mitigation through enhanced sequestration of atmospheric carbon dioxide into soil organic carbon. Few studies, however, have estimated the total carbon (C) input to soil, i.e. derived both from plant material (shoot and root) and phyllo- and rhizodeposition. Selection of cover crop species should account for multiple objectives, such as C inputs to soil, nitrate leaching reduction and positive residual effects on the following main crop. However, trade-offs between these objectives may occur. The aim of this study was to investigate the performance of the cover crop species winter rye, hairy vetch and oilseed radish, and to assess the ability of mixtures to overcome potential trade-offs. A randomized split-plot field trial was conducted to compare cover crop treatments and a weeded control under high and low soil nitrogen (N) availability. Multiple-pulse labeling with 14C-CO2 was carried out to trace net cover crop-derived rhizodeposition C. Soil mineral N was measured to 1.5 m depth in autumn, as well as grain and N yield in the subsequent spring barley. Cover crop species accumulated between 1250 and 2580 kg C ha−1, with significantly higher total C input (in shoot, root and phyllo- and rhizodeposits) for the mixtures compared with pure stands of either vetch or radish, while the results for rye were in between. The quantity of C lost via phyllo- and rhizodeposition (qClvPR) showed a significant positive correlation with root C and was highest for the mixtures and rye. The relative ClvPR ranged between 7% and 14% of total cover crop-derived C and tended to decrease under higher soil N availability. All cover crop treatments were able to decrease soil mineral N (0–1.5 m), with radish displaying the highest N leaching reduction potential. Despite substantial differences in cover crop total N uptake and C:N ratios, no significant differences were observed in the subsequent main crop grai

Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.

Little is known how the deep root systems of perennial crops contribute to deeper and better resource use when intercropped with annuals in arable fields. Therefore, we aimed at measuring the capacity of deep-rooted perennials, alfalfa (Medicago sativa L.) and curly dock (Rumex crispus L.) to access nutrients located under neighboring annuals at 1.0 and 2.5 m of soil depth. Alfalfa and curly dock were able to access the tracer sources (15N and Cs) placed at a distance farther than 0.4 m under the annual crop strips. As a result, the reliance on deeper soil layers for nutrient uptake by the crops grown at the borders became greater compared with the crops grown in the mid-strips. Combination of an annual cereal (winter rye) and a perennial legume (alfalfa) having contrasting root systems exhibited higher resource complementarity compared with intercroppings having more similar root systems or in the absence of legumes. Our results demonstrated that the deep-rooted perennials, when intercropped with annuals, can induce vertical niche complementarity, especially at deeper soil layers. This was assumed to be due to the differences in rooting depth and deep root activity the crop components, however, the magnitude of the effects depended on choice of crop combinations, and on types of tracers used in the study.

Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72-0.91) than with genomic models alone (0.55-0.86). The correlation between predictions and phenotypes varied from 0.17-0.28 for control plants and 0.23-0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GEControl and GEDrought) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.