RootPainter: deep learning segmentation of biological images with corrective annotation.

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.

Semifield root phenotyping: Root traits for deep nitrate uptake.

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 image-based 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.

Root and xylem anatomy varies with root length, root order, soil depth and environment in intermediate wheatgrass (Kernza®) and alfalfa.

BACKGROUND AND AIMS: Deep roots (i.e. >1 m depth) are important for crops to access water when the topsoil is dry. Root anatomy and hydraulic conductance play important roles in the uptake of soil water, particularly water located deep in the soil. We investigated whether root and xylem anatomy vary as a function of root type, order and length, or with soil depth in roots of two deep-rooted perennial crops: intermediate wheatgrass [Thinopyrum intermedium (Kernza®)] and alfalfa (Medicago sativa). We linked the expression of these anatomical traits to the plants' capacity to take up water from deep soil layers. METHODS: Using laser ablation tomography, we compared the roots of the two crops for cortical area, number and size of metaxylem vessels, and their estimated root axial hydraulic conductance (ERAHCe). The deepest roots investigated were located at soil depths of 2.25 and at 3.5 m in the field and in rhizoboxes, respectively. Anatomical differences were characterized along 1-m-long individual roots, among root types and orders, as well as between environmental conditions. KEY RESULTS: For both crops, a decrease in the number and diameter, or both, of metaxylem vessels along individual root segments and with soil depth in the field resulted in a decrease in ERAHCe. Alfalfa, with a greater number of metaxylem vessels per root throughout the soil profile and, on average, a 4-fold greater ERAHCe, took up more water from the deep soil layers than intermediate wheatgrass. Root anatomical traits were significantly different across root types, classes and growth conditions. CONCLUSIONS: Root anatomical traits are important tools for the selection of crops with enhanced exploitation of deep soil water. The development and breeding of perennial crops for improved subsoil exploitation will be aided by greater understanding of root phenotypes linked to deep root growth and activity.

Digging roots is easier with AI.

The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76), and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1-5 cm cm-3) as well with low RLD (0.1-0.3 cm cm-3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Root system-based limits to agricultural productivity and efficiency: the farming systems context.

Background There has been renewed global interest in both genetic and management strategies to improve root system function in order to improve agricultural productivity and minimize environmental damage. Improving root system capture of water and nutrients is an obvious strategy, yet few studies consider the important interactions between the genetic improvements proposed, and crop management at a system scale that will influence likely success. Scope To exemplify these interactions, the contrasting cereal-based farming systems of Denmark and Australia were used, where the improved uptake of water and nitrogen from deeper soil layers has been proposed to improve productivity and environmental outcomes in both systems. The analysis showed that water and nitrogen availability, especially in deeper layers (>1 m), was significantly affected by the preceding crops and management, and likely to interact strongly with deeper rooting as a specific trait of interest. Conclusions In the semi-arid Australian environment, grain yield impacts from storage and uptake of water from depth (>1 m) could be influenced to a stronger degree by preceding crop choice (0·42 t ha-1), pre-crop fallow management (0·65 t ha-1) and sowing date (0·63 t ha-1) than by current genetic differences in rooting depth (0·36 t ha-1). Matching of deep-rooted genotypes to management provided the greatest improvements related to deep water capture. In the wetter environment of Denmark, reduced leaching of N was the focus. Here the amount of N moving below the root zone was also influenced by previous crop choice or cover crop management (effects up to 85 kg N ha-1) and wheat crop sowing date (up to 45 kg ha-1), effects which over-ride the effects of differences in rooting depth among genotypes. These examples highlight the need to understand the farming system context and important G × E × M interactions in studies on proposed genetic improvements to root systems for improved productivity or environmental outcomes.

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics.

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.

Molecular diversity of the entomopathogenic fungal Metarhizium community within an agroecosystem.

The entomopathogenic fungal Metarhizium anisopliae lineage harbors cryptic diversity and was recently split into several species. Metarhizium spp. are frequently isolated from soil environments, but the abundance and distribution of the separate species in local communities is still largely unknown. Entomopathogenic isolates of Metarhizium spp. were obtained from 32 bulked soil samples of a single agroecosystem in Denmark using Tenebrio molitor as bait insect. To assess the Metarhizium community in soil from the agricultural field and surrounding hedgerow, 123 isolates were identified by sequence analysis of 5' end of elongation factor 1-α and their genotypic diversity characterized by multilocus simple sequence repeat (SSR) typing. Metarhizium brunneum was most frequent (78.8%) followed by M. robertsii (14.6%), while M. majus and M. flavoviride were infrequent (3.3% each) revealing co-occurrence of at least four Metarhizium species in the soil of the same agroecosystem. Based on SSR fragment length analysis five genotypes of M. brunneum and six genotypes of M. robertsii were identified among the isolates. A single genotype within M. brunneum predominated (72.3% of all genotypes) while the remaining genotypes of M. brunneum and M. robertsii were found at low frequencies throughout the investigated area indicating a diverse Metarhizium community. The results may indicate potentially favorable adaptations of the predominant M. brunneum genotype to the agricultural soil environment.

Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments.

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 ([Formula: see text]) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ([Formula: see text]) 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 [Formula: see text] 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 [Formula: see text] and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

Health biomarkers in a rat model after intake of organically grown carrots.

BACKGROUND: Organic food is perceived as being of better quality and healthier than conventional foods although the scientific research on organic foodstuffs is highly contradictory. The aim of the present study was to investigate if intake of carrots from four different cultivation systems grown in two consecutive years would influence various biomarkers of health in a rat model. All rats were fed a diet with 40% carrot content. The carrots were grown under conventional (C), 'minimalistic' organic (O1), organic (O2), or 'very' organic cultivation systems (O3). A control group (CO) being fed standard rat chow was included. RESULTS: The plasma α-tocopherol concentration was higher in the O2 carrot-based diet group than in the C carrot based-diet group in one year, while all other health biomarkers or nutrient content differences were observed between the CO diet and the carrot-based diets. CONCLUSION: This well-controlled field study demonstrated no clear influence of cultivation methods or harvest year on the nutritional quality of carrots or effect of cultivation methods on health-related biomarkers in a sensitive rat model. However, the experimental set-up and selected biomarkers could be used as a framework for further studies of health in relation to organic foodstuff.

Multi-method comparison of carrot quality from a conventional and three organic cropping systems with increasing levels of nutrient recycling.

BACKGROUND: There is a need to advance the study of the effects of organic and conventional systems on product quality. In particular, little is known about the importance of different farming practices concerning nutrient cycling and the use of external inputs within organic farming for the quality characteristics of the products. In this study the quality characteristics of carrot grown under different farming practices (conventional and three organic cropping systems) over a two-year period were analysed with the aim of discriminating between organic and conventional and investigating the effect of different organic farming practices concerning nutrient recycling and use of external nutrient input. RESULTS: All quality characteristics measured did not give a clear differentiation between the carrots from the different growing systems, even when multivariate statistical evaluation (principal component analysis) was applied, because of the significance of the differences between the field replicates within each management system and of the seasonality. Only some tendencies were emphasised over the two years that could be related to the fertilisation practices and the external inputs used. CONCLUSION: The results indicated that it was not possible to discriminate over the years between carrots from conventional and different organic cropping systems even though controlled conditions and a multi-method approach of analysis were adopted.

Dynamics of Deep Water and N Uptake of Oilseed Rape (Brassica napus L.) Under Varied N and Water Supply.

Enhanced nitrogen (N) and water uptake from deep soil layers may increase resource use efficiency while maintaining yield under stressed conditions. Winter oilseed rape (Brassica napus L.) can develop deep roots and access deep-stored resources such as N and water to sustain its growth and productivity. Less is known of the performance of deep roots under varying water and N availability. In this study, we aimed to evaluate the effects of reduced N and water supply on deep N and water uptake for oilseed rape. Oilseed rape plants grown in outdoor rhizotrons were supplied with 240 and 80 kg N ha-1, respectively, in 2019 whereas a well-watered and a water-deficit treatment were established in 2020. To track deep water and N uptake, a mixture of 2H2O and Ca(15NO3)2 was injected into the soil column at 0.5- and 1.7-m depths. δ2H in transpiration water and δ15N in leaves were measured after injection. δ15N values in biomass samples were also measured. Differences in N or water supply had less effect on root growth. The low N treatment reduced water uptake throughout the soil profile and altered water uptake distribution. The low N supply doubled the 15N uptake efficiency at both 0.5 and 1.7 m. Similarly, water deficit in the upper soil layers led to compensatory deep water uptake. Our findings highlight the increasing importance of deep roots for water uptake, which is essential for maintaining an adequate water supply in the late growing stage. Our results further indicate the benefit of reducing N supply for mitigating N leaching and altering water uptake from deep soil layers, yet at a potential cost of biomass reduction.

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.

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.

Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare.

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.

Deep-rooted perennial crops differ in capacity to stabilize C inputs in deep soil layers.

Comprehensive climate change mitigation necessitates soil carbon (C) storage in cultivated terrestrial ecosystems. Deep-rooted perennial crops may help to turn agricultural soils into efficient C sinks, especially in deeper soil layers. Here, we compared C allocation and potential stabilization to 150 cm depth from two functionally distinct deep-rooted perennials, i.e., lucerne (Medicago sativa L.) and intermediate wheatgrass (kernza; Thinopyrum intermedium), representing legume and non-legume crops, respectively. Belowground C input and stabilization was decoupled from nitrogen (N) fertilizer rate in kernza (100 and 200 kg mineral N ha-1), with no direct link between increasing mineral N fertilization, rhizodeposited C, and microbial C stabilization. Further, both crops displayed a high ability to bring C to deeper soil layers and remarkably, the N2-fixing lucerne showed greater potential to induce microbial C stabilization than the non-legume kernza. Lucerne stimulated greater microbial biomass and abundance of N cycling genes in rhizosphere soil, likely linked to greater amino acid rhizodeposition, hence underlining the importance of coupled C and N for microbial C stabilization efficiency. Inclusion of legumes in perennial cropping systems is not only key for improved productivity at low fertilizer N inputs, but also appears critical for enhancing soil C stabilization, in particular in N limited deep subsoils.

Core-labelling technique (CLT): a novel combination of the ingrowth-core method and tracer technique for deep root study.

BACKGROUND: Ingrowth-core method is a useful tool to determine fine root growth of standing crops by inserting root-free soil in mesh-bags for certain period of time. However, the root density observed by the method does not directly explain the nutrient uptake potential of crop plants as it varies over soil depth and incubation time. We have inserted an access-tube up to 4.2 m of soil depth with openings directly under crop plants, through which ingrowth-cores containing labelled soil with nutrient tracers were installed, called core-labelling technique (CLT). The main advantage of CLT would be its capacity to determine both root density and root activity from the same crop plants in deep soil layers. We tested the validity of the new method using a model crop species, alfalfa (Medicago sativa) against three depth-levels (1.0, 2.5 and 4.2 m), three sampling spots with varying distance (0-0.36, 0.36-0.72 and > 5 m from core-labelled spot), two sampling times (week 4 and 8), and two plant parts (young and old leaves) under two field experiments (spring and autumn). RESULTS: Using CLT, we were able to observe both deep root growth and root activity up to 4.2 m of soil depth. Tracer concentrations revealed that there was no sign of tracer-leakage to adjacent areas which is considered to be advantageous over the generic tracer-injection. Root activity increased with longer incubation period and tracer concentrations were higher in younger leaves only for anionic tracers. CONCLUSIONS: Our results indicate that CLT can lead to a comprehensive deep root study aiming at measuring both deep root growth and root activity from the same plants. Once produced and installed, the access-tubes and ingrowth-cores can be used for a long-term period, which reduces the workload and cost for the research. Therefore, CLT has a wide range of potential applications to the research involving roots in deep soil layers, which requires further confirmation by future experiments.

Genomic prediction of yield and root development in wheat under changing water availability.

BACKGROUND: Deeper roots help plants take up available resources in deep soil ensuring better growth and higher yields under conditions of drought. A large-scale semi-field root phenotyping facility was developed to allow a water availability gradient and detect potential interaction of genotype by water availability gradient. Genotyped winter wheat lines were grown as rows in four beds of this facility, where indirect genetic effects from neighbors could be important to trait variation. The objective was to explore the possibility of genomic prediction for grain-related traits and deep root traits collected via images taken in a minirhizotron tube under each row of winter wheat measured. RESULTS: The analysis comprised four grain-related traits: grain yield, thousand-kernel weight, protein concentration, and total nitrogen content measured on each half row that were harvested separately. Two root traits, total root length between 1.2 and 2 m depth and root length in four intervals on each tube were also analyzed. Two sets of models with or without the effects of neighbors from both sides of each row were applied. No interaction between genotypes and changing water availability were detected for any trait. Estimated genomic heritabilities ranged from 0.263 to 0.680 for grain-related traits and from 0.030 to 0.055 for root traits. The coefficients of genetic variation were similar for grain-related and root traits. The prediction accuracy of breeding values ranged from 0.440 to 0.598 for grain-related traits and from 0.264 to 0.334 for root traits. Including neighbor effects in the model generally increased the estimated genomic heritabilities and accuracy of predicted breeding values for grain yield and nitrogen content. CONCLUSIONS: Similar relative amounts of additive genetic variance were found for both yield traits and root traits but no interaction between genotypes and water availability were detected. It is possible to obtain accurate genomic prediction of breeding values for grain-related traits and reasonably accurate predicted breeding values for deep root traits using records from the semi-field facility. Including neighbor effects increased the estimated additive genetic variance of grain-related traits and accuracy of predicting breeding values. High prediction accuracy can be obtained although heritability is low.

The cytosine methylation landscape of spring barley revealed by a new reduced representation bisulfite sequencing pipeline, WellMeth.

Patterns and level of cytosine methylation vary widely among plant species and are associated with genome size as well as the proportion of transposons and other repetitive elements in the genome. We explored epigenetic patterns and diversity in a representative proportion of the spring barley (Hordeum vulgare L.) genome across several commercial and historical cultivars. This study adapted a genotyping-by-sequencing (GBS) approach for the detection of methylated cytosines in genomic DNA. To analyze the data, we developed WellMeth, a complete pipeline for analysis of reduced representation bisulfite sequencing. WellMeth enabled quantification of context-specific DNA methylation at the single-base resolution as well as identification of differentially methylated sites (DMCs) and regions (DMRs). On average, DNA methylation levels were significantly higher than what is commonly observed in many plants species, reaching over 10-fold higher levels than those in Arabidopsis thaliana (L.) Heynh. in the CHH methylation. Preferential methylation was observed within and at the edges of long-terminal repeats (LTR) retrotransposons Gypsy and Copia. From a pairwise comparison of cultivars, numerous DMRs could be identified of which more than 5,000 were conserved within the analyzed set of barley cultivars. The subset of regions overlapping with genes showed enrichment in gene ontology (GO) categories associated with chromatin and cellular structure and organization. A significant correlation between genetic and epigenetic distances suggests that a considerable portion of methylated regions is under strict genetic control in barley. The data presented herein represents the first step in efforts toward a better understanding of genome-level structural and functional aspects of methylation in barley.

Digging Deeper for Agricultural Resources, the Value of Deep Rooting.

In the quest for sustainable intensification of crop production, we discuss the option of extending the root depth of crops to increase the volume of soil exploited by their root systems. We discuss the evidence that deeper rooting can be obtained by appropriate choice of crop species, by plant breeding, or crop management and its potential contributions to production and sustainable development goals. Many studies highlight the potentials of deeper rooting, but we evaluate its contributions to sustainable intensification of crop production, the causes of the limited research into deep rooting of crops, and the research priorities to fill the knowledge gaps.

Construction of a large-scale semi-field facility to study genotypic differences in deep root growth and resources acquisition.

BACKGROUND: Roots are vital organs for plants, and the effective use of resources from the soil is important for yield stability. However, phenotypic variation in root traits among crop genotypes is mostly unknown and field screening of root development is costly and labour demanding. As a consequence, new methods are needed to investigate root traits of fully grown crops under field conditions, particularly roots in the deeper soil horizons. RESULTS: We developed a new phenotyping facility (RadiMax) for the study of root growth and soil resource acquisition under semi-field conditions. The facility consists of 4 units each covering 400 m2 and containing 150 minirhizotrons, allowing root observation in the 0.4 m-1.8 m or 0.7 m-2.8 m soil depth interval. Roots are observed through minirhizotrons using a multispectral imaging system. Plants are grown in rows perpendicular to a water stress gradient created by a multi-depth sub-irrigation system and movable rainout shelters. The water stress gradient allows for a direct link between root observations and the development of stress response in the canopy. CONCLUSION: To test the concept and technical features, selected spring barley (Hordeum vulgare L.) cultivars were grown in the system for two seasons. The system enabled genotypic differences for deep root growth to be observed, and clear aboveground physiological response was also visible along the water stress gradient. Although further technical development and field validation are ongoing, the semi-field facility concept offers novel possibilities for characterising genotypic differences in the effective use of soil resources in deeper soil layers.

Testing deep placement of an 15N tracer as a method for in situ deep root phenotyping of wheat, barley and ryegrass.

BACKGROUND: Deep rooting is one of the most promising plant traits for improving crop yield under water-limited conditions. Most root phenotyping methods are designed for laboratory-grown plants, typically measuring very young plants not grown in soil and not allowing full development of the root system. RESULTS: This study introduced the 15N tracer method to detect genotypic variations of deep rooting and N uptake, and to support the minirhizotron method. The method was tested in a new semifield phenotyping facility on two genotypes of winter wheat, seven genotypes of spring barley and four genotypes of ryegrass grown along a drought stress gradient in four individual experiments. The 15N labeled fertilizer was applied at increasing soil depths from 0.4 to 1.8 m or from 0.7 to 2.8 m through a subsurface tracer supply system, and sampling of aboveground biomass was conducted to measure the 15N uptake. The results confirm that the 15N labeling system could identify the approximate extension of the root system. The results of 15N labeling as well as root measurements made by minirhizotrons showed rather high variation. However, in the spring barley experiment, we did find correlations between root observations and 15N uptake from the deepest part of the root zone. The labeled crop rows mostly had significantly higher 15N enrichment than their neighbor rows. CONCLUSION: We concluded that the 15N tracer method is promising as a future method for deep root phenotyping because the method will be used for phenotyping for deep root function rather than deep root growth. With some modifications to the injection principle and sampling process to reduce measurement variability, we suggest that the 15N tracer method may be a useful tool for deep root phenotyping. The results demonstrated that the minirhizotrons observed roots of the tested rows rather than their neighboring rows.