Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: A focus on cover crops.

Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical and biotic properties of soil. However, cover crops, a group of plants that provide ecosystem services, can be utilised during fallow periods or used as an intercrop to restore soil health. The effectiveness of ecosystem services provided by cover crops varies widely as very little breeding has occurred in these species. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. In this study, we provide an overview of the range of belowground ecosystem services provided by cover crop roots: (1) soil structural remediation, (2) capture of soil resources and (3) maintenance of the rhizosphere and building of organic matter content. Based on the ecosystem services described, we outline current and promising phenotyping technologies and breeding strategies in cover crops that can enhance agricultural sustainability through improvement of root traits.

Maize lateral root developmental plasticity induced by mild water stress. II: Genotype-specific spatio-temporal effects on determinate development.

Maize lateral roots exhibit determinate growth, whereby the meristem is genetically programmed to stop producing new cells. To explore whether lateral root determinacy is modified under water deficits, we studied two maize genotypes (B73 and FR697) with divergent responses of lateral root growth to mild water stress using an experimental system that provided near-stable water potential environments throughout lateral root development. First-order laterals of the primary root system of FR697 exhibited delayed determinacy when grown at a water potential of -0.28 MPa, resulting in longer and wider roots than in well-watered (WW) controls. In B73, in contrast, neither the length nor width of lateral roots was affected by water deficit. In water-stressed FR697, root elongation continued at or above the maximum rate in WW roots for 3 days longer, and was still 45% of maximum when WW roots approached their determinate length. Maintenance of root elongation was associated with sustained rates of cell production. In addition, kinematic analyses showed that reductions in tissue expansion rates with aging were delayed in the longitudinal, radial and tangential planes throughout the root growth zone. Thus, this study reveals large genotypic differences in the interaction of water stress with developmental determinacy of maize lateral roots.

Maize lateral root developmental plasticity induced by mild water stress. I: Genotypic variation across a high-resolution series of water potentials.

Lateral root developmental plasticity induced by mild water stress was examined across a high-resolution series of growth media water potentials (Ψw ) in two genotypes of maize. The suitability of several media for imposing near-stable Ψw treatments on transpiring plants over prolonged growth periods was assessed. Genotypic differences specific to responses of lateral root growth from the primary root system occurred between cultivars FR697 and B73 over a narrow series of water stress treatments ranging in Ψw from -0.25 to -0.40 MPa. In FR697, both the average length and number of first-order lateral roots were substantially enhanced at a Ψw of -0.25 MPa compared with well-watered controls. These effects were separated spatially, occurring primarily in the upper and lower regions of the axial root, respectively. Furthermore, first-order lateral roots progressively increased in diameter with increasing water stress, resulting in a maximum 2.3-fold increase in root volume at a Ψw of -0.40 MPa. In B73, in contrast, the length, diameter, nor number of lateral roots was increased in any of the water stress treatments. The genotype-specific responses observed over this narrow range of Ψw demonstrate the necessity of high-resolution studies at mild stress levels for characterization of lateral root developmental plasticity.

Root system architecture and environmental flux analysis in mature crops using 3D root mesocosms.

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft3 (1.27 m3), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.

Super resolution for root imaging.

PREMISE: High-resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above-ground plant attributes. However, the acquisition of high-resolution images of plant roots is more challenging than above-ground data collection. An effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. METHODS: We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non-plant-root images, (ii) training with plant-root images, and (iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. The architectures of the SR models were based on two state-of-the-art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. RESULTS: In our experiments, we observed that the SR models improved the quality of low-resolution images of plant roots in an unseen data set in terms of the signal-to-noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non-root data sets. DISCUSSION: The incorporation of a deep learning-based SR model in the imaging process enhances the quality of low-resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal-to-noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.