Greater exploitation of subsoil resources by annual crops would afford multiple benefits, including greater water and N acquisition in most agroecosystems, and greater sequestration of atmospheric C. Constraints to root growth in the subsoil include soil acidity (an edaphic stress complex consisting of toxic levels of Al, inadequate levels of P and Ca, and often toxic levels of Mn), soil compaction, hypoxia, and suboptimal temperature. Multiple root phenes under genetic control are associated with adaptation to these constraints, opening up the possibility of breeding annual crops with root traits improving subsoil exploration. Adaptation to Al toxicity, hypoxia, and P deficiency are intensively researched, adaptation to soil hardness and suboptimal temperature less so, and adaptations to Ca deficiency and Mn toxicity are poorly understood. The utility of specific phene states may vary among soil taxa and management scenarios, interactions which in general are poorly understood. These traits and issues merit research because of their potential value in developing more productive, sustainable, benign, and resilient agricultural systems.

We present examples of root plasticity in response to soil compaction and discuss their role in plant performance. Subsequently, we propose a plastic ideotype for soil compaction tolerance.

Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput. Here, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size. DIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://dirt.iplantcollaborative.org/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science.

Reliable techniques for screening large numbers of plants for root traits are still being developed, but include aeroponic, hydroponic and agar plate systems. Coupled with digital cameras and image analysis software, these systems permit the rapid measurement of root numbers, length and diameter in moderate (typically <1000) numbers of plants. Usually such systems are employed with relatively small seedlings, and information is recorded in 2D. Recent developments in X-ray microtomography have facilitated 3D non-invasive measurement of small root systems grown in solid media, allowing angular distributions to be obtained in addition to numbers and length. However, because of the time taken to scan samples, only a small number can be screened (typically <10 per day, not including analysis time of the large spatial datasets generated) and, depending on sample size, limited resolution may mean that fine roots remain unresolved. Although agar plates allow differences between lines and genotypes to be discerned in young seedlings, the rank order may not be the same when the same materials are grown in solid media. For example, root length of dwarfing wheat (Triticum aestivum L.) lines grown on agar plates was increased by ~40% relative to wild-type and semi-dwarfing lines, but in a sandy loam soil under well watered conditions it was decreased by 24–33%. Such differences in ranking suggest that significant soil environment–genotype interactions are occurring. Developments in instruments and software mean that a combination of high-throughput simple screens and more in-depth examination of root–soil interactions is becoming viable.