Dynamic root growth and architecture responses to limiting nutrient availability: linking physiological models and experimentation

In recent years the study of root phenotypic plasticity in response to sub-optimal environmental factors and the genetic control of these responses have received renewed attention. As a path to increased productivity, in particular for low fertility soils, several applied research projects worldwide target the improvement of crop root traits both in plant breeding and biotechnology contexts. To assist these tasks and address the challenge of optimizing root growth and architecture for enhanced mineral resource use, the development of realistic simulation models is of great importance. We review this research field from a modeling perspective focusing particularly on nutrient acquisition strategies for crop production on low nitrogen and low phosphorous soils. Soil heterogeneity and the dynamics of nutrient availability in the soil pose a challenging environment in which plants have to forage efficiently for nutrients in order to maintain their internal nutrient homeostasis throughout their life cycle. Mathematical models assist in understanding plant growth strategies and associated root phenes that have potential to be tested and introduced in physiological breeding programs. At the same time, we stress that it is necessary to carefully consider model assumptions and development from a whole plant-resource allocation perspective and to introduce or refine modules simulating explicitly root growth and architecture dynamics through ontogeny with reference to key factors that constrain root growth. In this view it is important to understand negative feedbacks such as plant-plant competition. We conclude by briefly touching on available and developing technologies for quantitative root phenotyping from lab to field, from quantification of partial root profiles in the field to 3D reconstruction of whole root systems. Finally, we discuss how these approaches can and should be tightly linked to modeling to explore the root phenome. (C) 2013 Elsevier Inc. All rights reserved.