Modelling plant root systems: nutrient uptake, resilience and trait optimisation - Schäfer, Ernst Dirk (2021)
The green revolution led to a drastic increase in crop yields through chemical fertilisers, dwarf varieties and introduction of new methods of cultivation. We now face challenges such as climate change and soil degradation that require the development of crops resilient to adverse conditions in order to maintain an adequate food supply for the still growing world population. In order to maintain crop yields in soils with low nutrient availability or drought conditions we need to get a better understanding of the interactions between root system traits, soil environment and plant development. Compared to shoots, roots are hard to study because they are hidden from view by the soil. This makes mathematical models of roots a powerful tool to help us study root systems. We use OpenSimRoot, a functional-structural plant model to study the effects of various root system architectures on plant development in challenging environments, adding new functionality to expand the capabilities of OpenSimRoot. Our simulations showed that the effect of root loss on plant development depends on nutrient availability, plant species and root system phenotype, varying from very detrimental to slightly beneficial. Simulations of plants under drought implied that parsimonious and deeper rooting phenotypes perform better because of a large reduction in root carbon costs, increasing water uptake efficiency. We also show that machine learning techniques are a useful tool for root trait optimisation over a very large space of possible root system architectures. Our findings show that root system architecture has a large impact on plant development, especially in challenging environments and if we want to breed crops which are suited to deal with the challenges ahead of us we need to think about roots just as much as shoots. Our work also shows the benefits of interdisciplinary approaches by combining mathematical modelling with statistical machine learning in order to increase our understanding of biological systems. We hope our work will lead to increased collaborations across disciplines so that we may gain a better understanding of the hidden half of plants.