Tiina Roose

Data intensive modelling of the rhizosphere processes

We rely on soil to support crops and provide a host of ’free services’. Soil buffers the hydrological system, greatly reducing the risk of flooding after heavy rain; soil contains very large quantities of carbon, which would otherwise be released into the atmosphere, where it would contribute to climate change. The aim of this project was to develop a state-of-the-art image-based model of the physical and chemical properties of soil and soil-root interactions, i.e. a quantitative model of the rhizosphere based on fundamental scientific laws. This was realised by a combination of an innovative, data-rich fusion of structural and chemical imaging methods, integration of experimental efforts, and application of mathematically sound homogenisation/scale-up techniques.

FertiliserAI

One of the biggest challenges facing the agricultural industry is achieving high crop yields with the use of fertilisers while minimising the environmental side effects of fertiliser application and production. In this highly weather-reliant industry, developing weather-dependent fertilisation strategies will help maintain crop production and reduce greenhouse gas emissions. This is particularly important for phosphorus and nitrogen fertilisers, which have a large environmental footprint. One approach to achieving this goal is by harnessing new computational technologies/AI within a precision agriculture framework.

Result

The development of sophisticated models that integrate large datasets to simulate rhizosphere processes with high accuracy made it possible to address several specific science questions. The study contributes to the understanding of how the soil around the root, i.e. the rhizosphere, functions and influences soil ecosystems at multiple scales. It examines the roles that the root-soil interface micromorphology and mycorrhizae play in plant nutrient uptake, as well as the effect of plant-exuded mucilage on soil morphology and mechanics and the resulting field- and ecosystem-scale soil functions. The study proposes methods to translate this knowledge from the single root scale to root system, field, and ecosystem scales to predict how climate change, different soil management strategies, and plant breeding will influence soil fertility.

Impact

“The Nation that destroys its soil destroys itself“, Franklin D. Roosevelt famously said in 1937, and these words still hold true to this day. The topic of soil health will only grow in importance due to population increase and global warming. However, surprisingly, our knowledge of how soil and plants interact is rather limited. This is because soil is very difficult to study due to its opacity, frailty, and the different spatial scales associated with the key phenomena in soil.

We now have tools to observe the processes at the smallest relevant scale and the ability to translate these findings to the large scale, resulting in new state-of-the-art multi-scale models for soil-root processes. Such models allow investigation of questions such as: Would a change in root architecture be useful? What management-induced changes to soil structure are desirable for future environments? To what extent can roots adapt to stresses in the soil physical environment?