Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Tackling hidden hunger through soils This report describes the work completed during a week long data study group hosted by the Alan Turing Institute. The challenge was provided by Rothamsted Research and looks at predicting soil and plant physicochemical properties from soil infrared (IR) spectra. Three datasets were explored and modelled using a combination of established and more recent data-science strategies. Due to the size, scope and variety in the datasets, multiple conclusions were drawn. Overall, our preliminary findings indicate that soil physiochemical properties were easier to model than plant physicochemical properties. Decision tree based methods were used consistently throughout the three datasets and were overall more robust than other approaches considered in our analysis. Our results are in line with the current literature; IR data can be an effective predictor of the physicochemical properties of soil and by extension, the health of the soil.