Agroecosystems in a Climate Crisis: Using Big Data to understand the Out of the Ordinary

Disruptive climate change is already being observed in the UK, with 2020 being the first year to feature in the top-10 for temperature, precipitation and sunlight. Between 2011-2020, the UK has been, on average, 0.5C warmer than the 1981-2010 baseline climatic mean and 1.1C warmer than 1961-1990. The frequency of extreme precipitation is expected to increase by a factor of 1.5-3 with a 2C temperature rise, and to increase non-linearly with further warming.

Climate change therefore poses an immediate and direct risk to the UK's agroecosystems (e.g., through drought, flooding, pests, disease), but our understanding of this risk is poor.

Big Data provides an opportunity to address this. For example, using satellite sensing to make inferences at regional or national scales, or using data from data-rich research platforms. The North Wyke Farm Platform (NWFP; http://resources.rothamsted.ac.uk/farmplatform) is one such platform - a world-leading experiment that captures data to characterize four contrasting farm systems.

Collections include: (i) water chemistry/flow, soil moisture; (ii) greenhouse gas emissions; (iii) meteorological; (iv) surveys for soils/crop nutrients, soil fauna, floristics; and (v) crop/livestock performance; that are typically coupled with external collections such as those provided via remote sensing.

This project aims to detect extreme events and anomalous observations in the platform's high-dimensional datasets using statistical and machine learning methods. We propose a range of detection techniques, each capturing spatial, temporal and scale effects, where new methodologies will result. A coupled aim is an understanding of the impacts of identified extremes/anomalies for statistical inference when the platform's systems are compared for their resilience.

Here, linear mixed models (LMMs) and structural equation models (SEMs) will be adapted in two distinct ways so that uncertainties are more accurately captured: (1) to be robust to extremes/anomalies (down-weighting) and (2) to better capture them (up-weighting) via extreme-value theory.