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

破坏性的气候变化已经在英国被观察到，2020年是第一个进入温度、降水和日照前10名的年份。2011-2020年间，英国平均比1981-2010年基线气候平均值高0.5摄氏度，比1961-1990年高1.1摄氏度。极端降水频率预计将随着2摄氏度的升高而增加1.5-3倍，并随着进一步变暖而非线性增加。因此，气候变化对英国的农业生态系统构成了直接和直接的风险(例如，通过干旱、洪水、虫害、疾病)，但我们对这种风险的理解很差。大数据为解决这一问题提供了机会。例如，使用卫星遥感在区域或国家范围内做出推断，或使用数据丰富的研究平台提供的数据。北威克农场平台(North Wyke Farm Platform；http://resources.rothamsted.ac.uk/farmplatform))就是这样一个平台--这是一个世界领先的实验，它捕获数据以表征四个不同的农场系统。收集的数据包括：(1)水化学/流动、土壤湿度；(2)温室气体排放；(3)气象；(4)土壤/作物养分、土壤动物、植物群的调查；(5)作物/牲畜的表现；通常与通过遥感提供的外部收集相结合。该项目旨在利用统计和机器学习方法在平台的高维数据集中发现极端事件和异常观测。我们提出了一系列检测技术，每一种都捕捉到了空间、时间和尺度效应，新的方法将会产生。一个结合的目标是了解在比较平台的系统的弹性时确定的极端/异常情况的影响，以便进行统计推断。这里，线性混合模型(LMM)和结构方程模型(SEM)将以两种不同的方式进行调整，以便更准确地捕捉不确定性：(1)对极端/异常具有健壮性(降权)；(2)通过极值理论更好地捕捉它们(升权)。

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.