Resilience of UK crop yields to changing climate extremes

Abstract

Abstract. Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate potential future impacts of climate projections on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Past and projected climate conditions are considered for key wheat growth stages (Foundation, Construction and Production). Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is partially explained by seasonal metrics of temperature and precipitation, including mean, extremes, and intra-seasonal variability. Strong associations between climate and yield anomalies occur during years with cumulative climate impacts across growth stages, when climate extremes ‘escape’ the ability of farmers to adapt through agronomic means. We then analyse the latest 2.2 km UK Climate Projections for the UK’s three main wheat-growing regions. Climate projections indicate that on average across the three regions, the Foundation growth stage (broadly 1st October to 9th April) is likely to become warmer and wetter, while the Construction (10th April to 10th June) and Production (11th June to 26th July) stages are likely to become warmer and slightly drier. An analogue approach, comparing historical climate conditions with future climate projections, reveals a mixed picture of future climate conditions for UK crop yields. Projected warmer winter night temperatures are likely to prove beneficial in the Foundation stage, but concurrent increases in heavy rain may be detrimental. Similarly, warmer and drier mean conditions may enhance yields during the Production stage, but increases in high temperatures and heat variability may increase plant stress, while decreases in rainfall may also threaten adequate water supply. Since future climatic conditions are likely to move outside the historically observed range, there may be challenges for agriculture to adapt management practices to realise any potential benefits.

Current data and modeling bottlenecks for predicting crop yields in the United Kingdom

“…This includes crop management practices and protection information for which relatively little data for crops other than wheat is available. The large amount of data that is available on wheat, combined with the fact that wheat is the dominant arable crop grown globally, may explain the proliferation of models centered around predicting yields of wheat (Frich et al, 2002;Slater et al, 2021). This may also partially explain why previous studies have demonstrated that yield prediction accuracy is relatively high for wheat compared to other crops (Iizumi et al, 2013;Doi et al, 2020).…”

“…Many datasets reporting measurements of these variables are available at both global and field scales, though there was still a relative lack of field scale data compared to the number of stastical models requiring this data input as indicated by the low relative proportion value indicated in Figure 1. This abundance of data and the strong associations between increased temperature, increased precipitation and increased crop growth may account for these variables being widely incorporated into current crop yield models (Slater et al, 2021). However, there has been a significant increase in yield volatility for major UK crops such as wheat in recent years which can only be partially explained by seasonal variation in temperature and precipitation (Iizumi and Ramankutty, 2016;Hunt et al, 2019;Slater et al, 2021).…”

Experimental evaluation of biological regeneration of arable soil: The effects of grass-clover leys and arbuscular mycorrhizal inoculants on wheat growth, yield, and shoot pathology

“…Winter wheat, barley, and oilseed rape together accounted for 77% of the UK's cropped area in 2018 (Defra, 2020 ; Harkness et al, 2020 ). However, since 1996 (Knight et al, 2012 ; Slater et al, 2021 ), average yields have plateaued despite a fairly recent world record of wheat yields being achieved in the UK; for example, up to 16.5 t ha −1 from one field in 2015 (Hennessy, 2016 ). The lack of significant yield gain over 25 years has coincided with a period of revolutionary technological advances, including in wheat genetic mapping and marker-assisted breeding to increase yields, and select disease resistance and quality traits (Kuchel et al, 2007 ; Dhariwal and Randhawa, 2022 ; Pandurangan et al, 2022 ).…”

“…It has been estimated that 80% of agricultural land is experiencing increasingly severe degradation (Pimentel and Burgess, 2013 ), especially in intensively cultivated soils, which suffer unsustainable erosion losses (Borrelli et al, 2017 ; Evans et al, 2020 ). These soil and biological effects on crops are compounded by an increasing frequency of excessive rainfall and drought events, which are linked to climate change (Lowe, 2018 ; Slater et al, 2021 ). Extreme weather challenges to arable farming are expected to intensify, with further potential yield declines of 20% due to water stress being predicted (Putelat et al, 2021 ).…”

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

“…Ravuri et al, 2021;Neri et al, 2019), and geographical domains (from point to street-level, single river catchment through to global approaches). Hybrid models have been applied to predict a variety of hydrometeorological variables, including extreme heat and precipitation (Miller et al, 2021;Najafi et al, 2021;Miao et al, 2019;Ma et al, 2022), seasonal climate variables (Golian et al, 2022;Baker et al, 2020), tropical cyclones/hurricanes (Vecchi et al, 2011;Murakami et al, 2016;Kang and Elsner, 2020;Klotzbach et al, 2020), streamflow (Wood and Schaake, 2008;Mendoza et al, 2017;Rasouli et al, 2012;Duan et al, 2020), flooding (Slater and Villarini, 2018), drought (Madadgar et al, 2016;Wu et al, 2021), sea level (Khouakhi et al, 2019), and reservoir levels (Tian et al, 2021), over a range of and predictions have numerous operational and strategic applications, including water resources planning, reservoir inflow management (Tian et al, 2021;Essenfelder et al, 2020), surface water flooding (Rözer et al, 2021), flood risk mitigation, navigation (Meißner et al, 2017), and agricultural crop forecasting (Cao et al, 2022;Slater et al, 2021b). The envisaged dynamical predictors may include various model outputs such as meteorological forecasts with lead times up to 14 days; initialized climate predictions with sub-seasonal to decadal lead times; sub-seasonal runoff predictions, and/or land surface 110 2 Hybrid forecasting Hybrid forecasting encompasses approaches for pre-/post-processing hydroclimate predictions (Section 2.1), and for developing predictive models themselves, including short-term hybrid forecasts (Section 2.2), or sub-seasonal to decadal predictions (Section 2.3), and the integration of ML within parallel and coupled hybrid models (Section 2.4 and Table 3).…”